title: Isolating noise and amplifying signal with quantum Cheshire cat

abstract:
  The quantum Cheshire cat is a phenomenon in which an object, identified with
a "cat", is dissociated from a property of the object, identified with the
"grin" of the cat. We propose a thought experiment, similar to this phenomenon,
with an interferometric setup, where a property (a component of polarization)
of an object (photon) can be separated from the object itself and can
simultaneously be amplified when it is already decoupled from its object. We
further show that this setup can be used to dissociate two complementary
properties, e.g. two orthogonal components of polarization of a photon and
identified with the grin and the snarl of a cat, from each other and one of
them can be amplified while being detached from the other. Moreover, we extend
the work to a noisy scenario, effected by a spin-orbit coupling -like
additional interaction term in the Hamiltonian for the measurement process,
with the object in this scenario being identified with a "confused Cheshire
cat". We devise a gedanken experiment in which such a "confusion" can be
successfully dissociated from the system, and we find that the dissociation
helps in amplification of signals.


title:
Improving Time Sensitivity for Question Answering over Temporal
  Knowledge Graphs

abstract:
  Question answering over temporal knowledge graphs (KGs) efficiently uses
facts contained in a temporal KG, which records entity relations and when they
occur in time, to answer natural language questions (e.g., "Who was the
president of the US before Obama?"). These questions often involve three
time-related challenges that previous work fail to adequately address: 1)
questions often do not specify exact timestamps of interest (e.g., "Obama"
instead of 2000); 2) subtle lexical differences in time relations (e.g.,
"before" vs "after"); 3) off-the-shelf temporal KG embeddings that previous
work builds on ignore the temporal order of timestamps, which is crucial for
answering temporal-order related questions. In this paper, we propose a
time-sensitive question answering (TSQA) framework to tackle these problems.
TSQA features a timestamp estimation module to infer the unwritten timestamp
from the question. We also employ a time-sensitive KG encoder to inject
ordering information into the temporal KG embeddings that TSQA is based on.
With the help of techniques to reduce the search space for potential answers,
TSQA significantly outperforms the previous state of the art on a new benchmark
for question answering over temporal KGs, especially achieving a 32% (absolute)
error reduction on complex questions that require multiple steps of reasoning
over facts in the temporal KG.


title:
Optimal Regularity in Transmission Problems]{Optimal regularity for
  variational solutions of free transmission problems

abstract:
  In this article we study functionals of the type considered in \cite{HS21},
i.e. $$ J(v):=\int_{B_1} A(x,u)|\nabla u|^2 +f(x,u)u+ Q(x)\lambda (u)\,dx $$
here $A(x,u)= A_+(x)\chi_{\{u>0\}}+A_-(x) \chi _{\{u<0\}}$, $f(x,u)=
f_+(x)\chi_{\{u>0\}}+f_-(x) \chi _{\{u<0\}}$ and $\lambda(x,u) = \lambda_+(x)
\chi_{\{u>0\}} + \lambda_-(x) \chi_{\{u\le 0\}}$. We prove the optimal
$C^{0,1^-}$ regularity of minimizers of the functional indicated above (with
precise H\"older estimates) when the coefficients $A_{\pm}$ are continuous
functions and $\mu \le A_{\pm}\le \frac{1}{\mu}$ for some $0<\mu<1$, with $f
\in L^N(B_1)$ and $Q$ bounded. We do this by presenting a new compactness
argument and approximation theory similar to the one developed by L. Caffarelli
in \cite{Ca89} to treat the regularity theory for solutions to fully nonlinear
PDEs. Moreover, we introduce the $\mathcal{T}_{a,b}$ operator that allows one
to transfer minimizers from the transmission problems to the
Alt-Caffarelli-Friedman type functionals, {in small scales,} allowing this way
the study of the regularity theory of minimizers of Bernoulli type free
transmission problems.


title:
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment
  Analysis with ASR Errors

abstract:
  Multimodal sentiment analysis has attracted increasing attention and lots of
models have been proposed. However, the performance of the state-of-the-art
models decreases sharply when they are deployed in the real world. We find that
the main reason is that real-world applications can only access the text
outputs by the automatic speech recognition (ASR) models, which may be with
errors because of the limitation of model capacity. Through further analysis of
the ASR outputs, we find that in some cases the sentiment words, the key
sentiment elements in the textual modality, are recognized as other words,
which makes the sentiment of the text change and hurts the performance of
multimodal sentiment models directly. To address this problem, we propose the
sentiment word aware multimodal refinement model (SWRM), which can dynamically
refine the erroneous sentiment words by leveraging multimodal sentiment clues.
Specifically, we first use the sentiment word position detection module to
obtain the most possible position of the sentiment word in the text and then
utilize the multimodal sentiment word refinement module to dynamically refine
the sentiment word embeddings. The refined embeddings are taken as the textual
inputs of the multimodal feature fusion module to predict the sentiment labels.
We conduct extensive experiments on the real-world datasets including
MOSI-Speechbrain, MOSI-IBM, and MOSI-iFlytek and the results demonstrate the
effectiveness of our model, which surpasses the current state-of-the-art models
on three datasets. Furthermore, our approach can be adapted for other
multimodal feature fusion models easily. Data and code are available at
https://github.com/albertwy/SWRM.


title:
When A Conventional Filter Meets Deep Learning: Basis Composition
  Learning on Image Filters

abstract:
  Image filters are fast, lightweight and effective, which make these
conventional wisdoms preferable as basic tools in vision tasks. In practical
scenarios, users have to tweak parameters multiple times to obtain satisfied
results. This inconvenience heavily discounts the efficiency and user
experience. We propose basis composition learning on single image filters to
automatically determine their optimal formulas. The feasibility is based on a
two-step strategy: first, we build a set of filtered basis (FB) consisting of
approximations under selected parameter configurations; second, a dual-branch
composition module is proposed to learn how the candidates in FB are combined
to better approximate the target image. Our method is simple yet effective in
practice; it renders filters to be user-friendly and benefits fundamental
low-level vision problems including denoising, deraining and texture removal.
Extensive experiments demonstrate that our method achieves an appropriate
balance among the performance, time complexity and memory efficiency.


title:
Omni-frequency Channel-selection Representations for Unsupervised
  Anomaly Detection

abstract:
  Density-based and classification-based methods have ruled unsupervised
anomaly detection in recent years, while reconstruction-based methods are
rarely mentioned for the poor reconstruction ability and low performance.
However, the latter requires no costly extra training samples for the
unsupervised training that is more practical, so this paper focuses on
improving this kind of method and proposes a novel Omni-frequency
Channel-selection Reconstruction (OCR-GAN) network to handle anomaly detection
task in a perspective of frequency. Concretely, we propose a Frequency
Decoupling (FD) module to decouple the input image into different frequency
components and model the reconstruction process as a combination of parallel
omni-frequency image restorations, as we observe a significant difference in
the frequency distribution of normal and abnormal images. Given the correlation
among multiple frequencies, we further propose a Channel Selection (CS) module
that performs frequency interaction among different encoders by adaptively
selecting different channels. Abundant experiments demonstrate the
effectiveness and superiority of our approach over different kinds of methods,
e.g., achieving a new state-of-the-art 98.3 detection AUC on the MVTec AD
dataset without extra training data that markedly surpasses the
reconstruction-based baseline by +38.1 and the current SOTA method by +0.3.
Source code will be available at https://github.com/zhangzjn/OCR-GAN.


title:
Multi-Area Distribution System State Estimation via Distributed Tensor
  Completion

abstract:
  This paper proposes a model-free distribution system state estimation method
based on tensor completion using canonical polyadic decomposition. In
particular, we consider a setting where the network is divided into multiple
areas. The measured physical quantities at buses located in the same area are
processed by an area controller. A three-way tensor is constructed to collect
these measured quantities. The measurements are analyzed locally to recover the
full state information of the network. A distributed closed-form iterative
algorithm based on the alternating direction method of multipliers is developed
to obtain the low-rank factors of the whole network state tensor where
information exchange happens only between neighboring areas. The convergence
properties of the distributed algorithm and the sufficient conditions on the
number of samples for each smaller network that guarantee the identifiability
of the factors of the state tensor are presented. To demonstrate the efficacy
of the proposed algorithm and to check the identifiability conditions,
numerical simulations are carried out using the IEEE 123-bus system.


title:
A Comprehensive Review of Quantum Random Number Generators: Concepts,
  Classification and the Origin of Randomness

abstract:
  Random numbers are central to cryptography and various other tasks. The
intrinsic probabilistic nature of quantum mechanics has allowed us to construct
a large number of quantum random number generators (QRNGs) that are distinct
from the traditional true number generators. This article provides a review of
the existing QRNGs with a focus on various possible features of QRNGs (e.g.,
self-testing, device independence, semi-device independence) that are not
achievable on the classical world. It also discusses the origin, applicability,
and other facets of randomness. Specifically, the origin of randomness is
explored from the perspective of a set of hierarchical axioms for quantum
mechanics, implying that succeeding axioms can be regarded as a superstructure
constructed on top of a structure built by the preceding axioms. The axioms
considered are: (Q1) incompatibility and uncertainty; (Q2) contextuality; (Q3)
entanglement; (Q4) nonlocality and (Q5) indistinguishability of identical
particles. Relevant toy generalized probability theories (GPTs) are introduced,
and it is shown that the origin of random numbers in different types of QRNGs
known today are associated with different layers of nonclassical theories and
all of them do not require all the features of quantum mechanics. Further,
classification of the available QRNGs has been done and the technological
challenges associated with each class is critically analyzed. Commercially
available QRNGs are also compared.


title:
Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and
  Counting

abstract:
  Nuclear segmentation, classification and quantification within Haematoxylin &
Eosin stained histology images enables the extraction of interpretable
cell-based features that can be used in downstream explainable models in
computational pathology (CPath). However, automatic recognition of different
nuclei is faced with a major challenge in that there are several different
types of nuclei, some of them exhibiting large intraclass variability. In this
work, we propose an approach that combine Separable-HoverNet and
Instance-YOLOv5 to indentify colon nuclei small and unbalanced. Our approach
can achieve mPQ+ 0.389 on the Segmentation and Classification-Preliminary Test
Dataset and r2 0.599 on the Cellular Composition-Preliminary Test Dataset on
ISBI 2022 CoNIC Challenge.


title: Private Convex Optimization via Exponential Mechanism

abstract:
  In this paper, we study private optimization problems for non-smooth convex
functions $F(x)=\mathbb{E}_i f_i(x)$ on $\mathbb{R}^d$. We show that modifying
the exponential mechanism by adding an $\ell_2^2$ regularizer to $F(x)$ and
sampling from $\pi(x)\propto \exp(-k(F(x)+\mu\|x\|_2^2/2))$ recovers both the
known optimal empirical risk and population loss under $(\epsilon,\delta)$-DP.
Furthermore, we show how to implement this mechanism using $\widetilde{O}(n
\min(d, n))$ queries to $f_i(x)$ for the DP-SCO where $n$ is the number of
samples/users and $d$ is the ambient dimension. We also give a (nearly)
matching lower bound $\widetilde{\Omega}(n \min(d, n))$ on the number of
evaluation queries.
  Our results utilize the following tools that are of independent interest: (1)
We prove Gaussian Differential Privacy (GDP) of the exponential mechanism if
the loss function is strongly convex and the perturbation is Lipschitz. Our
privacy bound is \emph{optimal} as it includes the privacy of Gaussian
mechanism as a special case and is proved using the isoperimetric inequality
for strongly log-concave measures. (2) We show how to sample from
$\exp(-F(x)-\mu \|x\|^2_2/2)$ for $G$-Lipschitz $F$ with $\eta$ error in total
variation (TV) distance using $\widetilde{O}((G^2/\mu) \log^2(d/\eta))$
unbiased queries to $F(x)$. This is the first sampler whose query complexity
has \emph{polylogarithmic dependence} on both dimension $d$ and accuracy
$\eta$.


title:
On minima of difference of theta functions and application to hexagonal
  crystallization

abstract:
  Let $z=x+iy \in \mathbb{H}:=\{z= x+ i y\in\mathbb{C}: y>0\}$ and $ \theta
(\alpha;z)=\sum_{(m,n)\in\mathbb{Z}^2 } e^{-\alpha \frac{\pi }{y }|mz+n|^2}$ be
the theta function associated with the lattice $L ={\mathbb Z}\oplus z{\mathbb
Z}$. In this paper we consider the following minimization problem of difference
of two theta functions \begin{equation}\aligned\nonumber \min_{ \mathbb{H} }
\Big(\theta (\alpha; z)-\beta\theta (2\alpha; z)\Big) \endaligned\end{equation}
where $\alpha \geq 1$ and $ \beta \in (-\infty, +\infty)$. We prove that there
is a critical value $\beta_c=\sqrt2$ (independent of $\alpha$) such that if
$\beta\leq\beta_c$, the minimizer is $\frac{1}{2}+i\frac{\sqrt3}{2}$ (up to
translation and rotation) which corresponds to the hexagonal lattice, and if
$\beta>\beta_c$, the minimizer does not exist. Our result partially answers
some questions raised in \cite{Bet2016, Bet2018, Bet2020, Bet2019AMP} and gives
a new proof in the crystallization of hexagonal lattice under Yukawa potential.


title: Joint Beamforming and Reflection Design for RIS-assisted ISAC Systems

abstract:
  In this paper, we investigate the potential of employing reconfigurable
intelligent surface (RIS) in integrated sensing and communication (ISAC)
systems. In particular, we consider an RIS-assisted ISAC system in which a
multi-antenna base station (BS) simultaneously performs multi-user multi-input
singleoutput (MU-MISO) communication and target detection. We aim to jointly
design the transmit beamforming and receive filter of the BS, and the
reflection coefficients of the RIS to maximize the sum-rate of the
communication users, while satisfying a worst-case radar output signal-to-noise
ratio (SNR), the transmit power constraint, and the unit modulus property of
the reflecting coefficients. An efficient iterative algorithm based on
fractional programming (FP), majorization-minimization (MM), and alternative
direction method of multipliers (ADMM) is developed to solve the complicated
non-convex problem. Simulation results verify the advantage of the proposed
RIS-assisted ISAC scheme and the effectiveness of the developed algorithm.


title:
PhyCOM: A Multi-Layer Parametric Network for Joint Linear Impairments
  Compensation and Symbol Detection

abstract:
  In this paper, we focus on the joint impairments compensation and symbol
detection problem in communication systems. First, we introduce a new
multi-layer channel model that represents the underlying physics of multiple
impairments in communication systems. This model is composed of widely linear
parametric layers that describe the input-output relationship of the front-end
impairments and channel effects. Using this particular model, we show that the
joint compensation and zero-forcing detection problem can be solved by a
particular feedforward network called PhyCOM. Because of the small number of
network parameters, a PhyCOM network can be trained efficiently using
sophisticated optimization algorithms and a limited number of pilot symbols.
  Numerical examples are provided to demonstrate the effectiveness of PhyCOM
networks with communication systems corrupted by transmitter and receiver IQ
imbalances, carrier frequency offset, finite impulse response channels, and
transmitter and receiver phase noise distortions. Compared to conventional
digital signal processing approaches, simulation results show that the proposed
technique is much more flexible and offers better statistical performance both
in terms of MSE and SER with a moderate increase of the computation complexity.


title: Energy evolution of T-odd gluon TMDs at small $x$

abstract:
  We study the energy or TMD evolution of the three leading twist dipole type
T-odd gluon TMDs inside a transversely polarized nucleon, all of which at small
$x$ dynamically originate from the spin dependent odderon. Their energy
dependence presents a unique opportunity to study the polarization dependent
TMD evolution in the small-$x$ region, where the distributions are identical up
to a normalization constant at tree level. We further propose to study their
evolution via azimuthal asymmetries in virtual photon-jet production in
polarized proton-proton collisions at RHIC. We present model predictions for
the asymmetries as functions of the large jet or photon transverse momentum and
$Q^2$ which set the hard scales in this process.


title: A Pattern Collection for Blockchain Governance

abstract:
  Blockchain technology has been exploited to build next-generation
applications with its decentralised nature. However, serious concerns are
raised about the trustworthiness of the blockchain due to the virtue of the
vulnerabilities in on-chain algorithmic mechanisms and tedious debates in the
off-chain community. Accordingly, the governance of blockchain has received
great attention recently to enhance the trustworthiness of all decisions
directing a blockchain platform. This research domain is still immature,
however, and there is a lack of systematic knowledge to guide the practitioners
on how to design and implement governance methods for blockchain. Therefore, we
have performed an SLR to understand the state-of-art in this topic, and
extractd 14 governance patterns regarding the four layers (i.e., data,
platform, application, and community) of blockchain ecosystem. To effectively
use the discovered patterns, we demonstrate lifecycle stages of these four
layers and involved stakeholders in blockchain ecosystem, as a guidance for
effective use of the patterns, which has yet to be presented and introduced in
existing works of this topic.


title:
Chiral symmetry restoration and the $\Delta$ matter formation in neutron
  stars

abstract:
  We analyze the effects of chiral symmetry restoration in hadronic matter,
including the lowest-lying baryonic resonance $\Delta$ based on the parity
doublet model. We study the role of $\Delta$ and its chiral partner on the
equation of state (EoS) of dense matter under neutron star (NS) conditions of
$\beta$-equilibrium and charge neutrality. We find that the softening of the
EoS driven by the early onset of $\Delta$ matter due to partial restoration of
chiral symmetry allows accommodating the modern multi-messenger astrophysical
constraints on the mass, radius, and tidal deformability. The softening above
the saturation density is accompanied by subsequent stiffening at high
densities. We also find that the matter composition in the NS cores may be
different upon variations of the repulsive interactions of $\Delta$ baryons in
hadronic matter.


title:
Bidirectional Pricing and Demand Response for Nanogrids with HVAC
  Systems

abstract:
  Owing to the fluctuant renewable generation and power demand, the energy
surplus or deficit in each nanogrid is embodied differently across time. To
stimulate local renewable energy consumption and minimize the long-term energy
cost, some issues still remain to be explored: when and how the energy demand
and bidirectional trading prices are scheduled considering personal comfort
preferences and environmental factors. For this purpose, the demand response
and two-way pricing problems concurrently for nanogrids and a public monitoring
entity (PME) are studied with exploiting the large potential thermal elastic
ability of heating, ventilation and air-conditioning (HVAC) units. Different
from nanogrids, in terms of minimizing time-average costs, PME aims to set
reasonable prices and optimize profits by trading with nanogrids and the main
grid bi-directionally. In particular, such bilevel energy management problem is
formulated as a stochastic form in a long-term horizon. Since there are
uncertain system parameters, time-coupled queue constraints and the interplay
of bilevel decision-making, it is challenging to solve the formulated problems.
To this end, we derive a form of relaxation based on Lyapunov optimization
technique to make the energy management problem tractable without forecasting
the related system parameters. The transaction between nanogrids and PME is
captured by a one-leader and multi-follower Stackelberg game framework. Then,
theoretical analysis of the existence and uniqueness of Stackelberg equilibrium
(SE) is developed based on the proposed game property. Following that, we
devise an optimization algorithm to reach the SE with less information
exchange. Numerical experiments validate the effectiveness of the proposed
approach.


title: ArabGend: Gender Analysis and Inference on Arabic Twitter

abstract:
  Gender analysis of Twitter can reveal important socio-cultural differences
between male and female users. There has been a significant effort to analyze
and automatically infer gender in the past for most widely spoken languages'
content, however, to our knowledge very limited work has been done for Arabic.
In this paper, we perform an extensive analysis of differences between male and
female users on the Arabic Twitter-sphere. We study differences in user
engagement, topics of interest, and the gender gap in professions. Along with
gender analysis, we also propose a method to infer gender by utilizing
usernames, profile pictures, tweets, and networks of friends. In order to do
so, we manually annotated gender and locations for ~166K Twitter accounts
associated with ~92K user location, which we plan to make publicly available at
http://anonymous.com. Our proposed gender inference method achieve an F1 score
of 82.1%, which is 47.3% higher than majority baseline. In addition, we also
developed a demo and made it publicly available.


title:
Gas Adsorption and Diffusion Behaviors in Interfacial Systems Composed
  of a Polymer of Intrinsic Microporosity and Amorphous Silica: A Molecular
  Simulation Study

abstract:
  We investigate the adsorption and diffusion behaviors of CO2, CH4, and N2 in
interfacial systems composed of a polymer of intrinsic microporosity (PIM-1)
and amorphous silica using grand canonical Monte Carlo (GCMC) and molecular
dynamics (MD) simulations. We build model systems of mixed matrix membranes
(MMMs) with PIM-1 chains sandwiched between silica surfaces. Gas adsorption
analysis using GCMC simulations shows that gas molecules are preferentially
adsorbed in microcavities distributed near silica surfaces, resulting in an
increase in the solubility coefficients of CO2, CH4, and N2 compared to bulk
PIM-1. In contrast, diffusion coefficients obtained from MD simulations and
then calibrated using the dual-mode sorption model show different tendencies
depending on gas species: CO2 diffusivity decreases in MMMs compared to PIM-1,
whereas CH4 and N2 diffusivities increase. These differences are attributed to
competing effects of silica surfaces: the emergence of larger pores as a result
of chain packing disruption, which enhances gas diffusion, and a
quadrupole-dipole interaction between gas molecules and silica surface hydroxyl
groups, which retards gas diffusion. The former has a greater impact on CH4 and
N2 diffusivities, whereas the latter has a greater impact on CO2 diffusivity
due to the strong quadrupole-dipole interaction between CO2 and surface
hydroxyls. These findings add to our understanding of gas adsorption and
diffusion behaviors in the vicinity of PIM-1/silica interfaces, which are
unobtainable in experimental studies.


title: Generalised point vortices on a plane

abstract:
  A three-vortex system on a plane is known to be minimally superintegrable in
the Liouville sense. In this work, integrable generalisations of the
three-vortex planar model, which involve root vectors of simple Lie algebras,
are proposed. It is shown that a generalised system, which is governed by a
positive definite Hamiltonian, admits a natural integrable extension by spin
degrees of freedom. It is emphasised that the n-vortex planar model and plenty
of its generalisations enjoy the nonrelativistic scale invariance, which gives
room for possible holographic applications.


title: TableFormer: Robust Transformer Modeling for Table-Text Encoding

abstract:
  Understanding tables is an important aspect of natural language
understanding. Existing models for table understanding require linearization of
the table structure, where row or column order is encoded as an unwanted bias.
Such spurious biases make the model vulnerable to row and column order
perturbations. Additionally, prior work has not thoroughly modeled the table
structures or table-text alignments, hindering the table-text understanding
ability. In this work, we propose a robust and structurally aware table-text
encoding architecture TableFormer, where tabular structural biases are
incorporated completely through learnable attention biases. TableFormer is (1)
strictly invariant to row and column orders, and, (2) could understand tables
better due to its tabular inductive biases. Our evaluations showed that
TableFormer outperforms strong baselines in all settings on SQA, WTQ and
TabFact table reasoning datasets, and achieves state-of-the-art performance on
SQA, especially when facing answer-invariant row and column order perturbations
(6% improvement over the best baseline), because previous SOTA models'
performance drops by 4% - 6% when facing such perturbations while TableFormer
is not affected.


title:
Evidence for the first-order topological phase transition in a Kitaev
  spin liquid candidate $\alpha$-RuCl$ _3$

abstract:
  The Kitaev quantum spin liquid (QSL) on the two-dimensional honeycomb lattice
epitomizes an entangled topological state, where the spins fractionalize into
Majorana fermions. This state has aroused tremendous interest because it
harbors non-Abelian anyon excitations. The half-integer quantized thermal Hall
(HIQTH) conductance observed in $\alpha$-RuCl$_3$ is a key signature of these
excitations. However, the fate of this topologically nontrivial state at
intense fields remains largely elusive. Here, we report the thermal
conductivity $\kappa$ and specific heat $C$ of $\alpha$-RuCl$_3$ with in-plane
magnetic fields $H$. For the field direction perpendicular to the Ru-Ru bond,
where the HIQTH effect is observed, we find a discontinuous jump in $\kappa(H)$
and a peak anomaly in $C(H)$ at $\mu_0H^*\approx11$\,T, evidencing a weak
first-order phase transition. Remarkably, the HIQTH effect vanishes close to
$H^*$. Furthermore, we find that the spin-fractionalization feature is retained
well above $H^\ast$. These imply the emergence of the phase transition that
separates two QSL phases with distinct topological properties.


title: Radio properties of the OH megamaser galaxy IIZw 096

abstract:
  Based on the two epochs EVN archive data from OH line observations of IIZw
096, we confirm that the high-resolution OH emission in this source mainly
comes from two spots (OH1 and OH2) of comp D1 of this merging system. We found
no significant variations in the OH line emission. The OH 1665 MHz line
emission is detected at about 6 $\sigma$ level in the OH1 region by combining
two epoch EVN observations. We found that the comp D1 shows the brightest CO,
HCO+ line emission, as well as multi-band radio continuum emission. The
environment around D1 shows no clear velocity structure associated with
circular motions, making it different from most other OHMs in the literature,
which might have been caused by an effect during the merger stage. Meanwhile,
we found that the CO emission shows three velocity structures around D1,
including the central broad FWHM region, the double peak region where the CO
line profile shows two separated peaks, and the region of the high-velocity
clouds where the CO line peaks at a high velocity ($\sim$ 11000 \kms). \HI in
absorption also show high-velocity clouds around the D1 region, which might be
due to inflows caused by the merging of two or more galaxy components. Based on
the high-resolution K-band VLA and L-band VLBA observations of the radio
continuum emission, we derived the brightness temperature in the range $10^{5}$
K to $10^{6}$ K, which is consistent with other starburst dominant OHM sources
in the literature. The multi-band VLA observations show that the radio
continuum emission of comp D might also have contributions from free-free
emission, besides synchrotron emission. As a concenquence, these results
support a starburst origin for the OHMs, without the presence of an AGN.


title: Evolution of scalar field resonances on braneworld

abstract:
  In this work, we investigate numerical evolution of massive Kaluza-Klein~(KK)
modes of a scalar field in a thick brane. We derive the Klein-Gordon equation
in five dimensional spacetime, and obtain the evolution equation and the
Schr\"odinger-like equation. With the resonances of the scalar KK modes as the
initial data, the scalar field is evolved with the maximally dissipative
boundary condition. The results show that there are scalar KK resonant
particles with long life on the brane, which indicates that these resonances
might be viewed as one of the candidates for dark matter.


title: Slice-Aware Resource Calendaring in Cloud-based Radio Access Networks

abstract:
  Network slicing has been introduced in 5G/6G networks to address the
challenge of providing new services with different and sometimes conflicting
requirements. With SDN and NFV technologies being used in the design of 5G and
6G wireless network slicing, as well as the centralization of control over
these technologies, new services such as resource calendaring can also be used
in wireless networks. In bandwidth calendaring, traffic with a low latency
sensitivity and a high volume is shifted to later time slots so that
applications with a high latency sensitivity can be served instead. We discuss
how to calendar radio resources in the C-RAN architecture, which also makes use
of network slicing. This is referred to as Slice-Aware Radio Resource
Calendaring. A model of the problem is developed as an ILP problem and two
heuristic algorithms are proposed for solving it due to complexity of optimal
solution. Observations have shown that when resources are shared between
tenants, the number of accepted requests increases.


title: Compositional Inverses of AGW-PPs

abstract:
  In this paper, we present two methods to obtain the compositional inverses of
AGW-PPs. We improve some known results in this topic.


title:
Increasing the Peneteration of Renewables by Releasing Merchant Energy
  Storage Flexibility

abstract:
  It is anticipated that large-scale energy storage facility (ESF) will become
an essential part of the future energy markets to increase the penetration of
renewables. In this paper, a new optimization algorithm is developed to
participate in large-scale merchant ESF to deal with renewables fluctuations.
The ESF is an investor-owned and independently-operated, seeking to maximize
its total profit while hedging the system against net-load variation. To do so,
an exact computationally efficient bi-level mixed-integer linear programming
(MILP) is envisaged. The ESF operator tries to maximize its own profit at the
upper level while ISO seeks to clear the markets at the lower level. In
principle, the ESF will be able to construct its bidding/offering curves to
buy/sell from/to different markets. To maintain the tractability of the
problem, each lower level is replaced by its KKT optimality conditions, and
nonlinearities are converted into linear equivalents using strong duality
theory rendering the single-level MILP and consequently recast as an MPEC.
Additionally, the proposed framework is constructed according to the
information gap decision theory (IGDT) instrument to capture the adverse impact
of uncertainty on the profit of ESF.


title:
Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for
  Grammar Induction and Text Representation

abstract:
  Recently CKY-based models show great potential in unsupervised grammar
induction thanks to their human-like encoding paradigm, which runs recursively
and hierarchically, but requires $O(n^3)$ time-complexity. Recursive
Transformer based on Differentiable Trees (R2D2) makes it possible to scale to
large language model pre-training even with complex tree encoder by introducing
a heuristic pruning method. However, the rule-based pruning approach suffers
from local optimum and slow inference issues. In this paper, we fix those
issues in a unified method. We propose to use a top-down parser as a
model-based pruning method, which also enables parallel encoding during
inference. Typically, our parser casts parsing as a split point scoring task,
which first scores all split points for a given sentence, and then recursively
splits a span into two by picking a split point with the highest score in the
current span. The reverse order of the splits is considered as the order of
pruning in R2D2 encoder. Beside the bi-directional language model loss, we also
optimize the parser by minimizing the KL distance between tree probabilities
from parser and R2D2. Our experiments show that our Fast-R2D2 improves
performance significantly in grammar induction and achieves competitive results
in downstream classification tasks.


title:
Excitations in the ordered and paramagnetic states of honeycomb magnet
  Na2Co2TeO6

abstract:
  Na2Co2TeO6 is a proposed approximate Kitaev magnet, yet its actual magnetic
interactions are elusive due to a lack of knowledge on the full excitation
spectrum. Here, using inelastic neutron scattering and single crystals, we
determine the system's temperature-dependent magnetic excitations over the
entire Brillouin zone. Without committing to specific models, we unveil a
distinct signature of the third-nearest-neighbor coupling in the spin waves,
which signifies the associated distance as an emerging "soft link" in the
ordered state. The presence of at least six non-overlapping spin-wave branches
is at odds with all models proposed to date. Above the ordering temperature,
persisting dynamic correlations can be described by equal-time magnetic
structure factors of a hexagonal cluster, which reveal the leading
instabilities. Our result sets definitive constraint on theoretical models for
Na2Co2TeO6 and provides new insight for the materialization of the Kitaev
model.


title:
ProgressLabeller: Visual Data Stream Annotation for Training
  Object-Centric 3D Perception

abstract:
  Visual perception tasks often require vast amounts of labelled data,
including 3D poses and image space segmentation masks. The process of creating
such training data sets can prove difficult or time-intensive to scale up to
efficacy for general use. Consider the task of pose estimation for rigid
objects. Deep neural network based approaches have shown good performance when
trained on large, public datasets. However, adapting these networks for other
novel objects, or fine-tuning existing models for different environments,
requires significant time investment to generate newly labelled instances.
Towards this end, we propose ProgressLabeller as a method for more efficiently
generating large amounts of 6D pose training data from color images sequences
for custom scenes in a scalable manner. ProgressLabeller is intended to also
support transparent or translucent objects, for which the previous methods
based on depth dense reconstruction will fail. We demonstrate the effectiveness
of ProgressLabeller by rapidly create a dataset of over 1M samples with which
we fine-tune a state-of-the-art pose estimation network in order to markedly
improve the downstream robotic grasp success rates. ProgressLabeller will be
made publicly available soon.


title:
Continuous Covering on Networks: Strong Mixed Integer Programming
  Formulations

abstract:
  Covering problems are well-studied in the domain of Operations Research, and,
more specifically, in Location Science. When the location space is a network,
the most frequent assumption is to consider the candidate facility locations,
the points to be covered, or both, to be discrete sets. In this work, we study
the set-covering location problem when both candidate locations and demand
points are continuous sets on a network. This variant has received little
attention, and the scarce existing approaches have focused on particular cases,
such as tree networks and integer covering radius. Here we study the general
problem and present a Mixed Integer Linear Programming formulation (MILP) for
networks with edges' lengths no greater than the covering radius. The model
does not lose generality, as any edge not satisfying this condition can be
partitioned into subedges of appropriate lengths without changing the problem.
We propose a preprocessing algorithm to reduce the size of the MILP, and devise
tight big-$M$ constants and valid inequalities to strengthen our formulations.
Moreover, a second MILP is proposed, which admits edges' lengths greater than
the covering radius. As opposed to existing formulations of the problem
(including the first MILP proposed herein), the number of variables and
constraints of this second model does not depend on the lengths of the
network's edges. This second model represents a scalable approach that
particularly suits real-world networks, whose edges are usually greater than
the covering radius. Our computational experiments show the strengths and
limitations of our exact approach on both real-world and random networks. Our
formulations are also tested against an existing exact method.


title: Online Unit Profit Knapsack with Untrusted Predictions

abstract:
  A variant of the online knapsack problem is considered in the settings of
trusted and untrusted predictions. In Unit Profit Knapsack, the items have unit
profit, and it is easy to find an optimal solution offline: Pack as many of the
smallest items as possible into the knapsack. For Online Unit Profit Knapsack,
the competitive ratio is unbounded. In contrast, previous work on online
algorithms with untrusted predictions generally studied problems where an
online algorithm with a constant competitive ratio is known. The prediction,
possibly obtained from a machine learning source, that our algorithm uses is
the average size of those smallest items that fit in the knapsack. For the
prediction error in this hard online problem, we use the ratio
$r=\frac{a}{\hat{a}}$ where $a$ is the actual value for this average size and
$\hat{a}$ is the prediction. The algorithm presented achieves a competitive
ratio of $\frac{1}{2r}$ for $r\geq 1$ and $\frac{r}{2}$ for $r\leq 1$. Using an
adversary technique, we show that this is optimal in some sense, giving a
trade-off in the competitive ratio attainable for different values of $r$. Note
that the result for accurate advice, $r=1$, is only $\frac{1}{2}$, but we show
that no algorithm knowing the value $a$ can achieve a competitive ratio better
than $\frac{e-1}{e}\approx 0.6321$ and present an algorithm with a matching
upper bound. We also show that this latter algorithm attains a competitive
ratio of $r\frac{e-1}{e}$ for $r \leq 1$ and $\frac{e-r}{e}$ for $1 \leq r <
e$, and no algorithm can be better for both $r<1$ and $1\leq r<e$.


title:
"Is Whole Word Masking Always Better for Chinese BERT?": Probing on
  Chinese Grammatical Error Correction

abstract:
  Whole word masking (WWM), which masks all subwords corresponding to a word at
once, makes a better English BERT model. For the Chinese language, however,
there is no subword because each token is an atomic character. The meaning of a
word in Chinese is different in that a word is a compositional unit consisting
of multiple characters. Such difference motivates us to investigate whether WWM
leads to better context understanding ability for Chinese BERT. To achieve
this, we introduce two probing tasks related to grammatical error correction
and ask pretrained models to revise or insert tokens in a masked language
modeling manner. We construct a dataset including labels for 19,075 tokens in
10,448 sentences. We train three Chinese BERT models with standard
character-level masking (CLM), WWM, and a combination of CLM and WWM,
respectively. Our major findings are as follows: First, when one character
needs to be inserted or replaced, the model trained with CLM performs the best.
Second, when more than one character needs to be handled, WWM is the key to
better performance. Finally, when being fine-tuned on sentence-level downstream
tasks, models trained with different masking strategies perform comparably.


title:
The Elliptic Ginibre Ensemble: A Unifying Approach to Local and Global
  Statistics for Higher Dimensions

abstract:
  The elliptic Ginibre ensemble of complex non-Hermitian random matrices allows
to interpolate between the rotational invariant Ginibre ensemble and the
Gaussian unitary ensemble of Hermitian random matrices. It corresponds to a
two-dimensional one-component Coulomb gas in a quadrupolar field, at inverse
temperature $\beta=2$. Furthermore, it represents a determinantal point process
in the complex plane with corresponding kernel of planar Hermite polynomials.
Our main tool is a saddle point analysis of a single contour integral
representation of this kernel. We provide a unifying approach to rigorously
derive several known and new results of local and global spectral statistics,
including in higher dimensions. First, we prove the global statistics in the
elliptic Ginibre ensemble first derived by Forrester and Jancovici. The
limiting kernel receives its main contribution from the boundary of the
limiting elliptic droplet of support. In the Hermitian limit, there is a know
correspondence between non-interacting fermions in a trap in $d$ real
dimensions $\mathbb{R}^d$ and the $d$-dimensional harmonic oscillator. We
present a rigorous proof for the local $d$-dimensional bulk (sine-) and edge
(Airy-) kernel first defined by Dean et al., complementing recent results by
Deleporte and Lambert. Using the same relation to the $d$-dimensional harmonic
oscillator in $d$ complex dimensions $\mathbb{C}^d$, we provide new local bulk
and edge statistics at weak and strong non-Hermiticity, where the former
interpolates between correlations in $d$ real and $d$ complex dimensions. For
$\mathbb{C}^d$ with $d=1$ this corresponds to non-interacting fermions in a
rotating trap.


title: On subspaces whose weak* derived sets are proper and norm dense

abstract:
  We study long chains of iterated weak* derived sets, that is sets of all
weak* limits of bounded nets, of subspaces with the additional property that
the penultimate weak* derived set is a proper norm dense subspace of the dual.
We extend the result of Ostrovskii and show, that in the dual of any
non-quasi-reflexive Banach space containing an infinite-dimensional subspace
with separable dual, we can find for any countable successor ordinal {\alpha} a
subspace, whose weak* derived set of order {\alpha} is proper and norm dense.


title:
Structured Output Feedback Control for Linear Quadratic Regulator Using
  Policy Gradient Method

abstract:
  We consider the static output feedback control for Linear Quadratic Regulator
problems with the feedback gain constraints from the perspective of the
reinforcement learning. In the model based setting, we introduce the gradient
projection method and show its global convergence to stationary points. To
solve the problem in the model free setting, we propose the policy gradient
algorithm based on the gradient projection method and show its global
convergence to $\varepsilon$-stationary points. In addition, we introduce a
variance reduction technique and show both theoretically and numerically that
it significantly reduces the variance in the gradient estimation. We also show
in the numerical experiments that the model free approach performs almost as
well as the model based approach.


title: Lack of evidence for an odderon at small t

abstract:
  It is fundamental that the phase of an elastic scattering amplitude is
related to its energy variation. We repeat a previous fit to proton-proton and
proton-antiproton elastic scattering data from 13 to 13000 GeV, taking better
account of the very high accuracy of the 13 TeV data. The conclusion remains
that there is no evidence for the existence of an odderon in the small-t data


title:
Efficient Globally-Optimal Correspondence-Less Visual Odometry for
  Planar Ground Vehicles

abstract:
  The motion of planar ground vehicles is often non-holonomic, and as a result
may be modelled by the 2 DoF Ackermann steering model. We analyse the
feasibility of estimating such motion with a downward facing camera that exerts
fronto-parallel motion with respect to the ground plane. This turns the motion
estimation into a simple image registration problem in which we only have to
identify a 2-parameter planar homography. However, one difficulty that arises
from this setup is that ground-plane features are indistinctive and thus hard
to match between successive views. We encountered this difficulty by
introducing the first globally-optimal, correspondence-less solution to
plane-based Ackermann motion estimation. The solution relies on the
branch-and-bound optimisation technique. Through the low-dimensional
parametrisation, a derivation of tight bounds, and an efficient implementation,
we demonstrate how this technique is eventually amenable to accurate real-time
motion estimation. We prove its property of global optimality and analyse the
impact of assuming a locally constant centre of rotation. Our results on real
data finally demonstrate a significant advantage over the more traditional,
correspondence-based hypothesise-and-test schemes.


title:
FP-Loc: Lightweight and Drift-free Floor Plan-assisted LiDAR
  Localization

abstract:
  We present a novel framework for floor plan-based, full six degree-of-freedom
LiDAR localization. Our approach relies on robust ceiling and ground plane
detection, which solves part of the pose and supports the segmentation of
vertical structure elements such as walls and pillars. Our core contribution is
a novel nearest neighbour data structure for an efficient look-up of nearest
vertical structure elements from the floor plan. The registration is realized
as a pair-wise regularized windowed pose graph optimization. Highly efficient,
accurate and drift-free long-term localization is demonstrated on multiple
scenes.


title:
Ultrafast enhancement of interfacial exchange coupling in ferromagnetic
  bilayer

abstract:
  Fast spin manipulation in magnetic heterostructures, where magnetic
interactions between different materials often define the functionality of
devices, is a key issue in the development of ultrafast spintronics. Although
recently developed optical approaches such as ultrafast spin-transfer and
spin-orbit torques open new pathways to fast spin manipulation, these processes
do not fully utilize the unique possibilities offered by interfacial magnetic
coupling effects in ferromagnetic multilayer systems. Here, we experimentally
demonstrate ultrafast photo-enhanced interfacial exchange interactions in the
ferromagnetic Co$_2$FeAl/(Ga,Mn)As system at low laser fluence levels. The
excitation efficiency of Co$_2$FeAl with the (Ga,Mn)As layer is 30-40 times
higher than the case with the GaAs layer at 5 K due to a photo-enhanced
exchange coupling interaction via photoexcited charge transfer between the two
ferromagnetic layers. In addition, the coherent spin precessions persist to
room temperature, excluding the drive of photo-enhanced magnetization in the
(Ga,Mn)As layer and indicating a proximity-effect-related optical excitation
mechanism. The results highlight the importance of considering the range of
interfacial exchange interactions in ferromagnetic heterostructures and how
these magnetic coupling effects can be utilized for ultrafast, low-power spin
manipulation.


title: Quantum Riemann-Hilbert problems for the resolved conifold

abstract:
  We study the quantum Riemann-Hilbert problems determined by the refined
Donaldson-Thomas theory on the resolved conifold. Using the solutions to
classical Riemann-Hilbert problems by Beidgeland, we give explicit solutions in
terms of multiple sine functions with unequal parameters. The new feature of
the solutions is that the valid region of the quantum parameter
$q^{\frac{1}{2}}=\exp(\pi i \tau)$ varies on the space of stability conditions
and BPS $t$-plane. Comparing the solutions with the partition function of
refined Chern-Simons theory and invoking large $N$ string duality, we find that
the solution contains the non-perturbative completion of the refined
topological string on the resolved conifold. Therefore solving the quantum
Riemann-Hilbert problems provides a possible non-perturbative definition for
the Donaldson-Thomas theory.


title: A Domain-Theoretic Framework for Robustness Analysis of Neural Networks

abstract:
  We present a domain-theoretic framework for validated robustness analysis of
neural networks. We first analyze the global robustness of a general class of
networks. Then, using the fact that, over finite-dimensional Banach spaces, the
domain-theoretic L-derivative coincides with Clarke's generalized gradient, we
extend our framework for attack-agnostic local robustness analysis. Our
framework is ideal for designing algorithms which are correct by construction.
We exemplify this claim by developing a validated algorithm for estimation of
Lipschitz constant of feedforward regressors. We prove the completeness of the
algorithm over differentiable networks, and also over general position ReLU
networks. Within our domain model, differentiable and non-differentiable
networks can be analyzed uniformly. We implement our algorithm using
arbitrary-precision interval arithmetic, and present the results of some
experiments. Our implementation is truly validated, as it handles
floating-point errors as well.


title:
Earthquake Control: An Emerging Application for Robust Control. Theory
  and Experimental Tests

abstract:
  This paper addresses the possibility of using robust control theory for
preventing earthquakes through fluid injections in the earth's crust. The
designed robust controllers drive aseismically a fault system to a new
equilibrium point of lower energy by tracking a slow reference signal. The
control design is based on a reduced-order nonlinear model able to reproduce
earthquake-like instabilities. Uncertainties related to the frictional and
mechanical properties of the underlying physical process and external
perturbations are considered. Two kinds of controllers are derived. The first
one is based on sliding-mode theory and leads to local finite-time convergence
of the tracking error and rejection of Lipschitz w.r.t. time perturbations. The
second controller is based on LQR control and presents global exponential
stability of the tracking error and rejection of Lipschitz w.r.t. states
perturbations. Both controllers generate a continuous control signal,
attenuating the chattering effect in the case of the sliding-mode algorithms.
The developed controllers are tested extensively and compared on the basis of
numerical simulations and experiments in the laboratory. The present work opens
new perspectives for the application of robust nonlinear control theory to
complex geosystems, earthquakes and sustainable energy production.


title:
Comparison to control oscillations in high-order Finite Volume schemes
  via physical constraint limiters, neural networks and polynomial annihilation

abstract:
  The construction of high-order structure-preserving numerical schemes to
solve hyperbolic conservation laws has attracted a lot of attention in the last
decades and various different ansatzes exist. In this paper, we compare three
completely different approaches, i.e. physical constraint limiting, deep neural
networks and the application of polynomial annihilation to construct high-order
oscillation free Finite Volume (FV) blending schemes. We further analyze their
analytical and numerical properties. We demonstrate that all techniques can be
used and yield highly efficient FV methods but also come with some additional
drawbacks which we point out. Our investigation of the different blending
strategies should lead to a better understanding of those techniques and can be
transferred to other numerical methods as well which use similar ideas.


title:
RFI Identification Based On Deep-Learning]{A Robust RFI Identification
  For Radio Interferometry based on a Convolutional Neural Network

abstract:
  The rapid development of new generation radio interferometers such as the
Square Kilometer Array (SKA) has opened up unprecedented opportunities for
astronomical research. However, anthropogenic Radio Frequency Interference
(RFI) from communication technologies and other human activities severely
affects the fidelity of observational data. It also significantly reduces the
sensitivity of the telescopes. We proposed a robust Convolutional Neural
Network (CNN) model to identify RFI based on machine learning methods. We
overlaid RFI on the simulation data of SKA1-LOW to construct three visibility
function datasets. One dataset was used for modeling, and the other two were
used for validating the model's usability. The experimental results show that
the Area Under the Curve (AUC) reaches 0.93, with satisfactory accuracy and
precision. We then further investigated the effectiveness of the model by
identifying the RFI in the actual observational data from LOFAR and MeerKAT.
The results show that the model performs well. The overall effectiveness is
comparable to AOFlagger software and provides an improvement over existing
methods in some instances.


title: Homogeneous Transit Timing Analyses of Ten Exoplanet Systems

abstract:
  We study the transit timings of 10 exoplanets in order to investigate
potential Transit Timing Variations (TTVs) in them. We model their available
ground-based light curves, some presented here and others taken from the
literature, and homogeneously measure the mid-transit times. We statistically
compare our results with published values and find that the measurement errors
agree. However, in terms of recovering the possible frequencies, homogeneous
sets can be found to be more useful, of which no statistically relevant example
has been found for the planets in our study. We corrected the ephemeris
information of all ten planets we studied and provide these most precise light
elements as references for future transit observations with space-borne and
ground-based instruments. We found no evidence for secular or periodic changes
in the orbital periods of the planets in our sample, including the ultra-short
period WASP-103 b, whose orbit is expected to decay on an observable timescale.
Therefore, we derive the lower limits for the reduced tidal quality factors
(Q$^{\prime}_{\star}$) for the host stars based on best fitting quadratic
functions to their timing data. We also present a global model of all available
data for WASP-74 b, which has a Gaia parallax-based distance value ~25% larger
than the published value.


title:
Towards Decentralized Identity Management in Multi-stakeholder 6G
  Networks

abstract:
  Trust-building mechanisms among network entities of different administrative
domains will gain significant importance in 6G because a future mobile network
will be operated cooperatively by a variety of different stakeholders rather
than by a single mobile network operator. The use of trusted third party issued
certificates for initial trust establishment in multi-stakeholder 6G networks
is only advisable to a limited extent, as trusted third parties not only
represent single point of failures or attacks, but they also cannot guarantee
global independence due to national legislation and regulatory or political
influence. This article proposes to decentralize identity management in 6G
networks in order to enable secure mutual authentication between network
entities of different trust domains without relying on a trusted third party
and to empower network entities with the ability to shape and strengthen
cross-domain trust relationships by the exchange of verifiable credentials. A
reference model for decentralized identity management in 6G is given as an
initial guide for the fundamental design of a common identity management system
whose operation and governance is distributed equally across multiple trust
domains of interconnected and multi-stakeholder 6G ecosystems.


title:
Uniqueness results and enclosure properties for hypersurfaces with
  boundary in weighted cylinders

abstract:
  For a Riemannian manifold $M$, possibly with boundary, we consider the
Riemannian product $M\times\mathbb{R}^k$ with a smooth positive function that
weights the Riemannian measures. In this work we characterize parabolic
hypersurfaces with non-empty boundary and contained within certain regions of
$M\times\mathbb{R}^k$ with suitable weights. Our results include half-space and
Bernstein-type theorems in weighted cylinders. We also generalize to this
setting some classical properties about the confinement of a compact minimal
hypersurface to certain regions of Euclidean space according to the position of
its boundary. Finally, we show interesting situations where the statements are
applied, some of them in relation to the singularities of the mean curvature
flow.


title: Adversarial samples for deep monocular 6D object pose estimation

abstract:
  Estimating 6D object pose from an RGB image is important for many real-world
applications such as autonomous driving and robotic grasping. Recent deep
learning models have achieved significant progress on this task but their
robustness received little research attention. In this work, for the first
time, we study adversarial samples that can fool deep learning models with
imperceptible perturbations to input image. In particular, we propose a Unified
6D pose estimation Attack, namely U6DA, which can successfully attack several
state-of-the-art (SOTA) deep learning models for 6D pose estimation. The key
idea of our U6DA is to fool the models to predict wrong results for object
instance localization and shape that are essential for correct 6D pose
estimation. Specifically, we explore a transfer-based black-box attack to 6D
pose estimation. We design the U6DA loss to guide the generation of adversarial
examples, the loss aims to shift the segmentation attention map away from its
original position. We show that the generated adversarial samples are not only
effective for direct 6D pose estimation models, but also are able to attack
two-stage models regardless of their robust RANSAC modules. Extensive
experiments were conducted to demonstrate the effectiveness, transferability,
and anti-defense capability of our U6DA on large-scale public benchmarks. We
also introduce a new U6DA-Linemod dataset for robustness study of the 6D pose
estimation task. Our codes and dataset will be available at
\url{https://github.com/cuge1995/U6DA}.


title:
Interpolation and stability estimates for edge and face virtual elements
  of general order

abstract:
  We develop interpolation error estimates for general order standard and
serendipity edge and face virtual elements in two and three dimensions.
Contextually, we investigate the stability properties of the associated L2
discrete bilinear forms. These results are fundamental tools in the analysis of
general order virtual elements, e.g., for electromagnetic problems.


title:
Automatic Depression Detection via Learning and Fusing Features from
  Visual Cues

abstract:
  Depression is one of the most prevalent mental disorders, which seriously
affects one's life. Traditional depression diagnostics commonly depends on
rating with scales, which can be labor-intensive and subjective. In this
context, Automatic Depression Detection (ADD) has been attracting more
attention for its low cost and objectivity. ADD systems are able to detect
depression automatically from some medical records, like video sequences.
However, it remains challenging to effectively extract depression-specific
information from long sequences, thereby hindering a satisfying accuracy. In
this paper, we propose a novel ADD method via learning and fusing features from
visual cues. Specifically, we firstly construct Temporal Dilated Convolutional
Network (TDCN), in which multiple Dilated Convolution Blocks (DCB) are designed
and stacked, to learn the long-range temporal information from sequences. Then,
the Feature-Wise Attention (FWA) module is adopted to fuse different features
extracted from TDCNs. The module learns to assign weights for the feature
channels, aiming to better incorporate different kinds of visual features and
further enhance the detection accuracy. Our method achieves the
state-of-the-art performance on the DAIC_WOZ dataset compared to other
visual-feature-based methods, showing its effectiveness.


title:
Iterative Optimization of Quarter Sampling Masks for Non-Regular
  Sampling Sensors

abstract:
  Non-regular sampling can reduce aliasing at the expense of noise. Recently,
it has been shown that non-regular sampling can be carried out using a
conventional regular imaging sensor when the surface of its individual pixels
is partially covered. This technique is called quarter sampling (also 1/4
sampling), since only one quarter of each pixel is sensitive to light. For this
purpose, the choice of a proper sampling mask is crucial to achieve a high
reconstruction quality. In the scope of this work, we present an iterative
algorithm to improve an arbitrary quarter sampling mask which results in a
continuous increase of the reconstruction quality. In terms of the
reconstruction algorithms, we test two simple algorithms, namely, linear
interpolation and nearest neighbor interpolation, as well as two more
sophisticated algorithms, namely, steering kernel regression and frequency
selective extrapolation. Besides PSNR gains of +0.31 dB to +0.68 dB relative to
a random quarter sampling mask resulting from our optimized mask, visually
noticeable enhancements are perceptible.


title: Comprehensive Analysis of the Object Detection Pipeline on UAVs

abstract:
  An object detection pipeline comprises a camera that captures the scene and
an object detector that processes these images. The quality of the images
directly affects the performance of the object detector. Many works nowadays
focus either on improving the image quality or improving the object detection
models independently, but neglect the importance of joint optimization of the
two subsystems. In this paper, we first empirically analyze the influence of
seven parameters (quantization, compression, resolution, color model, image
distortion, gamma correction, additional channels) in remote sensing
applications. For our experiments, we utilize three UAV data sets from
different domains and a mixture of large and small state-of-the-art object
detector models to provide an extensive evaluation of the influence of the
pipeline parameters. Additionally, we realize an object detection pipeline
prototype on an embedded platform for an UAV and give a best practice
recommendation for building object detection pipelines based on our findings.
We show that not all parameters have an equal impact on detection accuracy and
data throughput, and that by using a suitable compromise between parameters we
are able to improve detection accuracy for lightweight object detection models,
while keeping the same data throughput.


title:
Temporal Perceiver: A General Architecture for Arbitrary Boundary
  Detection

abstract:
  Generic Boundary Detection (GBD) aims at locating general boundaries that
divide videos into semantically coherent and taxonomy-free units, and could
server as an important pre-processing step for long-form video understanding.
Previous research separately handle these different-level generic boundaries
with specific designs of complicated deep networks from simple CNN to LSTM.
Instead, in this paper, our objective is to develop a general yet simple
architecture for arbitrary boundary detection in videos. To this end, we
present Temporal Perceiver, a general architecture with Transformers, offering
a unified solution to the detection of arbitrary generic boundaries. The core
design is to introduce a small set of latent feature queries as anchors to
compress the redundant input into fixed dimension via cross-attention blocks.
Thanks to this fixed number of latent units, it reduces the quadratic
complexity of attention operation to a linear form of input frames.
Specifically, to leverage the coherence structure of videos, we construct two
types of latent feature queries: boundary queries and context queries, which
handle the semantic incoherence and coherence regions accordingly. Moreover, to
guide the learning of latent feature queries, we propose an alignment loss on
cross-attention to explicitly encourage the boundary queries to attend on the
top possible boundaries. Finally, we present a sparse detection head on the
compressed representations and directly output the final boundary detection
results without any post-processing module. We test our Temporal Perceiver on a
variety of detection benchmarks, ranging from shot-level, event-level, to
scene-level GBD. Our method surpasses the previous state-of-the-art methods on
all benchmarks, demonstrating the generalization ability of our temporal
perceiver.


title: Collaborative Robot Mapping using Spectral Graph Analysis

abstract:
  In this paper, we deal with the problem of creating globally consistent pose
graphs in a centralized multi-robot SLAM framework. For each robot to act
autonomously, individual onboard pose estimates and maps are maintained, which
are then communicated to a central server to build an optimized global map.
However, inconsistencies between onboard and server estimates can occur due to
onboard odometry drift or failure. Furthermore, robots do not benefit from the
collaborative map if the server provides no feedback in a computationally
tractable and bandwidth-efficient manner. Motivated by this challenge, this
paper proposes a novel collaborative mapping framework to enable accurate
global mapping among robots and server. In particular, structural differences
between robot and server graphs are exploited at different spatial scales using
graph spectral analysis to generate necessary constraints for the individual
robot pose graphs. The proposed approach is thoroughly analyzed and validated
using several real-world multi-robot field deployments where we show
improvements of the onboard system up to 90%.


title:
Ill-posedness issue for the 2D viscous shallow water equations in some
  critical Besov spaces

abstract:
  We study the Cauchy problem of the 2D viscous shallow water equations in some
critical Besov spaces $\dot B^{\frac{2}{p}}_{p,1}(\mathbb{R}^2)\times \dot
B^{\frac{2}{p}-1}_{p,q}(\mathbb{R}^2)$. As is known, this system is locally
well-posed for large initial data as well as globally well-posed for small
initial data in $\dot B^{\frac{2}{p}}_{p,1}(\mathbb{R}^2)\times \dot
B^{\frac{2}{p}-1}_{p,1}(\mathbb{R}^2)$ for $p<4$ and ill-posed in $\dot
B^{\frac{2}{p}}_{p,1}(\mathbb{R}^2)\times \dot
B^{\frac{2}{p}-1}_{p,1}(\mathbb{R}^2)$ for $p>4$. In this paper, we prove that
this system is ill-posed for the critical case $p=4$ in the sense of "norm
inflation". Furthermore, we also show that the system is ill-posed in $\dot
B^{\frac{1}{2}}_{4,1}(\mathbb{R}^2)\times \dot
B^{-\frac{1}{2}}_{4,q}(\mathbb{R}^2)$ for any $q\neq 2$


title:
Microscopic modelling of exciton-polariton diffusion coefficients in
  atomically thin semiconductors

abstract:
  In the strong light-matter coupling regime realized e.g. by integrating
semiconductors into optical microcavities, polaritons as new hybrid
light-matter quasi-particles are formed. The corresponding change in the
dispersion relation has a large impact on optics, dynamics and transport
behaviour of semiconductors. In this work, we investigate the strong-coupling
regime in hBN-encapsulated MoSe$_2$ monolayers focusing on exciton-polariton
diffusion. Applying a microscopic approach based on the exciton density matrix
formalism combined with the Hopfield approach, we predict a drastic increase of
the diffusion coefficients by two to three orders of magnitude in the strong
coupling regime. We explain this behaviour by the much larger polariton group
velocity and suppressed polariton-phonon scattering channels with respect to
the case of bare excitons. Our study contributes to a better microscopic
understanding of polariton diffusion in atomically thin semiconductors.


title: Symmetric Zinbiel superalgebras

abstract:
  The notion of symmetric Zinbiel superalgebras is introduced. We prove that
the nilpotency index of a symmetric Zinbiel superalgebra is not greater than 4
and describe two-generated symmetric Zinbiel algebras and odd generated
superalgebras. We discuss identities of mono and binary symmetric Zinbiel and
Leibniz algebras. It is proven that each quadratic Zinbiel algebra is 2-step
nilpotent. Also, we study double extensions of symmetric Zinbiel algebras.


title: Detecting commonality and variability in use-case diagram variants

abstract:
  The use-case diagram is a software artifact. Thus, as with any software
artifact, the use-case diagrams change across time through the software
development life cycle. Therefore, several versions of the same diagram are
existed at distinct times. Thus, comparing all use-case diagram variants to
detect common and variable use-cases becomes one of the main challenges in the
product line reengineering field. The contribution of this paper is to suggest
an automatic approach to compare a collection of use-case diagram variants and
detect both commonality and variability. In our work, every use-case represents
a feature. The proposed approach visualizes the detected features using formal
concept analysis, where common and variable features are introduced to software
engineers. The proposed approach was applied on a mobile media case study to be
validated. The findings confirm the importance and the performance of the
suggested approach as all common and variable features were precisely detected
via formal concept analysis and latent semantic indexing.


title: Lower defect groups and vertices of simple modules

abstract:
  We compare lower defect groups associated with $p$-regular classes and
vertices of simple modules for a block of a finite group algebra. We show that
lower defect groups are contained in vertices of simple modules after suitable
reordering. Moreover, for a block of principal type, we show that a $p$-regular
lower defect group which contains a vertex of simple module is a defect group
of the block.


title: VScript: Controllable Script Generation with Audio-Visual Presentation

abstract:
  Automatic script generation could save a considerable amount of resources and
offer inspiration to professional scriptwriters. We present VScript, a
controllable pipeline that generates complete scripts including dialogues and
scene descriptions, and presents visually using video retrieval and aurally
using text-to-speech for spoken dialogue. With an interactive interface, our
system allows users to select genres and input starting words that control the
theme and development of the generated script. We adopt a hierarchical
structure, which generates the plot, then the script and its audio-visual
presentation. We also introduce a novel approach to plot-guided dialogue
generation by treating it as an inverse dialogue summarization. Experiment
results show that our approach outperforms the baselines on both automatic and
human evaluations, especially in terms of genre control.


title: Quick Multi-Robot Motion Planning by Combining Sampling and Search

abstract:
  We propose a novel algorithm to solve multi-robot motion planning (MRMP)
rapidly, that we call Simultaneous Sampling-and-Search Planning (SSSP).
Conventional MRMP studies mostly take the form of two-phase planning that
constructs roadmaps then finds inter-robot collision-free paths on those
roadmaps. In contrast, SSSP performs roadmap construction and collision-free
pathfinding at the same time. This is realized by uniting techniques of
single-robot sampling-based motion planning and search techniques of
multi-agent pathfinding on discretized environments. SSSP ensures to find a
solution if exists on the search space defined by the Cartesian product of
robot-wise roadmaps. Our empirical evaluation in various scenarios demonstrates
that SSSP significantly outperforms standard approaches to MRMP, i.e., solving
more problem instances much faster. We also applied SSSP to planning for 32
ground robots in a dense situation.


title:
First do not fall: learning to exploit the environment with a damaged
  humanoid robot

abstract:
  Humanoid robots could replace humans in hazardous situations but most of such
situations are equally dangerous for them, which means that they have a high
chance of being damaged and fall. We hypothesize that humanoid robots would be
mostly used in buildings, which makes them likely to be close to a wall. To
avoid a fall, they can therefore lean on the closest wall, like a human would
do, provided that they find in a few milliseconds where to put the hand(s).
This article introduces a method, called D-Reflex, that learns a neural network
that chooses this contact position given the wall orientation, the wall
distance, and the posture of the robot. This contact position is then used by a
whole-body controller to reach a stable posture. We show that D-Reflex allows a
simulated TALOS robot (1.75m, 100kg, 30 degrees of freedom) to avoid more than
75% of the avoidable falls.


title:
Explainability for identification of vulnerable groups in machine
  learning models

abstract:
  If a prediction model identifies vulnerable individuals or groups, the use of
that model may become an ethical issue. But can we know that this is what a
model does? Machine learning fairness as a field is focused on the just
treatment of individuals and groups under information processing with machine
learning methods. While considerable attention has been given to mitigating
discrimination of protected groups, vulnerable groups have not received the
same attention. Unlike protected groups, which can be regarded as always
vulnerable, a vulnerable group may be vulnerable in one context but not in
another. This raises new challenges on how and when to protect vulnerable
individuals and groups under machine learning. Methods from explainable
artificial intelligence (XAI), in contrast, do consider more contextual issues
and are concerned with answering the question "why was this decision made?".
Neither existing fairness nor existing explainability methods allow us to
ascertain if a prediction model identifies vulnerability. We discuss this
problem and propose approaches for analysing prediction models in this respect.


title:
Stability analysis of microscopic models for traffic flow with lane
  changing

abstract:
  This paper investigates the mathematical modeling and the stability of
multi-lane traffic in the microscopic scale, studying a model based on two
interaction terms. To do this we propose simple lane changing conditions and we
study the stability of the steady states starting from the model in the
one-lane case and extending the results to the generic multi-lane case with the
careful design of the lane changing rules. We compare the results with
numerical tests, that confirm the predictions of the linear stability analysis
and also show that the model is able to reproduce stop & go waves, a typical
feature of congested traffic.


title: Entanglement Renormalization of a $T\bar{T}$-deformed CFT

abstract:
  In this work we use cMERA, a continuous tensor network, to study a
$T\bar{T}$-deformed scalar CFT on the line. We show that the entanglement
renormalization flow implemented by a (Gaussian) cMERA captures some features
which fit well with the known properties of the interacting theory. We discuss
about the non-localities induced by the $T\bar{T}$ deformation at some length
scales by computing the entanglement entropy on the cMERA optimized state. We
find that the kind of non-locality generated by those terms can be considered
as a mild-one, in the sense that it does not violate the area law of
entanglement. In the context of the conjectured connection between cMERA and
holography, we find that at first insight a finite bulk radius can be defined
in the putative geometric dual description of cMERA. However, the entropy
analysis contradicts the proposal that no geometry can be ascribed to the
region outside this radial cutoff.


title:
Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive
  Noise

abstract:
  In this paper, we introduce an adaptive graph normalized least mean pth power
(GNLMP) algorithm for graph signal processing (GSP) that utilizes GSP
techniques, including bandlimited filtering and node sampling, to estimate
sampled graph signals under impulsive noise. Different from least-squares-based
algorithms, such as the adaptive GSP Least Mean Squares (GLMS) algorithm and
the normalized GLMS (GNLMS) algorithm, the GNLMP algorithm has the ability to
reconstruct a graph signal that is corrupted by non-Gaussian noise with
heavy-tailed characteristics. Compared to the recently introduced adaptive GSP
least mean pth power (GLMP) algorithm, the GNLMP algorithm reduces the number
of iterations to converge to a steady graph signal. The convergence condition
of the GNLMP algorithm is derived, and the ability of the GNLMP algorithm to
process multidimensional time-varying graph signals with multiple features is
demonstrated as well. Simulations show the performance of the GNLMP algorithm
in estimating steady-state and time-varying graph signals is faster than GLMP
and more robust in comparison to GLMS and GNLMS.


title: The evolutionary stability of partial migration with Allee effects

abstract:
  An Allee effect occurs when the per-capita growth rate increases at low
densities. Here, we investigate the evolutionary stability of a partial
migration population with migrant population experiencing Allee effects.
Partial migration is a unique form of phenotypic diversity wherein migrant and
non-migrant individuals coexist together. It is shown that when Allee effect is
incorporated, the population undergoes a bifurcation as the fraction of
migrating population increases from zero to unity. Using an evolutionary game
theoretic approach, we prove the existence of a unique evolutionary stable
strategy (ESS). It is also shown that the ESS is the only ideal free
distribution (IFD) that arises in the context of partially migrating
population.


title: Enhanced Dip Coating on a Soft Substrate

abstract:
  A solid, withdrawn from a liquid bath, entrains a thin liquid film. This
simple process, first described by Landau, Levich and Derjaguin (LLD), is
commonly observed in everyday life. It also plays a central role in liquid
capture by animals, and is widely used for surface-coating purposes in
industry. Motivated by the emerging interest in the mechanics of very soft
materials, and in particular the resulting elastocapillary coupling, we develop
a dip-coating model that accounts for the additional presence of a soft solid
layer atop the rigid plate. The elastic response of this soft layer is
described by a Winkler's foundation. Using a combination of numerical, scaling
and asymptotic-matching methods, we find a new softness-dependent power-law
regime for the thickness of entrained liquid at small capillary number, which
corresponds to a modified physics at play in the dynamic meniscus. The
crossover between this regime and the classical dip-coating one occurs when the
substrate's deformation is comparable to the thickness of the entrained liquid
film.


title:
Determination of atomic multiphoton ionization phases by trichromatic
  multichannel wave packet interferometry

abstract:
  We present a multichannel photoelectron interferometry technique based on
trichromatic pulse shaping for unambiguous determination of quantum phases in
multiphoton ionization (MPI) of potassium atoms. The colors of the laser field
are chosen to produce three energetically separated photoelectron
interferograms in the continuum. While the red pulse is two-photon resonant
with the $3d$-state resulting in a (2+1) resonance-enhanced MPI (REMPI), a
(1+2) REMPI occurs via the non-resonant intermediate $4p$-state with an initial
green or blue pulse. We show that ionization via a non-resonant intermediate
state lifts the degeneracy of photoelectron interferograms from pathways
consisting of permutations of the colors. The analysis of the interferograms
reveals a phase shift of $\pm \pi/2$ depending on the sign of the detunings in
the (1+2) REMPI pathways. In addition, we demonstrate that the photoionization
time delay in the resonant (2+1) REMPI pathway gives rise to a linear spectral
phase in the photoelectron spectra. Insights into the underlying MPI processes
are gained through an analytic perturbative description and numerical
simulations of a trichromatic driven three level system coupled to the
continuum.


title:
Differentially private training of residual networks with scale
  normalisation

abstract:
  We investigate the optimal choice of replacement layer for Batch
Normalisation (BN) in residual networks (ResNets) for training with
Differentially Private Stochastic Gradient Descent (DP-SGD) and study the
phenomenon of scale mixing in residual blocks, whereby the activations on the
two branches are scaled differently. Our experimental evaluation indicates that
a hyperparameter search over 1-64 Group Normalisation (GN) groups improves the
accuracy of ResNet-9 and ResNet-50 considerably in both benchmark (CIFAR-10)
and large-image (ImageNette) tasks. Moreover, Scale Normalisation, a simple
modification to the model architecture by which an additional normalisation
layer is introduced after the residual block's addition operation further
improves the utility of ResNets allowing us to achieve state-of-the-art results
on CIFAR-10.


title:
Finding global solutions of some inverse optimal control problems using
  penalization and semismooth Newton methods

abstract:
  We present a method to solve a special class of parameter identification
problems for an elliptic optimal control problem to global optimality. The
bilevel problem is reformulated via the optimal-value function of the
lower-level problem. The reformulated problem is nonconvex and standard
regularity conditions like Robinson's CQ are violated. Via a relaxation of the
constraints, the problem can be decomposed into a family of convex problems and
this is the basis for a solution algorithm. The convergence properties are
analyzed. It is shown that a penalty method can be employed to solve this
family of problems while maintaining convergence speed. For an example problem,
the use of the identity as penalty function allows for the solution by a
semismooth Newton method. Numerical results are presented. Difficulties and
limitations of our approach to solve a nonconvex problem to global optimality
are discussed.


title:
Magnetic Braking of Accreting T Tauri Stars II: Torque Formulation
  Spanning Spin-Up and Spin-Down Regimes

abstract:
  The magnetic interaction between a classical T Tauri star and its surrounding
accretion disk is thought to influence its rotational evolution. We use 2.5D
magnetohydrodynamic, axisymmetric simulations of star-disk interaction,
computed via the PLUTO code, to calculate the net torque acting on these stars.
We divide the net torque into three contributions: accretion (spin-up), stellar
winds (spin-down), and magnetospheric ejections (MEs) (spin-up or down). In
Paper I, we explored interaction regimes in which the stellar magnetosphere
truncates the inner disk at a location spinning faster than the star, resulting
in a strong net spin-up contribution from accretion and MEs ("steady accretion"
regime). In this paper, we investigate interaction regimes in which the
truncation radius gets closer to and even exceeds corotation, where it is
possible for the disk material to gain angular momentum and be periodically
ejected by the centrifugal barrier ("propeller" regime). This reduces the
accretion torque, can change the sign of the ME torque, and can result in a net
stellar spin-down configuration. These results suggest it is possible to have a
net spin-down stellar torque even for truncation radii within the corotation
radius ($R_\text{t} \gtrsim 0.7 R_\text{co}$). We fit semi-analytic functions
for the truncation radius, and the torque associated with star-disk interaction
(i.e., the sum of accretion and ME torques) and stellar wind, allowing for the
prediction of the net stellar torque for a parameter regime covering both net
spin-up and spin-down configurations, as well as the possibility of
investigating rotational evolution via 1D stellar evolution codes.


title: Design Techniques for Incremental Non-Regular Image Sampling Patterns

abstract:
  Even though image signals are typically acquired on a regular two dimensional
grid, there exist many scenarios where non-regular sampling is possible.
Non-regular sampling can remove aliasing. In terms of the non-regular sampling
patterns, there is a high degree of freedom in how to actually arrange the
sampling positions. In literature, random patterns show higher reconstruction
quality compared to regular patterns due to reduced aliasing effects. On the
downside, random patterns feature large void areas which is also
disadvantageous. In the scope of this work, we present two techniques to design
optimized non-regular image sampling patterns for arbitrary sampling densities.
Both techniques create incremental sampling patterns, i.e., one pixel position
is added in each step until the desired sampling density is reached. Our
proposed patterns increase the reconstruction quality by more than +0.5 dB in
PSNR for a broad density range. Visual comparisons are provided.


title: BERT-LID: Leveraging BERT to Improve Spoken Language Identification

abstract:
  Language identification is a task of automatically determining the identity
of a language conveyed by a spoken segment. It has a profound impact on the
multilingual interoperability of an intelligent speech system. Despite language
identification attaining high accuracy on medium or long utterances (>3s), the
performance on short utterances (<=1s) is still far from satisfactory. We
propose an effective BERT-based language identification system (BERT-LID) to
improve language identification performance, especially on short-duration
speech segments. To adapt BERT into the LID pipeline, we drop in a conjunction
network prior to BERT to accommodate the frame-level Phonetic
Posteriorgrams(PPG) derived from the frontend phone recognizer and then
fine-tune the conjunction network and BERT pre-trained model together. We
evaluate several variations within this piped framework, including combining
BERT with CNN, LSTM, DPCNN, and RCNN. The experimental results demonstrate that
the best-performing model is RCNN-BERT. Compared with the prior works, our
RCNN-BERT model can improve the accuracy by about 5% on long-segment
identification and 18% on short-segment identification. The outperformance of
our model, especially on the short-segment task, demonstrates the applicability
of our proposed BERT-based approach on language identification.


title: Superradiance of Spin Defects in Silicon Carbide for Maser Applications

abstract:
  Masers as telecommunication amplifiers have been known for decades, yet their
application is strongly limited due to extreme operating conditions requiring
vacuum techniques and cryogenic temperatures. Recently, a new generation of
masers has been invented based on optically pumped spin states in pentacene and
diamond. In this study, we pave the way for masers based on spin S = 3/2
silicon vacancy (V_Si) defects in silicon carbide (SiC) to overcome the
microwave generation threshold and discuss the advantages of this highly
developed spin hosting material. To achieve population inversion, we optically
pump the V_Si into their mS = +-1/2 spin sub-states and additionally tune the
Zeeman energy splitting by applying an external magnetic field. In this way,
the prerequisites for stimulated emission by means of resonant microwaves in
the 10 GHz range are fulfilled. On the way to realising a maser, we were able
to systematically solve a series of subtasks that improved the underlying
relevant physical parameters of the SiC samples. Among others, we investigated
the pump efficiency as a function of the optical excitation wavelength and the
angle between the magnetic field and the defect symmetry axis in order to boost
the population inversion factor, a key figure of merit for the targeted
microwave oscillator. Furthermore, we developed a high-Q sapphire microwave
resonator (Q ~ 10^4 - 10^5) with which we find indications of superradiant
stimulated microwave emission. In summary, SiC with optimized spin defect
density and thus spin relaxation rates is well on its way of becoming a
suitable maser gain material with wide-ranging applications.


title: Machine Learning for Particle Flow Reconstruction at CMS

abstract:
  We provide details on the implementation of a machine-learning based particle
flow algorithm for CMS. The standard particle flow algorithm reconstructs
stable particles based on calorimeter clusters and tracks to provide a global
event reconstruction that exploits the combined information of multiple
detector subsystems, leading to strong improvements for quantities such as jets
and missing transverse energy. We have studied a possible evolution of particle
flow towards heterogeneous computing platforms such as GPUs using a graph
neural network. The machine-learned PF model reconstructs particle candidates
based on the full list of tracks and calorimeter clusters in the event. For
validation, we determine the physics performance directly in the CMS software
framework when the proposed algorithm is interfaced with the offline
reconstruction of jets and missing transverse energy. We also report the
computational performance of the algorithm, which scales approximately linearly
in runtime and memory usage with the input size.

categories: physics.data-an cs.LG hep-ex physics.ins-det stat.ML


title: Counting stars: a survey on flexible Skyline Query approaches

abstract:
  Nowadays, as the quantity of data to process began to rise, so did the need
for a method to discern what pieces of information could be useful for the
user; in response, researchers focused their efforts on improving the already
existing ranking methods or creating new ones starting from them. This survey
will be presented a small list of some of the most known and/or most recent
solutions proposed, with some possible applications for them, concerning a
state of the art restricted to around the last ten years, comparing their
performance with the traditional one top-k and skyline queries.


title:
Towards IID representation learning and its application on biomedical
  data

abstract:
  Due to the heterogeneity of real-world data, the widely accepted independent
and identically distributed (IID) assumption has been criticized in recent
studies on causality. In this paper, we argue that instead of being a
questionable assumption, IID is a fundamental task-relevant property that needs
to be learned. Consider $k$ independent random vectors $\mathsf{X}^{i = 1,
\ldots, k}$, we elaborate on how a variety of different causal questions can be
reformulated to learning a task-relevant function $\phi$ that induces IID among
$\mathsf{Z}^i := \phi \circ \mathsf{X}^i$, which we term IID representation
learning.
  For proof of concept, we examine the IID representation learning on
Out-of-Distribution (OOD) generalization tasks. Concretely, by utilizing the
representation obtained via the learned function that induces IID, we conduct
prediction of molecular characteristics (molecular prediction) on two
biomedical datasets with real-world distribution shifts introduced by a)
preanalytical variation and b) sampling protocol. To enable reproducibility and
for comparison to the state-of-the-art (SOTA) methods, this is done by
following the OOD benchmarking guidelines recommended from WILDS. Compared to
the SOTA baselines supported in WILDS, the results confirm the superior
performance of IID representation learning on OOD tasks. The code is publicly
accessible via https://github.com/CTPLab/IID_representation_learning.


title: On the validity of the Euler-Lagrange system without growth assumptions

abstract:
  The constrained minimisers of convex integral functionals of the form
$\mathscr F(v)=\int_\Omega F(\nabla^k v(x))\mathrm d x $ defined on Sobolev
mappings $v\in \mathrm W^{k,1}_g(\Omega , \mathbb R^N )\cap K$, where $K$ is a
closed convex subset of the Dirichlet class $\mathrm W^{k,1}_{g}(\Omega ,
\mathbb R^N ),$ are characterised as the energy solutions to the Euler-Lagrange
inequality for $\mathscr F$. We assume that the essentially smooth integrand
$F\colon \mathbb R^{N} \otimes \odot^{k}\mathbb R^{n} \to \mathbb
R\cup\{+\infty\}$ is convex, lower semi-continuous, proper and at least
super-linear at infinity. In the unconstrained case $K=\mathrm
W^{k,1}_{g}(\Omega , \mathbb R^N )$, if the integrand $F$ is convex,
real-valued, and satisfies a demi-coercivity condition, then $$ \int_{\Omega}
\! F^{\prime}(\nabla^{k} u) \cdot \nabla^{k}\phi \, \mathrm d x =0 $$ holds for
all $\phi \in \mathrm W_{0}^{k}( \Omega , \mathbb R^{N})$, where $\nabla^{k} u$
is the absolutely continuous part of the vector measure $D^{k}u$.


title: On closed subgroups of precompact groups

abstract:
  It is a Theorem of W.~ W. Comfort and K.~ A. Ross that if $G$ is a subgroup
of a compact Abelian group, and $S$ denotes those continuous homomorphisms from
$G$ to the one-dimensional torus, then the topology on $G$ is the initial
topology given by $S$. {Assume that $H$ is a subgroup of $G$. We study how} the
choice of $S$ affects the topological placement and properties of $H$ in $G$.
Among other results, we have {made significant} progress toward the solution of
the following specific questions: How many totally bounded group topologies
does $G$ admit such that $H$ is a closed (dense) subgroup? If $C_S$ denotes the
poset of all subgroups of $G$ that are $S$-closed, ordered by inclusion, does
$C_S$ has a greatest (resp. smallest) element? We say that a totally bounded
(topological, resp.) group is an \textit{SC-group} (\textit{topologically
simple}, resp.) if all its subgroups are closed (if $G$ and $\{e\}$ are its
only possible closed normal subgroups, resp.) {In addition, we investigate the
following questions.} How many SC-(topologically simple totally bounded, resp.)
group topologies does an arbitrary Abelian group $G$ admit?


title: Cu-doping effects on the ferromagnetic semimetal CeAuGe

abstract:
  We present a study of Cu-substitution effects in 4f-Ce intermetallic compound
CeAu1-xCuxGe, with potentially unusual electronic states, in the whole
concentration range (x = 0.0 - 1.0). The parent CeAuGe compound, crystallizing
in a non-centrosymmetric hexagonal structure, is a ferromagnetic semimetal with
Curie temperature 10 K. Cu-doping on Au-site of CeAuGe, CeAu1-xCuxGe, changes
the crystal structure from the non-centrosymmetric (P63mc) to centrosymmetric
(P63/mmc) space group at the concentration x ~ 0.5, where the c-lattice
constant has a maximum value. Magnetic susceptibility and electrical
resistivity measurements reveal that all Cu-doped compounds undergo magnetic
phase transition near 10 K, with the maximum transition temperature of 12 K for
x = 0.5. The neutron powder diffraction experiments show the ferromagnetic
ordering of Ce3+ magnetic moments with a value of ~ 1.2 Bohr magneton at 1.8 K,
oriented perpendicular to the hexagonal c-axis. By using symmetry analysis, we
have found the solutions for the magnetic structure in the ferromagnetic
Shubnikov space groups Cmc'21' and P21'/m' for x < 0.5 and x >= 0.5,
respectively. Electrical resistivity exhibits a metallic temperature behaviour
in all compounds. The resistivity has a local minimum in the paramagnetic state
due to Kondo effects at high doping x = 0.8 and 1.0. At the small Cu-doping
level, x = 0.2, the resistivity shows a broad feature at the ferromagnetic
transition temperature and an additional transition-like peculiarity at 2.5 K
in the ferromagnetic state.