title:
Lower Bound of $l_{1}$ Norm of Coherence of Bipartite Qubit-Qudit System
  and its Application in the Detection of Entangled Tripartite
  Qudit-Qubit-Qudit System

abstract:
  Quantum coherence and quantum entanglement are two strong pillars in quantum
information theory. We study here for the possibility of any connection between
these two important aspects of quantum mechanics while studying the
entanglement detection problem for the detection of bipartite higher
dimensional entangled states and multipartite entangled states. To achieve our
goal, we derive the lower bound $L$ of $l_{1}$ norm of coherence of bipartite
qubit-qudit system using the criterion that detect entanglement. Furthermore,
we deduce the upper bound $U$ of $l_{1}$ norm of coherence of separable
bipartite qubit-qudit system using the separability criterion. Thus, we find
that if any $l_{1}$ norm of coherence of bipartite qubit-qudit system is
greater than the upper bound $U$ then the given qubit-qudit state is entangled.
Finally, we obtained the upper bound $U_{1}$ of $l_{1}$ norm of coherence of
separable tripartite state lies either in $2 \otimes d \otimes d$ or $d \otimes
2 \otimes d$ or $d \otimes d \otimes 2$ dimensional Hilbert space using the
upper bound $U$. We have shown that if the $l_{1}$ norm of coherence of any
tripartite $qubit-qudit-qudit$ or $qudit-qubit-qudit$ or $qudit-qudit-qubit$
system is greater than the derived upper bound $U_{1}$ then the given
tripartite system represent an entangled state.


title:
Replicate, Reuse, Repeat: Capturing Non-Linear Communication via Session
  Types and Graded Modal Types

abstract:
  Session types provide guarantees about concurrent behaviour and can be
understood through their correspondence with linear logic, with propositions as
sessions and proofs as processes. However, a strictly linear setting is
somewhat limiting, since there exist various useful patterns of communication
that rely on non-linear behaviours. For example, shared channels provide a way
to repeatedly spawn a process with binary communication along a fresh linear
session-typed channel. Non-linearity can be introduced in a controlled way in
programming through the general concept of graded modal types, which are a
framework encompassing various kinds of coeffect typing (describing how
computations make demands on their context). This paper shows how graded modal
types can be leveraged alongside session types to enable various non-linear
concurrency behaviours to be re-introduced in a precise manner in a type system
with a linear basis. The ideas here are demonstrated using Granule, a
functional programming language with linear, indexed, and graded modal types.


title: Asynchronous Sessions with Input Races

abstract:
  We propose a calculus for asynchronous multiparty sessions where input
choices with different senders are allowed in processes. We present a type
system that accepts such input races provided they do not hinder lock-freedom.


title: Higher-order Context-free Session Types in System F

abstract:
  We present an extension of System F with higher-order context-free session
types. The mixture of functional types with session types has proven to be a
challenge for type equivalence formalization: whereas functional type
equivalence is often rule-based, session type equivalence usually follows a
semantic approach based on bisimulations. We propose a unifying approach that
handles the equivalence of functional and session types together. We present
three notions of type equivalence: a syntactic rule-based version, a semantic
bisimulation-based version, and an algorithmic version by reduction to the
problem of bisimulation of simple grammars. We prove that the three notions
coincide and derive a decidability result for the type equivalence problem of
higher-order context-free session types.


title: Provable GPU Data-Races in Static Race Detection

abstract:
  We extend the theory behind the Faial tool-chain, which can soundly prove
that CUDA programs (aka, kernels) are data-race free using specialized
behavioral types called memory access protocols (MAPs). In this paper we extend
the theory of MAPs to characterize kernels for which alarms can be identified
as true alarms. We introduce a core calculus for CUDA, which we named BabyCUDA,
and a behavioral type system for it. We show that if a BabyCUDA program can be
assigned a MAP, then any alarm raised by Faial for this program is a true
alarm.


title: Lang-n-Send: Processes That Send Languages

abstract:
  We present Lang-n-Send, a pi-calculus that is equipped with language
definitions. Processes can define languages in operational semantics, and use
them to execute programs. Furthermore, processes can send and receive pieces of
operational semantics through channels.
  We present a reduction semantics for Lang-n-Send, and we offer examples that
demonstrate some of the scenarios that Lang-n-Send captures.


title:
Correlations of gas, dust and young stellar populations in the spiral
  galaxy NGC 7331

abstract:
  Molecular clouds (MCs) in galaxies are complex places with many phases. It
is, therefore, essential to study the physics and kinematics of the MCs using
multiple emission lines. We probe the physics of the molecular gas and dust in
the nearby spiral galaxy NGC 7331 using multiple emission lines, i.e. carbon
monoxide (CO), 24{\mu}m and far-ultraviolet (FUV) data. 14 positions were
targeted across the gaseous disc of NGC 7331. We found that CO intensities, gas
mass, gas surface density, and 24{\mu}m-to-FUV flux ratio (i.e. the extinction)
increase up to about 40 arcsec from the centre and then start to decrease.
There is a positive correlation between most of the pair of parameters studied
(except FUV flux density). The beam-averaged physical parameters on the eastern
side of the disc show higher median values than those on the western side. Our
results indicate that the star formation activity, stellar populations and
overall physical properties of the ISM are different on either side of the
disc. Our study provides notable insights into the complex nature of the
interstellar medium (ISM) in galaxies and has the potential to provoke future
higher-resolution studies yet to come.


title:
Can Unsupervised Knowledge Transfer from Social Discussions Help
  Argument Mining?

abstract:
  Identifying argument components from unstructured texts and predicting the
relationships expressed among them are two primary steps of argument mining.
The intrinsic complexity of these tasks demands powerful learning models. While
pretrained Transformer-based Language Models (LM) have been shown to provide
state-of-the-art results over different NLP tasks, the scarcity of manually
annotated data and the highly domain-dependent nature of argumentation restrict
the capabilities of such models. In this work, we propose a novel transfer
learning strategy to overcome these challenges. We utilize argumentation-rich
social discussions from the ChangeMyView subreddit as a source of unsupervised,
argumentative discourse-aware knowledge by finetuning pretrained LMs on a
selectively masked language modeling task. Furthermore, we introduce a novel
prompt-based strategy for inter-component relation prediction that compliments
our proposed finetuning method while leveraging on the discourse context.
Exhaustive experiments show the generalization capability of our method on
these two tasks over within-domain as well as out-of-domain datasets,
outperforming several existing and employed strong baselines.


title:
Active colloid with externally induced periodic bipolar motility and its
  cooperative motion

abstract:
  Active matter physics has been developed with various types of self-propelled
particles, including those with polar and bipolar motility and beyond. However,
the bipolar motions experimentally realized so far have been either random
along the axis or periodic at intrinsic frequencies. Here we report another
kind of bipolar active particles, whose periodic bipolar self-propulsion is set
externally at a controllable frequency. We used Quincke rollers -- dielectric
particles suspended in a conducting liquid driven by an electric field -- under
an AC electric field instead of the usually used DC field. Reciprocating motion
of a single particle at the external frequency was observed experimentally and
characterized theoretically as stable periodic motion. Experimentally, we
observed not only the reciprocating motion but also non-trivial Active Brownian
Particle (ABP)-like persistent motion in a long time scale. This resulted in a
Lorentzian spectrum around zero frequency, which is not accounted for by a
simple extension of the conventional model of Quincke rollers to the AC field.
It was found that ABP-like motion can be reproduced by considering the
top-bottom asymmetry in the experimental system. Moreover, we found a
rotational diffusion coefficient much larger than the thermal one, as also
reported in previous experiments, which may have resulted from roughness of the
electrode surface. We also found self-organized formation of small clusters,
such as doublets and triplets, and characterized cooperative motion of
particles therein. The AC Quincke rollers reported here may serve as a model
experimental system of bipolar active matter, which appears to deserve further
investigations.


title: Oka domains in Euclidean spaces

abstract:
  In this paper we find surprisingly small Oka domains in Euclidean spaces
$\mathbb C^n$ of dimension $n>1$ at the very limit of what is possible. Under a
mild geometric assumption on a closed unbounded convex set $E$ in $\mathbb C^n$
we show that $\mathbb C^n\setminus E$ is an Oka domain. In particular, there
are Oka domains which are only slightly bigger than a halfspace, the latter
being neither Oka nor hyperbolic. This gives families of smooth real
hypersurfaces $\Sigma_t\subset \mathbb C^n$ $(t\in\mathbb R)$ dividing $\mathbb
C^n$ in an unbounded hyperbolic domain and an Oka domain such that at the
threshold value $t=0$ the hypersurface $\Sigma_0$ is a hyperplane and the
character of the two sides gets reversed. More generally, we show that if $E$
is a closed set in $\mathbb C^n$ for $n>1$ whose projective closure $\overline
E\subset\mathbb C\mathbb P^n$ avoids a hyperplane $\Lambda\subset\mathbb
C\mathbb P^n$ and is polynomially convex in $\mathbb C\mathbb P^n\setminus
\Lambda\cong\mathbb C^n$, then $\mathbb C^n\setminus E$ is an Oka domain.


title:
Quantum-accelerated algorithms for generating random primitive
  polynomials over finite fields

abstract:
  Despite the wide applications of primitive polynomials over finite fields in
computer science, there is no classical algorithm for generating random degree
$n$ primitive polynomials over some finite field $\mathbb{F}_q$ that runs in
time polynomial in $n$, except that additional information like the prime
factorization of $q^n-1$ were given in advance. In this paper, we concentrate
on the problem of generating random primitive polynomials over finite fields
and show how quantum algorithms can be employed to solve it efficiently, which
paves the way for future applications of primitive polynomials in code-based
cryptography, code-based identification and signature schemes, keyed
communication protocols and so on.


title:
Nobeyama Survey of Inward Motions toward Cores in Orion Identified by
  SCUBA-2

abstract:
  In this study, 36 cores (30 starless and 6 protostellar) identified in Orion
were surveyed to search for inward motions. We used the Nobeyama 45 m radio
telescope, and mapped the cores in the $J = 1\rightarrow0$ transitions of
HCO$^+$, H$^{13}$CO$^+$, N$_2$H$^+$, HNC, and HN$^{13}$C. The asymmetry
parameter $\delta V$, which was the ratio of the difference between the HCO$^+$
and H$^{13}$CO$^+$ peak velocities to the H$^{13}$CO$^+$ line width, was biased
toward negative values, suggesting that inward motions were more dominant than
outward motions. Three starless cores (10% of all starless cores surveyed) were
identified as cores with blue-skewed line profiles (asymmetric profiles with
more intense blue-shifted emission), and another two starless cores (7%) were
identified as candidate blue-skewed line profiles. The peak velocity difference
between HCO$^+$ and H$^{13}$CO$^+$ of them was up to 0.9 km s$^{-1}$,
suggesting that some inward motions exceeded the speed of sound for the
quiescent gas ($\sim10-17$ K). The mean of $\delta V$ of the five
aforementioned starless cores was derived to be $-$0.5$\pm$0.3. One core,
G211.16$-$19.33North3, observed using the ALMA ACA in DCO$^+$ $J =
3\rightarrow2$ exhibited blue-skewed features. Velocity offset in the
blue-skewed line profile with a dip in the DCO$^+$ $J = 3\rightarrow2$ line was
larger ($\sim 0.5$ km s$^{-1}$) than that in HCO$^+$ $J = 1\rightarrow0$ ($\sim
0.2$ km s$^{-1}$), which may represent gravitational acceleration of inward
motions. It seems that this core is at the last stage in the starless phase,
judging from the chemical evolution factor version 2.0 (CEF2.0).


title: Automatic Speech recognition for Speech Assessment of Preschool Children

abstract:
  The acoustic and linguistic features of preschool speech are investigated in
this study to design an automated speech recognition (ASR) system. Acoustic
fluctuation has been highlighted as a significant barrier to developing
high-performance ASR applications for youngsters. Because of the epidemic,
preschool speech assessment should be conducted online. Accordingly, there is a
need for an automatic speech recognition system. We were confronted with new
challenges in our cognitive system, including converting meaningless words from
speech to text and recognizing word sequence. After testing and experimenting
with several models we obtained a 3.1\% phoneme error rate in Persian. Wav2Vec
2.0 is a paradigm that could be used to build a robust end-to-end speech
recognition system.


title:
Evidence for coalescence in lightly doped strontium-substituted
  lanthanum cuprate samples

abstract:
  By comparing the experimental results obtained in the study of the electron
transport and magnetic properties of samples of lightly doped strontium
substituted lanthanum cuprates and the results of the theoretical analysis in
the reduced elastic scattering space (RPS), we constructed the classical
self-consistent strong-binding energy density profile in the superconducting
mode for this material. It is found that the imaginary part of the RPS can be
zero, almost constant, or strongly self-consistent, depending on the Sr
impurity concentration x. Supposing the Sr impurity concentration is very
dilute, a constant imaginary part of the elastic scattering cross-section
brings a sub-melted coalescent superconducting metallic state similar to the
constant lifetime in the normal state, except for the unitary narrow, sharp
peak at zero frequency. A broadening of the peak occurs for optimal
concentration of Sr atoms, mixing a more significant amount of nodal fermion
quasiparticles with Cooper pairs and making the energy density profile strongly
self-consistent dependent.

categories: cond-mat.supr-con cond-mat.dis-nn cond-mat.mtrl-sci physics.comp-ph


title: RSSI-CSI Measurement and Variation Mitigation with Commodity WiFi Device

abstract:
  Owing to the plentiful information released by the commodity devices, WiFi
signals have been widely studied for various wireless sensing applications. In
many works, both received signal strength indicator (RSSI) and the channel
state information (CSI) are utilized as the key factors for precise sensing.
However, the calculation and relationship between RSSI and CSI is not explained
in detail. Furthermore, there are few works focusing on the measurement
variation of the WiFi signal which impacts the sensing results. In this paper,
the relationship between RSSI and CSI is studied in detail and the measurement
variation of amplitude and phase information is investigated by extensive
experiments. In the experiments, transmitter and receiver are directly
connected by power divider and RF cables and the signal transmission is
quantitatively controlled by RF attenuators. By changing the intensity of
attenuation, the measurement of RSSI and CSI is carried out under different
conditions. From the results, it is found that in order to get a reliable
measurement of the signal amplitude and phase by commodity WiFi, the
attenuation of the channels should not exceed 60 dB. Meanwhile, the difference
between two channels should be lower than 10 dB. An active control mechanism is
suggested to ensure the measurement stability. The findings and criteria of
this work is promising to facilitate more precise sensing technologies with
WiFi signal.


title:
Some suggestions concerning the conjecture in: 'Tractable semi-algebraic
  approximation using Christoffel-Darboux kernel'

abstract:
  In 'Tractable semi-algebraic approximation using Christoffel-Darboux kernel'
Marx, Pauwels, Weisser, Henrion and Lasserre conjectured, that the
approximation rate $\mathcal O(\frac 1 {\sqrt(d)})$ of a Lipschitz functions by
a semi-algebraic function induced by a Christoffel- Darboux kernel of degree
$d$ in the $L^1$ norm can be improved for more regular functions. Here we will
show, that for semi-algebraic and definable functions the results can be
strengthened to a rational approximation rate in the $L^\infty$ norm.


title:
Dimension compression and expansion under homeomorphisms with
  exponentially integrable distortion

abstract:
  We improve both dimension compression and expansion bounds for homeomorphisms
with $p$-exponentially integrable distortion. To the first direction we also
introduce estimates for the compression multifractal spectra, which will be
used to estimate compression of dimension, and for the rotational multifractal
spectra. For establishing the expansion case we use the multifractal spectra of
the inverse mapping and construct examples proving sharpness.


title: An Ensemble Approach for Facial Expression Analysis in Video

abstract:
  Human emotions recognization contributes to the development of human-computer
interaction. The machines understanding human emotions in the real world will
significantly contribute to life in the future. This paper will introduce the
Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. The paper
focuses on solving the problem of the valence-arousal estimation and action
unit detection. For valence-arousal estimation, we conducted two stages:
creating new features from multimodel and temporal learning to predict
valence-arousal. First, we make new features; the Gated Recurrent Unit (GRU)
and Transformer are combined using a Regular Networks (RegNet) feature, which
is extracted from the image. The next step is the GRU combined with Local
Attention to predict valence-arousal. The Concordance Correlation Coefficient
(CCC) was used to evaluate the model.


title:
Making Heads or Tails: Towards Semantically Consistent Visual
  Counterfactuals

abstract:
  A visual counterfactual explanation replaces image regions in a query image
with regions from a distractor image such that the system's decision on the
transformed image changes to the distractor class. In this work, we present a
novel framework for computing visual counterfactual explanations based on two
key ideas. First, we enforce that the \textit{replaced} and \textit{replacer}
regions contain the same semantic part, resulting in more semantically
consistent explanations. Second, we use multiple distractor images in a
computationally efficient way and obtain more discriminative explanations with
fewer region replacements. Our approach is $\mathbf{27\%}$ more semantically
consistent and an order of magnitude faster than a competing method on three
fine-grained image recognition datasets. We highlight the utility of our
counterfactuals over existing works through machine teaching experiments where
we teach humans to classify different bird species. We also complement our
explanations with the vocabulary of parts and attributes that contributed the
most to the system's decision. In this task as well, we obtain state-of-the-art
results when using our counterfactual explanations relative to existing works,
reinforcing the importance of semantically consistent explanations.


title: FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization

abstract:
  MLP-like models built entirely upon multi-layer perceptrons have recently
been revisited, exhibiting the comparable performance with transformers. It is
one of most promising architectures due to the excellent trade-off between
network capability and efficiency in the large-scale recognition tasks.
However, its generalization performance to heterogeneous tasks is inferior to
other architectures (e.g., CNNs and transformers) due to the extensive
retention of domain information. To address this problem, we propose a novel
frequency-aware MLP architecture, in which the domain-specific features are
filtered out in the transformed frequency domain, augmenting the invariant
descriptor for label prediction. Specifically, we design an adaptive Fourier
filter layer, in which a learnable frequency filter is utilized to adjust the
amplitude distribution by optimizing both the real and imaginary parts. A
low-rank enhancement module is further proposed to rectify the filtered
features by adding the low-frequency components from SVD decomposition.
Finally, a momentum update strategy is utilized to stabilize the optimization
to fluctuation of model parameters and inputs by the output distillation with
weighted historical states. To our best knowledge, we are the first to propose
a MLP-like backbone for domain generalization. Extensive experiments on three
benchmarks demonstrate significant generalization performance, outperforming
the state-of-the-art methods by a margin of 3%, 4% and 9%, respectively.


title:
A new subband non linear prediction coding algorithm for narrowband
  speech signal: The nADPCMB MLT coding scheme

abstract:
  This paper focuses on a newly developed transparent nADPCMB MLT speech coding
algorithm. Our coder first decomposes the narrowband speech signal in subbands,
a non linear ADPCM scheme is then performed in each subband. The signal subband
decomposition is piloted by the equivalent Modulated Lapped Transform (MLT)
filter bank. The novelty of this algorithm is the non linear approach, based on
neural networks, to subband prediction coding. We have evaluated the
performance of the nADPCMB MLT coding algorithm with a session of formal
listening based on the five grade impairment scale standardized within ITU - T
Recommendation P.800.


title: Binomial Approximation to Locally Dependent CDO

abstract:
  In this paper, we develop Stein's method for binomial approximation using the
stop-loss metric that allows one to obtain a bound on the error term between
the expectation of call functions. We obtain the results for a locally
dependent collateralized debt obligation (CDO), under certain conditions on
moments. The results are also exemplified for an independent CDO. Finally, it
is shown that our bounds are sharper than the existing bounds.


title: Wide band sub-band speech coding using nonlinear prediction

abstract:
  We compare a wide band sub-band speech coder using ADPCM schemes with linear
prediction against the same scheme with nonlinear prediction based on
multi-layer perceptrons. Exhaustive results are presented in each band, and the
full signal. Our proposed scheme with non-linear neural net prediction
outperforms the linear scheme up to 2 dB in SEGSNR. In addition, we propose a
simple method based on a non-linearity in order to obtain a synthetic wide band
signal from a narrow band signal.


title: Same and interconvertible high-pressure ice phases

abstract:
  Most experimentally known high-pressure ice phases have a body-centred cubic
(bcc) oxygen lattice. Our atomistic simulations show that, amongst these bcc
ice phases, ices VII, VII' and X are the same thermodynamic phase under
different conditions, whereas superionic ice VII'' has a first-order phase
boundary with ice VII'. Moreover, at about 300 GPa, ice X transforms into the
Pbcm phase with a sharp structural change but no apparent activation barrier,
whilst at higher pressures the barrier gradually increases. Our study thus
clarifies the phase behaviour of the high-pressure insulating ices and reveals
peculiar solid-solid transition mechanisms not known in other systems.


title:
Reputation structure in indirect reciprocity under noisy and private
  assessment

abstract:
  Evaluation relationships are pivotal for maintaining a cooperative society. A
formation of the evaluation relationships has been discussed in terms of
indirect reciprocity, by modeling dynamics of good or bad reputations among
individuals. Recently, a situation that individuals independently evaluate
others with errors (i.e., noisy and private reputation) is considered, where
the reputation structure (from what proportion of individuals in the population
each receives good reputations, defined as goodness here) becomes complex, and
thus has been studied mainly with numerical simulations. The present study
gives a theoretical analysis of such complex reputation structure. We formulate
a stochastic transition of goodness of individuals. By considering a large
population, we derive dynamics of the frequency distribution of goodnesses. An
equilibrium state of the dynamics is approximated by a summation of Gaussian
functions. We demonstrate that the theoretical solution well fits the numerical
calculation. From the theoretical solution, we obtain a new interpretation of
the complex reputation structure. This study provides a novel mathematical
basis for cutting-edge studies on indirect reciprocity.


title:
Facial Expression Classification using Fusion of Deep Neural Network in
  Video for the 3rd ABAW3 Competition

abstract:
  For computers to recognize human emotions, expression classification is an
equally important problem in the human-computer interaction area. In the 3rd
Affective Behavior Analysis In-The-Wild competition, the task of expression
classification includes eight classes with six basic expressions of human faces
from videos. In this paper, we employ a transformer mechanism to encode the
robust representation from the backbone. Fusion of the robust representations
plays an important role in the expression classification task. Our approach
achieves 30.35\% and 28.60\% for the $F_1$ score on the validation set and the
test set, respectively. This result shows the effectiveness of the proposed
architecture based on the Aff-Wild2 dataset.


title: Two-timescale Resource Allocation for Automated Networks in IIoT

abstract:
  The rapid technological advances of cellular technologies will revolutionize
network automation in industrial internet of things (IIoT). In this paper, we
investigate the two-timescale resource allocation problem in IIoT networks with
hybrid energy supply, where temporal variations of energy harvesting (EH),
electricity price, channel state, and data arrival exhibit different
granularity. The formulated problem consists of energy management at a large
timescale, as well as rate control, channel selection, and power allocation at
a small timescale. To address this challenge, we develop an online solution to
guarantee bounded performance deviation with only causal information.
Specifically, Lyapunov optimization is leveraged to transform the long-term
stochastic optimization problem into a series of short-term deterministic
optimization problems. Then, a low-complexity rate control algorithm is
developed based on alternating direction method of multipliers (ADMM), which
accelerates the convergence speed via the decomposition-coordination approach.
Next, the joint channel selection and power allocation problem is transformed
into a one-to-many matching problem, and solved by the proposed price-based
matching with quota restriction. Finally, the proposed algorithm is verified
through simulations under various system configurations.


title:
Transcendence and continued fraction expansion of values of Hecke-Mahler
  series

abstract:
  Let $\theta$ and $\rho$ be real numbers with $0 \le \theta, \rho < 1$ and
$\theta$ irrational. We show that the Hecke-Mahler series $$ F_{\theta, \rho}
(z_1, z_2) = \sum_{k_1 \ge 1} \, \sum_{k_2 = 1}^{\lfloor k_1 \theta + \rho
\rfloor} \, z_1^{k_1} z_2^{k_2}, $$ where $\lfloor \cdot \rfloor$ denotes the
integer part function, takes transcendental values at any algebraic point
$(\beta, \alpha)$ with $0 < |\beta|, |\beta \alpha^\theta | < 1$. This extends
earlier results of Mahler (1929) and Loxton and van der Poorten (1977), who
settled the case $\rho=0$. Furthermore, for positive integers $b$ and $a$, with
$b \ge 2$ and $a$ congruent to $1$ modulo $b-1$, we give the continued fraction
expansion of the number
  $$
  {(b-1)^2\over b} F_{\theta, \rho} \left({1\over b}, {1\over
a}\right)+{\lfloor \theta+\rho\rfloor(b-1)\over b^2a},
  $$ from which we derive a formula giving the irrationality exponent of
$F_{\theta, \rho} (1/b, 1/a)$.


title:
Kinematics of the Galaxy from Young Open Star Clusters with Data from
  the Gaia EDR3 Catalogue

abstract:
  We have analyzed the kinematics of open star clusters (OSCs) with the proper
motions and distances calculated by Hao et al. based on Gaia EDR3 data. The
mean line-of-sight velocities are known for a number of clusters from this
list. We show that the Galactic rotation parameters determined from samples of
OSCs with various ages are in good agreement between themselves. The most
interesting results have been obtained from a sample of 967 youngest OSCs with
a mean age of 18 Myr. In particular, we have found the following parameters of
the angular velocity of Galactic rotation using only their proper motions and
distances:
  $\Omega_0 =28.01\pm0.15$ km s$^{-1}$ kpc$^{-1},$
  $\Omega^{'}_0=-3.674\pm0.040$ km s$^{-1}$ kpc$^{-2},$ and
  $\Omega^{''}_0=0.565\pm0.023$ km s$^{-1}$ kpc$^{-3}$. The circular rotation
velocity of the solar neighborhood around the Galactic center here is
$V_0=226.9\pm3.1$ km s$^{-1}$ for the adopted Galactocentric distance of the
Sun $R_0=8.1\pm0.1$ kpc. The parameters of the spiral density wave have been
determined from the space velocities of 233 young clusters. The amplitudes of
the radial and tangential velocity perturbations produced by the spiral density
wave are $f_R=9.1\pm0.8$ km s$^{-1}$ and $f_\theta=4.6\pm1.2$ km s$^{-1}$,
respectively; the perturbation wavelengths are $\lambda_R=3.3\pm0.5$ kpc and
$\lambda_\theta=2.6\pm0.6$ kpc for the the adopted four-armed spiral pattern.
The Sun's phase in the spiral density wave has been found to be
$(\chi_\odot)_R\approx-180^\circ$.


title:
Identifying Boundary Conditions with the Syntax and Semantic Information
  of Goals

abstract:
  In goal-oriented requirement engineering, boundary conditions(BC) are used to
capture the divergence of goals, i.e., goals cannot be satisfied as a whole in
some circumstances. As the goals are formally described by temporal logic,
solving BCs automatically helps engineers judge whether there is negligence in
the goal requirements. Several efforts have been devoted to computing the BCs
as well as to reducing the number of redundant BCs as well. However, the
state-of-the-art algorithms do not leverage the logic information behind the
specification and are not efficient enough for use in reality. In addition,
even though reducing redundant BCs are explored in previous work, the computed
BCs may be still not valuable to engineering for debugging. In this paper, we
start from scratch to establish the fundamental framework for the BC problem.
Based on that, we first present a new approach SyntacBC to identify BCs with
the syntax information of goals. The experimental results show that this method
is able to achieve a >1000X speed-up than the previous state-of-the-art
methodology. Inspired by the results, we found the defects of BCs solved by our
method and also by the previous ones, i.e., most of BCs computed are difficult
for debugging. As a result, we leverage the semantics information of goals and
propose an automata-based method for solving BCs, namely SemanticBC, which can
not only find the minimal scope of divergence, but also produce
easy-to-understand BCs without losing performance.


title:
On the off-shell superfield Lagrangian formulation of $4D$,
  $\mathcal{N}{=}\,1$ supersymmetric infinite spin theory

abstract:
  We develop a complete off-shell Lagrangian description of the free $4D, {\cal
N}=1$ supersymmetric theory of infinite spin. Bosonic and fermionic fields are
formulated in terms of spin-tensor fields with dotted and undotted indices. The
corresponding Lagrangians for bosonic and fermionic infinite spin fields
entering into the on-shell supersymmetric model are derived within the BRST
method. Lagrangian for this supersymmetric model is written in terms of the
complex infinite spin bosonic field and infinite spin fermionic Weyl field
subject to supersymmetry transformations. The fields involved into the on-shell
supersymmetric Lagrangian can be considered as components of six infinite spin
chiral and antichiral multiplets. These multiplets are extended to the
corresponding infinite spin chiral and antichiral superfields so that two
chiral and antichiral superfields contain among the components the basic fields
of an infinite spin supermultiplet and extra four chiral and antichiral
superfields containing only the auxiliary fields needed for the Lagrangian
formulation. The superfield Lagrangian is constructed in terms of these six
chiral and antichiral supefields, and we show that the component form of this
superfield Lagrangian exactly coincides with the previously found component
supersymmetric Lagrangian after eliminating the component fields added to
construct (anti)chiral superfields.


title:
Privileged Attribution Constrained Deep Networks for Facial Expression
  Recognition

abstract:
  Facial Expression Recognition (FER) is crucial in many research domains
because it enables machines to better understand human behaviours. FER methods
face the problems of relatively small datasets and noisy data that don't allow
classical networks to generalize well. To alleviate these issues, we guide the
model to concentrate on specific facial areas like the eyes, the mouth or the
eyebrows, which we argue are decisive to recognise facial expressions. We
propose the Privileged Attribution Loss (PAL), a method that directs the
attention of the model towards the most salient facial regions by encouraging
its attribution maps to correspond to a heatmap formed by facial landmarks.
Furthermore, we introduce several channel strategies that allow the model to
have more degrees of freedom. The proposed method is independent of the
backbone architecture and doesn't need additional semantic information at test
time. Finally, experimental results show that the proposed PAL method
outperforms current state-of-the-art methods on both RAF-DB and AffectNet.


title:
Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some
  benchmarks

abstract:
  The Donate Speech campaign has so far succeeded in gathering approximately
3600 hours of ordinary, colloquial Finnish speech into the Lahjoita puhetta
(Donate Speech) corpus. The corpus includes over twenty thousand speakers from
all the regions of Finland and from all age brackets. The primary goals of the
collection were to create a representative, large-scale resource to study
spontaneous spoken Finnish and to accelerate the development of language
technology and speech-based services. In this paper, we present the collection
process and the collected corpus, and showcase its versatility through multiple
use cases. The evaluated use cases include: automatic speech recognition of
spontaneous speech, detection of age, gender, dialect and topic and metadata
analysis. We provide benchmarks for the use cases, as well down loadable,
trained baseline systems with open-source code for reproducibility. One further
use case is to verify the metadata and transcripts given in this corpus itself,
and to suggest artificial metadata and transcripts for the part of the corpus
where it is missing.


title:
Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named
  Entity Recognition

abstract:
  Named entity recognition (NER) is the process of recognising and classifying
important information (entities) in text. Proper nouns, such as a person's
name, an organization's name, or a location's name, are examples of entities.
The NER is one of the important modules in applications like human resources,
customer support, search engines, content classification, and academia. In this
work, we consider NER for low-resource Indian languages like Hindi and Marathi.
The transformer-based models have been widely used for NER tasks. We consider
different variations of BERT like base-BERT, RoBERTa, and AlBERT and benchmark
them on publicly available Hindi and Marathi NER datasets. We provide an
exhaustive comparison of different monolingual and multilingual
transformer-based models and establish simple baselines currently missing in
the literature. We show that the monolingual MahaRoBERTa model performs the
best for Marathi NER whereas the multilingual XLM-RoBERTa performs the best for
Hindi NER. We also perform cross-language evaluation and present mixed
observations.


title:
The Optical Two and Three-Dimensional Fundamental Plane Correlations for
  More than 180 Gamma-Ray Burst Afterglows with Swift/UVOT, RATIR, and the
  SUBARU Telescope

abstract:
  Gamma-ray bursts (GRBs) are fascinating events due to their panchromatic
nature. We study optical plateaus in GRB afterglows via an extended search into
archival data. We comprehensively analyze all published GRBs with known
redshifts and optical plateaus observed by many ground-based telescopes (e.g.,
Subaru Telescope, RATIR) around the world and several space-based observatories
such as the Neil Gehrels Swift Observatory. We fit 502 optical light curves
(LCs), showing the existence of the plateau in 181 cases. This sample is 77\%
larger than the previous one \citep{dainotti2021b}, and it is the largest
compilation so far of optical plateaus. We discover the 3D fundamental plane
relation at optical wavelengths using this sample. This correlation is between
the rest-frame time at the end of the plateau emission, $T^{*}_{\rm opt}$, its
optical luminosity, $L_{\rm opt}$, and the peak in the optical prompt emission,
$L_{\rm peak, opt}$, thus resembling the three-dimensional (3D) X-ray
fundamental plane relation \citep{dainotti2016}. We correct our sample for
redshift evolution and selection effects, discovering that this correlation is
indeed intrinsic to GRB physics. We investigate the rest-frame end time
distributions in X-rays and optical ($T^{*}_{\rm opt}$, $T^{*}_{\rm X}$), and
conclude that the plateau is achromatic only when selection biases are not
considered. We also investigate if the 3D optical correlation may be a new
discriminant between optical GRB classes and {\bf find that there is no
significant separation between the classes compared to the Gold sample plane
after correcting for evolution.}


title: Neural Reflectance for Shape Recovery with Shadow Handling

abstract:
  This paper aims at recovering the shape of a scene with unknown,
non-Lambertian, and possibly spatially-varying surface materials. When the
shape of the object is highly complex and that shadows cast on the surface, the
task becomes very challenging. To overcome these challenges, we propose a
coordinate-based deep MLP (multilayer perceptron) to parameterize both the
unknown 3D shape and the unknown reflectance at every surface point. This
network is able to leverage the observed photometric variance and shadows on
the surface, and recover both surface shape and general non-Lambertian
reflectance. We explicitly predict cast shadows, mitigating possible artifacts
on these shadowing regions, leading to higher estimation accuracy. Our
framework is entirely self-supervised, in the sense that it requires neither
ground truth shape nor BRDF. Tests on real-world images demonstrate that our
method outperform existing methods by a significant margin. Thanks to the small
size of the MLP-net, our method is an order of magnitude faster than previous
CNN-based methods.


title:
SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG
  Signal Classification

abstract:
  In this article, we propose a sparse spectra graph convolutional network
(SSGCNet) for solving Epileptic EEG signal classification problems. The aim is
to achieve a lightweight deep learning model without losing model
classification accuracy. We propose a weighted neighborhood field graph (WNFG)
to represent EEG signals, which reduces the redundant edges between graph
nodes. WNFG has lower time complexity and memory usage than the conventional
solutions. Using the graph representation, the sequential graph convolutional
network is based on a combination of sparse weight pruning technique and the
alternating direction method of multipliers (ADMM). Our approach can reduce
computation complexity without effect on classification accuracy. We also
present convergence results for the proposed approach. The performance of the
approach is illustrated in public and clinical-real datasets. Compared with the
existing literature, our WNFG of EEG signals achieves up to 10 times of
redundant edge reduction, and our approach achieves up to 97 times of model
pruning without loss of classification accuracy.


title: A Measurement of the Effective Mean-Free-Path of Solar Wind Protons

abstract:
  Weakly collisional plasmas are subject to nonlinear relaxation processes,
which can operate at rates much faster than the particle collision frequencies.
This causes the plasma to respond like a magnetised fluid despite having long
particle mean-free-paths. In this Letter the effective collisional mechanisms
are modelled in the plasma kinetic equation to produce density, pressure and
magnetic field responses to compare with spacecraft measurements of the solar
wind compressive fluctuations at 1 AU. This enables a measurement of the
effective mean-free-path of the solar wind protons, found to be 4.35 $\times
10^5$ km, which is $\sim 10^3$ times shorter than the collisional
mean-free-path. These measurements are shown to support the effective fluid
behavior of the solar wind at scales above the proton gyroradius and
demonstrate that effective collision processes alter the thermodynamics and
transport of weakly collisional plasmas.


title: Improved Communication Complexity of Fault-Tolerant Consensus

abstract:
  Consensus is one of the most thoroughly studied problems in distributed
computing, yet there are still complexity gaps that have not been bridged for
decades. In particular, in the classical message-passing setting with
processes' crashes, since the seminal works of Bar-Joseph and Ben-Or [1998]
\cite{Bar-JosephB98} and Aspnes and Waarts [1996, 1998]
\cite{AspnesW-SICOMP-96,Aspnes-JACM-98} in the previous century, there is still
a fundamental unresolved question about communication complexity of fast
randomized Consensus against a (strong) adaptive adversary crashing processes
arbitrarily online. The best known upper bound on the number of communication
bits is $\Theta(\frac{n^{3/2}}{\sqrt{\log{n}}})$ per process, while the best
lower bound is $\Omega(1)$. This is in contrast to randomized Consensus against
a (weak) oblivious adversary, for which time-almost-optimal algorithms
guarantee amortized $O(1)$ communication bits per process \cite{GK-SODA-10}. We
design an algorithm against adaptive adversary that reduces the communication
gap by nearly linear factor to $O(\sqrt{n}\cdot\text{polylog } n)$ bits per
process, while keeping almost-optimal (up to factor $O(\log^3 n)$) time
complexity $O(\sqrt{n}\cdot\log^{5/2} n)$.
  More surprisingly, we show this complexity indeed can be lowered further, but
at the expense of increasing time complexity, i.e., there is a {\em trade-off}
between communication complexity and time complexity. More specifically, our
main Consensus algorithm allows to reduce communication complexity per process
to any value from $\text{polylog } n$ to $O(\sqrt{n}\cdot\text{polylog } n)$,
as long as Time $\times$ Communication $= O(n\cdot \text{polylog } n)$.
Similarly, reducing time complexity requires more random bits per process,
i.e., Time $\times$ Randomness $=O(n\cdot \text{polylog } n)$.


title:
k-Rater Reliability: The Correct Unit of Reliability for Aggregated
  Human Annotations

abstract:
  Since the inception of crowdsourcing, aggregation has been a common strategy
for dealing with unreliable data. Aggregate ratings are more reliable than
individual ones. However, many natural language processing (NLP) applications
that rely on aggregate ratings only report the reliability of individual
ratings, which is the incorrect unit of analysis. In these instances, the data
reliability is under-reported, and a proposed k-rater reliability (kRR) should
be used as the correct data reliability for aggregated datasets. It is a
multi-rater generalization of inter-rater reliability (IRR). We conducted two
replications of the WordSim-353 benchmark, and present empirical, analytical,
and bootstrap-based methods for computing kRR on WordSim-353. These methods
produce very similar results. We hope this discussion will nudge researchers to
report kRR in addition to IRR.


title:
Quasar standardization: Overcoming Selection Biases and Redshift
  Evolution

abstract:
  Quasars (QSOs) are extremely luminous active galatic nuclei currently
observed up to redshift $z=7.642$. As such, they have the potential to be the
next rung of the cosmic distance ladder beyond SNe Ia, if they can reliably be
used as cosmological probes. The main issue in adopting QSOs as standard
candles (similarly to Gamma-Ray Bursts) is the large intrinsic scatter in the
relations between their observed properties. This could be overcome by finding
correlations among their observables that are intrinsic to the physics of QSOs
and not artifacts of selection biases and/or redshift evolution. The
reliability of these correlations should be verified through well-established
statistical tests. The correlation between the ultraviolet (UV) and X-ray
fluxes developed by Risaliti \& Lusso is one of the most promising relations.
We apply a statistical method to correct this relation for redshift evolution
and selection biases. \textbf{Remarkably, we recover the the same parameters of
the slope and the normalization as Risaliti \& Lusso. Our results establish the
reliability of this relation, which is intrinsic to the QSO properties and not
merely an effect of selection biases or redshift evolution. Hence,} the
possibility to standardize QSOs as cosmological candles, thereby extending the
Hubble diagram up to $z=7.54$.


title:
NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep
  Neural Networks

abstract:
  Deep learning has recently been widely applied to many applications across
different domains, e.g., image classification and audio recognition. However,
the quality of Deep Neural Networks (DNNs) still raises concerns in the
practical operational environment, which calls for systematic testing,
especially in safety-critical scenarios. Inspired by software testing, a number
of structural coverage criteria are designed and proposed to measure the test
adequacy of DNNs. However, due to the blackbox nature of DNN, the existing
structural coverage criteria are difficult to interpret, making it hard to
understand the underlying principles of these criteria. The relationship
between the structural coverage and the decision logic of DNNs is unknown.
Moreover, recent studies have further revealed the non-existence of correlation
between the structural coverage and DNN defect detection, which further posts
concerns on what a suitable DNN testing criterion should be.
  In this paper, we propose the interpretable coverage criteria through
constructing the decision structure of a DNN. Mirroring the control flow graph
of the traditional program, we first extract a decision graph from a DNN based
on its interpretation, where a path of the decision graph represents a decision
logic of the DNN. Based on the control flow and data flow of the decision
graph, we propose two variants of path coverage to measure the adequacy of the
test cases in exercising the decision logic. The higher the path coverage, the
more diverse decision logic the DNN is expected to be explored. Our large-scale
evaluation results demonstrate that: the path in the decision graph is
effective in characterizing the decision of the DNN, and the proposed coverage
criteria are also sensitive with errors including natural errors and
adversarial examples, and strongly correlated with the output impartiality.


title:
A novel view: edge isoperimetric methods and reliability evaluation of
  several kinds of conditional edge-connectivity of interconnection networks

abstract:
  Reliability evaluation and fault tolerance of an interconnection network of
some parallel and distributed systems are discussed separately under various
link-faulty hypotheses in terms of different $\mathcal{P}$-conditional
edge-connectivity. With the help of edge isoperimetric problem's method in
combinatorics, this paper mainly offers a novel and unified view to investigate
the $\mathcal{P}$-conditional edge-connectivities of hamming graph $K_{L}^{n}$
with satisfying the property that each minimum $\mathcal{P}$-conditional
edge-cut separates the $K_{L}^{n}$ just into two components, such as
$L^{t}$-extra edge-connectivity, $t$-embedded edge-connectivity, cyclic
edge-connectivity, $(L-1)t$-super edge-connectivity, $(L-1)t$-average
edge-connectivity and $L^{t}$-th isoperimetric edge-connectivity. They share
the same values in form of $(L-1)(n-t)L^{t}$ (except for cyclic
edge-connectivity), which equals to the minimum number of links-faulty
resulting in an $L$-ary-$n$-dimensional sub-layer from $K_{L}^{n}$. Besides, we
also obtain the exact values of $h$-extra edge-connectivity and $h$-th
isoperimetric edge-connectivity of hamming graph $K_{L}^{n}$ for each $h\leq
L^{\lfloor {\frac{n}{2}} \rfloor}$. For the case $L=2$, $K_2^n=Q_n$ is
$n$-dimensional hypercube. Our results can be applied to more generalized class
of networks, called $n$-dim-ensional bijective connection networks, which
contains hypercubes, twisted cubes, crossed cubes, M\"obius cubes, locally
twisted cubes and so on. Our results improve several previous results on this
topic.


title:
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point
  Cloud Generation

abstract:
  We propose WarpingGAN, an effective and efficient 3D point cloud generation
network. Unlike existing methods that generate point clouds by directly
learning the mapping functions between latent codes and 3D shapes, Warping-GAN
learns a unified local-warping function to warp multiple identical pre-defined
priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D
shapes driven by local structure-aware semantics. In addition, we also
ingeniously utilize the principle of the discriminator and tailor a stitching
loss to eliminate the gaps between different partitions of a generated shape
corresponding to different priors for boosting quality. Owing to the novel
generating mechanism, WarpingGAN, a single lightweight network after one-time
training, is capable of efficiently generating uniformly distributed 3D point
clouds with various resolutions. Extensive experimental results demonstrate the
superiority of our WarpingGAN over state-of-the-art methods in terms of
quantitative metrics, visual quality, and efficiency. The source code is
publicly available at https://github.com/yztang4/WarpingGAN.git.


title: A Rationale-Centric Framework for Human-in-the-loop Machine Learning

abstract:
  We present a novel rationale-centric framework with human-in-the-loop --
Rationales-centric Double-robustness Learning (RDL) -- to boost model
out-of-distribution performance in few-shot learning scenarios. By using static
semi-factual generation and dynamic human-intervened correction, RDL exploits
rationales (i.e. phrases that cause the prediction), human interventions and
semi-factual augmentations to decouple spurious associations and bias models
towards generally applicable underlying distributions, which enables fast and
accurate generalisation. Experimental results show that RDL leads to
significant prediction benefits on both in-distribution and out-of-distribution
tests compared to many state-of-the-art benchmarks -- especially for few-shot
learning scenarios. We also perform extensive ablation studies to support
in-depth analyses of each component in our framework.


title: Learning Dense Correspondence from Synthetic Environments

abstract:
  Estimation of human shape and pose from a single image is a challenging task.
It is an even more difficult problem to map the identified human shape onto a
3D human model. Existing methods map manually labelled human pixels in real 2D
images onto the 3D surface, which is prone to human error, and the sparsity of
available annotated data often leads to sub-optimal results. We propose to
solve the problem of data scarcity by training 2D-3D human mapping algorithms
using automatically generated synthetic data for which exact and dense 2D-3D
correspondence is known. Such a learning strategy using synthetic environments
has a high generalisation potential towards real-world data. Using different
camera parameter variations, background and lighting settings, we created
precise ground truth data that constitutes a wider distribution. We evaluate
the performance of models trained on synthetic using the COCO dataset and
validation framework. Results show that training 2D-3D mapping network models
on synthetic data is a viable alternative to using real data.


title: Response strengths of open systems at exceptional points

abstract:
  Open quantum and wave systems exhibit exotic degeneracies at exceptional
points in parameter space that have attracted considerable attention in various
fields of physics, including optics and photonics. One reason is the strong
response of open systems at such degeneracies to external perturbations and
excitations. We introduce two characteristics of exceptional points that
quantity the response in terms of energy eigenvalues and eigenstates,
intensity, and dynamics. The concept is verified for several physically
relevant examples. This work provides a new perspective on the physics of
exceptional points.


title:
Rubik's Cube Operator: A Plug And Play Permutation Module for Better
  Arranging High Dimensional Industrial Data in Deep Convolutional Processes

abstract:
  The convolutional neural network (CNN) has been widely applied to process the
industrial data based tensor input, which integrates data records of
distributed industrial systems from the spatial, temporal, and system dynamics
aspects. However, unlike images, information in the industrial data based
tensor is not necessarily spatially ordered. Thus, directly applying CNN is
ineffective. To tackle such issue, we propose a plug and play module, the
Rubik's Cube Operator (RCO), to adaptively permutate the data organization of
the industrial data based tensor to an optimal or suboptimal order of
attributes before being processed by CNNs, which can be updated with subsequent
CNNs together via the gradient-based optimizer. The proposed RCO maintains K
binary and right stochastic permutation matrices to permutate attributes of K
axes of the input industrial data based tensor. A novel learning process is
proposed to enable learning permutation matrices from data, where the
Gumbel-Softmax is employed to reparameterize elements of permutation matrices,
and the soft regularization loss is proposed and added to the task-specific
loss to ensure the feature diversity of the permuted data. We verify the
effectiveness of the proposed RCO via considering two representative learning
tasks processing industrial data via CNNs, the wind power prediction (WPP) and
the wind speed prediction (WSP) from the renewable energy domain. Computational
experiments are conducted based on four datasets collected from different wind
farms and the results demonstrate that the proposed RCO can improve the
performance of CNN based networks significantly.


title:
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of
  Stationary Policies

abstract:
  This paper gives the first polynomial-time algorithm for tabular Markov
Decision Processes (MDP) that enjoys a regret bound \emph{independent on the
planning horizon}. Specifically, we consider tabular MDP with $S$ states, $A$
actions, a planning horizon $H$, total reward bounded by $1$, and the agent
plays for $K$ episodes. We design an algorithm that achieves an
$O\left(\mathrm{poly}(S,A,\log K)\sqrt{K}\right)$ regret in contrast to
existing bounds which either has an additional $\mathrm{polylog}(H)$
dependency~\citep{zhang2020reinforcement} or has an exponential dependency on
$S$~\citep{li2021settling}. Our result relies on a sequence of new structural
lemmas establishing the approximation power, stability, and concentration
property of stationary policies, which can have applications in other problems
related to Markov chains.


title:
The expected values and limiting behaviours for the Gutman index,
  Schultz index, multiplicative degree-Kirchhoff index and additive
  degree-kirchhoff index of a random cyclooctane chain

abstract:
  In this paper, we first introduce the explicit analytical formulas for the
expected values of the Gutman and Schultz indices for a random cyclooctane
chain COCn. Meanwhile, the explicit formulas of the variances of the Gutman and
Schultz indices for a random cyclooctane chain are determined and we prove
these two indices are asymptotically subject to normal distribution.
Furthermore, we are surprised to find the variances of Kf*(COCn) and Kf+(COCn)
for a random cyclooctane chain based on the known results of others' paper and
they are asymptotically subject to normal distribution.


title: Metalenses with polarization-independent adaptive nano-antennas

abstract:
  Metalens research has made major advances in recent years. These advances
rely on the simple design principle of arranging meta-atoms in regular arrays
to create an arbitrary phase and polarization profile. Unfortunately, the
concept of equally spaced meta-atoms reaches its limit for high deflection
angles where the deflection efficiency decreases. The efficiency can be
increased using nano-antennas with multiple elements, but their polarization
sensitivity hinders their application in metalenses. Here, we show that by
designing polarization-insensitive dimer nano-antennas and abandoning the
principle of equally spaced unit cells, polarization-independent ultrahigh
numerical aperture (NA=1.48) oil-immersion operation with an efficiency of 43%
can be demonstrated. This represents a significant improvement on other
polarization-independent designs at visible wavelength. We also use this single
layer metalens to replace a conventional objective lens and demonstrate the
confocal scanning microscopic imaging of a grating with a period of 300 nm at
532 nm operating wavelength. Overall, our results experimentally demonstrate a
novel design concept that further improves metalens performance.


title: TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference

abstract:
  Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Learning
models for Time Series analysis. We introduce an automated exploration approach
and a library of optimized kernels to map TCNs on Parallel Ultra-Low Power
(PULP) microcontrollers. Our approach minimizes latency and energy by
exploiting a layer tiling optimizer to jointly find the tiling dimensions and
select among alternative implementations of the causal and dilated
1D-convolution operations at the core of TCNs. We benchmark our approach on a
commercial PULP device, achieving up to 103X lower latency and 20.3X lower
energy than the Cube-AI toolkit executed on the STM32L4 and from 2.9X to 26.6X
lower energy compared to commercial closed-source and academic open-source
approaches on the same hardware target.


title: Multitasking Framework for Unsupervised Simple Definition Generation

abstract:
  The definition generation task can help language learners by providing
explanations for unfamiliar words. This task has attracted much attention in
recent years. We propose a novel task of Simple Definition Generation (SDG) to
help language learners and low literacy readers. A significant challenge of
this task is the lack of learner's dictionaries in many languages, and
therefore the lack of data for supervised training. We explore this task and
propose a multitasking framework SimpDefiner that only requires a standard
dictionary with complex definitions and a corpus containing arbitrary simple
texts. We disentangle the complexity factors from the text by carefully
designing a parameter sharing scheme between two decoders. By jointly training
these components, the framework can generate both complex and simple
definitions simultaneously. We demonstrate that the framework can generate
relevant, simple definitions for the target words through automatic and manual
evaluations on English and Chinese datasets. Our method outperforms the
baseline model by a 1.77 SARI score on the English dataset, and raises the
proportion of the low level (HSK level 1-3) words in Chinese definitions by
3.87%.


title: Modelling volume-outcome relationships in health care

abstract:
  Despite the ongoing strong interest in associations between quality of care
and the volume of health care providers, a unified statistical framework for
analyzing them is missing, and many studies suffer from poor statistical
modelling choices. We propose a flexible, additive mixed model for studying
volume-outcome associations in health care that takes into account individual
patient characteristics as well as provider-specific effects through a
multi-level approach. More specifically, we treat volume as a continuous
variable, and its effect on the considered outcome is modelled as a smooth
function. We take account of different case-mixes by including patient-specific
risk factors and of clustering on the provider level through random intercepts.
This strategy enables us to extract a smooth volume effect as well as
volume-independent provider effects. These two quantities can be compared
directly in terms of their magnitude, which gives insight into the sources of
variability of quality of care. Based on a causal DAG, we derive conditions
under which the volume-effect can be interpreted as a causal effect. The paper
provides confidence sets for each of the estimated quantities relying on joint
estimation of all effects and parameters. Our approach is illustrated through
simulation studies and an application to German health care data.
  Keywords: health care quality measurement, volume-outcome analysis, minimum
provider volume, additive regression models, random intercept


title: The Fixed Sub-Center: A Better Way to Capture Data Complexity

abstract:
  Treating class with a single center may hardly capture data distribution
complexities. Using multiple sub-centers is an alternative way to address this
problem. However, highly correlated sub-classes, the classifier's parameters
grow linearly with the number of classes, and lack of intra-class compactness
are three typical issues that need to be addressed in existing multi-subclass
methods. To this end, we propose to use Fixed Sub-Center (F-SC), which allows
the model to create more discrepant sub-centers while saving memory and cutting
computational costs considerably. The F-SC specifically, first samples a class
center Ui for each class from a uniform distribution, and then generates a
normal distribution for each class, where the mean is equal to Ui. Finally, the
sub-centers are sampled based on the normal distribution corresponding to each
class, and the sub-centers are fixed during the training process avoiding the
overhead of gradient calculation. Moreover, F-SC penalizes the Euclidean
distance between the samples and their corresponding sub-centers, it helps
remain intra-compactness. The experimental results show that F-SC significantly
improves the accuracy of both image classification and fine-grained recognition
tasks.


title:
Towards Escaping from Language Bias and OCR Error: Semantics-Centered
  Text Visual Question Answering

abstract:
  Texts in scene images convey critical information for scene understanding and
reasoning. The abilities of reading and reasoning matter for the model in the
text-based visual question answering (TextVQA) process. However, current
TextVQA models do not center on the text and suffer from several limitations.
The model is easily dominated by language biases and optical character
recognition (OCR) errors due to the absence of semantic guidance in the answer
prediction process. In this paper, we propose a novel Semantics-Centered
Network (SC-Net) that consists of an instance-level contrastive semantic
prediction module (ICSP) and a semantics-centered transformer module (SCT).
Equipped with the two modules, the semantics-centered model can resist the
language biases and the accumulated errors from OCR. Extensive experiments on
TextVQA and ST-VQA datasets show the effectiveness of our model. SC-Net
surpasses previous works with a noticeable margin and is more reasonable for
the TextVQA task.


title: No evidence for EDE from Planck data in extended scenarios

abstract:
  The latest data release from the ACT CMB experiment (in combination with
previous WMAP data) shows evidence for an Early Dark Energy component at more
than $3$ standard deviations. The same conclusion has been recently shown to
hold when temperature data from the Planck experiment limited to intermediate
angular scales ($l \le 650$) are included while it vanishes when the full
Planck dataset is considered. However, it has been shown that the full Planck
dataset exhibits an anomalous lensing component and a preference for a closed
universe at the level of three standard deviation. It is therefore of utmost
importance to investigate if these anomalies could anti-correlate with an early
dark energy component and hide its presence during the process of parameter
extraction. Here we demonstrate that extended parameters choices as curvature,
equation of state of dark energy and lensing amplitude $A_L$ have no impact on
the Planck constraints on EDE. In practice, EDE does not solve Planck angular
spectra anomalies. This indicates that current CMB evidence for an EDE
component comes essentially from the ACT-DR4 dataset.


title: A Note on the Gessel Numbers

abstract:
  The Gessel number $P(n,r)$ represents the number of lattice paths in a plane
with unit horizontal and vertical steps from $(0,0)$ to $(n+r,n+r-1)$ that
never touch any of the points from the set $\{(x,x)\in \mathbb{Z}^2: x \geq
r\}$. In this paper, we use combinatorial arguments to derive a recurrence
relation between $P(n,r)$ and $P(n-1,r+1)$. Also, we give a new proof for a
well-known closed formula for $P(n,r)$. Moreover, a new combinatorial
interpretation for the Gessel numbers is presented.


title:
Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based
  Gesture Recognition

abstract:
  Human-machine interaction is gaining traction in rehabilitation tasks, such
as controlling prosthetic hands or robotic arms. Gesture recognition exploiting
surface electromyographic (sEMG) signals is one of the most promising
approaches, given that sEMG signal acquisition is non-invasive and is directly
related to muscle contraction. However, the analysis of these signals still
presents many challenges since similar gestures result in similar muscle
contractions. Thus the resulting signal shapes are almost identical, leading to
low classification accuracy. To tackle this challenge, complex neural networks
are employed, which require large memory footprints, consume relatively high
energy and limit the maximum battery life of devices used for classification.
This work addresses this problem with the introduction of the Bioformers. This
new family of ultra-small attention-based architectures approaches
state-of-the-art performance while reducing the number of parameters and
operations of 4.9X. Additionally, by introducing a new inter-subjects
pre-training, we improve the accuracy of our best Bioformer by 3.39%, matching
state-of-the-art accuracy without any additional inference cost. Deploying our
best performing Bioformer on a Parallel, Ultra-Low Power (PULP) microcontroller
unit (MCU), the GreenWaves GAP8, we achieve an inference latency and energy of
2.72 ms and 0.14 mJ, respectively, 8.0X lower than the previous
state-of-the-art neural network, while occupying just 94.2 kB of memory.


title: 3D inhomogeneous self-accelerating beams

abstract:
  We propose and generate a new class of structured light fulfilling
quantum-like coherent states based on a set of circular Airy vortex modes. Such
coherent-state wave packets possess strong focus with both radial and angular
self-accelerations, which exploit more general 3D inhomogeneous velocity
control with global spatial symmetry of multilayer rotation akin to galactic
kinematics, as termed galaxy waves. Galaxy waves are endowed with new degrees
of freedom to control strong focusing and acceleration of 3D structured light,
promising numerous applications in optical trapping, manufacturing, and
nonlinear optics.


title:
Drastic change in magnetic anisotropy of UTe2 under pressure revealed by
  125Te-NMR

abstract:
  To investigate the normal-state magnetic properties of UTe2 under pressure,
we perform 125Te nuclear magnetic resonance (NMR) measurements up to 2 GPa.
Below 1.2 GPa, the b-axis NMR Knight shift shows a broad maximum at the
so-called T_chimax on cooling, which is consistent with the magnetization
measurement under pressure. T_chimax decreases with increasing pressure and
disappears at the critical pressure Pc = 1.7 GPa, above which superconductivity
is destroyed. This tendency is also observed in the temperature dependence of
the nuclear spin-lattice relaxation rate 1/T1. At low pressures, 1/T1 shows a
conventional Fermi-liquid behavior (1/T1T = constant) at low temperatures,
indicating the formation of the heavy-fermion state. Above Pc, 1/T1T follows a
1/T behavior without any crossover to the heavy-fermion state down to the
lowest temperature (~3 K). In addition, the NMR signals disappear below 3~K,
due to the influence of the magnetically ordered moments. From the pressure
dependence of the T_chimax and Knight shift, it was found that the Fermi
surface character is abruptly changed at Pc, and that superconductivity is
observed only in the heavy-fermion state.


title:
Downtime Optimized Live Migration of Industrial Real-Time Control
  Services

abstract:
  Live migration of services is a prerequisite for various use cases that must
be fulfilled for the realization of Industry 4.0. In addition, many different
types of services need to provide mobility and consequently need to be migrated
live. These can be offloaded algorithms from mobile devices, such as unmanned
vehicles or robots, security services, communication services or classic
control tasks. In particular, the latter place very high demands on determinism
and latency. Here, it is of utmost importance that the downtime of the service
is as low as possible. Since existing live migration approaches try to optimize
multiple metrics such as downtime, migration time as well as energy
consumption, which are equally relevant in the IT domain, it is not possible to
use any of these approaches without adoptions.
  Therefore, a novel concept is proposed that builds on top of both existing
migration approaches as well as virtualization technologies and aims primarily
at minimizing service downtime. Furthermore, the concept is evaluated using a
test environment. The results show that a sub-millisecond downtime can be
achieved with the proposed concept. Moreover, the total migration time is in
the range of several hundred milliseconds for the highest performance setting
and two seconds for a non-invasive approach.


title:
Nonreciprocal waveguide-QED for spinning cavities with multiple coupling
  points

abstract:
  We investigate chiral emission and the single-photon scattering of spinning
cavities coupled to a meandering waveguide at multiple coupling points. It is
shown that nonreciprocal photon transmissions occur in the cavities-waveguide
system, which stems from interference effects among different coupling points,
and frequency shifts induced by the Sagnac effect. The nonlocal interference is
akin to the mechanism in giant atoms. In the single-cavity setup, by optimizing
the spinning velocity and number of coupling points, the chiral factor can
approach 1, and the chiral direction can be freely switched. Moreover,
destructive interference gives rise to the complete photon transmission in one
direction over the whole optical frequency band, with no analogy in other
quantum setups. In the multiple-cavity system, we also investigate the photon
transport properties. The results indicate a directional information flow
between different nodes. Our proposal provides a novel way to achieve quantum
nonreciprocal devices, which can be applied in large-scale quantum chiral
networks with optical waveguides.


title:
SelfRemaster: Self-Supervised Speech Restoration with
  Analysis-by-Synthesis Approach Using Channel Modeling

abstract:
  We present a self-supervised speech restoration method without paired speech
corpora. Because the previous general speech restoration method uses artificial
paired data created by applying various distortions to high-quality speech
corpora, it cannot sufficiently represent acoustic distortions of real data,
limiting the applicability. Our model consists of analysis, synthesis, and
channel modules that simulate the recording process of degraded speech and is
trained with real degraded speech data in a self-supervised manner. The
analysis module extracts distortionless speech features and distortion features
from degraded speech, while the synthesis module synthesizes the restored
speech waveform, and the channel module adds distortions to the speech
waveform. Our model also enables audio effect transfer, in which only acoustic
distortions are extracted from degraded speech and added to arbitrary
high-quality audio. Experimental evaluations with both simulated and real data
show that our method achieves significantly higher-quality speech restoration
than the previous supervised method, suggesting its applicability to real
degraded speech materials.


title: Projective Integrable Mechanical Billiards

abstract:
  In this paper, we use the projective dynamical approach to integrable
mechanical billiards as in [Zhao, 2021] to establish the integrability of
natural mechanical billiards with the Lagrange problem, which is the
superposition of two Kepler problems and a Hooke problem, with the Hooke center
at the middle of the Kepler centers, as the underlying mechanical systems, and
with any combinations of confocal conic sections with foci at the Kepler
centers as the reflection wall, in the plane, on the sphere, and in the
hyperbolic plane. This covers many previously known integrable mechanical
billiards, especially the integrable Hooke, Kepler and two-center billiards in
the plane, as has been investigated in [Takeuchi-Zhao, 2021], as subcases. The
approach of [Takeuchi-Zhao, 2021] based on conformal correspondence has been
also applied to integrable Kepler billiards in the hyperbolic plane to
illustrate their equivalence with the corresponding integrable Hooke billiards
on the hemisphere and in the hyperbolic plane as well.


title:
Probing the electronic and local structure of Sr$_{2-x}$La$_x$CoNbO$_6$
  using near-edge and extended x-ray absorption fine structures

abstract:
  We report the electronic and local structural investigation of double
pervoskites Sr$_{2-x}$La$_x$CoNbO$_6$ ($x=$ 0--1) using x-ray absorption near
edge structure (XANES) and extended x-ray absorption fine structures (EXAFS) at
the Nb, Co, and Sr $K$-edges. The $ab$ $initio$ simulations and detailed
analysis of the Nb and Co $K$-edge XANES spectra demonstrate that the observed
pre-edge features arise from the transition of 1$s$ electrons to the mixed
$p-d$ hybridized states. We reveal a $z$-out Jahn-Teller (JT) distortion in the
CoO$_6$ octahedra, which decreases monotonically due to an enhancement in the
JT inactive Co$^{2+}$ ions with $x$ . On the other hand, the $z$-in distortion
in NbO$_6$ octahedra remains unaltered up to $x=$ 0.4 and then decreases with
further increase in $x$. This sudden change in the local coordination around Nb
atoms is found to be responsible for the evolution of the antiferromagnetic
interactions in $x \geqslant$ 0.6 samples. Also, we establish a correlation
between the degree of octahedral distortion and intensity of the white line
feature in the XANES spectra and possible reason for this are discussed. More
interestingly, we observe the signature of KN$_1$ double electron excitation in
the Sr $K$-edge EXAFS spectra for all the samples, which is found to be in good
agreement with the $Z$+1 approximation. Further, the Co L$_{2,3}$ edge shows
the reduction in the crystal field strength and hence an increase in the charge
transfer energy ($\Delta_{ct}$) with the La substitution.


title:
mcBERT: Momentum Contrastive Learning with BERT for Zero-Shot Slot
  Filling

abstract:
  Zero-shot slot filling has received considerable attention to cope with the
problem of limited available data for the target domain. One of the important
factors in zero-shot learning is to make the model learn generalized and
reliable representations. For this purpose, we present mcBERT, which stands for
momentum contrastive learning with BERT, to develop a robust zero-shot slot
filling model. mcBERT uses BERT to initialize the two encoders, the query
encoder and key encoder, and is trained by applying momentum contrastive
learning. Our experimental results on the SNIPS benchmark show that mcBERT
substantially outperforms the previous models, recording a new
state-of-the-art. Besides, we also show that each component composing mcBERT
contributes to the performance improvement.


title:
Frenkel pair formation energy for cubic Fe$_3$O$_4$ in DFT+U
  calculations

abstract:
  Ab initio modelling of point defects in the cubic phase of magnetite faces
two problems: the cubic structure becomes unstable below the Verwey temperature
and there is no consensus on the electronic structure of the cubic phase (if
there is a band gap, what type of symmetry of the wavefunction should be
considered and how to describe strong electronic correlations). In this paper,
we show that the comparison of the experimental data on the band gap and the
Frenkel pair formation energy with the first-principles calculations allows to
determine a consistent DFT+U model of cubic Fe$_3$O$_4$.


title:
Generating Data to Mitigate Spurious Correlations in Natural Language
  Inference Datasets

abstract:
  Natural language processing models often exploit spurious correlations
between task-independent features and labels in datasets to perform well only
within the distributions they are trained on, while not generalising to
different task distributions. We propose to tackle this problem by generating a
debiased version of a dataset, which can then be used to train a debiased,
off-the-shelf model, by simply replacing its training data. Our approach
consists of 1) a method for training data generators to generate high-quality,
label-consistent data samples; and 2) a filtering mechanism for removing data
points that contribute to spurious correlations, measured in terms of
z-statistics. We generate debiased versions of the SNLI and MNLI datasets, and
we evaluate on a large suite of debiased, out-of-distribution, and adversarial
test sets. Results show that models trained on our debiased datasets generalise
better than those trained on the original datasets in all settings. On the
majority of the datasets, our method outperforms or performs comparably to
previous state-of-the-art debiasing strategies, and when combined with an
orthogonal technique, product-of-experts, it improves further and outperforms
previous best results of SNLI-hard and MNLI-hard.


title:
Euler-Rodrigues Parameters: A Quantum Circuit to Calculate Rigid-Body
  Rotations

abstract:
  The use of vectorial parameterization to create geometrical representations
in computational models has a large number of applications. One particular
application is the calculation of the 3D rotational motion of rigid bodies,
that could be used for the spatial location estimation from objects. Provided
the algebraic nature of this problem, it could benefit from Quantum Computing,
in particular several vectors could be superposed to be transformed with a
single operation, providing a quantum processing advantage. In this article, we
propose an implementation of a Quantum Computing algorithm to compute
Euler-Rodrigues Parameters to model rigid body rotations to transform arbitrary
functions, rotating multiple vectors in superposition. We developed this
algorithm using Qiskit, taking into account the limitations imposed by the
current Noisy Intermediate Scale Quantum (NISQ) devices, such as the reduced
number of qubits available and the limited coherence time.


title: Transformers Meet Visual Learning Understanding: A Comprehensive Review

abstract:
  Dynamic attention mechanism and global modeling ability make Transformer show
strong feature learning ability. In recent years, Transformer has become
comparable to CNNs methods in computer vision. This review mainly investigates
the current research progress of Transformer in image and video applications,
which makes a comprehensive overview of Transformer in visual learning
understanding. First, the attention mechanism is reviewed, which plays an
essential part in Transformer. And then, the visual Transformer model and the
principle of each module are introduced. Thirdly, the existing
Transformer-based models are investigated, and their performance is compared in
visual learning understanding applications. Three image tasks and two video
tasks of computer vision are investigated. The former mainly includes image
classification, object detection, and image segmentation. The latter contains
object tracking and video classification. It is significant for comparing
different models' performance in various tasks on several public benchmark data
sets. Finally, ten general problems are summarized, and the developing
prospects of the visual Transformer are given in this review.


title:
Integrality of Stickelberger elements and annihilation of natural Galois
  modules

abstract:
  To each Galois extension $L/K$ of number fields with Galois group $G$ and
each integer $r \leq 0$ one can associate Stickelberger elements in the centre
of the rational group ring $\mathbb{Q}[G]$ in terms of values of Artin
$L$-series at $r$. We show that the denominators of their coefficients are
bounded by the cardinality of the commutator subgroup $G'$ of $G$ whenever $G$
is nilpotent. Moreover, we show that, after multiplication by $|G'|$ and away
from $2$-primary parts, they annihilate the class group of $L$ if $r=0$ and
higher Quillen $K$-groups of the ring of integers in $L$ if $r<0$. This
generalizes recent progress on conjectures of Brumer and of Coates and Sinnott
from abelian to nilpotent extensions.
  For arbitrary $G$ we show that the denominators remain bounded along the
cyclotomic $\mathbb{Z}_p$-tower of $L$ for every odd prime $p$. This allows us
to give an affirmative answer to a question of Greenberg and of Gross on the
behaviour of $p$-adic Artin $L$-series at zero.


title: Essentially entropic lattice Boltzmann model: Theory and simulations

abstract:
  We present a detailed description of the essentially entropic lattice
Boltzmann model. The entropic lattice Boltzmann model guarantees unconditional
numerical stability by iteratively solving the nonlinear entropy evolution
equation. In this paper we explain the construction of closed-form analytic
solutions to this equation. We demonstrate that near equilibrium this exact
solution reduces to the standard lattice Boltzmann model. We consider a few
test cases to show that the exact solution does not exhibit any significant
deviation from the iterative solution. We also extend the analytical solution
for the ES-BGK model to remove the limitation on the Prandtl number for heat
transfer problems. The simplicity of the exact solution removes the
computational overhead and algorithmic complexity associated with the entropic
lattice Boltzmann models.


title: Diffusivity dependence of the transition path ensemble

abstract:
  Transition pathways of stochastic dynamical systems are typically
approximated by instantons. Here we show, using a dynamical system containing
two competing pathways, that at low-to-intermediate temperatures, instantons
can fail to capture the most likely transition pathways. We construct an
approximation which includes fluctuations around the instanton and, by
comparing with the results of an accurate and efficient path-space Monte Carlo
sampling method, find this approximation to hold for a wide range of
temperatures. Our work delimits the applicability of large deviation theory and
provides methods to probe these limits numerically.


title:
Personalized incentives as feedback design in generalized Nash
  equilibrium problems

abstract:
  We investigate both stationary and time-varying, nonmonotone generalized Nash
equilibrium problems that exhibit symmetric interactions among the agents,
which are known to be potential. As may happen in practical cases, however, we
envision a scenario in which the formal expression of the underlying potential
function is not available, and we design a semi-decentralized Nash equilibrium
seeking algorithm. In the proposed two-layer scheme, a coordinator iteratively
integrates the (possibly noisy and sporadic) agents' feedback to learn the
pseudo-gradients of the agents, and then design personalized incentives for
them. On their side, the agents receive those personalized incentives, compute
a solution to an extended game, and then return feedback measurements to the
coordinator. In the stationary setting, our algorithm returns a Nash
equilibrium in case the coordinator is endowed with standard learning policies,
while it returns a Nash equilibrium up to a constant, yet adjustable, error in
the time-varying case. As a motivating application, we consider the ridehailing
service provided by several companies with mobility as a service orchestration,
necessary to both handle competition among firms and avoid traffic congestion,
which is also adopted to run numerical experiments verifying our results.


title:
Duality-Induced Regularizer for Semantic Matching Knowledge Graph
  Embeddings

abstract:
  Semantic matching models -- which assume that entities with similar semantics
have similar embeddings -- have shown great power in knowledge graph embeddings
(KGE). Many existing semantic matching models use inner products in embedding
spaces to measure the plausibility of triples and quadruples in static and
temporal knowledge graphs. However, vectors that have the same inner products
with another vector can still be orthogonal to each other, which implies that
entities with similar semantics may have dissimilar embeddings. This property
of inner products significantly limits the performance of semantic matching
models. To address this challenge, we propose a novel regularizer -- namely,
DUality-induced RegulArizer (DURA) -- which effectively encourages the entities
with similar semantics to have similar embeddings. The major novelty of DURA is
based on the observation that, for an existing semantic matching KGE model
(primal), there is often another distance based KGE model (dual) closely
associated with it, which can be used as effective constraints for entity
embeddings. Experiments demonstrate that DURA consistently and significantly
improves the performance of state-of-the-art semantic matching models on both
static and temporal knowledge graph benchmarks.


title:
Why are the solutions to overdetermined problems usually "as symmetric
  as possible"?

abstract:
  In this paper, we study the symmetry properties of nondegenerate critical
points of shape functionals using the implicit function theorem. We show that,
if a shape functional is invariant with respect to some continuous group of
rotations, then its nondegenerate critical points (bounded open sets with
smooth enough boundary) share the same symmetries. We also consider the case
where the shape functional exhibits translational invariance in addition to
just rotational invariance. Finally, we study the applications of this result
to the theory of one/two-phase overdetermined problems of Serrin-type. En
passant, we give a simple proof of the fact that the ball is the only
nondegenerate critical point of the Lagrangian associated to the maximization
problem for the torsional rigidity under a volume constraint. We remark that
the proof does not rely on either the method of moving planes or rearrangement
techniques.


title:
Why the mean anomaly at epoch is not used in tests of non-Newtonian
  gravity?

abstract:
  The mean anomaly at epoch $\eta$ is one of the standard six Keplerian orbital
elements in terms of which the motion of the two-body problem is parameterized.
Along with the argument of pericenter $\omega$, $\eta$ experiences long-term
rates of change induced, among other things, by general relativity and several
modified models of gravity. Thus, in principle, it may be fruitfully adopted
together with $\omega$ in several tests of post-Newtonian gravity performed
with astronomical and astrophysical binary systems. This would allow to enhance
the gravitational signature one is interested in and to disentangle some
competing disturbing effects acting as sources of systematic bias. Nonetheless,
for some reasons unknown to the present author, $\eta$ has never been used so
far by astronomers in actual data reductions. This note aims to raise interest
in the community about the possible practical use of such an orbital element
or, at least, to induce experts in astronomical data processing to explicitly
make clear if it is not possible to use $\eta$ for testing gravitational models
and, in this case, why.


title:
Feasibility Study of Magnetism-based Indoor Positioning Methods in an
  Incineration Plant

abstract:
  In an incineration plant, remote operation from a centralized control room is
now possible, but inspection and cleaning of equipment still require a worker
to visit the site. When the plant owner reduces the number of workers due to
operation costs, it will be standard for a single worker to visit the site.
Therefore, it is necessary to monitor the location of workers in real-time to
detect unexpected human accidents quickly. Conventional methods use radio
waves, such as Wi-Fi and Bluetooth, but there is little demand for
communication equipment in the incineration plant. However, there is not enough
demand for communication facilities in the incineration plant. It is too large
to bear the cost of installing wireless access points, and Bluetooth Low Energy
(BLE) beacons just for positioning. Therefore, we are focusing on magnetism
using for indoor positioning method. In addition, the incineration plant has a
lot of types of equipment that contains a wide range of magnetized metals,
large motors, and generators. We could observe the magnetic peculiarity at each
point. Based on these assumptions, we have developed a new indoor positioning
method at the incineration plant. This paper describes the development of an
indoor positioning system for an incineration plant. And we propose three
methods for fingerprinting matching: Point matching, Path matching, and DTW
matching. The average positioning errors of these methods are 6.89 m, 0.05 m,
and 0.06 m, respectively.


title:
Gyrokinetic Theory of Low-frequency Electromagnetic Waves in
  Finite-$\beta$ Anisotropic Plasmas

abstract:
  We present a gyrokinetic theory for the electromagnetic waves and
instabilities with frequencies much lower than the ion cyclotron frequency in
finite-$\beta$ anisotropic uniform plasmas. Here, $\beta$ is the ratio between
plasma and magnetic pressures. Kinetic effects due to both the finite Larmor
radii and wave-particle resonances are fully kept in the analysis.
Corresponding linear dispersion relation and wave polarizations, valid for
general $\beta$ value and perpendicular wavelength, are then specifically
derived for a bi-Maxwellian plasma. Analytic expressions for the criteria of
kinetic firehose and mirror instabilities are also given. The mode frequency,
stability, and wave polarization of a broad spectrum of normal modes are then
investigated numerically in a systematic study over a set of parameters. Our
study clearly demonstrates that, due to the finite ion Larmor radius effect,
the ion-sound wave, mirror mode, and shear Alfven wave are intrinsically
coupled.


title: A view of the peakon world through the lens of approximation theory

abstract:
  Peakons (peaked solitons) are a particular type of solution admitted by
certain nonlinear PDEs, most famously the Camassa-Holm shallow water wave
equation. These solutions take the form of a train of peak-shaped waves,
interacting in a particle-like fashion. In this article we give an overview of
the mathematics of peakons, with special emphasis on the connections to
classical problems in analysis, such as Pad\'e approximation, mixed
Hermite-Pad\'e approximation, multi-point Pad\'e approximation, continued
fractions of Stieltjes type and (bi)orthogonal polynomials. The exposition
follows the chronological development of our understanding, exploring the
peakon solutions of the Camassa-Holm, Degasperis-Procesi, Novikov, Geng-Xue and
modified Camassa-Holm (FORQ) equations. All of these paradigm examples are
integrable systems arising as the compatibility condition of a Lax pair, and a
recurring theme in the context of peakons is the need to properly interpret
these Lax pairs in the sense of Schwartz's theory of distributions. We trace
out the path leading from distributional Lax pairs to explicit formulas for
peakon solutions via a multitude of approximation-theoretic problems, and
illustrate the dynamics of peakons with graphics.


title:
Onto4MAT: A Swarm Shepherding Ontology for Generalised Multi-Agent
  Teaming

abstract:
  Research in multi-agent teaming has increased substantially over recent
years, with knowledge-based systems to support teaming processes typically
focused on delivering functional (communicative) solutions for a team to act
meaningfully in response to direction. Enabling humans to effectively interact
and team with a swarm of autonomous cognitive agents is an open research
challenge in Human-Swarm Teaming research, partially due to the focus on
developing the enabling architectures to support these systems. Typically,
bi-directional transparency and shared semantic understanding between agents
has not prioritised a designed mechanism in Human-Swarm Teaming, potentially
limiting how a human and a swarm team can share understanding and
information\textemdash data through concepts and contexts\textemdash to achieve
a goal. To address this, we provide a formal knowledge representation design
that enables the swarm Artificial Intelligence to reason about its environment
and system, ultimately achieving a shared goal. We propose the Ontology for
Generalised Multi-Agent Teaming, Onto4MAT, to enable more effective teaming
between humans and teams through the biologically-inspired approach of
shepherding.


title:
On the area-preserving Willmore flow of small bubbles sliding on a
  domain's boundary

abstract:
  We consider the area-preserving Willmore evolution of surfaces that are close
to a half-sphere with a small radius, sliding on the boundary S of a domain
while meeting it orthogonally. We prove that the flow exists for all times and
keeps a 'half-spherical' shape. Additionally, we investigate the asymptotic
behaviour of the flow and prove that for large times the barycenter of the
surfaces approximately follows an explicit ordinary differential equation.
Imposing additional conditions on the mean curvature of S, we then establish
convergence of the flow.


title:
Optimal MIMO Combining for Blind Federated Edge Learning with Gradient
  Sparsification

abstract:
  We provide the optimal receive combining strategy for federated learning in
multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows
the clients to perform individual gradient sparsification which greatly
improves performance in scenarios with heterogeneous (non i.i.d.) training
data. The proposed method beats the benchmark by a wide margin.


title: Transient hydrodynamic phonon transport in two-dimensional disk geometry

abstract:
  This paper is a continuation of our work on the transient hydrodynamic phonon
transport from three-dimensional to two-dimensional materials. In the previous
work [Zhang \textit{et al.}, Int. J. Heat Mass Transfer 181, 121847 (2021)], a
transient heat conduction phenomenon proving the existence and uniqueness of
hydrodynamic phonon transport in three-dimensional materials was found, namely,
using a heating laser pulse to heat the materials under the environment
temperature, after the heating laser is removed, the transient temperature
could be lower than the environment temperature. Whether this phenomenon could
appear in two-dimensional materials needs further study. In this paper, the
transient heat conduction in two-dimensional disk geometry is studied based on
the phonon Boltzmann transport equation (BTE). Our results show that this
phenomenon could appear in two-dimensional disk geometry and only appear in the
hydrodynamic regime. In addition, the possibility of this phenomenon observed
by experiments is theoretically discussed by using the experimental parameters
as the input of the phonon BTE. Results show that this phenomenon could appear
in a single-layer suspended graphene disk with diameter $7~\mu$m in the
temperature range of $50-150$ K. The present work could provide theoretical
guidance for the future experimental proof of hydrodynamic phonon transport in
two-dimensional materials.