title: Implicit Mentoring: The Unacknowledged Developer Efforts in Open Source

abstract:
  Mentoring is traditionally viewed as a dyadic, top-down apprenticeship. This
perspective, however, overlooks other forms of informal mentoring taking place
in everyday activities in which developers invest time and effort, but remain
unacknowledged. Here, we investigate the different flavors of mentoring in Open
Source Software (OSS) to define and identify implicit mentoring. We first
define implicit mentoring--situations where contributors guide others through
instructions and suggestions embedded in everyday (OSS) activities--through
formative interviews with OSS contributors, a literature review, and
member-checking. Next, through an empirical investigation of Pull Requests
(PRs) in 37 Apache Projects, we build a classifier to extract implicit
mentoring and characterize it through the dual lenses of experience and gender.
Our analysis of 107,895 PRs shows that implicit mentoring occurs (27.41% of all
PRs include implicit mentoring) and it does not follow the traditional dyadic,
top-down apprenticeship model. When considering the gender of mentor-mentee
pairs, we found pervasive homophily--a preference to mentor those who are of
the same gender--in 93.81% cases. In the cross-gender mentoring instances,
women were more likely to mentor men.


title:
End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC
  Estimation

abstract:
  Neural vocoders have recently demonstrated high quality speech synthesis, but
typically require a high computational complexity. LPCNet was proposed as a way
to reduce the complexity of neural synthesis by using linear prediction (LP) to
assist an autoregressive model. At inference time, LPCNet relies on the LP
coefficients being explicitly computed from the input acoustic features. That
makes the design of LPCNet-based systems more complicated, while adding the
constraint that the input features must represent a clean speech spectrum. We
propose an end-to-end version of LPCNet that lifts these limitations by
learning to infer the LP coefficients from the input features in the frame rate
network. Results show that the proposed end-to-end approach equals or exceeds
the quality of the original LPCNet model, but without explicit LP analysis. Our
open-source end-to-end model still benefits from LPCNet's low complexity, while
allowing for any type of conditioning features.


title:
Optimal QRAM and improved unitary synthesis by quantum circuits with any
  number of ancillary qubits

abstract:
  As a cornerstone for many quantum linear algebraic and quantum machine
learning algorithms, QRAM aims to provide the transformation of
$|i\rangle|0^n\rangle \to |i\rangle|\psi_i\rangle$ for all $i\in \{0,1\}^k$ for
the given $n$-qubit states $|\psi_i\rangle$. In this paper, we construct a
quantum circuit for implementing QRAM, with depth
$O\left(n+k+\frac{2^{n+k}}{n+k+m}\right)$ and size $O\left(2^{n+k}\right)$ for
any given number $m$ of ancillary qubits. These bounds, which can also be
viewed as time-space tradeoff for the transformation, are optimal for any
integer parameters $m,k\ge 0$ and $n\ge 1$. Our construction induces a circuit
for the standard quantum state preparation (QSP) problem of $|0^n\rangle \to
|\psi\rangle$ for any $|\psi\rangle$, pinning down its depth complexity to
$\Theta(n+2^{n}/(n+m))$ and its size complexity to $\Theta(2^{n})$ for any $m$.
This improves results in a recent line of investigations of QSP with ancillary
qubits. Another fundamental problem, unitary synthesis, asks to implement a
general $n$-qubit unitary by a quantum circuit. Previous work shows a lower
bound of $\Omega(n+4^n/(n+m))$ and an upper bound of $O(n2^n)$ for
$m=\Omega(2^n/n)$ ancillary qubits. In this paper, we quadratically shrink this
gap by presenting a quantum circuit of depth of
$O\left(n2^{n/2}+\frac{n^{1/2}2^{3n/2}}{m^{1/2}}\right)$.


title:
Topological correlations in three dimensional classical Ising models: an
  exact solution with a continuous phase transition

abstract:
  We study a three-dimensional (3D) classical Ising model that is exactly
solvable when some coupling constants take certain imaginary values. The
solution combines and generalizes the Onsager-Kaufman solution of the 2D Ising
model and the solution of Kitaev's honeycomb model, leading to a
three-parameter phase diagram with a third order phase transition between two
distinct phases. Interestingly, the phases of this model are distinguished by
topological features: the expectation value of a certain family of loop
observables depend only on the topology of the loop (whether the loop is
contractible), and are quantized at rational values that differ in the two
phases. We show that a related exactly solvable 3D classical statistical model
with real coupling constants also shows the topological features of one of
these phases. Furthermore, even in the model with complex parameters, the
partition function has some physical relevance, as it can be interpreted as the
transition amplitude of a quantum dynamical process and may shed light on
dynamical quantum phase transitions.

categories: cond-mat.stat-mech math-ph math.MP quant-ph


title:
On the global well-posedness and decay of a free boundary problem of the
  Navier-Stokes equation in unbounded domains

abstract:
  In this paper, we establish the unique existence and some decay properties of
a global solution of a free boundary problem of the incompressible
Navier-Stokes equations in $L_p$ in time and $L_q$ in space framework in a
uniformly $H^2_\infty$ domain $\Omega\subset \BR^N$ for $N\geq4$. We assume the
unique solvability of the weak Dirichlet problem for the Poisson equation and
the $L_q$-$L_r$ estimates for the Stokes semigroup. The novelty of this paper
is that we do not assume the compactness of the boundary, which is essentially
used in the case of exterior domains proved by Shibata \cite{Shiba17CIME}. The
restriction $N\geq 4$ is required to deduce an estimate for the nonlinear term
$\bG(\bu)$ arising from $\divv\bv=0$. However, we establish the results in the
half space $\hsp$ for $N\geq 3$ by reducing the linearized problem to the
problem with $\bG=\fb0$, where $\bG$ is the right member corresponding to
$\bG(\bu)$.


title: $H\mathbb{F}_2$-synthetic homotopy groups of topological modular forms

abstract:
  To any Adams type spectrum $E$, Pstr\k{a}gowski produced a symmetric monoidal
stable $\infty$-category $\mathcal{S}yn_E$ whose objects are, in a sense,
"formal Adams spectral sequences". $\mathcal{S}yn_E$ comes equipped with a lax
symmetric monoidal functor $\nu_E:Sp\to \mathcal{S}yn_E$ from classical
spectra, which embeds $\mathcal{S}p$ fully and faithfully in $\mathcal{S}yn_E$.
In this paper, for $E=H\mathbb{F}_2$ we compute the bigraded synthetic homotopy
groups $\pi_{*,*}(\nu_{H\mathbb{F}_2}tmf_2^{\wedge}),$ together with synthetic
analogs of $2$-, $\eta$-, and $\nu$-hidden extensions.


title: Stirling numbers associated with sequences of polynomials

abstract:
  Let P be the set of the sequence of polynomials of degree n. The aim of this
paper is to study the Stirling numbers of the second kind associated with P and
of the first kind associated with P, in a unified and systematic way with the
help of umbral calculus technique. Our results are illustrated with many
examples which give rise to interesting inverse relations in each case.


title:
ATOMS: ALMA Three-millimeter Observations of Massive Star-forming
  regions-IX. A pilot study towards IRDC G034.43+00.24 on multi-scale
  structures and gas kinematics

abstract:
  We present a comprehensive study of the gas kinematics associated with
density structures at different spatial scales in the filamentary infrared dark
cloud, G034.43+00.24 (G34). This study makes use of the H13CO+ (1-0) molecular
line data from the ALMA Three-millimeter Observations of Massive Star-forming
regions (ATOMS) survey, which has spatial and velocity resolution of 0.04 pc
and 0.2 km/s, respectively. Several tens of dendrogram structures have been
extracted in the position-position-velocity space of H13CO+, which include 21
small-scale leaves and 20 larger-scale branches. Overall, their gas motions are
supersonic but they exhibit the interesting behavior where leaves tend to be
less dynamically supersonic than the branches. For the larger-scale, branch
structures, the observed velocity-size relation (i.e., velocity
variation/dispersion versus size) are seen to follow the Larson scaling
exponent while the smaller-scale, leaf structures show a systematic deviation
and display a steeper slope. We argue that the origin of the observed
kinematics of the branch structures is likely to be a combination of turbulence
and gravity-driven ordered gas flows. In comparison, gravity-driven chaotic gas
motion is likely at the level of small-scale leaf structures. The results
presented in our previous paper and this current follow-up study suggest that
the main driving mechanism for mass accretion/inflow observed in G34 varies at
different spatial scales. We therefore conclude that a scale-dependent combined
effect of turbulence and gravity is essential to explain the star-formation
processes in G34.


title: Convergence of Oja's online principal component flow

abstract:
  Online principal component analysis (PCA) has been an efficient tool in
practice to reduce dimension. However, convergence properties of the
corresponding ODE are still unknown, including global convergence, stable
manifolds, and convergence rate. In this paper, we focus on the stochastic
gradient ascent (SGA) method proposed by Oja. By regarding the corresponding
ODE as a Landau-Lifshitz-Gilbert (LLG) equation on the Stiefel manifold, we
proved global convergence of the ODE. Moreover, we developed a new technique to
determine stable manifolds. This technique analyzes the rank of the initial
datum. Using this technique, we derived the explicit expression of the stable
manifolds. As a consequence, exponential convergence to stable equilibrium
points was also proved. The success of this new technique should be attributed
to the semi-decoupling property of the SGA method: iteration of previous
components does not depend on that of later ones. As far as we know, our result
is the first complete one on the convergence of an online PCA flow, providing
global convergence, explicit characterization of stable manifolds, and closed
formula of exponential convergence depending on the spectrum gap.


title: Exploring Classic Quantitative Strategies

abstract:
  The goal of this paper is to debunk and dispel the magic behind the black-box
quantitative strategies. It aims to build a solid foundation on how and why the
techniques work. This manuscript crystallizes this knowledge by deriving from
simple intuitions, the mathematics behind the strategies. This tutorial doesn't
shy away from addressing both the formal and informal aspects of quantitative
strategies. By doing so, it hopes to provide readers with a deeper
understanding of these techniques as well as the when, the how and the why of
applying these techniques. The strategies are presented in terms of both
S\&P500 and SH510300 data sets. However, the results from the tests are just
examples of how the methods work; no claim is made on the suggestion of real
market positions.


title:
Dual-Resonator Kinetic-Inductance Detector for Distinction between
  Signal and 1/f Frequency Noise

abstract:
  Astronomical Kinetic Inductance Detectors (KIDs), similar to quantum
information devices, experience performance limiting noise from materials. In
particular, 1/f (frequency) noise can be a dominant noise mechanism, which
arises from Two-Level System defects (TLSs) in the circuit dielectrics and
material interfaces. Here we present a Dual-Resonator KID (DuRKID), which is
designed for improved signal to noise (or noise equivalent power) relative to
1/f-noise limited KIDs. We first show the DuRKID schematic, fabricated circuit,
and we follow with a description of the intended operation, first measurements,
theory, and discussion. The circuit consists of two superconducting resonators
sharing an electrical capacitance bridge of 4 capacitors, each of which hosts
TLSs. The device is intended to operate using hybridization of the modes, which
causes TLSs to either couple to one mode or the other, depending upon which
capacitor they reside in. In contrast, the intended KID signal is directed to
an inductor, and due to hybridization this causes correlated frequency changes
in both (hybridized) modes. Therefore, one can distinguish photon signal from
TLS frequency noise. To achieve hybridization, a TiN inductor is current biased
to allow tuning of one bare resonator mode into degeneracy with the other and
measurements show that the intended resonator modes frequency tune and
hybridize as expected. The interresonator coupling and unintentional coupling
of the 2 resonators to transmission lines are also characterized in
measurements. In the theory, based on a quantum-information-science modes, we
calculate the 4-port S parameters and simulate the 1/f frequency noise of the
device. The study reveals that the DuRKID can exhibit a large and fundamental
performance advantage over 1/f-noise-limited KID detectors.


title: Web of Scholars: A Scholar Knowledge Graph

abstract:
  In this work, we demonstrate a novel system, namely Web of Scholars, which
integrates state-of-the-art mining techniques to search, mine, and visualize
complex networks behind scholars in the field of Computer Science. Relying on
the knowledge graph, it provides services for fast, accurate, and intelligent
semantic querying as well as powerful recommendations. In addition, in order to
realize information sharing, it provides an open API to be served as the
underlying architecture for advanced functions. Web of Scholars takes advantage
of knowledge graph, which means that it will be able to access more knowledge
if more search exist. It can be served as a useful and interoperable tool for
scholars to conduct in-depth analysis within Science of Science.


title:
Are We Ready for Robust and Resilient SLAM? A Framework For Quantitative
  Characterization of SLAM Datasets

abstract:
  Reliability of SLAM systems is considered one of the critical requirements in
many modern autonomous systems. This directed the efforts to developing many
state-of-the-art systems, creating challenging datasets, and introducing
rigorous metrics to measure SLAM system performance. However, the link between
datasets and performance in the robustness/resilience context has rarely been
explored. In order to fill this void, characterization the operating conditions
of SLAM systems is essential in order to provide an environment for
quantitative measurement of robustness and resilience. In this paper, we argue
that for proper evaluation of SLAM performance, the characterization of SLAM
datasets serves as a critical first step. The study starts by reviewing
previous efforts for quantitative characterization of SLAM datasets. Then, the
problem of perturbations characterization is discussed and the linkage to SLAM
robustness/resilience is established. After that, we propose a novel, generic
and extendable framework for quantitative analysis and comparison of SLAM
datasets. Additionally, a description of different characterization parameters
is provided. Finally, we demonstrate the application of our framework by
presenting the characterization results of three SLAM datasets: KITTI,
EuroC-MAV, and TUM-VI highlighting the level of insights achieved by the
proposed framework.


title:
Distributed Online Optimization in Time-Varying Unbalanced Networks
  without Explicit Subgradients

abstract:
  This paper studies a distributed online constrained optimization problem over
time-varying unbalanced digraphs without explicit subgradients. In sharp
contrast to the existing algorithms, we design a novel consensus-based
distributed online algorithm with a local randomized zeroth-order oracle and
then rescale the oracle by constructing row-stochastic matrices, which aims to
address the unbalancedness of time-varying digraphs. Under mild conditions, the
average dynamic regret over a time horizon is shown to asymptotically converge
at a sublinear rate provided that the accumulated variation grows sublinearly
with a specific order. Moreover, the counterpart of the proposed algorithm when
subgradients are available is also provided, along with its dynamic regret
bound, which reflects that the convergence of our algorithm is essentially not
affected by the zeroth-order oracle. Simulations on distributed targets
tracking problem and dynamic sparse signal recovery problem in sensor networks
are employed to demonstrate the effectiveness of the proposed algorithm.


title:
Optimal Investment in a Large Population of Competitive and
  Heterogeneous Agents

abstract:
  This paper studies a stochastic utility maximization game under relative
performance concerns in finite agent and infinite agent settings, where a
continuum of agents interact through a graphon (see definition below). We
consider an incomplete market model in which agents have CARA utilities, and we
obtain characterizations of Nash equilibria in both the finite agent and
graphon paradigms. Under modest assumptions on the denseness of the interaction
graph among the agents, we establish convergence results for the Nash
equilibria and optimal utilities of the finite player problem to the infinite
player problem. This result is achieved as an application of a general backward
propagation of chaos type result for systems of interacting forward-backward
stochastic differential equations, where the interaction is heterogeneous and
through the control processes, and the generator is of quadratic growth. In
addition, characterizing the graphon game gives rise to a novel form of
infinite dimensional forward-backward stochastic differential equation of
Mckean-Vlasov type, for which we provide well-posedness results. An interesting
consequence of our result is the computation of the competition indifference
capital, i.e., the capital making an investor indifferent between whether or
not to compete.


title: A nonlinear semigroup approach to Hamilton-Jacobi equations--revisited

abstract:
  We consider the Hamilton-Jacobi equation \[{H}(x,Du)+\lambda(x)u=c,\quad x\in
M, \] where $M$ is a connected, closed and smooth Riemannian manifold. The
functions ${H}(x,p)$ and $\lambda(x)$ are continuous. ${H}(x,p)$ is convex,
coercive with respect to $p$, and $\lambda(x)$ changes the signs. The first
breakthrough to this model was achieved by Jin-Yan-Zhao \cite{JYZ} under the
Tonelli conditions. In this paper, we consider more detailed structure of the
viscosity solution set and large time behavior of the viscosity solution on the
Cauchy problem.


abstract:
  We propose Multivariate Quantile Function Forecaster (MQF$^2$), a global
probabilistic forecasting method constructed using a multivariate quantile
function and investigate its application to multi-horizon forecasting. Prior
approaches are either autoregressive, implicitly capturing the dependency
structure across time but exhibiting error accumulation with increasing
forecast horizons, or multi-horizon sequence-to-sequence models, which do not
exhibit error accumulation, but also do typically not model the dependency
structure across time steps. MQF$^2$ combines the benefits of both approaches,
by directly making predictions in the form of a multivariate quantile function,
defined as the gradient of a convex function which we parametrize using
input-convex neural networks. By design, the quantile function is monotone with
respect to the input quantile levels and hence avoids quantile crossing. We
provide two options to train MQF$^2$: with energy score or with maximum
likelihood. Experimental results on real-world and synthetic datasets show that
our model has comparable performance with state-of-the-art methods in terms of
single time step metrics while capturing the time dependency structure.


title:
The Larger The Fairer? Small Neural Networks Can Achieve Fairness for
  Edge Devices

abstract:
  Along with the progress of AI democratization, neural networks are being
deployed more frequently in edge devices for a wide range of applications.
Fairness concerns gradually emerge in many applications, such as face
recognition and mobile medical. One fundamental question arises: what will be
the fairest neural architecture for edge devices? By examining the existing
neural networks, we observe that larger networks typically are fairer. But,
edge devices call for smaller neural architectures to meet hardware
specifications. To address this challenge, this work proposes a novel Fairness-
and Hardware-aware Neural architecture search framework, namely FaHaNa. Coupled
with a model freezing approach, FaHaNa can efficiently search for neural
networks with balanced fairness and accuracy, while guaranteed to meet hardware
specifications. Results show that FaHaNa can identify a series of neural
networks with higher fairness and accuracy on a dermatology dataset. Target
edge devices, FaHaNa finds a neural architecture with slightly higher accuracy,
5.28x smaller size, 15.14% higher fairness score, compared with MobileNetV2;
meanwhile, on Raspberry PI and Odroid XU-4, it achieves 5.75x and 5.79x
speedup.


title:
A comparison estimate for singular $p$-Laplace equations and its
  consequences

abstract:
  Comparison estimates are an important technical device in the study of
regularity problems for quasilinear possibly degenerate elliptic and parabolic
equations. Such tools have been employed indispensably in many papers of
Mingione, Duzaar-Mingione, and Kuusi-Mingione, etc. on certain measure datum
problems to obtain pointwise bounds for solutions and their full or fractional
derivatives in terms of appropriate linear or nonlinear potentials. However, a
comparison estimate for $p$-Laplace type elliptic equations with measure data
is still unavailable in the strongly singular case $1< p\leq
\frac{3n-2}{2n-1}$, where $n\geq 2$ is the dimension of the ambient space. This
issue will be completely resolved in this work by proving a comparison estimate
in a slightly larger range $1<p<3/2$. Applications include a `sublinear'
Poincar\'e type inequality, pointwise bounds for solutions and their
derivatives by Wolff's and Riesz's potentials, respectively. Some global
pointwise and weighted estimates are also obtained for bounded domains, which
enable us to treat a quasilinear Riccati type equation with possibly sublinear
growth in the gradient.


title: Absolute Zero-Shot Learning

abstract:
  Considering the increasing concerns about data copyright and privacy issues,
we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i.e., training
a classifier with zero real data. The key innovation is to involve a teacher
model as the data safeguard to guide the AZSL model training without data
leaking. The AZSL model consists of a generator and student network, which can
achieve date-free knowledge transfer while maintaining the performance of the
teacher network. We investigate `black-box' and `white-box' scenarios in AZSL
task as different levels of model security. Besides, we also provide discussion
of teacher model in both inductive and transductive settings. Despite
embarrassingly simple implementations and data-missing disadvantages, our AZSL
framework can retain state-of-the-art ZSL and GZSL performance under the
`white-box' scenario. Extensive qualitative and quantitative analysis also
demonstrates promising results when deploying the model under `black-box'
scenario.


title: Cosmic ray transport near the Sun

abstract:
  The strongly diverging magnetic field lines in the very inner heliosphere,
through the associated magnetic focusing/mirroring forces, can, potentially,
lead to highly anisotropic galactic cosmic ray distributions close to the Sun.
Using a simplified analytical approach, validated by numerical simulations, we
study the behaviour of the galactic cosmic ray distribution in this newly
explored region of the heliosphere and find that significant anisotropies can
be expected inside 0.2 au.


title:
Wave nature-based nonlocal correlation via projection measurements
  between space-like separated interferometric systems

abstract:
  Indistinguishability in quantum mechanics is an essential concept to
understanding mysterious quantum features such as self-interference of a single
photon and two-photon nonlocal correlation. Delayed-choice experiments are for
the cause-effect violation via post-measurements of photons in an
interferometric system. Recently, a macroscopic version of the delayed-choice
experiments has been demonstrated using the classical means of Poisson
distributed photons in a noninterfering Mach-Zehnder interferometer (NMZI).
Here, a coherence version of the nonlocal correlation is presented using the
wave nature of photons in space-like separated orthogonally polarized NMZIs.
For this, polarization-basis projections of the pair of NMZI output photons
onto a set of polarizers are measured coincidently, where each output photon
comprises orthogonal polarization bases. Because of the coherence feature of
the output photons via coincidence detection, a phase sensitive nonlocal
correlation is achieved. As a result, indistinguishability limited to a single
or entangled photon pairs needs to be reconsidered with basis (polarization)
randomness for the nonlocal correlation.


title: Efficient CDF Approximations for Normalizing Flows

abstract:
  Normalizing flows model a complex target distribution in terms of a bijective
transform operating on a simple base distribution. As such, they enable
tractable computation of a number of important statistical quantities,
particularly likelihoods and samples. Despite these appealing properties, the
computation of more complex inference tasks, such as the cumulative
distribution function (CDF) over a complex region (e.g., a polytope) remains
challenging. Traditional CDF approximations using Monte-Carlo techniques are
unbiased but have unbounded variance and low sample efficiency. Instead, we
build upon the diffeomorphic properties of normalizing flows and leverage the
divergence theorem to estimate the CDF over a closed region in target space in
terms of the flux across its \emph{boundary}, as induced by the normalizing
flow. We describe both deterministic and stochastic instances of this
estimator: while the deterministic variant iteratively improves the estimate by
strategically subdividing the boundary, the stochastic variant provides
unbiased estimates. Our experiments on popular flow architectures and UCI
benchmark datasets show a marked improvement in sample efficiency as compared
to traditional estimators.


title:
Improving fairness in speaker verification via Group-adapted Fusion
  Network

abstract:
  Modern speaker verification models use deep neural networks to encode
utterance audio into discriminative embedding vectors. During the training
process, these networks are typically optimized to differentiate arbitrary
speakers. This learning process biases the learning of fine voice
characteristics towards dominant demographic groups, which can lead to an
unfair performance disparity across different groups. This is observed
especially with underrepresented demographic groups sharing similar voice
characteristics. In this work, we investigate the fairness of speaker
verification models on controlled datasets with imbalanced gender
distributions, providing direct evidence that model performance suffers for
underrepresented groups. To mitigate this disparity we propose the
group-adapted fusion network (GFN) architecture, a modular architecture based
on group embedding adaptation and score fusion. We show that our method
alleviates model unfairness by improving speaker verification both overall and
for individual groups. Given imbalanced group representation in training, our
proposed method achieves overall equal error rate (EER) reduction of 9.6% to
29.0% relative, reduces minority group EER by 13.7% to 18.6%, and results in
20.0% to 25.4% less EER disparity, compared to baselines. The approach is
applicable to other types of training data skew in speaker recognition systems.


title: One-relator hierarchies

abstract:
  We prove that one-relator groups with negative immersions are hyperbolic and
virtually special; this resolves a recent conjecture of Louder and Wilton. The
main new tool we develop is a refinement of the classic Magnus--Moldavanskii
hierarchy for one-relator groups. We introduce the notions of stable
HNN-extensions and stable hierarchies. We then show that a one-relator group is
hyperbolic and has a quasi-convex one-relator hierarchy if and only if it does
not contain a Baumslag--Solitar subgroup and has a stable one-relator
hierarchy. Finally, we give an algorithm that, given as input a one-relator
group, verifies if it has a stable hierarchy and, if so, determines whether or
not it contains a Baumslag--Solitar subgroup.


title:
Reinforcement Learning from Demonstrations by Novel Interactive Expert
  and Application to Automatic Berthing Control Systems for Unmanned Surface
  Vessel

abstract:
  In this paper, two novel practical methods of Reinforcement Learning from
Demonstration (RLfD) are developed and applied to automatic berthing control
systems for Unmanned Surface Vessel. A new expert data generation method,
called Model Predictive Based Expert (MPBE) which combines Model Predictive
Control and Deep Deterministic Policy Gradient, is developed to provide high
quality supervision data for RLfD algorithms. A straightforward RLfD method,
model predictive Deep Deterministic Policy Gradient (MP-DDPG), is firstly
introduced by replacing the RL agent with MPBE to directly interact with the
environment. Then distribution mismatch problem is analyzed for MP-DDPG, and
two techniques that alleviate distribution mismatch are proposed. Furthermore,
another novel RLfD algorithm based on the MP-DDPG, called Self-Guided
Actor-Critic (SGAC) is present, which can effectively leverage MPBE by
continuously querying it to generate high quality expert data online. The
distribution mismatch problem leading to unstable learning process is addressed
by SGAC in a DAgger manner. In addition, theoretical analysis is given to prove
that SGAC algorithm can converge with guaranteed monotonic improvement.
Simulation results verify the effectiveness of MP-DDPG and SGAC to accomplish
the ship berthing control task, and show advantages of SGAC comparing with
other typical reinforcement learning algorithms and MP-DDPG.


title:
Decoupling and Schr\"odinger maximal estimates for finite type phases in
  higher dimensions

abstract:
  In this article, we establish an $\ell^2$ decoupling inequality for the
hypersurface \[\Big\{(\xi_1,...,\xi_{n-1},\xi_1^m+...+\xi_{n-1}^m):
(\xi_1,...,\xi_{n-1}) \in [0,1]^{n-1}\Big\},\] where $m\geq 4$ is an even
number, associated with the decomposition adapted to finite type hypersurfaces
from our previous work. The key ingredients of the proof include an $\ell^2$
decoupling inequality for the hypersurfaces
\[\Big\{(\xi_1,...,\xi_{n-1},\phi_1(\xi_1)+...+\phi_s(\xi_s)+\xi_{s+1}^m+...+\xi_{n-1}^m):
(\xi_1,...,\xi_{n-1}) \in [0,1]^{n-1}\Big\},\] $0 \leq s \leq n-1$, with
$\phi_1,...,\phi_s$ being non-degenerate. As an application, we generalize the
Schr\"{o}dinger maximal estimates in [Z. Li, J. Zhao and T. Zhao,
arXiv:2111.00897] to higher dimensions.


title:
Analytic approach to the formation of a three-dimensional black string
  from a dust cloud

abstract:
  In the three-dimensional low-energy string theory, we study the formation of
a black string from a dust cloud. We analytically obtain two distinct classes
of exact solutions with arbitrary functions responsible for mass distributions
of the dust cloud. The first and second kinds of solutions may describe
collapsing dusts but the first kind is only for the inhomogeneous dust
distribution while the second kind has a homogeneous limit. The finite collapse
time and the Israel junction conditions tell us that the first kind solution
describes the desired collapsing phenomenon, whereas the scale factor in the
inner spacetime for the second kind turns out to be trivial. In the first kind
solution, specific collapsing models can be realized by choosing an appropriate
inhomogeneous dust distribution consistent with the Israel junction conditions.
Consequently, the inhomogeneous dust cloud eventually collapses to the black
string although the homogeneous dust cloud does not guarantee the formation of
the black string in our setting. The space-like curvature singularities occur
at the finite collapse time and they can be cloaked by the horizon of the black
string.


title: Motion of Poly-vectors in Fractal Clifford Spaces

abstract:
  Fractal Clifford Spaces (FCS) may be considered as a challenging approach to
the unification of micro-physics and macro-physics. Trajectories of these
manifolds are described by different poly-vectors describing paths and their
deviation equations for not only to test particles but also for charged,
rotating and rotating charged objects. Due to relaxing the condition of
differentiability for all extended objects in the manifold. In this approach, a
specialized Bazanski Lagrangian will be discussed to describe the fractal
version of extended objects in a fractal space-time.


title: Unified treatment of Artin-type problems

abstract:
  Since Hooley's seminal 1967 resolution of Artin's primitive root conjecture
under the Generalized Riemann Hypothesis, numerous variations of the conjecture
have been considered. We present a framework generalizing and unifying many
previously considered variants, and prove results in this full generality
(under GRH).


title:
EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Autonomous
  Vehicle Perception

abstract:
  Autonomous vehicles use multiple sensors, large deep-learning models, and
powerful hardware platforms to perceive the environment and navigate safely. In
many contexts, some sensing modalities negatively impact perception while
increasing energy consumption. We propose EcoFusion: an energy-aware sensor
fusion approach that uses context to adapt the fusion method and reduce energy
consumption without affecting perception performance. EcoFusion performs up to
9.5% better at object detection than existing fusion methods with approximately
60% less energy and 58% lower latency on the industry-standard Nvidia Drive PX2
hardware platform. We also propose several context-identification strategies,
implement a joint optimization between energy and performance, and present
scenario-specific results.


title:
Flock navigation with dynamic hierarchy and subjective weights using
  nonlinear MPC

abstract:
  We propose a model predictive control (MPC) based approach to a flock control
problem with obstacle avoidance capability in a leader-follower framework,
utilizing the future trajectory prediction computed by each agent. We employ
the traditional Reynolds' flocking rules (cohesion, separation, and alignment)
as a basis, and tailor the model to fit a navigation (as opposed to formation)
purpose. In particular, we introduce several concepts such as the credibility
and the importance of the gathered information from neighbors, and dynamic
trade-offs between references. They are based on the observations that
near-future predictions are more reliable, agents closer to leaders are
implicit carriers of more educated information, and the predominance of either
cohesion or alignment is dictated by the distance between the agent and its
neighbors. These features are incorporated in the MPC formulation, and their
advantages are discussed through numerical simulations.


title:
Measuring the cosmological density field twice: A novel test of dark
  energy using the CMB quadrupole

abstract:
  The scattering of cosmic microwave background (CMB) radiation in galaxy
clusters induces polarization signals according to the quadrupole anisotropy in
the photon distribution at the cluster location. This `remote quadrupole'
derived from the measurements of the induced polarization provides an
opportunity for reconstructing primordial fluctuations on large scales. We
discuss that comparing the local CMB quadrupoles predicted by these
reconstructed primordial fluctuations and the direct measurements done by CMB
satellites may enable us to test the dark energy beyond cosmic variance limits.


title:
Scalable Query Answering under Uncertainty to Neuroscientific
  Ontological Knowledge: The NeuroLang Approach

abstract:
  Researchers in neuroscience have a growing number of datasets available to
study the brain, which is made possible by recent technological advances. Given
the extent to which the brain has been studied, there is also available
ontological knowledge encoding the current state of the art regarding its
different areas, activation patterns, key words associated with studies, etc.
Furthermore, there is an inherent uncertainty associated with brain scans
arising from the mapping between voxels -- 3D pixels -- and actual points in
different individual brains. Unfortunately, there is currently no unifying
framework for accessing such collections of rich heterogeneous data under
uncertainty, making it necessary for researchers to rely on ad hoc tools. In
particular, one major weakness of current tools that attempt to address this
kind of task is that only very limited propositional query languages have been
developed. In this paper, we present NeuroLang, an ontology language with
existential rules, probabilistic uncertainty, and built-in mechanisms to
guarantee tractable query answering over very large datasets. After presenting
the language and its general query answering architecture, we discuss
real-world use cases showing how NeuroLang can be applied to practical
scenarios for which current tools are inadequate.


title:
Dense Multi-Agent Navigation Using Voronoi Cells and Congestion
  Metric-based Replanning

abstract:
  We present a decentralized path-planning algorithm for navigating multiple
differential-drive robots in dense environments. In contrast to prior
decentralized methods, we propose a novel congestion metric-based replanning
that couples local and global planning techniques to efficiently navigate in
scenarios with multiple corridors. To handle dense scenes with narrow passages,
our approach computes the initial path for each agent to its assigned goal
using a lattice planner. Based on neighbors' information, each agent performs
online replanning using a congestion metric that tends to reduce the collisions
and improves the navigation performance. Furthermore, we use the Voronoi cells
of each agent to plan the local motion as well as a corridor selection strategy
to limit the congestion in narrow passages. We evaluate the performance of our
approach in complex warehouse-like scenes and demonstrate improved performance
and efficiency over prior methods. In addition, our approach results in a
higher success rate in terms of collision-free navigation to the goals.


title:
Cold and hot nuclear matter effects on $J/\psi$ production at RHIC-BES
  energies

abstract:
  Integrated and differential $J/\psi\ R_{AA}$ are systematically studied in
Au-Au collisions at RHIC-BES energies in a transport approach, including cold
and hot nuclear effects respectively in the initial condition and the collision
terms. With decreasing energy, while the temperature, life time and size of the
QGP fireball decrease, the nuclear absorption of the initially produced
charmonia is more and more strong, and the nuclear shadowing effect on
charmonium regeneration goes to anti-shadowing first and then to shadowing
again. As a competition between the cold and hot nuclear effects, the QGP phase
is still important for charmonium production at $\sqrt{s_{NN}}$=200, 62.4, 54.4
and 39 GeV but becomes negligible at $\sqrt{s_{NN}}$=14.5 GeV.


title:
Trajectory planning in Dynamics Environment : Application for Haptic
  Perception in Safe HumanRobot Interaction

abstract:
  In a human-robot interaction system, the most important thing to consider is
the safety of the user. This must be guaranteed in order to implement a
reliable system. The main objective of this paper is to generate a safe motion
scheme that takes into account the obstacles present in a virtual reality (VR)
environment. The work is developed using the MoveIt software in ROS to control
an industrial robot UR5. Thanks to this, we will be able to set up the planning
group, which is realized by the UR5 robot with a 6-sided prop and the base of
the manipulator, in order to plan feasible trajectories that it will be able to
execute in the environment. The latter is based on the interior of a vehicle,
containing a person (which would be the user in this case) for which the
configuration will also be made to be taken into account in the system. To do
this, we first investigated the software's capabilities and options for path
planning, as well as the different ways to execute the movements. We also
compared the different trajectory planning algorithms that the software is
capable of using in order to determine which one is best suited for the task.
Finally, we proposed different mobility schemes to be executed by the robot
depending on the situation it is facing. The first one is used when the robot
has to plan trajectories in a safe space, where the only obstacle to avoid is
the user's workspace. The second one is used when the robot has to interact
with the user, where a dummy model represents the user's position as a function
of time, which is the one to be avoided.


title:
Commonsense Reasoning for Identifying and Understanding the Implicit
  Need of Help and Synthesizing Assistive Actions

abstract:
  Human-Robot Interaction (HRI) is an emerging subfield of service robotics.
While most existing approaches rely on explicit signals (i.e. voice, gesture)
to engage, current literature is lacking solutions to address implicit user
needs. In this paper, we present an architecture to (a) detect user implicit
need of help and (b) generate a set of assistive actions without prior
learning. Task (a) will be performed using state-of-the-art solutions for Scene
Graph Generation coupled to the use of commonsense knowledge; whereas, task (b)
will be performed using additional commonsense knowledge as well as a sentiment
analysis on graph structure. Finally, we propose an evaluation of our solution
using established benchmarks (e.g. ActionGenome dataset) along with human
experiments. The main motivation of our approach is the embedding of the
perception-decision-action loop in a single architecture.


title:
Towards a general framework of Randomized Benchmarking for non-Markovian
  Noise

abstract:
  The rapid progress in the development of quantum devices is in large part due
to the availability of a wide range of characterization techniques allowing to
probe, test and adjust them. Nevertheless, these methods often make use of
approximations that hold in rather simplistic circumstances. In particular,
assuming that error mechanisms stay constant in time and have no dependence in
the past, is something that will be impossible to do as quantum processors
continue scaling up in depth and size. We establish a theoretical framework for
the Randomized Benchmarking protocol encompassing temporally-correlated,
so-called non-Markovian noise, at the gate level, for any gate set belonging to
a wide class of finite groups. We obtain a general expression for the Average
Sequence Fidelity (ASF) and propose a way to obtain average gate fidelities of
full non-Markovian noise processes. Moreover, we obtain conditions that are
fulfilled when an ASF displays authentic non-Markovian deviations. Finally, we
show that even though gate-dependence does not translate into a perturbative
term within the ASF, as in the Markovian case, the non-Markovian sequence
fidelity nevertheless remains stable under small gate-dependent perturbations.


title:
A local limit theorem for convergent random walks on relatively
  hyperbolic groups

abstract:
  We study random walks on relatively hyperbolic groups whose law is
convergent, in the sense that the derivative of its Green function is finite at
the spectral radius.When parabolic subgroups are virtually abelian, we prove
that for such a random walk satisfies a local limit theorem of the form
$p_n(e,e)\sim CR^{-n}n^{-d/2}$, where $p_n(e,e)$ is the probability of
returning to the origin at time $n$, $R$ is the inverse of the spectral radius
of the random walk and $d$ is the minimal rank of a parabolic subgroup along
which the random walk is spectrally degenerate.This concludes the
classification all possible behaviour for $p_n(e,e)$ on such groups.


title: Generalised tensors and traces

abstract:
  Tensors and traceouts are generalised, so that systems can be partitioned
according to almost arbitrary logical predicates. One might have feared that
the familiar interrelations between the notions of unitarity, complete
positivity, trace-preservation, non-signalling causality, locality and
localizability that are standard in quantum theory be jeopardized as the
partitioning of systems becomes both logical and dynamical. Such interrelations
in fact carry through, although a new notion, consistency, becomes
instrumental.


title: Distributed and Mobile Message Level Relaying/Replaying of GNSS Signals

abstract:
  With the introduction of Navigation Message Authentication (NMA), future
Global Navigation Satellite Systems (GNSSs) prevent spoofing by simulation,
i.e., the generation of forged satellite signals based on public information.
However, authentication does not prevent record-and-replay attacks, commonly
termed as meaconing. These attacks are less powerful in terms of adversarial
control over the victim receiver location and time, but by acting at the signal
level, they are not thwarted by NMA. This makes replaying/relaying attacks a
significant threat for GNSS. While there are numerous investigations on
meaconing, the majority does not rely on actual implementation and experimental
evaluation in real-world settings. In this work, we contribute to the
improvement of the experimental understanding of meaconing attacks. We design
and implement a system capable of real-time, distributed, and mobile meaconing,
built with off-the-shelf hardware. We extend from basic distributed attacks,
with signals from different locations relayed over the Internet and replayed
within range of the victim receiver(s): this has high bandwidth requirements
and thus depends on the quality of service of the available network to work. To
overcome this limitation, we propose to replay on message level, including the
authentication part of the payload. The resultant reduced bandwidth enables the
attacker to operate in mobile scenarios, as well as to replay signals from
multiple GNSS constellations and/or bands simultaneously. Additionally, the
attacker can delay individually selected satellite signals to potentially
influence the victim position and time solution in a more fine-grained manner.
Our versatile test-bench, enabling different types of replaying/relaying
attacks, facilitates testing realistic scenarios towards new and improved
replaying/relaying-focused countermeasures in GNSS receivers.


title: Training Adaptive Reconstruction Networks for Inverse Problems

abstract:
  Neural networks are full of promises for the resolution of ill-posed inverse
problems. In particular, physics informed learning approaches already seem to
progressively gradually replace carefully hand-crafted reconstruction
algorithms, for their superior quality. The aim of this paper is twofold. First
we show a significant weakness of these networks: they do not adapt efficiently
to variations of the forward model. Second, we show that training the network
with a family of forward operators allows to solve the adaptivity problem
without compromising the reconstruction quality significantly. All our
experiments are carefully devised on partial Fourier sampling problems arising
in magnetic resonance imaging (MRI).


title:
Alleviating Datapath Conflicts and Design Centralization in Graph
  Analytics Acceleration

abstract:
  Previous graph analytics accelerators have achieved great improvement on
throughput by alleviating irregular off-chip memory accesses. However, on-chip
side datapath conflicts and design centralization have become the critical
issues hindering further throughput improvement. In this paper, a general
solution, Multiple-stage Decentralized Propagation network (MDP-network), is
proposed to address these issues, inspired by the key idea of trading latency
for throughput. Besides, a novel High throughput Graph analytics accelerator,
HiGraph, is proposed by deploying MDP-network to address each issue in
practice. The experiment shows that compared with state-of-the-art accelerator,
HiGraph achieves up to 2.2x speedup (1.5x on average) as well as better
scalability.


title: The Kakeya conjecture on local fields of positive characteristic

abstract:
  We state and prove an analogue of the Kakeya conjecture for the local field
$\mathbb{F}_q(\!(t)\!)$. This extends Arsovski's result on the Kakeya
conjecture to local fields of positive characteristic.


title: Prompt-Learning for Short Text Classification

abstract:
  In the short text, the extremely short length, feature sparsity, and high
ambiguity pose huge challenges to classification tasks. Recently, as an
effective method for tuning Pre-trained Language Models for specific downstream
tasks, prompt-learning has attracted a vast amount of attention and research.
The main intuition behind the prompt-learning is to insert the template into
the input and convert the text classification tasks into equivalent cloze-style
tasks. However, most prompt-learning methods expand label words manually or
only consider the class name for knowledge incorporating in cloze-style
prediction, which will inevitably incur omissions and bias in short text
classification tasks. In this paper, we propose a simple short text
classification approach that makes use of prompt-learning based on
knowledgeable expansion. Taking the special characteristics of short text into
consideration, the method can consider both the short text itself and class
name during expanding label words space. Specifically, the top $N$ concepts
related to the entity in the short text are retrieved from the open Knowledge
Graph like Probase, and we further refine the expanded label words by the
distance calculation between selected concepts and class labels. Experimental
results show that our approach obtains obvious improvement compared with other
fine-tuning, prompt-learning, and knowledgeable prompt-tuning methods,
outperforming the state-of-the-art by up to 6 Accuracy points on three
well-known datasets.


title: ACC for foliated log canonical thresholds

abstract:
  We show the sets of foliated log canonical thresholds on surfaces and
threefolds satisfy the ascending chain condition.


title: Top quarks as a probe to quantum information

abstract:
  Top quark pairs produced at the Large Hadron Collider (LHC) provide a unique
window into quantum information theory at high energies. One of the most
ubiquitous measurements of quantum information is the violation of Bell's
inequality. We explore what would be necessary to observe a violation of Bell's
inequality and the dependence of this on the initial state of the top quark
pair. Furthermore, we show how a more general application of quantum
information theory in the realm of quantum computing can be leveraged to
perform offline reconstruction of primary vertices. We perform some
optimizations of the running parameters of the quantum annealer and compare
them to a non-optimized performance. Lastly, we discuss the future outlook of
both these topics and the steps to be taken.


title:
Ferroelectric control of magnetic skyrmions in two-dimensional van der
  Waals heterostructures

abstract:
  Magnetic skyrmions are chiral nanoscale spin textures which are usually
induced by Dzyaloshinskii-Moriya interaction (DMI). Recently, magnetic
skyrmions have been observed in two-dimensional (2D) van der Waals (vdW)
ferromagnetic materials, such as Fe$_{3}$GeTe$_{2}$. The electric control of
skyrmions is important for their potential application in low-power memory
technologies. Here, we predict that DMI and magnetic skyrmions in a
Fe$_{3}$GeTe$_{2}$ monolayer can be controlled by ferroelectric polarization of
an adjacent 2D vdW ferroelectric In$_{2}$Se$_{3}$. Based on density functional
theory and atomistic spin-dynamics modeling, we find that the interfacial
symmetry breaking produces a sizable DMI in a
Fe$_{3}$GeTe$_{2}$/In$_{2}$Se$_{3}$ vdW heterostructure. We show that the
magnitude of DMI can be controlled by ferroe-lectric polarization reversal,
leading to creation and annihilation of skyrmions. Furthermore, we find that
the sign of DMI in a In$_{2}$Se$_{3}$/Fe$_{3}$GeTe$_{2}$/In$_{2}$Se$_{3}$
heterostructure changes with ferroelectric switching reversing the skyrmion
chirality. The predicted electrically controlled skyrmion formation may be
interesting for spintronic applications.


title:
Energy-efficient Training of Distributed DNNs in the Mobile-edge-cloud
  Continuum

abstract:
  We address distributed machine learning in multi-tier (e.g.,
mobile-edge-cloud) networks where a heterogeneous set of nodes cooperate to
perform a learning task. Due to the presence of multiple data sources and
computation-capable nodes, a learning controller (e.g., located in the edge)
has to make decisions about (i) which distributed ML model structure to select,
(ii) which data should be used for the ML model training, and (iii) which
resources should be allocated to it. Since these decisions deeply influence one
another, they should be made jointly. In this paper, we envision a new approach
to distributed learning in multi-tier networks, which aims at maximizing ML
efficiency. To this end, we propose a solution concept, called RightTrain, that
achieves energy-efficient ML model training, while fulfilling learning time and
quality requirements. RightTrain makes high-quality decisions in polynomial
time. Further, our performance evaluation shows that RightTrain closely matches
the optimum and outperforms the state of the art by over 50%.


title: Quantum Squeezing of Slow-Light Solitons

abstract:
  We investigate the quantum squeezing of slow-light solitons generated in a
$\Lambda$-shaped three-level atomic system working under condition of
electromagnetically induced transparency (EIT). We show that due to the giant
Kerr nonlinearity contributed from the EIT effect, significant quantum
squeezing of the slow-light soliton can be realized within a short propagation
distance. The results reported here are helpful for understanding the quantum
property of slow-light solitons and for realizing light squeezing via EIT in
cold atomic gases experimentally.


title:
A Semi-Synthetic Dataset Generation Framework for Causal Inference in
  Recommender Systems

abstract:
  Accurate recommendation and reliable explanation are two key issues for
modern recommender systems. However, most recommendation benchmarks only
concern the prediction of user-item ratings while omitting the underlying
causes behind the ratings. For example, the widely-used Yahoo!R3 dataset
contains little information on the causes of the user-movie ratings. A solution
could be to conduct surveys and require the users to provide such information.
In practice, the user surveys can hardly avoid compliance issues and sparse
user responses, which greatly hinders the exploration of causality-based
recommendation. To better support the studies of causal inference and further
explanations in recommender systems, we propose a novel semi-synthetic data
generation framework for recommender systems where causal graphical models with
missingness are employed to describe the causal mechanism of practical
recommendation scenarios. To illustrate the use of our framework, we construct
a semi-synthetic dataset with Causal Tags And Ratings (CTAR), based on the
movies as well as their descriptive tags and rating information collected from
a famous movie rating website. Using the collected data and the causal graph,
the user-item-ratings and their corresponding user-item-tags are automatically
generated, which provides the reasons (selected tags) why the user rates the
items. Descriptive statistics and baseline results regarding the CTAR dataset
are also reported. The proposed data generation framework is not limited to
recommendation, and the released APIs can be used to generate customized
datasets for other research tasks.


title: Sign representation of single-peaked preferences and Bruhat orders

abstract:
  Single-peaked preferences and domains are extensively researched in social
science and economics. In this study, we examine the interval property as well
as combinatorial structure of single-peaked preferences on a fixed Left-Right
social axis. We introduce a sign representation of single-peaked preferences;
consequently, some cardinalities of single-peaked domains are easily obtained.
Basic operations on the sign representation, which completely define the Bruhat
poset, are also provided. The applications to known results and an isomorphic
relation with associated rhombus tiling are given. Finally, we some discussions
of related topics.


title:
Initial-Boundary Value Problems on a Half-Strip for the Generalized
  Kawahara-Zakharov-Kuznetsov Equation

abstract:
  Initial-boundary value problems on a half-strip with different types of
boundary conditions for the generalized Kawahara-Zakharov-Kuznetsov equation
with nonlinearity of higher order are considered. In particular, nonlinearity
can be quadratic and cubic. Results on global existence and uniqueness in
classes of weak and strong solutions and large-time decay of small solutions
are established. The solutions are considered in weighted at infinity Sobolev
spaces. The use of weighted spaces is crucial for the study. To this end new
interpolating inequalities in weighted anisotropic Sobolev spaces are
established. Both exponential and power weights are admissible.


title:
Low-complexity Joint Beamforming for RIS-Aided Multi-User Downlink over
  Correlated Channels

abstract:
  This paper considers the reconfigurable intelligent surface (RIS)-assisted
communication scenario, where an RIS is used to assist the base station (BS)
for serving multiple users. The RIS consisting of passive reflecting elements
can manipulate the reflected direction of the incoming electromagnetic waves
and thus it offers a new design dimension to the system designer. To maximize
the sum rate, the active beamforming at the BS and the passive phase shifts at
the RIS need to be jointly optimized, which is an NP-hard problem. In this
work, we consider the joint active and passive (JAPB) beamforming problem over
correlated fading channels. To facilitate practical implementation, we propose
two low-complexity schemes along with user grouping to solve JAPB. Besides, we
theoretically analyze the mean correlation coefficient between two cascade RIS
channels and obtain a closed-form expression for arbitrary phase-shift values.
Asymptotic analysis is also conducted to get insights into the channel
correlation of cascade RIS channels when the numbers of BS antennas and RIS
elements are large. Simulation results are presented to validate the analysis
accuracy of the derived mean correlation coefficient. Also, the sum rate
performance of the proposed methods under different system settings is
evaluated and compared with the benchmark that optimizes the RIS phase shifts
using element-wise successive refinement.


title:
Superconductive materials with MgB2-like structures from data-driven
  screening

abstract:
  Finding viable superconducting materials is of interest to the physics
community as the superconductors are the playground to manifest many appealing
quantum phenomena. This work exemplifies an end-to-end materials discovery
towards novel MgB2-like superconductors, starting from the data-driven compound
screening all the way to the experimental materialization. In addition to the
known superconducting compounds, CaB2 (Tc = 9.4 ~ 28.6 K), SrGa2 (Tc = 0.1 ~
2.4 K), BaGa2 (Tc = 0.3 ~ 3.3 K), BaAu2 (Tc = 0.0 ~ 0.5 K), and LaCu2 (Tc = 0.1
~ 2.2 K) are newly discovered, out of ~182,000 starting structures, to be the
most promising superconducting compounds that share similar atomic structures
with MgB2. Moreover, BaGa2 is experimentally synthesized and measured to
confirm that the compound is a BCS superconductor with Tc = 0.36 K, in good
agreement with our theoretical predictions. This work provides a "once and for
all" study for the MgB2-like superconductors and showcases that it is feasible
to discover new materials via a data-driven approach.


title:
Preformer: Predictive Transformer with Multi-Scale Segment-wise
  Correlations for Long-Term Time Series Forecasting

abstract:
  Transformer-based methods have shown great potential in long-term time series
forecasting. However, most of these methods adopt the standard point-wise
self-attention mechanism, which not only becomes intractable for long-term
forecasting since its complexity increases quadratically with the length of
time series, but also cannot explicitly capture the predictive dependencies
from contexts since the corresponding key and value are transformed from the
same point. This paper proposes a predictive Transformer-based model called
{\em Preformer}. Preformer introduces a novel efficient {\em Multi-Scale
Segment-Correlation} mechanism that divides time series into segments and
utilizes segment-wise correlation-based attention for encoding time series. A
multi-scale structure is developed to aggregate dependencies at different
temporal scales and facilitate the selection of segment length. Preformer
further designs a predictive paradigm for decoding, where the key and value
come from two successive segments rather than the same segment. In this way, if
a key segment has a high correlation score with the query segment, its
successive segment contributes more to the prediction of the query segment.
Extensive experiments demonstrate that our Preformer outperforms other
Transformer-based methods.


title: Weak supersymmetric $su(N|1)$ quantum systems

abstract:
  We present several examples of supersymmetric quantum mechanical systems with
weak superalgebra $su(N|1)$. One of them is the weak $su(N|1)$ oscillator. It
has a singlet ground state, $N +1$ degenerate states at the first excited
level, etc. Starting from the level $k = N+1$, the system has complete
supersymmetric multiplets at each level involving $2^N$ degenerate states. Due
to the fact that the supermultiplets are not complete for $k \leq N$, the
Witten index represents a nontrivial function of $\beta$.
  This system can be deformed with keeping the algebra intact. The index is
invariant under such deformation. The deformed system is not exactly solved,
but the invariance of the index implies that the energies of the states at the
first $N$ levels of the spectrum are not shifted, and we are dealing with a
quasi-exactly solvable system.
  Another system represents a weak generalisation of the superconformal
mechanics with $N$ complex supercharges. Also in this case, starting from a
certain energy, the spectrum involves only complete supersymmetric $2^N$-plets.
(There also exist normalizable states with lower energies, but they do not have
normalizable superpartners. To keep supersymmetry, we have to eliminate these
states.)


title: Packing and covering in higher dimensions

abstract:
  The present work surveys problems in $n$-dimensional space with $n$ large.
Early development in the study of packing and covering in high dimensions was
motivated by the geometry of numbers. Subsequent results, such as the discovery
of the Leech lattice and the linear programming bound, which culminated in the
recent solution of the sphere packing problem in dimensions 8 and 24, were
influenced by coding theory. After mentioning the known results concerning
existence of economical packings and coverings we discuss the different methods
yielding upper bounds for the density of packing congruent balls. We summarize
the few results on upper bounds for the packing density of general convex
bodies. The paper closes with some remarks on the structure of optimal
arrangements.


title: Deepfake Detection for Facial Images with Facemasks

abstract:
  Hyper-realistic face image generation and manipulation have givenrise to
numerous unethical social issues, e.g., invasion of privacy,threat of security,
and malicious political maneuvering, which re-sulted in the development of
recent deepfake detection methodswith the rising demands of deepfake forensics.
Proposed deepfakedetection methods to date have shown remarkable detection
perfor-mance and robustness. However, none of the suggested deepfakedetection
methods assessed the performance of deepfakes withthe facemask during the
pandemic crisis after the outbreak of theCovid-19. In this paper, we thoroughly
evaluate the performance ofstate-of-the-art deepfake detection models on the
deepfakes withthe facemask. Also, we propose two approaches to enhance
themasked deepfakes detection:face-patchandface-crop. The experi-mental
evaluations on both methods are assessed through the base-line deepfake
detection models on the various deepfake datasets.Our extensive experiments
show that, among the two methods,face-cropperforms better than theface-patch,
and could be a trainmethod for deepfake detection models to detect fake faces
withfacemask in real world.


title: Deep Graph Learning for Anomalous Citation Detection

abstract:
  Anomaly detection is one of the most active research areas in various
critical domains, such as healthcare, fintech, and public security. However,
little attention has been paid to scholarly data, i.e., anomaly detection in a
citation network. Citation is considered as one of the most crucial metrics to
evaluate the impact of scientific research, which may be gamed in multiple
ways. Therefore, anomaly detection in citation networks is of significant
importance to identify manipulation and inflation of citations. To address this
open issue, we propose a novel deep graph learning model, namely GLAD (Graph
Learning for Anomaly Detection), to identify anomalies in citation networks.
GLAD incorporates text semantic mining to network representation learning by
adding both node attributes and link attributes via graph neural networks. It
exploits not only the relevance of citation contents but also hidden
relationships between papers. Within the GLAD framework, we propose an
algorithm called CPU (Citation PUrpose) to discover the purpose of citation
based on citation texts. The performance of GLAD is validated through a
simulated anomalous citation dataset. Experimental results demonstrate the
effectiveness of GLAD on the anomalous citation detection task.


title: Exploratory Methods for Relation Discovery in Archival Data

abstract:
  In this article we propose a holistic approach to discover relations in art
historical communities and enrich historians' biographies and archival
descriptions with graph patterns relevant to art historiographic enquiry. We
use exploratory data analysis to detect patterns, we select features, and we
use them to evaluate classification models to predict new relations, to be
recommended to archivists during the cataloguing phase. Results show that
relations based on biographical information can be addressed with higher
precision than relations based on research topics or institutional relations.
Deterministic and a priori rules present better results than probabilistic
methods.


title: Local well-posedness and global existence for the Popowicz system

abstract:
  Popowicz system, as the interacting system of Camassa-Holm and
Degasperis-Procesi equations, has attracted some attention in recent years. In
this paper, we first study the local well-posedness for the cauchy problem of
Popowicz system in nonhomogeneous Besov spaces $B^s_{p,r}\times B^s_{p,r}$ with
$s> \max\{2, \frac{1}{p}+\frac{3}{2}\}$ or $(s=2, 2\leq p \leq \infty, 1\leq
r\leq 2)$. Moreover, a new blow-up criterion and global existence with
different initial values are obtained.


title: The SIRAH force-field and interactions between short DNA duplexes

abstract:
  In recent years, short DNA duplexes have been studied as an interesting
self-assembling system and a building block for DNA-based nanotechnologies.
Numerical simulation studies for the determination of the full phase diagram of
short duplexes require, as an input ingredient, a simplified but reliable
force-field able to capture the main features of duplex-duplex interaction. We
used the coarse-grained SIRAH force field in the implicit solvent approximation
to study the interaction between two short duplexes of double-strand DNA as a
function of the relative positions and orientations and the salt concentration,
in the cases of 8 and 20 base pairs. We discuss the consequences of our study
to determine new simple but qualitatively reliable model potentials.


title: FastRPB: a Scalable Relative Positional Encoding for Long Sequence Tasks

abstract:
  Transformers achieve remarkable performance in various domains, including
NLP, CV, audio processing, and graph analysis. However, they do not scale well
on long sequence tasks due to their quadratic complexity w.r.t. the inputs
length. Linear Transformers were proposed to address this limitation. However,
these models have shown weaker performance on the long sequence tasks comparing
to the original one. In this paper, we explore Linear Transformer models,
rethinking their two core components. Firstly, we improved Linear Transformer
with Shift-Invariant Kernel Function SIKF, which achieve higher accuracy
without loss in speed. Secondly, we introduce FastRPB which stands for Fast
Relative Positional Bias, which efficiently adds positional information to
self-attention using Fast Fourier Transformation. FastRPB is independent of the
self-attention mechanism and can be combined with an original self-attention
and all its efficient variants. FastRPB has O(N log(N)) computational
complexity, requiring O(N) memory w.r.t. input sequence length N.


title:
Spin canting in nonlinear terahertz magnon dynamics revealed by
  magnetorefractive probing in orthoferrite

abstract:
  We excite the spin precession in rare-earth orthoferrite YFeO3 by the
magnetic field of intense terahertz pulse and probe its dynamics by transient
absorption change in the near infrared. The observed waveforms contain
quasi-ferromagnetic-mode magnon oscillation and its second harmonics with a
comparably strong amplitude. The result can be explained by dielectric function
derived from magnetorefractive Hamiltonian. We reveal that the strong second
harmonic signal microscopically originates from novel dynamics of the
quasi-ferromagnetic mode magnon at nonlinear regime, wherein spin canting angle
periodically oscillates.


title: Miscellaneous problems about packing and covering

abstract:
  In this paper we discuss various special problems on packing and covering.
Among others we survey the problems and results concerning finite arrangements,
Minkowskian, saturated, compact, and totally separable packings. We discuss
shortest path problems and questions about stability of packings.


title:
The DoF Region of Two-User MIMO Broadcast Channel with Delayed
  Imperfect-Quality CSIT

abstract:
  The channel state information at the transmitter (CSIT) play an important
role in the performance of wireless networks. The CSIT model can be delayed and
imperfect-quality, since the feedback link has a delay and the channel state
information (CSI) feedback has distortion. In this paper, we thus characterize
the degrees-of-freedom (DoF) region of the two-user multiple-input
multiple-output (MIMO) broadcast channel with delayed imperfect-quality CSIT,
where the antenna configurations can be arbitrary. The converse proof of DoF
region is based on the enhancement of physically degraded channel. The
achievability proof of DoF region is through a novel transmission scheme
design, where the duration of each phase and the amount of transmitted symbols
are configured based on the imperfect-quality of delayed CSIT. As a result, we
show that the DoF region with delayed imperfect-quality CSIT is located between
the DoF region with no CSIT and the DoF region with delayed CSIT.


title:
The mean and variance of the distribution of shortest path lengths of
  random regular graphs

abstract:
  The distribution of shortest path lengths (DSPL) of random networks provides
useful information on their large scale structure. In the special case of
random regular graphs (RRGs), which consist of $N$ nodes of degree $c \ge 3$,
the DSPL, denoted by $P(L=\ell)$, follows a discrete Gompertz distribution.
Using the discrete Laplace transform we derive a closed-form expression for the
moment generating function of the DSPL of RRGs. From the moment generating
function we obtain closed-form expressions for the mean and variance of the
DSPL. More specifically, we find that the mean distance between pairs of
distinct nodes is given by $\langle L \rangle=\frac{\ln N}{\ln (c-1)} +
\frac{1}{2}- \frac{ \ln c - \ln (c-2) +\gamma}{\ln (c-1)} + \mathcal{O} \left(
\frac{\ln N}{N} \right)$, where $\gamma$ is the Euler-Mascheroni constant.
While the leading term is known, this result includes a novel correction term,
which yields very good agreement with the results obtained from direct
numerical evaluation of $\langle L \rangle$ via the tail-sum formula and with
the results obtained from computer simulations. However, it does not account
for an oscillatory behavior of $\langle L \rangle$ as a function of $c$ or $N$.
These oscillations are negligible in the sparse networks but detectable in the
dense networks. We also derive an expression for the variance ${\rm Var}(L)$ of
the DSPL, which captures the overall dependence of the variance on $c$ but does
not account for the oscillations. The oscillations are due to the discrete
nature of the shell structure around a random node. They reflect the profile of
the filling of new shells as $N$ is increased. The results for the mean and
variance are compared to the corresponding results obtained in other types of
random networks. The relation between the mean distance and the diameter is
discussed.


title:
Wong-Zakai approximation and support theorem for 2D and 3D stochastic
  convective Brinkman-Forchheimer equations

abstract:
  In this work, we demonstrate the Wong-Zakai approximation results for two and
three dimensional stochastic convective Brinkman-Forchheimer (SCBF) equations
forced by Hilbert space valued Wiener noise on bounded domains. Even though the
existence and uniqueness of a pathwise strong solution to SCBF equations is
known, the existence of a unique solution to the approximating system is not
immediate from the solvability results of SCBF equations, and we prove it by
using Faedo-Galerkin approximation technique and monotonicity arguments.
Moreover, as an application of the Wong-Zakai approximation, we obtain the
support of the distribution of solutions to SCBF equations.


title:
Unravelling Saturable Absorption of Thulium-doped Fibres for New Level
  of Integrated Femtosecond Pulse Generation

abstract:
  The ultrashort pulse laser operation is crucially defined by engaging
explicit modulators. Conventionally, they are laborious to incorporate into an
all-fibre design or involve the design of sophisticated cavities. We exploit
Thulium-doped fibre as the gain and simple, highly integrated saturable
absorber with a modulation depth of 23% and a high thermal damage threshold.
Hence, pulses as short as 350fs carrying 1nJ energy at a repetition rate of
45MHz have been demonstrated. The laser average output power showed long-term
stability with ~1%. The utilised concept of all-fibre saturable absorber
provides technological grounds for a down-scaled, highly integrated laser
architecture.


title:
Construction of $G^2$ planar Hermite interpolants with prescribed arc
  lengths

abstract:
  In this paper we address the problem of constructing $G^2$ planar
Pythagorean--hodograph (PH) spline curves, that interpolate points, tangent
directions and curvatures, and have prescribed arc-length. The interpolation
scheme is completely local. Each spline segment is defined as a PH biarc curve
of degree $7$, which results in having a closed form solution of the $G^2$
interpolation equations depending on four free parameters. By fixing two of
them to zero, it is proven that the length constraint can be satisfied for any
data and any chosen ratio between the two boundary tangents. Length
interpolation equation reduces to one algebraic equation with four solutions in
general. To select the best one, the value of the bending energy is observed.
Several numerical examples are provided to illustrate the obtained theoretical
results and to numerically confirm that the approximation order is $5$.


title: Localizing Small Apples in Complex Apple Orchard Environments

abstract:
  The localization of fruits is an essential first step in automated
agricultural pipelines for yield estimation or fruit picking. One example of
this is the localization of apples in images of entire apple trees. Since the
apples are very small objects in such scenarios, we tackle this problem by
adapting the object proposal generation system AttentionMask that focuses on
small objects. We adapt AttentionMask by either adding a new module for very
small apples or integrating it into a tiling framework. Both approaches clearly
outperform standard object proposal generation systems on the MinneApple
dataset covering complex apple orchard environments. Our evaluation further
analyses the improvement w.r.t. the apple sizes and shows the different
characteristics of our two approaches.


title: Hilbert points in Hilbert space-valued $L^p$ spaces

abstract:
  Let $H$ be a Hilbert space and $(\Omega,\mathcal{F},\mu)$ a probability
space. A Hilbert point in $L^p(\Omega; H)$ is a nontrivial function $\varphi$
such that $\|\varphi\|_p \leq \|\varphi+f\|_p$ whenever $\langle f, \varphi
\rangle = 0$. We demonstrate that $\varphi$ is a Hilbert point in $L^p(\Omega;
H)$ for some $p\neq2$ if and only if $\|\varphi(\omega)\|_H$ assumes only the
two values $0$ and $C>0$. We also obtain a geometric description of when a sum
of independent Rademacher variables is a Hilbert point.


title:
Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion
  Network for Action Recognition

abstract:
  Action recognition has been a heated topic in computer vision for its wide
application in vision systems. Previous approaches achieve improvement by
fusing the modalities of the skeleton sequence and RGB video. However, such
methods have a dilemma between the accuracy and efficiency for the high
complexity of the RGB video network. To solve the problem, we propose a
multi-modality feature fusion network to combine the modalities of the skeleton
sequence and RGB frame instead of the RGB video, as the key information
contained by the combination of skeleton sequence and RGB frame is close to
that of the skeleton sequence and RGB video. In this way, the complementary
information is retained while the complexity is reduced by a large margin. To
better explore the correspondence of the two modalities, a two-stage fusion
framework is introduced in the network. In the early fusion stage, we introduce
a skeleton attention module that projects the skeleton sequence on the single
RGB frame to help the RGB frame focus on the limb movement regions. In the late
fusion stage, we propose a cross-attention module to fuse the skeleton feature
and the RGB feature by exploiting the correlation. Experiments on two
benchmarks NTU RGB+D and SYSU show that the proposed model achieves competitive
performance compared with the state-of-the-art methods while reduces the
complexity of the network.


title: Spectroscopic observation of a transition region network jet

abstract:
  Ubiquitous transition region (TR) network jets are considered to be
substantial sources of mass and energy to the corona and solar wind. We conduct
a case study of a network jet to better understand the nature of mass flows
along its length and the energetics involved in its launch. We present an
observation of a jet with the Interface Region Imaging Spectrograph (IRIS),
while also using data from the Solar Dynamics Observatory (SDO) to provide
further context. The jet was located within a coronal hole close to the disk
center. We find that a blueshifted secondary component of TR emission is
associated with the jet and is persistent along its spire. This component
exhibits upward speeds of approximately 20-70 km s$^{-1}$ and shows enhanced
line broadening. However, plasma associated with the jet in the upper
chromosphere shows downflows of 5-10 km s$^{-1}$. Finally, the jet emanates
from a seemingly unipolar magnetic footpoint. While a definitive magnetic
driver is not discernible for this event, we infer that the energy driving the
network jet is deposited at the top of the chromosphere, indicating that TR
network jets are driven from the mid-atmospheric layers of the Sun. The energy
flux associated with the line broadening indicates that the jet could be
powered all the way into the solar wind.


title:
Cooperative Behavioral Planning for Automated Driving using Graph Neural
  Networks

abstract:
  Urban intersections are prone to delays and inefficiencies due to static
precedence rules and occlusions limiting the view on prioritized traffic.
Existing approaches to improve traffic flow, widely known as automatic
intersection management systems, are mostly based on non-learning reservation
schemes or optimization algorithms. Machine learning-based techniques show
promising results in planning for a single ego vehicle. This work proposes to
leverage machine learning algorithms to optimize traffic flow at urban
intersections by jointly planning for multiple vehicles. Learning-based
behavior planning poses several challenges, demanding for a suited input and
output representation as well as large amounts of ground-truth data. We address
the former issue by using a flexible graph-based input representation
accompanied by a graph neural network. This allows to efficiently encode the
scene and inherently provide individual outputs for all involved vehicles. To
learn a sensible policy, without relying on the imitation of expert
demonstrations, the cooperative planning task is phrased as a reinforcement
learning problem. We train and evaluate the proposed method in an open-source
simulation environment for decision making in automated driving. Compared to a
first-in-first-out scheme and traffic governed by static priority rules, the
learned planner shows a significant gain in flow rate, while reducing the
number of induced stops. In addition to synthetic simulations, the approach is
also evaluated based on real-world traffic data taken from the publicly
available inD dataset.


title:
Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal
  OCT Images

abstract:
  Inpainting shadowed regions cast by superficial blood vessels in retinal
optical coherence tomography (OCT) images is critical for accurate and robust
machine analysis and clinical diagnosis. Traditional sequence-based approaches
such as propagating neighboring information to gradually fill in the missing
regions are cost-effective. But they generate less satisfactory outcomes when
dealing with larger missing regions and texture-rich structures. Emerging deep
learning-based methods such as encoder-decoder networks have shown promising
results in natural image inpainting tasks. However, they typically need a long
computational time for network training in addition to the high demand on the
size of datasets, which makes it difficult to be applied on often small medical
datasets. To address these challenges, we propose a novel multi-scale shadow
inpainting framework for OCT images by synergically applying sparse
representation and deep learning: sparse representation is used to extract
features from a small amount of training images for further inpainting and to
regularize the image after the multi-scale image fusion, while convolutional
neural network (CNN) is employed to enhance the image quality. During the image
inpainting, we divide preprocessed input images into different branches based
on the shadow width to harvest complementary information from different scales.
Finally, a sparse representation-based regularizing module is designed to
refine the generated contents after multi-scale feature aggregation.
Experiments are conducted to compare our proposal versus both traditional and
deep learning-based techniques on synthetic and real-world shadows. Results
demonstrate that our proposed method achieves favorable image inpainting in
terms of visual quality and quantitative metrics, especially when wide shadows
are presented.


title:
Recommended high performance telescope system design for the TianQin
  project

abstract:
  China is planning to construct a new space-borne gravitational-wave (GW)
observatory, the TianQin project, in which the spaceborne telescope is an
important component in laser interferometry. The telescope is aimed to transmit
laser beams between the spacecrafts for the measurement of the displacements
between proof-masses in long arms. The telescope should have ultra-small
wavefront deviation to minimize noise caused by pointing error, ultra-stable
structure to minimize optical path noise caused by temperature jitter,
ultra-high stray light suppression ability to eliminate background noise. In
this paper, we realize a telescope system design with ultra-stable structure as
well as ultra-low wavefront distortion for the space-based GW detection
mission. The design requirements demand extreme control of high image quality
and extraordinary stray light suppression ability. Based on the primary
aberration theory, the initial structure design of the mentioned four-mirror
optical system is explored. After optimization, the maximum RMS wavefront error
is less than lamda/300 over the full field of view (FOV), which meets the noise
budget on the telescope design. The stray light noise caused by the back
reflection of the telescope is also analyzed. The noise at the position of
optical bench is less than 10-10 of the transmitted power, satisfying the
requirements of space gravitational-wave detection. We believe that our design
can be a good candidate for TianQin project, and can also be a good guide for
the space telescope design in any other similar science project.


title: Packing and covering properties of sequences of convex bodies

abstract:
  This paper suveys different variants of the following problem: Given a convex
set $K$ and a sequence $\{C_i\}$ of convex bodies in $E^n$, is it possible to
pack the sequence of bodies in $K$ or cover $K$ with the bodies? Algorithmic
versions of these problems are on-line packing and on-line covering: The bodies
of the sequence are given one at a time and the algorithm is to decide on the
placement of the arriving body before the next body is revealed; once placed,
the body cannot be moved.


title:
Quantization of topological indices in critical chains at low
  temperatures

abstract:
  Various types of topological phenomena at criticality are currently under
active research. In this paper we suggest to generalize the known topological
quantities to finite temperatures, allowing us to consider gapped and critical
(gapless) systems on the same footing. It is then discussed that the
quantization of the topological indices, also at critically, is retrieved by
taking the low-temperature limit. This idea is explicitly illustrated on a
simple case study of chiral critical chains where the quantization is shown
analytically and verified numerically. The formalism is also applied for
studying robustness of the topological indices to various types of disordering
perturbations.


title: Going beyond ER=EPR in the SYK model

abstract:
  We discuss generalizations of the TFD to a density matrix on the doubled
Hilbert space. We suggest that a semiclassical wormhole corresponds to a
certain class of such density matrices, and specify how they are constructed.
Different semi-classical profiles correspond to different non-overlapping
density matrices. We show that this language allows for a finer criteria for
when the wormhole is semiclassical, which goes beyond entanglement. Our main
tool is the SYK model. We focus on the simplest class of such density matrices,
in a scaling limit where the ER bridge is captured by chords going from one
space to another, encoding correlations in the microscopic Hamiltonian. The
length of the wormhole simply encodes the extent these correlations are eroded
when flowing from one side to the other.

categories: hep-th cond-mat.stat-mech cond-mat.str-el math-ph math.MP math.OA


title:
Measurement of the polarisation of single top quarks and antiquarks
  produced in the $t$-channel at $\sqrt{s}=13$ TeV and bounds on the $tWb$
  dipole operator from the ATLAS experiment

abstract:
  A simultaneous measurement of the three components of the top-quark and
top-antiquark polarisation vectors in $t$-channel single-top-quark production
is presented. This analysis is based on data from proton--proton collisions at
a centre-of-mass energy of 13 TeV corresponding to an integrated luminosity of
139 fb$^{-1}$, collected with the ATLAS detector at the LHC. Selected events
contain exactly one isolated electron or muon, large missing transverse
momentum and exactly two jets, one being $b$-tagged. Stringent selection
requirements are applied to discriminate $t$-channel single-top-quark events
from the background contributions. The top-quark and top-antiquark polarisation
vectors are measured from the distributions of the direction cosines of the
charged-lepton momentum in the top-quark rest frame. The three components of
the polarisation vector for the selected top-quark event sample are $P_{x'}
=0.01\pm0.18$, $P_{y'} = -0.029\pm0.027$, $P_{z'} =0.91\pm0.10$ and for the
top-antiquark event sample they are $P_{x'} = -0.02\pm0.20$, $P_{y'} =
-0.007\pm0.051$, $P_{z'} = -0.79\pm 0.16$. Normalised differential
cross-sections corrected to a fiducial region at the stable-particle level are
presented as a function of the charged-lepton angles for top-quark and
top-antiquark events inclusively and separately. These measurements are in
agreement with Standard Model predictions. The angular differential
cross-sections are used to derive bounds on the complex Wilson coefficient of
the dimension-six $\mathcal{O}_{tW}$ operator in the framework of an effective
field theory. The obtained bounds are $C_{tW} \in\ [-0.9,1.4]$ and $C_{itW} \in
[-0.8,0.2]$, both at 95\% confidence level.


title: Multiple arrangements

abstract:
  This paper surveys the theory of multiple packings and coverings. The study
of multiple arrangements started in the 60s of the last century, and it was
restricted mostly to lattice arrangements on the plane or of general
arrangements of balls. We emphasize two new topics which were intensively
investigated recently: decomposition of multiple coverings into simple
coverings and characterization of multiple tilings.


title:
Multi-Teacher Knowledge Distillation for Incremental Implicitly-Refined
  Classification

abstract:
  Incremental learning methods can learn new classes continually by distilling
knowledge from the last model (as a teacher model) to the current model (as a
student model) in the sequentially learning process. However, these methods
cannot work for Incremental Implicitly-Refined Classification (IIRC), an
incremental learning extension where the incoming classes could have two
granularity levels, a superclass label and a subclass label. This is because
the previously learned superclass knowledge may be occupied by the subclass
knowledge learned sequentially. To solve this problem, we propose a novel
Multi-Teacher Knowledge Distillation (MTKD) strategy. To preserve the subclass
knowledge, we use the last model as a general teacher to distill the previous
knowledge for the student model. To preserve the superclass knowledge, we use
the initial model as a superclass teacher to distill the superclass knowledge
as the initial model contains abundant superclass knowledge. However,
distilling knowledge from two teacher models could result in the student model
making some redundant predictions. We further propose a post-processing
mechanism, called as Top-k prediction restriction to reduce the redundant
predictions. Our experimental results on IIRC-ImageNet120 and IIRC-CIFAR100
show that the proposed method can achieve better classification accuracy
compared with existing state-of-the-art methods.


title:
Compositional Model Checking of Consensus Protocols Specified in TLA+
  via Interaction-Preserving Abstraction

abstract:
  Consensus protocols are widely used in building reliable distributed software
systems and its correctness is of vital importance. TLA+ is a lightweight
formal specification language which enables precise specification of system
design and exhaustive checking of the design without any human effort. The
features of TLA+ make it widely used in the specification and model checking of
consensus protocols, both in academia and industry. However, the application of
TLA+ is limited by the state explosion problem in model checking. Though
compositional model checking is essential to tame the state explosion problem,
existing compositional checking techniques do not sufficiently consider the
characteristics of TLA+.
  In this work, we propose the Interaction-Preserving Abstraction (IPA)
framework, which leverages the features of TLA+ and enables practical and
efficient compositional model checking of consensus protocols specified in
TLA+. In the IPA framework, system specification is partitioned into multiple
modules, and each module is divided to the internal part and the interaction
part. The basic idea of the interaction-preserving abstraction is to omit the
internal part of each module, such that another module cannot distinguish
whether it is interacting with the original module or the coarsened abstract
one.
  We use the IPA framework to the compositional checking of the TLA+
specification of two consensus protocols Raft and ParallelRaft. Raft is a
consensus protocol which is originally developed in the academia and then
widely used in industry. ParallelRaft is the replication protocol in PolarFS,
the distributed file system for the commercial database Alibaba PoloarDB. We
demonstrate that the IPA framework is easy to use in realistic scenarios and at
the same time significantly reduces the model checking cost.


title: Addition and Differentiation of ZX-diagrams

abstract:
  The ZX-calculus is a powerful framework for reasoning in quantum computing.
It provides in particular a compact representation of matrices of interests. A
peculiar property of the ZX-calculus is the absence of a formal sum allowing
the linear combinations of arbitrary ZX-diagrams. The universality of the
formalism guarantees however that for any two ZX-diagrams, the sum of their
interpretations can be represented by a ZX-diagram. We introduce a general,
inductive definition of the addition of ZX-diagrams, relying on the
construction of controlled diagrams. Based on this addition technique, we
provide an inductive differentiation of ZX-diagrams.
  Indeed, given a ZX-diagram with variables in the description of its angles,
one can differentiate the diagram according to one of these variables.
Differentiation is ubiquitous in quantum mechanics and quantum computing (e.g.
for solving optimization problems). Technically, differentiation of ZX-diagrams
is strongly related to summation as witnessed by the product rules.
  We also introduce an alternative, non inductive, differentiation technique
rather based on the isolation of the variables. Finally, we apply our results
to deduce a diagram for an Ising Hamiltonian.


title:
Nonexistence for parabolic differential inequalities with convection
  terms in exterior domains

abstract:
  We are concerned with the nonexistence of sign-changing global weak solutions
for a class of semilinear parabolic differential inequalities with convection
terms in exterior domains. A weight function of the form $t^\alpha |x|^\sigma$
is considered in front of the power nonlinearity. Two types of non-homogeneous
boundary conditions are investigated: Neumann-type and Dirichlet-type boundary
conditions. Using a unified approach, for each case, we establish sufficient
criteria for the nonexistence of global weak solutions. When $\alpha=0$, the
critical exponent in the sense of Fujita is obtained. This exponent is bigger
than that found previously by Zheng and Wang (2008) in the case of homogeneous
Neumann and Dirichlet boundary conditions.


title:
Deep Metric Learning-Based Semi-Supervised Regression With Alternate
  Learning

abstract:
  This paper introduces a novel deep metric learning-based semi-supervised
regression (DML-S2R) method for parameter estimation problems. The proposed
DML-S2R method aims to mitigate the problems of insufficient amount of labeled
samples without collecting any additional samples with target values. To this
end, the proposed DML-S2R method is made up of two main steps: i) pairwise
similarity modeling with scarce labeled data; and ii) triplet-based metric
learning with abundant unlabeled data. The first step aims to model pairwise
sample similarities by using a small number of labeled samples. This is
achieved by estimating the target value differences of labeled samples with a
Siamese neural network (SNN). The second step aims to learn a triplet-based
metric space (in which similar samples are close to each other and dissimilar
samples are far apart from each other) when the number of labeled samples is
insufficient. This is achieved by employing the SNN of the first step for
triplet-based deep metric learning that exploits not only labeled samples but
also unlabeled samples. For the end-to-end training of DML-S2R, we investigate
an alternate learning strategy for the two steps. Due to this strategy, the
encoded information in each step becomes a guidance for learning the other
step. The experimental results confirm the success of DML-S2R compared to the
state-of-the-art semi-supervised regression methods. The code of the proposed
method is publicly available at https://git.tu-berlin.de/rsim/DML-S2R.