title: Adaptive Task Planning for Large-Scale Robotized Warehouses

abstract:
  Robotized warehouses are deployed to automatically distribute millions of
items brought by the massive logistic orders from e-commerce. A key to
automated item distribution is to plan paths for robots, also known as task
planning, where each task is to deliver racks with items to pickers for
processing and then return the rack back. Prior solutions are unfit for
large-scale robotized warehouses due to the inflexibility to time-varying item
arrivals and the low efficiency for high throughput. In this paper, we propose
a new task planning problem called TPRW, which aims to minimize the end-to-end
makespan that incorporates the entire item distribution pipeline, known as a
fulfilment cycle. Direct extensions from state-of-the-art path finding methods
are ineffective to solve the TPRW problem because they fail to adapt to the
bottleneck variations of fulfillment cycles. In response, we propose Efficient
Adaptive Task Planning, a framework for large-scale robotized warehouses with
time-varying item arrivals. It adaptively selects racks to fulfill at each
timestamp via reinforcement learning, accounting for the time-varying
bottleneck of the fulfillment cycles. Then it finds paths for robots to
transport the selected racks. The framework adopts a series of efficient
optimizations on both time and memory to handle large-scale item throughput.
Evaluations on both synthesized and real data show an improvement of $37.1\%$
in effectiveness and $75.5\%$ in efficiency over the state-of-the-arts.


title: Gradient Descent, Stochastic Optimization, and Other Tales

abstract:
  The goal of this paper is to debunk and dispel the magic behind black-box
optimizers and stochastic optimizers. 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 gradient descent and stochastic optimization methods. 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 algorithms.
  Gradient descent is one of the most popular algorithms to perform
optimization and by far the most common way to optimize machine learning tasks.
Its stochastic version receives attention in recent years, and this is
particularly true for optimizing deep neural networks. In deep neural networks,
the gradient followed by a single sample or a batch of samples is employed to
save computational resources and escape from saddle points. In 1951, Robbins
and Monro published \textit{A stochastic approximation method}, one of the
first modern treatments on stochastic optimization that estimates local
gradients with a new batch of samples. And now, stochastic optimization has
become a core technology in machine learning, largely due to the development of
the back propagation algorithm in fitting a neural network. The sole aim of
this article is to give a self-contained introduction to concepts and
mathematical tools in gradient descent and stochastic optimization.


title:
Predicting and Optimizing for Energy Efficient ACMV Systems:
  Computational Intelligence Approaches

abstract:
  In this study, a novel application of neural networks that predict thermal
comfort states of occupants is proposed with accuracy over 95%, and two
optimization algorithms are proposed and evaluated under two real cases
(general offices and lecture theatres/conference rooms scenarios) in Singapore.
The two optimization algorithms are Bayesian Gaussian process optimization
(BGPO) and augmented firefly algorithm (AFA). Based on our earlier studies, the
models of energy consumption were developed and well-trained through neural
networks. This study focuses on using novel active approaches to evaluate
thermal comfort of occupants and so as to solves a multiple-objective problem
that aims to balance energy-efficiency of centralized air-conditioning systems
and thermal comfort of occupants. The study results show that both BGPO and AFA
are feasible to resolve this no prior knowledge-based optimization problem
effectively. However, the optimal solutions of AFA are more consistent than
those of BGPO at given sample sizes. The best energy saving rates (ESR) of BGPO
and AFA are around -21% and -10% respectively at energy-efficient user
preference for both Case 1 and Case 2. As a result, an potential benefit of
S$1219.1 can be achieved annually for this experimental laboratory level in
Singapore.


title: Convex Augmentation for Total Variation Based Phase Retrieval

abstract:
  Phase retrieval is an important problem with significant physical and
industrial applications. In this paper, we consider the case where the
magnitude of the measurement of an underlying signal is corrupted by Gaussian
noise. We introduce a convex augmentation approach for phase retrieval based on
total variation regularization. In contrast to popular convex relaxation models
like PhaseLift, our model can be efficiently solved by a modified semi-proximal
alternating direction method of multipliers (sPADMM). The modified sPADMM is
more general and flexible than the standard one, and its convergence is also
established in this paper. Extensive numerical experiments are conducted to
showcase the effectiveness of the proposed method.


title:
$\pi$ Flux Phase and Superconductivity for Lattice Fermions Coupled to
  Classical Gauge Fields

abstract:
  We study superconducting lattice fermions coupled to classical gauge fields.
Namely, without the gauge fields, the lattice fermions show superconducting
long-range order. In the previous paper, the existence of the long-range order
was proved by the present author. This paper is the continuing part of the
previous one. More precisely, the interactions between fermions were assumed to
be a Bardeen-Cooper-Schrieffer-type pairing which is a nearest-neighbour
two-body interaction on the hypercubic lattice $\mathbb{Z}^d$ with the
dimension $d\ge 3$. In the present paper, we deal with the corresponding gauged
model by introducing classical U(1) gauge fields. We prove that a certain
configuration of gauge fields which yields the $\pi$ flux for all the
plaquettes in the lattice minimizes the ground-state energy of the fermion
system. Since this configuration of the gauge fields exactly coincides with the
hopping amplitudes of the Hamiltonian of the previous paper, the ground state
of the whole system shows superconducting long-range order. Any configuration
of gauge fields which is gauge-equivalent to this special configuration of the
gauge fields also yields another ground state. However, if the Cooper pair
correlations are averaged over the gauge-equivalent class, then they are
vanishing except for the on-site correlation because the Cooper pair
correlations are not necessarily gauge invariant. On the other hand, the string
Cooper pair correlations which are gauge invariant always show the same
long-range order for any configuration of gauge fields in the gauge-equivalent
class.


title:
Porous media equations with nonlinear gradient noise and Dirichlet
  boundary conditions

abstract:
  We establish pathwise existence of solutions for porous media and fast
diffusion equations with nonlinear gradient noise, in the full regime
$m\in(0,\infty)$ and for any initial data in $L^2$. Moreover, if the initial
data is positive, solutions are pathwise unique. In turn, the solution map of
these equations is a continuous function of the driving noise and it generates
an associated random dynamical system. Finally, in the regime
$m\in\{1\}\cup(2,\infty)$, all the aforementioned results also hold for signed
initial data.


title: Bridging Walking and Slithering -- Stokesian Locomotion

abstract:
  Both legged locomotion and slithering motions typically utilize periodic
gaits -- repeating cycles of body shape change that produce a net motion
through the world. Legged locomotion can be viewed from the perspective of
piece-wise contact constraint formation and removal. Slithering and low
Reynolds number swimming operate under continuous constraints of force balance,
wherein dissipation removes the ability to accumulate momentum. Here we discuss
how to bridge the gap between these domains of motion, thereby, among other
benefits, producing models for the space of legged locomotion with slipping.
The connective fabric is the use of a "Stokesian", or "local connection" model.


title: The Rise and Fall of Robotic World (A case study of WALL-E)

abstract:
  The current trend in technology shows that the robots will soon be seen
interacting with humans and handling different tasks more efficiently in
future. With advancement in artificial intelligence it can be foreseen that
robots will definitely take over all of the major and minor jobs, will socially
interact with humans and will minimize the burden of human significantly. With
the passage of time humans will forget about their reality and the labour work
they currently do by themselves. That age will be exactly the peak age of
robots and we can call it age of the rise of the robots. With the rise of
robots, they will start taking decision by themselves regarding the human life
and the planet. Human will again start to minimize the influence of robots and
take control of their life with the help of some robots. The WALL-E narrates
such a story of human life dependency on robots in spaceship when earth is
destroyed because of some catastrophe. The clash between human and robots
occurs when humans find the earth is survivable and should go back but robots
do not allow them. With the help of some social robots humans again take
control and take the ship back to earth. The major part of the movie is about
the a lonely robot living on planet earth, the love of two robots, their
interaction. Those two robots help humans to move back to earth.


title: Lateral Stability of Vehicle with Interlocking Spikes

abstract:
  The interlock drive system generates traction by penetrating narrow
articulated spikes into the ground and by using the strength of the deeper soil
layers to resist horizontal draft forces. The system promises good tractive
performance in low gravity environments where tires generate little traction
due to low vehicle weight. Possible applications include heavy-duty vehicles
for civil engineering tasks like earthmoving or mining excavation. Safe vehicle
operation in complex terrain geometry requires lateral vehicle stability to
prevent vehicle rollover. Good lateral stability is a particular requirement
for excavation and piling operations where the margins of safety define the
terrain geometry that can be worked in, and it is a major constraint in
operational planning. An earthmoving vehicle that can operate at a high roll
angle reduces the need to maintain ramps in pits and on piles and can shorten
and simplify the paths for individual maneuvers. Here we report on several
field trials on the lateral stability of an earthmoving vehicle that uses the
interlock drive systems. We find that the vehicle can work well at roll angles
of up to 20{\deg}, but that it needs further improvement if work at roll angles
of 30{\deg} or more is required.


title: Traction of Interlocking Spikes on a Granular Material

abstract:
  The interlock drive system generates traction by inserting narrow articulated
spikes into the ground and by leveraging the soil's strength to resist
horizontal draft forces. The system promises high tractive performance in low
gravity environments where tires have little traction for lack of weight. At
Earth and Space 2021 we reported the performance of such spikes on a silty clay
loam, a cohesive soil. We found that in such soil, traction below a critical
depth is provided by a zone of lateral soil failure. We also found that the
articulation translates a horizontal draft force into a vertical penetration
force strong enough to penetrate a narrow spike to a depth where the soil can
sustain the draft force, in a self-regulating way. It is conceivable that a
granular material like regolith or sand with little to no cohesive strength
provides less vertical penetration resistance and less resistance to a
horizontal draft force than a cohesive soil, which leads to the question of
whether and how much tractive force an interlocking spike can generate on a
granular material. Here we report on field trials that study different spike
designs in dry and unsaturated moist sand. The results demonstrate that a loose
granular material requires larger spikes than a cohesive soil, that these
larger spikes penetrate dry and moist sand reliably, and that they promise good
tractive efficiency. The trials indicate that on sand, a larger spike diameter
can improve the pull/weight ratio without a loss of tractive performance.


title: GPUNet: Searching the Deployable Convolution Neural Networks for GPUs

abstract:
  Customizing Convolution Neural Networks (CNN) for production use has been a
challenging task for DL practitioners. This paper intends to expedite the model
customization with a model hub that contains the optimized models tiered by
their inference latency using Neural Architecture Search (NAS). To achieve this
goal, we build a distributed NAS system to search on a novel search space that
consists of prominent factors to impact latency and accuracy. Since we target
GPU, we name the NAS optimized models as GPUNet, which establishes a new SOTA
Pareto frontier in inference latency and accuracy. Within 1$ms$, GPUNet is 2x
faster than EfficientNet-X and FBNetV3 with even better accuracy. We also
validate GPUNet on detection tasks, and GPUNet consistently outperforms
EfficientNet-X and FBNetV3 on COCO detection tasks in both latency and
accuracy. All of these data validate that our NAS system is effective and
generic to handle different design tasks. With this NAS system, we expand
GPUNet to cover a wide range of latency targets such that DL practitioners can
deploy our models directly in different scenarios.


title: Cornering Optics

abstract:
  We show that the category of optics in a monoidal category arises naturally
from the free cornering of that category. Further, we show that the free
cornering of a monoidal category is a natural setting in which to work with
comb diagrams over that category. The free cornering admits an intuitive
graphical calculus, which in light of our work may be used to reason about
optics and comb diagrams.


title: Quantum entanglement and anthropology

abstract:
  We find that the set of local quantum operations and classical communication
for multiparty quantum states can be considered as analogous to online meetings
between members of a population. Moreover, monotonicity properties of quantum
and classical correlations of quantum states of shared systems also carry over
to relations between members of the population, giving rise to what may be
termed as a second law of anthropology.


title:
AFD Types Sparse Representations vs. the Karhunen-Loeve Expansion for
  Decomposing Stochastic Processes

abstract:
  This article introduces adaptive Fourier decomposition (AFD) type methods,
emphasizing on those that can be applied to stochastic processes and random
fields, mainly including stochastic adaptive Fourier decomposition and
stochastic pre-orthogonal adaptive Fourier decomposition. We establish their
algorithms based on the covariant function and prove that they enjoy the same
convergence rate as the Karhunen-Loeve (KL) decomposition. The AFD type methods
are compared with the KL decomposition. In contrast with the latter, the AFD
type methods do not need to compute eigenvalues and eigenfunctions of the
kernel-integral operator induced by the covariance function, and thus
considerably reduce the computation complexity and computer consumes. Various
kinds of dictionaries offer AFD flexibility to solve problems of a great
variety, including different types of deterministic and stochastic equations.
The conducted experiments show, besides the numerical convenience and fast
convergence, that the AFD type decompositions outperform the KL type in
describing local details, in spite of the proven global optimality of the
latter.

categories: math.ST cs.NA math.FA math.NA math.PR stat.TH


title: Markov chain approximations for nonsymmetric processes

abstract:
  The aim of this article is to prove that diffusion processes in
$\mathbb{R}^d$ with a drift can be approximated by suitable Markov chains on
$n^{-1}\mathbb{Z}^d$. Moreover, we investigate sufficient conditions on the
conductances which guarantee convergence of the associated Markov chains to
such Markov processes. Analogous questions are answered for a large class of
nonsymmetric jump processes. The proofs of our results rely on regularity
estimates for weak solutions to the corresponding nonsymmetric parabolic
equations and Dirichlet form techniques.


title:
Unveiling the charge distribution of a GaAs-based nanoelectronic device:
  A large experimental data-set approach

abstract:
  In quantum nanoelectronics, numerical simulations have become an ubiquitous
tool. Yet the comparison with experiments is often done at a qualitative level
or restricted to a single device with a handful of fitting parameters. In this
work, we assess the predictive power of these simulations by comparing the
results of a single model with a large experimental data set of 110 devices
with 48 different geometries. The devices are quantum point contacts of various
shapes and sizes made with electrostatic gates deposited on top of a high
mobility GaAs/GaAlAs two dimensional electron gas. We study the pinch-off
voltages applied on the gates to deplete the two-dimensional electron gas in
various spatial positions. We argue that the pinch-off voltages are a very
robust signature of the charge distribution in the device. The large
experimental data set allows us to critically review the modeling and arrive at
a robust one-parameter model that can be calibrated in situ, a crucial step for
making predictive simulations.


title:
APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient
  Point Cloud Classification

abstract:
  Point-cloud-based 3D classification task involves aggregating features from
neighbor points. In previous works, each source point is often selected as a
neighbor by multiple center points. Thus each source point has to participate
in calculation multiple times with high memory consumption. Meanwhile, to
pursue higher accuracy, these methods rely on a complex local aggregator to
extract fine geometric representation, which slows down the network. To address
these issues, we propose a new local aggregator of linear complexity, coined as
APP. Specifically, we introduce an auxiliary container as an anchor to exchange
features between the source point and the aggregating center. Each source point
pushes its feature to only one auxiliary container, and each center point pulls
features from only one auxiliary container. This avoids the re-computation of
each source point. To facilitate the learning of the local structure, we use an
online normal estimation module to provide the explainable geometric
information to enhance our APP modeling capability. The constructed network is
more efficient than all the previous baselines with a clear margin while only
occupying a low memory. Experiments on both synthetic and real datasets verify
that APP-Net reaches comparable accuracies with other networks. We will release
the complete code to help others reproduce the APP-Net.


title: Stability-driven Contact Reconstruction From Monocular Color Images

abstract:
  Physical contact provides additional constraints for hand-object state
reconstruction as well as a basis for further understanding of interaction
affordances. Estimating these severely occluded regions from monocular images
presents a considerable challenge. Existing methods optimize the hand-object
contact driven by distance threshold or prior from contact-labeled datasets.
However, due to the number of subjects and objects involved in these indoor
datasets being limited, the learned contact patterns could not be generalized
easily. Our key idea is to reconstruct the contact pattern directly from
monocular images, and then utilize the physical stability criterion in the
simulation to optimize it. This criterion is defined by the resultant forces
and contact distribution computed by the physics engine.Compared to existing
solutions, our framework can be adapted to more personalized hands and diverse
object shapes. Furthermore, an interaction dataset with extra physical
attributes is created to verify the sim-to-real consistency of our methods.
Through comprehensive evaluations, hand-object contact can be reconstructed
with both accuracy and stability by the proposed framework.


title:
Model-based Deep Learning Receiver Design for Rate-Splitting Multiple
  Access

abstract:
  Effective and adaptive interference management is required in next generation
wireless communication systems. To address this challenge, Rate-Splitting
Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the
transmitter and successive interference cancellation (SIC) at the receivers,
has been intensively studied in recent years, albeit mostly under the
assumption of perfect Channel State Information at the Receiver (CSIR) and
ideal capacity-achieving modulation and coding schemes. To assess its practical
performance, benefits, and limits under more realistic conditions, this work
proposes a novel design for a practical RSMA receiver based on model-based deep
learning (MBDL) methods, which aims to unite the simple structure of the
conventional SIC receiver and the robustness and model agnosticism of deep
learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol
Error Rate (SER), throughput performance through Link-Level Simulations (LLS),
and average training overhead. Also, a comparison with the SIC receiver, with
perfect and imperfect CSIR, is given. Results reveal that the MBDL outperforms
by a significant margin the SIC receiver with imperfect CSIR, due to its
ability to generate on demand non-linear symbol detection boundaries in a pure
data-driven manner.


title:
The electromagnetic Sigma-to-Lambda transition form factors with
  coupled-channel effects in the space-like region

abstract:
  Using dispersion theory, the electromagnetic Sigma-to-Lambda transition form
factors are expressed as the product of the pion electromagnetic form factor
and the $\pi\pi\to\Sigma\Lambda$ scattering amplitudes with the latter
estimated from SU(3) chiral perturbation theory including the baryon decuplet
as explicit degrees of freedom. The contribution of the $K\bar{K}$ channel is
also taken into account and the $\pi\pi$-$K\bar{K}$ coupled-channel effect is
included by means of a two-channel Muskhelishvili-Omn\`{e}s representation. It
is found that the electric transition form factor shows a significant shift
after the inclusion of $K\bar{K}$ channel, while the magnetic transition form
factor is only weakly affected. The error bands of the Sigma-to-Lambda
transition form factors from the uncertainties of the couplings in three-flavor
chiral perturbation are estimated by a bootstrap sampling method.


title: Weighted Counting of Matchings in Unbounded-Treewidth Graph Families

abstract:
  We consider a weighted counting problem on matchings, denoted
$\textrm{PrMatching}(\mathcal{G})$, on an arbitrary fixed graph family
$\mathcal{G}$. The input consists of a graph $G\in \mathcal{G}$ and of rational
probabilities of existence on every edge of $G$, assuming independence. The
output is the probability of obtaining a matching of $G$ in the resulting
distribution, i.e., a set of edges that are pairwise disjoint. It is known
that, if $\mathcal{G}$ has bounded treewidth, then
$\textrm{PrMatching}(\mathcal{G})$ can be solved in polynomial time. In this
paper we show that, under some assumptions, bounded treewidth in fact
characterizes the tractable graph families for this problem. More precisely, we
show intractability for all graph families $\mathcal{G}$ satisfying the
following treewidth-constructibility requirement: given an integer $k$ in
unary, we can construct in polynomial time a graph $G \in \mathcal{G}$ with
treewidth at least $k$. Our hardness result is then the following: for any
treewidth-constructible graph family $\mathcal{G}$, the problem
$\textrm{PrMatching}(\mathcal{G})$ is intractable. This generalizes known
hardness results for weighted matching counting under some restrictions that do
not bound treewidth, e.g., being planar, 3-regular, or bipartite; it also
answers a question left open in Amarilli, Bourhis and Senellart (PODS'16). We
also obtain a similar lower bound for the weighted counting of edge covers.


title:
A Note on "A survey of preference estimation with unobserved choice set
  heterogeneity" by Gregory S. Crawford, Rachel Griffith, and Alessandro Iaria

abstract:
  Crawford's et al. (2021) article on estimation of discrete choice models with
unobserved or latent consideration sets, presents a unified framework to
address the problem in practice by using "sufficient sets", defined as a
combination of past observed choices. The proposed approach is sustained in a
re-interpretation of a consistency result by McFadden (1978) for the problem of
sampling of alternatives, but the usage of that result in Crawford et al.
(2021) is imprecise in an important matter. It is stated that consistency would
be attained if any subset of the true consideration set is used for estimation,
but McFadden (1978) shows that, in general, one needs to do a sampling
correction that depends on the protocol used to draw the choice set. This note
derives the sampling correction that is required when the choice set for
estimation is built from past choices. Then, it formalizes the conditions under
which such correction would fulfill the uniform condition property and can
therefore be ignored when building practical estimators, such as the ones
analyzed by Crawford et al. (2021).


title:
Lightweight Image Enhancement Network for Mobile Devices Using
  Self-Feature Extraction and Dense Modulation

abstract:
  Convolutional neural network (CNN) based image enhancement methods such as
super-resolution and detail enhancement have achieved remarkable performances.
However, amounts of operations including convolution and parameters within the
networks cost high computing power and need huge memory resource, which limits
the applications with on-device requirements. Lightweight image enhancement
network should restore details, texture, and structural information from
low-resolution input images while keeping their fidelity. To address these
issues, a lightweight image enhancement network is proposed. The proposed
network include self-feature extraction module which produces modulation
parameters from low-quality image itself, and provides them to modulate the
features in the network. Also, dense modulation block is proposed for unit
block of the proposed network, which uses dense connections of concatenated
features applied in modulation layers. Experimental results demonstrate better
performance over existing approaches in terms of both quantitative and
qualitative evaluations.


title: A proof of T. Willwacher's conjecture

abstract:
  In the present paper, we continue the study of the relation between the
cohomology of moduli stacks of smooth and proper curves and the cohomology of
ribbon graph complexes. The main results of this work are proofs of T.
Willwacher's conjecture and A. Caldararu's conjecture


title: Atomic-scale Oxygen-mediated Etching of 2D MoS$_2$ and MoTe$_2$

abstract:
  Some of the materials are more affected by oxidation than others. To
elucidate the oxidation-induced degradation mechanisms in transition metal
chalcogenides, the chemical effects in single layer MoS$_2$ and MoTe$_2$ were
studied in situ in an electron microscope under controlled low-pressure oxygen
environments at room temperature.Oxidation is the main cause of degradation of
many two-dimensional materials, including transition metal dichalcogenides,
under ambient conditions. MoTe$_2$ is found to be reactive to oxygen, leading
to significant degradation above a pressure of 1$\times 10^{-7}$ torr.
Curiously, the common hydrocarbon contamination found on practically all
surfaces accelerates the damage rate significantly, by up to a factor of forty.
In contrast to MoTe$_2$, MoS$_2$ is found to be inert under oxygen environment,
with all observed structural changes being caused by electron irradiation only,
leading to well-defined pores with high proportion of molybdenum
nanowire-terminated edges. Using density functional theory calculations, a
further atomic-scale mechanism leading to the observed oxygen-related
degradation in MoTe$_2$ is proposed and the role of the carbon in the etching
is explored. Together, the results provide an important insight into the
oxygen-related deterioration of two-dimensional materials under ambient
conditions relevant in many fields.


title:
The N-shaped partition method: A novel parallel implementation of the
  Crank Nicolson algorithm

abstract:
  We develop an algorithm to solve tridiagonal systems of linear equations,
which appear in implicit finite-difference schemes of partial differential
equations (PDEs). In particular, the time-dependent Schr\"{o}dinger equation
(TDSE), is an ideal candidate to benefit from our proposed algorithm. Our
N-shaped partition method allows us to optimize the implementation of the
numerical calculation on parallel architectures, without memory size
constraints. Specifically, we discuss the possible realization of our method on
graphics processing units (GPUs) and the Message Passing Interface (MPI). For
instance, in GPU implementations, our scheme is particularly advantageous for
systems whose size exceeds the global memory of a single processor. Moreover,
because of its lack of memory constraints and the generality of the algorithm,
it is well-suited for mixed architectures, typically available in large high
performance computing (HPC) centres. We also provide an analytical estimation
of the optimal parameters to implement our algorithm, and test numerically the
suitability of our formula in a GPU implementation. Our method will be helpful
to tackle problems which require large spatial grids for which ab-initio
studies might be otherwise prohibitive both because of large shared-memory
requirements and computation times.


title:
Importance of intracluster scattering and relativistic corrections from
  tSZ effect with Cosmic Infrared Background

abstract:
  The Sunyaev-Zeldovich effect towards clusters of galaxies has become a
standard probe of cosmology. It is caused by the scattering of photons from the
cosmic microwave background (CMB) by the hot cluster electron gas. In a similar
manner, other photon backgrounds can be scattered when passing through the
cluster medium. This problem has been recently considered for the radio and the
cosmic infrared background. Here we revisit the discussion of the cosmic
infrared background (CIB) including several additional effects that were
omitted before. We discuss the {\it intracluster} scattering of the CIB and the
role of {\it relativistic} temperature corrections to the individual cluster
and all-sky averaged signals. We show that the all-sky CIB distortion
introduced by the scattering of the photon field was underestimated by a factor
of $\simeq 1.5$ due to neglecting the intracluster scattering contribution.
Energy is essentially transferred twice from the thermal electrons to the CIB.
We carefully clarify the origin of various effects in the calculation of the
average CIB and also scattered signals. The single-cluster CIB scattering
signal also exhibits a clear redshift dependence, which can be used in
cosmological analyses, as we describe both analytically and numerically. This
may open a new way for cosmological studies with future CMB experiments.


title:
Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation
  in Nighttime Semantic Segmentation

abstract:
  The performance of nighttime semantic segmentation is restricted by the poor
illumination and a lack of pixel-wise annotation, which severely limit its
application in autonomous driving. Existing works, e.g., using the twilight as
the intermediate target domain to perform the adaptation from daytime to
nighttime, may fail to cope with the inherent difference between datasets
caused by the camera equipment and the urban style. Faced with these two types
of domain shifts, i.e., the illumination and the inherent difference of the
datasets, we propose a novel domain adaptation framework via cross-domain
correlation distillation, called CCDistill. The invariance of illumination or
inherent difference between two images is fully explored so as to make up for
the lack of labels for nighttime images. Specifically, we extract the content
and style knowledge contained in features, calculate the degree of inherent or
illumination difference between two images. The domain adaptation is achieved
using the invariance of the same kind of difference. Extensive experiments on
Dark Zurich and ACDC demonstrate that CCDistill achieves the state-of-the-art
performance for nighttime semantic segmentation. Notably, our method is a
one-stage domain adaptation network which can avoid affecting the inference
time. Our implementation is available at https://github.com/ghuan99/CCDistill.


title: Bayesian Monitoring of COVID-19 in Sweden

abstract:
  In an effort to provide regional decision support for the public healthcare,
we design a data-driven compartment-based model of COVID-19 in Sweden. From
national hospital statistics we deduce parameter priors, and we develop linear
filtering techniques to drive the simulations given data in the form of daily
healthcare demands. We additionally put forward an optimization scheme which
enables a refined resolution of the reproduction number estimate, and which
also improves substantially on our confidence in the overall results thanks to
a parametric bootstrap procedure. Taken together we obtain a computationally
efficient Bayesian approach of predictive value which provides important
insight into the progression of the disease, including estimates of the
effective reproduction number, the infection fatality rate, and the
regional-level immunity. We successfully validate our posterior model against
several different sources, including outputs from extensive screening programs.
Since our required data in comparison is easy and non-sensitive to collect, we
argue that our approach is particularly promising as a tool to support
monitoring and decisions within public health.


title: Spin-resolved density response of the warm dense electron gas

abstract:
  We present extensive new \emph{ab initio} path integral Monte Carlo (PIMC)
results for the spin-resolved density response of the uniform electron gas
(UEG) at warm dense matter conditions. This allows us to unambiguously assess
the accuracy of previous theoretical approximations, thereby providing valuable
new insights for the future development of dielectric schemes. From a physical
perspective, we observe a nontrivial manifestation of an effective
electron--electron attraction that emerges in the spin-offdiagonal static
density response function at strong coupling, $r_s\gtrsim5$. All PIMC results
are freely available online and can be used to benchmark new approximations and
simulation schemes.


title:
Star-specific Key-homomorphic PRFs from Linear Regression and Extremal
  Set Theory

abstract:
  We introduce a novel method to derandomize the learning with errors (LWE)
problem by generating deterministic yet sufficiently independent LWE instances,
that are constructed via \emph{special} linear regression models. We also
introduce star-specific key-homomorphic (SSKH) pseudorandom functions (PRFs),
which are defined by the respective sets of parties that construct them. We use
our derandomized variant of LWE to construct a SSKH PRF family. The sets of
parties constructing SSKH PRFs are arranged as star graphs with possibly shared
vertices, i.e., some pair of sets have non-empty intersections. We reduce the
security of our SSKH PRF family to the hardness of LWE. To establish the
maximum number of SSKH PRFs that can be constructed -- by a set of parties --
in the presence of passive/active and external/internal adversaries, we prove
several bounds on the size of maximally cover-free at most $t$-intersecting
$k$-uniform family of sets $\mathcal{H}$, where the three properties are
defined as: (i) $k$-uniform: $\forall A \in \mathcal{H}: |A| = k$, (ii) at most
$t$-intersecting: $\forall A, B \in \mathcal{H}, B \neq A: |A \cap B| \leq t$,
(iii) maximally cover-free: $\forall A \in \mathcal{H}: A \not\subseteq
\bigcup\limits_{\substack{B \in \mathcal{H} \\ B \neq A}} B$. For the same
purpose, we define and compute the mutual information between different linear
regression hypotheses that are generated via overlapping training datasets.


title:
Accelerating Verified-Compiler Development with a Verified Rewriting
  Engine

abstract:
  Compilers are a prime target for formal verification, since compiler bugs
invalidate higher-level correctness guarantees, but compiler changes may become
more labor-intensive to implement, if they must come with proof patches. One
appealing approach is to present compilers as sets of algebraic rewrite rules,
which a generic engine can apply efficiently. Now each rewrite rule can be
proved separately, with no need to revisit past proofs for other parts of the
compiler. We present the first realization of this idea, in the form of a
framework for the Coq proof assistant. Our new Coq command takes normal proved
theorems and combines them automatically into fast compilers with proofs. We
applied our framework to improve the Fiat Cryptography toolchain for generating
cryptographic arithmetic, producing an extracted command-line compiler that is
about 1000$\times$ faster while actually featuring simpler compiler-specific
proofs.


title:
Quasiconvexity preserving property for fully nonlinear nonlocal
  parabolic equations

abstract:
  This paper is concerned with a general class of fully nonlinear parabolic
equations with monotone nonlocal terms. We investigate the quasiconvexity
preserving property of positive, spatially coercive viscosity solutions. We
prove that if the initial value is quasiconvex, the viscosity solution to the
Cauchy problem stays quasiconvex in space for all time. Our proof can be
regarded as a limit version of that for power convexity preservation as the
exponent tends to infinity. We also present several concrete examples to show
applications of our result.


title:
Quantum computing of the $^6$Li nucleus via ordered unitary coupled
  cluster

abstract:
  The variational quantum eigensolver (VQE) is an algorithm to compute ground
and excited state energy of quantum many-body systems. A key component of the
algorithm and an active research area is the construction of a parametrized
trial wavefunction -- a so called variational ansatz. The wavefunction
parametrization should be expressive enough, i.e. represent the true eigenstate
of a quantum system for some choice of parameter values. On the other hand, it
should be trainable, i.e. the number of parameters should not grow
exponentially with the size of the system. Here, we apply VQE to the problem of
finding ground and excited state energies of the odd-odd nucleus $^6$Li. We
study the effects of ordering fermionic excitation operators in the unitary
coupled clusters ansatz on the VQE algorithm convergence by using only
operators preserving the $J_z$ quantum number. The accuracy is improved by two
order of magnitude in the case of descending order. We first compute optimal
ansatz parameter values using a classical state-vector simulator with arbitrary
measurement accuracy and then use those values to evaluate energy eigenstates
of $^6$Li on a superconducting quantum chip from IBM. We post-process the
results by using error mitigation techniques and are able to reproduce the
exact energy with an error of 3.8% and 0.1% for the ground state and for the
first excited state of $^6$Li, respectively.


title:
WeatherBench Probability: A benchmark dataset for probabilistic
  medium-range weather forecasting along with deep learning baseline models

abstract:
  WeatherBench is a benchmark dataset for medium-range weather forecasting of
geopotential, temperature and precipitation, consisting of preprocessed data,
predefined evaluation metrics and a number of baseline models. WeatherBench
Probability extends this to probabilistic forecasting by adding a set of
established probabilistic verification metrics (continuous ranked probability
score, spread-skill ratio and rank histograms) and a state-of-the-art
operational baseline using the ECWMF IFS ensemble forecast. In addition, we
test three different probabilistic machine learning methods -- Monte Carlo
dropout, parametric prediction and categorical prediction, in which the
probability distribution is discretized. We find that plain Monte Carlo dropout
severely underestimates uncertainty. The parametric and categorical models both
produce fairly reliable forecasts of similar quality. The parametric models
have fewer degrees of freedom while the categorical model is more flexible when
it comes to predicting non-Gaussian distributions. None of the models are able
to match the skill of the operational IFS model. We hope that this benchmark
will enable other researchers to evaluate their probabilistic approaches.


title:
The Laplacians, Kirchhoff index and complexity of linear M\"obius and
  cylinder octagonal-quadrilateral networks

abstract:
  Spectrum graph theory not only facilitate comprehensively reflect the
topological structure and dynamic characteristics of networks, but also offer
significant and noteworthy applications in theoretical chemistry, network
science and other fields. Let $L_{n}^{8,4}$ represent a linear
octagonal-quadrilateral network, consisting of $n$ eight-member ring and $n$
four-member ring. The M\"{o}bius graph $Q_{n}(8,4)$ is constructed by reverse
identifying the opposite edges, whereas cylinder graph $Q'_{n}(8,4)$ identifies
the opposite edges by order. In this paper, the explicit formulas of Kirchhoff
indices and complexity of $Q_{n}(8,4)$ and $Q'_{n}(8,4)$ are demonstrated by
Laplacian characteristic polynomials according to decomposition theorem and
Vieta's theorem. In surprise, the Kirchhoff index of
$Q_{n}(8,4)$($Q'_{n}(8,4)$) is approximately one-third half of its Wiener index
as $n\to\infty$.


title: Motif-based Deep Graph Convolutional Networks

abstract:
  Graph Convolutional Network (GCN) is one of the most effective models in
graph embedding. It extracts shallow features and neighborhood information by
aggregating neighbor information to learn the embedding representation of
different nodes. However, the local topology information of many nodes in the
network is similar, the network obtained by shallow embedding represents the
network that is susceptible to structural noise, and the low-order embedding
cannot capture the high-order network structure; on the other hand, the deep
embedding undergoes multi-layer convolution. After the filters are stacked, the
embedded distribution is destroyed, and graph smoothing occurs. To address
these challenges, we propose a new framework that leverages network motifs to
learn deep features of the network from low-level embeddings under the
assumption of network homogeneity and transitivity, and then combines local
neighborhood information with deeper Global information fusion results in
accurate representation of nodes. On real datasets, our model achieves
significant performance improvement in link prediction and node classification.


title: TuGeBiC: A Turkish German Bilingual Code-Switching Corpus

abstract:
  In this paper we describe the process of collection, transcription, and
annotation of recordings of spontaneous speech samples from Turkish-German
bilinguals, and the compilation of a corpus called TuGeBiC. Participants in the
study were adult Turkish-German bilinguals living in Germany or Turkey at the
time of recording in the first half of the 1990s. The data were manually
tokenised and normalised, and all proper names (names of participants and
places mentioned in the conversations) were replaced with pseudonyms.
Token-level automatic language identification was performed, which made it
possible to establish the proportions of words from each language. The corpus
is roughly balanced between both languages. We also present quantitative
information about the number of code-switches, and give examples of different
types of code-switching found in the data. The resulting corpus has been made
freely available to the research community.


title: Topology Analysis of the XRP Network

abstract:
  XRP is one of the oldest, well-established cryptocurrencies. Despite the
popularity of XRP, little is known about its underlying peer-to-peer network.
The structural properties of a network impact its efficiency, security and
robustness. We aim to close the knowledge gap by providing a detailed analysis
of the XRP overlay network.
  In this paper we examine the graph-theoretic properties of the XRP Network
topology and its temporal characteristics. We crawl the XRP Network over two
months and collect 1,300 unique network snapshots. We uncover a small group of
nodes that act as a networking backbone. In addition, we observe a high network
churn, with a third of the nodes changing every five days. Our findings have
strong implications for the resilience and safety of the XRP Ledger.


title: The Shape of Central Quadrilaterals

abstract:
  The diagonals of a quadrilateral form four component triangles (in two ways).
For each of various shaped quadrilaterals, we examine 1000 triangle centers
located in these four component triangles. Using a computer, we determine when
the four centers form a special quadrilateral, such as a rhombus or a cyclic
quadrilateral. A typical result is the following. The diagonals of an
equidiagonal quadrilateral divide the quadrilateral into four nonoverlapping
triangles. Then the Nagel points of these four triangles form an orthodiagonal
quadrilateral.


title:
Power to the springs: Passive elements are sufficient to drive push-off
  in human walking

abstract:
  For the impulsive ankle push-off (APO) observed in human walking two
muscle-tendon-units (MTUs) spanning the ankle joint play an important role:
Gastrocnemius (GAS) and Soleus (SOL). GAS and SOL load the Achilles tendon to
store elastic energy during stance followed by a rapid energy release during
APO. We use a neuromuscular simulation (NMS) and a bipedal robot to investigate
the role of GAS and SOL on the APO. We optimize the simulation for a robust
gait and then sequentially replace the MTUs of (1) GAS, (2) SOL and (3) GAS and
SOL by linear springs. To validate the simulation, we implement NMS-3 on a
bipedal robot. Simulation and robot walk steady for all trials showing an
impulsive APO. Our results imply that the elastic MTU properties shape the
impulsive APO. For prosthesis or robot design that is, no complex ankle
actuation is needed to obtain an impulsive APO, if more mechanical intelligence
is incorporated in the design.


title:
COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both
  Party Personas

abstract:
  Maintaining a consistent persona is essential for building a human-like
conversational model. However, the lack of attention to the partner makes the
model more egocentric: they tend to show their persona by all means such as
twisting the topic stiffly, pulling the conversation to their own interests
regardless, and rambling their persona with little curiosity to the partner. In
this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue
generation Across both partY personas) that considers both parties as a "team":
expressing self-persona while keeping curiosity toward the partner, leading
responses around mutual personas, and finding the common ground. Specifically,
we first represent self-persona, partner persona and mutual dialogue all in the
concept sets. Then, we propose the Concept Set framework with a suite of
knowledge-enhanced operations to process them such as set algebras, set
expansion, and set distance. Based on these operations as medium, we train the
model by utilizing 1) concepts of both party personas, 2) concept relationship
between them, and 3) their relationship to the future dialogue. Extensive
experiments on a large public dataset, Persona-Chat, demonstrate that our model
outperforms state-of-the-art baselines for generating less egocentric, more
human-like, and higher quality responses in both automatic and human
evaluations.


title: A generalization of Newton-Maclaurin's inequalities

abstract:
  In this paper, we prove Newton-Maclaurin type inequalities for functions
obtained by linear combination of two neighboring primary symmetry functions,
which is a generalization of the classical Newton-Maclaurin inequality.


title:
Family of Two Dimensional Transition Metal Dichlorides Fundamental
  Properties, Structural Defects, and Environmental Stability

abstract:
  A large number of novel two-dimensional (2D) materials are constantly
discovered and deposed into the databases. Consolidate implementation of
machine learning algorithms and density functional theory (DFT) based
predictions have allowed creating several databases containing an unimaginable
amount of 2D samples. The next step in this chain, the investigation leads to a
comprehensive study of the functionality of the invented materials. In this
work, a family of transition metal dichlorides has been screened out for
systematical investigation of their structural stability, fundamental
properties, structural defects, and environmental stability via DFT based
calculations. The work highlights the importance of using the potential of the
invented materials and proposes a comprehensive characterization of a new
family of 2D materials.


title: Fundamental forces and their dynamics

abstract:
  In this essay, we wish to propose a general principle: \textit{the equation
of motion or dynamics of a fundamental force should not be prescribed but
instead be entirely driven by geometry of the appropriate spacetime manifold.}
The motivation for this pronouncement comes from the fact that the equation of
motion of general relativity follows from the geometry of Riemannian spacetime
manifold without appeal to anything else from outside. The driving differential
geometric property is the Bianchi identity satisfied by the Riemann curvature
tensor. Similarly it is geometry of the principal tangent bundle of fibre
spacetime manifold that may account for dynamics of the gauge vector fields. It
is the classical electric force for the Abelian gauge symmetry group while the
non-Abelian symmetry leads to the non-Abelian forces, the weak and the strong.
We shall also reflect on a unified picture of the basic forces, and the duality
correspondences it may inspire.


title: On the Role of Postconditions in Dynamic First-Order Epistemic Logic

abstract:
  Dynamic Epistemic Logic (DEL) is a logic that models information change in a
multi-agent setting through the use of action models with pre- and
post-conditions. In a recent work, DEL has been extended to first-order
epistemic logic (DFOEL), with a proof that the resulting Epistemic Planning
Problem is decidable, as long as action models pre- and post-conditions are
non-modal and the first-order domain is finite. Our contribution highlights the
role post-conditions have in DFOEL. We show that the Epistemic Planning Problem
with possibly infinite first-order domains is undecidable if the non-modal
event post-conditions may contain first-order quantifiers, while, on the
contrary, the problem becomes decidable when event post-conditions are
quantifier-free. The latter result is non-trivial and makes an extensive use of
automatic structures.


title:
A Polynomial-time Decentralised Algorithm for Coordinated Management of
  Multiple Intersections

abstract:
  Autonomous intersection management has the potential to reduce road traffic
congestion and energy consumption. To realize this potential, efficient
algorithms are needed. However, most existing studies locally optimize one
intersection at a time, and this can cause negative externalities on the
traffic network as a whole. Here, we focus on coordinating multiple
intersections, and formulate the problem as a distributed constraint
optimisation problem (DCOP). We consider three utility design approaches that
trade off efficiency and fairness. Our polynomial-time algorithm for
coordinating multiple intersections reduces the traffic delay by about 41%
compared to independent single intersection management approaches.


title: Quantum extraordinary-log universality of boundary critical behavior

abstract:
  Recent discovery of the extraordinary-log universality has generated
tremendous interest in classical and quantum boundary critical phenomena.
However, the classical-quantum correspondence of such a universality remains
elusive. Here, by utilizing quantum Monte Carlo simulations, we study the
quantum edge criticality of a two-dimensional Bose-Hubbard model featuring
emergent bulk criticality. On top of an insulating bulk, the open edges
experience a Kosterlitz-Thouless-like transition into superfluid phase when
sufficiently enhancing the hopping strength on edges. At the bulk critical
point, the edges exhibit the special, the ordinary and the extraordinary
critical phases. In the extraordinary phase, logarithms are involved in the
finite-size scaling of two-point correlation and superfluid stiffness, which
admit a classical-quantum correspondence for the extraordinary-log
universality. Thanks to the modern quantum emulator for interacting bosons in
lattices, the edge critical phases might be realized in experiments.


title: An invitation to formal power series

abstract:
  This is a lecture on the theory of formal power series developed entirely
without any analytic machinery. Combining ideas from various authors we are
able to prove Newton's binomial theorem, Jacobi's triple product, the
Rogers--Ramanujan identities and many other prominent results. We apply these
methods to derive several combinatorial theorems including Ramanujan's
partition congruences, generating functions of Stirling numbers and Jacobi's
four-square theorem. We further discuss formal Laurent series and multivariate
power series and end with a proof of MacMahon's master theorem.


title:
The Application of Energy and Laplacian Energy of Hesitancy Fuzzy Graph
  Based on Similarity Measures in Decision Making Problems

abstract:
  In this article, a new hesitancy fuzzy similarity measure is defined and then
used to develop the matrix of hesitancy fuzzy similarity measures, which is
subsequently used to classify hesitancy fuzzy graph using the working
procedure. We build a working procedure (Algorithm) for estimating the eligible
reputation scores values of experts by applying hesitancy fuzzy preference
relationships (HFPRs) and the usual similarity degree of one distinct HFPRs to
each other's. As the last step, we provide real time numerical examples to
demonstrate and validate our working procedure.


title:
Let's Agree to Agree: Targeting Consensus for Incomplete Preferences
  through Majority Dynamics

abstract:
  We study settings in which agents with incomplete preferences need to make a
collective decision. We focus on a process of majority dynamics where issues
are addressed one at a time and undecided agents follow the opinion of the
majority. We assess the effects of this process on various consensus notions --
such as the Condorcet winner -- and show that in the worst case, myopic
adherence to the majority damages existing consensus; yet, simulation
experiments indicate that the damage is often mild. We also examine scenarios
where the chair of the decision process can control the existence (or the
identity) of consensus, by determining the order in which the issues are
discussed.


title: On graded $S$-comultiplication modules

abstract:
  In this paper, we introduce the concept of graded $S$-comultiplication
modules. Several results concerning graded $S$-comultiplication modules are
proved. We show that $N$ is a graded $S$-second submodule of a graded
$S$-comultiplication $R$-module $M$ if and only if $Ann_R(N)$ is a graded
$S$-prime ideal of $R$ and there exists $x\in S$ such that
$xN\subseteq\bar{x}N$ for every $\bar{x}\in S$.


title: Toeplitz operators on the Hardy spaces of quotient domains

abstract:
  Let $\Omega$ be either the unit polydisc $\mathbb D^d$ or the unit ball
$\mathbb B_d$ in $\mathbb C^d$ and $G$ be a finite pseudoreflection group which
acts on $\Omega.$ Associated to each one-dimensional representation $\varrho$
of $G,$ we provide a notion of the (weighted) Hardy space
$H^2_\varrho(\Omega/G)$ on $\Omega/G.$ Subsequently, we show that each
$H^2_\varrho(\Omega/G)$ is isometrically isomorphic to the relative invariant
subspace of $H^2(\Omega)$ associated to the representation $\varrho.$ For
$\Omega=\mathbb D^d,$ $G=\mathfrak{S}_d,$ the permutation group on $d$ symbols
and $\varrho = $ the sign representation of $\mathfrak{S}_d,$ the Hardy space
$H^2_\varrho(\Omega/G)$ coincides to well-known notion of the Hardy space on
the symmetrized polydisc. We largely use invariant theory of the group $G$ to
establish identities involving Toeplitz operators on $H^2(\Omega)$ and
$H^2_\varrho(\Omega/G)$ which enable us to study algebraic properties (such as
generalized zero product problem, characterization of commuting Toeplitz
operators, compactness etc.) of Toeplitz operators on $H^2_\varrho(\Omega/G).$


title: Bubbles of nothing and AdS instabilities

abstract:
  In this contribution we review the ideas and results presented in the
articles arXiv:2002.01764 and arXiv:2110.08276 where the decay of AdS vacua
into bubbles of nothing is studied. We firstly consider AdS vacua in General
Relativity with a cosmological constant and in arbitrary dimensions. In this
context we study a class of gravitational instantons generalizing the Witten's
bubble of nothing and we discuss the derivation of the euclidean action.
Secondly we consider a "stringy" non-supersymmetric AdS$_4$ vacuum arising in a
compactification of massive IIA supergravity on a $S^6$ and preserving G$_2$
symmetry. This vacuum has been shown to have a fully stable KK spectrum and
also be protected against brane-jet decays. In this framework we discuss a
bubble solution in massive IIA connected to the G$_2$-invariant vacuum and
associated to an instability channel. We finally provide the interpretation of
this instability in terms of the nucleation of a bubble of nothing dressed up
with a D2 charge distribution.


title: Hierarchical Decompositions of Stochastic Pursuit-Evasion Games

abstract:
  In this work we present a hierarchical framework for solving discrete
stochastic pursuit-evasion games (PEGs) in large grid worlds. With a partition
of the grid world into superstates (e.g., "rooms"), the proposed approach
creates a two-resolution decision-making process, which consists of a set of
local PEGs at the original state level and an aggregated PEG at the superstate
level. Having much smaller cardinality, the local games can be easily solved to
a Nash equilibrium. To connect the decision-making at the two resolutions, we
use the Nash values of the local PEGs as the rewards for the aggregated game.
Through numerical simulations, we show that the proposed hierarchical framework
significantly reduces the computation overhead, while still maintaining a
satisfactory level of performance when competing against the flat Nash global
policies.


title: Anti-microbial properties of a multi-component alloy

abstract:
  High traffic touch surfaces such as doorknobs, countertops, and handrails can
be transmission points for the spread of pathogens, emphasizing the need to
develop materials that actively self-sanitize. Metals are frequently used for
these surfaces due to their durability, but many metals also possess
antimicrobial properties which function through a variety of mechanisms. This
work investigates metallic alloys comprised of several bioactive metals with
the target of achieving broad-spectrum, rapid bioactivity through synergistic
activity. An entropy-motivated stabilization paradigm is proposed to prepare
scalable alloys of copper, silver, nickel and cobalt. Using combinatorial
sputtering, thin-film alloys were prepared on 100 mm wafers with 50%
compositional grading of each element across the wafer. The films were then
annealed and investigated for alloy stability. Bioactivity testing was
performed on both the as-grown alloys and the annealed films using four
microorganisms -- Phi6, MS2, Bacillus subtilis and Escherichia coli -- as
surrogates for human viral and bacterial pathogens. Testing showed that after
30 s of contact with some of the test alloys, Phi6, an enveloped,
single-stranded RNA bacteriophage that serves as a SARS-CoV 2 surrogate, was
reduced up to 6.9 orders of magnitude (>99.9999%). Additionally, the
non-enveloped, double-stranded DNA bacteriophage MS2, and the Gram-negative E.
coli and Gram-positive B. subtilis bacterial strains showed a 5.0, 6.4, and 5.7
log reduction in activity after 30, 20 and 10 minutes, respectively.
Bioactivity in the alloy samples showed a strong dependence on the composition,
with the log reduction scaling directly with the Cu content. Concentration of
Cu by phase separation after annealing improved activity in some of the
samples. The results motivate a variety of themes which can be leveraged to
design ideal bioactive surfaces.


title:
Every signed planar graph without cycles of length from 4 to 7 is
  3-colorable

abstract:
  Hu and Li investigate the signed graph version of Erd$\ddot{\mathrm{o}}$s
problem: Is there a constant $c$ such that every signed planar graph without
$k$-cycles, where $4\leq k\leq c$, is $3$-colorable and prove that each signed
planar graph without cycles of length from 4 to 8 is 3-colorable. We give a
very short and simple proof of this result and improve it, based on a recent
observation.


title:
Granular surface flows confined between flat, frictional walls. Part 1.
  Kinematics

abstract:
  We report and analyse the results of extensive discrete element method
simulations of three-dimensional gravity driven flows of cohesionless granular
media over an erodible bed, the whole being confined between two flat and
frictional sidewalls. We focus on the role of sidewalls by performing
simulations for different gap widths ($W$) between the two confining sidewalls:
from $5$ to $30$ grain sizes ($d$). Our results indicate the existence of two
distinct regimes: regime I for flow angles smaller than the critical angle
$\theta_c\approx 40^\circ$ and regime II at flow angles larger than $\theta_c$
. Regime I corresponds to dense flows whereas flows belonging to regime II
exhibit a strong variation of the volume fraction through the depth. Three
relevant lengths are identified in the system: $W$ the gap between sidewalls,
$l$ the length characterizing the vertical variation of the volume fraction and
$h$ a characteristic length associated with the vertical variation of the
streamwise velocity. Using these lengths we can rescale the profiles of various
flow properties (e.g. streamwise velocity, granular temperature, particle
rotation...). In regime II, in contrast to regime I, $l$ and $h$ have a similar
behaviour. As a consequence, the rescaled profiles in regime II only involve
$h$ (or equivalently $l$) and $W$ . Other dissimilarities exist between regimes
I and II. In particular, the scaling of the flow rate with $h$ (at fixed $W$ )
differs in the two regimes, although they display a similar scaling with $W$
(at fixed flow angle).


title:
Vehicle Routing with Time-Dependent Travel Times: Theory, Practice, and
  Benchmarks

abstract:
  We develop theoretical foundations and practical algorithms for vehicle
routing with time-dependent travel times. We also provide new benchmark
instances and experimental results. First, we study basic operations on
piecewise linear arrival time functions. In particular, we devise a faster
algorithm to compute the pointwise minimum of a set of piecewise linear
functions and a monotonicity-preserving variant of the Imai-Iri algorithm to
approximate an arrival time function with fewer breakpoints.
  Next, we show how to evaluate insertion and deletion operations in tours
efficiently and update the underlying data structure faster than previously
known when a tour changes. Evaluating a tour also requires a scheduling step
which is non-trivial in the presence of time windows and time-dependent travel
times. We show how to perform this in linear time.
  Based on these results, we develop a local search heuristic to solve
real-world vehicle routing problems with various constraints efficiently and
report experimental results on classical benchmarks. Since most of these do not
have time-dependent travel times, we generate and publish new benchmark
instances that are based on real-world data. This data also demonstrates the
importance of considering time-dependent travel times in instances with tight
time windows.


title:
From viscous fluids to elastic solids: A perspective on the glass
  transition

abstract:
  A theory for the non-local stress in liquids captures the crossover from
viscous to elastic correlations upon supercooling. It explains the emergence of
long-ranged stress fields in glass which originate from the coupling of shear
stress to transverse deformations. The Goldstone mode in colloidal glass is
shown to be diffusive.


title: Low-Complexity Designs of Symbol-Level Precoding for MU-MISO Systems

abstract:
  Symbol-level precoding (SLP), which converts the harmful multi-user
interference (MUI) into beneficial signals, can significantly improve
symbol-error-rate (SER) performance in multi-user communication systems. While
enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol
precoder design suffers high computational complexity exponential with the
number of users, which is unaffordable in realistic systems. In this paper, we
propose a novel low-complexity grouped SLP (G-SLP) approach and develop
efficient design algorithms for typical max-min fairness and power minimization
problems. In particular, after dividing all users into several groups, the
precoders for each group are separately designed on a symbol-by-symbol basis by
only utilizing the symbol information of the users in that group, in which the
intra-group MUI is exploited using the concept of constructive interference
(CI) and the inter-group MUI is also effectively suppressed. In order to
further reduce the computational complexity, we utilize the Lagrangian dual,
Karush-Kuhn-Tucker (KKT) conditions and the majorization-minimization (MM)
method to transform the resulting problems into more tractable forms, and
develop efficient algorithms for obtaining closed-form solutions to them.
Extensive simulation results illustrate that the proposed G-SLP strategy and
design algorithms dramatically reduce the computational complexity without
causing significant performance loss compared with the traditional SLP schemes.


title:
Vector-valued fractal functions: Fractal dimension and Fractional
  calculus

abstract:
  There are many research available on the study of real-valued fractal
interpolation function and fractal dimension of its graph. In this paper, our
main focus is to study the dimensional results for vector-valued fractal
interpolation function and its Riemann-Liouville fractional integral. Here, we
give some results which ensure that dimensional results for vector-valued
functions are quite different from real-valued functions. We determine
interesting bounds for the Hausdorff dimension of the graph of vector-valued
fractal interpolation function. We also obtain bounds for the Hausdorff
dimension of associated invariant measure supported on the graph of
vector-valued fractal interpolation function. Next, We discuss more efficient
upper bound for the Hausdorff dimension of measure in terms of probability
vector and contraction ratios. Furthermore, we determine some dimensional
results for the graph of the Riemann-Liouville fractional integral of a
vector-valued fractal interpolation function.


title:
A Survey on Security Issues in Modern Implantable Devices: Solutions and
  Future Issues

abstract:
  Implantable Medical Devices (IMD) is a fast pace growing medical field and
continues to grow in the foreseeable future. Advancement in science and
technology has led to the IMD devices offering advanced medical treatments.
Modern IMDs can automatically monitor and manage different patients' health
conditions without any manual intervention from medical professionals. While
IMDs are also becoming more connected to enhance the delivery of care remotely
and provide the means for both patients and physicians to adjust therapy at the
comfort of their homes, it also increases security related concerns.
Adversaries could take advantage and exploit device vulnerabilities to
manipulate device settings remotely from anywhere around the world. This
manuscript reviews the current threats, security goals, and proposed solutions
by comparing them with their strengths and limitations. We also highlight the
emerging IMD technologies and innovative ideas for new designs and
implementations to improve the security of IMDs. Finally, we conclude the
article with future research directions toward securing IMD systems to light
the way for researchers.


title:
Modeling and mitigation of occupational safety risks in dynamic
  industrial environments

abstract:
  Identifying and mitigating safety risks is paramount in a number of
industries. In addition to guidelines and best practices, many industries
already have safety management systems (SMSs) designed to monitor and reinforce
good safety behaviors. The analytic capabilities to analyze the data acquired
through such systems, however, are still lacking in terms of their ability to
robustly quantify risks posed by various occupational hazards. Moreover, best
practices and modern SMSs are unable to account for dynamically evolving
environments/behavioral characteristics commonly found in many industrial
settings. This article proposes a method to address these issues by enabling
continuous and quantitative assessment of safety risks in a data-driven manner.
The backbone of our method is an intuitive hierarchical probabilistic model
that explains sparse and noisy safety data collected by a typical SMS. A fully
Bayesian approach is developed to calibrate this model from safety data in an
online fashion. Thereafter, the calibrated model holds necessary information
that serves to characterize risk posed by different safety hazards.
Additionally, the proposed model can be leveraged for automated decision
making, for instance solving resource allocation problems -- targeted towards
risk mitigation -- that are often encountered in resource-constrained
industrial environments. The methodology is rigorously validated on a simulated
test-bed and its scalability is demonstrated on real data from large
maintenance projects at a petrochemical plant.


title:
On the generalization capabilities of FSL methods through domain
  adaptation: a case study in endoscopic kidney stone image classification

abstract:
  Deep learning has shown great promise in diverse areas of computer vision,
such as image classification, object detection and semantic segmentation, among
many others. However, as it has been repeatedly demonstrated, deep learning
methods trained on a dataset do not generalize well to datasets from other
domains or even to similar datasets, due to data distribution shifts. In this
work, we propose the use of a meta-learning based few-shot learning approach to
alleviate these problems. In order to demonstrate its efficacy, we use two
datasets of kidney stones samples acquired with different endoscopes and
different acquisition conditions. The results show how such methods are indeed
capable of handling domain-shifts by attaining an accuracy of 74.38% and 88.52%
in the 5-way 5-shot and 5-way 20-shot settings respectively. Instead, in the
same dataset, traditional Deep Learning (DL) methods attain only an accuracy of
45%.


title: Thermoacoustic shocks in complex plasmas

abstract:
  The formation of thermoacoustic shocks is revealed in a fluid complex plasma.
The thermoacoustic wave mode can be damped (or anti-damped) when the
contribution from the thermoacoustic interaction is lower (or higher) than that
due to the particle collision and/or the kinematic viscosity. In the nonlinear
regime, the thermoacoustic wave, propagating with the acoustic speed, can
evolve into small amplitude shocks whose dynamics is governed by the
Bateman-Burgers equation with nonlocal nonlinearity. The latter can cause the
shock fronts to be stable (or unstable) depending on the collision frequency
remains below (or above) a critical value and the thermal feedback is positive.
The existence of different kinds of shocks and their characteristics are
analyzed with the system parameters that characterize the thermal feedback,
thermal diffusion, heat capacity per fluid particle, the particle collision and
the fluid viscosity. A good agreement between the analytical and numerical
results are also noticed.


title:
Fast Continuous and Integer L-shaped Heuristics Through Supervised
  Learning

abstract:
  We propose a methodology at the nexus of operations research and machine
learning (ML) leveraging generic approximators available from ML to accelerate
the solution of mixed-integer linear two-stage stochastic programs. We aim at
solving problems where the second stage is highly demanding. Our core idea is
to gain large reductions in online solution time while incurring small
reductions in first-stage solution accuracy by substituting the exact
second-stage solutions with fast, yet accurate supervised ML predictions. This
upfront investment in ML would be justified when similar problems are solved
repeatedly over time, for example, in transport planning related to fleet
management, routing and container yard management.
  Our numerical results focus on the problem class seminally addressed with the
integer and continuous L-shaped cuts. Our extensive empirical analysis is
grounded in standardized families of problems derived from stochastic server
location (SSLP) and stochastic multi knapsack (SMKP) problems available in the
literature. The proposed method can solve the hardest instances of SSLP in less
than 9% of the time it takes the state-of-the-art exact method, and in the case
of SMKP the same figure is 20%. Average optimality gaps are in most cases less
than 0.1%.


title: Algebraic Structure of Dirac Hamiltonians in Non-Commutative Phase Space

abstract:
  In this article we study two-dimensional Dirac Hamiltonians with
non-commutativity both in coordinates and momenta from an algebraic
perspective. In order to do so, we consider the graded Lie algebra
$\mathfrak{sl}(2|1)$ generated by Hermitian bilinear forms in the
non-commutative dynamical variables and the Dirac matrices in $2+1$ dimensions.
By further defining a total angular momentum operator, we are able to express a
class of Dirac Hamiltonians completely in terms of these operators. In this
way, we analyze the energy spectrum of some simple models by constructing and
studying the representation spaces of the unitary irreducible representations
of the graded Lie algebra $\mathfrak{sl}(2|1)\oplus \mathfrak{so}(2)$. As
application of our results, we consider the Landau model and a fermion in a
finite cylindrical well.


title:
Strange metal dynamics across the phase diagram of
  Bi$_{2}$Sr$_{2}$CuO$_{6+\delta}$ cuprates

abstract:
  Unlocking the mystery of the strange metal state has become the focal point
of high T$_{c}$ research, not because of its importance for superconductivity,
but because it appears to represent a truly novel phase of matter dubbed
`quantum supreme matter'. Detected originally through high magnetic field,
transport experiments, signatures of this phase have now been uncovered with a
variety of probes. Our high resolution optical data of the low T$_{c}$ cuprate
superconductor, Bi$_{2-x}$Pb$_{x}$Sr$_{2-y}$La$_{y}$CuO$_{6+\delta}$ allows us
to probe this phase over a large energy and temperature window. We demonstrate
that the optical signatures of the strange metal phase persist throughout the
phase diagram. The strange metal signatures in the optical conductivity are
two-fold, (i): a low energy Drude response with Drude width on the order of
temperature and (ii): a high energy conformal tail with doping dependent
power-law exponent. While the Drude weight evolves monotonously throughout the
entire doping range studied, the spectral weight contained in the high energy
conformal tail appears to be doping and temperature independent. Our analysis
further shows that the temperature dependence of the optical conductivity is
completely determined by the Drude parameters. Our results indicate that there
is no critical doping level inside the superconducting dome where the carrier
density starts to change drastically and that the previously observed 'return
to normalcy' is a consequence of the increasing importance of the Drude
component relative to the conformal tail with doping. Importantly, both the
doping and temperature dependence of the resistivity are largely determined by
the Drude width.


title:
Quantum Robustness Verification: A Hybrid Quantum-Classical Neural
  Network Certification Algorithm

abstract:
  In recent years, quantum computers and algorithms have made significant
progress indicating the prospective importance of quantum computing (QC).
Especially combinatorial optimization has gained a lot of attention as an
application field for near-term quantum computers, both by using gate-based QC
via the Quantum Approximate Optimization Algorithm and by quantum annealing
using the Ising model. However, demonstrating an advantage over classical
methods in real-world applications remains an active area of research. In this
work, we investigate the robustness verification of ReLU networks, which
involves solving a many-variable mixed-integer programs (MIPs), as a practical
application. Classically, complete verification techniques struggle with large
networks as the combinatorial space grows exponentially, implying that
realistic networks are difficult to be verified by classical methods. To
alleviate this issue, we propose to use QC for neural network verification and
introduce a hybrid quantum procedure to compute provable certificates. By
applying Benders decomposition, we split the MIP into a quadratic unconstrained
binary optimization and a linear program which are solved by quantum and
classical computers, respectively. We further improve existing hybrid methods
based on the Benders decomposition by reducing the overall number of iterations
and placing a limit on the maximum number of qubits required. We show that, in
a simulated environment, our certificate is sound, and provide bounds on the
minimum number of qubits necessary to approximate the problem. Finally, we
evaluate our method within simulation and on quantum hardware.


title: Beyond Neyman-Pearson

abstract:
  A standard practice in statistical hypothesis testing is to mention the
p-value alongside the accept/reject decision. We show the advantages of
mentioning an e-value instead. With p-values, we cannot use an extreme
observation (e.g. $p \ll \alpha$) for getting better frequentist decisions.
With e-values we can, since they provide Type-I risk control in a generalized
Neyman-Pearson setting with the decision task (a general loss function)
determined post-hoc, after observation of the data - thereby providing a handle
on "roving $\alpha$'s". When Type-II risks are taken into consideration, the
only admissible decision rules in the post-hoc setting turn out to be
e-value-based. We also propose to replace confidence intervals and
distributions by the *e-posterior*, which provides valid post-hoc frequentist
uncertainty assessments irrespective of prior correctness: if the prior is
chosen badly, e-intervals get wide rather than wrong, suggesting the
e-posterior minimax decision rule as a safer alternative for Bayes decisions.
The resulting "quasi-conditional paradigm" addresses foundational and practical
issues in statistical inference.


title:
Optical cold damping of neutral nanoparticles near the ground state in
  an optical lattice

abstract:
  We propose and demonstrate purely optical feedback cooling of neutral
nanoparticles in an optical lattice to an occupation number of about 1. The
cooling force is derived from the optical gradients of displaced optical
lattices produced with two sidebands on the trapping laser. To achieve highly
accurate position observations required for cooling near the ground state, we
reduce the laser intensity noise to a relative power noise of $6\times 10^{-8}$
/Hz in a frequency band of 30 kHz to 600 kHz. We establish a reproducible
method for neutralizing nanoparticles at high vacuum via a combination of
discharging and irradiating an ultraviolet light. Our results form an important
basis for the investigation of quantum mechanical properties of ultracold
nanoparticles and are also useful for precision measurements with neutral
nanoparticles.


title: Discriminants and toric K-theory

abstract:
  We discuss a categorical approach to the theory of discriminants in the
combinatorial language introduced by Gelfand, Kapranov and Zelevinsky. Our
point of view is inspired by homological mirror symmetry and provides
$K$--theoretic evidence for a conjecture presented by Paul Aspinwall in a
conference talk in Banff in March 2016 and later in a joint paper with Plesser
and Wang.


title:
Positive-Unlabeled Learning with Adversarial Data Augmentation for
  Knowledge Graph Completion

abstract:
  Most real-world knowledge graphs (KG) are far from complete and
comprehensive. This problem has motivated efforts in predicting the most
plausible missing facts to complete a given KG, i.e., knowledge graph
completion (KGC). However, existing KGC methods suffer from two main issues, 1)
the false negative issue, i.e., the candidates for sampling negative training
instances include potential true facts; and 2) the data sparsity issue, i.e.,
true facts account for only a tiny part of all possible facts. To this end, we
propose positive-unlabeled learning with adversarial data augmentation (PUDA)
for KGC. In particular, PUDA tailors positive-unlabeled risk estimator for the
KGC task to deal with the false negative issue. Furthermore, to address the
data sparsity issue, PUDA achieves a data augmentation strategy by unifying
adversarial training and positive-unlabeled learning under the
positive-unlabeled minimax game. Extensive experimental results demonstrate its
effectiveness and compatibility.


title:
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive
  Neighborhood Aggregation

abstract:
  Graph contrastive learning (GCL), as a popular approach to graph
self-supervised learning, has recently achieved a non-negligible effect. To
achieve superior performance, the majority of existing GCL methods elaborate on
graph data augmentation to construct appropriate contrastive pairs. However,
existing methods place more emphasis on the complex graph data augmentation
which requires extra time overhead, and pay less attention to developing
contrastive schemes specific to encoder characteristics. We argue that a better
contrastive scheme should be tailored to the characteristics of graph neural
networks (e.g., neighborhood aggregation) and propose a simple yet effective
method named FastGCL. Specifically, by constructing weighted-aggregated and
non-aggregated neighborhood information as positive and negative samples
respectively, FastGCL identifies the potential semantic information of data
without disturbing the graph topology and node attributes, resulting in faster
training and convergence speeds. Extensive experiments have been conducted on
node classification and graph classification tasks, showing that FastGCL has
competitive classification performance and significant training speedup
compared to existing state-of-the-art methods.


title:
Scattering-assisted and logic-controllable WGM laser in liquid crystal
  micropillar

abstract:
  Whispering gallery mode (WGM) microcavities can efficiently store and
manipulate light with strong light confinement and long photon lifetime, while
coupling light into and from WGMs is intrinsically hindered by their unique
feature of rotational symmetry. Here, a scattering-assisted liquid crystal (LC)
micropillar WGM laser is proposed. WGM lasing at the surface of the micropillar
is obviously enhanced by fluorescence scattering in the core of the
micropillar. Besides, weak scattering of LC molecules also builds efficient
coupling channels between the laser modes and the axial transmission modes of
the micropillar-based waveguide, providing an all-in-one liquid WGM laser with
functions of self-seeding and self-guiding. Furthermore, based on the
hysteresis characteristics of the electrically anchored LC molecules under the
interaction of thermal force, an erasable read-write liquid memory device is
proposed, paving the way for the application of logic-controllable WGM lasers
in optical storage and optical control.


title: Rectifying No-Hair Theorems in Gauss-Bonnet theory

abstract:
  We revisit the no-hair theorems in Einstein-Scalar-Gauss-Bonnet theory with a
general coupling function between the scalar and the Gauss-Bonnet term in four
dimensional spacetime. In the case of the old no-hair theorem the surface term
has so far been ignored, but this plays a crucial role when the coupling
function does not vanish at infinity and the scalar field admits a power
expansion with respect to the inverse of the radial coordinate in that regime.
We also clarify that the novel no-hair theorem is always evaded for regular
black hole solutions without any restrictions as long as the regularity
conditions are satisfied.


title:
MemSeg: A semi-supervised method for image surface defect detection
  using differences and commonalities

abstract:
  Under the semi-supervised framework, we propose an end-to-end memory-based
segmentation network (MemSeg) to detect surface defects on industrial products.
Considering the small intra-class variance of products in the same production
line, from the perspective of differences and commonalities, MemSeg introduces
artificially simulated abnormal samples and memory samples to assist the
learning of the network. In the training phase, MemSeg explicitly learns the
potential differences between normal and simulated abnormal images to obtain a
robust classification hyperplane. At the same time, inspired by the mechanism
of human memory, MemSeg uses a memory pool to store the general patterns of
normal samples. By comparing the similarities and differences between input
samples and memory samples in the memory pool to give effective guesses about
abnormal regions; In the inference phase, MemSeg directly determines the
abnormal regions of the input image in an end-to-end manner. Through
experimental validation, MemSeg achieves the state-of-the-art (SOTA)
performance on MVTec AD datasets with AUC scores of 99.56% and 98.84% at the
image-level and pixel-level, respectively. In addition, MemSeg also has a
significant advantage in inference speed benefiting from the end-to-end and
straightforward network structure, which better meets the real-time requirement
in industrial scenarios.


title:
In-situ measurements of twist and bend elastic constants in oblique
  helicoidal cholesteric

abstract:
  Unique electro-optical properties of the oblique helicoidal cholesteric
(ChOH) stem from its heliconical director structure. An applied electric field
preserves the single harmonic modulation of the director while tuning the ChOH
period and the corresponding Bragg peak wavelength within a broad spectral
range. We use the response of ChOH to the electric field to measure the elastic
constants of twist K22 and bend K33 directly in the cholesteric phase. The
temperature dependencies of K22 and K33 allow us to determine the range of the
electric tunability of the ChOH pitch and the heliconical angle. The data are
important for understanding how molecular composition and chirality influence
macroscopic elastic properties of the chiral liquid crystals and for the
development of ChOH-based optical devices.


title:
EFT Diagrammatica II: Tracing the UV origin of bosonic D6 CPV and D8
  SMEFT operators

abstract:
  In recent times, SMEFT, along with a superlative repertoire of theoretical
and computational tools, has emerged as an efficacious platform to test the
viability of proposed BSM scenarios. With symmetry as the backbone, higher mass
dimensional ($\geq 5$) SMEFT operators constitute the \textit{lingua franca}
for studying and comparing the direct or indirect effects of UV models on low
energy observables. The steady increase in the accessible energy scales for
contemporary particle collision experiments prompts us to inspect effective
operators beyond the leading order and investigate their measurable impact as
well as their connections with the appropriate BSM proposals. We take the next
step in delineating the possible UV roots of SMEFT operators by extending our
diagrammatic approach, previously employed for CP, baryon, and lepton number
conserving dimension-6 operators, to the complete set of purely bosonic SMEFT
operators up to mass dimension-8. We catalogue a diverse array of Feynman
diagrams elucidating how the operators encapsulate heavy field propagators
while abiding by a notion of minimalism.


title:
AI-Driven Contextual Advertising: A Technology Report and Implication
  Analysis

abstract:
  Programmatic advertising consists in automated auctioning of digital ad
space. Every time a user requests a web page, placeholders on the page are
populated with ads from the highest-bidding advertisers. The bids are typically
based on information about the user, and to an increasing extent, on
information about the surrounding media context. The growing interest in
contextual advertising is in part a counterreaction to the current dependency
on personal data, which is problematic from legal and ethical standpoints. The
transition is further accelerated by developments in Artificial Intelligence
(AI), which allow for a deeper semantic understanding of context and, by
extension, more effective ad placement. In this article, we begin by
identifying context factors that have been shown in previous research to
positively influence how ads are received. We then continue to discuss
applications of AI in contextual advertising, where it adds value by, e.g.,
extracting high-level information about media context and optimising bidding
strategies. However, left unchecked, these new practices can lead to unfair ad
delivery and manipulative use of context. We summarize these and other concerns
for consumers, publishers and advertisers in an implication analysis.


title: Analytical model of optical properties of a metal nanorod

abstract:
  It is well known that the optical properties of spherical metal particles can
be described with the Rayleigh approximation or rigorous Mie theory. But for
the small metallic nanorods, a theory well capturing the resonance and
scattering features is still absent. In this paper, an analytical model is
developed for the metallic nanorod, considering the non-uniformly distributed
conduction current and surface charges. With the circuit parameters deduced
from the kinetic and electromagnetic energy of the plasmonic system, a formula
which agrees well with the simulations has been suggested for the resonance
wavelength. Moreover, by introducing the radiative resistance to the circuit
theory, the dipole moment, extinction spectrum, and the near-field enhancement
of nanorod have been derived analytically and confirmed numerically. These
results are valuable for understanding the plasmonic resonance and provide a
guideline for designing the light scattering and absorption.


title:
Edge modes as dynamical frames: charges from post-selection in generally
  covariant theories

abstract:
  We develop a framework based on the covariant phase space formalism that
identifies gravitational edge modes as dynamical reference frames. Previously
considered in gauge theory [arXiv:2109.06184], this construction relies on a
systematic post-selection procedure. Given a global variational principle, it
produces consistent dynamics for a spacetime subregion bounded by a
codimension-one timelike submanifold. By serving as dynamical reference frames
for the gauge group, edge modes enable the imposition of gauge-invariant
boundary conditions. In generally covariant theories, where the gauge group is
a group of diffeomorphisms, such reference frames play an even more fundamental
role, as they allow one to identify the subregion in a gauge-invariant manner.
This leads to the introduction of a frame-dressed spacetime, that we call
relational spacetime, and which serves as the primary arena in which to perform
post-selection. Just like in gauge theory, requiring the subregion
presymplectic structure to be conserved by the dynamics leads to an essentially
unique prescription and unambiguous Hamiltonian charges. Gauge diffeomorphisms
are integrable, irrespective of how they behave in the vicinity of the
time-like boundary, and generate a first-class constraint algebra. By contrast,
diffeomorphisms acting on relational spacetime are in general physical, and
those that are parallel to the timelike boundary are integrable. Upon further
restriction to relational diffeomorphisms that preserve the boundary
conditions, we obtain a subalgebra of conserved charges. Finally, we explain
how the boundary conditions and the conserved presymplectic structure can both
be encoded into suitable boundary actions. While our formalism applies to any
generally covariant theory, we illustrate it on vacuum general relativity, and
conclude with a detailed comparison of our findings to earlier works.


title: Realistic soft-body tearing under 10ms in VR

abstract:
  We present a novel integration of a real-time continuous tearing algorithm
for 3D meshes in VR, suitable for devices of low CPU/GPU specifications, along
with a suitable particle decomposition that allows soft-body deformations on
both the original and the torn model.


title: Semi-global controllability of a geometric wave equation

abstract:
  We prove the semi-global controllability and stabilization of the
$(1+1)$-dimensional wave maps equation with spatial domain $\mathbb{S}^1$ and
target $\mathbb{S}^k$. First we show that damping stabilizes the system when
the energy is strictly below the threshold $2\pi$, where harmonic maps appear
as obstruction for global stabilization. Then, we adapt an iterative control
procedure to get low-energy exact controllability of the wave maps equation.
This result is optimal in the case $k=1$.


title: A Novel Speech-Driven Lip-Sync Model with CNN and LSTM

abstract:
  Generating synchronized and natural lip movement with speech is one of the
most important tasks in creating realistic virtual characters. In this paper,
we present a combined deep neural network of one-dimensional convolutions and
LSTM to generate vertex displacement of a 3D template face model from
variable-length speech input. The motion of the lower part of the face, which
is represented by the vertex movement of 3D lip shapes, is consistent with the
input speech. In order to enhance the robustness of the network to different
sound signals, we adapt a trained speech recognition model to extract speech
feature, and a velocity loss term is adopted to reduce the jitter of generated
facial animation. We recorded a series of videos of a Chinese adult speaking
Mandarin and created a new speech-animation dataset to compensate the lack of
such public data. Qualitative and quantitative evaluations indicate that our
model is able to generate smooth and natural lip movements synchronized with
speech.


title:
Asymptotic properties of an optimal principal eigenvalue with spherical
  weight and Dirichlet boundary conditions

abstract:
  We consider a weighted eigenvalue problem for the Dirichlet laplacian in a
smooth bounded domain $\Omega\subset \mathbb{R}^N$, where the bang-bang weight
equals a positive constant $\overline{m}$ on a ball $B\subset\Omega$ and a
negative constant $-\underline{m}$ on $\Omega\setminus B$. The corresponding
positive principal eigenvalue provides a threshold to detect
persistence/extinction of a species whose evolution is described by the
heterogeneous Fisher-KPP equation in population dynamics. In particular, we
study the minimization of such eigenvalue with respect to the position of $B$
in $\Omega$. We provide sharp asymptotic expansions of the optimal eigenpair in
the singularly perturbed regime in which the volume of $B$ vanishes. We deduce
that, up to subsequences, the optimal ball concentrates at a point maximizing
the distance from $\partial\Omega$.


title:
Decay estimate of bivariate Chebyshev coefficients for functions with
  limited smoothness

abstract:
  We obtain the decay bounds for Chebyshev series coefficients of functions
with finite Vitali variation on the unit square. A generalization of the well
known identity, which relates exact and approximated coefficients, obtained
using the quadrature formula, is derived. An asymptotic $L^1$-approximation
error of finite partial sum for functions defined on the unit square is
deduced.


title:
Review of recent developments of the functional renormalization group
  for systems out of equilibrium

abstract:
  We recapitulate recent developments of the functional renormalization group
(FRG) approach to the steady state of systems out of thermal equilibrium. In
particular, we discuss second-order truncation schemes which account for the
frequency-dependence of the two particle vertex and which incorporate inelastic
processes. Our focus is on two different types of one-dimensional fermion
chains: i) infinite, open systems which feature a translation symmetry, and ii)
finite systems coupled to left and right reservoirs. In addition to giving a
detailed and unified review of the technical derivation of the FRG schemes, we
briefly summarize the main physical results.