title:
Difference of composition-differentiation operators from Hardy spaces to
  weighted Bergman spaces via harmonic analysis

abstract:
  In this paper, the boundedness and compactness of the difference of
composition-differentiation operators $D_\varphi-D_\psi$ acting from Hardy
spaces $H^p$ to weighted Bergman spaces $A^q_\alpha$ are completely
characterize for all $0<p,q<\infty$.


title:
Bounds on the smallest strong nonlocality set of multipartite quantum
  states

abstract:
  An orthogonal set of states in multipartite systems is called to be strong
quantum nonlocality if it is locally irreducible under every bipartition of the
subsystems [Phys. Rev. Lett. 122} 040403 (2019)]. In this work, we study a
subclass of locally irreducible sets: to preserving the orthogonality of the
states, only trivial local measurement can be performed by each partite. We
call set with this property is locally stable. We find that in the case of two
qubits systems locally stable set coincide with locally indistinguishable set.
Then we present a characterization of locally stable set via the dimensions of
some states depended on the given set. Moreover, we give two constructions of
strongly nonlocal sets in multipartite quantum systems with arbitrary local
dimensions. As a consequence, the two results give lower bound and upper bounds
on the size of the smallest set which is locally stable for each bipartition.
Our results provide a complete answer to an open question raised by Halder et.
al and a recent paper [Phys. Rev. A 105, 022209 (2022)}. Compared with all
previous relevant proofs, our proof here is quite concise. Our results show
that this kind of strong quantum nonlocality is an universal property in
quantum mechanics which does not depend on the number of participants and local
dimensions.


title:
PISA: A Binary-Weight Processing-In-Sensor Accelerator for Edge Image
  Processing

abstract:
  This work proposes a Processing-In-Sensor Accelerator, namely PISA, as a
flexible, energy-efficient, and high-performance solution for real-time and
smart image processing in AI devices. PISA intrinsically implements a
coarse-grained convolution operation in Binarized-Weight Neural Networks
(BWNNs) leveraging a novel compute-pixel with non-volatile weight storage at
the sensor side. This remarkably reduces the power consumption of data
conversion and transmission to an off-chip processor. The design is completed
with a bit-wise near-sensor processing-in-DRAM computing unit to process the
remaining network layers. Once the object is detected, PISA switches to typical
sensing mode to capture the image for a fine-grained convolution using only the
near-sensor processing unit. Our circuit-to-application co-simulation results
on a BWNN acceleration demonstrate acceptable accuracy on various image
datasets in coarse-grained evaluation compared to baseline BWNN models, while
PISA achieves a frame rate of 1000 and efficiency of ~1.74 TOp/s/W. Lastly,
PISA substantially reduces data conversion and transmission energy by ~84%
compared to a baseline CPU-sensor design.


title: Adaptivity and Confounding in Multi-Armed Bandit Experiments

abstract:
  We explore a new model of bandit experiments where a potentially
nonstationary sequence of contexts influences arms' performance.
Context-unaware algorithms risk confounding while those that perform correct
inference face information delays. Our main insight is that an algorithm we
call deconfounted Thompson sampling strikes a delicate balance between
adaptivity and robustness. Its adaptivity leads to optimal efficiency
properties in easy stationary instances, but it displays surprising resilience
in hard nonstationary ones which cause other adaptive algorithms to fail.


title: Electrical Charge Control of h-BN Single Photon Sources

abstract:
  Colour centres of hexagonal boron nitride (h-BN) have been discovered as
promising and practical single photon sources due to their high brightness and
narrow spectral linewidth at room-temperature. In order to realize h-BN based
photonic quantum communications, the ability to electrically activate the
single photon fluorescence using an external electric field is crucial. In this
work, we show the electrical switching of the photoluminescence from h-BN
quantum emitters, enabled by the controllable electron transfer from the nearby
charge reservoir. By tuning the Fermi level of graphene next to the h-BN
defects, we observed luminescence brightening of a quantum emitter upon the
application of a voltage due to the direct charge state manipulation. In
addition, the correlation measurement of the single photon sources with the
graphene's Raman spectroscopy allows us to extract the exact charge transition
level of quantum emitters, providing the information on the crystallographic
nature of the defect structure. With the complete on-off switching of emission
intensity of h-BN quantum emitters using a voltage, our result paves the way
for the van der Waals colour centre based photonic quantum information
processing, cryptography and memory applications.


title: Can the Quantization of Black Hole be detected?

abstract:
  As a mysterious celestial body predicted by General Relativity, black holes
have been confirmed by observations in recent years. But there are still many
unknown properties waiting for us to discover, one of the famous problems is
the quantization of black holes. The quantization of black holes gives new
research significance to black holes, but the quantization of black holes is
difficult to prove. Here we propose a new method to verify the quantization of
black holes: Carnot cycle. Our results show that if the black holes have
quantized energy levels, the work W they impose to us via the Carnot cycle
should also be quantized. We indirectly verify the quantization of black holes
by detecting the quantization of W, and contrast it with the classic Carnot
cycle. In addition, we have also verified the correctness of the second law of
thermodynamics under the premise of considering the quantization of black
holes.


title:
Critical Checkpoints for Evaluating Defence Models Against Adversarial
  Attack and Robustness

abstract:
  From past couple of years there is a cycle of researchers proposing a defence
model for adversaries in machine learning which is arguably defensible to most
of the existing attacks in restricted condition (they evaluate on some bounded
inputs or datasets). And then shortly another set of researcher finding the
vulnerabilities in that defence model and breaking it by proposing a stronger
attack model. Some common flaws are been noticed in the past defence models
that were broken in very short time. Defence models being broken so easily is a
point of concern as decision of many crucial activities are taken with the help
of machine learning models. So there is an utter need of some defence
checkpoints that any researcher should keep in mind while evaluating the
soundness of technique and declaring it to be decent defence technique. In this
paper, we have suggested few checkpoints that should be taken into
consideration while building and evaluating the soundness of defence models.
All these points are recommended after observing why some past defence models
failed and how some model remained adamant and proved their soundness against
some of the very strong attacks.


title:
An Analog Signal Processing EIC-PIC Solution for Coherent Data Center
  Interconnects

abstract:
  Data center interconnects (DCIs) will have to support throughputs of 400 Gbps
or more per wavelength in the near future. To achieve such high data rates,
coherent modulation and detection is used, which conventionally requires
high-speed data conversion and signal processing in the digital domain.
Alternatively, high-speed signal conditioning and processing could be carried
out in co-designed photonic and electronic integrated circuits, in the optical
and electrical analog domains, respectively, to achieve reduced power
consumption, latency, form factor, and cost. A few demonstrations of analog
domain processing electronic integrated circuits (EICs), including those of
equalizer and carrier phase recovery (CPR) modules showcase progress in this
direction in the literature. In this brief, for the first time, we present
integration of a silicon photonic integrated coherent receiver (ICR) module
with a CPR module, as a part of a complete coherent receiver solution. A phase
shifter in the ICR (fabricated in a 220 nm silicon-on-insulator technology)
receives feedback from a CPR EIC, and the combination compensates for the time
varying phase offset between the modulated signal and the unmodulated carrier
in the closed loop configuration. In this proof-of-concept demonstration, we
present experimental results obtained from the stand-alone silicon photonic ICR
along with its system level integration with CPR chip, for QPSK signals. The
technique can be extended to a higher-order modulation format, such as 16-QAM,
for data rate scaling. The proposed scheme is suitable for homodyne systems,
such as polarization multiplexed carrier based self-homodyne links.


title: Murasugi sum and extremal knot Floer homology

abstract:
  The aim of this paper is to study the behavior of knot Floer homology under
Murasugi sum. We establish a graded version of Ni's isomorphism between the
extremal knot Floer homology of Murasugi sum of two links and the tensor
product of the extremal knot Floer homology groups of the two summands. We
further prove that $\tau=g$ for each summand if and only if $\tau=g$ holds for
the Murasugi sum (with $\tau$ and $g$ defined appropriately for multi-component
links). Some applications are presented.


title:
Energy conservation law for weak solutions of the full compressible
  Navier-Stokes equations

abstract:
  We consider a sufficient condition for the energy conservation law of a weak
solution for the full compressible Navier-Stokes equations on the torus. We
prove that a weak solution constructed by Feireisl with certain integrability
conditions conserve the energy. Our assumption relaxes the regularity condition
for the density compared with existing results.


title:
Global conservation law for an even order elliptic system with
  antisymmetric potential

abstract:
  In this note, we refine the local conservation law obtained by Lamm-Rivi\`ere
for fourth order systems and de Longueville-Gastel for general even order
systems to a global conservation law.


title: Social Welfare Maximization in Cross-Silo Federated Learning

abstract:
  As one of the typical settings of Federated Learning (FL), cross-silo FL
allows organizations to jointly train an optimal Machine Learning (ML) model.
In this case, some organizations may try to obtain the global model without
contributing their local training, lowering the social welfare. In this paper,
we model the interactions among organizations in cross-silo FL as a public
goods game for the first time and theoretically prove that there exists a
social dilemma where the maximum social welfare is not achieved in Nash
equilibrium. To overcome this social dilemma, we employ the Multi-player
Multi-action Zero-Determinant (MMZD) strategy to maximize the social welfare.
With the help of the MMZD, an individual organization can unilaterally control
the social welfare without extra cost. Experimental results validate that the
MMZD strategy is effective in maximizing the social welfare.


title: Free Inclinations for Transneptunian Objects in the Main Kuiper Belt

abstract:
  There is a complex inclination structure present in the transneptunian object
(TNO) orbital distribution in the main classical belt region (between orbital
semimajor axes of 39 and 48 au). The long-term gravitational effects of the
giant planets make TNO orbits precess, but non-resonant objects maintain a
nearly constant 'free' inclination ($I_\text{free}$) with respect to a local
forced precession pole. Because of the likely cosmogonic importance of the
distribution of this quantity, we tabulate free inclinations for all main-belt
TNOs, each individually computed using barycentric orbital elements with
respect to each object's local forcing pole. We show that the simplest method,
based on the Laplace-Lagrange secular theory, is unable to give correct forcing
poles for objects near the $\nu_{18}$ secular resonance, resulting in poorly
conserved $I_\text{free}$ values in much of the main belt. We thus instead
implemented an averaged Hamiltonian to obtain the expected nodal precession for
each TNO, yielding significantly more accurate free inclinations for
non-resonant objects. For the vast majority (96\%) of classical belt TNOs,
these $I_\text{free}$ values are conserved to $<1^\circ$ over 4 Gyr numerical
simulations, demonstrating the advantage of using this well-conserved quantity
in studies of the TNO population and its primordial inclination profile; our
computed distributions only reinforce the idea of a very co-planar surviving
'cold' primordial population, overlain by a large $I$-width implanted 'hot'
population.


title:
Microplankton life histories revealed by holographic microscopy and deep
  learning

abstract:
  The marine microbial food web plays a central role in the global carbon
cycle. Our mechanistic understanding of the ocean, however, is biased towards
its larger constituents, while rates and biomass fluxes in the microbial food
web are mainly inferred from indirect measurements and ensemble averages. Yet,
resolution at the level of the individual microplankton is required to advance
our understanding of the oceanic food web. Here, we demonstrate that, by
combining holographic microscopy with deep learning, we can follow
microplanktons throughout their lifespan, continuously measuring their three
dimensional position and dry mass. The deep learning algorithms circumvent the
computationally intensive processing of holographic data and allow rapid
measurements over extended time periods. This permits us to reliably estimate
growth rates, both in terms of dry mass increase and cell divisions, as well as
to measure trophic interactions between species such as predation events. The
individual resolution provides information about selectivity, individual
feeding rates and handling times for individual microplanktons. This method is
particularly useful to explore the flux of carbon through micro-zooplankton,
the most important and least known group of primary consumers in the global
oceans. We exemplify this by detailed descriptions of micro-zooplankton feeding
events, cell divisions, and long term monitoring of single cells from division
to division.

categories: physics.bio-ph cond-mat.soft cs.LG q-bio.QM


title:
Probing Inner-Edge of Dead Zones in Protoplanetary Disks with ALMA and
  Next Generation Very Large Array

abstract:
  The discovery of substructures in protoplanetary disks with ALMA has provided
us key insights on the formation of planets. However, observational constraints
on the formation of rocky planets have been still sparse, especially because of
the limited spatial resolution. The inner edge of so-called dead zone is one of
the preferential sites of rocky planet formation. We investigate the
capabilities of ALMA and ngVLA for observing a dust concentration expected at
the inner edge of the dead-zone around a Herbig star. Herbig Ae/Be stars are
useful laboratories for exploring the evolution of rocky grains in
protoplanetary disks because of their high luminosity which pushes the
dead-zone inner edge outward. We find that, thanks to its unprecedented angular
resolution and sensitivity, ngVLA can detect the dust concentration at the
dead-zone inner edge, with a reasonable integration time of 10 hrs at
$\lambda=3,7$ mm and 1 cm. The dust concentration is expected to be optically
thick at the ALMA wavelengths and cannot be spatially resolved due to its
limited resolution. On the other hand, the flux density from the inner disk
regions ($\sim$3--4 au) observed with current VLA is higher for disks with a
dust ring, and hence would be a useful indicator that help us choose potential
candidates of disks having a dust concentration at the inner most region. With
these observations we can characterize the process of dust concentration in the
innermost disk regions, where rocky planets can form.


title: Task Specific Attention is one more thing you need for object detection

abstract:
  Various models have been proposed to solve the object detection problem.
However, most of them require many hand-designed components to demonstrate good
performance. To mitigate these issues, Transformer based DETR and its variant
Deformable DETR were suggested. They solved much of the complex issue of
designing a head of object detection model but it has not been generally clear
that the Transformer-based models could be considered as the state-of-the-art
method in object detection without doubt. Furthermore, as DETR adapted
Transformer method only for the detection head, but still with including CNN
for the backbone body, it has not been certain that it would be possible to
build the competent end-to-end pipeline with the combination of attention
modules. In this paper, we propose that combining several attention modules
with our new Task Specific Split Transformer(TSST) is a fairly good enough
method to produce the best COCO results without traditionally hand-designed
components. By splitting generally purposed attention module into two separated
mission specific attention module, the proposed method addresses the way to
design simpler object detection models than before. Extensive experiments on
the COCO benchmark demonstrate the effectiveness of our approach. Code is
released at https://github.com/navervision/tsst


title: CLSEG: Contrastive Learning of Story Ending Generation

abstract:
  Story Ending Generation (SEG) is a challenging task in natural language
generation. Recently, methods based on Pre-trained Language Models (PLM) have
achieved great prosperity, which can produce fluent and coherent story endings.
However, the pre-training objective of PLM-based methods is unable to model the
consistency between story context and ending. The goal of this paper is to
adopt contrastive learning to generate endings more consistent with story
context, while there are two main challenges in contrastive learning of SEG.
First is the negative sampling of wrong endings inconsistent with story
contexts. The second challenge is the adaptation of contrastive learning for
SEG. To address these two issues, we propose a novel Contrastive Learning
framework for Story Ending Generation (CLSEG), which has two steps:
multi-aspect sampling and story-specific contrastive learning. Particularly,
for the first issue, we utilize novel multi-aspect sampling mechanisms to
obtain wrong endings considering the consistency of order, causality, and
sentiment. To solve the second issue, we well-design a story-specific
contrastive training strategy that is adapted for SEG. Experiments show that
CLSEG outperforms baselines and can produce story endings with stronger
consistency and rationality.


title: Guide Local Feature Matching by Overlap Estimation

abstract:
  Local image feature matching under large appearance, viewpoint, and distance
changes is challenging yet important. Conventional methods detect and match
tentative local features across the whole images, with heuristic consistency
checks to guarantee reliable matches. In this paper, we introduce a novel
Overlap Estimation method conditioned on image pairs with TRansformer, named
OETR, to constrain local feature matching in the commonly visible region. OETR
performs overlap estimation in a two-step process of feature correlation and
then overlap regression. As a preprocessing module, OETR can be plugged into
any existing local feature detection and matching pipeline, to mitigate
potential view angle or scale variance. Intensive experiments show that OETR
can boost state-of-the-art local feature matching performance substantially,
especially for image pairs with small shared regions. The code will be publicly
available at https://github.com/AbyssGaze/OETR.


title:
Generally applicable holographic torque measurement for optically
  trapped particles

abstract:
  We present a method to measure the optical torque applied to particles of
arbitrary shape held in an optical trap, inferred from the change of angular
momentum of light induced by the particle. All torque components can be
determined from a single interference pattern recorded by a camera in the back
focal plane of a high-NA condenser collecting all forward scattered light. We
derive explicit expressions mapping the measured complex field in this plane to
the torque components. The required phase is retrieved by an iterative
algorithm, using the known position of the optical traps as constraints. The
torque pertaining to individual particles is accessible, as well as separate
spin or orbital parts of the total torque.


title: Tackling benign nonconvexity with smoothing and stochastic gradients

abstract:
  Non-convex optimization problems are ubiquitous in machine learning,
especially in Deep Learning. While such complex problems can often be
successfully optimized in practice by using stochastic gradient descent (SGD),
theoretical analysis cannot adequately explain this success. In particular, the
standard analyses do not show global convergence of SGD on non-convex
functions, and instead show convergence to stationary points (which can also be
local minima or saddle points). We identify a broad class of nonconvex
functions for which we can show that perturbed SGD (gradient descent perturbed
by stochastic noise -- covering SGD as a special case) converges to a global
minimum (or a neighborhood thereof), in contrast to gradient descent without
noise that can get stuck in local minima far from a global solution. For
example, on non-convex functions that are relatively close to a convex-like
(strongly convex or PL) function we show that SGD can converge linearly to a
global optimum.


title:
QED effects on phase transition and Ruppeiner geometry of
  Euler-Heisenberg-AdS black holes

abstract:
  Taking the quantum electrodynamics (QED) effect into account, we study the
black hole phase transition and Ruppeiner geometry for the Euler-Heisenberg
anti-de Sitter black hole in the extended phase space. For negative and small
positive QED parameter, we observe a small/large black hole phase transition
and reentrant phase transition, respectively. While a large positive value of
the QED parameter ruins the phase transition. The phase diagrams for each case
are explicitly exhibited. Then we construct the Ruppeiner geometry in the
thermodynamic parameter space. Different features of the corresponding scalar
curvature are shown for both the small/large black hole phase transition and
reentrant phase transition cases. Of particular interest is that an additional
region of positive scalar curvature indicating dominated repulsive interaction
among black hole microstructure is present for the black hole with a small
positive QED parameter. Furthermore, the universal critical phenomena are also
observed for the scalar curvature of the Ruppeiner geometry. These results
indicate that the QED parameter has a crucial influence on the black hole phase
transition and microstructure.


title: Interpolation and Regularization for Causal Learning

abstract:
  We study the problem of learning causal models from observational data
through the lens of interpolation and its counterpart -- regularization. A
large volume of recent theoretical, as well as empirical work, suggests that,
in highly complex model classes, interpolating estimators can have good
statistical generalization properties and can even be optimal for statistical
learning. Motivated by an analogy between statistical and causal learning
recently highlighted by Janzing (2019), we investigate whether interpolating
estimators can also learn good causal models. To this end, we consider a simple
linearly confounded model and derive precise asymptotics for the *causal risk*
of the min-norm interpolator and ridge-regularized regressors in the
high-dimensional regime. Under the principle of independent causal mechanisms,
a standard assumption in causal learning, we find that interpolators cannot be
optimal and causal learning requires stronger regularization than statistical
learning. This resolves a recent conjecture in Janzing (2019). Beyond this
assumption, we find a larger range of behavior that can be precisely
characterized with a new measure of *confounding strength*. If the confounding
strength is negative, causal learning requires weaker regularization than
statistical learning, interpolators can be optimal, and the optimal
regularization can even be negative. If the confounding strength is large, the
optimal regularization is infinite, and learning from observational data is
actively harmful.


title:
Convergence analysis of a finite difference method for stochastic
  Cahn--Hilliard equation

abstract:
  This paper presents the convergence analysis of the spatial finite difference
method (FDM) for the stochastic Cahn--Hilliard equation with Lipschitz
nonlinearity and multiplicative noise. Based on fine estimates of the discrete
Green function, we prove that both the spatial semi-discrete numerical solution
and its Malliavin derivative have strong convergence order $1$. Further, by
showing the negative moment estimates of the exact solution, we obtain that the
density of the spatial semi-discrete numerical solution converges in
$L^1(\mathbb R)$ to the exact one. Finally, we apply an exponential Euler
method to discretize the spatial semi-discrete numerical solution in time and
show that the temporal strong convergence order is nearly $\frac38$, where a
difficulty we overcome is to derive the optimal H\"older continuity of the
spatial semi-discrete numerical solution.


title:
Efficient solution of 3D elasticity problems with smoothed aggregation
  algebraic multigrid and block arithmetics

abstract:
  Efficient solution of 3D elasticity problems is an important part of many
industrial and scientific applications. Smoothed aggregation algebraic
multigrid using rigid body modes for the tentative prolongation operator
construction is an efficient and robust choice for the solution of linear
systems arising from the discretization of elasticity equations. The system
matrices on every level of the multigrid hierarchy have block structure, so
using block representation and block arithmetics should significantly improve
the solver efficiency. However, the tentative prolongation operator
construction may only be done using scalar representation. The paper proposes a
couple of practical approaches for enabling the use of block arithmetics with
smoothed aggregation algebraic multigrid based on the open-source AMGCL
library. It is shown on the example of two real-world model problems that the
suggested improvements may speed up the solution by 50% and reduce the memory
requirements for the preconditioner by 30%. The implementation is
straightforward and only requires a minimal amount of code.


title: Fast K\"otter-Nielsen-H\o holdt Interpolation over Skew Polynomial Rings

abstract:
  Skew polynomials are a class of non-commutative polynomials that have several
applications in computer science, coding theory and cryptography. In
particular, skew polynomials can be used to construct and decode evaluation
codes in several metrics, like e.g. the Hamming, rank, sum-rank and skew
metric. In this paper we propose a fast divide-and-conquer variant of the
K\"otter-Nielsen-H{\o}holdt (KNH) interpolation over free modules over skew
polynomial rings. The proposed KNH interpolation can be used to solve the
interpolation step of interpolation-based decoding of (interleaved) Gabidulin,
linearized Reed-Solomon and skew Reed-Solomon codes efficiently, which have
various applications in coding theory and code-based quantum-resistant
cryptography.


title: Optimization flows landing on the Stiefel manifold

abstract:
  We study a continuous-time system that solves the optimization problem over
the set of orthogonal matrices, which is also known as the Stiefel manifold.
The resulting optimization flow follows a path that is not always on the
manifold but eventually lands on the manifold. We introduce a generalized
Stiefel manifold to which we extend the canonical metric of the Stiefel
manifold. We show that the vector field of the proposed flow can be interpreted
as the sum of a Riemannian gradient on a generalized Stiefel manifold and a
normal vector. Moreover, we prove that the proposed flow globally converges to
the set of critical points, and any local minimum and isolated critical point
is asymptotically stable.


title:
Towards better understanding and better generalization of few-shot
  classification in histology images with contrastive learning

abstract:
  Few-shot learning is an established topic in natural images for years, but
few work is attended to histology images, which is of high clinical value since
well-labeled datasets and rare abnormal samples are expensive to collect. Here,
we facilitate the study of few-shot learning in histology images by setting up
three cross-domain tasks that simulate real clinics problems. To enable
label-efficient learning and better generalizability, we propose to incorporate
contrastive learning (CL) with latent augmentation (LA) to build a few-shot
system. CL learns useful representations without manual labels, while LA
transfers semantic variations of the base dataset in an unsupervised way. These
two components fully exploit unlabeled training data and can scale gracefully
to other label-hungry problems. In experiments, we find i) models learned by CL
generalize better than supervised learning for histology images in unseen
classes, and ii) LA brings consistent gains over baselines. Prior studies of
self-supervised learning mainly focus on ImageNet-like images, which only
present a dominant object in their centers. Recent attention has been paid to
images with multi-objects and multi-textures. Histology images are a natural
choice for such a study. We show the superiority of CL over supervised learning
in terms of generalization for such data and provide our empirical
understanding for this observation. The findings in this work could contribute
to understanding how the model generalizes in the context of both
representation learning and histological image analysis. Code is available.


title: Controllability of Networked Sampled-data Systems

abstract:
  The controllability of networked sampled-data systems with zero-order
samplers on the control and transmission channels is explored, where single-
and multi-rate sampling patterns are considered, respectively. The effects of
sampling on controllability of networked systems are analyzed, with some
sufficient and/or necessary controllability conditions derived. Different from
the sampling control of single systems, the pathological sampling of node
systems could be eliminated by the network structure and inner couplings under
some conditions. While for singular topology matrix, the pathological sampling
of single nodes will cause the entire system to lose controllability. Moreover,
controllability of networked systems with specific node dynamics will not be
affected by any periodic sampling. All the results indicate that whether a
networked system is under pathological sampling or not is jointly determined by
mutually coupled factors.


title: VLP: A Survey on Vision-Language Pre-training

abstract:
  In the past few years, the emergence of pre-training models has brought
uni-modal fields such as computer vision (CV) and natural language processing
(NLP) to a new era. Substantial works have shown they are beneficial for
downstream uni-modal tasks and avoid training a new model from scratch. So can
such pre-trained models be applied to multi-modal tasks? Researchers have
explored this problem and made significant progress. This paper surveys recent
advances and new frontiers in vision-language pre-training (VLP), including
image-text and video-text pre-training. To give readers a better overall grasp
of VLP, we first review its recent advances from five aspects: feature
extraction, model architecture, pre-training objectives, pre-training datasets,
and downstream tasks. Then, we summarize the specific VLP models in detail.
Finally, we discuss the new frontiers in VLP. To the best of our knowledge,
this is the first survey on VLP. We hope that this survey can shed light on
future research in the VLP field.


title:
A cylindrical coordinates approach concerning internal waves for the
  Antarctic Circumpolar Current

abstract:
  In this paper, we devise a new exact and partially explicit solution to the
governing equations of geophysical fluid dynamics for an inviscid and
incompressible azimuth flow with a discontinuous density distribution and
subjected to forcing terms in terms of cylindrical coordinates. The obtained
solution represents a steady, purely azimuthal, stratified flow with an
associated free surface and an interface that is suitable for describing the
Antarctic Circumpolar Current. Resorting to a functional analysis, we
demonstrate that the relationship between the imposed pressure at the free
surface and the resulting surface deformation is well-defined and show that the
continuity of the pressure along the interface generates an equation that
describes implicitly the shape of the interface. Moreover, a particular example
is considered to show that the interface can be determined explicitly. Finally,
we derive an infinite regularity about the interface and obtain the expected
monotonicity properties between the surface pressure and its distortion.


title:
Ponderomotive squeezing of light by a levitated nanoparticle in free
  space

abstract:
  A mechanically compliant element can be set into motion by the interaction
with light. In turn, this light-driven motion can give rise to ponderomotive
correlations in the electromagnetic field. In optomechanical systems, cavities
are often employed to enhance these correlations up to the point where they
generate quantum squeezing of light. In free-space scenarios, where no cavity
is used, observation of squeezing remains possible but challenging due to the
weakness of the interaction, and has not been reported so far. Here, we measure
the ponderomotively squeezed state of light scattered by a nanoparticle
levitated in a free-space optical tweezer. We observe a reduction of the
optical fluctuations by up to $25$~\% below the vacuum level, in a bandwidth of
about $15$~kHz. Our results are well explained by a linearized dipole
interaction between the nanoparticle and the electromagnetic continuum. These
ponderomotive correlations open the door to quantum-enhanced sensing and
metrology with levitated systems, such as force measurements below the standard
quantum limit.


title: Can Interpretable Reinforcement Learning Manage Assets Your Way?

abstract:
  Personalisation of products and services is fast becoming the driver of
success in banking and commerce. Machine learning holds the promise of gaining
a deeper understanding of and tailoring to customers' needs and preferences.
Whereas traditional solutions to financial decision problems frequently rely on
model assumptions, reinforcement learning is able to exploit large amounts of
data to improve customer modelling and decision-making in complex financial
environments with fewer assumptions. Model explainability and interpretability
present challenges from a regulatory perspective which demands transparency for
acceptance; they also offer the opportunity for improved insight into and
understanding of customers. Post-hoc approaches are typically used for
explaining pretrained reinforcement learning models. Based on our previous
modeling of customer spending behaviour, we adapt our recent reinforcement
learning algorithm that intrinsically characterizes desirable behaviours and we
transition to the problem of asset management. We train inherently
interpretable reinforcement learning agents to give investment advice that is
aligned with prototype financial personality traits which are combined to make
a final recommendation. We observe that the trained agents' advice adheres to
their intended characteristics, they learn the value of compound growth, and,
without any explicit reference, the notion of risk as well as improved policy
convergence.


title: A note on cohomology of Clifford algebras

abstract:
  In this article we construct a cochain complex of a complex Clifford algebra
with coefficients in itself in a combinatorial fashion and we call the
corresponding cohomology by {\it Clifford cohomology.} We show that {\it
Clifford cohomology} controls the deformation of a complex Clifford algebra.


title:
Axial-vector transition form factors of the baryon octet to the baryon
  decuplet with flavor SU(3) symmetry breaking

abstract:
  We investigate the axial-vector transition form factors of the baryon octet
to the baryon decuplet within the framework of the chiral quark-soliton model,
with the effects of flavor SU(3) symmetry breaking included. We consider the
rotational $1/N_c$ corrections and regard the strange current quark mass as a
perturbation. We compare the present results for the $\Delta \to N$
axial-vector transition with those from other models and lattice QCD. We also
compute all possible axial-vector transitions from the baryon decuplet to the
octet with the strangeness changed, i.e., $|\Delta S|=1$. We obtain the value
of the essential form factor $C_5^A$ for the $\Delta \to N$ axial-vector
transition at the zero momentum transfer ($Q^2=0$). Furthermore, the present
results are in good agreement with those fitted with the T2K data. We extract
the value of the axial-vector mass $M_A$ compared to the data.


title:
Precise magnetization measurements down to 500 mK using a miniature 3He
  cryostat and a closed-cycle 3He gas handling system installed in a SQUID
  magnetometer without continuous-cooling functionality

abstract:
  We have conducted precise magnetization measurements down to 0.5 K with a
miniature 3He cryostat and a closed-cycle 3He gas handling system for a
commercial superconducting quantum interference device magnetometer [Magnetic
Property Measurement System (Quantum Design)]. The gas handling system contains
two sorption pumps filled with granular charcoals. We pressurize 3He gas up to
ambient pressure for the liquification at 3 K and then pump the vaper for
cooling. The lowest sample temperature is ~ 0.5 K and it can persist for 34
hours. We demonstrate the performance of the system by observing the Meissner
effect of aluminum below the superconducting transition temperature ~ 1 K. We
also measured the magnetization curve of the heavy fermion superconductor
CeCoIn5 resulting in successful observation of the lower critical field at 0.5
K.


title: A relation between Wiener index and Mostar index for daisy cubes

abstract:
  Daisy cubes are a class of isometric subgraphs of the hypercubes Q n. Daisy
cubes include some previously well known families of graphs like Fibonacci
cubes and Lucas cubes. Moreover they appear in chemical graph theory. Two
distance invariants, Wiener and Mostar indices, have been introduced in the
context of the mathematical chemistry. The Wiener index W (G) is the sum of
distance between over all unordered pairs of vertices of a graph G. The Mostar
index Mo(G) is a measure of how far G is from being distance balanced. In this
paper we establish that the Wiener and the Mostar indices of a daisy cube G are
linked by the relation 2W (G) -- Mo(G) = |V (G)||E(G)|. We give also an
expression of Wiener and Mostar indices for daisy cubes.


title: Analysis of optimal preconditioners for CutFEM

abstract:
  In this paper we consider a class of unfitted finite element methods for
scalar elliptic problems. These so-called CutFEM methods use standard finite
element spaces on a fixed unfitted triangulation combined with the Nitsche
technique and a ghost penalty stabilization. As a model problem we consider the
application of such a method to the Poisson interface problem. We introduce and
analyze a new class of preconditioners that is based on a subspace
decomposition approach. The unfitted finite element space is split into two
subspaces, where one subspace is the standard finite element space associated
to the background mesh and the second subspace is spanned by all cut basis
functions corresponding to nodes on the cut elements. We will show that this
splitting is stable, uniformly in the discretization parameter and in the
location of the interface in the triangulation. Based on this we introduce an
efficient preconditioner that is uniformly spectrally equivalent to the
stiffness matrix. Using a similar splitting, it is shown that the same
preconditioning approach can also be applied to a fictitious domain CutFEM
discretization of the Poisson equation. Results of numerical experiments are
included that illustrate optimality of such preconditioners for the Poisson
interface problem and the Poisson fictitious domain problem.


title:
The peculiar chemical abundance of the transitional millisecond pulsar
  PSR J1023+0038 -- Li enhancement

abstract:
  Using high-resolution optical spectroscopy we determine the chemical
abundance of the secondary star in the binary millisecond pulsar PSR
J1023+0038. We measure a metallicity of [Fe/H] = 0.48 +/- 0.04 which is higher
than the Solar value and in general find that the element abundances are
different compared to the secondary stars in X-ray binaries and stars in the
solar neighbourhood of similar Fe content. Our results suggest that the pulsar
was formed in a supernova explosion. We find that supernova models, where
matter that has been processed in the supernova is captured by the secondary
star leading to abundance anomalies, qualitatively agree with the observations.
We measure Li abundance of A(Li) = 3.66 +/- 0.20, which is anomalously high
compared to the Li abundance of stars with the same effective temperature,
irrespective of the age of the system. Furthermore, the Li abundance in PSR
J1023+0038 is higher than the Cosmic value and what is observed in young
Population I stars and so provides unambiguous evidence for fresh Li
production. The most likely explanation is the interaction of high energy
gamma-rays or relativistic protons from the pulsar wind or intrabinary shock
with the CNO nuclei in the secondary star's atmosphere via spallation which
leads to substantial Li enrichment in the secondary star's atmosphere.


title:
Electron spins interaction in the spin-Peierls phase of the organic spin
  chain (o -DMTTF) 2 X (X = Cl, Br, I)

abstract:
  We investigate the electron spin resonance of the organic spin-Peierls chain
(o-DMTTF)2X with X = Cl, Br and I. We describe the temperature dependence of
the spin gap during the phase transition and quantify the dimerization
parameter $\delta$. At the lowest temperatures, the susceptibility is governed
by defects in the spin dimerized chain. Such strongly correlated defects are
the consequence of breaks in the translational symmetry of the chain. In the
vicinity of the defects the spins are polarized antiferomagnetically forming a
magnetic soliton: a spin 1 2 quasi-particle of size ruled by $\delta$ pinned to
the defects. For (o-DMTTF)2Br and (o-DMTTF)2Cl, we show that the one-half of
the total number of solitons are in isolation (as singles) whereas the other
half form pairs (soliton dimers)with a strong magnetic coupling. The Rabi
oscillations of both the single-soliton and the soliton-dimer are observed,
which is a prerequisite in the context of quantum information.


title: A stabilization mechanism for many-body localization in two dimensions

abstract:
  Experiments in cold atom systems see almost identical signatures of many body
localization (MBL) in both one-dimensional ($d=1$) and two-dimensional ($d=2$)
systems despite the thermal avalanche hypothesis showing that the MBL phase is
unstable for $d>1$. Underpinning the thermal avalanche argument is the
assumption of exponential localization of local integrals of motion (LIOMs). In
this work we demonstrate that addition of a confining potential -- as is
typical in experimental setups -- allows a non-interacting disordered system to
have super-exponentially (Gaussian) localized wavefunctions, and an interacting
disordered system to undergo a localization transition. Moreover, we show that
Gaussian localization of MBL LIOMs shifts the quantum avalanche critical
dimension from $d=1$ to $d=2$, potentially bridging the divide between the
experimental demonstrations of MBL in these systems and existing theoretical
arguments that claim that such demonstrations are impossible.


title: The Rotation of Magnetic Flux Rope Formed during Solar Eruption

abstract:
  The eruptions of solar filaments often show rotational motion about their
rising direction, but it remains elusive what mechanism governs such rotation
and how the rotation is related to the initial morphology of the pre-eruptive
filament (and co-spatial sigmoid), filament chirality, and magnetic helicity.
The conventional view regarding the rotation as a result of a magnetic flux
rope (MFR) under-going the ideal kink instability still has confusion in
explaining these relationships. Here we proposed an alternative explanation for
the rotation during eruptions, by analyzing a magnetohydrodynamic simulation in
which magnetic reconnection initiates an eruption from a sheared arcade
configuration and an MFR is formed during eruption through the reconnection.
The simulation reproduces a reverse S-shaped MFR with dextral chirality, and
the axis of this MFR rotates counterclockwise while rising, which compares
favorably with a typical filament eruption observed from dual viewing angles.
By calculating the twist and writhe numbers of the modeled MFR during its
eruption, we found that accompanied with the rotation, the nonlocal writhe of
the MFR's axis decreases while the twist of its surrounding field lines
increases, and this is distinct from the kink instability, which converts
magnetic twist into writhe of the MFR axis.


title:
Longtime behavior for homoenergetic solutions in the collision dominated
  regime for hard potentials

abstract:
  In this paper, we consider a particular class of solutions to the Boltzmann
equation which are referred to as homoenergetic solutions. They describe the
dynamics of a dilute gas due to collisions and the action of either a shear, a
dilation or a combination of both. We prove that solutions with initially high
temperature remain close and converge to a Maxwellian distribution with
temperature going to infinity. Furthermore, we give precise asymptotic formulas
for the temperature. This local stability result is a consequence of a dominant
shear and the homogeneity $ \gamma>0 $ of the collision operator with respect
to relative velocities. The proof relies on an ansatz which is motivated by a
Hilbert-type expansion. We consider both non-cutoff and cutoff kernels.


title:
High-redshift cosmography: Application and comparison with different
  methods

abstract:
  Cosmography is used in cosmological data processing in order to constrain the
kinematics of the universe in a model-independent way. In this paper, we first
investigate the effect of the ultraviolet (UV) and X-ray relation of a quasar
on cosmological constraints. By fitting the quasar relation and cosmographic
parameters simultaneously, we find that the 4$\sigma$ deviation from the
cosmological constant cold dark matter ($\Lambda$CDM) model disappears. Next,
utilizing the Pantheon sample and 31 long gamma-ray bursts (LGRBs), we make a
comparison among the different cosmographic expansions ($z$-redshift,
$y$-redshift, $E(y)$, $\log(1+z)$, $\log(1+z)+k_{ij}$, and Pad$\rm \acute{e}$
approximations) with the third-order and fourth-order expansions. The expansion
order can significantly affect the results, especially for the $y$-redshift
method. Through analysis from the same sample, the lower-order expansion is
preferable, except the $y$-redshift and $E(y)$ methods. For the $y$-redshift
and $E(y)$ methods, despite adopting the same parameterization of $y=z/(1+z)$,
the performance of the latter is better than that of the former. Logarithmic
polynomials, $\log(1+z)$ and $\log(1+z) + k_{ij}$, perform significantly better
than $z$-redshift, $y$-redshift, and $E(y)$ methods, but worse than Pad$\rm
\acute{e}$ approximations. Finally, we comprehensively analyze the results
obtained from different samples. We find that the Pad$\rm \acute{e}_{(2,1)}$
method is suitable for both low and high redshift cases. The Pad$\rm
\acute{e}_{(2,2)}$ method performs well in a high-redshift situation. For the
$y$-redshift and $E(y)$ methods, the only constraint on the first two
parameters ($q_{0}$ and $j_{0}$) is reliable.


title: Improving Test Automation Maturity: a Multivocal Literature Review

abstract:
  Mature test automation is key for achieving software quality at speed. In
this paper, we present a multivocal literature review with the objective to
survey and synthesize the guidelines given in the literature for improving test
automation maturity. We selected and reviewed 81 primary studies, consisting of
26 academic literature and 55 grey literature sources. From primary studies, we
extracted 26 test automation best practices (e.g., Define an effective test
automation strategy, Set up good test environments, Develop high-quality test
scripts) and collected many pieces of advice (e.g., in forms of
implementation/improvement approaches, technical techniques, concepts,
experience-based heuristics) on how to conduct these best practices. We made
main observations: (1) There are only 6 best practices whose positive effect on
maturity improvement have been evaluated by academic studies using formal
empirical methods; (2) Several technical related best practices in this MLR
were not presented in test maturity models; (3) Some best practices can be
linked to success factors and maturity impediments proposed by other scholars;
(4) Most pieces of advice on how to conduct proposed best practices were
identified from experience studies and their effectiveness need to be further
evaluated with cross-site empirical evidence using formal empirical methods;
(5) In the literature, some advice on how to conduct certain best practices are
conflicting, and some advice on how to conduct certain best practices still
need further qualitative analysis.


title:
Nematicity and fractional magnetization plateaus induced by spin-lattice
  coupling in the classical kagome-lattice Heisenberg antiferromagnet

abstract:
  We investigate the effect of spin-lattice coupling (SLC) on the magnetic
properties of the classical kagome-lattice Heisenberg antiferromagnet (KHAF)
using improved Monte Carlo updates. The lattice modes are represented by
Einstein site phonons, which introduce effective further-neighbor spin
interactions in addition to the nearest-neighbor biquadratic interactions. In
the weak SLC, the macroscopically degenerate coplanar ground state remains at
zero field, while a $\sqrt{3} \times \sqrt{3}$ ordered phase accompanied by a
1/3-magnetization plateau appears in external magnetic fields. In the strong
SLC, we find a nematic order at zero field and a 1/9-magnetization plateau
associated with a $3 \times 3$ collinear order. Near the phase transition
between the 1/9- and 1/3-plateau states, the ergodicity in the single spin flip
is practically broken, and slow dynamics appear. We propose that relevant KHAFs
with strong SLC would be realized in spinel-based materials.


title: Clifford systems, harmonic maps and metrics with non-negative curvature

abstract:
  Associated with a symmetric Clifford system $\{P_0, P_1,\cdots, P_{m}\}$ on
$\mathbb{R}^{2l}$, there is a canonical vector bundle $\eta$ over $S^{l-1}$.
For $m=4$ and $8$, we construct explicitly its characteristic map, and
completely determine when the sphere bundle $S(\eta)$ admits a cross-section.
These generalize the related results in \cite{St51} and \cite{Ja58}
respectively. As an application, we establish new harmonic representatives of
certain elements in homotopy groups of spheres (cf. \cite{PT97} \cite{PT98}).
By a suitable choice of Clifford system, we construct a metric of non-negative
curvature on $M_+$, the inhomogeneous focal submanifold of OT-FKM type with
$m=3$, which is diffeomorphic to $S(\eta)$.


title: Central limit theorem for full discretization of parabolic SPDE

abstract:
  In order to characterize the fluctuation between the ergodic limit and the
time-averaging estimator of a full discretization in a quantitative way, we
establish a central limit theorem for the full discretization of the parabolic
stochastic partial differential equation. The theorem shows that the normalized
time-averaging estimator converges to a normal distribution with the variance
being the same as that of the continuous case, where the scale used for the
normalization corresponds to the temporal strong convergence order of the
considered full discretization. A key ingredient in the proof is to extract an
appropriate martingale difference series sum from the normalized time-averaging
estimator so that the convergence to the normal distribution of such a sum and
the convergence to zero in probability of the remainder are well balanced. The
main novelty of our method to balance the convergence lies in proposing an
appropriately modified Poisson equation so as to possess the space-independent
regularity estimates. As a byproduct, the full discretization is shown to
fulfill the weak law of large numbers, namely, the time-averaging estimator
converges to the ergodic limit in probability.


title:
Design for implementation of discrete-time quantum walk with circulant
  matrix on graph by optical polarizing elements

abstract:
  In this paper, we introduce a quantum walk whose local scattering at each
vertex is denoted by a unitary circulant matrix; namely the circulant quantum
walk. We also introduce another quantum walk induced by the circulant quantum
walk; namely the optical quantum walk, whose underlying graph is a $2$-regular
directed graph and obtained by blowing up the original graph in some way. We
propose a design of an optical circuit which implements the stationary state of
the optical quantum walk. We show that if the induced optical quantum walk does
not have $+1$ eigenvalue, then the stationary state of the optical quantum walk
gives that of the original circulant quantum walk. From this result, we give a
useful condition for the setting of the circulant quantum walks which can be
implemented by this optical circuit.


title:
VCVTS: Multi-speaker Video-to-Speech synthesis via cross-modal knowledge
  transfer from voice conversion

abstract:
  Though significant progress has been made for speaker-dependent
Video-to-Speech (VTS) synthesis, little attention is devoted to multi-speaker
VTS that can map silent video to speech, while allowing flexible control of
speaker identity, all in a single system. This paper proposes a novel
multi-speaker VTS system based on cross-modal knowledge transfer from voice
conversion (VC), where vector quantization with contrastive predictive coding
(VQCPC) is used for the content encoder of VC to derive discrete phoneme-like
acoustic units, which are transferred to a Lip-to-Index (Lip2Ind) network to
infer the index sequence of acoustic units. The Lip2Ind network can then
substitute the content encoder of VC to form a multi-speaker VTS system to
convert silent video to acoustic units for reconstructing accurate spoken
content. The VTS system also inherits the advantages of VC by using a speaker
encoder to produce speaker representations to effectively control the speaker
identity of generated speech. Extensive evaluations verify the effectiveness of
proposed approach, which can be applied in both constrained vocabulary and open
vocabulary conditions, achieving state-of-the-art performance in generating
high-quality speech with high naturalness, intelligibility and speaker
similarity. Our demo page is released here:
https://wendison.github.io/VCVTS-demo/


title:
Speaker Identity Preservation in Dysarthric Speech Reconstruction by
  Adversarial Speaker Adaptation

abstract:
  Dysarthric speech reconstruction (DSR), which aims to improve the quality of
dysarthric speech, remains a challenge, not only because we need to restore the
speech to be normal, but also must preserve the speaker's identity. The speaker
representation extracted by the speaker encoder (SE) optimized for speaker
verification has been explored to control the speaker identity. However, the SE
may not be able to fully capture the characteristics of dysarthric speakers
that are previously unseen. To address this research problem, we propose a
novel multi-task learning strategy, i.e., adversarial speaker adaptation (ASA).
The primary task of ASA fine-tunes the SE with the speech of the target
dysarthric speaker to effectively capture identity-related information, and the
secondary task applies adversarial training to avoid the incorporation of
abnormal speaking patterns into the reconstructed speech, by regularizing the
distribution of reconstructed speech to be close to that of reference speech
with high quality. Experiments show that the proposed approach can achieve
enhanced speaker similarity and comparable speech naturalness with a strong
baseline approach. Compared with dysarthric speech, the reconstructed speech
achieves 22.3% and 31.5% absolute word error rate reduction for speakers with
moderate and moderate-severe dysarthria respectively. Our demo page is released
here: https://wendison.github.io/ASA-DSR-demo/


title:
Spectral properties of soft X-ray transient MAXI J0637$-$430 using
  AstroSat

abstract:
  Soft X-ray transients are systems that are detected when they go into an
outburst, wherein their X-ray luminosity increases several orders of magnitude.
These outbursts are markers of the poorly understood change in the spectral
state of these systems from low/hard state to high/soft state. We report the
spectral properties of one such soft X-ray transient: MAXI J0637$-$430, with
data from the \textit{SXT} and \textit{LAXPC} instruments on-board
\textit{AstroSat} mission. The source was observed for a total of $\sim$ 60 ks
over two observations on 8$^{th}$ and 21$^{st}$ November, 2019 soon after its
discovery. Flux resolved spectral analysis of the source indicates the presence
of a multi-colour blackbody component arising from the accretion disk and a
thermal Comptonization component. The stable low temperature ($\sim$ 0.55
$keV$) of the blackbody component, points to a cool accretion disk with an
inner disk radius of the order of a few hundred $km$. In addition, we report
the presence of a relativistically broadened Gaussian line at 6.4 $keV$. The
disk dominated flux and photon power law index of $\gtrapprox 2$ and a constant
inner disk radius indicate the source to be in the soft state. From the study
we conclude that MAXI J0637$-$430 is a strong black hole X-ray binary
candidate.


title: Guaranteed constraint satisfaction in Koopman-based optimal control

abstract:
  We present an approach for guaranteed constraint satisfaction by means of
data-based optimal control, where the model is unknown and has to be obtained
from measurement data. To this end, we utilize the Koopman framework and an
eDMD-based bilinear surrogate modeling approach for control systems to show an
error bound on predicted observables, i.e., functions of the state. This result
is then applied to the constraints of the optimal control problem to show that
satisfaction of tightened constraints in the purely data-based surrogate model
implies constraint satisfaction for the original system.


title: Homogeneous geodesics in sub-Riemannian geometry

abstract:
  We study homogeneous geodesics of sub-Riemannian manifolds, i.e., normal
geodesics that are orbits of one-parametric subgroups of isometries. We obtain
a criterion for a geodesic to be homogeneous in terms of its initial momentum.
We prove that any weakly commutative sub-Riemannian homogeneous space is
geodesic orbit, that means all geodesics are homogeneous. We discuss some
examples of geodesic orbit sub-Riemannian manifolds. In particular, we show
that geodesic orbit Carnot groups are only groups of step $1$ and $2$. Finally,
we get a broad condition for existence of at least one homogeneous geodesic.


title: Optimal control of thermodynamic port-Hamiltonian Systems

abstract:
  We consider the problem of minimizing the entropy, energy, or exergy
production for state transitions of irreversible port-Hamiltonian systems
subject to control constraints. Via a dissipativity-based analysis we show that
optimal solutions exhibit the manifold turnpike phenomenon with respect to the
manifold of thermodynamic equilibria. We illustrate our analytical findings via
numerical results for a heat exchanger.


title: Asymptotic control theory for a closed string II

abstract:
  We develop an asymptotical control theory for one of the simplest distributed
(infinite dimensional) oscillating systems, namely, for a closed string under a
bounded load applied to a single distinguished point. We find exact classes of
string states that admit complete damping and an asymptotically exact value of
the required time. By using approximate reachable sets instead of exact ones,
we design a feedback control, which turns out to be asymptotically optimal. The
main results are an exact algebraic formula for asymptotic shape of the
reachable sets, asymptotically optimal time of motion, and an asymptotically
optimal control thus constructed.


title:
First-principles study on the specific heat jump in the glass transition
  of silica glass and the Prigogine-Defay ratio

abstract:
  The most important characteristic of glass transition is a jump in specific
heat $\Delta C_{p}$. Despite its significance, no standard theory exists to
describe it. In this study, first-principles molecular-dynamics (MD)
simulations are used to describe the glass transition of silica glass, which
presents many challenges. The novel view that state variables are extended to
include the equilibrium positions of atoms $\{ \bar{\bf R}_{j} \}$ is fully
used in analyzing the simulation results. Decomposing the internal energy into
three components (structural, phonon, and thermal expansion energies) reveals
that the jump $\Delta C_{p}$ of silica glass is entirely determined by the
component of structural energy. The reason for the small $\Delta C_{p}$ is its
high glass-transition temperature, which makes the fluctuation in the
structural energy insensitive to temperature changes. This significantly
affects how the Prigogine-Defay ratio $\it{\Pi}$ is interpreted, which was
previously unknown. The ratio $\it{\Pi}$ represents the ratio of the total
energy change to the contribution of thermal expansion energy at the glass
transition. The general property, $\it{\Pi}>1$, of glasses indicates that glass
transitions occur mainly by changes in the structural energy. Silica glass is
an extreme case in that the transition occurs entirely by the change in
internal structure, such as the distribution of the bending angle of Si--O--Si
bond.


title:
Imaging dynamic exciton interactions and coupling in transition metal
  dichalcogenides

abstract:
  Transition metal dichalcogenides (TMDs) are regarded as a possible materials
platform for quantum information science and related device applications. In
TMD monolayers, the dephasing time and inhomogeneity are crucial parameters for
any quantum information application. In TMD heterostructures, coupling strength
and interlayer exciton lifetimes are also parameters of interest. However, many
demonstrations in TMDs can only be realized at specific spots on the sample,
presenting a challenge to the scalability of these applications. Here, using
multi-dimensional coherent imaging spectroscopy (MDCIS), we shed light on the
underlying physics - including dephasing, inhomogeneity, and strain - for a
MoSe$_2$ monolayer and identify both promising and unfavorable areas for
quantum information applications. We furthermore apply the same technique to a
MoSe$_2$/WSe$_2$ heterostructure. Despite the notable presence of strain and
dielectric environment changes, coherent and incoherent coupling, as well as
interlayer exciton lifetimes are mostly robust across the sample. This
uniformity is despite a significantly inhomogeneous interlayer exciton
photoluminescence distribution that suggests a bad sample for device
applications. This robustness strengthens the case for TMDs as a
next-generation materials platform in quantum information science and beyond.


title:
Cut-and-join operators in cohomological field theory and topological
  recursion

abstract:
  We construct a cubic cut-and-join operator description for the partition
function of the Chekhov-Eynard-Orantin topological recursion for a local
spectral curve with simple ramification points. In particular, this class
contains partition functions of all semi-simple cohomological field theories.
The cut-and-join description leads to an algebraic version of topological
recursion. For the same partition functions we also derive N families of the
Virasoro constraints and prove that these constraints, supplemented by a
deformed dimension constraint, imply the cut-and-join description.


title: Combinatorial Properties of primitive words with Non-primitive Product

abstract:
  Let $\mathcal{A}$ be an alphabet of size $n\ge 2$. In this paper, we give a
complete description of primitive words $p\neq q$ over an alphabet
$\mathcal{A}$ of size $n\geq2$ such that $pq$ is non-primitive and $|p|=2|q|$.
In particular, if $l$ is s a positive integer, we count the cardinality of the
set $\mathcal{E}(l,\mathcal{A})$ of all couples $(p,q)$ of primitive words such
that $|p|=2|q|=2l$ and $pq$ is non-primitive. Then we give a combinatorial
formula for this cardinality and its asymptotic behavior, as $l$ or $n$ goes to
infinity.


title: Quantum-limited thermometry of a Fermi gas with a charged spin particle

abstract:
  We investigate the sensitivity of an ion sensor in determining the
temperature of an atomic Fermi gas. Our study extends to charged impurities the
proposal by M. T. Mitchison et al. Phys. Rev. Lett. 125, 080402 (2020), where
atomic neutral impurities were used as an in situ thermometer of the quantum
gas. We find that the long-range character of the atom-ion interaction enhances
the thermometer's sensitivity for certain system parameters. In addition, we
investigate the impact of the ion quantum motional state on the sensitivity by
assuming that it is confined in a harmonic trap. We observe that the
temperature sensitivity of the ion is noticeably influenced by its spatial
extension, making the latter a versatile tool to be manipulated for improving
the thermometer performance. We finally discuss our findings in the context of
current experimental atom-ion mixtures.


title:
Toward a Smart Resource Allocation Policy via Artificial Intelligence in
  6G Networks: Centralized or Decentralized?

abstract:
  In this paper, we design a new smart softwaredefined radio access network
(RAN) architecture with important properties like flexibility and traffic
awareness for sixth generation (6G) wireless networks. In particular, we
consider a hierarchical resource allocation framework for the proposed smart
soft-RAN model, where the software-defined network (SDN) controller is the
first and foremost layer of the framework. This unit dynamically monitors the
network to select a network operation type on the basis of distributed or
centralized resource allocation architectures to perform decision-making
intelligently. In this paper, our aim is to make the network more scalable and
more flexible in terms of achievable data rate, overhead, and complexity
indicators. To this end, we introduce a new metric, throughput overhead
complexity (TOC), for the proposed machine learning-based algorithm, which
makes a trade-off between these performance indicators. In particular, the
decision making based on TOC is solved via deep reinforcement learning (DRL),
which determines an appropriate resource allocation policy. Furthermore, for
the selected algorithm, we employ the soft actor-critic method, which is more
accurate, scalable, and robust than other learning methods. Simulation results
demonstrate that the proposed smart network achieves better performance in
terms of TOC compared to fixed centralized or distributed resource management
schemes that lack dynamism. Moreover, our proposed algorithm outperforms
conventional learning methods employed in other state-of-the-art network
designs.


title:
Generalization of the $\epsilon$-BBS and the Schensted insertion
  algorithm

abstract:
  The $\epsilon$-BBS is the family of solitonic cellular automata obtained via
the ultradiscretization of the elementary Toda orbits, which is a parametrized
family of integrable systems unifying the Toda equation and the relativistic
Toda equation. In this paper, we derive the $\epsilon$-BBS with many kinds of
balls and give its conserved quantities by the Schensted insertion algorithm
which is introduced in combinatorics. To prove this, we extend birational
transformations of the continuous elementary Toda orbits to the discrete hungry
elementary Toda orbits.


title:
Private Information Retrieval from Colluding and Byzantine Servers with
  Binary Reed-Muller Codes

abstract:
  In this work, a flexible and robust private information retrieval (PIR)
scheme based on binary non-maximum distance separable (non-MDS) codes is
considered. This combines previous works on PIR schemes based on transitive
non-MDS codes on one hand, and PIR from MDS-coded Byzantine and non-responsive
servers on the other hand. More specifically, a PIR scheme employing binary
Reed-Muller (RM) codes tolerant to colluding, Byzantine, and non-responsive
servers is constructed, and bounds for the achievable rates are derived under
certain conditions. The construction of such schemes turns out to be much more
involved than for MDS codes. Namely, the binary query vectors have to be
selected with great care to hit the desired information sets, which is
technically challenging as will be shown.


title:
A Free Lunch with Influence Functions? Improving Neural Network
  Estimates with Concepts from Semiparametric Statistics

abstract:
  Parameter estimation in the empirical fields is usually undertaken using
parametric models, and such models are convenient because they readily
facilitate statistical inference. Unfortunately, they are unlikely to have a
sufficiently flexible functional form to be able to adequately model real-world
phenomena, and their usage may therefore result in biased estimates and invalid
inference. Unfortunately, whilst non-parametric machine learning models may
provide the needed flexibility to adapt to the complexity of real-world
phenomena, they do not readily facilitate statistical inference, and may still
exhibit residual bias. We explore the potential for semiparametric theory (in
particular, the Influence Function) to be used to improve neural networks and
machine learning algorithms in terms of (a) improving initial estimates without
needing more data (b) increasing the robustness of our models, and (c) yielding
confidence intervals for statistical inference. We propose a new neural network
method MultiNet, which seeks the flexibility and diversity of an ensemble using
a single architecture. Results on causal inference tasks indicate that MultiNet
yields better performance than other approaches, and that all considered
methods are amenable to improvement from semiparametric techniques under
certain conditions. In other words, with these techniques we show that we can
improve existing neural networks for `free', without needing more data, and
without needing to retrain them. Finally, we provide the expression for
deriving influence functions for estimands from a general graph, and the code
to do so automatically.


title: Lightweight Multi-Drone Detection and 3D-Localization via YOLO

abstract:
  In this work, we present and evaluate a method to perform real-time multiple
drone detection and three-dimensional localization using state-of-the-art
tiny-YOLOv4 object detection algorithm and stereo triangulation. Our computer
vision approach eliminates the need for computationally expensive stereo
matching algorithms, thereby significantly reducing the memory footprint and
making it deployable on embedded systems. Our drone detection system is highly
modular (with support for various detection algorithms) and capable of
identifying multiple drones in a system, with real-time detection accuracy of
up to 77\% with an average FPS of 332 (on Nvidia Titan Xp). We also test the
complete pipeline in AirSim environment, detecting drones at a maximum distance
of 8 meters, with a mean error of $23\%$ of the distance. We also release the
source code for the project, with pre-trained models and the curated synthetic
stereo dataset.


title:
Quasiparticle density of states and triplet correlations in
  superconductor/ferromagnetic-insulator structures

abstract:
  A ferromagnetic insulator (FI) in contact with a superconductor (S) is known
to induce a spin splitting of the BCS density of states at the FI/S interface.
This spin splitting causes the Cooper pairs to reduce their singlet-state
correlations and acquire odd-in-frequency triplet correlations. We consider a
diffusive FI/S bilayer with a sharp magnetic domain wall in the FI and study
the local quasiparticle density of states and triplet superconducting
correlations. In the case of collinear alignment of the domains, we obtain
analytical results by solving the Usadel equation. For a small enough exchange
field, we also find an analytical expression for arbitrary magnetic textures,
which reveals how the triplet component vector depends on the local
magnetization of the FI. For an arbitrary angle between the magnetizations and
the strength of the exchange field, we numerically solve the problem of a sharp
domain wall. We finally propose two different setups based on FI/S/F stacks,
where F is a ferromagnetic layer, to filter out singlet pairs and detect the
presence of triplet correlations via tunneling differential conductance
measurements.


title:
AMS_ADRN at SemEval-2022 Task 5: A Suitable Image-text Multimodal Joint
  Modeling Method for Multi-task Misogyny Identification

abstract:
  Women are influential online, especially in image-based social media such as
Twitter and Instagram. However, many in the network environment contain gender
discrimination and aggressive information, which magnify gender stereotypes and
gender inequality. Therefore, the filtering of illegal content such as gender
discrimination is essential to maintain a healthy social network environment.
In this paper, we describe the system developed by our team for SemEval-2022
Task 5: Multimedia Automatic Misogyny Identification. More specifically, we
introduce two novel system to analyze these posts: a multimodal multi-task
learning architecture that combines Bertweet for text encoding with ResNet-18
for image representation, and a single-flow transformer structure which
combines text embeddings from BERT-Embeddings and image embeddings from several
different modules such as EfficientNet and ResNet. In this manner, we show that
the information behind them can be properly revealed. Our approach achieves
good performance on each of the two subtasks of the current competition,
ranking 15th for Subtask A (0.746 macro F1-score), 11th for Subtask B (0.706
macro F1-score) while exceeding the official baseline results by high margins.


title: Measurement-based deterministic imaginary time evolution

abstract:
  We introduce a method to perform imaginary time evolution in a controllable
quantum system using measurements and conditional unitary operations. Using a
Suzuki-Trotter decomposition, a sequence of measurements can produce the
evolution approximating imaginary time evolution of an arbitrary Hamiltonian,
up to a random sign coefficient. The randomness due to measurement is corrected
using conditional unitary operations, making the evolution deterministic. The
number of gates for a single iteration typically scales as a polynomial of the
number of qubits in the system. We demonstrate the approach with several
examples, including the transverse field Ising model and quantum search.


title:
The harm of class imbalance corrections for risk prediction models:
  illustration and simulation using logistic regression

abstract:
  Methods to correct class imbalance, i.e. imbalance between the frequency of
outcome events and non-events, are receiving increasing interest for developing
prediction models. We examined the effect of imbalance correction on the
performance of standard and penalized (ridge) logistic regression models in
terms of discrimination, calibration, and classification. We examined random
undersampling, random oversampling and SMOTE using Monte Carlo simulations and
a case study on ovarian cancer diagnosis. The results indicated that all
imbalance correction methods led to poor calibration (strong overestimation of
the probability to belong to the minority class), but not to better
discrimination in terms of the area under the receiver operating characteristic
curve. Imbalance correction improved classification in terms of sensitivity and
specificity, but similar results were obtained by shifting the probability
threshold instead. Our study shows that outcome imbalance is not a problem in
itself, and that imbalance correction may even worsen model performance.


title:
Predicting Sex and Stroke Success -- Computer-aided Player Grunt
  Analysis in Tennis Matches

abstract:
  Professional athletes increasingly use automated analysis of meta- and signal
data to improve their training and game performance. As in other related
human-to-human research fields, signal data, in particular, contain important
performance- and mood-specific indicators for automated analysis. In this
paper, we introduce the novel data set SCORE! to investigate the performance of
several features and machine learning paradigms in the prediction of the sex
and immediate stroke success in tennis matches, based only on vocal expression
through players' grunts. The data was gathered from YouTube, labelled under the
exact same definition, and the audio processed for modelling. We extract
several widely used basic, expert-knowledge, and deep acoustic features of the
audio samples and evaluate their effectiveness in combination with various
machine learning approaches. In a binary setting, the best system, using
spectrograms and a Convolutional Recurrent Neural Network, achieves an
unweighted average recall (UAR) of 84.0 % for the player sex prediction task,
and 60.3 % predicting stroke success, based only on acoustic cues in players'
grunts of both sexes. Further, we achieve a UAR of 58.3 %, and 61.3 %, when the
models are exclusively trained on female or male grunts, respectively.


title: Fine-Tuning in the 2HDM

abstract:
  The Two-Higgs Doublet Model (2HDM) is one of the most popular and natural
extensions of the Higgs sector; but it has two potential fine-tuning problems,
related to the electroweak (EW) breaking and the requirement of alignment with
the SM Higgs boson. We have quantified the fine-tunings obtaining analytical
expressions, both in terms of the initial 2HDM parameters and the physical ones
(masses, mixing angles, etc.). We also provide simple approximate expressions.
We have taken into account that the fine-tunings are not independent and
removed the "double counting" by projecting the variations of the alignment
onto the constant-${\rm v}^2$ hypersurface. The EW and the alignment
fine-tunings become severe in different, even opposite, regions of the
parameter space, namely in the regimes of large and small extra-Higgs masses,
respectively; emerging an intermediate region, $500\ {\rm GeV} \leq \{m_H, m_A,
m_{H^\pm}\} \leq 700\ {\rm GeV}$, where both are acceptably small. We also
discuss a remarkable trend that is not obvious at first glance. Namely, for
large $\tan\beta$ both the EW and the alignment fine-tunings become mitigated.
In consequence, the 2HDM becomes quite natural for $\tan\beta\geq {\cal O}
(10)$, even if $m_H, m_A, m_{H^\pm}$ are as large as 1500 GeV. We explain why
this is not the case for the 2HDM stemming from supersymmetry. We have
illustrated all these trends by numerically analyzing several representative
scenarios.


title: On Formality and Combinatorial Formality for hyperplane arrangements

abstract:
  A hyperplane arrangement is called formal provided all linear dependencies
among the defining forms of the hyperplanes are generated by ones corresponding
to intersections of codimension two. The significance of this notion stems from
the fact that complex arrangements with aspherical complements are formal. The
aim of this note is twofold. While work of Yuzvinsky shows that formality is
not combinatorial, in our first main theorem we prove that the combinatorial
property of niceness of arrangements does entail formality. Our second main
theorem shows that formality is hereditary, i.e. is passed to restrictions.
This is rather counter-intuitive, as in contrast the known sufficient
conditions for formality, i.e. asphericity, freeness and niceness (owed to our
first theorem), are not hereditary themselves. We also demonstrate that the
stronger property of $k$-formality, due to Brandt and Terao, is not hereditary.


title:
Event-Triggered Distributed Model Predictive Control for Platoon
  Coordination at Hubs in a Transport System

abstract:
  This paper considers the problem of hub-based platoon coordination for a
large-scale transport system, where trucks have individual utility functions to
optimize. An event-triggered distributed model predictive control method is
proposed to solve the optimal scheduling of waiting times at hubs for
individual trucks. In this distributed framework, trucks are allowed to decide
their waiting times independently and only limited information is shared
between trucks. Both the predicted reward gained from platooning and the
predicted cost for waiting at hubs are included in each truck's utility
function. The performance of the coordination method is demonstrated in a
simulation with one hundred trucks over the Swedish road network.


title:
Measurement of the charm mixing parameter $y_{CP} - y_{CP}^{K\pi}$ using
  two-body $D^0$ meson decays

abstract:
  A measurement of the ratios of the effective decay widths of $D^0 \to
\pi^-\pi^+$ and $D^0 \to K^-K^+$ decays over that of $D^0 \to K^-\pi^+$ decays
is performed with the LHCb experiment using proton-proton collisions at a
centre-of-mass energy of $13 \, \mathrm{TeV}$, corresponding to an integrated
luminosity of $6 \, \mathrm{fb^{-1}}$. These observables give access to the
charm mixing parameters $y_{CP}^{\pi\pi} - y_{CP}^{K\pi}$ and $y_{CP}^{KK} -
y_{CP}^{K\pi}$, and are measured as $y_{CP}^{\pi\pi} - y_{CP}^{K\pi} = (6.57
\pm 0.53 \pm 0.16) \times 10^{-3}$, $y_{CP}^{KK} - y_{CP}^{K\pi} = (7.08 \pm
0.30 \pm 0.14) \times 10^{-3}$, where the first uncertainties are statistical
and the second systematic. The combination of the two measurements is $y_{CP} -
y_{CP}^{K\pi} = (6.96 \pm 0.26 \pm 0.13) \times 10^{-3}$, which is four times
more precise than the previous world average.


title:
Comparison of an Apocalypse-Free and an Apocalypse-Prone First-Order
  Low-Rank Optimization Algorithm

abstract:
  We compare two first-order low-rank optimization algorithms, namely
$\text{P}^2\text{GD}$ (Schneider and Uschmajew, 2015), which has been proven to
be apocalypse-prone (Levin et al., 2021), and its apocalypse-free version
$\text{P}^2\text{GDR}$ obtained by equipping $\text{P}^2\text{GD}$ with a
suitable rank reduction mechanism (Olikier et al., 2022). Here an apocalypse
refers to the situation where the stationarity measure goes to zero along a
convergent sequence whereas it is nonzero at the limit. The comparison is
conducted on two simple examples of apocalypses, the original one (Levin et
al., 2021) and a new one. We also present a potential side effect of the rank
reduction mechanism of $\text{P}^2\text{GDR}$ and discuss the choice of the
rank reduction parameter.


title:
Large-Scale Acoustic Characterization of Singaporean Children's English
  Pronunciation

abstract:
  In this work, we investigate pronunciation differences in English spoken by
Singaporean children in relation to their American and British counterparts by
conducting Kmeans clustering and Archetypal analysis on selected vowel pairs
and approximants. Given that Singapore adopts British English as the
institutional standard due to historical reasons, one might expect Singaporean
children to follow British pronunciation patterns. Indeed, Singaporean and
British children are more similar in their production of syllable-final /r/ --
they do not lower their third formant nearly as much as American children do,
suggesting a lack of rhoticity. Interestingly, Singaporean children also
present similar patterns to American children when it comes to their fronting
of vowels as demonstrated across various vowels including TRAP-BATH split
vowels. Singaporean children's English also demonstrated characteristics that
do not resemble any of the other two populations. We observe that Singaporean
children's vowel height characteristics are distinct from both that of American
and British children. In tense and lax vowel pairs, we also consistently
observe that the distinction is less conspicuous for Singaporean children
compared to the other speaker groups. Further, while American and British
children demonstrate lowering of F1 and F2 formants in transitions into
syllable-final /l/s, a wide gap between F2 and F3 formants, and small
difference between F1 and F2 formants, all of these are not exhibited in
Singaporean children's pronunciation. These findings point towards potential
sociolinguistic implications of how Singapore English might be evolving to
embody more than British pronunciation characteristics. Furthermore, these
findings also suggest that Singapore English could be have been influenced by
languages beyond American and British English, potentially due to Singapore's
multilingual environment.


title: Assemblages and steering in general probabilistic theories

abstract:
  We study steering in the framework of general probabilistic theories. We show
that for dichotomic assemblages, steering can be characterized in terms of a
certain tensor cross norm, which is also related to a steering degree given by
steering robustness. Another contribution is the observation that steering in
GPTs can be conveniently treated using Choquet theory for probability measures
on the state space. In particular, we find a variational expression for
universal steering degree for dichotomic assemblages and obtain conditions
characterizing unsteerable states analogous to some conditions recently found
for the quantum case. The setting also enables us to rather easily extend the
results to infinite dimensions and arbitrary numbers of measurements with
arbitrary outcomes.


title: Iterative Learning for Instance Segmentation

abstract:
  Instance segmentation is a computer vision task where separate objects in an
image are detected and segmented. State-of-the-art deep neural network models
require large amounts of labeled data in order to perform well in this task.
Making these annotations is time-consuming. We propose for the first time, an
iterative learning and annotation method that is able to detect, segment and
annotate instances in datasets composed of multiple similar objects. The
approach requires minimal human intervention and needs only a bootstrapping set
containing very few annotations. Experiments on two different datasets show the
validity of the approach in different applications related to visual
inspection.


title:
Implications of the quantum nature of the black hole horizon on the
  gravitational-wave ringdown

abstract:
  Motivated by capturing putative quantum effects at the horizon scale, we
model the black hole horizon as a membrane with fluctuations following a
Gaussian profile. By extending the membrane paradigm at the semiclassical
level, we show that the quantum nature of the black hole horizon implies
partially reflective boundary conditions and a frequency-dependent
reflectivity. This generically results into a modified quasi-normal mode
spectrum and the existence of echoes in the postmerger signal. On a similar
note, we derive the horizon boundary condition for a braneworld black hole that
could originate from quantum corrections on the brane. This scenario also leads
to a modified gravitational-wave ringdown. We discuss general implications of
these findings for scenarios predicting quantum corrections at the horizon
scale.


title: Prepare your video for streaming with Segue

abstract:
  We identify new opportunities in video streaming, involving the joint
consideration of offline video chunking and online rate adaptation. Due to a
video's complexity varying over time, certain parts are more likely to cause
performance impairments during playback with a particular rate adaptation
algorithm. To address such an issue, we propose Segue, which carefully uses
variable-length video segments, and augment specific segments with additional
bitrate tracks. The key novelty of our approach is in making such decisions
based on the video's time-varying complexity and the expected rate adaptation
behavior over time. We propose and implement several methods for such
adaptation-aware chunking. Our results show that Segue substantially reduces
rebuffering and quality fluctuations, while maintaining video quality
delivered; Segue improves QoE by 9% on average, and by 22% in low-bandwidth
conditions. Finally, we view our problem framing as a first step in a new
thread on algorithmic and design innovation in video streaming, and leave the
reader with several interesting open questions.


title: How to Manage Tiny Machine Learning at Scale: An Industrial Perspective

abstract:
  Tiny machine learning (TinyML) has gained widespread popularity where machine
learning (ML) is democratized on ubiquitous microcontrollers, processing sensor
data everywhere in real-time. To manage TinyML in the industry, where mass
deployment happens, we consider the hardware and software constraints, ranging
from available onboard sensors and memory size to ML-model architectures and
runtime platforms. However, Internet of Things (IoT) devices are typically
tailored to specific tasks and are subject to heterogeneity and limited
resources. Moreover, TinyML models have been developed with different
structures and are often distributed without a clear understanding of their
working principles, leading to a fragmented ecosystem. Considering these
challenges, we propose a framework using Semantic Web technologies to enable
the joint management of TinyML models and IoT devices at scale, from modeling
information to discovering possible combinations and benchmarking, and
eventually facilitate TinyML component exchange and reuse. We present an
ontology (semantic schema) for neural network models aligned with the World
Wide Web Consortium (W3C) Thing Description, which semantically describes IoT
devices. Furthermore, a Knowledge Graph of 23 publicly available ML models and
six IoT devices were used to demonstrate our concept in three case studies, and
we shared the code and examples to enhance reproducibility:
https://github.com/Haoyu-R/How-to-Manage-TinyML-at-Scale


title: Infinite Product Representation for the Szego Kernel for an Annulus

abstract:
  The Szego kernel has many applications to problems in conformal mapping and
satisfies the Kerzman-Stein integral equation. The Szego kernel for an annulus
can be expressed as a bilateral series. In this paper, we show how to represent
the Szego kernel for an annulus as a basic bilateral series and an infinite
product representation through the application of the Ramanujan's sum. The
infinite product clearly exhibits the zero of the Szego kernel for an annulus.
Its connection with basic gamma function and modified Jacobi theta function is
also presented. The results are extended to the Szego kernel for general
annulus and weighted Szego kernel. Numerical comparisons on computing the Szego
kernel for an annulus based on the bilateral series and the infinite product
are also presented.


title: Towards Simple and Accurate Human Pose Estimation with Stair Network

abstract:
  In this paper, we focus on tackling the precise keypoint coordinates
regression task. Most existing approaches adopt complicated networks with a
large number of parameters, leading to a heavy model with poor
cost-effectiveness in practice. To overcome this limitation, we develop a small
yet discrimicative model called STair Network, which can be simply stacked
towards an accurate multi-stage pose estimation system. Specifically, to reduce
computational cost, STair Network is composed of novel basic feature extraction
blocks which focus on promoting feature diversity and obtaining rich local
representations with fewer parameters, enabling a satisfactory balance on
efficiency and performance. To further improve the performance, we introduce
two mechanisms with negligible computational cost, focusing on feature fusion
and replenish. We demonstrate the effectiveness of the STair Network on two
standard datasets, e.g., 1-stage STair Network achieves a higher accuracy than
HRNet by 5.5% on COCO test dataset with 80\% fewer parameters and 68% fewer
GFLOPs.


title: Caplet pricing in affine models for risk-free rates

abstract:
  Risk-free rates (RFRs) play a central role in the reform of interest rate
benchmarks. We study a model for RFRs driven by a general affine process. In
this context, under minimal assumptions, we derive explicit valuation formulas
for forward-looking and backward-looking caplets/floorlets, term-basis caplets
as well as 1-month and 3-month RFR futures contracts.


title: Unbraided wiring diagrams for Stein fillings of lens spaces

abstract:
  In a previous work, we constructed a planar Lefschetz fibration on each Stein
filling of any lens space equipped with its canonical contact structure. Here
we describe an algorithm to draw an unbraided wiring diagram whose associated
planar Lefschetz fibration obtained by the method of Plamenevskaya and
Starkston, where the lens space with its canonical contact structure is viewed
as the contact link of a cyclic quotient singularity, is equivalent to the
Lefschetz fibration we constructed on each Stein filling of the lens space at
hand. Coupled with the work of Plamenevskaya and Starkston, we obtain the
following result as a corollary: The wiring diagram we describe can be extended
to an arrangement of symplectic graphical disks in $\mathbb{C}^2$ with marked
points, including all the intersection points of these disks, so that by
removing the proper transforms of these disks from the blowup of $\mathbb{C}^2$
along those marked points one recovers the Stein filling along with the
Lefschetz fibration. Moreover, the arrangement is related to the decorated
plane curve germ representing the cyclic quotient singularity by a smooth
graphical homotopy.
  As another application, we set up an explicit bijection between the Stein
fillings of any lens space with its canonical contact structure, and the Milnor
fibers of the corresponding cyclic quotient singularity, which was first
obtained by N\'{e}methi and Popescu-Pampu, using different methods.


title: Polynomial bounds for chromatic number VII. Disjoint holes

abstract:
  A hole in a graph $G$ is an induced cycle of length at least four, and a
$k$-multihole in $G$ is a set of pairwise disjoint and nonadjacent holes. It is
well known that if $G$ does not contain any holes then its chromatic number is
equal to its clique number. In this paper we show that, for any $k$, if $G$
does not contain a $k$-multihole, then its chromatic number is at most a
polynomial function of its clique number. We show that the same result holds if
we ask for all the holes to be odd or of length four; and if we ask for the
holes to be longer than any fixed constant or of length four. This is part of a
broader study of graph classes that are polynomially $\chi$-bounded.


title: Truck Platoon Formation at Hubs: An Optimal Release Time Rule

abstract:
  We consider a hub-based platoon coordination problem in which vehicles arrive
at a hub according to an independent and identically distributed stochastic
arrival process. The vehicles wait at the hub, and a platoon coordinator, at
each time-step, decides whether to release the vehicles from the hub in the
form of a platoon or wait for more vehicles to arrive. The platoon release time
problem is modeled as a stopping rule problem wherein the objective is to
maximize the average platooning benefit of the vehicles located at the hub and
there is a cost of having vehicles waiting at the hub. We show that the
stopping rule problem is monotone and the optimal platoon release time policy
will therefore be in the form of a one time-step look-ahead rule. The
performance of the optimal release rule is numerically compared with (i) a
periodic release time rule and (ii) a non-causal release time rule where the
coordinator knows all the future realizations of the arrival process. Our
numerical results show that the optimal release time rule achieves a close
performance to that of the non-causal rule and outperforms the periodic rule,
especially when the arrival rate is low.


title:
Analysis of Dual Band and Survey Photometry of Two Low Mass Ratio
  Contact Binary Systems

abstract:
  The study presents photometric analysis of the completely eclipsing contact
binary systems TYC 8351-1081-1 and ASAS J210406-0522.3. TYC 8351-1081-1 is an
extremely low mass ratio (q=0.086) system with high degree of contact (f=0.66)
while ASAS J210406-0522.3 is found to be in marginal contact (f=0.08) with a
relatively low mass ratio of 0.272. There is good thermal contact in both
systems with only a small difference in the temperature of the components. The
systems have been observed by a number sky surveys over the past 20 years. We
compare the light curve solutions from up to three of these surveys and find
that survey photometric data manually analysed is robust and yields results
comparable to dedicated ground based photometry. There is evidence of
significant luminosity transfer from the primary to the secondary, on the order
of 0.5LJ for TYC 8351-1081-1 and 0.06LJ for ASAS J210406-0522.3. There appears
to be no change in period of either system over the past 20 years and
theoretical angular momentum loss is below current measurement threshold in
both cases. We also show that the mass ratio and separation are well above the
theoretical values for orbital instability in both cases. As would be expected,
the density of the secondary components is significantly higher relative to the
primary.