title:
Diverse Text Generation via Variational Encoder-Decoder Models with
  Gaussian Process Priors

abstract:
  Generating high quality texts with high diversity is important for many NLG
applications, but current methods mostly focus on building deterministic models
to generate higher quality texts and do not provide many options for promoting
diversity. In this work, we present a novel latent structured variable model to
generate high quality texts by enriching contextual representation learning of
encoder-decoder models. Specifically, we introduce a stochastic function to map
deterministic encoder hidden states into random context variables. The proposed
stochastic function is sampled from a Gaussian process prior to (1) provide
infinite number of joint Gaussian distributions of random context variables
(diversity-promoting) and (2) explicitly model dependency between context
variables (accurate-encoding). To address the learning challenge of Gaussian
processes, we propose an efficient variational inference approach to
approximate the posterior distribution of random context variables. We evaluate
our method in two typical text generation tasks: paraphrase generation and text
style transfer. Experimental results on benchmark datasets demonstrate that our
method improves the generation quality and diversity compared with other
baselines.


title: Parameterized algorithm for replicated objects with local reads

abstract:
  We consider the problem of implementing linearizable objects that support
both read and read-modify-write (RMW) operations in message-passing systems
with process crashes. Since in many systems read operations vastly outnumber
RMW operations, we are interested in implementations that emphasize the
efficiency of read operations.
  We present a parametrized algorithm for partially synchronous systems where
processes have access to external clocks that are synchronized within
$\epsilon$. With this algorithm, every read operation is local (intuitively, it
does not trigger messages). If a read is not concurrent with a conflicting RMW,
it is performed immediately with no waiting; furthermore, even with a
concurrent conflicting RMW, a read experiences very little delay in the
worst-case. For example, the algorithm's parameters can be set to ensure that
every read takes $\epsilon$ time in the worst-case. To the best of our
knowledge this is the first algorithm to achieve this bound in the partially
synchronous systems that we assume here. Our parametrized algorithm generalizes
the (non-parameterized) lease-based algorithm of Chandra et al. [6] where the
worst-case time for reads is $3\delta$, where $\delta$ is the maximum message
delay.
  The algorithm's parameters can be used to trade-off the worst-case times for
read and RMW operations. They can also be used to take advantage of the fact
that in many message-passing systems the delay of most messages is order of
magnitudes smaller than the maximum message delay~$\delta$: for example, the
parameters can be set so that, in "nice" periods where message delays are
$\delta^* \ll \delta$, reads take at most $\epsilon$ time while RMWs take at
most $3 \delta^*$ time.


title: Dual Quaternion Matrices in Multi-Agent Formation Control

abstract:
  Three kinds of dual quaternion matrices associated with the mutual visibility
graph, namely the relative configuration adjacency matrix, the logarithm
adjacency matrix and the relative twist adjacency matrix, play important roles
in multi-agent formation control. In this paper, we study their properties and
applications. We show that the relative configuration adjacency matrix and the
logarithm adjacency matrix are all Hermitian matrices, and thus have very nice
spectral properties. We introduce dual quaternion Laplacian matrices, and prove
a Gershgorin-type theorem for square dual quaternion Hermitian matrices, for
studying properties of dual quaternion Laplacian matrices. The role of the dual
quaternion Laplacian matrices in formation control is discussed.


title:
On-Chip High Extinction Ratio Single-Stage Mach-Zehnder Interferometer
  based on Multimode Interferometer

abstract:
  On-chip high extinction ratio Mach-Zehnder interferometers (MZI) have always
attracted interest from researchers as it can be used in many applications in
astrophotonics, optical switching, programmable photonic circuits, and quantum
information. However, in previous research studies, ultra-high extinction ratio
on-chip MZIs have only been achieved by using a multi-stage MZI approach. In
this paper, we investigate a high extinction ratio single-stage MZI based on
two cascaded multimode interferometers (MMI). We determine that TM noise is an
important factor that can prevent us from achieving a high extinction ratio
MZI. By introducing a bend-based TM filter without additional loss, we
experimentally demonstrate that such a TM filter can improve the maximum
extinction ratio of the MMI-MZI by more than 10 dB. With the TM filter, we
report a record high 61.2 dB extinction ratio in a single stage, thermally
tunable MMI-MZI with only 1.5 dB insertion loss and more than 60nm bandwidth.
These results pave the way for many interesting applications.


title:
Tangential contact between free and fixed boundaries for variational
  solutions to variable coefficient Bernoulli type Free boundary problems

abstract:
  In this paper, we show that given appropriate boundary data, the free
boundary and the fixed boundary of minimizers of functionals of type
\eqref{functional} contact each other in a tangential fashion. We prove this
result via classification of the global profiles, adapting the ideas from
\cite{KKS06}.


title: The Projectivization Matroid of a $q$-Matroid

abstract:
  In this paper, we investigate the relation between a $q$-matroid and its
associated matroid called the projectivization matroid. The latter arises by
projectivizing the groundspace of the $q$-matroid and considering the
projective space as the groundset of the associated matroid on which is defined
a rank function compatible with that of the $q$-matroid. We show that the
projectivization map is a functor from categories of $q$-matroids to categories
of matroids, which allows to prove new results about maps of $q$-matroids. We
furthermore show the characteristic polynomial of a $q$-matroid is equal to
that of the projectivization matroid. We use this relation to establish a
recursive formula for the characteristic polynomial of a $q$-matroid in terms
of the characteristic polynomial of its minors. Finally we use the
projectivization matroid to prove a $q$-analogue of the critical theorem in
terms of $\mathbb{F}_{q^m}$-linear rank metric codes and $q$-matroids.


title: Clues in Tweets: Twitter-Guided Discovery and Analysis of SMS Spam

abstract:
  With its critical role in business and service delivery through mobile
devices, SMS (Short Message Service) has long been abused for spamming, which
is still on the rise today possibly due to the emergence of A2P bulk messaging.
The effort to control SMS spam has been hampered by the lack of up-to-date
information about illicit activities. In our research, we proposed a novel
solution to collect recent SMS spam data, at a large scale, from Twitter, where
users voluntarily report the spam messages they receive. For this purpose, we
designed and implemented SpamHunter, an automated pipeline to discover SMS spam
reporting tweets and extract message content from the attached screenshots.
Leveraging SpamHunter, we collected from Twitter a dataset of 21,918 SMS spam
messages in 75 languages, spanning over four years. To our best knowledge, this
is the largest SMS spam dataset ever made public. More importantly, SpamHunter
enables us to continuously monitor emerging SMS spam messages, which
facilitates the ongoing effort to mitigate SMS spamming. We also performed an
in-depth measurement study that sheds light on the new trends in the spammer's
strategies, infrastructure and spam campaigns. We also utilized our spam SMS
data to evaluate the robustness of the spam countermeasures put in place by the
SMS ecosystem, including anti-spam services, bulk SMS services, and text
messaging apps. Our evaluation shows that such protection cannot effectively
handle those spam samples: either introducing significant false positives or
missing a large number of newly reported spam messages.


title: Soft Threshold Ternary Networks

abstract:
  Large neural networks are difficult to deploy on mobile devices because of
intensive computation and storage. To alleviate it, we study ternarization, a
balance between efficiency and accuracy that quantizes both weights and
activations into ternary values. In previous ternarized neural networks, a hard
threshold {\Delta} is introduced to determine quantization intervals. Although
the selection of {\Delta} greatly affects the training results, previous works
estimate {\Delta} via an approximation or treat it as a hyper-parameter, which
is suboptimal. In this paper, we present the Soft Threshold Ternary Networks
(STTN), which enables the model to automatically determine quantization
intervals instead of depending on a hard threshold. Concretely, we replace the
original ternary kernel with the addition of two binary kernels at training
time, where ternary values are determined by the combination of two
corresponding binary values. At inference time, we add up the two binary
kernels to obtain a single ternary kernel. Our method dramatically outperforms
current state-of-the-arts, lowering the performance gap between full-precision
networks and extreme low bit networks. Experiments on ImageNet with ResNet-18
(Top-1 66.2%) achieves new state-of-the-art.
  Update: In this version, we further fine-tune the experimental
hyperparameters and training procedure. The latest STTN shows that ResNet-18
with ternary weights and ternary activations achieves up to 68.2% Top-1
accuracy on ImageNet. Code is available at: github.com/WeixiangXu/STTN.


title: Analysis of Joint Speech-Text Embeddings for Semantic Matching

abstract:
  Embeddings play an important role in many recent end-to-end solutions for
language processing problems involving more than one data modality. Although
there has been some effort to understand the properties of single-modality
embedding spaces, particularly that of text, their cross-modal counterparts are
less understood. In this work, we study a joint speech-text embedding space
trained for semantic matching by minimizing the distance between paired
utterance and transcription inputs. This was done through dual encoders in a
teacher-student model setup, with a pretrained language model acting as the
teacher and a transformer-based speech encoder as the student. We extend our
method to incorporate automatic speech recognition through both pretraining and
multitask scenarios and found that both approaches improve semantic matching.
Multiple techniques were utilized to analyze and evaluate cross-modal semantic
alignment of the embeddings: a quantitative retrieval accuracy metric,
zero-shot classification to investigate generalizability, and probing of the
encoders to observe the extent of knowledge transfer from one modality to
another.


title:
Structural stability of interior subsonic steady-states to hydrodynamic
  model for semiconductors with sonic boundary

abstract:
  For the stationary hydrodynamic model for semiconductors with sonic boundary,
represented by Euler-Poisson equations, it possesses the various physical
solutions including interior subsonic solutions/interior supersonic
solutions/shock transonic solutions/$C^1$-smooth transonic solutions. However,
the structural stability for these physical solutions is challenging and has
remained open as we know. In this paper, we investigate the structural
stability of interior subsonic solutions when the doping profiles are
restricted in the subsonic region. The main result is proved by using the local
(weighted) singularity analysis and the monotonicity argument. Both the result
itself and techniques developed here will give us some truly enlightening
insights into our follow-up study on the structural stability of the remaining
types of solutions.


title:
A Vanka-based parameter-robust multigrid relaxation for the Stokes-Darcy
  Brinkman problems

abstract:
  We propose a block-structured multigrid relaxation scheme for solving the
Stokes-Darcy Brinkman equations discretized by the marker and cell scheme. An
element-based additive Vanka smoother is used to solve the corresponding
shifted Laplacian operator. Using local Fourier analysis, we present the
stencil for the additive Vanka smoother and derive an optimal smoothing factor
for Vanka-based Braess-Sarazin relaxation for the Stokes-Darcy Brinkman
equations. Although the optimal damping parameter is dependent on meshsize and
physical parameter, it is very close to one. Numerical results of two-grid and
V(1,1)-cycle are presented, which show high efficiency of the proposed
relaxation scheme and its robustness to physical parameters and the meshsize.
Using a damping parameter equal to one gives almost the same results as these
for the optimal damping parameter at a lower computational overhead.


title:
Mesenchymal stem cells as carrier cells to enable effective intratumoral
  delivery of oncolytic virus for oncolytic virotherapy: a systematic review

abstract:
  Oncolytic viruses, which may be naturally occurring or genetically
engineered, are a type of virus that infects and destroy cancer cells
preferentially. Owing to their selectivity, they outperform conventional
chemotherapy and radiotherapy, which both have a tendency to impact non-target
cells and cause unwanted adverse side effects. Oncolytic virotherapy is a type
of cancer treatment in which oncolytic viruses are deliberately introduced into
patients affected with cancers in order for them to infect and destroy cancer
cells locally or systemically, in a manner analogous to chemotherapy but with a
greater degree of selectivity. Multiple studies indicate that oncolytic
virotherapy is effective in vitro but in vivo findings remain ambiguous due to
the approach's primary limitation: inefficient therapeutic agent delivery to
its target, which is heavily influenced by the immune system. Here, we propose
overcoming this limitation by exploiting a recent discovery in cancer research:
a carrier cell. By exploiting their tumor-promoting activities, mesenchymal
stem cells may be employed for cancer therapy by serving as a carrier for the
oncolytic viruses toward their target. This approach directly addresses the
limitation of conventional oncolytic virotherapy, where oncolytic viruses are
often poorly delivered after systemic administration.


title: Few remarks on essential modules

abstract:
  In the present paper, modules over integral domains and principal ideal
domains that are proper essential extensions of some submodules are classified.
We introduce a new class of modules that we call $\mathrm{SM}$ modules and show
that the class of Artinian modules, locally finite modules, and modules of
finite lengths are all proper subclasses of SM modules. We also show that
non-semisimple $\mathrm{SM}$ modules possess essential socles. Further, we show
that non-semieimple modules over integral domains with nonempty torsion-free
parts do not possess essential socles.


title:
Elliptic soliton solutions: $\tau$ functions, vertex operators and
  bilinear identities

abstract:
  We establish a bilinear framework for elliptic soliton solutions which are
composed by the Lam\'e-type plane wave factors. $\tau$ functions in Hirota's
form are derived and vertex operators that generate such $\tau$ functions are
presented. Bilinear identities are constructed and an algorithm to calculate
residues and bilinear equations is formulated. These are investigated in detail
for the KdV equation and sketched for the KP hierarchy. Degenerations by the
periods of elliptic functions are investigated, giving rise to the bilinear
framework associated with trigonometric/hyperbolic and rational functions.
Reductions by dispersion relation are considered by employing the so-called
elliptic $N$-th roots of the unity. $\tau$ functions, vertex operators and
bilinear equations of the KdV hierarchy and Boussinesq equation are obtained
from those of the KP. We also formulate two ways to calculate bilinear
derivatives involved with the Lam\'e-type plane wave factors, which shows that
such type of plane wave factors result in quasi-gauge property of bilinear
equations.


title: Ferroelectric transition of a chiral molecular crystal BINOL2DMSO

abstract:
  We report dielectric, thermodynamical, acoustic, and optical properties of a
chiral molecular crystal, 1,1'-bi-2-naphthol 2-dimethylsulfoxide (BINOL2DMSO).
We find two successive phase transitions at Tc1=190 K and Tc2=125 K. The first
transition at Tc1 is characterized by an order-disorder transition of the guest
molecules DMSO along with ferroelectricity. At the second transition of Tc2,
the crystal structure deforms from tetragonal to monoclinic, leading to domain
formation. Low-temperature x-ray diffraction suggests that the space group
changes from P4_12_12 (P4_32_12) to P4_1 (P4_3) at Tc1, and down to P112_1 at
Tc2.


title: $N=2$ quantum chiral superfields and quantum super bundles

abstract:
  We give the superalgebra of $N=2$ chiral (and antichiral) quantum superfields
realized as a subalgebra of the quantum supergroup $\mathrm{SL}_q(4|2)$. The
multiplication law in the quantum supergroup induces a coaction on the set of
chiral superfields. We also realize the quantum deformation of the chiral
Minkowski superspace as a quantum principal bundle.


title:
First muon acceleration and muon linear accelerator for measuring the
  muon anomalous magnetic moment and electric dipole moment

abstract:
  Muon acceleration using a radio-frequency accelerator was recently
demonstrated for the first time. Measurement of the muon anomalous magnetic
moment and electric dipole moment at Japan Proton Accelerator Research Complex
is the first experiment using accelerated muon beams, and construction will
begin soon. The radio-frequency accelerator used in the experiment and the
first muon acceleration are described in this paper.


title: Dynamic Focus-aware Positional Queries for Semantic Segmentation

abstract:
  Most of the latest top semantic segmentation approaches are based on vision
Transformers, particularly DETR-like frameworks, which employ a set of queries
in the Transformer decoder. Each query is composed of a content query that
preserves semantic information and a positional query that provides positional
guidance for aggregating the query-specific context. However, the positional
queries in the Transformer decoder layers are typically represented as fixed
learnable weights, which often encode dataset statistics for segments and can
be inaccurate for individual samples. Therefore, in this paper, we propose to
generate positional queries dynamically conditioned on the cross-attention
scores and the localization information of the preceding layer. By doing so,
each query is aware of its previous focus, thus providing more accurate
positional guidance and encouraging the cross-attention consistency across the
decoder layers. In addition, we also propose an efficient way to deal with
high-resolution cross-attention by dynamically determining the contextual
tokens based on the low-resolution cross-attention maps to perform local
relation aggregation. Our overall framework termed FASeg (Focus-Aware semantic
Segmentation) provides a simple yet effective solution for semantic
segmentation. Extensive experiments on ADE20K and Cityscapes show that our
FASeg achieves state-of-the-art performance, e.g., obtaining 48.3% and 49.6%
mIoU respectively for single-scale inference on ADE20K validation set with
ResNet-50 and Swin-T backbones, and barely increases the computation
consumption from Mask2former. Source code will be made publicly available at
https://github.com/zip-group/FASeg.


title:
Staring at the Shadows of Archaic Galaxies: Damped Ly$\alpha$ and Metal
  Absorbers toward a Young $z \sim 6$ Weak-line Quasar

abstract:
  We characterize the Ly$\alpha$ halo and absorption systems toward PSO
J083+11, a unique $z=6.3401$ weak-line quasar, using Gemini/GNIRS,
Magellan/FIRE, and VLT/MUSE data. Strong absorptions by hydrogen and several
metal lines (e.g., CII, MgII, and OI) are discovered in the spectrum, which
indicates the presence of: (i) a proximate sub-damped Ly$\alpha$ (sub-DLA)
system at $z=6.314$ and (ii) a MgII absorber at $z=2.2305$. To describe the
observed damping wing signal, we model the Ly$\alpha$ absorption with a
combination of a sub-DLA with the neutral hydrogen column density of $\log
N_\mathrm{HI} = 20.03 \pm 0.30$ cm$^{-2}$ and absorption from the intergalactic
medium with a neutral fraction of around 10 percent. The sub-DLA toward PSO
J083+11 has an abundance ratio of [C/O] $=-0.04 \pm 0.33$ and metallicity of
[O/H] $=-2.19 \pm 0.44$, similar to those of low-redshift metal-poor DLAs.
These measurements suggest that the sub-DLA might truncate PSO J083+11's
proximity zone size and complicate the quasar lifetime measurement. However,
this quasar shows no sign of a Ly$\alpha$ halo in the MUSE data cube, where the
estimated $1\sigma$ limit of surface brightness is $2.76 \times 10^{-18}$ erg
s$^{-1}$ cm$^{-2}$ arcsec$^{-2}$ at aperture size of 1 arcsecond, or equivalent
to a Ly$\alpha$ luminosity of $\leq 43.46$ erg s$^{-1}$. This non-detection,
while being only weak independent evidence on its own, is at least consistent
with a young quasar scenario, as expected for a quasar with a short accretion
timescale.


title:
Angular dependence of the upper critical induction of clean $s$- and
  $d_{x^2-y^2}$-wave superconductors with self-consistent ellipsoidal effective
  mass and Zeeman anisotropies

abstract:
  We employ the Schr{\"o}dinger-Dirac method generalized to an ellipsoidal
effective mass anisotropy in order to treat the spin and orbital effective mass
anisotropies self consistently, which is important when Pauli-limiting effects
on the upper critical field characteristic of singlet superconductivity are
present. By employing the Klemm-Clem transformations to map the equations of
motion into isotropic form, we then calculate the upper critical magnetic
induction $B_{c2}(\theta, \phi, T)$ at arbitrary directions and temperatures
$T$ for isotropic $s$-wave and for anisotropic $d_{x^2-y^2}$-wave
superconducting order parameters. As for anisotropic $s$-wave superconductors,
the reduced upper critical field $b_{c2}$ is largest in the direction of the
lowest effective mass, and is proportional to the universal orientation factor
$\alpha(\theta,\phi)$. However, for $d_{x^2-y^2}$-wave pairing, ${\bm
B}_{c2}(\pi/2,\phi,T)$ exhibits either a four-fold pattern with $C_4$ symmetry
just below the transition temperature $T_c$ that rotates by $\pi/4$ as $T$ is
lowered, or a two-fold pattern with $C_2$ symmetry, depending upon the planar
effective mass anisotropy. This provides a new method to distinguish these
pairing symmetries in clean unconventional superconductors.


title: Lecture notes on the Fundamental Structures of Differential Geometry

abstract:
  This is the first part of the lecture notes that grew out of the special
course given during the 2021-2022 academic year. In these lecture notes we
present an approach to the fundamental structures of differential geometry that
uses the vernacular of sheaves, differential operators and horizontal
subbundles.


title: Differentiable Rendering for Synthetic Aperture Radar Imagery

abstract:
  There is rising interest in integrating signal and image processing pipelines
into deep learning training to incorporate more domain knowledge. This can lead
to deep neural networks that are trained more robustly and with limited data,
as well as the capability to solve ill-posed inverse problems. In particular,
there is rising interest in differentiable rendering, which allows explicitly
modeling geometric priors and constraints in the optimization pipeline using
first-order methods such as backpropagation. Existing efforts in differentiable
rendering have focused on imagery from electro-optical sensors, particularly
conventional RGB-imagery. In this work, we propose an approach for
differentiable rendering of Synthetic Aperture Radar (SAR) imagery, which
combines methods from 3D computer graphics with neural rendering. We
demonstrate the approach on the inverse graphics problem of 3D Object
Reconstruction from limited SAR imagery using high-fidelity simulated SAR data.


title: Open-closed homotopy algebra in superstring field theory

abstract:
  We construct open-closed superstring interactions based on the open-closed
homotopy algebra structure. It provides a classical open superstring field
theory on general closed-superstring-field backgrounds described by classical
solutions of the nonlinear equation of motion of the closed superstring field
theory. We also give the corresponding WZW-like action through the map
connecting the homotopy-based and WZW-like formulations.


title: Multivariate orthogonal spline systems

abstract:
  In this article we consider orthonormal systems consisting of tensor products
of splines. We show some convergence results of the corresponding orthogonal
series including a.e. convergence and unconditional convergence in $L^p$ for
$1<p<\infty$, where the latter is proved under some geometric conditions on the
involved partitions that depend on the spline order.


title:
Optimisation of Operating High Voltage of the large area Resistive Plate
  Chamber for ICAL experiment

abstract:
  The Resistive Plate Chamber is a widely used detector in high energy physics.
The operating potential of this chamber is determined by the optimisation of
the efficiency and noise rate of the device. This optimisation is based on the
assumption that the performance of the device over the whole surface area is
uniform. The INO-ICAL experiment is going to use $\sim$ 30000 RPC of size
$\sim$2\,m$\times$2\,m. All the RPC will have to pass a minimum quality
assurance criteria, but may not be able to maintain a good uniformity over the
whole surface area, particularly for the whole running period of about twenty
years. This paper describes the choice of the optimum operating HV for an RPC
of non-uniform response.


title:
Some identities involving degenerate Stirling numbers arising from
  normal ordering

abstract:
  In this paper, we derive some identities and recurrence relations for the
degenerate Stirling numbers of the first kind and of the second kind which are
degenerate versions of the ordinary Stirling numbers of the first kind and of
the second kind. They are deduced from the normal oderings of degenerate
integral powers of the number operator and their inversions, certain relations
of boson operators and from the recurrence relations of the Stirling numbers
themselves. Here we note that, while the normal ordering of an integral power
of the number operator is expressed with the help of the Stirling numbers of
the second kind, that of a degenerate integral power of the number operator is
represented by means of the degenerate Stirling numbers of the second kind.


title: Generalized Kazdan-Warner equations on foliated manifolds

abstract:
  On compact foliated manifolds, we extend the theorem on the existence and the
uniqueness of the solutions of generalized Kazdan-Warner equations. Examples of
PDEs which we solve include the transverse Hitchin equation for a diagonal
harmonic metric on basic cyclic Higgs bundles over a complex codimension one
foliated manifold.


title:
BatchFormerV2: Exploring Sample Relationships for Dense Representation
  Learning

abstract:
  Attention mechanisms have been very popular in deep neural networks, where
the Transformer architecture has achieved great success in not only natural
language processing but also visual recognition applications. Recently, a new
Transformer module, applying on batch dimension rather than spatial/channel
dimension, i.e., BatchFormer [18], has been introduced to explore sample
relationships for overcoming data scarcity challenges. However, it only works
with image-level representations for classification. In this paper, we devise a
more general batch Transformer module, BatchFormerV2, which further enables
exploring sample relationships for dense representation learning. Specifically,
when applying the proposed module, it employs a two-stream pipeline during
training, i.e., either with or without a BatchFormerV2 module, where the
batchformer stream can be removed for testing. Therefore, the proposed method
is a plug-and-play module and can be easily integrated into different vision
Transformers without any extra inference cost. Without bells and whistles, we
show the effectiveness of the proposed method for a variety of popular visual
recognition tasks, including image classification and two important dense
prediction tasks: object detection and panoptic segmentation. Particularly,
BatchFormerV2 consistently improves current DETR-based detection methods (e.g.,
DETR, Deformable-DETR, Conditional DETR, and SMCA) by over 1.3%. Code will be
made publicly available.


title: Cartan $F(R)$ Gravity and Equivalent Scalar-Tensor Theory

abstract:
  We investigate the Cartan formalism in $F(R)$ gravity. $F(R)$ gravity has
been introduced as a theory to explain cosmological accelerated expansion by
replacing the Ricci scalar $R$ in the Einstein-Hilbert action with a function
of $R$. As is well-known, $F(R)$ gravity is rewritten as a scalar-tensor theory
by using the conformal transformation. Cartan $F(R)$ gravity is described based
on the Riemann-Cartan geometry formulated by the vierbein. In the Cartan
formalism, the Ricci scalar $R$ is divided into two parts, one derived from the
Levi-Civita connection and the other from the torsion. A kinetic term for a
scalar field is constructed from the torsion part. It is shown that a class of
the Cartan $F(R)$ gravity can be rewritten as an equivalent scalar-tensor
theory without any conformal transformations. As a simple case, we apply the
result to the Starobinsky model.


title:
Monte Carlo Physarum Machine: Characteristics of Pattern Formation in
  Continuous Stochastic Transport Networks

abstract:
  We present Monte Carlo Physarum Machine: a computational model suitable for
reconstructing continuous transport networks from sparse 2D and 3D data. MCPM
is a probabilistic generalization of Jones's 2010 agent-based model for
simulating the growth of Physarum polycephalum slime mold. We compare MCPM to
Jones's work on theoretical grounds, and describe a task-specific variant
designed for reconstructing the large-scale distribution of gas and dark matter
in the Universe known as the Cosmic web. To analyze the new model, we first
explore MCPM's self-patterning behavior, showing a wide range of continuous
network-like morphologies -- called "polyphorms" -- that the model produces
from geometrically intuitive parameters. Applying MCPM to both simulated and
observational cosmological datasets, we then evaluate its ability to produce
consistent 3D density maps of the Cosmic web. Finally, we examine other
possible tasks where MCPM could be useful, along with several examples of
fitting to domain-specific data as proofs of concept.


title: Age of Information with Hybrid-ARQ: A Unified Explicit Result

abstract:
  Delivering timely status updates in a timeliness-critical communication
system is of paramount importance to assist accurate and efficient decision
making. Therefore, the topic of analyzing Age of Information has aroused new
research interest. This paper contributes to new results in this area by
systematically analyzing the AoI of two types of Hybrid Automatic Repeat
reQuest (HARQ) techniques that have been newly standardized in the Release-16
5G New Radio (NR) specifications, namely reactive HARQ and proactive HARQ.
Under a code-based status update system with non-trivial coding delay,
transmission delay, propagation delay, decoding delay, and feedback delay, we
derive unified closed-form average AoI and average Peak AoI expressions for
reactive HARQ and proactive HARQ, respectively. Based on the obtained explicit
expressions, we formulate an AoI minimization problem to investigate the
age-optimal codeblock assignment strategy in the finite block-length (FBL)
regime. Through case studies and analytical results, we provide comparative
insights between reactive HARQ and proactive HARQ from a perspective of
freshness of information. The numerical results and optimization solutions show
that proactive HARQ draws its strength from both age performance and system
robustness, thus enabling the potential to provide new system advancement of a
freshness-critical status update system.


title: Homomorphisms of (n,m)-graphs with respect to generalised switch

abstract:
  An $(n,m)$-graph has $n$ different types of arcs and $m$ different types of
edges. A homomorphism of an $(n,m)$-graph $G$ to an $(n,m)$-graph $H$ is a
vertex mapping that preserves adjacency type and directions. Notice that, in an
$(n,m)$-graph a vertex can possibly have $(2n+m)$ different types of neighbors.
In this article, we study homomorphisms of $(n,m)$-graphs while an Abelian
group acts on the set of different types of neighbors of a vertex.


title: Neutral meson mixing in the $B^0_{(s)}$ sector from lattice QCD

abstract:
  We summarise the current status of lattice QCD computations of neutral meson
parameters in the $B_d^0$ and the $B_s^0$ systems. We comment on recent results
and anticipate further improvements on the uncertainties in the future.


title:
Mixed Chiral and Achiral Character in Substituted Ethane: A Next
  Generation QTAIM Perspective

abstract:
  We use the newly introduced spanning stress tensor trajectory
$U_{\sigma}$-space construction within next generation quantum theory of atoms
in molecules (NG-QTAIM) for a chirality investigation of singly and doubly
substituted ethane with halogen substituents: F, Cl, Br. A lack of achiral
character in $U_{\sigma}$-space was discovered for singly substituted ethane.
The resultant axial bond critical point (BCP) sliding responded more strongly
to the increase in atomic number of the substituted halogen than the chirality.
The presence of the very light F atom was found responsible for a very high
degree of achiral character of the doubly substituted ethane.


title: A remark on $p$-adic Siegel Eisenstein series

abstract:
  A generalization of Serre's $p$-adic Eisenstein series in the case of Siegel
modular forms is studied and a coincidence between a $p$-adic Siegel Eisenstein
series and a genus theta series associated with a quaternary quadratic form is
proved.


title:
FT-EALU: Fault Tolerant Arithmetic and Logic Unit for Critical Embedded
  and Real time Systems

abstract:
  In this paper, a fault-tolerant approach to mitigate transient and permanent
faults of arithmetic and logic operations of embedded processors called FT-EALU
is proposed. In this method, each operation is replicated in time and the
derived final results are voted to generate the final output. To consider the
effect of permanent faults, replicating identical operations in time is not
sufficient, and diversifying the operands is required. To this aim in FT-EALU,
we consider three distinct versions of input data and apply the target
operation to them serially in time. To avoid high time overhead, we employ
simple operators such as shift and swap to make an appropriate diversion in
input data. Our proposed fault tolerance approach passes the replicated and
diverse results to a novel weighted voter that is designed based on the
reward/punishment strategy. For each version of execution, based on the
proposed weighting approach a corresponding weight according to its correction
capability confronting several faulty scenarios is defined. This weight defines
the reliability of the result of each version of execution and determines its
effect on the final result. The final result is generated bit by bit based on
the weight of each execution and its computed result. These weights are
determined statically through a design-time learning scheme according to
applying several types of faults on various data bits. Based on the capability
of execution versions on mitigating the permanent faults, positive or negative
scores are assigned to them. These scores are integrated for several cases and
normalized to derive the appropriate weight of each execution at bit level.
Several experiments are performed to show the efficiency of our proposed
approach and based on them, FT-EALU is capable of correcting about 84.93% and
69.71% of permanent injected faults on single and double bits of input data.


title: Direct Dense Pose Estimation

abstract:
  Dense human pose estimation is the problem of learning dense correspondences
between RGB images and the surfaces of human bodies, which finds various
applications, such as human body reconstruction, human pose transfer, and human
action recognition. Prior dense pose estimation methods are all based on Mask
R-CNN framework and operate in a top-down manner of first attempting to
identify a bounding box for each person and matching dense correspondences in
each bounding box. Consequently, these methods lack robustness due to their
critical dependence on the Mask R-CNN detection, and the runtime increases
drastically as the number of persons in the image increases. We therefore
propose a novel alternative method for solving the dense pose estimation
problem, called Direct Dense Pose (DDP). DDP first predicts the instance mask
and global IUV representation separately and then combines them together. We
also propose a simple yet effective 2D temporal-smoothing scheme to alleviate
the temporal jitters when dealing with video data. Experiments demonstrate that
DDP overcomes the limitations of previous top-down baseline methods and
achieves competitive accuracy. In addition, DDP is computationally more
efficient than previous dense pose estimation methods, and it reduces jitters
when applied to a video sequence, which is a problem plaguing the previous
methods.


title: Probabilistic Implicit Scene Completion

abstract:
  We propose a probabilistic shape completion method extended to the continuous
geometry of large-scale 3D scenes. Real-world scans of 3D scenes suffer from a
considerable amount of missing data cluttered with unsegmented objects. The
problem of shape completion is inherently ill-posed, and high-quality result
requires scalable solutions that consider multiple possible outcomes. We employ
the Generative Cellular Automata that learns the multi-modal distribution and
transform the formulation to process large-scale continuous geometry. The local
continuous shape is incrementally generated as a sparse voxel embedding, which
contains the latent code for each occupied cell. We formally derive that our
training objective for the sparse voxel embedding maximizes the variational
lower bound of the complete shape distribution and therefore our progressive
generation constitutes a valid generative model. Experiments show that our
model successfully generates diverse plausible scenes faithful to the input,
especially when the input suffers from a significant amount of missing data. We
also demonstrate that our approach outperforms deterministic models even in
less ambiguous cases with a small amount of missing data, which infers that
probabilistic formulation is crucial for high-quality geometry completion on
input scans exhibiting any levels of completeness.


title:
Multi-modality Associative Bridging through Memory: Speech Sound
  Recollected from Face Video

abstract:
  In this paper, we introduce a novel audio-visual multi-modal bridging
framework that can utilize both audio and visual information, even with
uni-modal inputs. We exploit a memory network that stores source (i.e., visual)
and target (i.e., audio) modal representations, where source modal
representation is what we are given, and target modal representations are what
we want to obtain from the memory network. We then construct an associative
bridge between source and target memories that considers the interrelationship
between the two memories. By learning the interrelationship through the
associative bridge, the proposed bridging framework is able to obtain the
target modal representations inside the memory network, even with the source
modal input only, and it provides rich information for its downstream tasks. We
apply the proposed framework to two tasks: lip reading and speech
reconstruction from silent video. Through the proposed associative bridge and
modality-specific memories, each task knowledge is enriched with the recalled
audio context, achieving state-of-the-art performance. We also verify that the
associative bridge properly relates the source and target memories.


title:
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender
  System

abstract:
  While personalization increases the utility of recommender systems, it also
brings the issue of filter bubbles. E.g., if the system keeps exposing and
recommending the items that the user is interested in, it may also make the
user feel bored and less satisfied. Existing work studies filter bubbles in
static recommendation, where the effect of overexposure is hard to capture. In
contrast, we believe it is more meaningful to study the issue in interactive
recommendation and optimize long-term user satisfaction. Nevertheless, it is
unrealistic to train the model online due to the high cost. As such, we have to
leverage offline training data and disentangle the causal effect on user
satisfaction.
  To achieve this goal, we propose a counterfactual interactive recommender
system (CIRS) that augments offline reinforcement learning (offline RL) with
causal inference. The basic idea is to first learn a causal user model on
historical data to capture the overexposure effect of items on user
satisfaction. It then uses the learned causal user model to help the planning
of the RL policy. To conduct evaluation offline, we innovatively create an
authentic RL environment (KuaiEnv) based on a real-world fully observed user
rating dataset. The experiments show the effectiveness of CIRS in bursting
filter bubbles and achieving long-term success in interactive recommendation.
The implementation of CIRS is available via
https://github.com/chongminggao/CIRS-codes.


title:
FoV-Net: Field-of-View Extrapolation Using Self-Attention and
  Uncertainty

abstract:
  The ability to make educated predictions about their surroundings, and
associate them with certain confidence, is important for intelligent systems,
like autonomous vehicles and robots. It allows them to plan early and decide
accordingly. Motivated by this observation, in this paper we utilize
information from a video sequence with a narrow field-of-view to infer the
scene at a wider field-of-view. To this end, we propose a temporally consistent
field-of-view extrapolation framework, namely FoV-Net, that: (1) leverages 3D
information to propagate the observed scene parts from past frames; (2)
aggregates the propagated multi-frame information using an attention-based
feature aggregation module and a gated self-attention module, simultaneously
hallucinating any unobserved scene parts; and (3) assigns an interpretable
uncertainty value at each pixel. Extensive experiments show that FoV-Net does
not only extrapolate the temporally consistent wide field-of-view scene better
than existing alternatives, but also provides the associated uncertainty which
may benefit critical decision-making downstream applications. Project page is
at http://charliememory.github.io/RAL21_FoV.


title: Improving Monocular Visual Odometry Using Learned Depth

abstract:
  Monocular visual odometry (VO) is an important task in robotics and computer
vision. Thus far, how to build accurate and robust monocular VO systems that
can work well in diverse scenarios remains largely unsolved. In this paper, we
propose a framework to exploit monocular depth estimation for improving VO. The
core of our framework is a monocular depth estimation module with a strong
generalization capability for diverse scenes. It consists of two separate
working modes to assist the localization and mapping. With a single monocular
image input, the depth estimation module predicts a relative depth to help the
localization module on improving the accuracy. With a sparse depth map and an
RGB image input, the depth estimation module can generate accurate
scale-consistent depth for dense mapping. Compared with current learning-based
VO methods, our method demonstrates a stronger generalization ability to
diverse scenes. More significantly, our framework is able to boost the
performances of existing geometry-based VO methods by a large margin.


title:
A decomposition algorithm for two-stage stochastic programs with
  nonconvex recourse

abstract:
  In this paper, we have studied a decomposition method for solving a class of
nonconvex two-stage stochastic programs, where both the objective and
constraints of the second-stage problem are nonlinearly parameterized by the
first-stage variable. Due to the failure of the Clarke regularity of the
resulting nonconvex recourse function, classical decomposition approaches such
as Benders decomposition and (augmented) Lagrangian-based algorithms cannot be
directly generalized to solve such models. By exploring an implicitly
convex-concave structure of the recourse function, we introduce a novel
decomposition framework based on the so-called partial Moreau envelope. The
algorithm successively generates strongly convex quadratic approximations of
the recourse function based on the solutions of the second-stage convex
subproblems and adds them to the first-stage master problem. Convergence under
both fixed scenarios and interior samplings is established. Numerical
experiments are conducted to demonstrate the effectiveness of the proposed
algorithm.


title:
The inf-sup constant for Crouzeix-Raviart triangular elements of any
  polynomial order

abstract:
  In this paper, we consider the discretization of the two-dimensional
stationary Stokes equation by Crouzeix-Raviart elements for the velocity of any
polynomial order $k\geq1$ on conforming triangulations and discontinuous
pressure approximations of order $k-1$. We will bound the inf-sup constant from
below independent of the mesh size and analyse its $k$-dependence. Our
assumptions on the mesh are quite general, for odd $k$ we require that the
triangulations contain at least one inner vertex while for even $k$ we assume
that the triangulations consist of more than a single triangle.


title: Into-TTS : Intonation Template based Prosody Control System

abstract:
  Intonations take an important role in delivering the intention of the
speaker. However, current end-to-end TTS systems often fail to model proper
intonations. To alleviate this problem, we propose a novel, intuitive method to
synthesize speech in different intonations using predefined intonation
templates. Prior to the acoustic model training, speech data are automatically
grouped into intonation templates by k-means clustering, according to their
sentence-final F0 contour. Two proposed modules are added to the end-to-end TTS
framework: intonation classifier and intonation encoder. The intonation
classifier recommends a suitable intonation template to the given text. The
intonation encoder, attached to the text encoder output, synthesizes speech
abiding the requested intonation template. Main contributions of our paper are:
(a) an easy-to-use intonation control system covering a wide range of users;
(b) better performance in wrapping speech in a requested intonation with
improved pitch distance and MOS; and (c) feasibility to future integration
between TTS and NLP, TTS being able to utilize contextual information. Audio
samples are available at https://srtts.github.io/IntoTTS.


title: On the Harnack inequality for antisymmetric $s$-harmonic functions

abstract:
  We prove the Harnack inequality for antisymmetric $s$-harmonic functions in a
general domain. This may be used in conjunction with the method of moving
planes to obtain quantitative stability results for symmetry and overdetermined
problems driven by the fractional Laplacian.
  The proof is split into two parts: an interior Harnack inequality away from
the plane of symmetry, and a boundary Harnack inequality close to the plane of
symmetry. We prove the interior Harnack inequality by establishing the weak
Harnack inequality for viscosity super-solutions and local boundedness for
viscosity sub-solutions. To prove the boundary Harnack inequality, we introduce
a new mean-value formula for antisymmetric $s$-harmonic functions.


title: FedSynth: Gradient Compression via Synthetic Data in Federated Learning

abstract:
  Model compression is important in federated learning (FL) with large models
to reduce communication cost. Prior works have been focusing on sparsification
based compression that could desparately affect the global model accuracy. In
this work, we propose a new scheme for upstream communication where instead of
transmitting the model update, each client learns and transmits a light-weight
synthetic dataset such that using it as the training data, the model performs
similarly well on the real training data. The server will recover the local
model update via the synthetic data and apply standard aggregation. We then
provide a new algorithm FedSynth to learn the synthetic data locally.
Empirically, we find our method is comparable/better than random masking
baselines in all three common federated learning benchmark datasets.


title: Two-Dimensional Ferromagnetic Half-Metallic Janus V2AsP Monolayer

abstract:
  Two-dimensional (2D) ferromagnetic materials present promising candidates for
spintronic devices, and the half-metallic materials with 100% spin polarization
at Fermi energy level are highly desired for many spin-based devices. 2D Janus
materials have attracted great attention in recent years due to their excellent
properties induced by breaking the symmetry. Here, using the density functional
theory, we report that the Janus V2AsP monolayer demonstrates a charming
ferromagnetic half-metallic feature. It is dynamically stable in view of the
absence of imaginary frequency phonon. The half-metallic gap is about 0.38 eV
and the spin splitting of about 1.34eV for the V2AsP monolayer. Interestingly,
a tensile strain of 4.9% can induce it to undergo a phase transition from
ferromagnetic to anti-ferromagnetic state. Moreover, the Curie temperature (Tc)
enhances with the increase of compressive strain. All there appealing
properties make the half-metallic Janus V2AsP monolayer a promising material
for 2D spintronic applications.


title: Direct search based on probabilistic descent in reduced spaces

abstract:
  Derivative-free algorithms seek the minimum value of a given objective
function without using any derivative information. The performance of these
methods often worsen as the dimension increases, a phenomenon predicted by
their worst-case complexity guarantees. Nevertheless, recent algorithmic
proposals have shown that incorporating randomization into otherwise
deterministic frameworks could alleviate this effect for direct-search methods.
The best guarantees and practical performance are obtained when employing a
random vector and its negative, which amounts to drawing directions in a random
one-dimensional subspace. Unlike for other derivative-free schemes, however,
the properties of these subspaces have not been exploited.
  In this paper, we study a generic direct-search algorithm in which the
polling directions are defined using random subspaces. Complexity guarantees
for such an approach are derived thanks to probabilistic properties related to
both the subspaces and the directions used within these subspaces. By
leveraging results on random subspace embeddings and sketching matrices, we
show that better complexity bounds are obtained for randomized instances of our
framework. A numerical investigation confirms the benefit of randomization,
particularly when done in subspaces, when solving problems of moderately large
dimension.


title: Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery

abstract:
  Articulation-centric 2D/3D pose supervision forms the core training objective
in most existing 3D human pose estimation techniques. Except for synthetic
source environments, acquiring such rich supervision for each real target
domain at deployment is highly inconvenient. However, we realize that standard
foreground silhouette estimation techniques (on static camera feeds) remain
unaffected by domain-shifts. Motivated by this, we propose a novel target
adaptation framework that relies only on silhouette supervision to adapt a
source-trained model-based regressor. However, in the absence of any auxiliary
cue (multi-view, depth, or 2D pose), an isolated silhouette loss fails to
provide a reliable pose-specific gradient and requires to be employed in tandem
with a topology-centric loss. To this end, we develop a series of
convolution-friendly spatial transformations in order to disentangle a
topological-skeleton representation from the raw silhouette. Such a design
paves the way to devise a Chamfer-inspired spatial topological-alignment loss
via distance field computation, while effectively avoiding any gradient
hindering spatial-to-pointset mapping. Experimental results demonstrate our
superiority against prior-arts in self-adapting a source trained model to
diverse unlabeled target domains, such as a) in-the-wild datasets, b)
low-resolution image domains, and c) adversarially perturbed image domains (via
UAP).


title:
Existence of a positive hyperbolic orbit in the presence of an elliptic
  orbit in three-dimensional Reeb flows

abstract:
  Nondegenerate periodic orbits in three-dimensional Reeb flows can be
classified into three types, positive hyperbolic, negative hyperbolic and
elliptic. As a problem which involves refining the three-dimensional Weinstein
conjecture, D. Cristofaro-Gardiner, M. Hutchings and D. Pomerleano proposed
whether every nondegenerate closed contact three manifold has at least one
positive hyperbolic orbit except for lens spaces. In the same paper, they
showed the existence of at least one simple hyperbolic orbit under $b_{1}>0$ by
the isomorphism between ECH and Seiberg-Witten Floer (co)homology, especially,
using the result that if $b_{1}>0$, the odd part of ECH which detects the
existence of a positive hyperbolic orbit does not vanish. But in the case of
$b_{1}=0$, such a way doesn't work. In the present paper, we prove the
existence of a positive hyperbolic orbit in the presence of at least one
elliptic orbit except for some well-known trivial cases under $b_{1}=0$. The
key points in this paper are the volume property with respect to ECH spectrums
and the compactification of the moduli spaces of certain $J$-holomorphic curves
counted by the $U$-map.


title: SPFNet:Subspace Pyramid Fusion Network for Semantic Segmentation

abstract:
  The encoder-decoder structure has significantly improved performance in many
vision tasks by fusing low-level and high-level feature maps. However, this
approach can hardly extract sufficient context information for pixel-wise
segmentation. In addition, extracting similar low-level features at multiple
scales could lead to redundant information. To tackle these issues, we propose
Subspace Pyramid Fusion Network (SPFNet). Specifically, we combine pyramidal
module and context aggregation module to exploit the impact of
multi-scale/global context information. At first, we construct a Subspace
Pyramid Fusion Module (SPFM) based on Reduced Pyramid Pooling (RPP). Then, we
propose the Efficient Global Context Aggregation (EGCA) module to capture
discriminative features by fusing multi-level global context features. Finally,
we add decoder-based subpixel convolution to retrieve the high-resolution
feature maps, which can help select category localization details. SPFM learns
separate RPP for each feature subspace to capture multi-scale feature
representations, which is more useful for semantic segmentation. EGCA adopts
shuffle attention mechanism to enhance communication across different
sub-features. Experimental results on two well-known semantic segmentation
datasets, including Camvid and Cityscapes, show that our proposed method is
competitive with other state-of-the-art methods.


title: Effective Field Theory of Dipolar Braiding Statistics in Two Dimensions

abstract:
  A rank-2 toric code (R2TC) Hamiltonian in two dimensions can be constructed
as a Higgsed descendant of rank-2 U(1) lattice gauge theory. As noted by the
authors recently, [Y.-T. Oh, J. Kim, E.-G. Moon, and J. H. Han, Phys. Rev. B
{\bf 105}, 045128] the quasiparticles in that model showed braiding statistics
that depends on the initial locations of the particles which participate in the
braiding. We argue that this new kind of statistical phase captures the total
dipole moment of quasiparticles encompassed in the braiding, whereas
conventional anyonic braiding sees the total charge. An Aharonov-Bohm
interpretation of such {\it dipolar braiding statistics} can be made in terms
of emergent, rank-1 vector potentials built from the underlying rank-2 gauge
fields. Pertinent field theories of the quasiparticle dynamics in the R2TC are
developed, which turn out to be highly interacting theories predicting their
constrained dynamics. The accompanying conservation laws are also of unusual
types. A {\it dipolar BF theory} of the rank-2 gauge fields is constructed and
shown to correctly capture the dipolar braiding statistics, in contrast to the
conventional BF theory capturing the {\it monopolar braiding statistics} of
anyons in the rank-1 toric code. A tight-binding model for the quasiparticle
dynamics in the R2TC involves two-particle as well as the one-particle hopping,
both of which are coupled to the tensor gauge fields.


title: Muon g-2 with overlap valence fermion

abstract:
  We present a lattice calculation of the leading order (LO) hadronic vacuum
polarization (HVP) contribution to the muon anomalous magnetic moment for the
connected light and strange quarks, $a^{\rm W}_{{\rm con}, l\,/s}$ in the
widely used window $t_0=0.4~\mathrm{fm}$, $t_1=1.0~\mathrm{fm}$,
$\Delta=0.15~\mathrm{fm}$, and also of $a^{\rm S}_{{\rm con}, l\,/s}$ in the
short distance region. We use the overlap fermion on 4 physical-point
ensembles. Two 2+1 flavors ensembles use the domain wall fermion (DWF) and
Iwasaki gauge actions at $a = 0.084$ and 0.114 fm, and two 2+1+1 flavors
ensembles use the highly-improved-staggered-quark (HISQ) and Symanzik gauge
actions at $a = 0.088$ and 0.121 fm. They have incorporated infinite volume
corrections from 3 additional DWF ensembles at ${\rm L}$ = 4.8, 6.4 and 9.6 fm
and physical pion mass. For the $a^{\rm W}_{{\rm con}, l}$, we find that our
results on the two smaller lattice spacings are consistent with those using the
unitary setup, but those at the two coarser lattice spacings have obviously
small differences compared to the unitary ones.


title:
Empirical Analysis of Lifelog Data using Optimal Feature Selection based
  Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming

abstract:
  Recent advancement in the field of pervasive healthcare monitoring systems
causes the generation of a huge amount of lifelog data in real-time. Chronic
diseases are one of the most serious health challenges in developing and
developed countries. According to WHO, this accounts for 73% of all deaths and
60% of the global burden of diseases. Chronic disease classification models are
now harnessing the potential of lifelog data to explore better healthcare
practices. This paper is to construct an optimal feature selection-based
unsupervised logistic regression model (OFS-ULR) to classify chronic diseases.
Since lifelog data analysis is crucial due to its sensitive nature; thus the
conventional classification models show limited performance. Therefore,
designing new classifiers for the classification of chronic diseases using
lifelog data is the need of the age. The vital part of building a good model
depends on pre-processing of the dataset, identifying important features, and
then training a learning algorithm with suitable hyper parameters for better
performance. The proposed approach improves the performance of existing methods
using a series of steps such as (i) removing redundant or invalid instances,
(ii) making the data labelled using clustering and partitioning the data into
classes, (iii) identifying the suitable subset of features by applying either
some domain knowledge or selection algorithm, (iv) hyper parameter tuning for
models to get best results, and (v) performance evaluation using Spark
streaming environment. For this purpose, two-time series datasets are used in
the experiment to compute the accuracy, recall, precision, and f1-score. The
experimental analysis proves the suitability of the proposed approach as
compared to the conventional classifiers and our newly constructed model
achieved highest accuracy and reduced training complexity among all among all.


title:
Evidence of changes in the low-latitude plasma drift under IMF $B_z$
  coupling: a TIEGCM simulation approach

abstract:
  Study of the dynamic nature of low-latitude ionosphere during geomagnetically
disturbed conditions, especially in the EIA and the magnetic equatorial regions
are vital for understanding the underlying physics as well as for mitigating
space weather hazards on the sophisticated technological systems essential for
human civilization. An important aspect of the space weather studies is the
thorough understanding of coupling between the solar wind and the terrestrial
magnetosphere-ionosphere system and subsequent influence on the low-latitude
ionosphere. This paper presents an effort to understand the influence of actual
values of the north-south component of Interplanetary Magnetic Field (IMF,
$B_z$) on the vertical plasma drifts at a location near the EIA and the
geomagnetic equator. The strong storm event of October 13, 2016, falling in the
descending phase of solar cycle 24, has been taken up as a case study.
Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM)
simulation runs have been performed under two scenarios: first when no coupling
is present (IMF $B_z$ = 0 nT) and second when actual observations of the values
of IMF $B_z$ is given as inputs to the model. Observations show when actual
data is fed to the model, there is significant shift of the vertical drift
towards westward, while there is an increase in the peak value of the westward
drift after the pre-reversal enhancement. This study is an initial effort to
understand the variations in low-latitude plasma motions during the main phase
of strong geomagnetic storms. This initial work will be followed up with
understanding the global plasma drift variations under the influence of other
components of the IMF near the EIA and the dip equatorial regions.

categories: physics.space-ph astro-ph.EP astro-ph.SR physics.geo-ph physics.plasm-ph


title:
Conditional Handover in 5G: Principles, Future Use Cases and FR2
  Performance

abstract:
  This paper elaborates on Conditional Handover (CHO) - a mobility feature
designed in Release 16 of 3rd Generation Partnership Project (3GPP), aimed at
improving the reliability of handover in cellular networks. CHO has turned out
to be a successful feature and attracted several other areas of mobile
networks, where increased reliability is also desirable (e.g. Non-Terrestrial
Networks or Integrated Access Backhaul). This paper explains the principles of
CHO, describes its future use in vertical areas and provides performance
results showing the gains CHO may offer to 5G systems at higher frequency
bands. Our simulative analysis was focused on the handover in Frequency Range 2
(FR2) and CHO recovery procedure. Results have shown that also at higher
frequencies CHO can substantially reduce the mobility failures compared to
baseline handover of Release 15. Moreover, it has been verified that CHO
recovery can be a useful means to allow faster reconnection to the network
after a failure. In certain scenarios CHO Recovery usage ratio was found to
exceed 80%. The paper also outlines the future research and standardization
directions in the area of CHO which appears to be a solid candidate for further
development in 3GPP Release 18 and beyond.


title: On a Stochastic Model of Diversification

abstract:
  We propose a definition of diversification as a binary relationship between
financial portfolios. According to it, a convex linear combination of several
risk positions with some weights is considered to be less risky than the
probabilistic mixture of the same risk positions with the same weights. It
turns out to be that the proposed partial ordering coincides with the
well-known second order stochastic dominance, but allows to take a look at it
from another perspective.


title:
Problem reports and team maturity in agile automotive software
  development

abstract:
  Background: Volvo Cars is pioneering an agile transformation on a large scale
in the automotive industry. Social psychological aspects of automotive software
development are an under-researched area in general. Few studies on team
maturity or group dynamics can be found specifically in the automotive software
engineering domain. Objective: This study is intended as an initial step to
fill that gap by investigating the connection between issues and problem
reports and team maturity. Method: We conducted a quantitative study with 84
participants from 14 teams and qualitatively validated the result with the
Release Train Engineer having an overview of all the participating teams.
Results: We find that the more mature a team is, the faster they seem to
resolve issues as provided through external feedback, at least in the two
initial team maturity stages. Conclusion: This study suggests that working on
team dynamics might increase productivity in modern automotive software
development departments, but this needs further investigation.


title: Verifying Weak and Strong k-Step Opacity in Discrete-Event Systems

abstract:
  Opacity is an important system-theoretic property expressing whether a system
may reveal its secret to a passive observer (an intruder) who knows the
structure of the system but has only limited observations of its behavior.
Several notions of opacity have been discussed in the literature, including
current-state opacity, k-step opacity, and infinite-step opacity. We
investigate weak and strong k-step opacity, the notions that generalize both
current-state opacity and infinite-step opacity, and ask whether the intruder
is not able to decide, at any instant, when respectively whether the system was
in a secret state during the last k observable steps. We design a new algorithm
verifying weak k-step opacity, the complexity of which is lower than the
complexity of existing algorithms and does not depend on the parameter k, and
show how to use it to verify strong k-step opacity by reducing strong k-step
opacity to weak k-step opacity. The complexity of the resulting algorithm is
again better than the complexity of existing algorithms and does not depend on
the parameter k.


title: Software Rasterization of 2 Billion Points in Real Time

abstract:
  We propose a software rasterization pipeline for point clouds that is capable
of brute-force rendering up to two billion points in real time (60fps).
Improvements over the state of the art are achieved by batching points in a way
that a number of batch-level optimizations can be computed before rasterizing
the points within the same rendering pass. These optimizations include frustum
culling, level-of-detail rendering, and choosing the appropriate coordinate
precision for a given batch of points directly within a compute workgroup.
Adaptive coordinate precision, in conjunction with visibility buffers, reduces
the number of loaded bytes for the majority of points down to 4, thus making
our approach several times faster than the bandwidth-limited state of the art.
Furthermore, support for LOD rendering makes our software-rasterization
approach suitable for rendering arbitrarily large point clouds, and to meet the
increased performance demands of virtual reality rendering.


title:
Quenched Invariance Principle for a Reflecting Diffusion in a Continuum
  Percolation Cluster

abstract:
  We consider a continuum percolation built over stationary ergodic point
processes. Assuming that the occupied region has a unique unbounded cluster and
the cluster satisfies volume regularity and isoperimetric condition, we prove a
quenched invariance principle for reflecting diffusions on the cluster.


title:
Implementation of AI/Deep Learning Disruption Predictor into a Plasma
  Control System

abstract:
  This paper reports on advances to the state-of-the-art deep-learning
disruption prediction models based on the Fusion Recurrent Neural Network
(FRNN) originally introduced a 2019 Nature publication. In particular, the
predictor now features not only the disruption score, as an indicator of the
probability of an imminent disruption, but also a sensitivity score in
real-time to indicate the underlying reasons for the imminent disruption. This
adds valuable physics-interpretability for the deep-learning model and can
provide helpful guidance for control actuators now that it is fully implemented
into a modern Plasma Control System (PCS). The advance is a significant step
forward in moving from modern deep-learning disruption prediction to real-time
control and brings novel AI-enabled capabilities relevant for application to
the future burning plasma ITER system. Our analyses use large amounts of data
from JET and DIII-D vetted in the earlier NATURE publication. In addition to
when a shot is predicted to disrupt, this paper addresses reasons why by
carrying out sensitivity studies. FRNN is accordingly extended to use many more
channels of information, including measured DIII-D signals such as (i) the
n1rms signal that is correlated with the n =1 modes with finite frequency,
including neoclassical tearing mode and sawtooth dynamics, (ii) the bolometer
data indicative of plasma impurity content, and (iii) q-min, the minimum value
of the safety factor relevant to the key physics of kink modes. The additional
channels and interpretability features expand the ability of the deep learning
FRNN software to provide information about disruption subcategories as well as
more precise and direct guidance for the actuators in a plasma control system.


title:
Enhanced Magnetic Quadrupole Moments in Nuclei with Octupole Deformation
  and their CP-violating effects in molecules

abstract:
  Nuclei with an octupole deformation have a non-zero electric octupole moment,
electric dipole moment (EDM), Schiff moment and magnetic quadrupole moment
(MQM) in the intrinsic frame which rotates with the nucleus. In a state with
definite angular momentum in the laboratory frame, these moments are forbidden
by parity (P) and time reversal invariance (T) conservation, meaning their
expectation values vanish due to nuclear rotation. However, nuclei with an
octupole deformation have doublets of close opposite parity rotational states
with the same spin, which are mixed by T,P-odd nuclear forces. This mixing
produces the orientation of the nuclear axis along nuclear spin and all moments
existing in the intrinsic frame appear in the laboratory frame (provided the
nuclear spin I is sufficiently large to allow such moment). Such a mechanism
produces enhanced T,P-violating nuclear moments. This enhancement also takes
place in nuclei with a soft octupole vibration mode. Schiff moments in such
nuclei have been calculated in previous works. In the present paper we consider
the magnetic quadrupole moment which appears in isotopes with nuclear spin $I
\geq 1$. Magnetic interaction between the nuclear MQM and electrons produces an
atomic EDM and T,P-violating nuclear spin - molecular axis interaction
constants for molecules in electronic states with non-zero electron angular
momentum. Measurements of these constants may be used to test CP-violation
theories and search for axion dark matter in atomic, molecular and solid state
experiments. Potential candidate nuclei include 153Eu, 161Dy, 221Fr, 223Fr,
223Ra, 223Rn, 225Ac, 227Ac, 229Th, 229Pa, 233U and 235U. We subsequently
consider molecules containing these nuclei (EuO, EuN+, RaF, AcO, AcN+, AcF+,
ThO and ThF+).


title: Weak topologies for unbounded nets in CAT($0$) spaces

abstract:
  Weak topologies that yield weak convergence for bounded sequences and nets in
CAT($0$) spaces have been studied in the past. We are here concerned with weak
topologies that yield weak convergence of unbounded sequences and nets. We
analyze two such topologies that generalize the weak topology on Hilbert spaces
and that agree with the strong topology on a CAT($0$) space if and only if the
space is locally compact.


title:
Explainable Online Lane Change Predictions on a Digital Twin with a
  Layer Normalized LSTM and Layer-wise Relevance Propagation

abstract:
  Artificial Intelligence and Digital Twins play an integral role in driving
innovation in the domain of intelligent driving. Long short-term memory (LSTM)
is a leading driver in the field of lane change prediction for manoeuvre
anticipation. However, the decision-making process of such models is complex
and non-transparent, hence reducing the trustworthiness of the smart solution.
This work presents an innovative approach and a technical implementation for
explaining lane change predictions of layer normalized LSTMs using Layer-wise
Relevance Propagation (LRP). The core implementation includes consuming live
data from a digital twin on a German highway, live predictions and explanations
of lane changes by extending LRP to layer normalized LSTMs, and an interface
for communicating and explaining the predictions to a human user. We aim to
demonstrate faithful, understandable, and adaptable explanations of lane change
prediction to increase the adoption and trustworthiness of AI systems that
involve humans. Our research also emphases that explainability and
state-of-the-art performance of ML models for manoeuvre anticipation go hand in
hand without negatively affecting predictive effectiveness.


title:
A study of photoionized gas in two HII regions of the N44 complex in the
  LMC using MUSE observations

abstract:
  We use the optical integral field observations with Multi-Unit Spectroscopic
Explorer (MUSE) on the Very Large Telescope, together with CLOUDY
photoionization models to study ionization structure and physical conditions of
two luminous HII regions in N44 star-forming complex of the Large Magellanic
Cloud. The spectral maps of various emission lines reveal a stratified
ionization geometry in N44 D1. The spatial distribution of [O I] 6300A emission
in N44 D1 indicates a partially covered ionization front at the outer boundary
of the H II region. These observations reveal that N44 D1 is a Blister HII
region. The [O I] 6300A emission in N44 C does not provide a well-defined
ionization front at the boundary, while patches of [S II] 6717 A and [O I]
6300A emission bars are found in the interior. The results of spatially
resolved MUSE spectra are tested with the photoionization models for the first
time in these HII regions. A spherically symmetric ionization-bounded model
with a partial covering factor, which is appropriate for a Blister HII region
can well reproduce the observed geometry and most of the diagnostic line ratios
in N44 D1. Similarly, in N44 C we apply a low density and optically thin model
based on the observational signatures. Our modeling results show that the
ionization structure and physical conditions of N44 D1 are mainly determined by
the radiation from an O5 V star. However, local X-rays, possibly from
supernovae or stellar wind, play a key role. In N44 C, the main contribution is
from three ionizing stars.


title: On The Model Size Selection For Speaker Identification

abstract:
  In this paper we evaluate the relevance of the model size for speaker
identification. We show that it is possible to improve the identification rates
if a different model size is used for each speaker. We also present some
criteria for selecting the model size, and a new algorithm that outperforms the
classical system with a fixed model size.


title: Nonlinear Vectorial Prediction with Neural Nets

abstract:
  In this paper we propose a nonlinear vectorial prediction scheme based on a
Multi Layer Perceptron. This system is applied to speech coding in an ADPCM
backward scheme. In addition a procedure to obtain a vectorial quantizer is
given, in order to achieve a fully vectorial speech encoder. We also present
several results with the proposed system


title: Technology and jobs: A systematic literature review

abstract:
  Does technological change destroy or create jobs? New technologies may
replace human workers, but can simultaneously create jobs if workers are needed
to use these technologies or if new economic activities emerge. Furthermore,
technology-driven productivity growth may increase disposable income,
stimulating a demand-induced expansion of employment. To synthesize the
existing knowledge on this question, we systematically review the empirical
literature on the past four decades of technological change and its impact on
employment, distinguishing between five broad technology categories (ICT,
Robots, Innovation, TFP-style, Other). Overall, we find across studies that the
labor-displacing effect of technology appears to be more than offset by
compensating mechanisms that create or reinstate labor. This holds for most
types of technology, suggesting that previous anxieties over widespread
technology-driven unemployment lack an empirical base, at least so far.
Nevertheless, low-skill, production, and manufacturing workers have been
adversely affected by technological change, and effective up- and reskilling
strategies should remain at the forefront of policy making along with targeted
social support systems.


title:
Learning Dynamic Correlations in Spatiotemporal Graphs for Motion
  Prediction

abstract:
  Human motion prediction is a challenge task due to the dynamic spatiotemporal
graph correlations in different motion sequences. How to efficiently represent
spatiotemporal graph correlations and model dynamic correlation variances
between different motion sequences is a challenge for spatiotemporal graph
representation in motion prediction. In this work, we present Dynamic
SpatioTemporal Graph Convolution (DSTD-GC). The proposed DSTD-GC decomposes
dynamic spatiotemporal graph modeling into a combination of Dynamic Spatial
Graph Convolution (DS-GC) and Dynamic Temporal Graph Convolution (DT-GC). As
human motions are subject to common constraints like body connections and
present dynamic motion patterns from different samples, we present Constrained
Dynamic Correlation Modeling strategy to represent the spatial/temporal graph
as a shared spatial/temporal correlation and a function to extract
temporal-specific /spatial-specific adjustments for each sample. The modeling
strategy represents the spatiotemporal graph with 28.6\% parameters of the
state-of-the-art static decomposition representation while also explicitly
models sample-specific spatiotemporal correlation variances. Moreover, we also
mathematically reformulating spatiotemporal graph convolutions and their
decomposed variants into a unified form and find that DSTD-GC relaxes strict
constraints of other graph convolutions, leading to a stronger representation
capability. Combining DSTD-GC with prior knowledge, we propose a powerful
spatiotemporal graph convolution network called DSTD-GCN which outperforms
state-of-the-art methods on the Human3.6M and CMU Mocap datasets in prediction
accuracy with fewest parameters.


title: REM: Routing Entropy Minimization for Capsule Networks

abstract:
  Capsule Networks ambition is to build an explainable and
biologically-inspired neural network model. One of their main innovations
relies on the routing mechanism which extracts a parse tree: its main purpose
is to explicitly build relationships between capsules. However, their true
potential in terms of explainability has not surfaced yet: these relationships
are extremely heterogeneous and difficult to understand. This paper proposes
REM, a technique which minimizes the entropy of the parse tree-like structure,
improving its explainability. We accomplish this by driving the model
parameters distribution towards low entropy configurations, using a pruning
mechanism as a proxy. We also generate static parse trees with no performance
loss, showing that, with REM, Capsule Networks build stronger relationships
between capsules.


title:
On the Cauchy problem of defocusing mKdV equation: long-time asymptotics
  under step-like initial data

abstract:
  We investigate the long time asymptotics for the Cauchy problem of the
defocusing modified Kortweg-de Vries (mKdV) equation with step-like initial
data, i.e., $q_{0}(x)=q(x,t=0)=C_{L}$, as $x<0$ and $q_{0}(x)=C_{R}$ as $x>0$,
where $C_{L}>C_{R}>0$ are arbitrary positive real numbers. We firstly develop
the direct scattering theory to establish the Riemann-Hilbert (RH) problem
associated with step-like initial data. Then by introducing the related $g$
function in different space-time regions and using the steepest descent
analysis, we deform the original matrix valued RH problem to explicitly solving
models. Finally we obtain the different long-time asymptotic behavior of the
solution of the Cauchy problem for defocusing mKdV equation in four different
space-time regions $\mathcal{R}_{\xi,I},
\mathcal{R}_{\xi,II},\mathcal{R}_{\xi,III}$ and $\mathcal{R}_{\xi,IV} $ in the
half-plane, where $\mathcal{R}_{\xi,I}$ and $ \mathcal{R}_{\xi,IV}$ are far
left field regions; and $\mathcal{R}_{\xi,II}$ and $ \mathcal{R}_{\xi,III}$ are
rarefaction wave regions.


title:
tPLCnet: Real-time Deep Packet Loss Concealment in the Time Domain Using
  a Short Temporal Context

abstract:
  This paper introduces a real-time time-domain packet loss concealment (PLC)
neural-network (tPLCnet). It efficiently predicts lost frames from a short
context buffer in a sequence-to-one (seq2one) fashion. Because of its seq2one
structure, a continuous inference of the model is not required since it can be
triggered when packet loss is actually detected. It is trained on 64h of
open-source speech data and packet-loss traces of real calls provided by the
Audio PLC Challenge. The model with the lowest complexity described in this
paper reaches a robust PLC performance and consistent improvements over the
zero-filling baseline for all metrics. A configuration with higher complexity
is submitted to the PLC Challenge and shows a performance increase of 1.07
compared to the zero-filling baseline in terms of PLC-MOS on the blind test set
and reaches a competitive 3rd place in the challenge ranking.


title:
A Modification of McFadden's $R^2$ for Binary and Ordinal Response
  Models

abstract:
  A lot of studies on the summary measures of predictive strength of
categorical response models consider the Likelihood Ratio Index (LRI), also
known as the McFadden-$R^2$, a better option than many other measures. We
propose a simple modification of the LRI that adjusts for the effect of the
number of response categories on the measure and that also rescales its values,
mimicking an underlying latent measure. The modified measure is applicable to
both binary and ordinal response models fitted by maximum likelihood. Results
from simulation studies and a real data example on the olfactory perception of
boar taint show that the proposed measure outperforms most of the widely used
goodness-of-fit measures for binary and ordinal models. The proposed $R^2$
interestingly proves quite invariant to an increasing number of response
categories of an ordinal model.


title:
An heterogeneous ubiquity theorem, application to self-similar measures
  with overlaps

abstract:
  In this article, one investigates in a very general frame mass transference
principles from ball to arbitrary open sets when the sequence of balls is
distributed according to a finite measure. As an application of the main
theorem, a mass transference principle is established when the measure is
self-similar with no separation conditions.


title:
GraFN: Semi-Supervised Node Classification on Graph with Few Labels via
  Non-Parametric Distribution Assignment

abstract:
  Despite the success of Graph Neural Networks (GNNs) on various applications,
GNNs encounter significant performance degradation when the amount of
supervision signals, i.e., number of labeled nodes, is limited, which is
expected as GNNs are trained solely based on the supervision obtained from the
labeled nodes. On the other hand,recent self-supervised learning paradigm aims
to train GNNs by solving pretext tasks that do not require any labeled nodes,
and it has shown to even outperform GNNs trained with few labeled nodes.
However, a major drawback of self-supervised methods is that they fall short of
learning class discriminative node representations since no labeled information
is utilized during training. To this end, we propose a novel semi-supervised
method for graphs, GraFN, that leverages few labeled nodes to ensure nodes that
belong to the same class to be grouped together, thereby achieving the best of
both worlds of semi-supervised and self-supervised methods. Specifically, GraFN
randomly samples support nodes from labeled nodes and anchor nodes from the
entire graph. Then, it minimizes the difference between two predicted class
distributions that are non-parametrically assigned by anchor-supports
similarity from two differently augmented graphs. We experimentally show that
GraFN surpasses both the semi-supervised and self-supervised methods in terms
of node classification on real-world graphs. The source code for GraFN is
available at https://github.com/Junseok0207/GraFN.


title:
Extraction of optimal subsequences of sequence of balls, and application
  to optimality estimates of mass transference principles

abstract:
  In this article, we prove that from any sequence of balls whose associated
limsup set has full $\mu$-measure, one can extract a well-distributed
subsequence of balls. From this, we deduce the optimality of various lower
bounds for the Hausdorff dimension of limsup sets of balls obtained by mass
transference principles.


title: Multivariate Microaggregation of Set-Valued Data

abstract:
  Data controllers manage immense data, and occasionally, it is released
publically to help the researchers to conduct their studies. However, this
publically shared data may hold personally identifiable information (PII) that
can be collected to re-identify a person. Therefore, an effective anonymization
mechanism is required to anonymize such data before it is released publically.
Microaggregation is one of the Statistical Disclosure Control (SDC) methods
that are widely used by many researchers. This method adapts the k-anonymity
principle to generate k-indistinguishable records in the same clusters to
preserve the privacy of the individuals. However, in these methods, the size of
the clusters is fixed (i.e., k records), and the clusters generated through
these methods may hold non-homogeneous records. By considering these issues, we
propose an adaptive size clustering technique that aggregates homogeneous
records in similar clusters, and the size of the clusters is determined after
the semantic analysis of the records. To achieve this, we extend the MDAV
microaggregation algorithm to semantically analyze the unstructured records by
relying on the taxonomic databases (i.e., WordNet), and then aggregating them
in homogeneous clusters. Furthermore, we propose a distance measure that
determines the extent to which the records differ from each other, and based on
this, homogeneous adaptive clusters are constructed. In experiments, we
measured the cohesiveness of the clusters in order to gauge the homogeneity of
records. In addition, a method is proposed to measure information loss caused
by the redaction method. In experiments, the results show that the proposed
mechanism outperforms the existing state-of-the-art solutions.


title: Swarm gradient dynamics for global optimization: the density case

abstract:
  Using jointly geometric and stochastic reformulations of nonconvex problems
and exploiting a Monge-Kantorovich gradient system formulation with vanishing
forces, we formally extend the simulated annealing method to a wide class of
global optimization methods. Due to an inbuilt combination of a gradient-like
strategy and particles interactions, we call them swarm gradient dynamics. As
in the original paper of Holley-Kusuoka-Stroock, the key to the existence of a
schedule ensuring convergence to a global minimizer is a functional inequality.
One of our central theoretical contributions is the proof of such an inequality
for one-dimensional compact manifolds. We conjecture the inequality to be true
in a much wider setting. We also describe a general method allowing for global
optimization and evidencing the crucial role of functional inequalities {\`a}
la {\L}ojasiewicz.


title: Diagrammatic Analysis for Parameterized Quantum Circuits

abstract:
  Diagrammatic representations of quantum algorithms and circuits offer novel
approaches to their design and analysis. In this work, we describe extensions
of the ZX-calculus especially suitable for parameterized quantum circuits, in
particular for computing observable expectation values as functions of or for
fixed parameters, which are important algorithmic quantities in a variety of
applications ranging from combinatorial optimization to quantum chemistry. We
provide several new ZX-diagram rewrite rules and generalizations for this
setting. In particular, we give formal rules for dealing with linear
combinations of ZX-diagrams, where the relative complex-valued scale factors of
each diagram must be kept track of, in contrast to most previously studied
single-diagram realizations where these coefficients can be effectively
ignored. This allows us to directly import a number useful relations from the
operator analysis to ZX-calculus setting, including causal cone and quantum
gate commutation rules. We demonstrate that the diagrammatic approach offers
useful insights into algorithm structure and performance by considering several
ans\"atze from the literature including realizations of hardware-efficient
ans\"atze and QAOA. We find that by employing a diagrammatic representation,
calculations across different ans\"atze can become more intuitive and
potentially easier approach systematically than by alternative means. Finally,
we outline how diagrammatic approaches may aid in the design and study of new
and more effective quantum circuit ans\"atze.


title:
Phase-field modeling of paramagnetic austenite-ferromagnetic martensite
  transformation coupled with mechanics and micromagnetics

abstract:
  A three-dimensional phase-field model is proposed for simulating the magnetic
martensitic phase transformation. The model considers a paramagnetic cubic
austenite to ferromagnetic tetragonal martensite transition, as it occurs in
magnetic Heusler alloys like Ni2 MnGa, and is based on a Landau 2-3-4
polynomial with temperature dependent coefficients. The
paramagnetic-ferromagnetic transition is recaptured by interpolating the
micromagnetic energy as a function of the order parameter for the ferroelastic
domains. The model is numerically implemented in real space by finite element
(FE) method. FE simulations in the martensitic state show that the model is
capable to correctly recapture the ferroelastic and -magnetic microstructures,
as well as the influence of external stimuli. Simulation results indicate that
the paramagnetic austenite to ferromagnetic martensite transition shifts
towards higher temperatures when a magnetic field or compressive stress is
applied. The dependence of the phase transition temperature shift on the
strength of the external stimulus is uncovered as well. Simulation of the phase
transition in magnetocaloric materials is of high interest for the development
of energy-efficient magnetocaloric cooling devices.


title: On Principal Value and Standard Extension of Distributions

abstract:
  For a holomorphic function f on a complex manifold M we explain in this
article that the distribution associated to |f | 2$\alpha$ (Log|f | 2) q f --N
by taking the corresponding limit on the sets {|f | $\ge$ $\epsilon$} when
$\epsilon$ goes to 0, coincides for ($\alpha$) non negative and q, N $\in$ N,
with the value at $\lambda$ = $\alpha$ of the meromorphic extension of the
distribution |f | 2$\lambda$ (Log|f | 2) q f --N. This implies that any
distribution in the D Mmodule generated by such a distribution has the Standard
Extension Property. This implies a non torsion result for the D M-module
generated by such a distribution. As an application of this result we determine
generators for the conjugate modules of the regular holonomic D-modules
associated to z($\sigma$) $\lambda$ , the power $\lambda$, where $\lambda$ is
any complex number, of the (multivalued) root of the universal equation of
degree k, z k + k j=1 (--1) h $\sigma$ h z k--h = 0 whose structure is studied
in [4].


title:
Characteristic polynomials of the weak order on classical and affine
  Coxeter groups

abstract:
  We find a simple product formula for the characteristic polynomial of the
permutations with a fixed descent set under the weak order. As a corollary we
obtain a simple product formula for the characteristic polynomial of
alternating permutations. We generalize these results to Coxeter groups. We
also find a formula for the generating function for the characteristic
polynomials of classical Coxeter groups, which is then related to affine
Coxeter groups.


title:
Organic Molecular Dynamics and Charge-carrier Lifetime in Lead Iodide
  Perovskite MAPbI$_3$

abstract:
  The long charge carrier lifetime of the hybrid organic-inorganic perovskites
(HOIPs) is the key for their remarkable performance as a solar cell material.
The microscopic mechanism for the long lifetime is still in debate. Here, by
using muon spin relaxation technique that probes the fluctuation of local
magnetic fields, we show that the muon depolarization rate ($\Delta$) of a
prototype HOIP methylammonium lead iodide (MAPbI$_3$) shows a sharp decrease
with increasing temperature in two steps above 120 K and 190 K across the
structural transition from orthorhombic to tetragonal structure at 162 K. Our
analysis shows that the reduction of $\Delta$ is quantitatively in agreement
with the expected behavior due to the rapid development of MA jumping rotation
around the $C_3$ and $C_4$ symmetry axes. Our results provide direct evidence
for the intimate relation between the rotation of the electric dipoles of MA
molecules and the charge carrier lifetime in HOIPs.


title: Stacked Tensegrity Mechanism for Medical Application

abstract:
  In this article a multi-segmented planar tensegrity mechanism was presented.
This mechanism has a three-segment structure with each segment residing on top
of another. The size of the segments may decrease proportionally from base to
top, resulting in a tapered shape from base to tip like an elephant trunk. The
system was mechanically formulated as having linear springs and cables
functioning as actuators. The singularities, as well as the stability of the
parallel mechanism, were analyzed by using the principle of minimum energy.
Optimization was also done to obtain the greatest angular deflection for a
segment according to a ratio between the size of the base and the moving
platform of the robotic system. The result of this work is a family of
mechanisms that can generate the same workspace for different stability
properties.


title: Towards Immersive Humanitarian Visualizations

abstract:
  This paper introduces immersive humanitarian visualization as a promising
research area in information visualization. Humanitarian visualizations are
data visualizations designed to promote human welfare. This paper explains why
immersive display technologies taken broadly (e.g, virtual reality, augmented
reality, ambient displays and physical representations) open up a range of
opportunities for humanitarian visualization. In particular, immersive displays
offer ways to make remote and hidden human suffering more salient. They also
offer ways to communicate quantitative facts together with qualitative
information and visceral experiences, in order to provide a holistic
understanding of humanitarian issues that could support more informed
humanitarian decisions. But despite some promising preliminary work, immersive
humanitarian visualization has not taken off as a research topic yet. The goal
of this paper is to encourage, motivate, and inspire future research in this
area.


title:
Regularity of the value function and quantitative propagation of chaos
  for mean field control problems

abstract:
  We investigate a mean field optimal control problem obtained in the limit of
the optimal control of large particle systems with forcing and terminal data
which are not assumed to be convex. We prove that the value function, which is
known to be Lipschitz continuous but not of class C 1 , in general, without
convexity, is actually smooth in an open and dense subset of the space of times
and probability measures. As a consequence, we prove a new quantitative
propagation of chaos-type result for the optimal solutions of the particle
system starting from this open and dense set.


title:
Convergence Analysis of Generalized ADMM with Majorization for Linearly
  Constrained Composite Convex Optimization

abstract:
  The generalized alternating direction method of multipliers (ADMM) of Xiao et
al. [{\tt Math. Prog. Comput., 2018}] aims at the two-block linearly
constrained composite convex programming problem, in which each block is in the
form of "nonsmooth + quadratic". However, in the case of non-quadratic (but
smooth), this method may fail unless the favorable structure of 'nonsmooth +
smooth' is no longer used. This paper aims to remedy this defect by using a
majorized technique to approximate the augmented Lagrangian function, so that
the corresponding subprobllem can be decomposed into some smaller problems and
then solved separately. Furthermore, the recent symmetric Gauss-Seidel (sGS)
decomposition theorem guarantees the equivalence between the bigger subproblem
and these smaller ones. This paper focuses on convergence analysis, that is, we
prove that the sequence generated by the proposed method converges globally to
a Karush-Kuhn-Tucker point of the considered problem. Finally, we do some
numerical experiments on a kind of simulated convex composite optimization
problems which illustrate that the proposed method is more efficient than its
compared ones.