title:
Nitrogen doped In$_2$O$_3$-ZnO nanocomposite thin film based sensitive
  and selective ethanol sensor

abstract:
  Nanocomposite metal oxide thin films exhibit assuring qualities in the field
of gas sensors because of the collective opportunities provided by the
heterointerface formation. In this work, we present the synthesis of nitrogen
doped mesoporous In$_2$O$_3$-ZnO nano-composite thin films by simple wet
chemical method using urea as the nitrogen precursor. SEM investigation
suggests formation of mesoporous nano-composite thin films, where the
uniformity of surface pore distribution depends on the relative proportion of
In$_2$O$_3$ and ZnO in the composites. HRTEM investigation suggest formation of
sharp interfaces between N-In$_2$O$_3$ and N-ZnO grains in the nano-composite
thin films. The nano-composite thin films have been tested for their ethanol
sensing performance over an extensive range of temperature, ethanol vapor
concentration and relative humidity. Nitrogen doped nano-composite thin film
with equal proportion of In$_2$O$_3$ and ZnO exhibits excellent ethanol sensing
performance at a reasonable operating temperature (~94 % at 200 {\deg}C for 50
ppm of ethanol), fast response time (~two seconds), stability over time,
enhanced resilience against humidity and selectivity to ethanol over various
other volatile organic compounds.


title:
Cosmic acceleration and energy conditions in $\Lambda$CDM symmetric
  teleparallel $f(Q)$ gravity

abstract:
  In this paper, we study a spatially homogeneous and isotropic FLRW
cosmological model in framework of the symmetric teleparallel $f(Q)$ gravity
proposed by Jimenez et al. (Physical Review D 98.4 (2018): 044048), where the
non-metricity $Q$ is responsible for the gravitational interaction. For the
whole analysis we have considered a special form of $f\left(Q\right)$ gravity
function i.e. $f\left( Q\right) =\alpha Q^{n}+\beta $ (where $\alpha $, $\beta
$, and $n$ are the dynamical model constant parameters) in the direction of
$t\left( z\right) =\frac{kt_{0}}{b}f\left( z\right)$, where, $f\left( z\right)
=W\left[ \frac{b}{k}e^{\frac{b-\ln \left(1+z\right) }{k}}\right] $ and $W$
denotes the Lambert function. We have discussed two cases of the function:
linear $f(Q)$ model towards $ n=1$ and quadratic for $n=2$. In both the model
we had verified the validity of the models with the help of energy conditions
along with some physical parameters like equation of state parameter, jerk
parameter, statefinder parameters are also discussed in details and compared it
with the observational data.


title:
Generalized Langevin Equation with a Non-Linear Potential of Mean Force
  and Non-Linear Memory Friction From a Hybrid Projection Scheme

abstract:
  We introduce a hybrid projection scheme that combines linear Mori projection
and conditional Zwanzig projection techniques and use it to derive a
Generalized Langevin Equation (GLE) for a general interacting many-body system.
The resulting GLE includes i) explicitly the potential of mean force (PMF) that
describes the equilibrium distribution of the system in the chosen space of
reaction coordinates, ii) a random force term that explicitly depends on the
initial state of the system, and iii) a memory friction contribution that
splits into two parts: a part that is linear in the past reaction-coordinate
velocity and a part that is in general non-linear in the past reaction
coordinates but does not depend on velocities. Our hybrid scheme thus combines
all desirable properties of the Zwanzig and Mori projection schemes. The
non-linear memory friction contribution is shown to be related to correlations
between the reaction-coordinate velocity and the random force. We present a
numerical method to compute all parameters of our GLE, in particular, the
non-linear memory friction function and the random force distribution, from a
trajectory in reaction coordinate space. We apply our method to the
dihedral-angle dynamics of a butane molecule in water obtained from atomistic
molecular dynamics simulations. For this example, we demonstrate that
non-linear memory friction is present and that the random force exhibits
significant non-Gaussian corrections. We also present the derivation of the GLE
for multidimensional reaction coordinates that are general functions of all
positions in the phase space of the underlying many-body system; this
corresponds to a systematic coarse-graining procedure that preserves not only
the correct equilibrium behavior but also the correct dynamics of the
coarse-grained system.


title:
Systematic Builder for All-Atom Simulations of Plastically Bonded
  Explosives

abstract:
  The shock to detonation transition in heterogeneous plastically bonded
explosives is dominated by energy localization into hotspots that arise from
the interaction of the shockwave with microstructural features and defects. The
complex polycrystalline structure of these materials leads to a network of
hotspot that can coalesce into deflagration and detonation waves. Significant
progress has been made on the formation and potency of hotspots using atomistic
simulations, but most of the work has focused on ideal and isolated defects.
Hence, developed a method, denoted PBXGen, to build realistic PBX
microstructures for all-atom simulations. PBXGen is generally applicable, and
we demonstrate it with two systems: an RDX-polystyrene PBX with a 3D
microstructure and a TATB-polystyrene with columnar grains. The resulting
structure exhibit key features of PBXs, albeit at smaller scales, and are
validated against experimental mechanical and shock properties.


title: Zero-Shot Aspect-Based Sentiment Analysis

abstract:
  Aspect-based sentiment analysis (ABSA) typically requires in-domain annotated
data for supervised training/fine-tuning. It is a big challenge to scale ABSA
to a large number of new domains. This paper aims to train a unified model that
can perform zero-shot ABSA without using any annotated data for a new domain.
We propose a method called contrastive post-training on review Natural Language
Inference (CORN). Later ABSA tasks can be cast into NLI for zero-shot transfer.
We evaluate CORN on ABSA tasks, ranging from aspect extraction (AE), aspect
sentiment classification (ASC), to end-to-end aspect-based sentiment analysis
(E2E ABSA), which show ABSA can be conducted without any human annotated ABSA
data.


title: Mitigating the quantum hype

abstract:
  We are in the midst of quantum hype with some excessive claims of quantum
computing potential, many vendors' and even some research organizations'
exaggerations, and a funding frenzy for very low technology readiness level
startups. Governments are contributing to this hype with their large quantum
initiatives and their technology sovereignty aspirations. Technology hypes are
not bad per se since they create emulation, drive innovations and also
contribute to attracting new talents. It works as scientists and vendors
deliver progress and innovation on a continuous basis after a so-called peak of
expectations. It fails with exaggerated overpromises and underdeliveries that
last too long. It could cut short research and innovation funding, creating
some sort of quantum winter. After looking at the shape and form of technology
and science hypes and driving some lessons from past hypes, we investigate the
current quantum hype and its specifics. We find that, although there is some
significant uncertainty on the potential to create real scalable quantum
computers, the scientific and vendor fields are relatively sane and solid
compared to other technology hypes. The vendors hype has some profound and
disruptive impact on the organization of fundamental research. Also, quantum
technologies comprise other fields like quantum telecommunications and quantum
sensing with a higher technology readiness level, which are less prone to hype.
We then make some proposals to mitigate the potential negative effects of the
current quantum hype including recommendations on scientific communication to
strengthen the trust in quantum science, vendor behavior improvements,
benchmarking methodologies, public education and putting in place a responsible
research and innovation approach.


title:
Knowledge Graph Based Waveform Recommendation: A New Communication
  Waveform Design Paradigm

abstract:
  Traditionally, a communication waveform is designed by experts based on
communication theory and their experiences on a case-by-case basis, which is
usually laborious and time-consuming. In this paper, we investigate the
waveform design from a novel perspective and propose a new waveform design
paradigm with the knowledge graph (KG)-based intelligent recommendation system.
The proposed paradigm aims to improve the design efficiency by structural
characterization and representations of existing waveforms and intelligently
utilizing the knowledge learned from them. To achieve this goal, we first build
a communication waveform knowledge graph (CWKG) with a first-order neighbor
node, for which both structured semantic knowledge and numerical parameters of
a waveform are integrated by representation learning. Based on the developed
CWKG, we further propose an intelligent communication waveform recommendation
system (CWRS) to generate waveform candidates. In the CWRS, an improved
involution1D operator, which is channel-agnostic and space-specific, is
introduced according to the characteristics of KG-based waveform representation
for feature extraction, and the multi-head self-attention is adopted to weigh
the influence of various components for feature fusion. Meanwhile, multilayer
perceptron-based collaborative filtering is used to evaluate the matching
degree between the requirement and the waveform candidate. Simulation results
show that the proposed CWKG-based CWRS can automatically recommend waveform
candidates with high reliability.


title:
Rain energy harvesting using atomically thin Gadolinium Telluride
  decorated 3D Printed nanogenerator

abstract:
  The 3D printing technology offers an innovative approach for developing
energy storage devices to create facile and low-cost customized electrodes for
modern electronics. Generating electric potential by moving a droplet of ionic
solution over two-dimensional (2D) materials is a novel method for rain energy
harvesting. This work demonstrated a liquid-solid contact electrification-based
3D printed nanogenerator where raindrop passes through the positively charged
ultrathin Gadolinium Telluride (Gd2Te3) sheets. Experimental results showed
that voltage as high as ~0.6 V could be generated by moving a droplet of ionic
solution on the decorated 3D printed nanogenerator. The output efficiency of
the nanogenerator is increased ~400% by enhancing the surface area of copious
3D printed porous structures. Density Functional Theory (DFT) calculations are
done, revealing that the high electrical conductivity of (112) surface of
Gd2Te3 is due to the p-type charge carriers. Additionally, we illustrate the
enhancement of the output performance (~0.8V) by using a graphite rod and
arbitrarily manipulating the surface charge. Therefore, this work can open up a
new avenue to advance scientific research of Blue energy harvesting and tackle
the energy crisis.


title:
Searches for $pp\rightarrow H^{\pm}h\rightarrow W^{\pm}hh$ signal in the
  MSSM model

abstract:
  We considered the new signature of a charged Higgs boson, $pp\rightarrow
H^{\pm}h\rightarrow W^{\pm}hh$, which is almost background free as the
promising to study of supersymmetry at the LHC with both $\sqrt{s} = 13$ TeV
and $\sqrt{s}= 14$ TeV. In the framework of MSSM model we used the latest
theoretical parameter space, which is consistent with the experimental
constraints from Large Hadron Collider and direct or indirect searches. In the
surviving regions with the help of Pythia 8.3 and Feynhiggs programs it was
found that production cross sections, $\sigma(pp\rightarrow H^{\pm}h)$, and
Branching Ratios, $BR(H^{\pm}\rightarrow W^{\pm}h)$, are the largest at
Benchmark Points 4 and 14 correspondingly.


title: $\mathcal{F}$-EBM: Energy Based Learning of Functional Data

abstract:
  Energy-Based Models (EBMs) have proven to be a highly effective approach for
modelling densities on finite-dimensional spaces. Their ability to incorporate
domain-specific choices and constraints into the structure of the model through
composition make EBMs an appealing candidate for applications in physics,
biology and computer vision and various other fields. In this work, we present
a novel class of EBM which is able to learn distributions of functions (such as
curves or surfaces) from functional samples evaluated at finitely many points.
Two unique challenges arise in the functional context. Firstly, training data
is often not evaluated along a fixed set of points. Secondly, steps must be
taken to control the behaviour of the model between evaluation points, to
mitigate overfitting. The proposed infinite-dimensional EBM employs a latent
Gaussian process, which is weighted spectrally by an energy function
parameterised with a neural network. The resulting EBM has the ability to
utilize irregularly sampled training data and can output predictions at any
resolution, providing an effective approach to up-scaling functional data. We
demonstrate the efficacy of our proposed approach for modelling a range of
datasets, including data collected from Standard and Poor's 500 (S\&P) and UK
National grid.


title: An updated glitch rate law inferred from radio pulsars

abstract:
  Radio pulsar glitches probe far-from-equilibrium processes involving stress
accumulation and relaxation in neutron star interiors. Previous studies of
glitch rates have focused on individual pulsars with as many recorded glitches
as possible. In this work we analyze glitch rates using all available data
including objects that have glitched never or once. We assume the glitch rate
follows a homogeneous Poisson process, and therefore exclude pulsars which
exhibit quasiperiodic glitching behavior. Calculating relevant Bayes factors
shows that a model in which the glitch rate $\lambda$ scales as a power of the
characteristic age $\tau$ is preferred over models which depend arbitrarily on
powers of the spin frequency $\nu$ and/or its time derivative $\dot{\nu}$. For
$\lambda = A (\tau/\tau\vref)^{-\gamma}$, where $\tau_{\rm ref}=1\ {\rm yr}$ is
a reference time, the posterior distributions are unimodal with
$A=\ModelAAglitch\ \rm{yr}^{-1}$, and $\gamma=\ModelAgammaglitch$. Importantly,
the data exclude with 99\% confidence the possibility $\gamma=1$ canvassed in
the literature. When objects with zero recorded glitches are included, the
age-based rate law is still preferred and the posteriors change to give
$A=\ModelAAall\ \rm{yr}^{-1}$, and $\gamma=\ModelAgammaall$. The updated
estimates still support increased glitch activity for younger pulsars, while
demonstrating that the large number of objects with zero glitches contain
important statistical information about the rate, provided that they are part
of the same population as opposed to a disjoint population which never glitches
for some unknown physical reason.


title:
Emerging negative Poisson's ratio driven by strong intralayer
  interaction response in rectangular transition metal chalcogenides

abstract:
  Auxetic behavior quantified by the negative Poisson's ratio (NPR) is commonly
attributed to geometry evolution with re-entrant mechanism or other mechanical
factors, which is thought to be independent of electronic structures. Thus,
searching for electronic effect dominated auxetic behavior is challenging.
Herein, from state-of-the-art first-principles calculations, by studying a
class of two-dimensional (2D) transition metal chalcogenides (TMCs), namely
X2Y2-type (X=Cu, Ag, Au; Y=O, S, Se) rectangular TMCs (R-TMCs), we identify
that the monolayer R-Cu2Se2 unconventionally demonstrates a
structure-independent anisotropic NPR. In contrast, the NPR is absent in other
R-TMCs. The emerging NPR is attributed to the strong strain response of
intralayer interaction in R-Cu2Se2, which can be traced to the lone pair
electrons and weak electronegativity of Se atoms under multi-orbital
hybridization. The emerging NPR would make R-Cu2Se2 a promising candidate in
electronics protection, and our study would provide valuable clues and useful
guidance for designing advanced auxetic materials.


title:
Spatial, spectral, temporal and polarisation resolved state tomography
  of light

abstract:
  The ability to measure polarisation, spectrum, temporal dynamics, and spatial
amplitude and phase of optical beams is essential to study fundamental
phenomena in laser dynamics, telecommunications and nonlinear optics. Current
characterisation techniques only apply in limited contexts. Non-interferometric
methods typically lack access to spatial phase, while phase-sensitive
approaches necessitate either an auxiliary reference source or an adequate
self-reference, neither of which is universally available. Regardless of the
reference, deciphering complex wavefronts of multiple co-propagating incoherent
fields remains particularly challenging. Here, we harness the principles of
quantum state tomography to circumvent these limitations. A full description of
an unknown beam is retrieved by measuring its temporally and spectrally
resolved density matrices for both polarisations, using a spatial light
modulator to display projective holograms and a single-mode fibre to guide the
collected signal to a high-speed photodiode and a spectrometer. Despite no
spatial resolution of the detector and the intensity-only character of the
collected signal, the method resolves multiple arbitrary spatial fields within
a single beam, including their phase and amplitude, as well as their spatial
coherence. Leveraging the coherence information unlocks unambiguous
determination of the spectral and temporal evolution of mutually incoherent
fields, even when these spectrally overlap or have an identical time delay. We
demonstrate these hallmark features by characterising the rich spatiotemporal
and spectral output of a vertical-cavity surface-emitting laser diode that has
so far resisted full analysis using existing techniques.


title:
Identifying stimulus-driven neural activity patterns in multi-patient
  intracranial recordings

abstract:
  Identifying stimulus-driven neural activity patterns is critical for studying
the neural basis of cognition. This can be particularly challenging in
intracranial datasets, where electrode locations typically vary across
patients. This chapter first presents an overview of the major challenges to
identifying stimulus-driven neural activity patterns in the general case. Next,
we will review several modality-specific considerations and approaches, along
with a discussion of several issues that are particular to intracranial
recordings. Against this backdrop, we will consider a variety of within-subject
and across-subject approaches to identifying and modeling stimulus-driven
neural activity patterns in multi-patient intracranial recordings. These
approaches include generalized linear models, multivariate pattern analysis,
representational similarity analysis, joint stimulus-activity models,
hierarchical matrix factorization models, Gaussian process models, geometric
alignment models, inter-subject correlations, and inter-subject functional
correlations. Examples from the recent literature serve to illustrate the major
concepts and provide the conceptual intuitions for each approach.


title:
Smartphone-based Hard-braking Event Detection at Scale for Road Safety
  Services

abstract:
  Road crashes are the sixth leading cause of lost disability-adjusted
life-years (DALYs) worldwide. One major challenge in traffic safety research is
the sparsity of crashes, which makes it difficult to achieve a fine-grain
understanding of crash causations and predict future crash risk in a timely
manner. Hard-braking events have been widely used as a safety surrogate due to
their relatively high prevalence and ease of detection with embedded vehicle
sensors. As an alternative to using sensors fixed in vehicles, this paper
presents a scalable approach for detecting hard-braking events using the
kinematics data collected from smartphone sensors. We train a Transformer-based
machine learning model for hard-braking event detection using concurrent sensor
readings from smartphones and vehicle sensors from drivers who connect their
phone to the vehicle while navigating in Google Maps. The detection model shows
superior performance with a $0.83$ Area under the Precision-Recall Curve
(PR-AUC), which is $3.8\times$better than a GPS speed-based heuristic model,
and $166.6\times$better than an accelerometer-based heuristic model. The
detected hard-braking events are strongly correlated with crashes from publicly
available datasets, supporting their use as a safety surrogate. In addition, we
conduct model fairness and selection bias evaluation to ensure that the safety
benefits are equally shared. The developed methodology can benefit many safety
applications such as identifying safety hot spots at road network level,
evaluating the safety of new user interfaces, as well as using routing to
improve traffic safety.


title:
Robust Dynamic State Estimator of Integrated Energy Systems based on
  Natural Gas Partial Differential Equations

abstract:
  The reliability and precision of dynamic database are vital for the optimal
operating and global control of integrated energy systems. One of the effective
ways to obtain the accurate states is state estimations. A novel robust dynamic
state estimation methodology for integrated natural gas and electric power
systems is proposed based on Kalman filter. To take full advantage of
measurement redundancies and predictions for enhancing the estimating accuracy,
the dynamic state estimation model coupling gas and power systems by gas
turbine units is established. The exponential smoothing technique and gas
physical model are integrated in Kalman filter. Additionally, the time-varying
scalar matrix is proposed to conquer bad data in Kalman filter algorithm. The
proposed method is applied to an integrated gas and power systems formed by
GasLib-40 and IEEE 39-bus system with five gas turbine units. The simulating
results show that the method can obtain the accurate dynamic states under three
different measurement error conditions, and the filtering performance are
better than separate estimation methods. Additionally, the proposed method is
robust when the measurements experience bad data.


title: Brain-Computer-Interface controlled robot via RaspberryPi and PiEEG

abstract:
  This paper presents Open-source software and a developed shield board for the
Raspberry Pi family of single-board computers that can be used to read EEG
signals. We have described the mechanism for reading EEG signals and
decomposing them into a Fourier series and provided examples of controlling
LEDs and a toy robot by blinking. Finally, we discussed the prospects of the
brain-computer interface for the near future and considered various methods for
controlling external mechanical objects using real-time EEG signals.


title:
Discovery of a Short-Period and Unusually Helium-Deficient Dwarf Nova
  KSP-OT-201701a by the KMTNet Supernova Program

abstract:
  We present the first ever discovery of a short-period and unusually
helium-deficient dwarf nova KSP-OT-201701a by the Korea Microlensing Telescope
Network Supernova Program. The source shows three superoutbursts, each led by a
precursor outburst, and several normal outbursts in BVI during the span of ~2.6
years with supercycle and normal cycle lengths of about 360 and 76 days,
respectively. Spectroscopic observations near the end of a superoutburst reveal
the presence of strong double-peaked HI emission lines together with weak HeI
emission lines. The helium-to-hydrogen intensity ratios measured by
HeI{\lambda}5876 and H{\alpha} lines are 0.10 {\pm} 0.01 at a quiescent phase
and 0.26 {\pm} 0.04 at an outburst phase, similar to the ratios found in
long-period dwarf novae while significantly lower than those in helium
cataclysmic variables (He CVs). Its orbital period of 51.91 {\pm} 2.50 minutes,
which is estimated based on time series spectroscopy, is a bit shorter than the
superhump period of 56.52 {\pm} 0.19 minutes, as expected from the
gravitational interaction between the eccentric disk and the secondary star. We
measure its mass ratio to be 0.37^{+0.32}_{-0.21} using the superhump period
excess of 0.089 {\pm} 0.053. The short orbital period, which is under the
period minimum, the unusual helium deficiency, and the large mass ratio suggest
that KSP-OT-201701a is a transition object evolving to a He CV from a
long-period dwarf nova with an evolved secondary star.


title:
CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in
  Dynamic Environments

abstract:
  The dynamic factors in the environment will lead to the decline of camera
localization accuracy due to the violation of the static environment assumption
of SLAM algorithm. Recently, some related works generally use the combination
of semantic constraints and geometric constraints to deal with dynamic objects,
but problems can still be raised, such as poor real-time performance, easy to
treat people as rigid bodies, and poor performance in low dynamic scenes. In
this paper, a dynamic scene-oriented visual SLAM algorithm based on object
detection and coarse-to-fine static probability named CFP-SLAM is proposed. The
algorithm combines semantic constraints and geometric constraints to calculate
the static probability of objects, keypoints and map points, and takes them as
weights to participate in camera pose estimation. Extensive evaluations show
that our approach can achieve almost the best results in high dynamic and low
dynamic scenarios compared to the state-of-the-art dynamic SLAM methods, and
shows quite high real-time ability.


title:
Schreier's type formulae and two scales for growth of Lie algebras and
  groups

abstract:
  Let $G$ be a free group of rank $n$ and $H\subset G$ its subgroup of finite
index. Then $H$ is also a free group and the rank $m$ of $H$ is determined by
Schreier's formula $m-1=(n-1)\cdot|G:H|.$
  Any subalgebra of a free Lie algebra is also free. But a straightforward
analogue of Schreier's formula for free Lie algebras does not exist, because
any subalgebra of finite codimension has an infinite number of generators.
  But the appropriate Schreier's formula for free Lie algebras exists in terms
of formal power series. There exists also a version in terms of exponential
generating functions. This is a survey on how these formulas are applied to
study 1) growth of finitely generated Lie algebras and groups and 2) the
codimension growth of varieties of Lie algebras. First, these formulae allow to
specify explicit formulas for generating functions of respective types for free
solvable (or more generally, polynilpotent) Lie algebras. Second, these
explicit formulas for generating functions are used to derive asymptotic for
these two types of the growth. These results can be viewed as analogues of the
Witt formula for free Lie algebras and groups. In case of Lie algebras, we
obtain two scales for respective types of growth. We also shortly mention the
situation on growth for other types of linear algebras.


title:
Distribution Embedding Networks for Meta-Learning with Heterogeneous
  Covariate Spaces

abstract:
  We propose Distribution Embedding Networks (DEN) for classification with
small data using meta-learning techniques. Unlike existing meta-learning
approaches that focus on image recognition tasks and require the training and
target tasks to be similar, DEN is specifically designed to be trained on a
diverse set of training tasks and applied on tasks whose number and
distribution of covariates differ vastly from its training tasks. Such property
of DEN is enabled by its three-block architecture: a covariate transformation
block followed by a distribution embedding block and then a classification
block. We provide theoretical insights to show that this architecture allows
the embedding and classification blocks to be fixed after pre-training on a
diverse set of tasks; only the covariate transformation block with relatively
few parameters needs to be updated for each new task. To facilitate the
training of DEN, we also propose an approach to synthesize binary
classification training tasks, and demonstrate that DEN outperforms existing
methods in a number of synthetic and real tasks in numerical studies.


title: Dirty derivatives for output feedback stabilization

abstract:
  Dirty derivatives are routinely used in industrial settings, particularly in
the implementation of the derivative term in PID control, and are especially
appealing due to their noise-attenuation and model-free characteristics. In
this paper, we provide a Lyapunov-based proof for the stability of linear
time-invariant control systems in controller canonical form when utilizing
dirty derivatives in place of observers for the purpose of output feedback.
This is, to the best of the authors' knowledge, the first time that stability
proofs are provided for the use of dirty derivatives in lieu of derivatives of
different orders. In the spirit of adaptive control, we also show how dirty
derivatives can be used for output feedback control when the control gain is
unknown.


title:
Switchable Topological Phase Transition and Novel Nonlinear Optical
  Properties in ReC2H Monolayer

abstract:
  Extensive investigations on topological phase transition (TPT) in
three-dimensional compounds have been done. whereas, rare in two-dimensional
systems, let alone noncentrosymmetric materials. In this work, based on
first-principles calculations, we explore an inversion symmetry broken
structural ReC2H monolayer. We reveal that it undergoes two TPTs, namely from
normal insulator to Z2 topological insulator and back to normal insulator, at
the critical biaxial strain of 2.3% and 7.8%, respectively. The band inversion
occurs at the generic momentum in the first TPT, while at high symmetric K
point in the second one. Usually, band inversion is identified by the exchange
in the components or irreducible representations of the wavefunctions. These
quantities can be easily obtained in theoretical calculation but hard to be
detected in experimental techniques like Angle-resolved photoemission
spectroscopy. It is well known that nonlinear optical (NLO) response is very
sensitive to the components and symmetries of the engaged bands, which also
incorporates information of band topology. Therefore, we study the shift
current, one of the widely explored NLO responses in noncentrosymmetric
systems, during the two TPTs. We find that in both cases band inversion leads
to the sign change of shift vectors around the momenta where the bandgap closes
and reopens. Whereas the shift current, as the overall contribution of shift
vectors weighted by the absorption rate in the whole Brillouin zone, may keep
its direction. This work offers insight that a scrutinized examination is
highly demanded in utilizing shift current to detect TPT.


title:
PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm
  Optimization

abstract:
  Physics-informed neural networks (PINNs) have recently emerged as a promising
application of deep learning in a wide range of engineering and scientific
problems based on partial differential equation models. However, evidence shows
that PINN training by gradient descent displays pathologies and stiffness in
gradient flow dynamics. In this paper, we propose the use of a hybrid particle
swarm optimization and gradient descent approach to train PINNs. The resulting
PSO-PINN algorithm not only mitigates the undesired behaviors of PINNs trained
with standard gradient descent, but also presents an ensemble approach to PINN
that affords the possibility of robust predictions with quantified uncertainty.
Experimental results using the Poisson, advection, and Burgers equations show
that PSO-PINN consistently outperforms a baseline PINN trained with Adam
gradient descent.


title:
Learning Representation from Neural Fisher Kernel with Low-rank
  Approximation

abstract:
  In this paper, we study the representation of neural networks from the view
of kernels. We first define the Neural Fisher Kernel (NFK), which is the Fisher
Kernel applied to neural networks. We show that NFK can be computed for both
supervised and unsupervised learning models, which can serve as a unified tool
for representation extraction. Furthermore, we show that practical NFKs exhibit
low-rank structures. We then propose an efficient algorithm that computes a low
rank approximation of NFK, which scales to large datasets and networks. We show
that the low-rank approximation of NFKs derived from unsupervised generative
models and supervised learning models gives rise to high-quality compact
representations of data, achieving competitive results on a variety of machine
learning tasks.


title:
Finite-temperature plasmons, damping and collective behavior for
  $\alpha-\mathcal{T}_3$ model

abstract:
  We have conducted a thorough theoretical and numerical investigation of the
electronic susceptibility, polarizability, plasmons, their damping rates, as
well as the static screening in pseudospin-1 Dirac cone materials with a flat
band, or for a general $\alpha - \mathcal{T}_3$ model, at finite temperatures.
This includes calculating the polarization function, plasmon dispersions and
their damping rates at arbitrary temperatures and obtaining analytical
approximations the long wavelength limit, low and high temperatures. We
demonstrate that the integral transformation of the polarization function
cannot be used directly for a dice lattice revealing some fundamental
properties and important applicability limits of the flat band dispersions
model. At $k_B T \ll E_F$, the largest temperature-induced change of the
polarization function and plasmons comes from the mismatch between the chemical
potential and the Fermi energy. We have also obtained a series of closed-form
semi-analytical expressions for the static limit of the polarization function
of an arbitrary $\alpha - \mathcal{T}_3$ material at any temperature with exact
analytical formulas for the high, low and zero temperature limits which is of
tremendous importance for all types of transport and screening calculations for
the flat band Dirac materials.


title:
Unsupervised Learning Based Hybrid Beamforming with Low-Resolution Phase
  Shifters for MU-MIMO Systems

abstract:
  Millimeter wave (mmWave) is a key technology for fifth-generation (5G) and
beyond communications. Hybrid beamforming has been proposed for large-scale
antenna systems in mmWave communications. Existing hybrid beamforming designs
based on infinite-resolution phase shifters (PSs) are impractical due to
hardware cost and power consumption. In this paper, we propose an
unsupervised-learning-based scheme to jointly design the analog precoder and
combiner with low-resolution PSs for multiuser multiple-input multiple-output
(MU-MIMO) systems. We transform the analog precoder and combiner design problem
into a phase classification problem and propose a generic neural network
architecture, termed the phase classification network (PCNet), capable of
producing solutions of various PS resolutions. Simulation results demonstrate
the superior sum-rate and complexity performance of the proposed scheme, as
compared to state-of-the-art hybrid beamforming designs for the most commonly
used low-resolution PS configurations.


title:
Model Averaging for Generalized Linear Models in Fragmentary Data
  Prediction

abstract:
  Fragmentary data is becoming more and more popular in many areas which brings
big challenges to researchers and data analysts. Most existing methods dealing
with fragmentary data consider a continuous response while in many applications
the response variable is discrete. In this paper we propose a model averaging
method for generalized linear models in fragmentary data prediction. The
candidate models are fitted based on different combinations of covariate
availability and sample size. The optimal weight is selected by minimizing the
Kullback-Leibler loss in the com?pleted cases and its asymptotic optimality is
established. Empirical evidences from a simulation study and a real data
analysis about Alzheimer disease are presented.


title: A group-based structure for perfect sequence covering arrays

abstract:
  An $(n,k)$-perfect sequence covering array with multiplicity $\lambda$,
denoted PSCA$(n,k,\lambda)$, is a multiset whose elements are permutations of
the sequence $(1,2, \dots, n)$ and which collectively contain each ordered
length $k$ subsequence exactly $\lambda$ times. The primary objective is to
determine for each pair $(n,k)$ the smallest value of $\lambda$, denoted
$g(n,k)$, for which a PSCA$(n,k,\lambda)$ exists; and more generally, the
complete set of values $\lambda$ for which a PSCA$(n,k,\lambda)$ exists. Yuster
recently determined the first known value of $g(n,k)$ greater than 1, namely
$g(5,3)=2$, and suggested that finding other such values would be challenging.
We show that $g(6,3)=g(7,3)=2$, using a recursive search method inspired by an
old algorithm due to Mathon. We then impose a group-based structure on a
perfect sequence covering array by restricting it to be a union of distinct
cosets of a prescribed nontrivial subgroup of the symmetric group $S_n$. This
allows us to determine the new results that $g(7,4)=2$ and $g(7,5) \in
\{2,3,4\}$ and $g(8,3) \in \{2,3\}$ and $g(9,3) \in \{2,3,4\}$. We also show
that, for each $(n,k) \in \{ (5,3), (6,3), (7,3), (7,4) \}$, there exists a
PSCA$(n,k,\lambda)$ if and only if $\lambda \ge 2$; and that there exists a
PSCA$(8,3,\lambda)$ if and only if $\lambda \ge g(8,3)$.


title:
A Reinforcement Learning Framework for PQoS in a Teleoperated Driving
  Scenario

abstract:
  In recent years, autonomous networks have been designed with Predictive
Quality of Service (PQoS) in mind, as a means for applications operating in the
industrial and/or automotive sectors to predict unanticipated Quality of
Service (QoS) changes and react accordingly. In this context, Reinforcement
Learning (RL) has come out as a promising approach to perform accurate
predictions, and optimize the efficiency and adaptability of wireless networks.
Along these lines, in this paper we propose the design of a new entity,
implemented at the RAN-level that, with the support of an RL framework,
implements PQoS functionalities. Specifically, we focus on the design of the
reward function of the learning agent, able to convert QoS estimates into
appropriate countermeasures if QoS requirements are not satisfied. We
demonstrate via ns-3 simulations that our approach achieves the best trade-off
in terms of QoS and Quality of Experience (QoE) performance of end users in a
teleoperated-driving-like scenario, compared to other baseline solutions.


title: Reasoning on the Air: An Implicit Semantic Communication Architecture

abstract:
  Semantic communication is a novel communication paradigm which draws
inspiration from human communication focusing on the delivery of the meaning of
a message to the intended users. It has attracted significant interest recently
due to its potential to improve efficiency and reliability of communication,
enhance users' quality-of-experience (QoE), and achieve smoother
cross-protocol/domain communication. Most existing works in semantic
communication focus on identifying and transmitting explicit semantic meaning,
e.g., labels of objects, that can be directly identified from the source
signal. This paper investigates implicit semantic communication in which the
hidden information, e.g., implicit causality and reasoning mechanisms of users,
that cannot be directly observed from the source signal needs to be transported
and delivered to the intended users. We propose a novel implicit semantic
communication (iSC) architecture for representing, communicating, and
interpreting the implicit semantic meaning. In particular, we first propose a
graph-inspired structure to represent implicit meaning of message based on
three key components: entity, relation, and reasoning mechanism. We then
propose a generative adversarial imitation learning-based reasoning mechanism
learning (GAML) solution for the destination user to learn and imitate the
reasoning process of the source user. We prove that, by applying GAML, the
destination user can accurately imitate the reasoning process of the users to
generate reasoning paths that follow the same probability distribution as the
expert paths. Numerical results suggest that our proposed architecture can
achieve accurate implicit meaning interpretation at the destination user.


title: Does A Special Relativistic Liouville Equation Exist?

abstract:
  The Liouville Equation, the starting point of non-relativistic,
non-equilibrium classical statistical mechanics, is problematic in special
relativity because of two problems. A relativistic Hamiltonian is claimed not
to exist for interacting particles and the problem of what time to use since
the particles will all have their own time as part of their space-time
coordinate. In this paper, I look at this problem and surprisingly found that
there is no special relativistic Liouville equation in 8N phase space, where N
is the number of particles, because the canonical Hamiltonian is zero for both
non-interacting and interacting particles. This is due to the parametrization
symmetry in defining a single time for the Liouville equation evolution, which
results in a constraint.This is similar to the fact that in general relativity,
diffeomorphism invariance of the theory always give a zero Hamiltonian because
of a constraint.


title: Life-long Learning for Reasoning-based Semantic Communication

abstract:
  Semantic communication is an emerging paradigm that focuses on understanding
and delivering semantics, or meaning of messages. Most existing semantic
communication solutions define semantic meaning as the meaning of object labels
recognized from a source signal, while ignoring intrinsic information that
cannot be directly observed. Moreover, existing solutions often assume the
recognizable semantic meanings are limited by a pre-defined label database. In
this paper, we propose a novel reasoning-based semantic communication
architecture in which the semantic meaning is represented by a graph-based
knowledge structure in terms of object-entity, relationships, and reasoning
rules. An embedding-based semantic interpretation framework is proposed to
convert the high-dimensional graph-based representation of semantic meaning
into a low-dimensional representation, which is efficient for channel
transmission. We develop a novel inference function-based approach that can
automatically infer hidden information such as missing entities and relations
that cannot be directly observed from the message. Finally, we introduce a
life-long model updating approach in which the receiver can learn from
previously received messages and automatically update the reasoning rules of
users when new unknown semantic entities and relations have been discovered.
Extensive experiments are conducted based on a real-world knowledge database
and numerical results show that our proposed solution achieves 76%
interpretation accuracy of semantic meaning at the receiver, notably when some
entities are missing in the transmitted message.


title: Active metric learning and classification using similarity queries

abstract:
  Active learning is commonly used to train label-efficient models by
adaptively selecting the most informative queries. However, most active
learning strategies are designed to either learn a representation of the data
(e.g., embedding or metric learning) or perform well on a task (e.g.,
classification) on the data. However, many machine learning tasks involve a
combination of both representation learning and a task-specific goal. Motivated
by this, we propose a novel unified query framework that can be applied to any
problem in which a key component is learning a representation of the data that
reflects similarity. Our approach builds on similarity or nearest neighbor (NN)
queries which seek to select samples that result in improved embeddings. The
queries consist of a reference and a set of objects, with an oracle selecting
the object most similar (i.e., nearest) to the reference. In order to reduce
the number of solicited queries, they are chosen adaptively according to an
information theoretic criterion. We demonstrate the effectiveness of the
proposed strategy on two tasks -- active metric learning and active
classification -- using a variety of synthetic and real world datasets. In
particular, we demonstrate that actively selected NN queries outperform
recently developed active triplet selection methods in a deep metric learning
setting. Further, we show that in classification, actively selecting class
labels can be reformulated as a process of selecting the most informative NN
query, allowing direct application of our method.


title:
Multi-task graph neural networks for simultaneous prediction of global
  and atomic properties in ferromagnetic systems

abstract:
  We introduce a multi-tasking graph convolutional neural network, HydraGNN, to
simultaneously predict both global and atomic physical properties and
demonstrate with ferromagnetic materials. We train HydraGNN on an open-source
ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with
a fixed body centered tetragonal (BCT) lattice structure and fixed volume to
simultaneously predict the mixing enthalpy (a global feature of the system),
the atomic charge transfer, and the atomic magnetic moment across
configurations that span the entire compositional range. By taking advantage of
underlying physical correlations between material properties, multi-task
learning (MTL) with HydraGNN provides effective training even with modest
amounts of data. Moreover, this is achieved with just one architecture instead
of three, as required by single-task learning (STL). The first convolutional
layers of the HydraGNN architecture are shared by all learning tasks and
extract features common to all material properties. The following layers
discriminate the features of the different properties, the results of which are
fed to the separate heads of the final layer to produce predictions. Numerical
results show that HydraGNN effectively captures the relation between the
configurational entropy and the material properties over the entire
compositional range. Overall, the accuracy of simultaneous MTL predictions is
comparable to the accuracy of the STL predictions. In addition, the
computational cost of training HydraGNN for MTL is much lower than the original
DFT calculations and also lower than training separate STL models for each
property.


title: On singularities of Ericksen-Leslie system in dimension three

abstract:
  In this paper, we consider the initial and boundary value problem of
Ericksen-Leslie system modeling nematic liquid crystal flows in dimension
three. Two examples of singularity at finite time are constructed. The first
example is constructed in a special axisymmetric class with suitable
axisymmetric initial and boundary data, while the second example is constructed
for an initial data with small energy but nontrivial topology. A counter
example of maximum principle to the system is constructed by utilizing the
Poiseuille flow in dimension one.


title:
Maximum Likelihood Estimation of Optimal Receiver Operating
  Characteristic Curves from Likelihood Ratio Observations

abstract:
  The optimal receiver operating characteristic (ROC) curve, giving the maximum
probability of detection as a function of the probability of false alarm, is a
key information-theoretic indicator of the difficulty of a binary hypothesis
testing problem (BHT). It is well known that the optimal ROC curve for a given
BHT, corresponding to the likelihood ratio test, is theoretically determined by
the probability distribution of the observed data under each of the two
hypotheses. In some cases, these two distributions may be unknown or
computationally intractable, but independent samples of the likelihood ratio
can be observed. This raises the problem of estimating the optimal ROC for a
BHT from such samples. The maximum likelihood estimator of the optimal ROC
curve is derived, and it is shown to converge to the true optimal ROC curve in
the \levy\ metric, as the number of observations tends to infinity. A classical
empirical estimator, based on estimating the two types of error probabilities
from two separate sets of samples, is also considered. The maximum likelihood
estimator is observed in simulation experiments to be considerably more
accurate than the empirical estimator, especially when the number of samples
obtained under one of the two hypotheses is small. The area under the maximum
likelihood estimator is derived; it is a consistent estimator of the true area
under the optimal ROC curve.


title:
Fractional Electromagnetic Response in Three-Dimensional Chiral
  Anomalous Semimetal

abstract:
  The magnetoelectric coupling of electrons in a three-dimensional solid can be
effectively described by axion electrodynamics. Here we report the discovery of
the fractional magnetoelectric effect in chiral anomalous semimetals of the
three-dimensional massless Wilson fermions, which have linear dispersions at
the energy crossing point, and break the chiral symmetry at generic momenta. In
the presence of electric and magnetic fields, the time-reversal and parity
symmetry breaking give rise to the quarter-quantized topological
magnetoelectric effect, which is directly related to the winding number 1/2 of
the band structure, and is only one half of that for topological insulators.
The fractional electromagnetic response can be revealed by the surface Hall
conductance and extracted from the measurement of the topological Kerr and
Faraday rotation. The transition-metal pentatelluride ZrTe5 with a
strain-tunable band gap provides a potential platform to test the effect
experimentally.


title:
Demystify Optimization and Generalization of Over-parameterized
  PAC-Bayesian Learning

abstract:
  PAC-Bayesian is an analysis framework where the training error can be
expressed as the weighted average of the hypotheses in the posterior
distribution whilst incorporating the prior knowledge. In addition to being a
pure generalization bound analysis tool, PAC-Bayesian bound can also be
incorporated into an objective function to train a probabilistic neural
network, making them a powerful and relevant framework that can numerically
provide a tight generalization bound for supervised learning. For simplicity,
we call probabilistic neural network learned using training objectives derived
from PAC-Bayesian bounds as {\it PAC-Bayesian learning}. Despite their
empirical success, the theoretical analysis of PAC-Bayesian learning for neural
networks is rarely explored. This paper proposes a new class of convergence and
generalization analysis for PAC-Bayes learning when it is used to train the
over-parameterized neural networks by the gradient descent method. For a wide
probabilistic neural network, we show that when PAC-Bayes learning is applied,
the convergence result corresponds to solving a kernel ridge regression when
the probabilistic neural tangent kernel (PNTK) is used as its kernel. Based on
this finding, we further characterize the uniform PAC-Bayesian generalization
bound which improves over the Rademacher complexity-based bound for
non-probabilistic neural network. Finally, drawing the insight from our
theoretical results, we propose a proxy measure for efficient hyperparameters
selection, which is proven to be time-saving.


title: Finite Relation Algebras

abstract:
  We will show that almost all nonassociative relation algebras are symmetric
and integral (in the sense that the fraction of both labelled and unlabelled
structures that are symmetric and integral tends to 1), and using a Fra\"iss\'e
limit, we will establish that the classes of all atom structures of
nonassociative relation algebras and relation algebras both have 0-1 laws. As a
consequence, we obtain improved asymptotic formulas for the numbers of these
structures and broaden some known probabilistic results on relation algebras.


title: On Perfect Sequence Covering Arrays

abstract:
  A PSCA$(v, t, \lambda)$ is a multiset of permutations of the $v$-element
alphabet $\{0, \dots, v-1\}$ such that every sequence of $t$ distinct elements
of the alphabet appears in the specified order in exactly $\lambda$ of the
permutations. For $v \geq t \geq 2$, we define $g(v, t)$ to be the smallest
positive integer $\lambda$ such that a PSCA$(v, t, \lambda)$ exists. We show
that $g(6, 3) = g(7, 3) = g(7, 4) = 2$ and $g(8, 3) = 3$. Using suitable
permutation representations of groups we make improvements to the upper bounds
on $g(v, t)$ for many values of $v \leq 32$ and $3\le t\le 6$. We also prove a
number of restrictions on the distribution of symbols among the columns of a
PSCA.


title: Quality-diversity for aesthetic evolution

abstract:
  Many creative generative design spaces contain multiple regions with
individuals of high aesthetic value. Yet traditional evolutionary computing
methods typically focus on optimisation, searching for the fittest individual
in a population. In this paper we apply quality-diversity search methods to
explore a creative generative system (an agent-based line drawing model). We
perform a random sampling of genotype space and use individual artist-assigned
evaluations of aesthetic quality to formulate a computable fitness measure
specific to the artist and this system. To compute diversity we use a
convolutional neural network to discriminate features that are dimensionally
reduced into two dimensions. We show that the quality-diversity search is able
to find multiple phenotypes of high aesthetic value. These phenotypes show
greater diversity and quality than those the artist was able to find using
manual search methods.


title: Population Calibration using Likelihood-Free Bayesian Inference

abstract:
  In this paper we develop a likelihood-free approach for population
calibration, which involves finding distributions of model parameters when fed
through the model produces a set of outputs that matches available population
data. Unlike most other approaches to population calibration, our method
produces uncertainty quantification on the estimated distribution. Furthermore,
the method can be applied to any population calibration problem, regardless of
whether the model of interest is deterministic or stochastic, or whether the
population data is observed with or without measurement error. We demonstrate
the method on several examples, including one with real data. We also discuss
the computational limitations of the approach. Immediate applications for the
methodology developed here exist in many areas of medical research including
cancer, COVID-19, drug development and cardiology.


title:
Rotationally-Invariant Circuits: Universality with the exchange
  interaction and two ancilla qubits

abstract:
  Universality of local unitary transformations is one of the cornerstones of
quantum computing with many applications and implications that go beyond this
field. However, it has been recently shown that this universality does not hold
in the presence of continuous symmetries: generic symmetric unitaries on a
composite system cannot be implemented, even approximately, using local
symmetric unitaries on the subsystems [I. Marvian, Nature Physics (2022)]. In
this work, we study qubit circuits formed from k-local rotationally-invariant
unitaries and fully characterize the constraints imposed by locality on the
realizable unitaries. We also present an interpretation of these constraints in
terms of the average energy of states with a fixed angular momentum.
Interestingly, despite these constraints, we show that, using a pair of ancilla
qubits, any rotationally-invariant unitary can be realized with the Heisenberg
exchange interaction, which is 2-local and rotationally-invariant. We also show
that a single ancilla is not enough to achieve universality. Finally, we
discuss applications of these results for quantum computing with semiconductor
quantum dots, quantum reference frames, and resource theories.

categories: quant-ph cond-mat.str-el hep-th math-ph math.MP nucl-th


title: Three-temperature modeling of laser-induced damage process in silicon

abstract:
  Laser excitation in silicon from femto- to pico-second time scales is
studied. We assume the Three-Temperature Model (3TM) which describes the
dynamics of the distinct quasi-temperatures for electrons, holes, and lattice.
Numerical results for damage threshold reproduce the experimental results not
only quantitatively, but qualitatively as well, showing dependence on laser
pulse duration. Comparison with experimental data suggests that electron
emission and thermal melting are both responsible for damage in silicon. We
found that electron-phonon relaxation time has a significant effect on pulse
duration dependence of electron emission.


title: Complexity classes of Polishable subgroups

abstract:
  In this paper we further develop the theory of canonical approximations of
Polishable subgroups of Polish groups, building on previous work of Solecki and
Farah--Solecki. In particular, we obtain a characterization of such canonical
approximations in terms of their Borel complexity class. As an application we
provide a complete list of all the possible Borel complexity classes of
Polishable subgroups of Polish groups or, equivalently, of the ranges of
continuous group homomorphisms between Polish groups. We also provide a
complete list of all the possible Borel complexity classes of the ranges of:
continuous group homomorphisms between non-Archimedean Polish groups;
continuous linear maps between separable Fr\'{e}chet spaces; continuous linear
maps between separable Banach spaces.


title: Predictive Closed-Loop Service Automation in O-RAN based Network Slicing

abstract:
  Network slicing provides introduces customized and agile network deployment
for managing different service types for various verticals under the same
infrastructure. To cater to the dynamic service requirements of these verticals
and meet the required quality-of-service (QoS) mentioned in the service-level
agreement (SLA), network slices need to be isolated through dedicated elements
and resources. Additionally, allocated resources to these slices need to be
continuously monitored and intelligently managed. This enables immediate
detection and correction of any SLA violation to support automated service
assurance in a closed-loop fashion. By reducing human intervention, intelligent
and closed-loop resource management reduces the cost of offering flexible
services. Resource management in a network shared among verticals (potentially
administered by different providers), would be further facilitated through open
and standardized interfaces. Open radio access network (O-RAN) is perhaps the
most promising RAN architecture that inherits all the aforementioned features,
namely intelligence, open and standard interfaces, and closed control loop.
Inspired by this, in this article we provide a closed-loop and intelligent
resource provisioning scheme for O-RAN slicing to prevent SLA violations. In
order to maintain realism, a real-world dataset of a large operator is used to
train a learning solution for optimizing resource utilization in the proposed
closed-loop service automation process. Moreover, the deployment architecture
and the corresponding flow that are cognizant of the O-RAN requirements are
also discussed.


title:
Piecewise geodesic Jordan curves I: weldings, explicit computations, and
  Schwarzian derivatives

abstract:
  We consider Jordan curves $\gamma=\cup_{j=1}^n \gamma_j$ in the Riemann
sphere for which each $\gamma_j$ is a hyperbolic geodesic in the complement of
the remaining arcs $\gamma\smallsetminus \gamma_j$. These curves are
characterized by the property that their conformal welding is piecewise
M\"obius. Among other things, we compute the Schwarzian derivatives of the
Riemann maps of the two regions in $\hat {\mathbb C}\smallsetminus \gamma$,
show that they form a rational function with second order poles at the
endpoints of the $\gamma_j,$ and show that the poles are simple if the curve
has continuous tangents. Our key tool is the explicit computation of all
geodesic pairs, namely pairs of chords $\gamma=\gamma_1\cup\gamma_2$ in a
simply connected domain $D$ such that $\gamma_j$ is a hyperbolic geodesic in
$D\smallsetminus \gamma_{3-j}$ for both $j=1$ and $j=2$.


title: Rearrangement Events on Circular Genomes

abstract:
  Early literature on genome rearrangement modelling views the problem of
computing evolutionary distances as an inherently combinatorial one. In
particular, attention was given to estimating distances using the minimum
number of events required to transform one genome into another. In hindsight,
this approach is analogous to early methods for inferring phylogenetic trees
from DNA sequences such as maximum parsimony -- both are motivated by the
principle that the true distance minimises evolutionary change, and both are
effective if this principle is a true reflection of reality. Recent literature
considers genome rearrangement under statistical models, continuing this
parallel with DNA-based methods; the goal here is to use model-based methods
(for example maximum likelihood techniques) to compute distance estimates that
incorporate the large number of rearrangement paths that can transform one
genome into another. Crucially, this approach requires one to decide upon a set
of feasible rearrangement events and, in this paper, we focus on characterising
well-motivated models for signed, uni-chromosomal circular genomes, where the
number of regions remains fixed. Since rearrangements are often mathematically
described using permutations, we isolate the sets of permutations representing
rearrangements that are biologically reasonable in this context, for example
inversions and translocations. We provide precise mathematical expressions for
these rearrangements, and then describe them in terms of the set of cuts made
in the genome when they are applied. We directly compare cuts to breakpoints,
and use this concept to count the distinct rearrangement actions which apply a
given number of cuts. Finally, we provide some examples of rearrangement
models, and include a discussion of some questions that arise when defining
plausible models.


title:
A Novel Assistive Controller Based on Differential Geometry for Users of
  the Differential-Drive Wheeled Mobile Robots

abstract:
  Certain wheeled mobile robots e.g., electric wheelchairs, can operate through
indirect joystick controls from users. Correct steering angle becomes essential
when the user should determine the vehicle direction and velocity, in
particular for differential wheeled vehicles since the vehicle velocity and
direction are controlled with only two actuating wheels. This problem gets more
challenging when complex curves should be realized by the user. A novel
assistive controller with safety constraints is needed to address these
problems. Also, the classic control methods mostly require the desired states
beforehand which completely contradicts human's spontaneous decisions on the
desired location to go. In this work, we develop a novel assistive control
strategy based on differential geometry relying on only joystick inputs and
vehicle states where the controller does not require any desired states. We
begin with explaining the vehicle kinematics and our designed Darboux frame
kinematics on a contact point of a virtual wheel and plane. Next, the geometric
controller using the Darboux frame kinematics is designed for having smooth
trajectories under certain safety constraints. We experiment our approach with
different participants and evaluate its performance in various routes.


title:
Identification of prognostic and predictive biomarkers in
  high-dimensional data with PPLasso

abstract:
  In clinical trials, identification of prognostic and predictive biomarkers is
essential to precision medicine. Prognostic biomarkers can be useful for the
prevention of the occurrence of the disease, and predictive biomarkers can be
used to identify patients with potential benefit from the treatment. Previous
researches were mainly focused on clinical characteristics, and the use of
genomic data in such an area is hardly studied. A new method is required to
simultaneously select prognostic and predictive biomarkers in high dimensional
genomic data where biomarkers are highly correlated. We propose a novel
approach called PPLasso (Prognostic Predictive Lasso) integrating prognostic
and predictive effects into one statistical model. PPLasso also takes into
account the correlations between biomarkers that can alter the biomarker
selection accuracy. Our method consists in transforming the design matrix to
remove the correlations between the biomarkers before applying the generalized
Lasso. In a comprehensive numerical evaluation, we show that PPLasso
outperforms the traditional Lasso approach on both prognostic and predictive
biomarker identification in various scenarios. Finally, our method is applied
to publicly available transcriptomic data from clinical trial RV144. Our method
is implemented in the PPLasso R package which will be soon available from the
Comprehensive R Archive Network (CRAN).


title:
Aggregation Service for Federated Learning: An Efficient, Secure, and
  More Resilient Realization

abstract:
  Federated learning has recently emerged as a paradigm promising the benefits
of harnessing rich data from diverse sources to train high quality models, with
the salient features that training datasets never leave local devices. Only
model updates are locally computed and shared for aggregation to produce a
global model. While federated learning greatly alleviates the privacy concerns
as opposed to learning with centralized data, sharing model updates still poses
privacy risks. In this paper, we present a system design which offers efficient
protection of individual model updates throughout the learning procedure,
allowing clients to only provide obscured model updates while a cloud server
can still perform the aggregation. Our federated learning system first departs
from prior works by supporting lightweight encryption and aggregation, and
resilience against drop-out clients with no impact on their participation in
future rounds. Meanwhile, prior work largely overlooks bandwidth efficiency
optimization in the ciphertext domain and the support of security against an
actively adversarial cloud server, which we also fully explore in this paper
and provide effective and efficient mechanisms. Extensive experiments over
several benchmark datasets (MNIST, CIFAR-10, and CelebA) show our system
achieves accuracy comparable to the plaintext baseline, with practical
performance.


title: Hybrid Neural Coded Modulation: Design and Training Methods

abstract:
  We propose a hybrid coded modulation scheme which composes of inner and outer
codes. The outer-code can be any standard binary linear code with efficient
soft decoding capability (e.g. low-density parity-check (LDPC) codes). The
inner code is designed using a deep neural network (DNN) which takes the
channel coded bits and outputs modulated symbols. For training the DNN, we
propose to use a loss function that is inspired by the generalized mutual
information. The resulting constellations are shown to outperform the
conventional quadrature amplitude modulation (QAM) based coding scheme for
modulation order 16 and 64 with 5G standard LDPC codes.


title: Toponomic Quantum Computation

abstract:
  Holonomic quantum computation makes use of non-abelian geometric phases,
associated to the evolution of a subspace of quantum states, to encode logical
gates. We identify a special class of subspaces, for which a sequence of
rotations results in a non-abelian holonomy of a topological nature, so that it
is invariant under any $SO(3)$-perturbation. Making use of a Majorana-like
stellar representation for subspaces, we give explicit examples of
topological-holonomic (or toponomic) NOT and CNOT gates.


title:
A Nano-Architecture for Fertility Monitoring via Intra-body
  Communication

abstract:
  Fertility monitoring in humans for either natural conception or artificial
insemination and fertilization has been a critical challenge both for the
treating physician and the treated patients. Eggs in human female can be
fertilized when they reach the Fallopian tube from the upper parts of the
reproductive system. However, no technology, till date, on its own could detect
the presence of eggs in the Fallopian tube and communicate their presence to
the consulting physician or nurse and the patient, so that the next step can be
initiated in a timely fashion. In this paper, we propose a conceptual
architecture from a communications engineering point of view. The architecture
combines intra-body nano-sensor network for detecting Fallopian tube activity,
with body-area network for receiving information from the intra-body network
and communicating the same over-the-air to the involved personnel
(physician/nurse/patient). Preliminary simulations have been conducted using
particle based stochastic simulator to investigate the relationship between
achievable information rates, signal to noise ratio (SNR) and distance. It has
been found that data rate as high as 300 Mbps is achievable at SNR 45. Hence,
the proposed architecture ensures transfer of information with near-zero
latency and minimum energy along with high throughput, so that necessary action
can be taken within the short time-window of the Fallopian tube activity.


title:
Performance of multilabel machine learning models and risk
  stratification schemas for predicting stroke and bleeding risk in patients
  with non-valvular atrial fibrillation

abstract:
  Appropriate antithrombotic therapy for patients with atrial fibrillation (AF)
requires assessment of ischemic stroke and bleeding risks. However, risk
stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive
capacity for patients with AF. Machine learning (ML) techniques may improve
predictive performance and support decision-making for appropriate
antithrombotic therapy. We compared the performance of multilabel ML models
with the currently used risk scores for predicting outcomes in AF patients.
Materials and Methods This was a retrospective cohort study of 9670 patients,
mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and
had 1-year follow-up. The primary outcome was ischemic stroke and major
bleeding admission. The secondary outcomes were all-cause death and event-free
survival. The discriminant power of ML models was compared with clinical risk
scores by the area under the curve (AUC). Risk stratification was assessed
using the net reclassification index. Results Multilabel gradient boosting
machine provided the best discriminant power for stroke, major bleeding, and
death (AUC = 0.685, 0.709, and 0.765 respectively) compared to other ML models.
It provided modest performance improvement for stroke compared to CHA2DS2-VASc
(AUC = 0.652), but significantly improved major bleeding prediction compared to
HAS-BLED (AUC = 0.522). It also had a much greater discriminant power for death
compared with CHA2DS2-VASc (AUC = 0.606). Also, models identified additional
risk features (such as hemoglobin level, renal function, etc.) for each
outcome. Conclusions Multilabel ML models can outperform clinical risk
stratification scores for predicting the risk of major bleeding and death in
non-valvular AF patients.


title:
Impact of spectral noise shape and correlations of laser beam jitter on
  acquiring optical links in space

abstract:
  We investigate how the probability of acquiring an optical link between a
scanning and a target spacecraft depends on the spectral shape, power and
dimensionality of the beam jitter, as well as on the choice of detector
integration time, beam detection radius and scan speed. For slow scans and long
integration times, the probability of failure (Pfail) is determined by the
integrated jitter power up to a critical frequency, which we verify by
comparing the results of an analytical model to those of Monte Carlo
simulations. Jitter above the critical frequency leads to a loss of correlation
between integration windows and decreases Pfail for both, 1d (radial) and 2d
(radial and tangential) jitter, as long as the RMS jitter amplitude does not
exceed the beam diameter. In the opposite limit of fast scans and short
integration times, emergent correlations between jitter fluctuations on two
adjacent scanning tracks also decrease Pfail. The analytical model is
additionally used to assess the effect of multiple overlapping tracks and the
impact of target drifts in the uncertainty plane.


title: Isotope Ratio Dual-Comb Spectrometer

abstract:
  We demonstrate the use of dual-comb spectroscopy for isotope ratio
measurements. We show that the analysis spectral range of a free-running
near-infrared dual-comb spectrometer can be extended to the mid-infrared by
difference frequency generation to target specific spectral regions suitable
for such measurements, and especially the relative isotopic ratio $\delta
{}^{13}$C. The measurements performed present a very good repeatability over
several days with a standard deviation below 2$\unicode{x2030}$ for a recording
time of a few tens of seconds, and the results are compatible with measurements
obtained using an isotope ratio mass spectrometer. Our setup also shows the
possibility to target several chemical species without any major modification,
which can be used to measure other isotopic ratios. Further improvements could
decrease the uncertainties of the measurements, and the spectrometer could thus
compete with isotope ratio spectrometers currently available on the market.


title: Online Bayesian Optimization for a Recoil Mass Separator

abstract:
  The SEparator for CApture Reactions (SECAR) is a next-generation recoil
separator system at the Facility for Rare Isotope Beams (FRIB) designed for the
direct measurement of capture reactions on unstable nuclei in inverse
kinematics. To maximize the performance of this system, stringent requirements
on the beam alignment to the central beam axis and on the ion-optical settings
need to be achieved. These can be difficult to attain through manual tuning by
human operators without potentially leaving the system in a sub-optimal and
irreproducible state. In this work, we present the first development of online
Bayesian optimization with a Gaussian process model to tune an ion beam through
a nuclear astrophysics recoil separator. We show that this method achieves
small incoming angular deviations (\textless 1 mrad) in an efficient and
reproducible manner that is at least three times faster than standard
hand-tuning. Additionally, we present a Bayesian method for experimental
optimization of the ion optics, and show that it validates the nominal
theoretical ion-optical settings of the device, and improves the mass
separation by 32\% for some beams.


title: Measuring Electric Dipole Moments of Trapped Sub-mm Particles

abstract:
  We present a method for measurements of electric dipole moments on (sub)-mm
size (basalt) particles levitated in an acoustic trap and centered within a
plate capacitor. If an electric field is applied the particles oscillate with
specific frequencies due to their permanent dipole moments. We observe dipole
moments on the order of $D_P = 10^{-15} ... 10^{-14} \rm \, C \, m $. The
dipole moment increases in small aggregates with the number of grains and is
larger for samples vibrated (tribocharged) before trapping. The basalt grains
show no sign of change in their dipole moment during measurements, implying a
timescale for charge mobility being at least larger than minutes.


title:
Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building
  and Multi-Floor Indoor Localization

abstract:
  Location fingerprinting based on RSSI becomes a mainstream indoor
localization technique due to its advantage of not requiring the installation
of new infrastructure and the modification of existing devices, especially
given the prevalence of Wi-Fi-enabled devices and the ubiquitous Wi-Fi access
in modern buildings. The use of AI/ML technologies like DNNs makes location
fingerprinting more accurate and reliable, especially for large-scale
multi-building and multi-floor indoor localization. The application of DNNs for
indoor localization, however, depends on a large amount of preprocessed and
deliberately-labeled data for their training. Considering the difficulty of the
data collection in an indoor environment, especially under the current epidemic
situation of COVID-19, we investigate three different methods of RSSI data
augmentation based on Multi-Output Gaussian Process (MOGP), i.e., by a single
floor, by neighboring floors, and by a single building; unlike Single-Output
Gaussian Process (SOGP), MOGP can take into account the correlation among RSSI
observations from multiple Access Points (APs) deployed closely to each other
(e.g., APs on the same floor of a building) by collectively handling them. The
feasibility of the MOGP-based RSSI data augmentation is demonstrated through
experiments based on the state-of-the-art RNN indoor localization model and the
UJIIndoorLoc, i.e., the most popular publicly-available multi-building and
multi-floor indoor localization database, where the RNN model trained with the
UJIIndoorLoc database augmented by using the whole RSSI data of a building in
fitting an MOGP model (i.e., by a single building) outperforms the other two
augmentation methods as well as the RNN model trained with the original
UJIIndoorLoc database, resulting in the mean three-dimensional positioning
error of 8.42 m.


title: In-flight production of an isomeric beam of $^{16}$N

abstract:
  An in-flight beam of $^{16}$N was produced via the single-neutron adding
($d$,$p$) reaction in inverse kinematics at the recently upgraded Argonne
Tandem Linear Accelerator System (ATLAS) in-flight system. The amount of the
$^{16}$N beam which resided in its excited 0.120-MeV $J^{\pi}=0^-$ isomeric
state (T$_{1/2}\approx5$ $\mu$s) was determined to be 40(5)% at a reaction
energy of 7.9(3) MeV/$u$, and 24(2)% at a reaction energy of 13.2(2) MeV/$u$.
The isomer measurements took place at an experimental station $\approx30$ m
downstream of the production target and utilized an Al beam-stopping foil and a
HPGe Clover detector. Composite $^{16}$N beam rate determinations were made at
the experimental station and the focal plane of the Argonne in-flight
radioactive ion-beam separator (RAISOR) with Si $\Delta$E-E telescopes. A
Distorted Wave Born Approximation (DWBA) approach was coupled with the known
spectroscopic information on $^{16}$N in order to estimate the relative
$^{16}$N isomer yields and composite $^{16}$N beam rates. In addition to the
observed reaction-energy dependence of the isomer fraction, a large sensitivity
to angular acceptance of the recoils was also observed.


title: A complex limit cycle not intersecting the real plane

abstract:
  We give a precise example of a polynomial vector field on $\mathbb{R}^2$
whose corresponding singular holomorphic foliation of $\mathbb{C}^2$ possesses
a complex limit cycle which does not intersect the real plane $\mathbb{R}^2$.


title: Study of Anisotropic Polytropes in $f(\mathcal{R},\mathrm{T})$ Theory

abstract:
  This paper examines the general formalism and applications of isotropic as
well as anisotropic polytropic stars in curvature-matter coupled gravity. For
this purpose, we consider static spherical and Schwarzschild spacetimes in the
interior and exterior regions, respectively. We use two polytropic equations of
state to obtain physically viable solutions of the field equations. The
hydrostatic equilibrium and Lane-Emden equations are developed for both
isotropic as well as anisotropic cases. We study the effects of anisotropic
pressure on the stellar structure. Moreover, we graphically inspect the
physical behavior of isotropic as well as anisotropic polytropes through energy
conditions and stability criterion. Finally, we discuss Tolman mass to explore
some characteristics of the models. It is concluded that more viable and stable
polytropes are found in this theory as compared to general relativity.


title:
Microstructural pattern formation during liquid metal dealloying:
  Phase-field simulations and theoretical analyses

abstract:
  In recent years, liquid metal dealloying (LMD) has emerged as a promising
material processing method to generate micro and nano-scale bicontinuous or
porous structures. Most previous studies focused on the experimental
characterization of the dealloying process and on the properties of the
dealloyed materials, leaving the theoretical study incomplete to fully
understand the fundamental mechanisms of LMD. In this paper, we use theoretical
models and phase-field simulations to clarify the kinetics and pattern
formation during LMD. Our study starts from a theoretical analysis of the 1D
dissolution of a binary precursor alloy, which reveals that the 1D dissolution
process involves two regimes. In the first regime, due to the low solubility of
one of the elements in the melt, it accumulates at the solid-liquid interface,
which reduces the dissolution kinetics. In the second regime, the interface
kinetics reaches a stationary regime where both elements of the precursor alloy
dissolve into the melt. Previous works revealed that in the early dealloying
stage, the dealloying front is destabilized by an interfacial spinodal
decomposition, which triggers the formation of interconnected ligaments. We
extend this line of work by proposing a linear stability analysis able to
predict the initial length-scale of the ligaments formed in the initial stage
of the dealloying. Combining this analysis with the 1D dissolution model
proposed here enables us to better understand the initial conditions
(composition of the precursor alloy and the melt) leading to a planar
dissolution without interface destabilization. Finally, we report a strong
influence of solid-state diffusion on dealloying that was overlooked in
previous studies. Although the solid-state diffusivity is four to five orders
of magnitude smaller than in the liquid phase, it is found to affect both
dissolution kinetics and ligament morphologies.


title: Arbitrary entanglement of three qubits via linear optics

abstract:
  We present a linear-optical scheme for generation of an arbitrary state of
three qubits. It requires only three independent particles in the input and
post-selection of the coincidence-type at the output. The success probability
of the protocol is equal for any desired state. Furthermore, the optical design
remains insensitive to particle statistics (bosons, fermions or anyons). This
approach builds upon the no-touching paradigm, which demonstrates the utility
of particle indistinguishability as a resource of entanglement for practical
applications.


title:
The CUHK-TENCENT speaker diarization system for the ICASSP 2022
  multi-channel multi-party meeting transcription challenge

abstract:
  This paper describes our speaker diarization system submitted to the
Multi-channel Multi-party Meeting Transcription (M2MeT) challenge, where
Mandarin meeting data were recorded in multi-channel format for diarization and
automatic speech recognition (ASR) tasks. In these meeting scenarios, the
uncertainty of the speaker number and the high ratio of overlapped speech
present great challenges for diarization. Based on the assumption that there is
valuable complementary information between acoustic features, spatial-related
and speaker-related features, we propose a multi-level feature fusion mechanism
based target-speaker voice activity detection (FFM-TS-VAD) system to improve
the performance of the conventional TS-VAD system. Furthermore, we propose a
data augmentation method during training to improve the system robustness when
the angular difference between two speakers is relatively small. We provide
comparisons for different sub-systems we used in M2MeT challenge. Our
submission is a fusion of several sub-systems and ranks second in the
diarization task.


title: Robust Vector Quantized-Variational Autoencoder

abstract:
  Image generative models can learn the distributions of the training data and
consequently generate examples by sampling from these distributions. However,
when the training dataset is corrupted with outliers, generative models will
likely produce examples that are also similar to the outliers. In fact, a small
portion of outliers may induce state-of-the-art generative models, such as
Vector Quantized-Variational AutoEncoder (VQ-VAE), to learn a significant mode
from the outliers. To mitigate this problem, we propose a robust generative
model based on VQ-VAE, which we name Robust VQ-VAE (RVQ-VAE). In order to
achieve robustness, RVQ-VAE uses two separate codebooks for the inliers and
outliers. To ensure the codebooks embed the correct components, we iteratively
update the sets of inliers and outliers during each training epoch. To ensure
that the encoded data points are matched to the correct codebooks, we quantize
using a weighted Euclidean distance, whose weights are determined by
directional variances of the codebooks. Both codebooks, together with the
encoder and decoder, are trained jointly according to the reconstruction loss
and the quantization loss. We experimentally demonstrate that RVQ-VAE is able
to generate examples from inliers even if a large portion of the training data
points are corrupted.


title:
Effect of Bjerrum pairs on electrostatic properties in an electrolyte
  solution near charged surfaces: A mean-field approach

abstract:
  In this paper, we investigate the consequences of ion association, coupled
with the considerations of finite size effects and orientational ordering of
Bjerrum pairs as well as ions and water molecules, on electric double layer
near charged surfaces. Based on the lattice statistical mechanics accounting
for finite sizes and dipole moments of ions, Bjerrum pairs and solvent
molecules, we consider the formation of Bjerrum pairs and derive the
mathematical expressions for Bjerrum pair number density as well as
cation/anion number density and water molecule number density.
  We reveal the several significant phenomena. Firstly, it is shown that our
approach naturally yields the equilibrium constant for dissociation-association
equilibrium between Bjerrum pairs and ions. Secondly, at low surface charge
densities, an increase in the bulk concentration of Bjerrum pairs enhances the
permittivity and decreases the differential capacitance. Next, for cases where
Bjerrum pairs in an alcohol electrolyte solution have a high value of dipole
moment, Bjerrum pair number density increases with decreasing distance from the
charged surface, and differential capacitance and permittivity is high compared
to ones for the cases with lower values of Bjerrum-pair dipole moments.
Finally, we show that the difference in concentration and dipole moment of
Bjerrum pairs can lead to some variation in osmotic pressure between two
similarly charged surfaces.


title:
A Comparison of Representation Learning Methods for Dimensionality
  Reduction of fMRI Scans for Classification of ADHD

abstract:
  This paper compares three feature representation techniques used to represent
resting state functional magnetic resonance (fMRI) scans. The proposed models
of feature representation consider the time averaged fMRI scans as raw
representation of image data. The effectiveness of the representation is
evaluated by using these features for classification of Attention Deficit
Hyperactivity Disorder (ADHD) patients from healthy controls. The
dimensionality reduction methods used for feature representation are
maximum-variance unfolding, locally linear embedding and auto-encoders. The
classifiers tested for classification purpose were neural net and support
vector machine. Using auto-encoders with four hidden layers along with a
support vector machine classifier yielded a classification accuracy of 61.25%
along with 65.69% sensitivity and 52.20% specificity.


title:
Ion partitioning effect on the electrostatic interaction between two
  charged soft surfaces

abstract:
  We theoretically investigate electrostatic properties between two charged
surfaces with a grafted polyelectrolyte layer in an aqueous electrolyte
solution by using the Poisson-Boltzmann approach accounting for ion
partitioning. In order to consider the ion partitioning effect, we focus on
changes of electrostatic properties due to the difference in dielectric
permittivity of the polyelectrolyte layer and the aqueous electrolyte solution.
We find that ion partitioning enhances electrostatic potential in the region
between two charged soft surfaces and hence increases the electrostatic
interaction between two charged soft surfaces. Ion partitioning effect on
osmotic pressure is enhanced not only by an increase in the thickness of
polyelectrolyte layer and Debye length but also by a decrease in ion radius.


title:
Projection-based Point Convolution for Efficient Point Cloud
  Segmentation

abstract:
  Understanding point cloud has recently gained huge interests following the
development of 3D scanning devices and the accumulation of large-scale 3D data.
Most point cloud processing algorithms can be classified as either point-based
or voxel-based methods, both of which have severe limitations in processing
time or memory, or both. To overcome these limitations, we propose
Projection-based Point Convolution (PPConv), a point convolutional module that
uses 2D convolutions and multi-layer perceptrons (MLPs) as its components. In
PPConv, point features are processed through two branches: point branch and
projection branch. Point branch consists of MLPs, while projection branch
transforms point features into a 2D feature map and then apply 2D convolutions.
As PPConv does not use point-based or voxel-based convolutions, it has
advantages in fast point cloud processing. When combined with a learnable
projection and effective feature fusion strategy, PPConv achieves superior
efficiency compared to state-of-the-art methods, even with a simple
architecture based on PointNet++. We demonstrate the efficiency of PPConv in
terms of the trade-off between inference time and segmentation performance. The
experimental results on S3DIS and ShapeNetPart show that PPConv is the most
efficient method among the compared ones. The code is available at
github.com/pahn04/PPConv.


title: The maximal curves and heat flow in fully affine geometry

abstract:
  In Euclidean geometry, the shortest distance between two points is a straight
line. Chern made a conjecture in 1977 that an affine maximal graph of a smooth,
locally uniformly convex function on two-dimensional Euclidean space
$\mathbb{R}^2$ must be a paraboloid. In 2000, Trudinger and Wang completed the
proof of this conjecture in affine geometry. (Caution: in these literatures,
the term "affine geometry" refers to "equi-affine geometry".) A natural problem
arises: Whether the hyperbola is the fully affine maximal curve in
$\mathbb{R}^2$? In this paper, by utilizing the evolution equations for curves,
we obtain the second variational formula for fully affine extremal curves in
$\mathbb{R}^2$, and show the fully affine maximal curves in $\mathbb{R}^2$ are
much more abundant and include the explicit curves $y=x^\alpha
~\left(\alpha\;\text{is a constant
and}\;\alpha\notin\{0,1,\frac{1}{2},2\}\right)$ and $y=x\log x$. At the same
time, we generalize the fundamental theory of curves in higher dimensions,
equipped with $\text{GA}(n)=\text{GL}(n)\ltimes\mathbb{R}^n$. Moreover, in
fully affine plane geometry, an isoperimetric inequality is investigated, and a
complete classification of the solitons for fully affine heat flow is provided.
We also study the local existence, uniqueness, and long-term behavior of this
fully affine heat flow. A closed embedded curve will converge to an ellipse
when evolving according to the fully affine heat flow is proved.


title: Grounding Answers for Visual Questions Asked by Visually Impaired People

abstract:
  Visual question answering is the task of answering questions about images. We
introduce the VizWiz-VQA-Grounding dataset, the first dataset that visually
grounds answers to visual questions asked by people with visual impairments. We
analyze our dataset and compare it with five VQA-Grounding datasets to
demonstrate what makes it similar and different. We then evaluate the SOTA VQA
and VQA-Grounding models and demonstrate that current SOTA algorithms often
fail to identify the correct visual evidence where the answer is located. These
models regularly struggle when the visual evidence occupies a small fraction of
the image, for images that are higher quality, as well as for visual questions
that require skills in text recognition. The dataset, evaluation server, and
leaderboard all can be found at the following link:
https://vizwiz.org/tasks-and-datasets/answer-grounding-for-vqa/.


title: Data Scaling Laws in NMT: The Effect of Noise and Architecture

abstract:
  In this work, we study the effect of varying the architecture and training
data quality on the data scaling properties of Neural Machine Translation
(NMT). First, we establish that the test loss of encoder-decoder transformer
models scales as a power law in the number of training samples, with a
dependence on the model size. Then, we systematically vary aspects of the
training setup to understand how they impact the data scaling laws. In
particular, we change the following (1) Architecture and task setup: We compare
to a transformer-LSTM hybrid, and a decoder-only transformer with a language
modeling loss (2) Noise level in the training distribution: We experiment with
filtering, and adding iid synthetic noise. In all the above cases, we find that
the data scaling exponents are minimally impacted, suggesting that marginally
worse architectures or training data can be compensated for by adding more
data. Lastly, we find that using back-translated data instead of parallel data,
can significantly degrade the scaling exponent.


title:
Ultrasensitive electrode-free and co-catalyst-free detection of
  nanomoles per hour hydrogen evolution for the discovery of new photocatalysts

abstract:
  High throughput theoretical methods are increasingly used to identify
promising photocatalytic materials for hydrogen generation from water as a
clean source of energy. While most promising water splitting candidates require
co-catalyst loading and electrical biasing, computational costs to predict them
apriori becomes large. It is therefore important to identify bare, bias-free
semiconductor photocatalysts with small initial hydrogen production rates,
often in the range of tens of nano-mols per hour, as these can become highly
efficient with further co-catalyst loading and biasing. Here we report a
sensitive hydrogen detection system suitable for screening new photocatalysts.
The hydrogen evolution rate of the prototypical rutile TiO2 loaded with 0.3 %
wt Pt is detected to be 78.0+-0.8 {\mu}mol/h/0.04g, comparable with the rates
reported in the literature. In contrast, sensitivity to an ultralow evolution
rate of 11.4+-0.3 nmol/h/0.04g is demonstrated for bare polycrystalline TiO2
without electrical bias. Two candidate photocatalysts, ZnFe2O4 (18.1+-0.2
nmol/h/0.04g) and Ca2PbO4 (35.6+-0.5 nmol/h/0.04g), without electrical bias or
co-catalyst loading, are demonstrated to be potentially superior to bare TiO2.
This work expands the techniques available for sensitive detection of
photocatalytic processes towards much faster screening of new candidate
photocatalytic materials in their bare state


title:
On the theory of capacities on locally compact spaces and its
  interaction with the theory of balayage

abstract:
  The paper deals with the theory of inner (outer) capacities on locally
compact spaces with respect to general function kernels, the main emphasis
being placed on the establishment of alternative characterizations of inner
(outer) capacities and inner (outer) capacitary measures for arbitrary sets.
The analysis is substantially based on the close interaction between the theory
of capacities and that of balayage. As a by-product, we provide a rigorous
justification of Fuglede's theories of inner and outer capacitary measures and
capacitability (Acta Math., 1960). The results obtained are largely new even
for the logarithmic, Newtonian, Green, $\alpha$-Riesz, and $\alpha$-Green
kernels.


title:
Semi-Supervised Trajectory-Feedback Controller Synthesis for Signal
  Temporal Logic Specifications

abstract:
  There are spatio-temporal rules that dictate how robots should operate in
complex environments, e.g., road rules govern how (self-driving) vehicles
should behave on the road. However, seamlessly incorporating such rules into a
robot control policy remains challenging especially for real-time applications.
In this work, given a desired spatio-temporal specification expressed in the
Signal Temporal Logic (STL) language, we propose a semi-supervised controller
synthesis technique that is attuned to human-like behaviors while satisfying
desired STL specifications. Offline, we synthesize a trajectory-feedback neural
network controller via an adversarial training scheme that summarizes past
spatio-temporal behaviors when computing controls, and then online, we perform
gradient steps to improve specification satisfaction. Central to the offline
phase is an imitation-based regularization component that fosters better policy
exploration and helps induce naturalistic human behaviors. Our experiments
demonstrate that having imitation-based regularization leads to higher
qualitative and quantitative performance compared to optimizing an STL
objective only as done in prior work. We demonstrate the efficacy of our
approach with an illustrative case study and show that our proposed controller
outperforms a state-of-the-art shooting method in both performance and
computation time.