title:
Beurling's Theorem for Valuation Hilbert Modules and Several Complex
  Variables

abstract:
  We develop a theory of Valuation Hilbert Modules and prove a version of
Beurling's theorem for these. Then we apply our version of Beurling's theorem
to obtain complete descriptions of the closed invariant subspaces of a number
of Hilbert spaces of analytic functions in several complex variables, including
$H^2$ of the polydisk the ball, and bounded symmetric domains, and weighted
$A^2$ spaces on complex analytic manifolds.


title:
On the High Dimensional RSA Algorithm -- A Public Key Cryptosystem Based
  on Lattice and Algebraic Number Theory

abstract:
  The most known of public key cryptosystem was introduced in 1978 by Rivest,
Shamir and Adleman [19] and now called the RSA public key cryptosystem in their
honor. Later, a few authors gave a simply extension of RSA over algebraic
numbers field( see [20]- [22]), but they require that the ring of algebraic
integers is Euclidean ring, this requirement is much more stronger than the
class number one condition. In this paper, we introduce a high dimensional form
of RSA by making use of the ring of algebraic integers of an algebraic number
field and the lattice theory. We give an attainable algorithm (see Algorithm I
below) of which is significant both from the theoretical and practical point of
view. Our main purpose in this paper is to show that the high dimensional RSA
is a lattice based on public key cryptosystem indeed, of which would be
considered as a new number in the family of post-quantum cryptography(see [17]
and [18]). On the other hand, we give a matrix expression for any algebraic
number fields (see Theorem 2.7 below), which is a new result even in the sense
of classical algebraic number theory.


title:
Autocorrelation, Wigner and Ambiguity Transforms on Polygons for
  Coherent Radiation Rendering

abstract:
  Simulating the radar illumination of large scenes generally relies on a
geometric model of light transport which largely ignores prominent wave
effects. This can be remedied through coherence ray-tracing, but this requires
the Wigner transform of the aperture. This diffraction function has been
historically difficult to generate, and is relevant in the fields of optics,
holography, synchrotron-radiation, quantum systems and radar. In this paper we
provide the Wigner transform of arbitrary polygons through geometric transforms
and the Stokes Fourier transform; and display its use in Monte-Carlo rendering.


title:
Mitigating Coriolis Effects in Centrifuge Simulators Through Allowing
  Small, Unperceived G-Vector Misalignments

abstract:
  When coupled with additional degrees of freedom, centrifuge-based motion
platforms can combine the agility of hexapod-based platforms with the ability
to sustain higher G-levels and an extended motion space, required for
simulating extreme maneuvers. However, the false and often nauseating
sensations of rotation, by Coriolis effects induced by the centrifuge rotation
in combination with rotations of the centrifuge cabin or the pilot's head, are
a major disadvantage. This paper discusses the development of a motion filter,
the Coherent Alignment Method (COHAM), which aims at reducing Coriolis effects
by allowing small mismatches in the G-vector alignment, reducing cabin
rotations. Simulations show that as long as these mismatches remain within a
region where humans perceive the G-vector as 'coherent', the Coherent Alignment
Zone (CAZ), the cabin angular accelerations can indeed be reduced. COHAM was
tested in a high G-maneuver task with a fixed CAZ threshold obtained in a
previous study. It was experimentally compared to an existing motion filter,
using metrics such as sickness, comfort and false cues. Results show that
sickness, dizziness and discomfort are reduced, making the centrifuge sessions
more bearable. It is recommended to further improve the filter design and
tuning, and test it with more fighter pilots.


title: The Existence of Dyon Solutions for Generalized Weinberg-Salam Model

abstract:
  The generalized Weinberg-Salam model which is presented in a recent study of
Kimm, Yoon and Cho, is arising in electroweak theory. In this paper, we prove
the existence and asymptotic behaviors at infinity of static and radially
symmetric dyon solutions to the boundary-value problem of this model. Moreover,
as a by product, the qualitative properties of dyon solutions are also
obtained. The methods used here are the extremum principle, the Schauder fixed
point theory and the shooting approach depending on one shooting parameter. We
provide an effective framework for constructing the dyon solutions in general
dimensions and develop the existing results.


title: Featherweight Assisted Vulnerability Discovery

abstract:
  Predicting vulnerable source code helps to focus attention on those parts of
the code that need to be examined with more scrutiny. Recent work proposed the
use of function names as semantic cues that can be learned by a deep neural
network (DNN) to aid in the hunt for vulnerability of functions.
  Combining identifier splitting, which splits each function name into its
constituent words, with a novel frequency-based algorithm, we explore the
extent to which the words that make up a function's name can predict
potentially vulnerable functions. In contrast to *lightweight* predictions by a
DNN that considers only function names, avoiding the use of a DNN provides
*featherweight* predictions. The underlying idea is that function names that
contain certain "dangerous" words are more likely to accompany vulnerable
functions. Of course, this assumes that the frequency-based algorithm can be
properly tuned to focus on truly dangerous words.
  Because it is more transparent than a DNN, the frequency-based algorithm
enables us to investigate the inner workings of the DNN. If successful, this
investigation into what the DNN does and does not learn will help us train more
effective future models.
  We empirically evaluate our approach on a heterogeneous dataset containing
over 73000 functions labeled vulnerable, and over 950000 functions labeled
benign. Our analysis shows that words alone account for a significant portion
of the DNN's classification ability. We also find that words are of greatest
value in the datasets with a more homogeneous vocabulary. Thus, when working
within the scope of a given project, where the vocabulary is unavoidably
homogeneous, our approach provides a cheaper, potentially complementary,
technique to aid in the hunt for source-code vulnerabilities. Finally, this
approach has the advantage that it is viable with orders of magnitude less
training data.


title:
Unveiling quantum entanglement and correlation of sub-Ohmic and Ohmic
  baths for quantum phase transitions in dissipative systems

abstract:
  By employing the spin-boson model in a dense limit of environmental modes,
quantum entanglement and correlation of sub-Ohmic and Ohmic baths for
dissipative quantum phase transitions are numerically investigated based on the
variational principle. With several measures borrowed from quantum information
theory, three different types of singularities are found for the first-order,
second-order, and Kosterlitz-Thouless phase transitions, respectively, and the
values of transition points and critical exponents are accurately determined.
Besides, the scaling form of the quantum discord in the Ohmic case is
identified, quite different from that in the sub-Ohmic regime. In a two-spin
model, two distinct behaviors of the quantum discord are uncovered: one is
related to the quantum entanglement between two spins and the other is decided
by the correlation function in the position space rather than the entanglement.


title: Amplitude bases in generic EFTs

abstract:
  We present for the first time an efficient algorithm to find a basis of
kinematically independent structures built of (massless and massive) spinor
helicity variables in four dimensions. This method provides a classification of
independent contact terms for the scattering amplitudes with generic masses,
spins and multiplicity, in any effective field theory (EFT). These contact
terms are in one-to-one correspondence with a complete set of irrelevant
operators in the EFT. As basic applications of our method, we classify the
$D^{2n} F^4$ contact terms in SU$(N)$ Yang-Mills theory for $n\leq 8$,
dimension-six operators involving five $W^\pm$, $Z$ and $\gamma$ vector bosons,
and spin-tidal effective interactions for spin-1 massive particles in
gravitational theories.


title: Pre-integration via Active Subspaces

abstract:
  Pre-integration is an extension of conditional Monte Carlo to quasi-Monte
Carlo and randomized quasi-Monte Carlo. It can reduce but not increase the
variance in Monte Carlo. For quasi-Monte Carlo it can bring about improved
regularity of the integrand with potentially greatly improved accuracy.
Pre-integration is ordinarily done by integrating out one of $d$ input
variables to a function. In the common case of a Gaussian integral one can also
pre-integrate over any linear combination of variables. We propose to do that
and we choose the first eigenvector in an active subspace decomposition to be
the pre-integrated linear combination. We find in numerical examples that this
active subspace pre-integration strategy is competitive with pre-integrating
the first variable in the principal components construction on the Asian option
where principal components are known to be very effective. It outperforms other
pre-integration methods on some basket options where there is no well
established default. We show theoretically that, just as in Monte Carlo,
pre-integration can reduce but not increase the variance when one uses
scrambled net integration. We show that the lead eigenvector in an active
subspace decomposition is closely related to the vector that maximizes a less
computationally tractable criterion using a Sobol' index to find the most
important linear combination of Gaussian variables. They optimize similar
expectations involving the gradient. We show that the Sobol' index criterion
for the leading eigenvector is invariant to the way that one chooses the
remaining $d-1$ eigenvectors with which to sample the Gaussian vector.


title:
Fine-grained rim formation via kinetic dust aggregation in shock waves
  around evaporating icy planetesimals

abstract:
  Fine-grained rims (FGRs) are frequently found around chondrules in primitive
chondrites. The remarkable feature of FGRs is their submicron-sized and
non-porous nature. The typical thickness of FGRs around chondrules is 10--100
$\mu$m. Recently, a novel idea was proposed for the origin of FGRs: high-speed
collisions between chondrules and fine dust grains called the kinetic dust
aggregation process. Experimental studies revealed that (sub)micron-sized
ceramic particles can stick to a ceramic substrate in a vacuum when the impact
velocity is approximately in the range of 0.1--1 km/s. In this study, we
examine the possibility of FGR formation via kinetic dust aggregation in
chondrule-forming shock waves. When shock waves are created by undifferentiated
icy planetesimals, fine dust grains would be released from the planetary
surface due to evaporation of icy planetesimals. We consider the dynamics of
chondrules behind the shock front and calculate the growth of FGRs via kinetic
dust aggregation based on simple one-dimensional calculations. We found that
non-porous FGRs with the thickness of 10--100 $\mu$m would be formed in shock
waves around evaporating icy planetesimals.


title: On the Multi-View Information Bottleneck Representation

abstract:
  In this work, we generalize the information bottleneck (IB) approach to the
multi-view learning context. The exponentially growing complexity of the
optimal representation motivates the development of two novel formulations with
more favorable performance-complexity tradeoffs. The first approach is based on
forming a stochastic consensus and is suited for scenarios with significant
{\em representation overlap} between the different views. The second method,
relying on incremental updates, is tailored for the other extreme scenario with
minimal representation overlap. In both cases, we extend our earlier work on
the alternating directional methods of multiplier (ADMM) solver and establish
its convergence and scalability. Empirically, we find that the proposed methods
outperform state-of-the-art approaches in multi-view classification problems
under a broad range of modelling parameters.


title:
Blind source separation of baseband RF communication signals using
  mixed-signal matrix multiplication circuit

abstract:
  An 8 x 8 mixed-signal matrix multiplier architecture based on 64 hybrid
capacitor-resistor multiplying digital to analogue converters implemented in a
65 nm CMOS technology was developed for the application of blind source
separation of baseband RF signals. The integrated circuit has 13-bit resolution
for each matrix weight and achieves a measured dynamic range of > 62 dB with a
bandwidth of > 15 MHz and typical power dissipation of < 30 mW per matrix row.
Separation of single-tone signal is measured to be better than 57 dBc.


title: PuzzleBots: Physical Coupling of Robot Swarms

abstract:
  Robot swarms have been shown to improve the ability of individual robots by
inter-robot collaboration. In this paper, we present the PuzzleBots - a
low-cost robotic swarm system where robots can physically couple with each
other to form functional structures with minimum energy consumption while
maintaining individual mobility to navigate within the environment. Each robot
has knobs and holes along the sides of its body so that the robots can couple
by inserting the knobs into the holes. We present the characterization of knob
design and the result of gap-crossing behavior with up to nine robots. We show
with hardware experiments that the robots are able to couple with each other to
cross gaps and decouple to perform individual tasks. We anticipate the
PuzzleBots will be useful in unstructured environments as individuals and
coupled systems in real-world applications.


title:
Cross-Channel Attention-Based Target Speaker Voice Activity Detection:
  Experimental Results for M2MeT Challenge

abstract:
  In this paper, we present the speaker diarization system for the
Multi-channel Multi-party Meeting Transcription Challenge (M2MeT) from team
DKU_DukeECE. As the highly overlapped speech exists in the dataset, we employ
an x-vector-based target-speaker voice activity detection (TS-VAD) to find the
overlap between speakers. For the single-channel scenario, we separately train
a model for each of the 8 channels and fuse the results. We also employ the
cross-channel self-attention to further improve the performance, where the
non-linear spatial correlations between different channels are learned and
fused. Experimental results on the evaluation set show that the single-channel
TS-VAD reduces the DER by over 75% from 12.68\% to 3.14%. The multi-channel
TS-VAD further reduces the DER by 28% and achieves a DER of 2.26%. Our final
submitted system achieves a DER of 2.98% on the AliMeeting test set, which
ranks 1st in the M2MET challenge.


title:
Joint Pilot Optimization, Target Detection and Channel Estimation for
  Integrated Sensing and Communication Systems

abstract:
  Radar sensing will be integrated into the 6G communication system to support
various applications. In this integrated sensing and communication system, a
radar target may also be a communication channel scatterer. In this case, the
radar and communication channels exhibit certain joint burst sparsity. We
propose a two-stage joint pilot optimization, target detection and channel
estimation scheme to exploit such joint burst sparsity and pilot beamforming
gain to enhance detection/estimation performance. In Stage 1, the base station
(BS) sends downlink pilots (DP) for initial target search, and the user sends
uplink pilots (UP) for channel estimation. Then the BS performs joint target
detection and channel estimation based on the reflected DP and received UP
signals. In Stage 2, the BS exploits the prior information obtained in Stage 1
to optimize the DP signal to achieve beamforming gain and further refine the
performance. A Turbo Sparse Bayesian inference algorithm is proposed for joint
target detection and channel estimation in both stages. The pilot optimization
problem in Stage 2 is a semi-definite programming with rank-1 constraints. By
replacing the rank-1 constraint with a tight and smooth approximation, we
propose an efficient pilot optimization algorithm based on the
majorization-minimization method. Simulations verify the advantages of the
proposed scheme.


title:
Kinetic Simulation on Electron, Proton and Helium Acceleration in a
  Nonrelativistic Quasiparallel shock

abstract:
  In addition to electrons and protons, nonrelativistic quasiparallel shocks
are expected to possess the ability to accelerate heavy ions. The shocks in
supernova remnants are generally supposed to be accelerators of the Galactic
cosmic rays, which consist of many species of particles. We investigate
diffusive shock acceleration (DSA) of electrons, protons and helium ions in a
nonrelativistic quasiparallel shock through 1D particle-in-cell (PIC)
simulation with a helium-to-proton number density ratio of $0.1$, which is
relevant for the Galactic cosmic rays. The simulation indicates that waves can
be excited by the flow of the energetic protons and helium ions upstream of the
nonrelativistic quasiparallel shock with a sonic Mach number of 14 and an
alfv\'{e}n Mach number of 19.5 in the shock rest frame, and the charged
particles are scattered by the self-generated waves and accelerated gradually.
Moreover, the spectra of the charged particles downstream of the shock are
thermal plus a nonthermal tail, and the acceleration is efficient with about
$7\%$ and $5.4\%$ of the bulk kinetic energy transferred into the nonthermal
protons and helium ions in the near downstream region at the end of the
simulation, respectively.


title:
Further refining Swampland Conjecture on inflation in general
  scalar-tensor theories of gravity

abstract:
  An alternative refined de Sitter conjecture giving rise to a natural
combination of the first and second derivatives of the scalar potential was
proposed recently by David Andriot and Christoph Roupec (Fortsch. Phys. 67
(2019) no.1-2, 1800105). In this work, we study the inflation models in a
general scalar-tensor theory with exponential and hyperbolic tangent forms of
potential as well as model with quantum corrected potential and examine whether
these three models of inflation can satisfy this further refining de Sitter
swampland conjecture or not. Regarding our analysis with proper choices of
parameters with proper choices of parameters $a,b=1-a$ and $q$, we find that
these three inflationary models can always satisfy this new refined swampland
conjecture. Therefore, all three inflationary models might all be in landscape
since the further refining de Sitter swampland conjecture is satisfied.


title: TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network

abstract:
  Signal measurements appearing in the form of time series are one of the most
common types of data used in medical machine learning applications. However,
such datasets are often small, making the training of deep neural network
architectures ineffective. For time-series, the suite of data augmentation
tricks we can use to expand the size of the dataset is limited by the need to
maintain the basic properties of the signal. Data generated by a Generative
Adversarial Network (GAN) can be utilized as another data augmentation tool.
RNN-based GANs suffer from the fact that they cannot effectively model long
sequences of data points with irregular temporal relations. To tackle these
problems, we introduce TTS-GAN, a transformer-based GAN which can successfully
generate realistic synthetic time-series data sequences of arbitrary length,
similar to the real ones. Both the generator and discriminator networks of the
GAN model are built using a pure transformer encoder architecture. We use
visualizations and dimensionality reduction techniques to demonstrate the
similarity of real and generated time-series data. We also compare the quality
of our generated data with the best existing alternative, which is an RNN-based
time-series GAN.


title:
A method for virtual optical sectioning and tomography utilizing shallow
  depth of field

abstract:
  A method is proposed for high-resolution, three-dimensional reconstruction of
internal structure of objects from planar transmission images. The described
approach can be used with any form of radiation or matter waves, in principle,
provided that the depth of field is smaller than the thickness of the sample.
The physical optics basis for the method is elucidated and the reconstruction
algorithm is presented in detail. A simulated example demonstrates an
application of the method to three-dimensional electron transmission imaging of
a nanoparticle under realistic radiation dose and spatial resolution
constraints. It is envisaged that the method can be applicable in
high-resolution transmission electron microscopy, soft X-ray microscopy,
ultrasound imaging and other areas.


title:
Exploration with Multi-Sample Target Values for Distributional
  Reinforcement Learning

abstract:
  Distributional reinforcement learning (RL) aims to learn a value-network that
predicts the full distribution of the returns for a given state, often modeled
via a quantile-based critic. This approach has been successfully integrated
into common RL methods for continuous control, giving rise to algorithms such
as Distributional Soft Actor-Critic (DSAC). In this paper, we introduce
multi-sample target values (MTV) for distributional RL, as a principled
replacement for single-sample target value estimation, as commonly employed in
current practice. The improved distributional estimates further lend themselves
to UCB-based exploration. These two ideas are combined to yield our
distributional RL algorithm, E2DC (Extra Exploration with Distributional
Critics). We evaluate our approach on a range of continuous control tasks and
demonstrate state-of-the-art model-free performance on difficult tasks such as
Humanoid control. We provide further insight into the method via visualization
and analysis of the learned distributions and their evolution during training.


title:
Exact dynamical correlations of nonlocal operators in quadratic open
  Fermion systems: a characteristic function approach

abstract:
  The dynamical correlations of nonlocal operators in general quadratic open
fermion systems is still a challenging problem. Here we tackle this problem by
developing a new formulation of open fermion many-body systems, namely, the
characteristic function approach. Illustrating the technique, we analyze a
finite Kitaev chain with boundary dissipation and consider anyon-type nonlocal
excitations. We give explicit formula for the Green's functions, demonstrating
an asymmetric light cone induced by the anyon statistical parameter and an
increasing relaxation rate with this parameter. We also analyze some other
types of nonlocal operator correlations such as the full counting statistics of
the charge number and the Loschmidt echo in a quench from the vacuum state. The
former shows clear signature of a nonequilibrium quantum phase transition,
while the later exhibits cusps at some critical times and hence demonstrates
dynamical quantum phase transitions.


title:
Towards a smooth unification from an ekpyrotic bounce to the dark energy
  era

abstract:
  In the context of a ghost free $f(R,\mathcal{G})$ model, we present a
non-singular cosmological scenario in which the universe initially contracts
through an ekpyrotic phase having a bouncing like behaviour, and following the
bounce, it smoothly transits to a matter or radiation like deceleration era
which is further smoothly connected to the dark energy era at present epoch. We
consider the Gauss-Bonnet (GB) coupling function in such a way that it gets
compatible with the event GW170817. Using suitable reconstruction technique, we
obtain the non-trivial scalar field potential as well as the GB coupling
function. Such scalar potential and GB coupling function source a smooth
unified scenario from an ekpyrotic bounce to the dark energy era with an
intermediate deceleration era. The occurrence of ekpyrotic contraction phase
justifies the resolution of the anisotropic problem (also known as BKL
instability) in the background evolution. The Hubble radius shows an asymmetric
behaviour around the bounce, in particular, the evolution of the Hubble radius
leads to the generation era of the primordial perturbation modes far before the
bounce in the deep sub-Hubble regime. Accordingly we perform the scalar and
tensor perturbation evolution in the present context, and as a result, the
scalar power spectrum at large scale modes is found to be problematic. Thus an
extended scenario is proposed where we consider a pre-ekpyrotic phase having
the equation of state parameter is less than unity, and re-examine the scalar
and tensor power spectra, on large scales that cross the Hubble radius during
the pre-ekpyrotic stage. In this regard, the GB coupling function shows
considerable effects in reducing the tensor to scalar ratio compared to the
case where the GB coupling is absent. Furthermore the dark energy epoch is
consistent with the Planck+SNe+BAO data.


title: Surface slices and homology spheres

abstract:
  Here we develop the theory of the diagrammatics of surface cross sections to
prove that there are an infinite number of homology 3-spheres smoothly
embeddable in a homology 4-sphere but not in a homotopy 4-sphere. Our primary
obstruction comes from work of Daemi.


title:
Bandwidth-Constrained Distributed Quickest Change Detection in
  Heterogeneous Sensor Networks: Anonymous vs Non-Anonymous Settings

abstract:
  The heterogeneous distribute quickest changed detection (HetDQCD) problem
with 1-bit feedback is studied, in which a fusion center monitors an abrupt
change through a bunch of heterogeneous sensors via anonymous 1-bit feedbacks.
Two fusion rules, one-shot and voting rules, are considered. We analyze the
performance in terms of the worst-case expected detection delay and the average
run length to false alarm for the two fusion rules. Our analysis unveils the
mixed impact of involving more sensors into the decision and enables us to find
near optimal choices of parameters in the two schemes. Notably, it is shown
that, in contrast to the homogeneous setting, the first alarm rule may no
longer lead to the best performance among one-shot schemes. The non-anonymous
setting is also investigated where a simple scheme that only accepts alarms
from the most informative sensors is shown to outperform all the above schemes
and the mixture CUSUM scheme for the anonymous HetDQCD, hinting at the price of
anonymity.


title: Triangle Graph Interest Network for Click-through Rate Prediction

abstract:
  Click-through rate prediction is a critical task in online advertising.
Currently, many existing methods attempt to extract user potential interests
from historical click behavior sequences. However, it is difficult to handle
sparse user behaviors or broaden interest exploration. Recently, some
researchers incorporate the item-item co-occurrence graph as an auxiliary. Due
to the elusiveness of user interests, those works still fail to determine the
real motivation of user click behaviors. Besides, those works are more biased
towards popular or similar commodities. They lack an effective mechanism to
break the diversity restrictions. In this paper, we point out two special
properties of triangles in the item-item graphs for recommendation systems:
Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we
propose a novel and effective framework named Triangle Graph Interest Network
(TGIN). For each clicked item in user behavior sequences, we introduce the
triangles in its neighborhood of the item-item graphs as a supplement. TGIN
regards these triangles as the basic units of user interests, which provide the
clues to capture the real motivation for a user clicking an item. We
characterize every click behavior by aggregating the information of several
interest units to alleviate the elusive motivation problem. The attention
mechanism determines users' preference for different interest units. By
selecting diverse and relative triangles, TGIN brings in novel and
serendipitous items to expand exploration opportunities of user interests.
Then, we aggregate the multi-level interests of historical behavior sequences
to improve CTR prediction. Extensive experiments on both public and industrial
datasets clearly verify the effectiveness of our framework.


title: Low-mass members of nearby young stellar moving groups from Gaia EDR3

abstract:
  Gaia EDR3 offers greatly improved kinematics for nearby objects, including
members of nearby young stellar moving groups (NYMGs). In this study, we aim to
identify low-mass NYMG members (spectral types of M0 to mid-L) in Gaia EDR3. We
calculated spatio-kinematic membership probabilities utilising a Bayesian
membership probability calculation scheme developed in our previous study. We
evaluated stellar youth primarily based on colour-magnitude diagram positions.
Combining spatio-kinematic membership assessment and youth evaluation, we
identified ~2900 low-mass NYMG candidate members including ~700 previously
claimed members. In the set of ~2200 new candidate members, ~550 appear to be
young based on NUV brightness. Our pilot spectroscopic study with WiFeS on the
ANU 2.3-m telescope observed 78 candidates, with 79 per cent confirmed as
members. Using our new member sample, we estimated an isochronal age of the
beta-Pictoris Moving Group. The mean age (~10 Myr), which is around half the
age of recent estimates, suggests either a truly younger age of the
beta-Pictoris Moving Group or inaccuracies in contemporary isochrones. As the
main results of this study, we provide lists of newly confirmed and candidate
low-mass NYMG members.


title:
Vanishing results from Lichnerowicz Laplacian on complete K\"{a}hler
  manifolds and applications

abstract:
  In this paper, we show several rigidity results for harmonic $(p,q)$-forms in
complete K\"{a}hler manifolds. We also give several applications to study
non-compact K\"{a}hler manifolds with parallel Bochner tensor or quaternion
K\"{a}hler manifolds. Our results are natural extensions of Petersen and Wink's
results in \cite{PW21, PW} in the setting of complete, non-compact K\"{a}hler
manifolds.


title: Hyper-Convolutions via Implicit Kernels for Medical Imaging

abstract:
  The convolutional neural network (CNN) is one of the most commonly used
architectures for computer vision tasks. The key building block of a CNN is the
convolutional kernel that aggregates information from the pixel neighborhood
and shares weights across all pixels. A standard CNN's capacity, and thus its
performance, is directly related to the number of learnable kernel weights,
which is determined by the number of channels and the kernel size (support). In
this paper, we present the \textit{hyper-convolution}, a novel building block
that implicitly encodes the convolutional kernel using spatial coordinates.
Hyper-convolutions decouple kernel size from the total number of learnable
parameters, enabling a more flexible architecture design. We demonstrate in our
experiments that replacing regular convolutions with hyper-convolutions can
improve performance with less parameters, and increase robustness against
noise. We provide our code here:
\emph{https://github.com/tym002/Hyper-Convolution}


title:
How Effective is Incongruity? Implications for Code-mix Sarcasm
  Detection

abstract:
  The presence of sarcasm in conversational systems and social media like
chatbots, Facebook, Twitter, etc. poses several challenges for downstream NLP
tasks. This is attributed to the fact that the intended meaning of a sarcastic
text is contrary to what is expressed. Further, the use of code-mix language to
express sarcasm is increasing day by day. Current NLP techniques for code-mix
data have limited success due to the use of different lexicon, syntax, and
scarcity of labeled corpora. To solve the joint problem of code-mixing and
sarcasm detection, we propose the idea of capturing incongruity through
sub-word level embeddings learned via fastText. Empirical results shows that
our proposed model achieves F1-score on code-mix Hinglish dataset comparable to
pretrained multilingual models while training 10x faster and using a lower
memory footprint


title: Multi-modal Sensor Fusion for Auto Driving Perception: A Survey

abstract:
  Multi-modal fusion is a fundamental task for the perception of an autonomous
driving system, which has recently intrigued many researchers. However,
achieving a rather good performance is not an easy task due to the noisy raw
data, underutilized information, and the misalignment of multi-modal sensors.
In this paper, we provide a literature review of the existing multi-modal-based
methods for perception tasks in autonomous driving. Generally, we make a
detailed analysis including over 50 papers leveraging perception sensors
including LiDAR and camera trying to solve object detection and semantic
segmentation tasks. Different from traditional fusion methodology for
categorizing fusion models, we propose an innovative way that divides them into
two major classes, four minor classes by a more reasonable taxonomy in the view
of the fusion stage. Moreover, we dive deep into the current fusion methods,
focusing on the remaining problems and open-up discussions on the potential
research opportunities. In conclusion, what we expect to do in this paper is to
present a new taxonomy of multi-modal fusion methods for the autonomous driving
perception tasks and provoke thoughts of the fusion-based techniques in the
future.


title:
"I Shake The Package To Check If It's Mine": A Study of Package Fetching
  Practices and Challenges of Blind and Low Vision People in China

abstract:
  With about 230 million packages delivered per day in 2020, fetching packages
has become a routine for many city dwellers in China. When fetching packages,
people usually need to go to collection sites of their apartment complexes or a
KuaiDiGui, an increasingly popular type of self-service package pickup machine.
However, little is known whether such processes are accessible to blind and low
vision (BLV) city dwellers. We interviewed BLV people (N=20) living in a large
metropolitan area in China to understand their practices and challenges of
fetching packages. Our findings show that participants encountered difficulties
in finding the collection site and localizing and recognizing their packages.
When fetching packages from KuaiDiGuis, they had difficulty in identifying the
correct KuaiDiGui, interacting with its touch screen, navigating the complex
on-screen workflow, and opening the target compartment. We discuss design
considerations to make the package fetching process more accessible to the BLV
community.


title: Portrait Segmentation Using Deep Learning

abstract:
  A portrait is a painting, drawing, photograph, or engraving of a person,
especially one depicting only the face or head and shoulders. In the digital
world the portrait of a person is captured by having the person as a subject in
the image and capturing the image of the person such that the background is
blurred. DSLRs generally do it by reducing the aperture to focus on very close
regions of interest and automatically blur the background. In this paper I have
come up with a novel approach to replicate the portrait mode from DSLR using
any smartphone to generate high quality portrait images.


title: A Note on Holevo quantity of $SU(2)$-invariant states

abstract:
  The Holevo quantity and the $SU(2)$-invariant states have particular
importance in quantum information processing. We calculate analytically the
Holevo quantity for bipartite systems composed of spin-$j$ and
spin-$\frac{1}{2}$ subsystems with $SU(2)$ symmetry, when the projective
measurements are performed on the spin-$\frac{1}{2}$ subsystem. The relations
among the Holevo quantity, the maximal values of the Holevo quantity and the
states are analyzed in detail. In particular, we show that the Holevo quantity
increases in the parameter region $F<F_d$ and decreases in region $F>F_d$ when
$j$ increases, where $F$ is function of temperature in thermal equilibrium and
$F_d=j/(2j+1)$, and the maximum value of the Holevo quantity is attained at
$F=1$ for all $j$. Moreover, when the dimension of system increases, the
maximal value of the Holevo quantity decreases.


title:
On the local existence of solutions to the Navier-Stokes-wave system
  with a free interface

abstract:
  We address a system of equations modeling a compressible fluid interacting
with an elastic body in dimension three. We prove the local existence and
uniqueness of a strong solution when the initial velocity belongs to the space
$H^{2+\epsilon}$ and the initial structure velocity is in $H^{1.5+\epsilon}$ ,
where $\epsilon \in (0, 1/2)$.


title:
On the compensator of certain special processes with respect to a market
  filtration generated by a Brownian bridge information process

abstract:
  We consider an information-based model in which the flow of information about
an event of interest (bankruptcy, decision, ...) occurring at a future random
time $\tau$ is modelled with the completed filtration generated by a Brownian
bridge with random length $\tau$ and pinning point $Z$. Assuming that the
distribution of $\tau$ is absolutely continuous with respect to the Lebesgue
measure, following P.-A. Meyer approach, we provide the explicit computation of
the compensator of the indicator process $\mathbb{I}_{[\tau,\infty)}$.
Moreover, by exploiting special properties of the bridge process we also give
the explicit expression of the compensator of the process
$Z\,\mathbb{I}_{[\tau,\infty)}$.


title:
From `Wow' to `Why': Guidelines for Creating the Opening of a Data Video
  with Cinematic Styles

abstract:
  Data videos are an increasingly popular storytelling form. The opening of a
data video critically influences its success as the opening either attracts the
audience to continue watching or bores them to abandon watching. However,
little is known about how to create an attractive opening. We draw inspiration
from the openings of famous films to facilitate designing data video openings.
First, by analyzing over 200 films from several sources, we derived six primary
cinematic opening styles adaptable to data videos. Then, we consulted eight
experts from the film industry to formulate 28 guidelines. To validate the
usability and effectiveness of the guidelines, we asked participants to create
data video openings with and without the guidelines, which were then evaluated
by experts and the general public. Results showed that the openings designed
with the guidelines were perceived to be more attractive, and the guidelines
were praised for clarity and inspiration.


title:
Spectrally Adapted Physics-Informed Neural Networks for Solving
  Unbounded Domain Problems

abstract:
  Solving analytically intractable partial differential equations (PDEs) that
involve at least one variable defined in an unbounded domain requires efficient
numerical methods that accurately resolve the dependence of the PDE on that
variable over several orders of magnitude. Unbounded domain problems arise in
various application areas and solving such problems is important for
understanding multi-scale biological dynamics, resolving physical processes at
long time scales and distances, and performing parameter inference in
engineering problems. In this work, we combine two classes of numerical
methods: (i) physics-informed neural networks (PINNs) and (ii) adaptive
spectral methods. The numerical methods that we develop take advantage of the
ability of physics-informed neural networks to easily implement high-order
numerical schemes to efficiently solve PDEs. We then show how recently
introduced adaptive techniques for spectral methods can be integrated into
PINN-based PDE solvers to obtain numerical solutions of unbounded domain
problems that cannot be efficiently approximated by standard PINNs. Through a
number of examples, we demonstrate the advantages of the proposed spectrally
adapted PINNs (s-PINNs) over standard PINNs in approximating functions, solving
PDEs, and estimating model parameters from noisy observations in unbounded
domains.


title:
Hydrogen and Battery Storage Technologies for Low Cost Energy
  Decarbonization in Distribution Networks

abstract:
  Deep energy decarbonization cannot be achieved without high penetration of
renewables. At higher renewable energy penetrations, the variability and
intermittent nature of solar photovoltaic (PV) electricity can cause ramping
issues with existing fossil fuel generation, requiring longer term energy
storage to increase the reliability of grid operation. A proton exchange
membrane electrolyzer can produce H2and serves as a utility controllable load.
The produced H2 can then be stored and converted back into electricity, or
mixed with natural gas, or used as transportation fuel, or chemical feedstock.
This paper considers the perspective of the distribution system operator that
operates the distributed energy resources on a standard IEEE 33-node
distribution network considering the technical and physical constraints with
the goal of minimizing total investment and operation cost. Different case
studies, at very high PV penetrations are considered to show the challenges and
path to net-zero emission energy production using H2 energy. Sensitivity of
utility PV costs and electrolyzer capital costs on producing H2 at $1/kg are
presented showing that the distribution network could produce 100% renewable
electricity and H2 could be produced with the same price by 2050 with
conservative cost estimates and by 2030 with accelerated cost declines.


title:
Zero-viscosity Limit for Boussinesq Equations with Vertical Viscosity
  and Navier Boundary in the Half Plane

abstract:
  In this paper we study the zero-viscosity limit of $2$-D Boussinesq equations
with vertical viscosity and zero diffusivity, which is a nonlinear system with
partial dissipation arising in atmospheric sciences and oceanic circulation.
The domain is taken as $\mathbb{R}_+^2$ with Navier-type boundary. We prove the
nonlinear stability of the approximate solution constructed by boundary layer
expansion in conormal Sobolev space. The optimal expansion order and
convergence rates for the inviscid limit are also identified in this paper. Our
paper extends the partial zero-dissipation limit results of Boussinesq system
with full dissipation by Chae D. $[Adv. Math. 203, no. 2, 2006]$ in the whole
space to the case with partial dissipation and Navier boundary in the half
plane.


title: FEAT: Face Editing with Attention

abstract:
  Employing the latent space of pretrained generators has recently been shown
to be an effective means for GAN-based face manipulation. The success of this
approach heavily relies on the innate disentanglement of the latent space axes
of the generator. However, face manipulation often intends to affect local
regions only, while common generators do not tend to have the necessary spatial
disentanglement. In this paper, we build on the StyleGAN generator, and present
a method that explicitly encourages face manipulation to focus on the intended
regions by incorporating learned attention maps. During the generation of the
edited image, the attention map serves as a mask that guides a blending between
the original features and the modified ones. The guidance for the latent space
edits is achieved by employing CLIP, which has recently been shown to be
effective for text-driven edits. We perform extensive experiments and show that
our method can perform disentangled and controllable face manipulations based
on text descriptions by attending to the relevant regions only. Both
qualitative and quantitative experimental results demonstrate the superiority
of our method for facial region editing over alternative methods.


title:
Painleve-type asymptotics for the defocusing Hirota equation in
  transition region

abstract:
  We consider the Cauchy problem for the classical Hirota equation on the line
with decaying initial data. Based on the spectral analysis of the Lax pair of
the Hirota equation, we first expressed the solution of the Cauchy problem in
terms of the solution of a Riemann-Hilbert problem. Further we apply nonlinear
steepest descent analysis to obtain the long-time asymptotics of the solution
in the critical transition region $|\frac{x}{t} -
\frac{\alpha^2}{3\beta}|t^{2/3} \leq M$, $M$ is a positive constant. Our result
shows that the long time asymptotics of the Hirota equation can be expressed in
terms of the solution of Painlev\'{e} $\mathrm{II}$ equation. Keywords: Hirota
equation, steepest descent method, Painlev\'{e} $\mathrm{II}$ equation,
long-time asymptotics.


title: First-passage Brownian functionals with stochastic resetting

abstract:
  We study the statistical properties of first-passage time functionals of a
one dimensional Brownian motion in the presence of stochastic resetting. A
first-passage functional is defined as $V=\int_0^{t_f} Z[x(\tau)]$ where $t_f$
is the first-passage time of a reset Brownian process $x(\tau)$, i.e., the
first time the process crosses zero. In here, the particle is reset to $x_R>0$
at a constant rate $r$ starting from $x_0>0$ and we focus on the following
functionals: (i) local time $T_{loc} = \int _0^{t_f}d \tau ~ \delta (x-x_R)$,
(ii) residence time $T_{res} = \int _0^{t_f} d \tau ~\theta (x-x_R)$, and (iii)
functionals of the form $A_n = \int _{0}^{t_f} d \tau [x(\tau)]^n $ with $n
>-2$. For first two functionals, we analytically derive the exact expressions
for the moments and distributions. Interestingly, the residence time moments
reach minima at some optimal resetting rates. A similar phenomena is also
observed for the moments of the functional $A_n$. Finally, we show that the
distribution of $A_n$ for large $A_n$ decays exponentially as $\sim
\text{exp}\left( -A_n/a_n\right)$ for all values of $n$ and the corresponding
decay length $a_n$ is also estimated. In particular, exact distribution for the
first passage time under resetting (which corresponds to the $n=0$ case) is
derived and shown to be exponential at large time limit in accordance with the
generic observation. This behavioural drift from the underlying process can be
understood as a ramification due to the resetting mechanism which curtails the
undesired long Brownian first passage trajectories and leads to an accelerated
completion. We confirm our results to high precision by numerical simulations.


title:
Fluorination Increases Hydrophobicity at the Macroscopic Level but not
  at the Microscopic Level

abstract:
  Hydrophobic interactions have been studied in detail in the past based on
hydrophobic polymers, such as polystyrene (PS). Because fluorinated materials
have relatively low surface energy, they often show both oleophobicity and
hydrophobicity at the macroscopic level. However, it remains unknown how
fluorination of hydrophobic polymer influences hydrophobicity at the
microscopic level. In this work, we synthesized PS and fluorine-substituted PS
(FPS) by reversible addition-fragmentation chain transfer polymerization
method. Contact angle measurements confirmed that FPS is more hydrophobic than
PS at the macroscopic level due to the introduction of fluorine. However,
single molecule force spectroscopy experiments showed that the forces required
to unfold the PS and FPS nanoparticles in water are indistinguishable,
indicating that the strength of the hydrophobic ffect that drives the
self-assembly of PS and FPS nanoparticles is the same at the microscopic level.
The divergence of hydrophobic effect at the macroscopic and microscopic level
may hint different underlying mechanisms: the hydrophobicity is dominated by
the solvent hydration at the microscopic level and the surface-associated
interaction at the macroscopic level.


title:
Learning the random variables in Monte Carlo simulations with stochastic
  gradient descent: Machine learning for parametric PDEs and financial
  derivative pricing

abstract:
  In financial engineering, prices of financial products are computed
approximately many times each trading day with (slightly) different parameters
in each calculation. In many financial models such prices can be approximated
by means of Monte Carlo (MC) simulations. To obtain a good approximation the MC
sample size usually needs to be considerably large resulting in a long
computing time to obtain a single approximation. In this paper we introduce a
new approximation strategy for parametric approximation problems including the
parametric financial pricing problems described above. A central aspect of the
approximation strategy proposed in this article is to combine MC algorithms
with machine learning techniques to, roughly speaking, learn the random
variables (LRV) in MC simulations. In other words, we employ stochastic
gradient descent (SGD) optimization methods not to train parameters of standard
artificial neural networks (ANNs) but to learn random variables appearing in MC
approximations. We numerically test the LRV strategy on various parametric
problems with convincing results when compared with standard MC simulations,
Quasi-Monte Carlo simulations, SGD-trained shallow ANNs, and SGD-trained deep
ANNs. Our numerical simulations strongly indicate that the LRV strategy might
be capable to overcome the curse of dimensionality in the $L^\infty$-norm in
several cases where the standard deep learning approach has been proven not to
be able to do so. This is not a contradiction to lower bounds established in
the scientific literature because this new LRV strategy is outside of the class
of algorithms for which lower bounds have been established in the scientific
literature. The proposed LRV strategy is of general nature and not only
restricted to the parametric financial pricing problems described above, but
applicable to a large class of approximation problems.


title:
WISE view of changing-look AGNs: evidence for a transitional stage of
  AGNs

abstract:
  The discovery of changing-look active galactic nuclei (CLAGNs) with the
significant change of optical broad emission lines (optical CLAGNs) and/or
strong variation of line-of-sight column densities (X-ray CLAGNs) challenges
the orientation-based AGN unification model. We explore mid-infrared (mid-IR)
properties for a sample of 57 optical CLAGNs and 11 X-ray CLAGNs based on the
{\it Wide-field Infrared Survey Explorer} ({\it WISE}) archive data. We find
that Eddington-scaled mid-IR luminosities of both optical and X-ray CLAGNs stay
just between low-luminosity AGNs (LLAGNs) and luminous QSOs. The average
Eddington-scaled mid-IR luminosities for optical and X-ray CLAGNs are $\sim
0.4$\% and $\sim 0.5$\%, respectively, which roughly correspond the bolometric
luminosity of transition between a radiatively inefficient accretion flow
(RIAF) and Shakura-Sunyaev disk (SSD). We estimate the time lags of the
variation in the mid-IR behind that in the optical band for 13 CLAGNs with
strong mid-IR variability, where the tight correlation between the time lag and
the bolometric luminosity ($\tau - L$) for CLAGNs roughly follows that found in
the luminous QSOs.


title: No weak epsilon nets for lines and convex sets in space

abstract:
  We prove that there exist no weak $\varepsilon$-nets of constant size for
lines and convex sets in $\mathbb{R}^d$.


title:
Microstructure Effects on Performance and Deactivation of Hierarchically
  Structured Porous Catalyst: a Pore Network Model

abstract:
  In this paper, the pore network model to investigate the reaction-diffusion
process in the hierarchically structured porous catalyst particle is extended
to consider the phenomenon of deactivation by coking. In this framework, the
interaction of internal particle pore structure and mass transfer under the
condition of coke deposition are examined. A primitive experimental
investigation has been performed as an introduction to the development of the
model. Then, the effect of structural features namely macroporosity and pore
size ratio, the deactivation properties, the maximum loading of coke as well as
the transport properties, the pore Damkohler number on the net reaction rate
and deactivation of the particle have been investigated. Three deactivation
mechanisms are accounted for, namely, the site coverage, the pore narrowing,
and the pore blockage. It is found that the deactivation of the catalyst
particle can be divided into two conditions: the kineticsal deactivation and
the structural deactivation. It is shown that depending on the Damkohler
number, increasing the macroporosity does not necessarily improve the
reactivity and deactivation resistance of the catalyst. The key finding of this
work is to demonstrate and quantify how changing the typical fresh catalyst
microstructure observed in the experimental characterization into a
hierarchical one influences the reactivity and deactivation.


title: Robust Anomaly Detection for Time-series Data

abstract:
  Time-series anomaly detection plays a vital role in monitoring complex
operation conditions. However, the detection accuracy of existing approaches is
heavily influenced by pattern distribution, existence of multiple normal
patterns, dynamical features representation, and parameter settings. For the
purpose of improving the robustness and guaranteeing the accuracy, this
research combined the strengths of negative selection, unthresholded recurrence
plots, and an extreme learning machine autoencoder and then proposed robust
anomaly detection for time-series data (RADTD), which can automatically learn
dynamical features in time series and recognize anomalies with low label
dependency and high robustness. Yahoo benchmark datasets and three tunneling
engineering simulation experiments were used to evaluate the performance of
RADTD. The experiments showed that in benchmark datasets RADTD possessed higher
accuracy and robustness than recurrence qualification analysis and extreme
learning machine autoencoder, respectively, and that RADTD accurately detected
the occurrence of tunneling settlement accidents, indicating its remarkable
performance in accuracy and robustness.


title:
The ion acoustic instability of the rotating cylindrical helicon
  discharge plasma

abstract:
  The kinetic theory for the cylindrical plasma, produced by the cylindrically
symmetric (azimuthal mode number m=0) helicon wave, is developed with
accounting for the cylindrical geometry and the radial inhomogeneity of the
helicon wave and plasma. This theory reveals macroscale effect of the azimuthal
steady rotation of electrons with a radially inhomogeneous angular velocity,
caused by radial inhomogeneity of the helicon electric field. It is found that
this sheared rotation as well as the electron density and temperature
inhomogeneity are responsible for the development of the high frequency ion
acoustic instability of the inhomogeneous cylindrical plasma. This instability
is spatially localized in the region of strong gradients of the helicon wave
electric field and of the plasma density, where it is more stronger than the
parametric instabilities driven by the oscillating motion of the electrons
relative to ions in the helicon wave.


title:
Portfolio Optimization on NIFTY Thematic Sector Stocks Using an LSTM
  Model

abstract:
  Portfolio optimization has been a broad and intense area of interest for
quantitative and statistical finance researchers and financial analysts. It is
a challenging task to design a portfolio of stocks to arrive at the optimized
values of the return and risk. This paper presents an algorithmic approach for
designing optimum risk and eigen portfolios for five thematic sectors of the
NSE of India. The prices of the stocks are extracted from the web from Jan 1,
2016, to Dec 31, 2020. Optimum risk and eigen portfolios for each sector are
designed based on ten critical stocks from the sector. An LSTM model is
designed for predicting future stock prices. Seven months after the portfolios
were formed, on Aug 3, 2021, the actual returns of the portfolios are compared
with the LSTM-predicted returns. The predicted and the actual returns indicate
a very high-level accuracy of the LSTM model.


title:
On (Global) Unique Continuation Properties of the Fractional Discrete
  Laplacian

abstract:
  We study various qualitative and quantitative (global) unique continuation
properties for the fractional discrete Laplacian. We show that while the
fractional Laplacian enjoys striking rigidity properties in the form of
(global) unique continuation properties, the fractional discrete Laplacian does
not enjoy these in general. While discretization thus counteracts the strong
rigidity properties of the continuum fractional Laplacian, by discussing
quantitative forms of unique continuation, we illustrate that these properties
can be recovered if exponentially small (in the lattice size) correction terms
are added. This in particular allows us to deduce uniform stability properties
for a discrete, linear inverse problem for the fractional Laplacian. We
complement these observations with a transference principle and the discussion
of these properties on the discrete torus.


title: Efficient primal heuristics for mixed-integer linear programs

abstract:
  This paper is a short report about our work for the primal task in the
Machine Learning for Combinatorial Optimization NeurIPS 2021 Competition. For
each dataset of our interest in the competition, we propose customized primal
heuristic methods to efficiently identify high-quality feasible solutions. The
computational studies demonstrate the superiority of our proposed approaches
over the competitors'.


title:
The enclosure method for the detection of variable order in fractional
  diffusion equations

abstract:
  This paper is concerned with a new type of inverse obstacle problem governed
by a variable-order time-fraction diffusion equation in a bounded domain. The
unknown obstacle is a region where the space dependent variable-order of
fractional time derivative of the governing equation deviates from a known
homogeneous background one. The observation data is given by the Neumann data
of the solution of the governing equation for a specially designed Dirichlet
data. Under a suitable jump condition on the deviation, it is shown that the
most recent version of the time domain enclosure method enables one to extract
information about the geometry of the obstacle and a qualitative nature of the
jump, from the observation data.


title:
Energy-Aware Edge Association for Cluster-based Personalized Federated
  Learning

abstract:
  Federated Learning (FL) over wireless network enables data-conscious services
by leveraging the ubiquitous intelligence at network edge for
privacy-preserving model training. As the proliferation of context-aware
services, the diversified personal preferences causes disagreeing conditional
distributions among user data, which leads to poor inference performance. In
this sense, clustered federated learning is proposed to group user devices with
similar preference and provide each cluster with a personalized model. This
calls for innovative design in edge association that involves user clustering
and also resource management optimization. We formulate an accuracy-cost
trade-off optimization problem by jointly considering model accuracy,
communication resource allocation and energy consumption. To comply with
parameter encryption techniques in FL, we propose an iterative solution
procedure which employs deep reinforcement learning based approach at cloud
server for edge association. The reward function consists of minimized energy
consumption at each base station and the averaged model accuracy of all users.
Under our proposed solution, multiple edge base station are fully exploited to
realize cost efficient personalized federated learning without any prior
knowledge on model parameters. Simulation results show that our proposed
strategy outperforms existing strategies in achieving accurate learning at low
energy cost.


title:
Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY
  50 Stocks

abstract:
  Portfolio design and optimization have been always an area of research that
has attracted a lot of attention from researchers from the finance domain.
Designing an optimum portfolio is a complex task since it involves accurate
forecasting of future stock returns and risks and making a suitable tradeoff
between them. This paper proposes a systematic approach to designing portfolios
using two algorithms, the critical line algorithm, and the hierarchical risk
parity algorithm on eight sectors of the Indian stock market. While the
portfolios are designed using the stock price data from Jan 1, 2016, to Dec 31,
2020, they are tested on the data from Jan 1, 2021, to Aug 26, 2021. The
backtesting results of the portfolios indicate while the performance of the CLA
algorithm is superior on the training data, the HRP algorithm has outperformed
the CLA algorithm on the test data.


title: IVeri: Privacy-Preserving Interdomain Verification

abstract:
  In an interdomain network, autonomous systems (ASes) often establish peering
agreements, so that one AS (agreement consumer) can influence the routing
policies of the other AS (agreement provider). Peering agreements are
implemented in the BGP configuration of the agreement provider. It is crucial
to verify their implementation because one error can lead to disastrous
consequences. However, the fundamental challenge for peering agreement
verification is how to preserve the privacy of both ASes involved in the
agreement. To this end, this paper presents IVeri, the first privacy-preserving
interdomain agreement verification system. IVeri models the interdomain
agreement verification problem as a SAT formula, and develops a novel,
efficient, privacy-serving SAT solver, which uses oblivious shuffling and
garbled circuits as the key building blocks to let the agreement consumer and
provider collaboratively verify the implementation of interdomain peering
agreements without exposing their private information. A prototype of IVeri is
implemented and evaluated extensively. Results show that IVeri achieves
accurate, privacy-preserving interdomain agreement verification with reasonable
overhead.


title:
The ratio $\mathcal{R}(D_s)$ for $B_s \to D_s \ell\nu_\ell$ by using the
  QCD light-cone sum rules within the framework of heavy quark effective field
  theory

abstract:
  In the paper, we study the $B_s\to D_s$ transition form factors by using the
light-cone sum rules within the framework of heavy quark effective field
theory. We adopt a chiral current correlation function to do the calculation,
the resultant transition form factors $f_+^{B_s\to D_s}(q^2)$ and $f_0^{B_s\to
D_s}(q^2)$ are dominated by the contribution of $D_s$-meson leading-twist
distribution amplitude, while the contributions from less certain $D_s$-meson
twist-3 distribution amplitudes are greatly suppressed. At the largest recoil
point, we obtain $f_{+,0}^{B_s \to D_s}(0)=0.533^{+0.112}_{-0.094}$. By further
extrapolating the transition form factors into all the physically allowable
$q^2$ region with the help of the $z$-series parametrization approach, we
calculate the branching fractions $\mathcal{B}(B_s \to D_s \ell^\prime
\nu_{\ell^\prime})$ with $(\ell^\prime= e,\mu)$ and $\mathcal{B}(B_s \to D_s
\tau \nu_\tau)$, which gives $\mathcal{R}(D_s)=0.334\pm 0.017$.


title: Investigating dark energy by electromagnetic frequency shifts

abstract:
  The observed red shift $z$ might be composed by the expansion red shift
$z_{\rm C}$ and an additional frequency shift $z_{\rm S}$, towards the red or
the blue, by considering Extended Theories of Electromagnetism (ETE). Indeed,
massive photon theories - the photon has a real mass as in the de Broglie-Proca
theory or an effective mass as in the Standard-Model Extension (SME), based on
Lorentz-Poincar\'e Symmetry Violation (LSV) - or Non-Linear Electro-Magnetism
(NLEM) theories may induce a cosmological expansion independent frequency shift
in presence of background (inter-) galactic electromagnetic fields, and where
of relevance LSV fields, even when both fields are constant. We have tested
this prediction considering the Pantheon Catalogue, composed by 1048 SNe Ia,
and 15 BAO data, for different cosmological models characterised by the absence
of a cosmological constant. From the data, we compute which values of $z_{\rm
S}$ match the observations, spanning cosmological parameters ($\Omega$
densities and Hubble-Lema\^itre constant) domains. We conclude that the
frequency shift $z_{\rm S}$ can support an alternative to accelerated
expansion, naturally accommodating each SN Ia position in the distance-modulus
versus red shift diagram, due to the light-path dependency of $z_{\rm S}$.
Finally, we briefly mention laboratory test approaches to investigate the
additional shift from ETE predictions.


title:
Diffractive deep neural network based adaptive optics scheme for vortex
  beam in oceanic turbulence

abstract:
  Vortex beam carrying orbital angular momentum (OAM) is disturbed by oceanic
turbulence (OT) when propagating in underwater wireless optical communication
(UWOC) system. Adaptive optics (AO) is used to compensate for distortion and
improve the performance of the UWOC system. In this work, we propose a
diffractive deep neural network (DDNN) based AO scheme to compensate for the
distortion caused by OT, where the DDNN is trained to obtain the mapping
between the distortion intensity distribution of the vortex beam and its
corresponding phase screen representating OT. The intensity pattern of the
distorted vortex beam obtained in the experiment is input to the DDNN model,
and the predicted phase screen can be used to compensate the distortion in real
time. The experiment results show that the proposed scheme can extract quickly
the characteristics of the intensity pattern of the distorted vortex beam, and
output accurately the predicted phase screen. The mode purity of the
compensated vortex beam is significantly improved, even with a strong OT. Our
scheme may provide a new avenue for AO techniques, and is expected to promote
the communication quality of UWOC system.


title: Cohomology of Quaternionic Foliations and Orbifolds

abstract:
  In this paper starting with a concise review of quaternionic geometry,
Kraines 4-form and quaternion K\"ahler manifolds, we define a transversely
quaternion K\"ahler foliation and the analogues of the earlier geometrical
objects for this case. Later, we review the Hodge theory for basic forms and
formulate its counterpart for our case, which enables us to reformulate some of
the results in Riemannian geometry obtained by A. Fujiki and V.Y. Kraines.
Finally, as any orbifold can be realized as the leaf space of a suitably
defined Riemannian foliation we are able to adapt our results to the orbifold
case.


title:
The Self-Driving Car: Crossroads at the Bleeding Edge of Artificial
  Intelligence and Law

abstract:
  Artificial intelligence (AI) features are increasingly being embedded in cars
and are central to the operation of self-driving cars (SDC). There is little or
no effort expended towards understanding and assessing the broad legal and
regulatory impact of the decisions made by AI in cars. A comprehensive
literature review was conducted to determine the perceived barriers, benefits
and facilitating factors of SDC in order to help us understand the suitability
and limitations of existing and proposed law and regulation. (1) existing and
proposed laws are largely based on claimed benefits of SDV that are still
mostly speculative and untested; (2) while publicly presented as issues of
assigning blame and identifying who pays where the SDC is involved in an
accident, the barriers broadly intersect with almost every area of society,
laws and regulations; and (3) new law and regulation are most frequently
identified as the primary factor for enabling SDC. Research on assessing the
impact of AI in SDC needs to be broadened beyond negligence and liability to
encompass barriers, benefits and facilitating factors identified in this paper.
Results of this paper are significant in that they point to the need for deeper
comprehension of the broad impact of all existing law and regulations on the
introduction of SDC technology, with a focus on identifying only those areas
truly requiring ongoing legislative attention.


title:
Extinction and re-initiation of methane detonation in dilute coal
  particle suspensions

abstract:
  In this study, methane detonation propagation in dilute coal particle
suspensions is studied based on Eulerian-Lagrangian method. Two-dimensional
configuration is considered, and a skeletal chemical mechanism (24 species and
104 reactions) is applied for methane combustion. The gas and particulate phase
equations are solved using an OpenFOAM code for two-phase compressible reacting
flow, RYrhoCentralFOAM. The effects of char combustion on methane detonation
dynamics are investigated and devolatized coal particles are modelled. The
results show that propagation of the methane detonation wave in coal particle
suspensions are considerably affected by coal particle concentration and size.
Detonation extinction occurs when the coal particle size is small and
concentration is high. The averaged lead shock speed generally decreases with
increased particle concentration and decreased particle size. Mean structure of
methane and coal particle hybrid detonation is analysed, based on the gas and
particle quantities. It is found that char combustion proceeds in the subsonic
region behind the detonation wave and heat release is relatively distributed
compared to that from gas phase reaction. Moreover, for 1 {\mu}m particle, if
the particle concentration is beyond a threshold value, detonation
re-initiation occurs after it is quenched at the beginning of the coal dust
suspensions. This is caused by hot spots from the shock focusing along the
reaction front in a decoupled detonation and these shocks are generated from
char combustion behind the lead shock. A regime map of detonation propagation
and extinction is predicted. It is found that the re-initiation location
decreases with the particle concentration and approaches a constant value when
the concentration exceeds 1000 g/m3. The results from this study are useful for
prevention and suppression of methane/coal dust hybrid explosion.


title:
The uniform electron gas at high temperatures: \emph{ab initio} path
  integral Monte Carlo simulations and analytical theory

abstract:
  We present extensive new \emph{ab initio} path integral Monte Carlo (PIMC)
simulations of the uniform electron gas (UEG) in the high-temperature regime,
$8\leq\theta=k_\textnormal{B}T/E_\textnormal{F}\leq128$. This allows us to
study the convergence of different properties towards the classical limit. In
particular, we investigate the classical relation between the static structure
factor $S(\mathbf{q})$ and the static local field correction $G(\mathbf{q})$,
which is only fulfilled at low densities. Moreover, we compare our new results
for the interaction energy to the parametrization of the UEG by Groth \emph{et
al.}~[PRL \textbf{119}, 135001 (2017)], which interpolates between PIMC results
for $\theta\leq8$ and the Debye-H\"uckel limit, and to higher order analytical
virial expansions. Finally, we consider the momentum distribution function
$n(\mathbf{q})$ and find an interaction-induced increase in the occupation of
the zero-momentum state even for $\theta\gtrsim32$. All PIMC data are freely
available online, and can be used as input for improved parametrizations and as
a rigorous benchmark for approximate methods.


title:
An alternative strategy for the use of a low-cost age-hardenable
  Fe-Si-Ti steel for automotive application

abstract:
  For High Strength Low Alloy (HSLA) steels or for age-hardenable steels
(maraging) the strengthening by precipitation is done before forming operation
in order to increase the yield stress as much as possible. In this publication
the advantages of an hardening thermal treatment after forming operation are
investigated in a low cost age-hardenable steel Fe-Si-Ti consistent with
automotive application.


title: Enhancing variational generation through self-decomposition

abstract:
  In this article we introduce the notion of Split Variational Autoencoder
(SVAE), whose output $\hat{x}$ is obtained as a weighted sum $\sigma \odot
\hat{x_1} + (1-\sigma) \odot \hat{x_2}$ of two generated images
$\hat{x_1},\hat{x_2}$, and $\sigma$ is a learned compositional map. The network
is trained as a usual Variational Autoencoder with a negative loglikelihood
loss between training and reconstructed images. The decomposition is
nondeterministic, but follows two main schemes, that we may roughly categorize
as either "syntactic" or "semantic". In the first case, the map tends to
exploit the strong correlation between adjacent pixels, splitting the image in
two complementary high frequency sub-images. In the second case, the map
typically focuses on the contours of objects, splitting the image in
interesting variations of its content, with more marked and distinctive
features. In this case, the Fr\'echet Inception Distance (FID) of $\hat{x_1}$
and $\hat{x_2}$ is usually lower (hence better) than that of $\hat{x}$, that
clearly suffers from being the average of the formers. In a sense, a SVAE
forces the Variational Autoencoder to {\em make choices}, in contrast with its
intrinsic tendency to average between alternatives with the aim to minimize the
reconstruction loss towards a specific sample. According to the FID metric, our
technique, tested on typical datasets such as Mnist, Cifar10 and Celeba, allows
us to outperform all previous purely variational architectures (not relying on
normalization flows).


title:
$s$-wave superconductivity in the noncentrosymmetric W$_3$Al$_2$C
  superconductor: An NMR study

abstract:
  We report on a microscopic study of the noncentrosymmetric superconductor
W$_3$Al$_2$C (with $T_c$ = 7.6 K), mostly by means of $^{27}$Al- and $^{13}$C
nuclear magnetic resonance (NMR). Since in this material the density of states
at the Fermi level is dominated by the tungsten's 5$d$ orbitals, we expect a
sizeable spin-orbit coupling (SOC) effect. The normal-state electronic
properties of W$_3$Al$_2$C resemble those of a standard metal, but with a
Korringa product $1/(T_{1}T)$ significantly smaller than that of metallic Al,
reflecting the marginal role played by $s$-electrons. In the superconducting
state, we observe a reduction of the Knight shift and an exponential decrease
of the NMR relaxation rate $1/T_1$, typical of $s$-wave superconductivity. This
is further supported by the observation of a small but distinct coherence peak
just below $T_c$ in the $^{13}$C NMR relaxation-rate, in agreement with the
fully-gapped superconducting state inferred from the electronic specific-heat
data well below $T_c$. The above features are compared to those of members of
the same family, in particular, Mo$_3$Al$_2$C, often claimed to exhibit
unconventional superconductivity. We discuss why, despite the enhanced SOC,
W$_3$Al$_2$C does not show spin-triplet features in its superconducting state
and consider the broader consequences of our results for noncentrosymmetric
superconductors in general.


title: A domain with non-plurisubharmonic $d$-balanced squeezing function

abstract:
  In this note, we give an example of a domain whose $d$-balanced squeezing
function is non-plurisubharmonic.


title: Minimal Leader Set for Controllability of k-distant Trees

abstract:
  Minimal controllability problem plays an important role in the field of
network control. A New concept-Minimum Perfect Critical Set (MPCS)is proposed.
Four different MPCSs were found for k-distant tree graphs. Based on this
concept of MPCS, an algorithm for finding the minimal leader set is provided.
Numerical experiments show that these theories enable the algorithm to find a
minimal leader set with a probability of more than 0.98. Further, some other
numerical characteristics of the minimal leader set of k-distant trees were
found.


title:
Branching Random Walks with Two Types of Particles on Multidimensional
  Lattices

abstract:
  We consider a continuous-time branching random walk on a multidimensional
lattice with two types of particles and an infinite number of initial
particles. The main results are devoted to the study of the generating function
and the limiting behavior of the moments of subpopulations generated by a
single particle of each type. We assume that particle types differ from each
other not only by the laws of branching, as in multi-type branching processes,
but also by the laws of walking. For a critical branching process at each
lattice point and recurrent random walk of particles, the effect of limit
spatial clustering of particles over the lattice is studied. A model
illustrating epidemic propagation is also considered. In this model, we
consider two types of particles: infected and immunity generated. Initially,
there is an infected particle that can infect others. Here, for the local
number of particles of each type at a lattice point, we study the moments and
their limiting behavior. Additionally, the effect of intermittency of the
infected particles is studied for a supercritical branching process at each
lattice point. Simulations are presented to demonstrate the effect of limit
clustering for the epidemiological model.


title: Simultaneous cooling by measuring one ancillary system

abstract:
  We present a simultaneous-cooling protocol for a double-resonator system via
projective measurements on an ancillary $V$-type qutrit. Through repeated
measurements on the ground state of the ancillary system, the two resonators
can be cooled down to their respective ground states from thermal states. With
respect to the measurement-based cooling, an optimized measurement-interval
$\tau_{\rm opt}$ is analytically obtained for the first time, which is
inversely proportional to the collective thermal Rabi frequency $\Omega_{\rm
th}$ as a function of the resonators' average population of the last round.
Under about only $20$ optimized measurements, the average population of the
target resonators can be reduced by $6$ orders in magnitude. Our simultaneous
or collective cooling protocol is scalable to the systems with more numbers of
resonators and robust to the fluctuation in the resonator frequency.


title:
Local PT-symmetric evolutions on separable states and violation of
  no-signaling

abstract:
  We show that local PT-symmetric evolutions can lead to violation of the
no-signaling principle for separable and even classically correlated bipartite
shared quantum states. For classically correlated states, specially chosen
PT-symmetric operations from a set of zero volume can also preserve the
principle. The violations can be removed by using a CPT inner product instead
of the traditional one.


title:
Combinatorial proofs and refinements of three partition theorems of
  Andrews

abstract:
  In his recent work, Andrews revisited two-color partitions with certain
restrictions on the differences between consecutive parts, and he established
three theorems linking these two-color partitions with more familiar kinds of
partitions. In this note, we provide bijective proofs as well as refinements of
those three theorems of Andrews. Our refinements take into account the numbers
of parts in each of the two colors.


title:
Unexpectedly Spontaneous Water Dissociation on Graphene Oxide Supported
  by Copper Substrate

abstract:
  Water dissociation is of fundamental importance in scientific fields and has
drawn considerable interest in diverse technological applications. However, the
high activation barrier of breaking the O-H bond within the water molecule has
been identified as the bottleneck, even for the water adsorbed on the graphene
oxide (GO). Herein, using the density functional theory calculations, we
demonstrate that the water molecule can be spontaneously dissociated on GO
supported by the (111) surface of the copper substrate (Copper-GO). This
process involves a proton transferring from water to the interfacial oxygen
group, and a hydroxide covalently bonding to GO. Compared to that on GO, the
water dissociation barrier on Copper-GO is significantly decreased to be less
than or comparable to thermal fluctuations. This is ascribed to the
orbital-hybridizing interaction between copper substrate and GO, which enhances
the reaction activity of interfacial oxygen groups along the basal plane of GO
for water dissociation. Our work provides a novel strategy to access water
dissociation via the substrate-enhanced reaction activity of interfacial oxygen
groups on GO and indicates that the substrate can serve as an essential key to
tune the catalytic performance of various two-dimensional material devices.


title: Ensembles de petite somme, structure de sous-criticit\'e

abstract:
  If $A$ and $B$ are two bounded sets of reals, Ruzsa proved a precise lower
bound of the measure of the sumset $A+B$ involving the ratio
$\lambda(A)/\lambda(B)$. De Roton established a structural result about the
critical sets of this lower bound. Here, we prove a generalization of de
Roton's work by establishing a result in a neighborhood of the case of
equality.


title: Visual Behaviors and Mobile Information Acquisition

abstract:
  It is common for people to engage in information acquisition tasks while on
the move. To understand how users' visual behaviors influence microlearning, a
form of mobile information acquisition, we conducted a shadowing study with 8
participants and identified three common visual behaviors: 'glance', 'inspect',
and 'drift'. We found that 'drift' best supports mobile information
acquisition. We also identified four user-related factors that can influence
the utilization of mobile information acquisition opportunities: situational
awareness, switching costs, ongoing cognitive processes, and awareness of
opportunities. We further examined how these user-related factors interplay
with device-related factors through a technology probe with 20 participants
using mobile phones and optical head-mounted displays (OHMDs). Results indicate
that different device platforms significantly influence how mobile information
acquisition opportunities are used: OHMDs can better support mobile information
acquisition when visual attention is fragmented. OHMDs facilitate shorter
visual switch-times between the task and surroundings, which reduces the mental
barrier of task transition. Mobile phones, on the other hand, provide a more
focused experience in more stable surroundings. Based on these findings, we
discuss trade-offs and design implications for supporting information
acquisition tasks on the move.


title:
Exponentially Stable Adaptive Control of MIMO Systems with Unknown
  Control Matrix

abstract:
  The scope of this research is a problem of the direct model reference
adaptive control of linear time-invariant multi-input multi-output (MIMO)
plants without any a priori knowledge about system matrices. To handle it, a
new method is proposed, which includes three main stages. Firstly, using the
well-known DREM procedure, the plant parametrization is made to obtain the
linear regressions, in which the plant matrices and state initial conditions
are the unknown parameters. Secondly, such regressions are substituted into the
known equations for the controller parameters calculation. Thirdly, the
controller parameters are identified using the novel adjustment law with
exponential rate of convergence. To the best of the authors knowledge, such a
method is the first one to provide the following features simultaneously: 1) it
is applicable for the generic completely unknown MIMO systems (e.g. without any
information about state or control allocation matrices, the sign of the latter,
etc.); 2) it guarantees the exponential convergence of both the parameter and
tracking errors under the mild requirement of the regressor finite excitation;
3) it ensures monotonicity of the transient curves of the control law
parameters matrices. The results of the conducted experiments with the model of
a rubber and ailerons control of a small passenger aircraft corroborate all the
theoretical results.


title:
Estimating the Euclidean Quantum Propagator with Deep Generative
  Modelling of Feynman paths

abstract:
  Feynman path integrals provide an elegant, classically-inspired
representation for the quantum propagator and the quantum dynamics, through
summing over a huge manifold of all possible paths. From computational and
simulational perspectives, the ergodic tracking of the whole path manifold is a
hard problem. Machine learning can help, in an efficient manner, to identify
the relevant subspace and the intrinsic structure residing at a small fraction
of the vast path manifold. In this work, we propose the concept of Feynman path
generator, which efficiently generates Feynman paths with fixed endpoints from
a (low-dimensional) latent space, by targeting a desired density of paths in
the Euclidean space-time. With such path generators, the Euclidean propagator
as well as the ground state wave function can be estimated efficiently for a
generic potential energy. Our work leads to a fresh approach for calculating
the quantum propagator, paves the way toward generative modelling of Feynman
paths, and may also provide a future new perspective to understand the
quantum-classical correspondence through deep learning.

categories: quant-ph cond-mat.dis-nn cs.LG physics.data-an


title: Pipe Overflow: Smashing Voice Authentication for Fun and Profit

abstract:
  Recent years have seen a surge of popularity of acoustics-enabled personal
devices powered by machine learning. Yet, machine learning has proven to be
vulnerable to adversarial examples. Large number of modern systems protect
themselves against such attacks by targeting the artificiality, i.e., they
deploy mechanisms to detect the lack of human involvement in generating the
adversarial examples. However, these defenses implicitly assume that humans are
incapable of producing meaningful and targeted adversarial examples. In this
paper, we show that this base assumption is wrong. In particular, we
demonstrate that for tasks like speaker identification, a human is capable of
producing analog adversarial examples directly with little cost and
supervision: by simply speaking through a tube, an adversary reliably
impersonates other speakers in eyes of ML models for speaker identification.
Our findings extend to a range of other acoustic-biometric tasks such as
liveness, bringing into question their use in security-critical settings in
real life, such as phone banking.


title:
Computing Transience Bounds of Emergency Call Centers: a Hierarchical
  Timed Petri Net Approach

abstract:
  A fundamental issue in the analysis of emergency call centers is to estimate
the time needed to return to a congestion-free regime after an unusual event
with a massive arrival of calls. Call centers can generally be represented by
timed Petri nets with a hierarchical structure, in which several layers
describe the successive steps of treatments of calls. We study a continuous
approximation of the Petri net dynamics (with infinitesimal tokens). Then, we
show that a counter function, measuring the deviation to the stationary regime,
coincides with the value function of a semi-Markov decision problem. Then, we
establish a finite time convergence result, exploiting the hierarchical
structure of the Petri net. We obtain an explicit bound for the transience
time, as a function of the initial marking and sojourn times. This is based on
methods from the theory of stochastic shortest paths and non-linear
Perron--Frobenius theory. We illustrate the bound on a case study of a medical
emergency call center.


title: Triple-meron crystal in high-spin Kitaev magnets

abstract:
  Spin textures with nontrivial topology hold great promise in future
spintronics applications since they are robust against local deformations. The
meron, as one of such spin textures, is widely believed to appear in pairs due
to its topological equivalence to a half skyrmion. Motivated by recent
progresses in high-spin Kitaev magnets, here we investigate numerically a
classical Kitaev-$\Gamma$ model with a single-ion anisotropy. An exotic spin
texture including three merons is discovered. Such a state features a peculiar
property with an odd number of merons in one magnetic unit cell and it can
induce the topological Hall effect.Therefore, these merons cannot be
dissociated from skyrmions as reported in the literature and a general
mechanism for such a deconfinement phenomenon calls for further studies. Our
work demonstrates that high-spin Kitaev magnets can host robust unconventional
spin textures and thus they offer a versatile platform not only for exploring
exotic states in spintronics but also for understanding the deconfinement
mechanism in the condensed-matter physics and the field theory.


title: Attempt to describe phase slips by means of an adiabatic approximation

abstract:
  As a plausibility test for the feasibility of extension of the quasiclassical
Keldysh--Usadel technique to slowly varying situations, we assess the influence
of the time-derivative term in the time-dependent Ginzburg--Landau equation. We
consider cases in which the superconducting state in a nanowire varies slowly,
either because the voltage applied on it is small, or because most of phase
drift takes place next to the boundaries. An approximation without this time
derivative can describe the superconducting state away from phase slips, but is
unable to predict the value or the existence of a critical voltage at which
evolution becomes non-stationary.


title:
Transport and emergent stratification in the equilibrated Eady model:
  the vortex gas scaling regime

abstract:
  We numerically and theoretically investigate the Boussinesq Eady model, where
a rapidly rotating density-stratified layer of fluid is subject to a meridional
temperature gradient in thermal wind balance with a uniform vertically sheared
zonal flow. Through a suite of numerical simulations, we show that the
transport properties of the resulting turbulent flow are governed by
quasi-geostrophic (QG) dynamics in the rapidly rotating strongly stratified
regime. The 'vortex gas' scaling predictions put forward in the context of the
two-layer QG model carry over to this fully 3D system: the functional
dependence of the meridional flux on the control parameters is the same, the
two ajustable parameters entering the theory taking slightly different values.
In line with the QG prediction, the meridional buoyancy flux is
depth-independent. The vertical buoyancy flux is such that turbulence
transports buoyancy along isopycnals, except in narrow layers near the the top
and bottom boundaries, the thickness of which decreases as the diffusivities go
to zero. The emergent (re)stratification is set by a simple balance between the
vertical buoyancy flux and diffusion along the vertical direction. Overall,
this study demonstrates how the vortex-gas scaling theory can be adapted to
quantitatively predict the magnitude and vertical structure of the meridional
and vertical buoyancy fluxes, and of the emergent stratification, without
additional fitting parameters.


title: On Vietoris--Rips complexes (with scale 3) of hypercube graphs

abstract:
  For a metric space $(X, d)$ and a scale parameter $r \geq 0$, the
Vitoris-Rips complex $\mathcal{VR}(X;r)$ is a simplicial complex on vertex set
$X$, where a finite set $\sigma \subseteq X$ is a simplex if and only if
diameter of $\sigma$ is at most $r$. For $n \geq 1$, let $\mathbb{I}_n$ denotes
the $n$-dimensional hypercube graph. In this paper, we show that
$\mathcal{VR}(\mathbb{I}_n;r)$ has non trivial reduced homology only in
dimensions $4$ and $7$. Therefore, we answer a question posed by Adamaszek and
Adams recently.
  A (finite) simplicial complex $\Delta$ is $d$-collapsible if it can be
reduced to the void complex by repeatedly removing a face of size at most $d$
that is contained in a unique maximal face of $\Delta$. The collapsibility
number of $\Delta$ is the minimum integer $d$ such that $\Delta$ is
$d$-collapsible. We show that the collapsibility number of
$\mathcal{VR}(\mathbb{I}_n;r)$ is $2^r$ for $r \in \{2, 3\}$.


title: A Review of Modern Fashion Recommender Systems

abstract:
  The textile and apparel industries have grown tremendously over the last
years. Customers no longer have to visit many stores, stand in long queues, or
try on garments in dressing rooms as millions of products are now available in
online catalogs. However, given the plethora of options available, an effective
recommendation system is necessary to properly sort, order, and communicate
relevant product material or information to users. Effective fashion RS can
have a noticeable impact on billions of customers' shopping experiences and
increase sales and revenues on the provider-side.
  The goal of this survey is to provide a review of recommender systems that
operate in the specific vertical domain of garment and fashion products. We
have identified the most pressing challenges in fashion RS research and created
a taxonomy that categorizes the literature according to the objective they are
trying to accomplish (e.g., item or outfit recommendation, size recommendation,
explainability, among others) and type of side-information (users, items,
context). We have also identified the most important evaluation goals and
perspectives (outfit generation, outfit recommendation, pairing recommendation,
and fill-in-the-blank outfit compatibility prediction) and the most commonly
used datasets and evaluation metrics.


title:
3D Map Reconstruction of an Orchard using an Angle-Aware Covering
  Control Strategy

abstract:
  In the last years, unmanned aerial vehicles are becoming a reality in the
context of precision agriculture, mainly for monitoring, patrolling and remote
sensing tasks, but also for 3D map reconstruction. In this paper, we present an
innovative approach where a fleet of unmanned aerial vehicles is exploited to
perform remote sensing tasks over an apple orchard for reconstructing a 3D map
of the field, formulating the covering control problem to combine the position
of a monitoring target and the viewing angle. Moreover, the objective function
of the controller is defined by an importance index, which has been computed
from a multi-spectral map of the field, obtained by a preliminary flight, using
a semantic interpretation scheme based on a convolutional neural network. This
objective function is then updated according to the history of the past
coverage states, thus allowing the drones to take situation-adaptive actions.
The effectiveness of the proposed covering control strategy has been validated
through simulations on a Robot Operating System.


title: Node-wise monotone barrier coupling law for central pattern generation

abstract:
  With the aim of providing new insights into the mechanisms behind the
emergence of collective behaviors via nonlinear coupling, we propose a
node-wise monotone barrier coupling law that imitates the behavior and
beneficial properties of neural central pattern generators (CPGs). In
particular, the coupling law 1) allows us to assign multiple central patterns
on the circle and 2) allows for rapid switching between different patterns via
simple `kicks'. In the end, we achieve full control by partitioning the state
space by utilizing a barrier effect and assigning unique steady-state behaviors
to each element of the resulting partition. We analyze the global behavior and
study the viability of the design.


title: Optimal Correlators and Waveforms for Mismatched Detection

abstract:
  We consider the classical Neymann-Pearson hypothesis testing problem of
signal detection, where under the null hypothesis ($\calH_0$), the received
signal is white Gaussian noise, and under the alternative hypothesis
($\calH_1$), the received signal includes also an additional non-Gaussian
random signal, which in turn can be viewed as a deterministic waveform plus
zero-mean, non-Gaussian noise. However, instead of the classical likelihood
ratio test detector, which might be difficult to implement, in general, we
impose a (mismatched) correlation detector, which is relatively easy to
implement, and we characterize the optimal correlator weights in the sense of
the best trade-off between the false-alarm error exponent and the
missed-detection error exponent. Those optimal correlator weights depend
(non-linearly, in general) on the underlying deterministic waveform under
$\calH_1$. We then assume that the deterministic waveform may also be free to
be optimized (subject to a power constraint), jointly with the correlator, and
show that both the optimal waveform and the optimal correlator weights may take
on values in a small finite set of typically no more than two to four levels,
depending on the distribution of the non-Gaussian noise component. Finally, we
outline an extension of the scope to a wider class of detectors that are based
on linear combinations of the correlation and the energy of the received
signal.