title:
PillarGrid: Deep Learning-based Cooperative Perception for 3D Object
  Detection from Onboard-Roadside LiDAR

abstract:
  3D object detection plays a fundamental role in enabling autonomous driving,
which is regarded as the significant key to unlocking the bottleneck of
contemporary transportation systems from the perspectives of safety, mobility,
and sustainability. Most of the state-of-the-art (SOTA) object detection
methods from point clouds are developed based on a single onboard LiDAR, whose
performance will be inevitably limited by the range and occlusion, especially
in dense traffic scenarios. In this paper, we propose \textit{PillarGrid}, a
novel cooperative perception method fusing information from multiple 3D LiDARs
(both on-board and roadside), to enhance the situation awareness for connected
and automated vehicles (CAVs). PillarGrid consists of four main phases: 1)
cooperative preprocessing of point clouds, 2) pillar-wise voxelization and
feature extraction, 3) grid-wise deep fusion of features from multiple sensors,
and 4) convolutional neural network (CNN)-based augmented 3D object detection.
A novel cooperative perception platform is developed for model training and
testing. Extensive experimentation shows that PillarGrid outperforms the SOTA
single-LiDAR-based 3D object detection methods with respect to both accuracy
and range by a large margin.


title:
V476 Cyg (Nova Cyg 1920) is currently a dwarf nova -- first such an
  object in the period gap?

abstract:
  V476 Cyg (Nova Cyg 1920) is a bright, fast nova reaching a photographic
magnitude of 2.0. Using the Zwicky Transient Facility (ZTF) public database, I
found that this nova is currently a dwarf nova with a cycle length of ~24 d.
Compared to other classical novae currently in dwarf nova-type states,
outbursts of V476 Cyg are rapidly rising and short with durations of a few
days. Based on the AAVSO observations, this nova was probably already in the
dwarf nova-type phase in 2016, 96 years after the nova eruption. I found a
possible orbital period of 0.1018002(6) d using the ZTF data, which would place
the object in the period gap. This supposed short orbital period appears to
explain the features and faint absolute magnitudes of the observed dwarf nova
outbursts. If this period is confirmed, V476 Cyg is a classical nova with the
shortest orbital period with distinct dwarf nova outbursts and in which a nova
eruption was recorded in the modern era. I also compared with the outburst
properties with V446 Her (Nova Her 1960), which currently shows SS Cyg-type
outbursts. The transition to the dwarf nova-phase in V476 Cyg occurred much
earlier (~100 yr) than what has been supposed (~1000 yr) for classical novae
below the period gap. V476 Cyg would not only provide an ideal laboratory of
the behavior of an irradiated accretion disk in which tidal instability is
expected to work, but also an ideal laboratory of the effect of a massive white
dwarf on dwarf nova outbursts.


title:
Wavelet Knowledge Distillation: Towards Efficient Image-to-Image
  Translation

abstract:
  Remarkable achievements have been attained with Generative Adversarial
Networks (GANs) in image-to-image translation. However, due to a tremendous
amount of parameters, state-of-the-art GANs usually suffer from low efficiency
and bulky memory usage. To tackle this challenge, firstly, this paper
investigates GANs performance from a frequency perspective. The results show
that GANs, especially small GANs lack the ability to generate high-quality high
frequency information. To address this problem, we propose a novel knowledge
distillation method referred to as wavelet knowledge distillation. Instead of
directly distilling the generated images of teachers, wavelet knowledge
distillation first decomposes the images into different frequency bands with
discrete wavelet transformation and then only distills the high frequency
bands. As a result, the student GAN can pay more attention to its learning on
high frequency bands. Experiments demonstrate that our method leads to 7.08
times compression and 6.80 times acceleration on CycleGAN with almost no
performance drop. Additionally, we have studied the relation between
discriminators and generators which shows that the compression of
discriminators can promote the performance of compressed generators.


title:
A Simple Combinatorial Proof of Szemer\'{e}di's Theorem via Three Levels
  of Infinities

abstract:
  We present a nonstandard simple elementary proof of Szemer\'{e}di's theorem
by a straightforward induction with the help of three levels of infinities and
four different elementary embeddings in a nonstandard universe.


title:
The $Z_3$ soft breaking in the I(2+1)HDM and its probes at present and
  future colliders

abstract:
  A $ Z_3 $ symmetric 3-Higgs Doublet Model (3HDM) with two inert doublets and
one active doublet (that plays the role of the Higgs doublet), the so-called
I(2+1)HDM, is studied. We discuss the behaviour of this 3HDM realisation when
one allows for a $ Z_3 $ soft breaking term. Such a symmetry enables the
presence of a two-component Dark Matter (DM) scenario in the form of
"Hermaphrodite DM", where the two inert candidates have opposite CP parity and
are protected by this discrete symmetry from decaying into Standard Model (SM)
particles. Furthermore, the two DM states are potentially distinguishable from
each other as they cannot be subsumed into a complex field, having different
masses and gauge couplings. With this in mind, we study differential spectra
with a distinctive shape from which the existence of two different DM component
distributions could be easily inferred. We prove this to be possible at the
Large Hadron Collider (LHC) via the $q\bar{q} \to 2l+H_1H_1$ and $q\bar{q} \to
2l+A_1A_1$ processes as well as at a future electron-positron machine via the
$e^{+}e^{-} \to 2l+H_1H_1$ and $e^{+}e^{-} \to 2l+A_1A_1$ channels, where
$l=e,\mu$.


title:
Hybrid Beamforming for Millimeter Wave MIMO Integrated Sensing and
  Communications

abstract:
  In this letter, we consider hybrid beamforming for millimeter wave (mmWave)
MIMO integrated sensing and communications (ISAC). We design the transmit beam
of a dual-functional radar-communication (DFRC) base station (BS), aiming at
approaching the objective radar beam pattern, subject to the constraints of the
signal to interference-plus-noise ratio (SINR) of communication users and total
transmission power of the DFRC BS. To provide additional degree of freedom for
the beam design problem, we introduce a phase vector to the objective beam
pattern and propose an alternating minimization method to iteratively optimize
the transmit beam and the phase vector, which involves second-order cone
programming and constrained least squared estimation, respectively. Then based
on the designed transmit beam, we determine the analog beamformer and digital
beamformer subject to the constant envelop constraint of phase shifter network
in mmWave MIMO, still using the alternating minimization method. Simulation
results show that under the same SINR constraint of communication users, larger
antenna array can achieve better radar beam quality.


title: A new theta cycle for $GSp_4$ and an Edixhoven type theorem

abstract:
  In this paper we investigate a new theta cycle for $GSp_4/\mathbb{Q}$ by
using author's theta operators defined in the previous work. In the course of
the construction we also modify the theta operators to work on any
characteristic including $p=2$ and any weight. As an application we discuss an
Edixhoven type theorem for $GSp_4/\mathbb{Q}$.


title:
Laboratory investigation of the interaction between the jet and
  background, from collisionless to strong collision

abstract:
  The interaction between the supersonic jet and background can influence the
process of star formation, and this interaction also results in a change of the
jet's velocity, direction and density through shock waves. However, due to the
limitations of current astronomical facilities, the fine shock structure and
the detailed interaction process still remain unclear. Here we investigate the
plasma dynamics under different collision states through laser-driven
experiments. A double-shock structure is shown in the optical diagnosis for
collision case, but the integrated self-emitting X-ray characteristic is
different. For solid plastic hemisphere obstacle, two-layer shock emission is
observed, and for the relatively low-density laser-driven plasma core, only one
shock emission is shown. And the plasma jets are deflected by $50 ^{\circ}$
through the interaction with the high-density background in both cases. For
collisionless cases, filament structures are observed, and the mean width of
filaments is roughly the same as the ion skin depth. High-energy electrons are
observed in all interaction cases. We present the detailed process of the shock
formation and filament instability through 2D/3D hydrodynamic simulations and
particle-in-cell simulations respectively. Our results can also be applied to
explain the shock structure in the Herbig-Haro (HH) 110/270 system, and the
experiments indicate that the impact point may be pushed into the inside part
of the cloud.


title:
A Two-Level Block Preconditioned Jacobi-Davidson Method for Computing
  Multiple and Clustered Eigenvalues

abstract:
  In this paper, we propose a two-level block preconditioned Jacobi-Davidson
(BPJD) method for solving discrete eigenvalue problems resulting from finite
element approximations of $2m$th $(m=1,2)$ order elliptic eigenvalue problems.
A new and efficient preconditioner is constructed by an overlapping domain
decomposition (DD). This method may compute the first several eigenpairs,
including simple, multiple and clustered cases. In each iteration, we only need
to solve a couple of parallel subproblems and one small scale eigenvalue
problem. The rigorous theoretical analysis reveals that the convergence rate of
the two-level BPJD method for the first several eigenvalues is bounded by
$c(H)(1-C\frac{\delta^{2m-1}}{H^{2m-1}})^{2}$, where $H$ is the diameter of
subdomains and $\delta$ is the overlapping size among subdomains. The constant
$C$ is independent of the mesh size $h$ and internal gaps among target
eigenvalues, which means that our method is optimal and cluster robust.
Meanwhile, the $H$-dependent constant $c(H)$ decreases monotonically to $1$, as
$H\to 0$, which means that more subdomains lead to the better convergence rate.
Numerical results supporting our theory are given.


title: Image Style Transfer: from Artistic to Photorealistic

abstract:
  The rapid advancement of deep learning has significantly boomed the
development of photorealistic style transfer. In this review, we reviewed the
development of photorealistic style transfer starting from artistic style
transfer and the contribution of traditional image processing techniques on
photorealistic style transfer, including some work that had been completed in
the Multimedia lab at the University of Alberta. Many techniques were discussed
in this review. However, our focus is on VGG-based techniques, whitening and
coloring transform (WCTs) based techniques, the combination of deep learning
with traditional image processing techniques.


title: Joint asymptotic expansions for Bessel functions

abstract:
  We study the classical problem of finding asymptotics for the Bessel
functions $J_{\nu}(z)$ and $Y_{\nu}(z)$ as the argument $z$ and the order $\nu$
approach infinity. We use blow-up analysis to find asymptotics for the modulus
and phase of the Bessel functions; this approach produces polyhomogeneous
conormal joint asymptotic expansions, valid in any regime. As a consequence,
our asymptotics may be differentiated term by term with respect to either
argument or order, allowing us to easily produce expansions for Bessel function
derivatives. We also discuss applications to spectral theory, in particular the
study of the Dirichlet eigenvalues of a disk.


title:
Channel Estimation for Wideband MmWave MIMO OFDM System Exploiting Block
  Sparsity

abstract:
  In this letter, we investigate time-domain channel estimation for wideband
millimeter wave (mmWave) MIMO OFDM system. By transmitting frequency-domain
pilot symbols as well as different beamforming vectors, we observe that the
time-domain mmWave MIMO channels exhibit channel delay sparsity and especially
block sparsity among different spatial directions. Then we propose a
time-domain channel estimation exploiting block sparsity (TDCEBS) scheme, which
always aims at finding the best nonzero block achieving the largest projection
of the residue at each iterations. In particular, we evaluate the system
performance using the QuaDRiGa which is recommended by 5G New Radio to generate
wideband mmWave MIMO channels. The effectiveness of the proposed TDCEBS scheme
is verified by the simulation results, as the proposed scheme outperforms the
existing schemes.


title:
Thermal emission from bow shocks II: 3D magnetohydrodynamic models of
  Zeta Ophiuchi

abstract:
  The nearby, massive, runaway star Zeta Ophiuchi has a large bow shock
detected in optical and infrared, and, uniquely among runaway O stars, diffuse
X-ray emission is detected from the shocked stellar wind. Previous
investigation of NGC 7635 found that numerical simulations over-predict the
X-ray emission by ~10 times. Here we make the first detailed computational
investigation of the bow shock of Zeta Ophiuchi, to test whether a simple model
of the bow shock can explain the observed nebula, and to compare the detected
X-ray emission with simulated emission maps. We re-analysed archival {\it
Chandra} observations of the thermal diffuse X-ray emission from the shocked
wind region of the bow shock, finding total unabsorbed X-ray flux (0.3-2 keV
band) corresponding to a diffuse luminosity of
$L_\mathrm{X}=2.33~(0.79-3.45)\times10^{29}$ergs$^{-1}$. 3D MHD simulations
were used to model the interaction of the star's wind with a uniform ISM using
a range of stellar and ISM parameters motivated by observational constraints.
Synthetic infrared, Ha, soft X-ray, emission measure, and radio 6\,GHz emission
maps were generated from three simulations, for comparison with relevant
observations. Simulations where the space velocity of Zeta Ophiuchi has a
significant radial velocity produce infrared emission maps with opening angle
of the bow shock in better agreement with observations than for the case where
motion is fully in the plane of the sky. The simulation with the highest
pressure has the closest match, with flux level within a factor of 2 of the
observational lower limit, and emission weighted temperature of
$\log_{10}(T_\mathrm{A}/\mathrm{K})=6.4$, although the morphology of the
diffuse emission appears somewhat different. Observed X-ray emission is a
filled bubble brightest near the star whereas simulations predict brightening
towards the contact discontinuity as density increases.


title:
Magnetic excitations, non-classicality and quantum wake spin dynamics in
  the short-range Hubbard chain

abstract:
  Recent work has demonstrated that quantum Fisher information (QFI), a witness
of multipartite entanglement, and magnetic Van Hove correlations $G(r,t)$, a
probe of local real-space real-time spin dynamics, can be successfully
extracted from inelastic neutron scattering on spin systems, through accurate
measurements of the dynamical spin structure factor $S(k,\omega)$. Here we
apply theoretically these ideas to the Hubbard chain away from the
strong-coupling limit. This model has a nontrivial redistribution of spectral
weight in $S(k,\omega)$ going from the non-interacting limit ($U=0$) to the
strong coupling limit ($U\rightarrow \infty$), where it reduces to the
Heisenberg quantum spin chain. We use the density matrix renormalization group
(DMRG) to find $S(k,\omega)$, from which QFI is then calculated. We find that
QFI grows with $U$, becoming capable of witnessing bipartite entanglement above
$U=2.5$ (in units of the hopping), where it also changes slope. This point is
also proximate to slope changes of the bandwidth $W(U)$ and the half-chain von
Neumann entanglement entropy. We compute $G(r,t)$ by Fourier-transforming
$S(k,\omega)$. The results indicate a crossover in the short-time
short-distance dynamics at low $U$ characterized by ferromagnetic lightcone
wavefronts, to a Heisenberg-like behavior at large $U$ featuring
antiferromagnetic lightcones and spatially period-doubled antiferromagnetism.
We find this crossover has largely been completed by $U=3$. Our results thus
provide evidence that, in several aspects, the strong-coupling limit of the
Hubbard chain is reached qualitatively already at a relatively modest
interaction strength. We discuss experimental candidates for observing the
$G(r,t)$ dynamics found at low $U$.


title:
Stable and Efficient Shapley Value-Based Reward Reallocation for
  Multi-Agent Reinforcement Learning of Autonomous Vehicles

abstract:
  With the development of sensing and communication technologies in networked
cyber-physical systems (CPSs), multi-agent reinforcement learning (MARL)-based
methodologies are integrated into the control process of physical systems and
demonstrate prominent performance in a wide array of CPS domains, such as
connected autonomous vehicles (CAVs). However, it remains challenging to
mathematically characterize the improvement of the performance of CAVs with
communication and cooperation capability. When each individual autonomous
vehicle is originally self-interest, we can not assume that all agents would
cooperate naturally during the training process. In this work, we propose to
reallocate the system's total reward efficiently to motivate stable cooperation
among autonomous vehicles. We formally define and quantify how to reallocate
the system's total reward to each agent under the proposed transferable utility
game, such that communication-based cooperation among multi-agents increases
the system's total reward. We prove that Shapley value-based reward
reallocation of MARL locates in the core if the transferable utility game is a
convex game. Hence, the cooperation is stable and efficient and the agents
should stay in the coalition or the cooperating group. We then propose a
cooperative policy learning algorithm with Shapley value reward reallocation.
In experiments, compared with several literature algorithms, we show the
improvement of the mean episode system reward of CAV systems using our proposed
algorithm.


title: Latin Hypercubes and Space-filling Designs

abstract:
  This chapter discusses a general design approach to planning computer
experiments, which seeks design points that fill a bounded design region as
uniformly as possible. Such designs are broadly referred to as space-filling
designs.


title:
Doubly Coupled Designs for Computer Experiments with both Qualitative
  and Quantitative Factors

abstract:
  Computer experiments with both qualitative and quantitative input variables
occur frequently in many scientific and engineering applications. How to choose
input settings for such experiments is an important issue for accurate
statistical analysis, uncertainty quantification and decision making. Sliced
Latin hypercube designs are the first systematic approach to address this
issue. However, it comes with the increasing cost associated with an increasing
large number of level combinations of the qualitative factors. For the reason
of run size economy, marginally coupled designs were proposed in which the
design for the quantitative factors is a sliced Latin hypercube design with
respect to each qualitative factor. The drawback of such designs is that the
corresponding data may not be able to capture the effects between any two (and
more) qualitative factors and quantitative factors. To balance the run size and
design efficiency, we propose a new type of designs, doubly coupled designs,
where the design points for the quantitative factors form a sliced Latin
hypercube design with respect to the levels of any qualitative factor and with
respect to the level combinations of any two qualitative factors, respectively.
The proposed designs have the better stratification property between the
qualitative and quantitative factors compared with marginally coupled designs.
The existence of the proposed designs is established. Several construction
methods are introduced, and the properties of the resulting designs are also
studied.


title:
A New and Flexible Design Construction for Orthogonal Arrays for Modern
  Applications

abstract:
  Orthogonal array, a classical and effective tool for collecting data, has
been flourished with its applications in modern computer experiments and
engineering statistics. Driven by the wide use of computer experiments with
both qualitative and quantitative factors, multiple computer experiments,
multi-fidelity computer experiments, cross-validation and stochastic
optimization, orthogonal arrays with certain structures have been introduced.
Sliced orthogonal arrays and nested orthogonal arrays are examples of such
arrays. This article introduces a flexible, fresh construction method which
uses smaller arrays and a special structure. The method uncovers the hidden
structure of many existing fixed-level orthogonal arrays of given run sizes,
possibly with more columns. It also allows fixed-level orthogonal arrays of
nearly strength three to be constructed, which are useful as there are not many
construction methods for fixed-level orthogonal arrays of strength three, and
also helpful for generating Latin hypercube designs with desirable
low-dimensional projections. Theoretical properties of the proposed method are
explored. As by-products, several theoretical results on orthogonal arrays are
obtained.


title: On local antimagic total labeling of amalgamation graphs

abstract:
  Let $G = (V,E)$ be a connected simple graph of order $p$ and size $q$. A
graph $G$ is called local antimagic (total) if $G$ admits a local antimagic
(total) labeling. A bijection $g : E \to \{1,2,\ldots,q\}$ is called a local
antimagic labeling of $G$ if for any two adjacent vertices $u$ and $v$, we have
$g^+(u) \ne g^+(v)$, where $g^+(u) = \sum_{e\in E(u)} g(e)$, and $E(u)$ is the
set of edges incident to $u$. Similarly, a bijection $f:V(G)\cup E(G)\to
\{1,2,\ldots,p+q\}$ is called a local antimagic total labeling of $G$ if for
any two adjacent vertices $u$ and $v$, we have $w_f(u)\ne w_f(v)$, where
$w_f(u) = f(u) + \sum_{e\in E(u)} f(e)$. Thus, any local antimagic (total)
labeling induces a proper vertex coloring of $G$ if vertex $v$ is assigned the
color $g^+(v)$ (respectively, $w_f(u)$). The local antimagic (total) chromatic
number, denoted $\chi_{la}(G)$ (respectively $\chi_{lat}(G)$), is the minimum
number of induced colors taken over local antimagic (total) labeling of $G$. In
this paper, we determined $\chi_{lat}(G)$ where $G$ is the amalgamation of
complete graphs.


title:
Auto-FedRL: Federated Hyperparameter Optimization for
  Multi-institutional Medical Image Segmentation

abstract:
  Federated learning (FL) is a distributed machine learning technique that
enables collaborative model training while avoiding explicit data sharing. The
inherent privacy-preserving property of FL algorithms makes them especially
attractive to the medical field. However, in case of heterogeneous client data
distributions, standard FL methods are unstable and require intensive
hyperparameter tuning to achieve optimal performance. Conventional
hyperparameter optimization algorithms are impractical in real-world FL
applications as they involve numerous training trials, which are often not
affordable with limited compute budgets. In this work, we propose an efficient
reinforcement learning~(RL)-based federated hyperparameter optimization
algorithm, termed Auto-FedRL, in which an online RL agent can dynamically
adjust hyperparameters of each client based on the current training progress.
Extensive experiments are conducted to investigate different search strategies
and RL agents. The effectiveness of the proposed method is validated on a
heterogeneous data split of the CIFAR-10 dataset as well as two real-world
medical image segmentation datasets for COVID-19 lesion segmentation in chest
CT and pancreas segmentation in abdominal CT.


title: A cluster structure on the coordinate ring of partial flag varieties

abstract:
  The main goal of this paper is to show that the (multi-homogeneous)
coordinate ring of a partial flag variety $\mathbb{C} [G / P_K^{-}]$ admits a
cluster algebra structure if $G$ is any simply-connected semisimple complex
algebraic group. We use derivation properties and a special lifting map to
prove that the cluster algebra structure $\mathcal{A}$ of the coordinate ring
$\mathbb{C}[N_K]$ of a Schubert cell constructed by Goodearl and Yakimov can be
lifted, in an explicit way, to a cluster structure $\hat{\mathcal{A}}$ living
in the coordinate ring of the corresponding partial flag variety. Then we use a
minimality condition to prove that the cluster algebra $\hat{\mathcal{A}}$ is
indeed equal to $\mathbb{C}[G / P_K^{-}]$.


title: Construction of Marginally Coupled Designs by Subspace Theory

abstract:
  Recent researches on designs for computer experiments with both qualitative
and quantitative factors have advocated the use of marginally coupled designs.
This paper proposes a general method of constructing such designs for which the
designs for qualitative factors are multi-level orthogonal arrays and the
designs for quantitative factors are Latin hypercubes with desirable
space-filling properties. Two cases are introduced for which we can obtain the
guaranteed low-dimensional space-filling property for quantitative factors.
Theoretical results on the proposed constructions are derived. For practical
use, some constructed designs for three-level qualitative factors are
tabulated.


title: Weak itinerant magnetic phases of La2Ni7

abstract:
  La2Ni7 is an intermetallic compound that is thought to have itinerant
magnetism with a small moment ordering below 65 K. A recent study of single
crystal samples by Ribeiro et. al. [Phys. Rev. B 105, 014412 (2022)] determined
detailed anisotropic H-T phase diagrams and revealed three zero-field magnetic
phase transitions at T1 ~ 61.0 K, T2 ~ 56.5K, and T3 ~ 42 K. In that study only
the highest temperature phase is shown to have a clear ferromagnetic component.
Here we present a single crystal neutron diffraction study determining the
propagation vector and magnetic moment direction of the three magnetically
ordered phases, two incommensurate and one commensurate, as a function of
temperature. The higher temperature phases have similar, incommensurate
propagation vectors, but with different ordered moment directions. At lower
temperatures the magnetic order becomes commensurate with magnetic moments
along the c direction as part of a first-order magnetic phase transition. We
find that the low-temperature commensurate magnetic order is consistent with a
proposal from earlier DFT calculations.


title: What Makes Reading Comprehension Questions Difficult?

abstract:
  For a natural language understanding benchmark to be useful in research, it
has to consist of examples that are diverse and difficult enough to
discriminate among current and near-future state-of-the-art systems. However,
we do not yet know how best to select text sources to collect a variety of
challenging examples. In this study, we crowdsource multiple-choice reading
comprehension questions for passages taken from seven qualitatively distinct
sources, analyzing what attributes of passages contribute to the difficulty and
question types of the collected examples. To our surprise, we find that passage
source, length, and readability measures do not significantly affect question
difficulty. Through our manual annotation of seven reasoning types, we observe
several trends between passage sources and reasoning types, e.g., logical
reasoning is more often required in questions written for technical passages.
These results suggest that when creating a new benchmark dataset, selecting a
diverse set of passages can help ensure a diverse range of question types, but
that passage difficulty need not be a priority.


title:
Reconfigurable MIMO towards Electro-magnetic Information Theory:
  Capacity Maximization Pattern Design

abstract:
  In this paper, we focus on the pattern reconfigurable multiple-input
multiple-output (PR-MIMO), a technique that has the potential to bridge the gap
between electro-magnetics and communications towards the emerging
Electro-magnetic Information Theory (EIT). Specifically, we focus on the
pattern design problem aimed at maximizing the channel capacity for
reconfigurable MIMO communication systems, where we firstly introduce the
matrix representation of PR-MIMO and further formulate a pattern design
problem. We decompose the pattern design into two steps, i.e., the correlation
modification process to optimize the correlation structure of the channel,
followed by the power allocation process to improve the channel quality based
on the optimized channel structure. For the correlation modification process,
we propose a sequential optimization framework with eigenvalue decomposition to
obtain near-optimal solutions. For the power allocation process, we provide a
closed-form power allocation scheme to redistribute the transmission power
among the modified subchannels. Numerical results show that the proposed
pattern design scheme offers significant improvements over legacy MIMO systems,
which motivates the application of PR-MIMO in wireless communication systems.


title: Continuity on directed spaces

abstract:
  We investigate two approximation relations on a T0 topological space, the
n-approximation, and the d-approximation, which are generalizations of the
way-below relation on a dcpo. Different kinds of continuous spaces are defined
by the two approximations and are all shown to be directed spaces. We show that
the continuity of a directed space is very similar to the continuity of a dcpo
in many aspects, which indicates that the notion of directed spaces is a
suitable topological extension of dcpos.The main results are: (1) A topological
space is continuous iff it is a retract of an algebraic space;(2) a directed
space X is core compact iff for any directed space Y, the topological product
is equal to the categorical product in DTop of X and Y respectively;(3) a
directed space is continuous (resp., algebraic, quasicontinuous,
quasialgebraic) iff the lattice of its closed subsets is continuous (resp.,
algebraic, quasicontinuous, quasialgebraic).


title:
The Principle of Diversity: Training Stronger Vision Transformers Calls
  for Reducing All Levels of Redundancy

abstract:
  Vision transformers (ViTs) have gained increasing popularity as they are
commonly believed to own higher modeling capacity and representation
flexibility, than traditional convolutional networks. However, it is
questionable whether such potential has been fully unleashed in practice, as
the learned ViTs often suffer from over-smoothening, yielding likely redundant
models. Recent works made preliminary attempts to identify and alleviate such
redundancy, e.g., via regularizing embedding similarity or re-injecting
convolution-like structures. However, a "head-to-toe assessment" regarding the
extent of redundancy in ViTs, and how much we could gain by thoroughly
mitigating such, has been absent for this field. This paper, for the first
time, systematically studies the ubiquitous existence of redundancy at all
three levels: patch embedding, attention map, and weight space. In view of
them, we advocate a principle of diversity for training ViTs, by presenting
corresponding regularizers that encourage the representation diversity and
coverage at each of those levels, that enabling capturing more discriminative
information. Extensive experiments on ImageNet with a number of ViT backbones
validate the effectiveness of our proposals, largely eliminating the observed
ViT redundancy and significantly boosting the model generalization. For
example, our diversified DeiT obtains 0.70%~1.76% accuracy boosts on ImageNet
with highly reduced similarity. Our codes are fully available in
https://github.com/VITA-Group/Diverse-ViT.


title:
Spatial distribution of electric field of equal probability quantum
  walks based on three-level quantum system

abstract:
  Based on the three-level quantum system, when it is in resonance, according
to any two lattice points closest to Hamiltonian coupling, electrons transition
from high energy level to low energy level and release photons; Or absorb
photons and transition from low energy level to high energy level, thus
obtaining the physical process of quantum walking along a straight line under
the condition of equal probability. Then, the optical radiation in the quantum
walk is mapped into a Gaussian pulse of the electric field, and the Maxwell's
equation is solved by the three dimensional finite-difference time-domain
method to obtain the spatial electric distributio. Finally, the physical
process ofquantum walking on two parallel lines is further discussed, involving
some physical properties such as electromagnetic coupling or coherence, quantum
state exchange and so on. The electric field coupling between two lines can be
calculated by FDTD, which provides a useful tool for the design and analysis of
quantum devices.


title:
Combining Deep Learning with Physics Based Features in
  Explosion-Earthquake Discrimination

abstract:
  This paper combines the power of deep-learning with the generalizability of
physics-based features, to present an advanced method for seismic
discrimination between earthquakes and explosions. The proposed method contains
two branches: a deep learning branch operating directly on seismic waveforms or
spectrograms, and a second branch operating on physics-based parametric
features. These features are high-frequency P/S amplitude ratios and the
difference between local magnitude (ML) and coda duration magnitude (MC). The
combination achieves better generalization performance when applied to new
regions than models that are developed solely with deep learning. We also
examined which parts of the waveform data dominate deep learning decisions
(i.e., via Grad-CAM). Such visualization provides a window into the black-box
nature of the machine-learning models and offers new insight into how the deep
learning derived models use data to make the decisions.


title:
Unicorns and Giraffes in the binary zoo: stripped giants with subgiant
  companions

abstract:
  We analyze two binary systems containing giant stars, V723 Mon ("the
Unicorn") and 2M04123153+6738486 ("the Giraffe"). Both giants orbit more
massive but less luminous companions, previously proposed to be mass-gap black
holes. Spectral disentangling reveals luminous companions with star-like
spectra in both systems. Joint modeling of the spectra, light curves, and
spectral energy distributions robustly constrains the masses, temperatures, and
radii of both components: the primaries are luminous, cool giants ($T_{\rm
eff,\,giant} = 3,800\,\rm K$ and $4,000\,\rm K$, $R_{\rm giant}=
22.5\,R_{\odot}$ and $25\,R_{\odot}$) with exceptionally low masses ($M_{\rm
giant} \approx 0.4\,M_{\odot}$) that likely fill their Roche lobes. The
secondaries are only slightly warmer subgiants ($T_{\rm eff,\,2} = 5,800\,\rm
K$ and $5,150\,\rm K$, $R_2= 8.3\,R_{\odot}$ and $9\,R_{\odot}$) and thus are
consistent with observed UV limits that would rule out main-sequence stars with
similar masses ($M_2 \approx 2.8\,M_{\odot}$ and $\approx 1.8\,M_{\odot}$). In
the Unicorn, rapid rotation blurs the spectral lines of the subgiant, making it
challenging to detect even at wavelengths where it dominates the total light.
Both giants have surface abundances indicative of CNO processing and subsequent
envelope stripping. The properties of both systems can be reproduced by binary
evolution models in which a $1-2\,M_{\odot}$ primary is stripped by a companion
as it ascends the giant branch. The fact that the companions are also evolved
implies either that the initial mass ratio was very near unity, or that the
companions are temporarily inflated due to rapid accretion. The Unicorn and
Giraffe offer a window into into a rarely-observed phase of binary evolution
preceding the formation of wide-orbit helium white dwarfs, and eventually,
compact binaries containing two helium white dwarfs.


title: Large Spin-2 Signals at the Cosmological Collider

abstract:
  We study the theory and phenomenology of massive spin-2 fields during the
inflation with nonzero background chemical potential, and extend the
cosmological collider physics to tensor modes. We identify a unique dimension-5
and parity-violating chemical potential operator for massive spin-2 fields,
which leads to a ghost-free linear theory propagating one scalar mode and two
tensor modes. The chemical potential greatly boosts the production of one
tensor mode even for very heavy spin-2 particles, and thereby leads to large
and distinct cosmological collider signals for massive spin-2 particles. The
large signals show up at the tree-level in both the curvature trispectrum and
the tensor-curvature mixed bispectrum.


title:
Synthesizing cross-design evidence and cross-format data using network
  meta-regression

abstract:
  In network meta-analysis (NMA), we synthesize all relevant evidence about
health outcomes with competing treatments. The evidence may come from
randomized controlled trials (RCT) or non-randomized studies (NRS) as
individual participant data (IPD) or as aggregate data (AD). We present a suite
of Bayesian NMA and network meta-regression (NMR) models allowing for
cross-design and cross-format synthesis. The models integrate a three-level
hierarchical model for synthesizing IPD and AD into four approaches. The four
approaches account for differences in the design and risk of bias in the RCT
and NRS evidence. These four approaches variously ignoring differences in risk
of bias, using NRS to construct penalized treatment effect priors and
bias-adjustment models that control the contribution of information from high
risk of bias studies in two different ways. We illustrate the methods in a
network of three pharmacological interventions and placebo for patients with
relapsing-remitting multiple sclerosis. The estimated relative treatment
effects do not change much when we accounted for differences in design and risk
of bias. Conducting network meta-regression showed that intervention efficacy
decreases with increasing participant age. We re-analysed a network of 431 RCT
comparing 21 antidepressants, and we did not observe material changes in
intervention efficacy when adjusting for studies high risk of bias. In summary,
the described suite of NMA/NMR models enables inclusion of all relevant
evidence while incorporating information on the within-study bias in both
observational and experimental data and enabling estimation of individualized
treatment effects through the inclusion of participant characteristics.


title: Rijke tube: A nonlinear oscillator

abstract:
  Dynamical systems theory has emerged as an interdisciplinary area of research
to characterize the complex dynamical transitions in real-world systems.
Various nonlinear dynamical phenomena and bifurcations have been discovered
over the decades using different reduced-order models of oscillators. Different
measures and methodologies have been developed theoretically to detect,
control, or suppress the nonlinear oscillations. However, obtaining such
phenomena experimentally is often challenging, time-consuming, and risky,
mainly due to the limited control of certain parameters during experiments.
With this review, we aim to introduce a paradigmatic and easily configurable
Rijke tube oscillator to the dynamical systems community. The Rijke tube is
commonly used by the combustion community as a prototype to investigate the
detrimental phenomena of thermoacoustic instability. Recent investigations in
such Rijke tubes have utilized various methodologies from dynamical systems
theory to better understand the occurrence of thermoacoustic oscillations,
their prediction and mitigation, both experimentally and theoretically. The
existence of various dynamical behaviors has been reported in single as well as
coupled Rijke tube oscillators. These behaviors include bifurcations, routes to
chaos, noise-induced transitions, synchronization, and suppression of
oscillations. Various early warning measures have been established to predict
thermoacoustic instabilities. Therefore, this review paper consolidates the
usefulness of a Rijke tube oscillator in terms of experimentally discovering
and modeling different nonlinear phenomena observed in physics; thus,
transcending the boundaries between the physics and the engineering
communities.


title: p-adic tight wavelet frames

abstract:
  We propose a simple method to construct step mask and corresponding step
wavelet functions that generate tight wavelet frames on the field of p-adic
numbers. To construct tight wavelet frames we do not use the principle of
unitary extension, we use Pontryagin's principle of duality.


title: Efficiency in Random Resource Allocation and Social Choice

abstract:
  We study efficiency in general collective choice problems when agents have
ordinal preferences and randomization is allowed. We establish the equivalence
between welfare maximization and ex-ante efficiency for general domains. We
relate ex-ante efficiency with ex-post efficiency, characterizing when the two
notions coincide. Our results have implications for well-studied mechanisms
including random serial dictatorship and a number of specific environments,
including the dichotomous, single-peaked, and social choice domains.


title: LesionPaste: One-Shot Anomaly Detection for Medical Images

abstract:
  Due to the high cost of manually annotating medical images, especially for
large-scale datasets, anomaly detection has been explored through training
models with only normal data. Lacking prior knowledge of true anomalies is the
main reason for the limited application of previous anomaly detection methods,
especially in the medical image analysis realm. In this work, we propose a
one-shot anomaly detection framework, namely LesionPaste, that utilizes true
anomalies from a single annotated sample and synthesizes artificial anomalous
samples for anomaly detection. First, a lesion bank is constructed by applying
augmentation to randomly selected lesion patches. Then, MixUp is adopted to
paste patches from the lesion bank at random positions in normal images to
synthesize anomalous samples for training. Finally, a classification network is
trained using the synthetic abnormal samples and the true normal data.
Extensive experiments are conducted on two publicly-available medical image
datasets with different types of abnormalities. On both datasets, our proposed
LesionPaste largely outperforms several state-of-the-art unsupervised and
semi-supervised anomaly detection methods, and is on a par with the
fully-supervised counterpart. To note, LesionPaste is even better than the
fully-supervised method in detecting early-stage diabetic retinopathy.


title: EventFormer: AU Event Transformer for Facial Action Unit Event Detection

abstract:
  Facial action units (AUs) play an indispensable role in human emotion
analysis. We observe that although AU-based high-level emotion analysis is
urgently needed by real-world applications, frame-level AU results provided by
previous works cannot be directly used for such analysis. Moreover, as AUs are
dynamic processes, the utilization of global temporal information is important
but has been gravely ignored in the literature. To this end, we propose
EventFormer for AU event detection, which is the first work directly detecting
AU events from a video sequence by viewing AU event detection as a multiple
class-specific sets prediction problem. Extensive experiments conducted on a
commonly used AU benchmark dataset, BP4D, show the superiority of EventFormer
under suitable metrics.


title: Taking an Emotional Look at Video Paragraph Captioning

abstract:
  Translating visual data into natural language is essential for machines to
understand the world and interact with humans. In this work, a comprehensive
study is conducted on video paragraph captioning, with the goal to generate
paragraph-level descriptions for a given video. However, current researches
mainly focus on detecting objective facts, ignoring the needs to establish the
logical associations between sentences and to discover more accurate emotions
related to video contents. Such a problem impairs fluent and abundant
expressions of predicted captions, which are far below human language tandards.
To solve this problem, we propose to construct a large-scale emotion and logic
driven multilingual dataset for this task. This dataset is named EMVPC
(standing for "Emotional Video Paragraph Captioning") and contains 53
widely-used emotions in daily life, 376 common scenes corresponding to these
emotions, 10,291 high-quality videos and 20,582 elaborated paragraph captions
with English and Chinese versions. Relevant emotion categories, scene labels,
emotion word labels and logic word labels are also provided in this new
dataset. The proposed EMVPC dataset intends to provide full-fledged video
paragraph captioning in terms of rich emotions, coherent logic and elaborate
expressions, which can also benefit other tasks in vision-language fields.
Furthermore, a comprehensive study is conducted through experiments on existing
benchmark video paragraph captioning datasets and the proposed EMVPC. The
stateof-the-art schemes from different visual captioning tasks are compared in
terms of 15 popular metrics, and their detailed objective as well as subjective
results are summarized. Finally, remaining problems and future directions of
video paragraph captioning are also discussed. The unique perspective of this
work is expected to boost further development in video paragraph captioning
research.


title: Bitcoin's Latency--Security Analysis Made Simple

abstract:
  Closed-form upper and lower bounds are developed for the security of the
Nakamoto consensus as a function of the confirmation depth. The bounds are easy
to compute using only geometric and binomial distributions. The gap between the
upper and lower bounds is small for Bitcoin's parameters.


title:
Throughput Maximization for UAV-enabled Integrated Periodic Sensing and
  Communication

abstract:
  Unmanned aerial vehicle (UAV) is expected to revolutionize the existing
integrated sensing and communication (ISAC) system and promise a more flexible
joint design. Nevertheless, the existing works on ISAC mainly focus on
exploring the performance of both functionalities simultaneously during the
entire considered period, which may ignore the practical asymmetric sensing and
communication requirements. In particular, always forcing sensing along with
communication may make it is harder to balance between these two
functionalities due to shared spectrum resources and limited transmit power. To
address this issue, we propose a new integrated periodic sensing and
communication mechanism for the UAV-enabled ISAC system to provide a more
flexible trade-off between two integrated functionalities. Specifically, the
system achievable rate is maximized via jointly optimizing UAV trajectory, user
association, target sensing selection, and transmit beamforming, while meeting
the sensing frequency and beam pattern gain requirement for the given targets.
Despite that this problem is highly non-convex and involves closely coupled
integer variables, we derive the closed-form optimal beamforming vector to
dramatically reduce the complexity of beamforming design, and present a tight
lower bound of the achievable rate to facilitate UAV trajectory design. Based
on the above results, we propose a penalty-based algorithm to efficiently solve
the considered problem. The optimal achievable rate and the optimal UAV
location are analyzed under a special case of infinity number of antennas.
Furthermore, we prove the structural symmetry between the optimal solutions in
different ISAC frames without location constraints and propose an efficient
algorithm for solving the problem with location constraints.


title:
Self-Sustaining Representation Expansion for Non-Exemplar
  Class-Incremental Learning

abstract:
  Non-exemplar class-incremental learning is to recognize both the old and new
classes when old class samples cannot be saved. It is a challenging task since
representation optimization and feature retention can only be achieved under
supervision from new classes. To address this problem, we propose a novel
self-sustaining representation expansion scheme. Our scheme consists of a
structure reorganization strategy that fuses main-branch expansion and
side-branch updating to maintain the old features, and a main-branch
distillation scheme to transfer the invariant knowledge. Furthermore, a
prototype selection mechanism is proposed to enhance the discrimination between
the old and new classes by selectively incorporating new samples into the
distillation process. Extensive experiments on three benchmarks demonstrate
significant incremental performance, outperforming the state-of-the-art methods
by a margin of 3%, 3% and 6%, respectively.


title:
Asymptotic expansion of solutions to the wave equation with
  space-dependent damping

abstract:
  We study the large time behavior of solutions to the wave equation with
space-dependent damping in an exterior domain. We show that if the damping is
effective, then the solution is asymptotically expanded in terms of solutions
of corresponding parabolic equations. The main idea to obtain the asymptotic
expansion is the decomposition of the solution of the damped wave equation into
the solution of the corresponding parabolic problem and the time derivative of
the solution of the damped wave equation with certain inhomogeneous term and
initial data. The estimate of the remainder term is an application of weighted
energy method with suitable supersolutions of the corresponding parabolic
problem.


title:
Preserving Lagrangian structure in data-driven reduced-order modeling of
  large-scale mechanical systems

abstract:
  This work presents a nonintrusive physics-preserving method to learn
reduced-order models (ROMs) of Lagrangian mechanical systems. Existing
intrusive projection-based model reduction approaches construct
structure-preserving Lagrangian ROMs by projecting the Euler-Lagrange equations
of the full-order model (FOM) onto a linear subspace. This Galerkin projection
step requires complete knowledge about the Lagrangian operators in the FOM and
full access to manipulate the computer code. In contrast, the proposed
Lagrangian operator inference approach embeds the mechanics into the operator
inference framework to develop a data-driven model reduction method that
preserves the underlying Lagrangian structure. The proposed approach exploits
knowledge of the governing equations (but not their discretization) to define
the form and parametrization of a Lagrangian ROM which can then be learned from
projected snapshot data. The method does not require access to FOM operators or
computer code. The numerical results demonstrate Lagrangian operator inference
on an Euler-Bernoulli beam model and a large-scale discretization of a soft
robot fishtail with 779,232 degrees of freedom. Accurate long-time predictions
of the learned Lagrangian ROMs far outside the training time interval
illustrate their generalizability.


title: Towards deconstructing the simplest seesaw mechanism

abstract:
  The triplet or type-II seesaw mechanism is the simplest way to endow
neutrinos with mass in the Standard Model (SM). Here we review its associated
theory and phenomenology, including restrictions from $S$, $T$, $U$ parameters,
neutrino experiments, charged lepton flavour violations as well as collider
searches. We also examine restrictions coming from requiring consistency of
electroweak symmetry breaking, i.e. perturbative unitarity and stability of the
vacuum. Finally, we discuss novel effects associated to the scalar mediator of
neutrino mass generation namely, (i) rare processes, e.g. $l_\alpha \to l_\beta
\gamma$ decays, at the intensity frontier, and also (ii) four-lepton signatures
in colliders at the high-energy frontier. These can be used to probe neutrino
properties in an important way, providing a test of the absolute neutrino mass
and mass-ordering, as well as of the atmospheric octant. They may also provide
the first evidence for charged lepton flavour violation in nature. In contrast,
neutrino non-standard interaction strengths are found to lie below current
detectability.


title:
MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired
  Images in OCT Scan

abstract:
  Deep learning models tend to underperform in the presence of domain shifts.
Domain transfer has recently emerged as a promising approach wherein images
exhibiting a domain shift are transformed into other domains for augmentation
or adaptation. However, with the absence of paired and annotated images, most
domain transfer methods mainly rely on adversarial networks and weak cycle
consistency, which could result in incomplete domain transfer or poor adherence
to the original image content. In this paper, we introduce MDT-Net to address
the limitations above through a multi-domain transfer model based on perceptual
supervision. Specifically, our model consists of an encoder-decoder network,
which aims to preserve anatomical structures, and multiple domain-specific
transfer modules, which guide the domain transition through feature
transformation. During the inference, MDT-Net can directly transfer images from
the source domain to multiple target domains at one time without any reference
image. To demonstrate the performance of MDT-Net, we evaluate it on RETOUCH
dataset, comprising OCT scans from three different scanner devices (domains),
for multi-domain transfer. We also take the transformed results as additional
training images for fluid segmentation in OCT scans in the tasks of domain
adaptation and data augmentation. Experimental results show that MDT-Net can
outperform other domain transfer models qualitatively and quantitatively.
Furthermore, the significant improvement in dice scores over multiple
segmentation models also demonstrates the effectiveness and efficiency of our
proposed method.


title:
Towards sub-30nm Contacted Gate Pitch, Forked Contact and
  Dynamically-Doped Nanosheets to Enhance Si and 2D Materials Device Scaling

abstract:
  We propose a novel Forked-Contacts, Dynamically-Doped Multigate transistor as
ultimate scaling booster for both Si and 2D materials in aggressively-scaled
nanosheet devices. Using accurate dissipative DFT-NEGF atomistic-simulation
fundamentals and cell layout extrinsics, we demonstrate superior and optimal
device characteristics and invertor energy - delays down to sub-30-nm pitches,
i.e., a 10 nm scaling boost compared to the nanosheet MOSFET references.


title: Search for muon-to-electron conversion with the COMET experiment

abstract:
  Charged Lepton Flavor Violation is expected to be one of the most powerful
tools to reveal physics beyond the Standard Model. The COMET experiment aims to
search for the neutrinoless coherent transition of a muon into an electron in
the field of a nucleus. Muon-to-electron conversion has never been observed,
and can be, and would be, clear evidence of new physics if discovered. The
experimental sensitivity of this process, defined as the ratio of the
muon-to-electron conversion rate to the total muon capture rate, is expected to
be significantly improved by a factor of 100 to 10,000 in the coming decade.
The COMET experiment will take place at J-PARC with single event sensitivities
of the orders of $10^{-15}$ and $10^{-17}$ in Phase-I and Phase-II,
respectively. The ambitious goal of the COMET experiment is achieved by
realizing a high-quality pulsed beam and an unprecedentedly powerful muon
source together with an excellent detector apparatus that can tolerate a severe
radiation environment. The construction of a new beam line, superconducting
magnets, detectors and electronics is in progress towards the forthcoming
Phase-I experiment. We present the experimental methods, sensitivity and
backgrounds along with recent status and prospects.


title: On the factoriality of q-deformed Araki-Woods von Neumann algebras

abstract:
  The $q$-deformed Araki-Woods von Neumann algebras
$\Gamma_q(\mathcal{H}_\mathbb{R}, U_t)^{\prime \prime}$ are factors for all
$q\in (-1,1)$ whenever $dim(\mathcal{H}_\mathbb{R})\geq 3$. When
$dim(\mathcal{H}_\mathbb{R})=2$ they are factors as well for all $q$ so long as
the parameter defining $(U_t)$ is `small' or $1$ $($trivial$)$ as the case may
be.


title: Excitations of the ferroelectric order

abstract:
  We identify the bosonic excitations in ferroelectrics that carry electric
dipoles from the phenomenological Landau-Ginzburg-Devonshire theory. The
"ferron" quasi-particles emerge from the concerted action of anharmonicity and
broken inversion symmetry. In contrast to magnons, the transverse excitations
of the magnetic order, the ferrons in displacive ferroelectrics are
longitudinal with respect to the ferroelectric order. Based on the ferron
spectrum, we predict temperature dependent pyroelectric and electrocaloric
properties, electric-field-tunable heat and polarization transport, and
ferron-photon hybridization.


title:
Complex structure and characterization of multi-photon split states in
  integrated circuits

abstract:
  Multi-photon split states, where each photon is in a different input mode,
represent an essential resource for various quantum applications. We formulate
the general structure of their reduced density matrices based on the symmetry
analysis, and identify the number of real and complex-valued independent
coefficients. Then, we show that the density matrix can be fully characterized
by measuring correlations after photon interference in a static circuit, where
the required outputs scale sub-quadratically versus the number of photons. We
present optimized circuit designs composed of coupled segmented waveguides,
representing a linear optical neural network, which minimize the reconstruction
error and facilitate robustness to fabrication deviations.


title:
The Health Gym: Synthetic Health-Related Datasets for the Development of
  Reinforcement Learning Algorithms

abstract:
  In recent years, the machine learning research community has benefited
tremendously from the availability of openly accessible benchmark datasets.
Clinical data are usually not openly available due to their highly confidential
nature. This has hampered the development of reproducible and generalisable
machine learning applications in health care. Here we introduce the Health Gym
- a growing collection of highly realistic synthetic medical datasets that can
be freely accessed to prototype, evaluate, and compare machine learning
algorithms, with a specific focus on reinforcement learning. The three
synthetic datasets described in this paper present patient cohorts with acute
hypotension and sepsis in the intensive care unit, and people with human
immunodeficiency virus (HIV) receiving antiretroviral therapy in ambulatory
care. The datasets were created using a novel generative adversarial network
(GAN). The distributions of variables, and correlations between variables and
trends over time in the synthetic datasets mirror those in the real datasets.
Furthermore, the risk of sensitive information disclosure associated with the
public distribution of the synthetic datasets is estimated to be very low.


title: On smooth plane models for modular curves of Shimura type

abstract:
  In this paper we prove that there are finitely many modular curves that admit
a smooth plane model. Moreover, if the degree of the model is greater than or
equal to 10, no such curve exists. For modular curves of Shimura type we show
that none can admit a smooth plane model of degree 5, 6 or 7.


title: Varying Coefficient Linear Discriminant Analysis for Dynamic Data

abstract:
  Linear discriminant analysis (LDA) is a vital classification tool in
statistics and machine learning. This paper investigates the varying
coefficient LDA model for dynamic data, with Bayes' discriminant direction
being a function of some exposure variable to address the heterogeneity. By
deriving a new discriminant direction function parallel with Bayes' direction,
we propose a least-square estimation procedure based on the B-spline
approximation. For high-dimensional regime, the corresponding data-driven
discriminant rule is more computationally efficient than the existed dynamic
linear programming rule. We also establish the corresponding theoretical
results, including estimation error bound and the uniform excess
misclassification rate. Numerical experiments on synthetic data and real data
both corroborate the superiority of our proposed classification method.


title:
Collective excitations in jammed states: ultrafast defect propagation
  and finite-size scaling

abstract:
  In crowded systems, particle currents can be mediated by propagating
collective excitations which are generated as rare events and have a finite
lifetime. The theoretical description of such excitations is hampered by the
problem of identifying complex many-particle transition states, calculation of
their free energies, and the evaluation of propagation mechanisms and
velocities. Here we show that these problems can be tackled for a highly jammed
system of hard spheres in a periodic potential. We derive generation rates of
collective excitations, their anomalously high velocities, explain the
occurrence of an apparent jamming transition and its strong dependence on the
system size. The particle currents follow a scaling behavior, where for small
systems the current is proportional to the generation rate and for large
systems given by the geometric mean of the generation rate and velocity. Our
theoretical approach is widely applicable to dense nonequilibrium systems in
confined geometries. It provides new perspectives for studying dynamics of
collective excitations in experiments.


title:
Machine learning technique for morphological classification of galaxies
  from SDSS. II. The image-based morphological catalogs of galaxies at
  0.02<z<0.1

abstract:
  We applied the image-based approach with a convolutional neural network model
to the sample of low-redshifts galaxies with $-24^{m}<M_{r}<-19.4^{m}$ from the
SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy
Zoo 2 (GZ2) dataset, considering them as the inference and training datasets,
respectively. As a result, we created the morphological catalog of 315782
galaxies at 0.02<z<0.1, where morphological five classes and 34 detailed
features (bar, rings, number of spiral arms, mergers, etc.) were first defined
for 216148 galaxies (inference dataset) by the image-based CNN classifier. For
the rest of galaxies the initial morphological classification was re-assigned
as in the GZ2 project.
  Our method shows the promising performance of morphological classification
attaining more 93 % of accuracy for five classes morphology prediction except
the cigar-shaped (75 %) and completely rounded (83 %) galaxies. Main results
are presented in the catalog of 19468 completely rounded, 27321 rounded
in-between, 3235 cigar-shaped, 4099 edge-on, 18615 spiral, and 72738 general
low-redshift galaxies of the studied SDSS sample. As for the classification of
galaxies by their detailed structural morphological features, our CNN model
gives the accuracy in the range of 92-99 % depending on features, a number of
galaxies with the given feature in the inference dataset, and the galaxy image
quality. We demonstrate that implication of the CNN model with adversarial
validation and adversarial image data augmentation improves classification of
smaller and fainter SDSS galaxies with $m_{r}$ <17.7.


title: Wormhole in $f(Q)$ gravity

abstract:
  In this paper, exact asymptotically flat wormhole solutions in the context of
symmetric teleparallel gravity, i.e., $f(Q)$ theory of gravity, are
investigated. Since modified theories of gravity provide new field equations,
we have analyzed some possible wormhole solutions by using modified field
equations. Four different forms of the $f(Q)$ function are considered then the
shape function is calculated with some different equations of state. Also we
have used a power-law shape function with these models, which lead to an
asymptotically barotropic equation of state. Some physical and mathematical
properties of the solutions, like energy conditions and boundary conditions,
are addressed .


title:
SSCU-Net: Spatial-Spectral Collaborative Unmixing Network for
  Hyperspectral Images

abstract:
  Linear spectral unmixing is an essential technique in hyperspectral image
processing and interpretation. In recent years, deep learning-based approaches
have shown great promise in hyperspectral unmixing, in particular, unsupervised
unmixing methods based on autoencoder networks are a recent trend. The
autoencoder model, which automatically learns low-dimensional representations
(abundances) and reconstructs data with their corresponding bases (endmembers),
has achieved superior performance in hyperspectral unmixing. In this article,
we explore the effective utilization of spatial and spectral information in
autoencoder-based unmixing networks. Important findings on the use of spatial
and spectral information in the autoencoder framework are discussed. Inspired
by these findings, we propose a spatial-spectral collaborative unmixing
network, called SSCU-Net, which learns a spatial autoencoder network and a
spectral autoencoder network in an end-to-end manner to more effectively
improve the unmixing performance. SSCU-Net is a two-stream deep network and
shares an alternating architecture, where the two autoencoder networks are
efficiently trained in a collaborative way for estimation of endmembers and
abundances. Meanwhile, we propose a new spatial autoencoder network by
introducing a superpixel segmentation method based on abundance information,
which greatly facilitates the employment of spatial information and improves
the accuracy of unmixing network. Moreover, extensive ablation studies are
carried out to investigate the performance gain of SSCU-Net. Experimental
results on both synthetic and real hyperspectral data sets illustrate the
effectiveness and competitiveness of the proposed SSCU-Net compared with
several state-of-the-art hyperspectral unmixing methods.


title:
End-to-End Multi-Tab Website Fingerprinting Attack: A Detection
  Perspective

abstract:
  Website fingerprinting attack (WFA) aims to deanonymize the website a user is
visiting through anonymous networks channels (e.g., Tor). Despite of remarkable
progress in the past years, most existing methods make implicitly a couple of
artificial assumptions that (1) only a single website (i.e., single-tab) is
visited each time, and (2) website fingerprinting data are pre-trimmed into a
single trace per website manually. In reality, a user often open multiple tabs
for multiple websites spontaneously. Indeed, this multi-tab WFA (MT-WFA)
setting has been studied in a few recent works, but all of them still fail to
fully respect the real-world situations. In particular, the overlapping
challenge between website fingerprinting has never been investigated in depth.
In this work, we redefine the problem of MT-WFA as detecting multiple monitored
traces, given a natural untrimmed traffic data including monitored traces,
unmonitored traces, and potentially unconstrained overlapping between them.
This eliminates the above assumptions, going beyond the conventional single
website fingerprint classification perspective taken by all previous WFA
methods. To tackle this realistic MT-WFA problem, we formulate a novel Website
Fingerprint Detection (WFD) model capable of detecting accurately the start and
end points of all the monitored traces and classifying them jointly, given
long, untrimmed raw traffic data. WFD is end-to-end, with the trace
localization and website classification integrated in a single unified
pipeline. To enable quantitative evaluation in our MT-WFA setting, we introduce
new performance metrics. Extensive experiments on several newly constructed
benchmarks show that our WFD outperforms the state-of-the-art alternative
methods in both accuracy and efficiency by a large margin, even with a very
small training set. Code is available at
https://github.com/WFDetector/WFDetection


title:
Integrable bi-Hamiltonian systems by Jacobi structure on real
  three-dimensional Lie groups

abstract:
  By Poissonization of Jacobi structures on real three-dimensional Lie groups
$\mathbf{G}$ and using the realizations of their Lie algebras, we obtain
integrable bi-Hamiltonian systems on $\mathbf{G}\otimes \mathbb{R}$.


title: MarkBERT: Marking Word Boundaries Improves Chinese BERT

abstract:
  We present a Chinese BERT model dubbed MarkBERT that uses word information.
Existing word-based BERT models regard words as basic units, however, due to
the vocabulary limit of BERT, they only cover high-frequency words and fall
back to character level when encountering out-of-vocabulary (OOV) words.
Different from existing works, MarkBERT keeps the vocabulary being Chinese
characters and inserts boundary markers between contiguous words. Such design
enables the model to handle any words in the same way, no matter they are OOV
words or not. Besides, our model has two additional benefits: first, it is
convenient to add word-level learning objectives over markers, which is
complementary to traditional character and sentence-level pre-training tasks;
second, it can easily incorporate richer semantics such as POS tags of words by
replacing generic markers with POS tag-specific markers. MarkBERT pushes the
state-of-the-art of Chinese named entity recognition from 95.4\% to 96.5\% on
the MSRA dataset and from 82.8\% to 84.2\% on the OntoNotes dataset,
respectively. Compared to previous word-based BERT models, MarkBERT achieves
better accuracy on text classification, keyword recognition, and semantic
similarity tasks.


title: Kundt Three Dimensional Left Invariant Spacetimes

abstract:
  Kundt spacetimes are of great importance to General Relativity. We show that
a Kundt spacetime is a Lorentz manifold with a non-singular isotropic geodesic
vector field having its orthogonal distribution integrable and determining a
totally geodesic foliation. We give the local structure of Kundt spacetimes and
some properties of left invariant Kundt structures on Lie groups. Finally, we
classify all left invariant Kundt structures on three dimensional simply
connected unimodular Lie groups.


title: Astrophysical and Cosmological Probes of Dark Matter

abstract:
  While astrophysical and cosmological probes provide a remarkably precise and
consistent picture of the quantity and general properties of dark matter, its
fundamental nature remains one of the most significant open questions in
physics. Obtaining a more comprehensive understanding of dark matter within the
next decade will require overcoming a number of theoretical challenges: the
groundwork for these strides is being laid now, yet much remains to be done.
Chief among the upcoming challenges is establishing the theoretical foundation
needed to harness the full potential of new observables in the astrophysical
and cosmological domains, spanning the early Universe to the inner portions of
galaxies and the stars therein. Identifying the nature of dark matter will also
entail repurposing and implementing a wide range of theoretical techniques from
outside the typical toolkit of astrophysics, ranging from effective field
theory to the dramatically evolving world of machine learning and
artificial-intelligence-based statistical inference. Through this work, the
theory frontier will be at the heart of dark matter discoveries in the upcoming
decade.

categories: hep-ph astro-ph.CO astro-ph.GA astro-ph.HE


title: Free groups as end homogeneity groups of $3$-manifolds

abstract:
  For every finitely generated free group $F$, we construct an irreducible open
$3$-manifold $M_F$ whose end set is homeomorphic to a Cantor set, and with the
end homogeneity group of $M_F$ isomorphic to $F$. The end homogeneity group is
the group of all self-homeomorphisms of the end set that extend to
homeomorphisms of the entire $3$-manifold. This extends an earlier result that
constructs, for each finitely generated abelian group $G$, an irreducible open
$3$-manifold $M_G$ with end homogeneity group $G$. The method used in the proof
of our main result also shows that if $G$ is a group with a Cayley graph in
$\mathbb{R}^3$ such that the graph automorphisms have certain nice extension
properties, then there is an irreducible open $3$-manifold $M_G$ with end
homogeneity group $G$.


title:
Differentiated Relevances Embedding for Group-based Referring Expression
  Comprehension

abstract:
  Referring expression comprehension (REC) aims to locate a certain object in
an image referred by a natural language expression. For joint understanding of
regions and expressions, existing REC works typically target on modeling the
cross-modal relevance in each region-expression pair within each single image.
In this paper, we explore a new but general REC-related problem, named
Group-based REC, where the regions and expressions can come from different
subject-related images (images in the same group), e.g., sets of photo albums
or video frames. Different from REC, Group-based REC involves differentiated
cross-modal relevances within each group and across different groups, which,
however, are neglected in the existing one-line paradigm. To this end, we
propose a novel relevance-guided multi-group self-paced learning schema (termed
RMSL), where the within-group region-expression pairs are adaptively assigned
with different priorities according to their cross-modal relevances, and the
bias of the group priority is balanced via an across-group relevance constraint
simultaneously. In particular, based on the visual and textual semantic
features, RMSL conducts an adaptive learning cycle upon triplet ranking, where
(1) the target-negative region-expression pairs with low within-group
relevances are used preferentially in model training to distinguish the primary
semantics of the target objects, and (2) an across-group relevance
regularization is integrated into model training to balance the bias of group
priority. The relevances, the pairs, and the model parameters are alternatively
updated upon a unified self-paced hinge loss.


title:
Computing the action ground state for the rotating nonlinear
  Schr\"odinger equation

abstract:
  We consider the computations of the action ground state for a rotating
nonlinear Schr\"odinger equation. It reads as a minimization of the action
functional under the Nehari constraint. In the focusing case, we identify an
equivalent formulation of the problem which simplifies the constraint. Based on
it, we propose a normalized gradient flow method with asymptotic Lagrange
multiplier and establish the energy-decaying property. Popular optimization
methods are also applied to gain more efficiency. In the defocusing case, we
prove that the ground state can be obtained by the unconstrained minimization.
Then the direct gradient flow method and unconstrained optimization methods are
applied. Numerical experiments show the convergence and accuracy of the
proposed methods in both cases, and comparisons on the efficiency are
discussed. Finally, the relation between the action and the energy ground
states are numerically investigated.


title:
ixpeobssim: a Simulation and Analysis Framework for the Imaging X-ray
  Polarimetry Explorer

abstract:
  ixpeobssim is a simulation and analysis framework, based on the Python
programming language and the associated scientific ecosystem, specifically
developed for the Imaging X-ray Polarimetry Explorer (IXPE). Given a source
model and the response functions of the telescopes, it is designed to produce
realistic simulated observations, in the form of event lists in FITS format,
containing a strict super-set of the information provided by standard IXPE
level-2 files. The core ixpeobssim simulation capabilities are complemented by
a full suite of post-processing applications, allowing for the implementation
of complex, polarization-aware analysis pipelines, and facilitating the
inter-operation with the standard visualization and analysis tools
traditionally in use by the X-ray community. We emphasize that, although a
significant part of the framework is specific to IXPE, the modular nature of
the underlying implementation makes it potentially straightforward to adapt it
to different missions with polarization capabilities.


title: Stateless or stateful FaaS? I'll take both!

abstract:
  Serverless computing has emerged as a very popular cloud technology, together
with its companion Function-as-a-Service (FaaS) programming model enabling
invocations of stateless functions from clients. An evolution of serverless is
now taking place, shifting it towards the edge of the network and broadening
its scope to stateful functions, as well. In this paper we argue that stateless
vs. stateful is not a dichotomy of the application per se, but rather a
time-varying property of most (if not all) applications, as confirmed by the
analysis of real traces collected in a production environment. Based on this
observation, we propose a mathematical formulation of a resource allocation
problem that jointly encompasses both operation modes, dubbed lambda vs. mu,
which can be solved efficiently at run-time by an edge orchestrator. We
evaluate the proposed solution via simulation experiments in realistic network
and workload conditions, which leads the way to the practical realization of a
system where applications can freely adapt their current operation mode and
optimize their performance at a minimum cost of operation from the network's
perspective.


title:
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge
  Distillation

abstract:
  The recent large-scale vision-language pre-training (VLP) of dual-stream
architectures (e.g., CLIP) with a tremendous amount of image-text pair data,
has shown its superiority on various multimodal alignment tasks. Despite its
success, the resulting models are not capable of multimodal generative tasks
due to the weak text encoder. To tackle this problem, we propose to augment the
dual-stream VLP model with a textual pre-trained language model (PLM) via
vision-language knowledge distillation (VLKD), enabling the capability for
multimodal generation. VLKD is pretty data- and computation-efficient compared
to the pre-training from scratch. Experimental results show that the resulting
model has strong zero-shot performance on multimodal generation tasks, such as
open-ended visual question answering and image captioning. For example, it
achieves 44.5% zero-shot accuracy on the VQAv2 dataset, surpassing the previous
state-of-the-art zero-shot model with $7\times$ fewer parameters. Furthermore,
the original textual language understanding and generation ability of the PLM
is maintained after VLKD, which makes our model versatile for both multimodal
and unimodal tasks.


title:
Local ultra-densification of single-walled carbon nanotube films:
  modeling and experiment

abstract:
  Fabrication of nanostructured metasurfaces poses a significant technological
and fundamental challenge. Despite developing novel systems that support
reversible elongation and distortion, their nanoscale patterning and control of
optical properties remain an open problem. Herein we report the atomic force
microscope lithography (AFML) application for nanoscale patterning of
single-walled carbon nanotube films and the associated reflection coefficient
tuning. We present models of bundling reorganization, formed-pattern stability,
and energy distribution describing mechanical behavior with mesoscopic distinct
element method (MDEM). All observed and calculated phenomena support each other
and present a platform for developing AFML patterned optical devices using
meshy nanostructured matter.

categories: cond-mat.mtrl-sci physics.comp-ph physics.ins-det physics.optics


title:
Joint CNN and Transformer Network via weakly supervised Learning for
  efficient crowd counting

abstract:
  Currently, for crowd counting, the fully supervised methods via density map
estimation are the mainstream research directions. However, such methods need
location-level annotation of persons in an image, which is time-consuming and
laborious. Therefore, the weakly supervised method just relying upon the
count-level annotation is urgently needed. Since CNN is not suitable for
modeling the global context and the interactions between image patches, crowd
counting with weakly supervised learning via CNN generally can not show good
performance. The weakly supervised model via Transformer was sequentially
proposed to model the global context and learn contrast features. However, the
transformer directly partitions the crowd images into a series of tokens, which
may not be a good choice due to each pedestrian being an independent
individual, and the parameter number of the network is very large. Hence, we
propose a Joint CNN and Transformer Network (JCTNet) via weakly supervised
learning for crowd counting in this paper. JCTNet consists of three parts: CNN
feature extraction module (CFM), Transformer feature extraction module (TFM),
and counting regression module (CRM). In particular, the CFM extracts crowd
semantic information features, then sends their patch partitions to TRM for
modeling global context, and CRM is used to predict the number of people.
Extensive experiments and visualizations demonstrate that JCTNet can
effectively focus on the crowd regions and obtain superior weakly supervised
counting performance on five mainstream datasets. The number of parameters of
the model can be reduced by about 67%~73% compared with the pure Transformer
works. We also tried to explain the phenomenon that a model constrained only by
count-level annotations can still focus on the crowd regions. We believe our
work can promote further research in this field.


title: GRAND+: Scalable Graph Random Neural Networks

abstract:
  Graph neural networks (GNNs) have been widely adopted for semi-supervised
learning on graphs. A recent study shows that the graph random neural network
(GRAND) model can generate state-of-the-art performance for this problem.
However, it is difficult for GRAND to handle large-scale graphs since its
effectiveness relies on computationally expensive data augmentation procedures.
In this work, we present a scalable and high-performance GNN framework GRAND+
for semi-supervised graph learning. To address the above issue, we develop a
generalized forward push (GFPush) algorithm in GRAND+ to pre-compute a general
propagation matrix and employ it to perform graph data augmentation in a
mini-batch manner. We show that both the low time and space complexities of
GFPush enable GRAND+ to efficiently scale to large graphs. Furthermore, we
introduce a confidence-aware consistency loss into the model optimization of
GRAND+, facilitating GRAND+'s generalization superiority. We conduct extensive
experiments on seven public datasets of different sizes. The results
demonstrate that GRAND+ 1) is able to scale to large graphs and costs less
running time than existing scalable GNNs, and 2) can offer consistent accuracy
improvements over both full-batch and scalable GNNs across all datasets.


title: BiBERT: Accurate Fully Binarized BERT

abstract:
  The large pre-trained BERT has achieved remarkable performance on Natural
Language Processing (NLP) tasks but is also computation and memory expensive.
As one of the powerful compression approaches, binarization extremely reduces
the computation and memory consumption by utilizing 1-bit parameters and
bitwise operations. Unfortunately, the full binarization of BERT (i.e., 1-bit
weight, embedding, and activation) usually suffer a significant performance
drop, and there is rare study addressing this problem. In this paper, with the
theoretical justification and empirical analysis, we identify that the severe
performance drop can be mainly attributed to the information degradation and
optimization direction mismatch respectively in the forward and backward
propagation, and propose BiBERT, an accurate fully binarized BERT, to eliminate
the performance bottlenecks. Specifically, BiBERT introduces an efficient
Bi-Attention structure for maximizing representation information statistically
and a Direction-Matching Distillation (DMD) scheme to optimize the full
binarized BERT accurately. Extensive experiments show that BiBERT outperforms
both the straightforward baseline and existing state-of-the-art quantized BERTs
with ultra-low bit activations by convincing margins on the NLP benchmark. As
the first fully binarized BERT, our method yields impressive 56.3 times and
31.2 times saving on FLOPs and model size, demonstrating the vast advantages
and potential of the fully binarized BERT model in real-world
resource-constrained scenarios.


title:
Pareto optimality, economy-effectiveness trade-offs and ion channel
  degeneracy: Improving population models of neurons

abstract:
  Nerve cells encounter unavoidable evolutionary trade-offs between multiple
tasks. They must consume as little energy as possible (be energy-efficient or
economical) but at the same time fulfil their functions (be functionally
effective). Neurons displaying best performance for such multi-task trade-offs
are said to be Pareto optimal. However, it is not understood how ion channel
parameters contribute to the Pareto optimal performance of neurons. Ion channel
degeneracy implies that multiple combinations of ion channel parameters can
lead to functionally similar neuronal behavior. Therefore, to simulate
functional behavior, instead of a single model, neuroscientists often use
populations of valid models with distinct ion conductance configurations. This
approach is called population (also database or ensemble) modeling. It remains
unclear, which ion channel parameters in a vast population of functional models
are more likely to be found in the brain. Here we propose that Pareto
optimality can serve as a guiding principle for addressing this issue. The
Pareto optimum concept can help identify the subpopulations of
conductance-based models with ion channel configurations that perform best for
the trade-off between economy and functional effectiveness. In this way, the
high-dimensional parameter space of neuronal models might be reduced to
geometrically simple low-dimensional manifolds. Therefore, Pareto optimality is
a promising framework for improving population modeling of neurons and their
circuits. We also discuss how Pareto inference might help deduce neuronal
functions from high-dimensional Patch-seq data. Furthermore, we hypothesize
that Pareto optimality might contribute to our understanding of observed ion
channel correlations in neurons.


title:
A theory for the flow of chemically-responsive polymer solutions:
  equilibrium and shear-induced phase separation

abstract:
  Chemically-responsive polymers are macromolecules that respond to local
variations of the chemical composition of the solution by changing their
conformation, with notable examples including polyelectrolytes, proteins and
DNA. The polymer conformation changes can occur in response to changes to the
pH, the ionic strength or to the concentration of a generic solute that
interacts with the polymer. These chemical stimuli can lead to drastic
variations of the polymer flexibility and even trigger a transition from a coil
to a globule polymer conformation. In many situations the spatial distribution
of the chemical stimuli can be highly inhomogeneous, which can lead to large
spatial variations of polymer conformation and of the rheological properties of
the mixture. In this paper, we develop a theory for the flow of a mixture of a
solute and chemically-responsive polymers. The approach is valid for generic
flows and inhomogeneous distributions of polymers and solutes. To model the
polymer conformation changes introduced by the interactions with the solute, we
consider the polymers as linear elastic dumbbells whose spring stiffness
depends on the solute concentration. We use the Onsager's variational formalism
to derive the equations governing the evolution of the variables, which unveils
novel couplings between the distribution of dumbbells and that of the solute.
Finally, we use a linear stability analysis to show that the governing
equations predict an equilibrium phase separation and a distinct shear-induced
phase separation whereby a homogeneous distribution of solute and dumbbells
spontaneously demix. Similar phase transitions have been observed in previous
experiments using stimuli-responsive polymers and may play an important role in
living systems.


title:
G2GML: Graph to Graph Mapping Language for Bridging RDF and Property
  Graphs

abstract:
  How can we maximize the value of accumulated RDF data? Whereas the RDF data
can be queried using the SPARQL language, even the SPARQL-based operation has a
limitation in implementing traversal or analytical algorithms. Recently, a
variety of database implementations dedicated to analyses on the property graph
(PG) model have emerged. Importing RDF datasets into these graph analysis
engines provides access to the accumulated datasets through various application
interfaces. However, the RDF model and the PG model are not interoperable.
Here, we developed a framework based on the Graph to Graph Mapping Language
(G2GML) for mapping RDF graphs to PGs to make the most of accumulated RDF data.
Using this framework, accumulated graph data described in the RDF model can be
converted to the PG model, which can then be loaded to graph database engines
for further analysis. For supporting different graph database implementations,
we redefined the PG model and proposed its exchangeable serialization formats.
We demonstrate several use cases, where publicly available RDF data are
extracted and converted to PGs. This study bridges RDF and PGs and contributes
to interoperable management of knowledge graphs, thereby expanding the use
cases of accumulated RDF data.


title:
Crystal-field effects competing with spin-orbit interactions in
  NaCeO$_2$

abstract:
  Ce compounds feature a remarkable diversity of electronic properties, which
motivated extensive investigations over the last decades. Inelastic neutron
scattering represents an important tool for understanding their underlying
electronic structures but in certain cases a straightforward interpretation of
the measured spectra is hampered by the presence of strong vibronic couplings.
The latter may give rise to extra spectral features, which complicates the
mapping of experimental data onto standard multiplet diagrams. To benchmark the
performance of embedded-cluster quantum chemical computational schemes for the
case of $4f$ systems, we here address the Ce 4$f^1$ multiplet structure of
NaCeO$_2$, an antiferromagnet with $D_{2d}$ magnetic-site symmetry for which
neutron scattering measurements indicate only weak vibronic effects. Very good
agreement with the experimental results is found in the computations, which
validates our computational approach and confirms NaCeO$_2$ as a 4$f$ magnet in
the intermediate coupling regime with equally strong 4$f$-shell spin-orbit and
crystal-field interactions.


title: Multibeam Satellite Communications with Energy Efficiency Optimization

abstract:
  Energy efficiency (EE) is an important aspect of satellite communications.
Different with the existing algorithms that typically use the first-order
Taylor lower bound approximation to convert non-convex EE maximization (EEM)
problems into convex ones, in this letter a two-step quadratic transformation
method is presented. In the first step, the fractional form of the achievable
rate over the total power consumption is converted into a non-fractional form
based on quadratic transformation. In the second step, the fractional form of
the signal power over the interference-and-noise power is further converted
into a non-fractional form, still based on quadratic transformation. After the
two-step quadratic transformation, the original EEM problem is converted into
an equivalent convex one. Then an alternating optimization algorithm is
presented to solve it by iteratively performing two stages until a stop
condition is satisfied. Simulation results show that the presented algorithm
can fast converge and its performance is better than that of the sequential
convex approximation algorithm and the multibeam interference mitigation
algorithm.


title:
A combined approach to the analysis of speech conversations in a contact
  center domain

abstract:
  The ever more accurate search for deep analysis in customer data is a really
strong technological trend nowadays, quite appealing to both private and public
companies. This is particularly true in the contact center domain, where speech
analytics is an extremely powerful methodology for gaining insights from
unstructured data, coming from customer and human agent conversations. In this
work, we describe an experimentation with a speech analytics process for an
Italian contact center, that deals with call recordings extracted from inbound
or outbound flows. First, we illustrate in detail the development of an
in-house speech-to-text solution, based on Kaldi framework, and evaluate its
performance (and compare it to Google Cloud Speech API). Then, we evaluate and
compare different approaches to the semantic tagging of call transcripts,
ranging from classic regular expressions to machine learning models based on
ngrams and logistic regression, and propose a combination of them, which is
shown to provide a consistent benefit. Finally, a decision tree inducer, called
J48S, is applied to the problem of tagging. Such an algorithm is natively
capable of exploiting sequential data, such as texts, for classification
purposes. The solution is compared with the other approaches and is shown to
provide competitive classification performances, while generating highly
interpretable models and reducing the complexity of the data preparation phase.
The potential operational impact of the whole process is thoroughly examined.


title:
Brownian fluctuations of the interface in a system with two linear
  attracted components and white noises

abstract:
  We concern the analysis of the long time behavior of interfaces in systems
with two components. Each component evolves according to 1-d Allen-Cahn
equation with Neumann boundary conditions, perturbed by small space-time white
noise and with symmetric double well potential in the interval
$[-\epsilon^{-1},\epsilon^{-1}]$. The two components interact with each other
by an attractive linear force. Instantons are defined as the stationary
solution of the Allen-Cahn equation without noise which connects two pure
phases. We prove that for time $t=\epsilon^{-1}$, in the limit $\epsilon
\rightarrow 0$, when initial states are close to an instanton, two components
stay close to the same instanton, whose center moves as a Brownian motion.


title:
SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object
  Detection

abstract:
  Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn
an object detector generalizing to a novel domain free of annotations. Recent
advances align class-conditional distributions by narrowing down cross-domain
prototypes (class centers). Though great success,they ignore the significant
within-class variance and the domain-mismatched semantics within the training
batch, leading to a sub-optimal adaptation. To overcome these challenges, we
propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD,
which completes mismatched semantics and reformulates the adaptation with graph
matching. Specifically, we design a Graph-embedded Semantic Completion module
(GSC) that completes mismatched semantics through generating hallucination
graph nodes in missing categories. Then, we establish cross-image graphs to
model class-conditional distributions and learn a graph-guided memory bank for
better semantic completion in turn. After representing the source and target
data as graphs, we reformulate the adaptation as a graph matching problem,
i.e., finding well-matched node pairs across graphs to reduce the domain gap,
which is solved with a novel Bipartite Graph Matching adaptor (BGM). In a
nutshell, we utilize graph nodes to establish semantic-aware node affinity and
leverage graph edges as quadratic constraints in a structure-aware matching
loss, achieving fine-grained adaptation with a node-to-node graph matching.
Extensive experiments verify that SIGMA outperforms existing works
significantly. Our code is available at
https://github.com/CityU-AIM-Group/SIGMA.


title:
Hydrogen phase-IV characterization by full account of quantum
  anharmonicity

abstract:
  We devise a framework to compute accurate phonons in molecular crystals even
in case of strong quantum anharmonicity. Our approach is based on the
calculation of the static limit of the phononic Matsubara Green's function from
path integral molecular dynamics simulations. Our method enjoys a remarkably
low variance, which allows one to compute accurate phonon frequencies after a
few picoseconds of nuclear dynamics, and it is further stabilized by the use of
appropriate constrained displacement operators. We applied it to solid hydrogen
at high pressure. For phase III, our predicted infrared (IR) and Raman active
vibrons agree very well with experiments. We then characterize the crystalline
symmetry of phase IV by direct comparison with vibrational data and we
determine the character of its Raman and IR vibron peaks.


title: Affine Volterra processes with jumps

abstract:
  The theory of affine processes has been recently extended to the framework of
stochastic Volterra equations with continuous trajectories. These so-called
affine Volterra processes overcome modeling shortcomings of affine processes
because they can have trajectories whose regularity is different from the
regularity of the paths of Brownian motion. More specifically, singular kernels
yield rough affine processes. This paper extends the theory by considering
affine stochastic Volterra equations with jumps. This extension is not
straightforward because the jump structure together with possible singularities
of the kernel may induce explosions of the trajectories.