title: Memory Complexity of Quantum Processes

abstract:
  Generic open quantum dynamics can be described by two seemingly very distinct
approaches: a top down approach by considering an (unknown) environment coupled
to the system and affects the observed dynamics of the system; or a bottom up
approach which tries to build an open quantum evolution model from the observed
data. The process tensor framework describes all the possible observations one
could possibly make on a quantum system, however it is computationally
inefficient and not predictive. Here we define the purified process tensor
which 1) allows efficient tomography as well as prediction for future process
and 2) naturally defines a stationary quantum process as well as a quantitative
and easy-to-evaluate definition of the memory complexity, or the degree of
non-Markovianity, for it. As such it allows to uncover the minimal open quantum
evolution model hidden in the observed data, completing the second approach for
understanding open quantum dynamics. The intimate connection between quantum
processes and classical stochastic processes is drawn in the end.


title:
Phased array beamforming methods for powering biomedical ultrasonic
  implants

abstract:
  Millimeter-scale implants using ultrasound for power and communication have
been proposed for a range of deep-tissue applications, including neural
recording and stimulation. However, published implementations have shown high
sensitivity to misalignment with the external ultrasound transducer. Ultrasonic
beamforming using a phased array to these implants can improve tolerance to
misalignment, reduce implant volume, and allow multiple implants to be operated
simultaneously in different locations. This paper details the design of a
custom planar phased array ultrasound system, which is capable of steering and
focusing ultrasound power within a 3D volume. Analysis and simulation is
performed to determine the choice of array element pitch, with special
attention given to maximizing the power available at the implant while meeting
FDA limits for diagnostic ultrasound. Time reversal is proposed as a
computationally simple approach to beamforming that is robust despite
scattering and inhomogeneity of the acoustic medium. This technique is
demonstrated both in active drive and pulse-echo modes, and it is
experimentally compared with other beamforming techniques by measuring energy
transfer efficiency at varying depths and angles. Simultaneous power delivery
to multiple implants is also demonstrated.


title:
Ensemble Domain Decomposition Algorithm for the Fully-mixed Random
  Stokes-Darcy Model with the Beavers-Joseph Interface Conditions

abstract:
  In this paper, an efficient ensemble domain decomposition algorithm is
proposed for fast solving the fully-mixed random Stokes-Darcy model with the
physically realistic Beavers-Joseph (BJ) interface conditions. We utilize the
Monte Carlo method for the coupled model with random inputs to derive some
deterministic Stokes-Darcy numerical models and use the idea of the ensemble to
realize the fast computation of multiple problems. One remarkable feature of
the algorithm is that multiple linear systems share a common coefficient matrix
in each deterministic numerical model, which significantly reduces the
computational cost and achieves comparable accuracy with the traditional
methods. Moreover, by domain decomposition, we can decouple the Stokes-Darcy
system into two smaller sub-physics problems naturally. Both mesh-dependent and
mesh-independent convergence rates of the algorithm are rigorously derived by
choosing suitable Robin parameters. Especially, for small hydraulic
conductivity in practice, the almost optimal geometric convergence can be
obtained by finite element discretization. Finally, two groups of numerical
experiments are conducted to validate and illustrate the exclusive features of
the proposed algorithm.


title: Disperse rotation operator DRT and use in some stream ciphers

abstract:
  The rotation operator is frequently used in several stream ciphers, including
HC-128, Rabbit, and Salsa20, the final candidates for eSTREAM. This is because
the rotation operator (ROT) is simple but has very good dispersibility. In this
paper, we propose a disperse rotation operator (DRT), which has the same
structure as ROT but has better dispersibility. In addition, the use of DRT
instead of ROT has shown that the quality of the output stream of all three
stream ciphers was significantly improved. However, the use of DRT instead of
ROT in the HC-128 stream cipher prevents the expansion of differential attacks
based on LSB.


title:
Low-Energy Excitations of Skyrmion Crystals in a Centrosymmetric
  Kondo-Lattice Magnet: Decoupled Spin-Charge Excitations and Nonreciprocity

abstract:
  We theoretically study spin and charge excitations of skyrmion crystals
stabilized by conduction-electron-mediated magnetic interactions via
spin-charge coupling in a centrosymmetric Kondo-lattice model by large-scale
spin-dynamics simulations combined with the kernel polynomial method. We reveal
clear segregation of spin and charge excitation channels and nonreciprocal
nature of the spin excitations governed by the Fermi-surface geometry, which
are unique to the skyrmion crystals in centrosymmetric itinerant hosts and can
be a source of novel physical phenomena.


title:
Closed-Form Second-Order Partial Derivatives of Rigid-Body Inverse
  Dynamics

abstract:
  Optimization-based control methods for robots often rely on first-order
dynamics approximation methods like in iLQR. Using second-order approximations
of the dynamics is expensive due to the costly second-order partial derivatives
of dynamics with respect to the state and control. Current approaches for
calculating these derivatives typically use automatic differentiation (AD) and
chain-rule accumulation or finite-difference. In this paper, for the first
time, we present closed-form analytical second-order partial derivatives of
inverse dynamics for rigid-body systems with floating base and multi-DoF
joints. A new extension of spatial vector algebra is proposed that enables the
analysis. A recursive $\mathcal{O}(Nd^2)$ algorithm is also provided where $N$
is the number of bodies and $d$ is the depth of the kinematic tree. A
comparison with AD in CasADi shows speedups of 1.5-3$\times$ for serial
kinematic trees with $N> 5$, and a C++ implementation shows runtimes of
$\approx$400$\mu s$ for a quadruped.


title:
Existence and uniqueness of solutions to non-Abelian multiple vortex
  equations on graphs

abstract:
  Let $G=(V,E)$ be a connected finite graph. We study a system of non-Abelian
multiple vortex equations on $G$. We established a necessary and sufficient
condition for the existence and uniqueness of solutions to the non-Abelian
multiple vortex equations.


title:
Smoothness of the fundamental solution of Schr\"odinger equations with
  mild trapping

abstract:
  In this short note, smoothness of the fundamental solution of Schr\"odinger
equations on a complete manifold is studied. It is shown that (1) the
fundamental solution is smooth under "mild" trapping conditions; (2) there is a
Riemannian manifold which is equal to Euclidean space outside a compact set
such that the fundamental solution is not smooth.


title:
The Dynamics of Q-learning in Population Games: a Physics-Inspired
  Continuity Equation Model

abstract:
  Although learning has found wide application in multi-agent systems, its
effects on the temporal evolution of a system are far from understood. This
paper focuses on the dynamics of Q-learning in large-scale multi-agent systems
modeled as population games. We revisit the replicator equation model for
Q-learning dynamics and observe that this model is inappropriate for our
concerned setting. Motivated by this, we develop a new formal model, which
bears a formal connection with the continuity equation in physics. We show that
our model always accurately describes the Q-learning dynamics in population
games across different initial settings of MASs and game configurations. We
also show that our model can be applied to different exploration mechanisms,
describe the mean dynamics, and be extended to Q-learning in 2-player and
n-player games. Last but not least, we show that our model can provide insights
into algorithm parameters and facilitate parameter tuning.


title:
Strong light-matter interactions between gap plasmons and
  two-dimensional excitons at ambient condition in a deterministic way

abstract:
  Strong exciton-plasmon interaction between the layered two-dimensional (2D)
semiconductors and gap plasmons shows a great potential to implement cavity
quantum-electrodynamics in ambient condition. However, achieving a robust
plasmon-exciton coupling with nanocavity is still very challenging, because the
layer area is usually small with conventional approaches. Here, we report on a
robust strong exciton-plasmon coupling between the gap mode of bowtie and the
excitons in MoS$_2$ layers with gold-assisted mechanical exfoliation and the
nondestructive wet transfer techniques for large-area layer. Benefiting from
the ultrasmall mode volume and strong in-plane field, the estimated effective
exciton number contributing to the coupling is largely reduced. With a
corrected exciton transition dipole moment, the exciton numbers are extracted
with 40 for the case of monolayer and 48 for 8 layers. Our work paves a way to
realize the strong coupling with 2D materials with few excitons at room
temperature.


title:
NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth
  Estimation

abstract:
  Estimating the accurate depth from a single image is challenging since it is
inherently ambiguous and ill-posed. While recent works design increasingly
complicated and powerful networks to directly regress the depth map, we take
the path of CRFs optimization. Due to the expensive computation, CRFs are
usually performed between neighborhoods rather than the whole graph. To
leverage the potential of fully-connected CRFs, we split the input into windows
and perform the FC-CRFs optimization within each window, which reduces the
computation complexity and makes FC-CRFs feasible. To better capture the
relationships between nodes in the graph, we exploit the multi-head attention
mechanism to compute a multi-head potential function, which is fed to the
networks to output an optimized depth map. Then we build a bottom-up-top-down
structure, where this neural window FC-CRFs module serves as the decoder, and a
vision transformer serves as the encoder. The experiments demonstrate that our
method significantly improves the performance across all metrics on both the
KITTI and NYUv2 datasets, compared to previous methods. Furthermore, the
proposed method can be directly applied to panorama images and outperforms all
previous panorama methods on the MatterPort3D dataset. The source code of our
method will be made public.


title: Understanding the impact of diffusion of CO in the astrochemical models

abstract:
  The mobility of lighter species on the surface of interstellar dust grains
plays a crucial role in forming simple through complex molecules. Carbon
monoxide is one of the most abundant molecules, its surface diffusion on the
grain surface is essential to forming many molecules. Recent laboratory
experiments found a diverse range of diffusion barriers for CO on the grain
surface, their use can significantly impact the abundance of several molecules.
The impact of different diffusion barriers of CO, in the astrochemical models,
is studied to understand its effect on the abundance of solid CO and the
species for which it is a reactant partner. A gas-grain network is used for
three different physical conditions; cold-core and warm-up models with slow and
fast heating rates. Two different ratios (0.3 and 0.5) between diffusion and
desorption barrier are utilized for all the species. For each physical
condition and ratio, six different models are run by varying diffusion barriers
of CO. Solid CO abundance for the models with the lowest diffusion barrier
yields less than 0.1% of water ice for cold clouds and a maximum of 0.4% for
slow and fast warm-up models. Also, solid CO$_2$ in dense clouds is
significantly overproduced (140 % of water). The abundance of H$_2$CO and
CH$_3$OH showed an opposite trend, and HCOOH, CH$_3$CHO, NH$_2$CO, and
CH$_3$COCH$_3$ are produced in lower quantities for models with low diffusion
barriers for CO. Considerable variation in abundance is observed between models
with the high and low diffusion barrier. Models with higher diffusion barriers
provide a relatively better agreement with the observed abundances when
compared with the models having lower diffusion barriers.


title:
Security Analysis of Two Recent Pairing-Free Certificateless Two-Party
  Authenticated Key Agreement Protocols for Smart Grid

abstract:
  Smart grids are intelligent power transmission networks that monitor and
control communication participants and grid nodes to ensure bidirectional flow
of information and power between all nodes. To secure the smart grid, it is
very important to design the key agreement protocol. The pairing-free
certificateless two-party authenticated key agreement protocol has been widely
studied and applied as a basic core protocol to protect the security of the
smart grid. Until now, various protocols have been proposed, and these
protocols are being introduced and operated not only in smart grid, but also in
smart cities, healthcare, and vehicle ad hoc networks. In this paper, we
analyzed the security properties of two recently proposed pairing-free
certificateless two-party authenticated key agreement protocols for Smart grid.
According to our analysis, these two protocols are insecure against basic
impersonation attacks of malicious key-generator centers, man-in-the-middle
attacks of malicious key generator centers, and key offset attacks. We also
found and pointed out some errors in the descriptions of these protocols.


title:
Large-scale Optimization of Partial AUC in a Range of False Positive
  Rates

abstract:
  The area under the ROC curve (AUC) is one of the most widely used performance
measures for classification models in machine learning. However, it summarizes
the true positive rates (TPRs) over all false positive rates (FPRs) in the ROC
space, which may include the FPRs with no practical relevance in some
applications. The partial AUC, as a generalization of the AUC, summarizes only
the TPRs over a specific range of the FPRs and is thus a more suitable
performance measure in many real-world situations. Although partial AUC
optimization in a range of FPRs had been studied, existing algorithms are not
scalable to big data and not applicable to deep learning. To address this
challenge, we cast the problem into a non-smooth difference-of-convex (DC)
program for any smooth predictive functions (e.g., deep neural networks), which
allowed us to develop an efficient approximated gradient descent method based
on the Moreau envelope smoothing technique, inspired by recent advances in
non-smooth DC optimization. To increase the efficiency of large data
processing, we used an efficient stochastic block coordinate update in our
algorithm. Our proposed algorithm can also be used to minimize the sum of
ranked range loss, which also lacks efficient solvers. We established a
complexity of $\tilde O(1/\epsilon^6)$ for finding a nearly $\epsilon$-critical
solution. Finally, we numerically demonstrated the effectiveness of our
proposed algorithms for both partial AUC maximization and sum of ranked range
loss minimization.


title:
Energy Decomposition along Reaction Coordinate and Energy Weighted
  Reactive Flux: 1. Theory

abstract:
  A theoretical framework is proposed for an energy decomposition scheme along
the reaction coordinate, in which the ensemble average of the potential energy
weighted with reactive flux intensity is decomposed into energy components at
the per-coordinate level. The decomposed energy quantity is demonstrated to be
closely related to the free energy along the reaction coordinate and its
connection to the emergent potential energy is provided. We also explore the
property of the reactive flux weighted by the potential energy in the subspace
of collective variables. A free energy analogue is then proposed in the
subspace of collective variables and the directional derivative of this free
energy analogue in the direction of the reactive flux is shown to be useful in
the identification of the reaction coordinate and the minimum free energy path.


title:
SMA-NBO: A Sequential Multi-Agent Planning with Nominal Belief-State
  Optimization in Target Tracking

abstract:
  In target tracking with mobile multi-sensor systems, sensor deployment
impacts the observation capabilities and the resulting state estimation
quality. Based on a partially observable Markov decision process (POMDP)
formulation comprised of the observable sensor dynamics, unobservable target
states, and accompanying observation laws, we present a distributed
information-driven solution approach to the multi-agent target tracking
problem, namely, sequential multi-agent nominal belief-state optimization
(SMA-NBO). SMA-NBO seeks to minimize the expected tracking error via receding
horizon control including a heuristic expected cost-to-go (HECTG). SMA-NBO
incorporates a computationally efficient approximation of the target
belief-state over the horizon. The agent-by-agent decision-making is capable of
leveraging on-board (edge) compute for selecting (sub-optimal) target-tracking
maneuvers exhibiting non-myopic cooperative fleet behavior. The optimization
problem explicitly incorporates semantic information defining target occlusions
from a world model. To illustrate the efficacy of our approach, a random
occlusion forest environment is simulated. SMA-NBO is compared to other
baseline approaches. The simulation results show SMA-NBO 1) maintains tracking
performance and reduces the computational cost by replacing the calculation of
the expected target trajectory with a single sample trajectory based on maximum
a posteriori estimation; 2) generates cooperative fleet decision by
sequentially optimizing single-agent policy with efficient usage of other
agents' policy of intent; 3) aptly incorporates the multiple weighted trace
penalty (MWTP) HECTG, which improves tracking performance with a
computationally efficient heuristic.


title:
The Velocity Statistics of Turbulent Clouds in the Presence of Gravity,
  Magnetic fields, Radiation, and Outflow Feedback

abstract:
  The interaction of turbulence, magnetic fields, self-gravity, and stellar
feedback within molecular clouds is crucial for understanding star formation.
We study the effects of self-gravity and outflow feedback on the properties of
the turbulent velocity via the structure function over length scales from
$\sim$ 0.01 pc to 2 pc. We analyze a series of three-dimensional,
magnetohydrodynamical (MHD) simulations of star cluster formation. We find
outflow feedback can change the scaling of velocity fluctuations but still
roughly being in between Kolmogorov and Burgers turbulence. We observe that
self-gravity and protostellar outflows increase the velocity fluctuations over
all length scales. Outflows can amplify the velocity fluctuations by up to a
factor of $\sim$7 on scales $\sim$ 0.01 - 0.2 pc and drive turbulence up to a
scale of $\sim$ 1 pc. The amplified velocity fluctuations provide more support
against gravity and enhance fragmentation on small scales. The self-gravity's
effect is more significant on smaller dense clumps and it increases the
fraction of the compressive velocity component up to a scale of $\sim$ 0.2 pc.
However, outflow feedback drives both solenoidal and compressive modes, but it
induces a higher fraction of solenoidal modes relative to compressive modes.
Thus, with outflows, the dense core ends up with a slightly higher fraction of
solenoidal modes. We find that the compressible fraction is fairly constant
with about 1/3 on scales $\sim$ 0.1 - 0.2 pc. The combined effect of enhanced
velocity dispersion and reduced compressive fraction contributes to a reduction
in the star formation rate.


title: SoftGroup for 3D Instance Segmentation on Point Clouds

abstract:
  Existing state-of-the-art 3D instance segmentation methods perform semantic
segmentation followed by grouping. The hard predictions are made when
performing semantic segmentation such that each point is associated with a
single class. However, the errors stemming from hard decision propagate into
grouping that results in (1) low overlaps between the predicted instance with
the ground truth and (2) substantial false positives. To address the
aforementioned problems, this paper proposes a 3D instance segmentation method
referred to as SoftGroup by performing bottom-up soft grouping followed by
top-down refinement. SoftGroup allows each point to be associated with multiple
classes to mitigate the problems stemming from semantic prediction errors and
suppresses false positive instances by learning to categorize them as
background. Experimental results on different datasets and multiple evaluation
metrics demonstrate the efficacy of SoftGroup. Its performance surpasses the
strongest prior method by a significant margin of +6.2% on the ScanNet v2
hidden test set and +6.8% on S3DIS Area 5 in terms of AP_50. SoftGroup is also
fast, running at 345ms per scan with a single Titan X on ScanNet v2 dataset.
The source code and trained models for both datasets are available at
\url{https://github.com/thangvubk/SoftGroup.git}.


title: Probing the dark photon via polarized DIS scattering at the HERA and EIC

abstract:
  The dark photon is widely predicted in many new physics beyond the Standard
Model. In this work, we propose to utilize the polarized lepton cross section
in neutral current DIS process at the HERA and upcoming Electron-Ion Collider
(EIC) to probe the properties of the dark photon. It shows that we can
constrain the mixing parameter $\epsilon<0.02$ when the dark photon mass
$m_{A_D}<10 ~{\rm GeV}$ at the HERA with polarized lepton beam and this bound
is comparable to the limit from the unpolarized HERA data. With the help of
high integrated luminosity and high electron beam polarization of the EIC, the
upper limit of the $\epsilon$ could be further improved. Depending on the
assumption of the systematic error of the cross section measurements, we obtain
$\epsilon<0.01\sim 0.02$ for the mass region $m_{A_D}<10~{\rm GeV}$ under the
integrated luminosity of $300~{\rm fb}^{-1}$. We also discuss the complementary
roles between the cross section and the single-spin asymmetry measurements in
probing the dark photon.


title: Measurable tilings by abelian group actions

abstract:
  Let $X$ be a measure space with a measure-preserving action $(g,x) \mapsto g
\cdot x$ of an abelian group $G$. We consider the problem of understanding the
structure of measurable tilings $F \odot A = X$ of $X$ by a measurable tile $A
\subset X$ translated by a finite set $F \subset G$ of shifts, thus the
translates $f \cdot A$, $f \in F$ partition $X$ up to null sets. Adapting
arguments from previous literature, we establish a "dilation lemma" that
asserts, roughly speaking, that $F \odot A = X$ implies $F^r \odot A = X$ for a
large family of integer dilations $r$, and use this to establish a structure
theorem for such tilings analogous to that established recently by the second
and fourth authors. As applications of this theorem, we completely classify
those random tilings of finitely generated abelian groups that are "factors of
iid", and show that measurable tilings of a torus $\mathbb{T}^d$ can always be
continuously (in fact linearly) deformed into a tiling with rational shifts,
with particularly strong results in the low-dimensional cases $d=1,2$ (in
particular resolving a conjecture of Conley, the first author, and Pikhurko in
the $d=1$ case).


title:
Restructuring in bimetallic core-shell nanoparticles: Real time
  observation

abstract:
  The formation process of core-shell bimetallic nanoparticles synthesized by
sputtering onto a substrate is observed in real time using an originally
developed acoustic technique. The technique enables us to evaluate the
structural change of nanoparticles at room temperature without contacting the
nanoparticles or substrate. In the experiments, the sputtering of metal A
followed by metal B tended to form B-shell/A-core nanoparticles. However, in
Pd-Au alloy system, notable restructuring occurred during synthesis, resulting
in the formation of A-shell/B-core nanoparticles. The formation process is
analyzed using the molecular dynamics simulation, revealing that this
restructuring occurs on a short timescale, and high diffusivity of Au plays an
important role.


title:
Quantum Entanglement in the Sachdev-Ye-Kitaev Model and its
  Generalizations

abstract:
  Entanglement is one of the most important concepts in quantum physics. We
review recent progress in understanding the quantum entanglement in many-body
systems using large-$N$ solvable models: the Sachdev-Ye-Kitaev (SYK) model and
its generalizations. We present the study of entanglement entropy in the
original SYK Model using three different approaches: the exact diagonalization,
the eigenstate thermalization hypothesis, and the path-integral representation.
For coupled SYK models, the entanglement entropy shows linear growth and
saturation at the thermal value. The saturation is related to replica wormholes
in gravity. Finally, we consider the steady-state entanglement entropy of
quantum many-body systems under repeated measurements. The traditional symmetry
breaking in the enlarged replica space leads to the measurement-induced
entanglement phase transition.


title:
Physics-informed neural network solution of thermo-hydro-mechanical
  (THM) processes in porous media

abstract:
  Physics-Informed Neural Networks (PINNs) have received increased interest for
forward, inverse, and surrogate modeling of problems described by partial
differential equations (PDE). However, their application to multiphysics
problem, governed by several coupled PDEs, present unique challenges that have
hindered the robustness and widespread applicability of this approach. Here we
investigate the application of PINNs to the forward solution of problems
involving thermo-hydro-mechanical (THM) processes in porous media, which
exhibit disparate spatial and temporal scales in thermal conductivity,
hydraulic permeability, and elasticity. In addition, PINNs are faced with the
challenges of the multi-objective and non-convex nature of the optimization
problem. To address these fundamental issues, we: (1)~rewrite the THM governing
equations in dimensionless form that is best suited for deep-learning
algorithms; (2)~propose a sequential training strategy that circumvents the
need for a simultaneous solution of the multiphysics problem and facilitates
the task of optimizers in the solution search; and (3)~leverage adaptive weight
strategies to overcome the stiffness in the gradient flow of the
multi-objective optimization problem. Finally, we apply this framework to the
solution of several synthetic problems in 1D and~2D.


title:
Code Synonyms Do Matter: Multiple Synonyms Matching Network for
  Automatic ICD Coding

abstract:
  Automatic ICD coding is defined as assigning disease codes to electronic
medical records (EMRs). Existing methods usually apply label attention with
code representations to match related text snippets. Unlike these works that
model the label with the code hierarchy or description, we argue that the code
synonyms can provide more comprehensive knowledge based on the observation that
the code expressions in EMRs vary from their descriptions in ICD. By aligning
codes to concepts in UMLS, we collect synonyms of every code. Then, we propose
a multiple synonyms matching network to leverage synonyms for better code
representation learning, and finally help the code classification. Experiments
on the MIMIC-III dataset show that our proposed method outperforms previous
state-of-the-art methods.


title: Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking

abstract:
  Visual tracking is adopted to extensive unmanned aerial vehicle (UAV)-related
applications, which leads to a highly demanding requirement on the robustness
of UAV trackers. However, adding imperceptible perturbations can easily fool
the tracker and cause tracking failures. This risk is often overlooked and
rarely researched at present. Therefore, to help increase awareness of the
potential risk and the robustness of UAV tracking, this work proposes a novel
adaptive adversarial attack approach, i.e., Ad$^2$Attack, against UAV object
tracking. Specifically, adversarial examples are generated online during the
resampling of the search patch image, which leads trackers to lose the target
in the following frames. Ad$^2$Attack is composed of a direct downsampling
module and a super-resolution upsampling module with adaptive stages. A novel
optimization function is proposed for balancing the imperceptibility and
efficiency of the attack. Comprehensive experiments on several well-known
benchmarks and real-world conditions show the effectiveness of our attack
method, which dramatically reduces the performance of the most advanced Siamese
trackers.


title:
Correct-N-Contrast: A Contrastive Approach for Improving Robustness to
  Spurious Correlations

abstract:
  Spurious correlations pose a major challenge for robust machine learning.
Models trained with empirical risk minimization (ERM) may learn to rely on
correlations between class labels and spurious attributes, leading to poor
performance on data groups without these correlations. This is particularly
challenging to address when spurious attribute labels are unavailable. To
improve worst-group performance on spuriously correlated data without training
attribute labels, we propose Correct-N-Contrast (CNC), a contrastive approach
to directly learn representations robust to spurious correlations. As ERM
models can be good spurious attribute predictors, CNC works by (1) using a
trained ERM model's outputs to identify samples with the same class but
dissimilar spurious features, and (2) training a robust model with contrastive
learning to learn similar representations for same-class samples. To support
CNC, we introduce new connections between worst-group error and a
representation alignment loss that CNC aims to minimize. We empirically observe
that worst-group error closely tracks with alignment loss, and prove that the
alignment loss over a class helps upper-bound the class's worst-group vs.
average error gap. On popular benchmarks, CNC reduces alignment loss
drastically, and achieves state-of-the-art worst-group accuracy by 3.6% average
absolute lift. CNC is also competitive with oracle methods that require group
labels.


title: Evolution of mixed strategies in monotone games

abstract:
  We consider the basic problem of approximating Nash equilibria in
noncooperative games. For monotone games, we design continuous time flows which
converge in an averaged sense to Nash equilibria. We also study mean field
equilibria, which arise in the large player limit of symmetric noncooperative
games. In this setting, we will additionally show that the approximation of
mean field equilibria is possible under a suitable monotonicity hypothesis.


title: Observing Axion Emission from Supernova with Collider Detectors

abstract:
  We consider a possibility to observe the axion emission from a nearby
supernova (SN) in the future, which can be known in advance by the pre-SN alert
system, by collider detectors like the LHC detectors (i.e., the ATLAS and the
CMS) and the ILC detectors (i.e., the ILD and SiD). The axion from the SN can
be converted to the photon by the strong magnetic field in the detector and the
photon can be detected by electromagnetic calorimeter. We estimate the numbers
of signal and background events due to a nearby SN and show that the number of
signal may be sizable. The axion emission from a nearby SN may be observed if,
at the time of the SN, the beam is stopped and the detector operation is
switched to the one for the SN axion search.


title: An Open Challenge for Inductive Link Prediction on Knowledge Graphs

abstract:
  An emerging trend in representation learning over knowledge graphs (KGs)
moves beyond transductive link prediction tasks over a fixed set of known
entities in favor of inductive tasks that imply training on one graph and
performing inference over a new graph with unseen entities. In inductive
setups, node features are often not available and training shallow entity
embedding matrices is meaningless as they cannot be used at inference time with
unseen entities. Despite the growing interest, there are not enough benchmarks
for evaluating inductive representation learning methods. In this work, we
introduce ILPC 2022, a novel open challenge on KG inductive link prediction. To
this end, we constructed two new datasets based on Wikidata with various sizes
of training and inference graphs that are much larger than existing inductive
benchmarks. We also provide two strong baselines leveraging recently proposed
inductive methods. We hope this challenge helps to streamline community efforts
in the inductive graph representation learning area. ILPC 2022 follows best
practices on evaluation fairness and reproducibility, and is available at
https://github.com/pykeen/ilpc2022.


title:
Tangential Navier-Stokes equations on evolving surfaces: Analysis and
  simulations

abstract:
  The paper considers a system of equations that models a lateral flow of a
Boussinesq--Scriven fluid on a passively evolving surface embedded in
$\mathbb{R}^3$. For the resulting Navier-Stokes type system, posed on a smooth
closed time-dependent surface, we introduce a weak formulation in terms of
functional spaces on a space-time manifold defined by the surface evolution.
The weak formulation is shown to be well-posed for any finite final time and
without smallness conditions on data. We further extend an unfitted finite
element method, known as TraceFEM, to compute solutions to the fluid system.
Convergence of the method is demonstrated numerically. In another series of
experiments we visualize lateral flows induced by smooth deformations of a
material surface.


title:
BatchFormer: Learning to Explore Sample Relationships for Robust
  Representation Learning

abstract:
  Despite the success of deep neural networks, there are still many challenges
in deep representation learning due to the data scarcity issues such as data
imbalance, unseen distribution, and domain shift. To address the
above-mentioned issues, a variety of methods have been devised to explore the
sample relationships in a vanilla way (i.e., from the perspectives of either
the input or the loss function), failing to explore the internal structure of
deep neural networks for learning with sample relationships. Inspired by this,
we propose to enable deep neural networks themselves with the ability to learn
the sample relationships from each mini-batch. Specifically, we introduce a
batch transformer module or BatchFormer, which is then applied into the batch
dimension of each mini-batch to implicitly explore sample relationships during
training. By doing this, the proposed method enables the collaboration of
different samples, e.g., the head-class samples can also contribute to the
learning of the tail classes for long-tailed recognition. Furthermore, to
mitigate the gap between training and testing, we share the classifier between
with or without the BatchFormer during training, which can thus be removed
during testing. We perform extensive experiments on over ten datasets and the
proposed method achieves significant improvements on different data scarcity
applications without any bells and whistles, including the tasks of long-tailed
recognition, compositional zero-shot learning, domain generalization, and
contrastive learning. Code will be made publicly available at
https://github.com/zhihou7/BatchFormer.


title:
Generation of control signals using second-Nyquist zone technique for
  superconducting qubit devices

abstract:
  There is growing interest in developing integrated room temperature control
electronics for the control and measurement of superconducting devices for
quantum computing applications. With the availability of faster DACs, it has
become possible to generate microwave signals with amplitude and phase controls
directly without requiring any analog mixer. In this report, we use the
evaluation kit ZCU111 to generate vector microwave pulses using the
second-Nyquist zone technique. We characterize the performance of the signal
generation and measure amplitude variation across second Nyquist zone,
single-sideband phase noise, and spurious-free dynamic range. We further
perform various time-domain measurements to characterize a superconducting
transmon qubit and benchmark our results against traditionally used analog
mixer setups.


title: Semi-supervised Learning using Robust Loss

abstract:
  The amount of manually labeled data is limited in medical applications, so
semi-supervised learning and automatic labeling strategies can be an asset for
training deep neural networks. However, the quality of the automatically
generated labels can be uneven and inferior to manual labels. In this paper, we
suggest a semi-supervised training strategy for leveraging both manually
labeled data and extra unlabeled data. In contrast to the existing approaches,
we apply robust loss for the automated labeled data to automatically compensate
for the uneven data quality using a teacher-student framework. First, we
generate pseudo-labels for unlabeled data using a teacher model pre-trained on
labeled data. These pseudo-labels are noisy, and using them along with labeled
data for training a deep neural network can severely degrade learned feature
representations and the generalization of the network. Here we mitigate the
effect of these pseudo-labels by using robust loss functions. Specifically, we
use three robust loss functions, namely beta cross-entropy, symmetric
cross-entropy, and generalized cross-entropy. We show that our proposed
strategy improves the model performance by compensating for the uneven quality
of labels in image classification as well as segmentation applications.


title:
Gaia Reveals Dark Matter Correlations Across the Fornax Dwarf Spheroidal
  Galaxy: An Assessment of Dark Matter Models

abstract:
  The predicted size of dark matter substructures in kilo-parsec scales is
model-dependent. Therefore, if the correlations between dark matter mass
densities, as a function of the distances between them, are measured via
observations, we can scrutinize dark matter scenarios. In this paper, we use
Gaia's data to infer the single-body phase-space density of the stars in the
Fornax dwarf spheroidal galaxy. The latter together with the Jeans equation,
after eliminating the gravitational potential using the Poisson equation,
reveals the mass density of dark matter as a function of its position in the
galaxy. We derive the correlations between dark matter mass densities as a
function of distances between them. For distances beyond 100 parsec, no
statistically significant correlation is observed. We use the results to limit
the parameter space of the Ginzburg-Landau statistical field theory of dark
matter mass densities, and subsequently shrink the parameter space of (i) a
classic gas dark matter and (ii) a superfluid dark matter. Our results can be
used to leave bounds on high-resolution N-body simulations by comparing the
correlations computed using their outputs and the measured ones via
observations.

categories: astro-ph.GA astro-ph.CO cond-mat.other hep-ph


title:
How Do Organizations Seek Cyber Assurance? Investigations on the
  Adoption of the Common Criteria and Beyond

abstract:
  Cyber assurance, which is the ability to operate under the onslaught of cyber
attacks and other unexpected events, is essential for organizations facing
inundating security threats on a daily basis. Organizations usually employ
multiple strategies to conduct risk management to achieve cyber assurance.
Utilizing cybersecurity standards and certifications can provide guidance for
vendors to design and manufacture secure Information and Communication
Technology (ICT) products as well as provide a level of assurance of the
security functionality of the products for consumers. Hence, employing security
standards and certifications is an effective strategy for risk management and
cyber assurance. In this work, we begin with investigating the adoption of
cybersecurity standards and certifications by surveying 258 participants from
organizations across various countries and sectors. Specifically, we identify
adoption barriers of the Common Criteria through the designed questionnaire.
Taking into account the seven identified adoption barriers, we show the
recommendations for promoting cybersecurity standards and certifications.
Moreover, beyond cybersecurity standards and certifications, we shed light on
other risk management strategies devised by our participants, which provides
directions on cybersecurity approaches for enhancing cyber assurance in
organizations.


title:
Cographic Splitting Of Graphic Matroids With Respect To A Set With Three
  Elements

abstract:
  In general, the splitting operation on binary matroids does not preserve the
graphicness and cographicness properties of binary matroids. In this paper, we
obtain a characterization of the class of graphic matroids whose splitting with
respect to a set of three elements gives cographic matroids. We also provide an
alternate proof to such characterization when the set contains two elements
which was provided by Borse et al.


title: Towards Smart Networking with SDN Enabled IPv6 Network

abstract:
  This paper presents the features and benefits of legacy IPv4 network
migration towards major two latest networking paradigms viz. Internet protocol
version 6 (IPv6) and the software-defined networking (SDN). These latest
networking paradigms are the enabler of future generation networking so that
the standards and requirements of fifth generation (5G) wireless networking can
be achieved. Features and migration approaches of IPv6 and SDN will be
separately discussed, then a joint migration approach of SDN and IPv6 network
termed as SoDIP6 network migration will be presented, and the integration of
SoDIP6 network as a backbone of 5G network will be introduced.


title:
Calculation of Sub-bands {1,2,5,6} for 64-Point Complex FFT and Its
  extension to N (=2^N) Point FFT

abstract:
  FFT algorithm is one of the most applied algorithmsin digital signal
processing. Digital signal processing hasgradually become important in
biomedical application. Herehardware implementation of FFTs have found useful
appli-cations for bio-wearable devices. However, for these devices, low-power
and low-area are of utmost importance.In this report, we investigate a
sub-structure of decimation-in-frequency (DIF) FFT where a number of sub-bands
areof interest to us. Specifically, we divide the range of frequencies into 8
sub-bands (0-7) and calculate 4 of them( 1,2,5,6). We show that using concepts
likepushingandradix22, the number of complex multiplications can be
dras-tically reduced for 16-point, 32-point and 64-point FFTswhile computing
those specific bands. Later, we also extendit toN= 2n-point FFT based on
optimized 64-point FFTstructure. The number of complex multiplications is
furtherreduced usingmerge-FFT. Our results show that the numberof
multiplications (and hence power) can be reduced greatlyusing our optimized
structure compared to an unoptimizedstructure. This can find application in
biomedical signal processing specifically while computingp ower spectral
density of a physiological time series where reducing computational power is of
utmost importance


title:
On signed graphs whose spectral radius does not exceed
  $\sqrt{2+\sqrt{5}}$

abstract:
  The Hoffman program with respect to any real or complex square matrix $M$
associated to a graph $G$ stems from Hoffman's pioneering work on the limit
points for the spectral radius of adjacency matrices of graphs does not exceed
$\sqrt{2+\sqrt{5}}$. A signed graph $\dot{G}=(G,\sigma)$ is a pair
$(G,\sigma),$ where $G=(V,E)$ is a simple graph and $\sigma: E(G)\rightarrow
\{+1,-1\}$ is the sign function. In this paper, we study the Hoffman program of
signed graphs. Here, all signed graphs whose spectral radius does not exceed
$\sqrt{2+\sqrt{5}}$ will be identified.


title: CAFE: Learning to Condense Dataset by Aligning Features

abstract:
  Dataset condensation aims at reducing the network training effort through
condensing a cumbersome training set into a compact synthetic one.
State-of-the-art approaches largely rely on learning the synthetic data by
matching the gradients between the real and synthetic data batches. Despite the
intuitive motivation and promising results, such gradient-based methods, by
nature, easily overfit to a biased set of samples that produce dominant
gradients, and thus lack global supervision of data distribution. In this
paper, we propose a novel scheme to Condense dataset by Aligning FEatures
(CAFE), which explicitly attempts to preserve the real-feature distribution as
well as the discriminant power of the resulting synthetic set, lending itself
to strong generalization capability to various architectures. At the heart of
our approach is an effective strategy to align features from the real and
synthetic data across various scales, while accounting for the classification
of real samples. Our scheme is further backed up by a novel dynamic bi-level
optimization, which adaptively adjusts parameter updates to prevent
over-/under-fitting. We validate the proposed CAFE across various datasets, and
demonstrate that it generally outperforms the state of the art: on the SVHN
dataset, for example, the performance gain is up to 11%. Extensive experiments
and analyses verify the effectiveness and necessity of proposed designs.


title:
Exploring Patch-wise Semantic Relation for Contrastive Learning in
  Image-to-Image Translation Tasks

abstract:
  Recently, contrastive learning-based image translation methods have been
proposed, which contrasts different spatial locations to enhance the spatial
correspondence. However, the methods often ignore the diverse semantic relation
within the images. To address this, here we propose a novel semantic relation
consistency (SRC) regularization along with the decoupled contrastive learning,
which utilize the diverse semantics by focusing on the heterogeneous semantics
between the image patches of a single image. To further improve the
performance, we present a hard negative mining by exploiting the semantic
relation. We verified our method for three tasks: single-modal and multi-modal
image translations, and GAN compression task for image translation.
Experimental results confirmed the state-of-art performance of our method in
all the three tasks.


title: Introduction to the combinatorial atlas

abstract:
  We give elementary self-contained proofs of the strong Mason conjecture
recently proved by Anari at. al. (arXiv:1811.01600) and Br\"and\'en--Huh
(arXiv:1902.03719), and of the classical Alexandrov--Fenchel inequality. Both
proofs use the combinatorial atlas technology recently introduced by the
authors (arXiv:2110.10740). We also give a formal relationship between
combinatorial atlases and Lorentzian polynomials.


title:
Improved convergence of the Arrow-Hurwicz iteration for the
  Navier-Stokes equation via grad-div stabilization and Anderson acceleration

abstract:
  We consider two modifications of the Arrow-Hurwicz (AH) iteration for solving
the incompressible steady Navier-Stokes equations for the purpose of
accelerating the algorithm: grad-div stabilization, and Anderson acceleration.
AH is a classical iteration for general saddle point linear systems and it was
later extended to Navier-Stokes iterations in the 1970's which has recently
come under study again. We apply recently developed ideas for grad-div
stabilization and divergence-free finite element methods along with Anderson
acceleration of fixed point iterations to AH in order to improve its
convergence. Analytical and numerical results show that each of these methods
improves AH convergence, but the combination of them yields an efficient and
effective method that is competitive with more commonly used solvers.


title: Kernel Density Estimation by Genetic Algorithm

abstract:
  This study proposes a data condensation method for multivariate kernel
density estimation by genetic algorithm. First, our proposed algorithm
generates multiple subsamples of a given size with replacement from the
original sample. The subsamples and their constituting data points are regarded
as $\it{chromosome}$ and $\it{gene}$, respectively, in the terminology of
genetic algorithm. Second, each pair of subsamples breeds two new subsamples,
where each data point faces either $\it{crossover}$, $\it{mutation}$, or
$\it{reproduction}$ with a certain probability. The dominant subsamples in
terms of fitness values are inherited by the next generation. This process is
repeated generation by generation and brings the sparse representation of
kernel density estimator in its completion. We confirmed from simulation
studies that the resulting estimator can perform better than other well-known
density estimators.


title:
Recent Advances in Vision Transformer: A Survey and Outlook of Recent
  Work

abstract:
  Vision Transformers (ViTs) are becoming more popular and dominating technique
for various vision tasks, compare to Convolutional Neural Networks (CNNs). As a
demanding technique in computer vision, ViTs have been successfully solved
various vision problems while focusing on long-range relationships. In this
paper, we begin by introducing the fundamental concepts and background of the
self-attention mechanism. Next, we provide a comprehensive overview of recent
top-performing ViT methods describing in terms of strength and weakness,
computational cost as well as training and testing dataset. We thoroughly
compare the performance of various ViT algorithms and most representative CNN
methods on popular benchmark datasets. Finally, we explore some limitations
with insightful observations and provide further research direction. The
project page along with the collections of papers are available at
https://github.com/khawar512/ViT-Survey


title: Private High-Dimensional Hypothesis Testing

abstract:
  We provide improved differentially private algorithms for identity testing of
high-dimensional distributions. Specifically, for $d$-dimensional Gaussian
distributions with known covariance $\Sigma$, we can test whether the
distribution comes from $\mathcal{N}(\mu^*, \Sigma)$ for some fixed $\mu^*$ or
from some $\mathcal{N}(\mu, \Sigma)$ with total variation distance at least
$\alpha$ from $\mathcal{N}(\mu^*, \Sigma)$ with $(\varepsilon, 0)$-differential
privacy, using only \[\tilde{O}\left(\frac{d^{1/2}}{\alpha^2} +
\frac{d^{1/3}}{\alpha^{4/3} \cdot \varepsilon^{2/3}} + \frac{1}{\alpha \cdot
\varepsilon}\right)\] samples if the algorithm is allowed to be computationally
inefficient, and only \[\tilde{O}\left(\frac{d^{1/2}}{\alpha^2} +
\frac{d^{1/4}}{\alpha \cdot \varepsilon}\right)\] samples for a computationally
efficient algorithm. We also provide a matching lower bound showing that our
computationally inefficient algorithm has optimal sample complexity. We also
extend our algorithms to various related problems, including mean testing of
Gaussians with bounded but unknown covariance, uniformity testing of product
distributions over $\{\pm 1\}^d$, and tolerant testing.
  Our results improve over the previous best work of Canonne, Kamath, McMillan,
Ullman, and Zakynthinou \cite{CanonneKMUZ20} for both computationally efficient
and inefficient algorithms, and even our computationally efficient algorithm
matches the optimal \emph{non-private} sample complexity of
$O\left(\frac{\sqrt{d}}{\alpha^2}\right)$ in many standard parameter settings.
In addition, our results show that, surprisingly, private identity testing of
$d$-dimensional Gaussians can be done with fewer samples than private identity
testing of discrete distributions over a domain of size $d$ \cite{AcharyaSZ18},
which refutes a conjectured lower bound of Canonne et al. \cite{CanonneKMUZ20}.

categories: cs.DS cs.CR cs.IT cs.LG math.IT math.ST stat.TH


title: Self-supervised Transparent Liquid Segmentation for Robotic Pouring

abstract:
  Liquid state estimation is important for robotics tasks such as pouring;
however, estimating the state of transparent liquids is a challenging problem.
We propose a novel segmentation pipeline that can segment transparent liquids
such as water from a static, RGB image without requiring any manual annotations
or heating of the liquid for training. Instead, we use a generative model that
is capable of translating images of colored liquids into synthetically
generated transparent liquid images, trained only on an unpaired dataset of
colored and transparent liquid images. Segmentation labels of colored liquids
are obtained automatically using background subtraction. Our experiments show
that we are able to accurately predict a segmentation mask for transparent
liquids without requiring any manual annotations. We demonstrate the utility of
transparent liquid segmentation in a robotic pouring task that controls pouring
by perceiving the liquid height in a transparent cup. Accompanying video and
supplementary materials can be found


title:
Curriculum-style Local-to-global Adaptation for Cross-domain Remote
  Sensing Image Segmentation

abstract:
  Although domain adaptation has been extensively studied in natural
image-based segmentation task, the research on cross-domain segmentation for
very high resolution (VHR) remote sensing images (RSIs) still remains
underexplored. The VHR RSIs-based cross-domain segmentation mainly faces two
critical challenges: 1) Large area land covers with many diverse object
categories bring severe local patch-level data distribution deviations, thus
yielding different adaptation difficulties for different local patches; 2)
Different VHR sensor types or dynamically changing modes cause the VHR images
to go through intensive data distribution differences even for the same
geographical location, resulting in different global feature-level domain gap.
To address these challenges, we propose a curriculum-style local-to-global
cross-domain adaptation framework for the segmentation of VHR RSIs. The
proposed curriculum-style adaptation performs the adaptation process in an
easy-to-hard way according to the adaptation difficulties that can be obtained
using an entropy-based score for each patch of the target domain, and thus well
aligns the local patches in a domain image. The proposed local-to-global
adaptation performs the feature alignment process from the locally semantic to
globally structural feature discrepancies, and consists of a semantic-level
domain classifier and an entropy-level domain classifier that can reduce the
above cross-domain feature discrepancies. Extensive experiments have been
conducted in various cross-domain scenarios, including geographic location
variations and imaging mode variations, and the experimental results
demonstrate that the proposed method can significantly boost the domain
adaptability of segmentation networks for VHR RSIs. Our code is available at:
https://github.com/BOBrown/CCDA_LGFA.


title: Most transient random walks have infinitely many cut times

abstract:
  We prove that if $(X_n)_{n\geq 0}$ is a random walk on a transient graph such
that the Green's function decays at least polynomially along the random walk,
then $(X_n)_{n\geq 0}$ has infinitely many cut times almost surely. This
condition applies in particular to any graph of spectral dimension strictly
larger than $2$. In fact, our proof applies to general (possibly nonreversible)
Markov chains satisfying a similar decay condition for the Green's function
that is sharp for birth-death chains. We deduce that a conjecture of Diaconis
and Freedman (Ann. Probab. 1980) holds for the same class of Markov chains, and
resolve a conjecture of Benjamini, Gurel-Gurevich, and Schramm (Ann. Probab.
2011) on the existence of infinitely many cut times for random walks of
positive speed.


title: Quantum simulation of Ising spins on Platonic graphs

abstract:
  We present quantum simulation experiments of Ising-like spins on Platonic
graphs, which are performed with two-dimensional arrays of Rydberg atoms and
quantum-wire couplings. The quantum wires are used to couple otherwise
uncoupled long-distance atoms, enabling topology-preserving transformtions of
the three-dimensional graphs to the two-dimensional plane. We implement three
Platonic graphs, tetrahedron, cube, and octahedron of Platonic solids, and
successfully probe their ground many-body spin configurations before and after
the quasi-adiabatic control of the system Hamiltonians from the paramagnetic
phase to anti-ferromagnetic-like phases. Our small-scale quantum simulations of
using less than 22 atoms are limited by experimental imperfections, which can
be easily improved by the state-of-the-art Rydberg-atom technologies for more
than 1000-atom scales. Our quantum-wire approach is expected to pave a new
route towards large-scale quantum simulations.


title: SegTAD: Precise Temporal Action Detection via Semantic Segmentation

abstract:
  Temporal action detection (TAD) is an important yet challenging task in video
analysis. Most existing works draw inspiration from image object detection and
tend to reformulate it as a proposal generation - classification problem.
However, there are two caveats with this paradigm. First, proposals are not
equipped with annotated labels, which have to be empirically compiled, thus the
information in the annotations is not necessarily precisely employed in the
model training process. Second, there are large variations in the temporal
scale of actions, and neglecting this fact may lead to deficient representation
in the video features. To address these issues and precisely model temporal
action detection, we formulate the task of temporal action detection in a novel
perspective of semantic segmentation. Owing to the 1-dimensional property of
TAD, we are able to convert the coarse-grained detection annotations to
fine-grained semantic segmentation annotations for free. We take advantage of
them to provide precise supervision so as to mitigate the impact induced by the
imprecise proposal labels. We propose an end-to-end framework SegTAD composed
of a 1D semantic segmentation network (1D-SSN) and a proposal detection network
(PDN).


title:
QaNER: Prompting Question Answering Models for Few-shot Named Entity
  Recognition

abstract:
  Recently, prompt-based learning for pre-trained language models has succeeded
in few-shot Named Entity Recognition (NER) by exploiting prompts as task
guidance to increase label efficiency. However, previous prompt-based methods
for few-shot NER have limitations such as a higher computational complexity,
poor zero-shot ability, requiring manual prompt engineering, or lack of prompt
robustness. In this work, we address these shortcomings by proposing a new
prompt-based learning NER method with Question Answering (QA), called QaNER.
Our approach includes 1) a refined strategy for converting NER problems into
the QA formulation; 2) NER prompt generation for QA models; 3) prompt-based
tuning with QA models on a few annotated NER examples; 4) zero-shot NER by
prompting the QA model. Comparing the proposed approach with previous methods,
QaNER is faster at inference, insensitive to the prompt quality, and robust to
hyper-parameters, as well as demonstrating significantly better low-resource
performance and zero-shot capability.


title:
Rethinking the role of normalization and residual blocks for spiking
  neural networks

abstract:
  Biologically inspired spiking neural networks (SNNs) are widely used to
realize ultralow-power energy consumption. However, deep SNNs are not easy to
train due to the excessive firing of spiking neurons in the hidden layers. To
tackle this problem, we propose a novel but simple normalization technique
called postsynaptic potential normalization. This normalization removes the
subtraction term from the standard normalization and uses the second raw moment
instead of the variance as the division term. The spike firing can be
controlled, enabling the training to proceed appropriating, by conducting this
simple normalization to the postsynaptic potential. The experimental results
show that SNNs with our normalization outperformed other models using other
normalizations. Furthermore, through the pre-activation residual blocks, the
proposed model can train with more than 100 layers without other special
techniques dedicated to SNNs.


title: Rydberg wire gates for universal quantum computation

abstract:
  Rydberg atom arrays offer flexible geometries of strongly-interacting neutral
atoms, which are useful for many quantum applications such as quantum
simulation and quantum computation. Here we consider a gate-based quantum
computing scheme for a Rydberg-atom array. We utilize auxiliary atoms which are
used as a quantum wire to mediate controllable interactions among data-qubit
atoms. We construct universal quantum gates for the data atoms, by using
single-atom addressing operations. Standard one-, two-, and multi-qubit
solutions are explicitly obtained as respective sequences of pulsed operations
acting on individual data and wire atoms. A detailed resource estimate is
provided for an experimental implementation of this scheme in a Rydberg quantum
simulator.


title: Interpretation of the puzzling gamma-ray spectrum of the Geminga halo

abstract:
  The gamma-ray halo around Geminga is formed owing to the slow diffusion of
the electrons released by the Geminga PWN. The latest HAWC and HESS
observations exhibit complex features in the TeV gamma-ray spectrum of the
Geminga halo. We first show that the new results cannot be interpreted by the
commonly used simple model, where a single power-law injection spectrum and an
energy index of $\delta=1/3$ for the diffusion coefficient are assumed. We then
propose a two-population electron injection model based on the x-ray
observations of the Geminga PWN, which consists of a population of freshly
accelerated electrons escaping from the PWN through rapid outflows and a
population trapped longer inside the PWN before escaping. The two-population
model interprets the HAWC and HESS data well, and the goodness of fit improves
significantly compared with the single power-law injection model. It also
predicts a different energy dependency of the gamma-ray profile from the single
power-law model, which could be tested by LHAASO in the coming future. We note
that a $\delta$ slightly larger than 1 is needed to fit the HAWC and HESS data
consistently. We also discuss the possible improvements by adopting the
two-zone diffusion model.


title:
The RATTLE Motion Planning Algorithm for Robust Online Parametric Model
  Improvement with On-Orbit Validation

abstract:
  Certain forms of uncertainty that robotic systems encounter can be explicitly
learned within the context of a known model, like parametric model
uncertainties such as mass and moments of inertia. Quantifying such parametric
uncertainty is important for more accurate prediction of the system behavior,
leading to safe and precise task execution. In tandem, providing a form of
robustness guarantee against prevailing uncertainty levels like environmental
disturbances and current model knowledge is also desirable. To that end, the
authors' previously proposed RATTLE algorithm, a framework for online
information-aware motion planning, is outlined and extended to enhance its
applicability to real robotic systems. RATTLE provides a clear tradeoff between
information-seeking motion and traditional goal-achieving motion and features
online-updateable models. Additionally, online-updateable low level control
robustness guarantees and a new method for automatic adjustment of information
content down to a specified estimation precision is proposed. Results of
extensive experimentation in microgravity using the Astrobee robots aboard the
International Space Station and practical implementation details are presented,
demonstrating RATTLE's capabilities for real-time, robust, online-updateable,
and model information-seeking motion planning capabilities under parametric
uncertainty.


title:
Momentum-Aware Trajectory Optimization and Control for Agile Quadrupedal
  Locomotion

abstract:
  In this paper, we present a versatile hierarchical offline planning
algorithm, along with and an online control pipeline for agile quadrupedal
locomotion. Our offline planner alternates between optimizing centroidal
dynamics for a reduced-order model and whole-body trajectory optimization, with
the aim of achieving dynamics consensus. Our novel momentum-inertia-aware
centroidal optimization, which uses an equimomental ellipsoid parameterization,
is able to generate highly acrobatic motions via "inertia shaping". Our
whole-body optimization approach significantly improves upon the quality of
standard DDP-based approaches by iteratively exploiting feedback from the
centroidal level. For online control, we have developed a novel linearization
of the full centroidal dynamics, and incorporated these into a convex model
predictive control scheme. Our controller can efficiently optimize for both
contact forces and joint accelerations in single optimization, enabling more
straightforward tracking for momentum-rich motions compared to existing
quadrupedal MPC controllers. We demonstrate the capability and generality of
our trajectory planner on four different dynamic maneuvers. We then present
hardware experiments on the MIT Mini Cheetah platform to demonstrate
performance of the entire planning and control pipeline on a twisting jump
maneuver.


title: Automated clustering of COVID-19 anti-vaccine discourse on Twitter

abstract:
  Attitudes about vaccination have become more polarized; it is common to see
vaccine disinformation and fringe conspiracy theories online. An observational
study of Twitter vaccine discourse is found in Ojea Quintana et al. (2021): the
authors analyzed approximately six months' of Twitter discourse -- 1.3 million
original tweets and 18 million retweets between December 2019 and June 2020,
ranging from before to after the establishment of Covid-19 as a pandemic. This
work expands upon Ojea Quintana et al. (2021) with two main contributions from
data science. First, based on the authors' initial network clustering and
qualitative analysis techniques, we are able to clearly demarcate and visualize
the language patterns used in discourse by Antivaxxers (anti-vaccination
campaigners and vaccine deniers) versus other clusters (collectively, Others).
Second, using the characteristics of Antivaxxers' tweets, we develop text
classifiers to determine the likelihood a given user is employing
anti-vaccination language, ultimately contributing to an early-warning
mechanism to improve the health of our epistemic environment and bolster (and
not hinder) public health initiatives.


title: A Characterization of Multiclass Learnability

abstract:
  A seminal result in learning theory characterizes the PAC learnability of
binary classes through the Vapnik-Chervonenkis dimension. Extending this
characterization to the general multiclass setting has been open since the
pioneering works on multiclass PAC learning in the late 1980s. This work
resolves this problem: we characterize multiclass PAC learnability through the
DS dimension, a combinatorial dimension defined by Daniely and Shalev-Shwartz
(2014).
  The classical characterization of the binary case boils down to empirical
risk minimization. In contrast, our characterization of the multiclass case
involves a variety of algorithmic ideas; these include a natural setting we
call list PAC learning. In the list learning setting, instead of predicting a
single outcome for a given unseen input, the goal is to provide a short menu of
predictions.
  Our second main result concerns the Natarajan dimension, which has been a
central candidate for characterizing multiclass learnability. This dimension
was introduced by Natarajan (1988) as a barrier for PAC learning. Whether the
Natarajan dimension characterizes PAC learnability in general has been posed as
an open question in several papers since. This work provides a negative answer:
we construct a non-learnable class with Natarajan dimension one.
  For the construction, we identify a fundamental connection between concept
classes and topology (i.e., colorful simplicial complexes). We crucially rely
on a deep and involved construction of hyperbolic pseudo-manifolds by
Januszkiewicz and Swiatkowski. It is interesting that hyperbolicity is directly
related to learning problems that are difficult to solve although no obvious
barriers exist. This is another demonstration of the fruitful links machine
learning has with different areas in mathematics.


title:
Segregated solutions for nonlinear Schr\"odinger systems with weak
  interspecies forces

abstract:
  We find positive non-radial solutions for a system of Schr\"odinger equations
in a weak fully attractive or repulsive regime in presence of an external
radial trapping potential that exhibits a maximum or a minimum at infinity.


title:
Dialogue Summaries as Dialogue States (DS2), Template-Guided
  Summarization for Few-shot Dialogue State Tracking

abstract:
  Annotating task-oriented dialogues is notorious for the expensive and
difficult data collection process. Few-shot dialogue state tracking (DST) is a
realistic solution to this problem. In this paper, we hypothesize that dialogue
summaries are essentially unstructured dialogue states; hence, we propose to
reformulate dialogue state tracking as a dialogue summarization problem. To
elaborate, we train a text-to-text language model with synthetic template-based
dialogue summaries, generated by a set of rules from the dialogue states. Then,
the dialogue states can be recovered by inversely applying the summary
generation rules. We empirically show that our method DS2 outperforms previous
works on few-shot DST in MultiWoZ 2.0 and 2.1, in both cross-domain and
multi-domain settings. Our method also exhibits vast speedup during both
training and inference as it can generate all states at once. Finally, based on
our analysis, we discover that the naturalness of the summary templates plays a
key role for successful training.


title:
Addressing the Shape-Radiance Ambiguity in View-Dependent Radiance
  Fields

abstract:
  We present a method for handling view-dependent information in radiance
fields to help with convergence and quality of 3D reconstruction. Radiance
fields with view-dependence suffers from the so called shape-radiance
ambiguity, which can lead to incorrect geometry given a high angular resolution
of view-dependent colors. We propose the addition of a difference plane in
front of each camera, with the purpose of separating view-dependent and
Lambertian components during training. We also propose an additional step where
we train, but do not store, a low-resolution view-dependent function that helps
to isolate the surface if such a separation is proven difficult. These
additions have a small impact on performance and memory usage but enables
reconstruction of scenes with highly specular components without any other
explicit handling of view-dependence such as Spherical Harmonics.


title:
The exoplanetary magnetosphere extension in Sun-like stars based on the
  solar wind - solar UV relation

abstract:
  Earth's magnetosphere extension is controlled by solar activity level via
solar wind properties. Understanding such a relation in the Solar System is
useful to predict the condition of exoplanetary magnetosphere near Sun-like
stars. We use measurements of a chromospheric proxy, the Ca II K index, and
solar wind OMNI parameters to connect the solar activity variations on the
decennial time scales to solar wind properties. The dataset span over the time
interval 1965-2021, which almost entirely covers the last 5 solar cycles. Using
both cross-correlation and mutual information analysis, a 3.2-year lag of the
solar wind speed with respect to the Ca II K index is found. Analogously, a
3.6-year lag is found with respect to the dynamic pressure. A correlation
between the solar wind dynamic pressure and the solar UV emission is therefore
found and used to derive the Earth's magnetopause standoff distance. Moreover,
the advantage of using a chromospheric proxy, such as the Ca II K index, opens
the possibility to extend the relation found for the Sun to Sun-like stars, by
linking stellar variability to stellar wind properties. The model is applied to
a sample of Sun-like stars as a case study, where we assume the presence of an
Earth-like exoplanet at 1 AU. Finally, we compare our results with previous
estimates of the magnetosphere extension for the same set of sun-like stars.


title:
Measurement of the branching fraction of the doubly Cabibbo-suppressed
  decay $D^0\to K^+\pi^-\pi^0$ and search for $D^0\to K^+\pi^-\pi^0\pi^0$

abstract:
  Using $2.93\,\rm fb^{-1}$ of $e^+e^-$ collision data collected at a
center-of-mass energy of 3.773\,GeV with the BESIII detector, we present a
measurement of the branching fraction of the doubly Cabibbo-suppressed (DCS)
decay $D^0\to K^+\pi^-\pi^0$ and a search for the DCS decay $D^0\to
K^+\pi^-\pi^0\pi^0$. The branching fraction of $D^0\to K^+\pi^-\pi^0$ is
determined to be $[3.13^{+0.60}_{-0.56}({\rm stat}) \pm 0.09({\rm syst})]
\times 10^{-4}$. No signal is observed for $D^0\to K^+\pi^-\pi^0\pi^0$ and an
upper limit of $3.6 \times 10^{-4}$ is set on the branching fraction at the
90\% C.L. We combine these results with the world-average branching fractions
of their counterpart Cabibbo-favored decays to determine the ratios of the
doubly Cabibbo-suppressed over the Cabibbo-favored branching fractions,
${\mathcal B}(D^0\to K^+\pi^-\pi^0)/{\mathcal B}(D^0\to K^-\pi^+\pi^0)=(0.22\pm
0.04)\%$~and ${\mathcal B}(D^0\to K^+\pi^-\pi^0\pi^0)/{\mathcal B}(D^0\to
K^-\pi^+\pi^0\pi^0)<0.40\%$ at the 90\% C.L., which correspond to $(0.75\pm
0.14)\tan^{4} \theta_C$~and $1.37\times \tan^{4} \theta_C$, respectively, where
$\theta_C$ is the Cabibbo angle.


title:
DareFightingICE Competition: A Fighting Game Sound Design and AI
  Competition

abstract:
  This paper presents a new competition -- at the 2022 IEEE Conference on Games
(CoG) -- called DareFightingICE Competition. The competition has two tracks: a
sound design track and an AI track. The game platform for this competition is
also called DareFightingICE, a fighting game platform. DareFightingICE is a
sound-design-enhanced version of FightingICE, used earlier in a competition at
CoG until 2021 to promote artificial intelligence (AI) research in fighting
games. In the sound design track, participants compete for the best sound
design, given the default sound design of DareFightingICE as a sample.
Participants of the AI track are asked to develop their AI algorithm that
controls a character given only sound as the input (blind AI) to fight against
their opponent; a sample deep-learning blind AI will be provided by us. Our
means to maximize the synergy between the two tracks are also described. This
competition serves to come up with effective sound designs for visually
impaired players, a group in the gaming community which has been mostly
ignored. To the best of our knowledge, DareFightingICE Competition is the first
of its kind within and outside of CoG.


title:
Self-Supervised Ego-Motion Estimation Based on Multi-Layer Fusion of RGB
  and Inferred Depth

abstract:
  In existing self-supervised depth and ego-motion estimation methods,
ego-motion estimation is usually limited to only leveraging RGB information.
Recently, several methods have been proposed to further improve the accuracy of
self-supervised ego-motion estimation by fusing information from other
modalities, e.g., depth, acceleration, and angular velocity. However, they
rarely focus on how different fusion strategies affect performance. In this
paper, we investigate the effect of different fusion strategies for ego-motion
estimation and propose a new framework for self-supervised learning of depth
and ego-motion estimation, which performs ego-motion estimation by leveraging
RGB and inferred depth information in a Multi-Layer Fusion manner. As a result,
we have achieved state-of-the-art performance among learning-based methods on
the KITTI odometry benchmark. Detailed studies on the design choices of
leveraging inferred depth information and fusion strategies have also been
carried out, which clearly demonstrate the advantages of our proposed
framework.


title:
Symmetry-protected Dirac nodal lines and large spin Hall effect in
  $\mathbf{V_6Sb_4}$ with kagome bilayer

abstract:
  Recently, a family of nonmagnetic kagome metals \textit{A}$\mathrm{V_3Sb_5}$
(\textit{A}=K, Rb, and Cs) has attracted significant attention for realizing
the intertwining of quantum order and nontrivial topology. However, these
compounds have been identified to host complex band structures. Therefore, it
is desirable to design and synthesize novel kagome materials with a simple band
topology and good transport properties. In this study, using first-principles
calculations, we present the electronic properties and the intrinsic spin Hall
effect of V$_6$Sb$_4$, the latest experimentally synthesized vanadium-based
compounds with a kagome bilayer. In the absence of spin-orbital coupling (SOC),
this compound is a Dirac nodal line semimetal with symmetry-protected nodal
rings near the Fermi level. Within the SOC, the spin-rotation symmetry breaks
the gaps of the nodal rings with a small band gap. Furthermore, based on the
Wannier tight-binding approach and the Kubo formula, we propose a large spin
Hall effect in V$_6$Sb$_4$, which intrinsically originates from the spin Berry
curvature. Our work further expands nonmagnetic kagome compounds for
applications in spintronics accompanied by exotic quantum order.


title: Neural Architecture Search using Progressive Evolution

abstract:
  Vanilla neural architecture search using evolutionary algorithms (EA)
involves evaluating each architecture by training it from scratch, which is
extremely time-consuming. This can be reduced by using a supernet to estimate
the fitness of every architecture in the search space due to its weight sharing
nature. However, the estimated fitness is very noisy due to the co-adaptation
of the operations in the supernet. In this work, we propose a method called
pEvoNAS wherein the whole neural architecture search space is progressively
reduced to smaller search space regions with good architectures. This is
achieved by using a trained supernet for architecture evaluation during the
architecture search using genetic algorithm to find search space regions with
good architectures. Upon reaching the final reduced search space, the supernet
is then used to search for the best architecture in that search space using
evolution. The search is also enhanced by using weight inheritance wherein the
supernet for the smaller search space inherits its weights from previous
trained supernet for the bigger search space. Exerimentally, pEvoNAS gives
better results on CIFAR-10 and CIFAR-100 while using significantly less
computational resources as compared to previous EA-based methods. The code for
our paper can be found in https://github.com/nightstorm0909/pEvoNAS


title:
A Backside-Illuminated Charge-Focusing Silicon SPAD with Enhanced
  Near-Infrared Sensitivity

abstract:
  A backside-illuminated (BSI) near-infrared enhanced silicon single-photon
avalanche diode (SPAD) for time-of-flight (ToF) light detection and ranging
applications is presented. The detector contains a 2 $\mu$m wide multiplication
region with a spherically-uniform electric field peak enforced by field-line
crowding. A charge-focusing electric field extends into a 10 $\mu$m deep
absorption volume, whereby electrons generated in all corners of the device can
move efficiently towards the multiplication region. The SPAD is integrated with
a customized 130 nm CMOS technology and a dedicated BSI process. The device has
a pitch of 15 $\mu$m, which has the potential to be scaled down without
significant performance loss. Furthermore, the detector achieves a photon
detection efficiency of 27% at 905 nm, with an excess bias of 3.5 V that is
controlled by integrated CMOS electronics, and a timing resolution of 240 ps.
By virtue of these features, the device architecture is well-suited for large
format ToF imaging arrays with integrated electronics.

categories: physics.ins-det physics.app-ph physics.optics quant-ph


title:
Electroweak Phase Transition and Gravitational Waves in the Type-II
  Seesaw Model

abstract:
  The type-II seesaw model is a possible candidate for simultaneously
explaining non-vanishing neutrino masses and the observed baryon asymmetry of
the Universe. In this work, we study in detail the pattern of phase transition
and the gravitational wave production of this model. We find a strong
first-order electroweak phase transition generically prefers positive Higgs
portal couplings and a light triplet below $\sim550$ GeV. In addition, we find
the gravitational wave yield generated during the phase transition would be at
the edge of BBO sensitivity and could be further examined by Ultimate-DECIGO.


title:
ViTransPAD: Video Transformer using convolution and self-attention for
  Face Presentation Attack Detection

abstract:
  Face Presentation Attack Detection (PAD) is an important measure to prevent
spoof attacks for face biometric systems. Many works based on Convolution
Neural Networks (CNNs) for face PAD formulate the problem as an image-level
binary classification task without considering the context. Alternatively,
Vision Transformers (ViT) using self-attention to attend the context of an
image become the mainstreams in face PAD. Inspired by ViT, we propose a
Video-based Transformer for face PAD (ViTransPAD) with short/long-range
spatio-temporal attention which can not only focus on local details with short
attention within a frame but also capture long-range dependencies over frames.
Instead of using coarse image patches with single-scale as in ViT, we propose
the Multi-scale Multi-Head Self-Attention (MsMHSA) architecture to accommodate
multi-scale patch partitions of Q, K, V feature maps to the heads of
transformer in a coarse-to-fine manner, which enables to learn a fine-grained
representation to perform pixel-level discrimination for face PAD. Due to lack
inductive biases of convolutions in pure transformers, we also introduce
convolutions to the proposed ViTransPAD to integrate the desirable properties
of CNNs by using convolution patch embedding and convolution projection. The
extensive experiments show the effectiveness of our proposed ViTransPAD with a
preferable accuracy-computation balance, which can serve as a new backbone for
face PAD.


title: Emergent space-time supersymmetry at disorder quantum critical point

abstract:
  We study the effect of disorder on the spacetime supersymmetry that is
proposed to emerge at the quantum critical point of pair density wave
transition in (2+1)D Dirac semimetals and (3+1)D Weyl semimetals. In the (2+1)D
Dirac semimetal, we consider three types of disorder, including random scalar
potential, random vector potential and random mass potential, while the random
mass disorder is absent in the (3+1)D Weyl semimetal. Via a systematic
renormalization group analysis, we find that any type of weak random disorder
is irrelevant due to the couplings between the disorder potential and the
Yukawa vertex. The emergent supersymmetry is thus stable for weak random
potentials. Our work will pave the way for exploration supersymmetry in
realistic condensed matter systems.


title: Graph Representation Learning Beyond Node and Homophily

abstract:
  Unsupervised graph representation learning aims to distill various graph
information into a downstream task-agnostic dense vector embedding. However,
existing graph representation learning approaches are designed mainly under the
node homophily assumption: connected nodes tend to have similar labels and
optimize performance on node-centric downstream tasks. Their design is
apparently against the task-agnostic principle and generally suffers poor
performance in tasks, e.g., edge classification, that demands feature signals
beyond the node-view and homophily assumption. To condense different feature
signals into the embeddings, this paper proposes PairE, a novel unsupervised
graph embedding method using two paired nodes as the basic unit of embedding to
retain the high-frequency signals between nodes to support node-related and
edge-related tasks. Accordingly, a multi-self-supervised autoencoder is
designed to fulfill two pretext tasks: one retains the high-frequency signal
better, and another enhances the representation of commonality. Our extensive
experiments on a diversity of benchmark datasets clearly show that PairE
outperforms the unsupervised state-of-the-art baselines, with up to 101.1\%
relative improvement on the edge classification tasks that rely on both the
high and low-frequency signals in the pair and up to 82.5\% relative
performance gain on the node classification tasks.


title: Third order fiducial predictions for Drell-Yan at the LHC

abstract:
  The Drell-Yan process at hadron colliders is a fundamental benchmark for the
study of strong interactions and the extraction of electro-weak parameters. The
outstanding precision of the LHC demands very accurate theoretical predictions
with a full account of fiducial experimental cuts. In this letter we present a
state-of-the-art calculation of the fiducial cross section and of differential
distributions for this process at third order in the strict fixed-order
expansion in the strong coupling, as well as including the all-order
resummation of logarithmic corrections. Together with these results, we present
a detailed study of the subtraction technique used to carry out the calculation
for different sets of experimental cuts, as well as of the sensitivity of the
fiducial cross section to infrared physics. We find that residual theory
uncertainties are reduced to the percent level and that the robustness of the
predictions can be improved by a suitable adjustment of fiducial cuts.


title:
Absence of confinement and non-Boltzmann stationary states of fractional
  Brownian motion in shallow external potentials

abstract:
  We study the diffusive motion of a particle in a subharmonic potential of the
form $U(x)=|x|^c$ ($0<c<2$) driven by long-range correlated, stationary
fractional Gaussian noise $\xi_{\alpha}(t)$ with $0<\alpha\le2$. In the absence
of the potential the particle exhibits free fractional Brownian motion with
anomalous diffusion exponent $\alpha$. While for an harmonic external potential
the dynamics converges to a Gaussian stationary state, from extensive numerical
analysis we here demonstrate that stationary states for shallower than harmonic
potentials exist only as long as the relation $c>2(1-1/\alpha)$ holds. We
analyse the motion in terms of the mean squared displacement and (when it
exists) the stationary probability density function (PDF). Moreover we discuss
analogies of non-stationarity of L{\'e}vy flights in shallow external
potentials.


title:
Ultralow loss hollow-core negative curvature fibers with nested
  elliptical antiresonance tubes

abstract:
  Hollow-core negative curvature fibers can confine light within air core and
have small nonlinearity and dispersion and high damage threshold, thereby
attracting a great deal of interest in the field of hollow core fibers.
However, reducing the loss of hollow-core negative curvature fibers is a
serious problem. On this basis, three new types of fibers with different nested
tube structures are proposed in the near-infrared spectral regions and compared
in detail with a previously proposed hollow-core negative curvature fiber. We
used finite-element method for numerical simulation studies of their
transmission loss, bending loss, and single-mode performance, and then the
transmission performance of various structural fibers is compared. We found
that the nested elliptical antiresonant fiber 1 has better transmission
performance than that of the three other types of fibers in the spectral range
of 0.72-1.6 {\mu}m. Results show that the transmission loss of the LP01 mode is
as low as 6.45*10-6 dB/km at {\lambda} = 1.06 {\mu}m. To the best of our
knowledge, the record low level of transmission loss of hollow-core
antiresonant fibers with nested tube structures was created. In addition, the
nested elliptical antiresonant fiber 1 has better bending resistance, and its
bending loss was below 2.99*10-2 dB/km at 5 cm bending radius.


title:
A modified weak Galerkin method for
  $\boldsymbol{H}(\mathrm{curl})$-elliptic problem

abstract:
  In this paper, we design and analysis a modified weak Galerkin (MWG) finite
element method for $\boldsymbol{H}(\mathrm{curl})-$elliptic problem. We first
introduce a new discrete weak curl operator and the MWG finite element space.
The modified weak Galerkin method does not require the penalty parameter by
comparing with traditional DG methods. We prove optimal error estimates in
energy norm. At last, we provide the numerical results to confirm these
theoretical results.


title:
First-principles calculations of the surface states of doped and alloyed
  topological materials via band unfolding method

abstract:
  One of the most remarkable characteristics of topological materials is that
they have special surface states, which are determined by the topological
properties of their bulk materials. The angle resolved photoemission
spectroscopy (ARPES) is a powerful tool to explore the surface states, which
allows to further investigate the topological phase transitions. However, it is
very difficult to compare the first-principle calculated band structures to the
ARPES results,when the systems are doped or alloyed, because the band
structures are heavily folded.We develop an efficient band unfolding method
based on numerical atomic orbitals (NAOs). We apply this method to study the
surface states of the non-magnetically and magnetically doped topological
insulators Bi$_2$Se$_3$ and the topological crystalline insulators
Pb$_{1-x}$Sn$_{x}$Te.


title:
Representing Mixtures of Word Embeddings with Mixtures of Topic
  Embeddings

abstract:
  A topic model is often formulated as a generative model that explains how
each word of a document is generated given a set of topics and
document-specific topic proportions. It is focused on capturing the word
co-occurrences in a document and hence often suffers from poor performance in
analyzing short documents. In addition, its parameter estimation often relies
on approximate posterior inference that is either not scalable or suffers from
large approximation error. This paper introduces a new topic-modeling framework
where each document is viewed as a set of word embedding vectors and each topic
is modeled as an embedding vector in the same embedding space. Embedding the
words and topics in the same vector space, we define a method to measure the
semantic difference between the embedding vectors of the words of a document
and these of the topics, and optimize the topic embeddings to minimize the
expected difference over all documents. Experiments on text analysis
demonstrate that the proposed method, which is amenable to mini-batch
stochastic gradient descent based optimization and hence scalable to big
corpora, provides competitive performance in discovering more coherent and
diverse topics and extracting better document representations.


title: Recovery time of a plasma-wakefield accelerator

abstract:
  The interaction of intense particle bunches with plasma can give rise to
plasma wakes capable of sustaining gigavolt-per-metre electric fields, which
are orders of magnitude higher than provided by state-of-the-art
radio-frequency technology. Plasma wakefields can, therefore, strongly
accelerate charged particles and offer the opportunity to reach higher particle
energies with smaller and hence more widely available accelerator facilities.
However, the luminosity and brilliance demands of high-energy physics and
photon science require particle bunches to be accelerated at repetition rates
of thousands or even millions per second, which are orders of magnitude higher
than demonstrated with plasma-wakefield technology. Here we investigate the
upper limit on repetition rates of beam-driven plasma accelerators by measuring
the time it takes for the plasma to recover to its initial state after
perturbation by a wakefield. The many-nanosecond-level recovery time measured
establishes the in-principle attainability of megahertz rates of acceleration
in plasmas. The experimental signatures of the perturbation are well described
by simulations of a temporally evolving parabolic ion channel, transferring
energy from the collapsing wake to the surrounding media. This result
establishes that plasma-wakefield modules could be developed as feasible
high-repetition-rate energy boosters at current and future particle-physics and
photon-science facilities.


title:
Data Augmentation as Feature Manipulation: a story of desert cows and
  grass cows

abstract:
  Data augmentation is a cornerstone of the machine learning pipeline, yet its
theoretical underpinnings remain unclear. Is it merely a way to artificially
augment the data set size? Or is it about encouraging the model to satisfy
certain invariance? In this work we consider another angle, and we study the
effect of data augmentation on the dynamic of the learning process. We find
that data augmentation can alter the relative importance of various features,
effectively making certain informative but hard to learn features more likely
to be captured in the learning process. Importantly, we show that this effect
is more pronounced for non-linear models, such as neural networks. Our main
contribution is a detailed analysis of data augmentation on the learning
dynamic for a two layer convolutional neural network in the recently proposed
multi-view model by Allen-Zhu and Li [2020]. We complement this analysis with
further experimental evidence that data augmentation can be viewed as a form of
feature manipulation.


title:
The Vicomtech Audio Deepfake Detection System based on Wav2Vec2 for the
  2022 ADD Challenge

abstract:
  This paper describes our submitted systems to the 2022 ADD challenge withing
the tracks 1 and 2. Our approach is based on the combination of a pre-trained
wav2vec2 feature extractor and a downstream classifier to detect spoofed audio.
This method exploits the contextualized speech representations at the different
transformer layers to fully capture discriminative information. Furthermore,
the classification model is adapted to the application scenario using different
data augmentation techniques. We evaluate our system for audio synthesis
detection in both the ASVspoof 2021 and the 2022 ADD challenges, showing its
robustness and good performance in realistic challenging environments such as
telephonic and audio codec systems, noisy audio, and partial deepfakes.


title: How can potential engineers seek and discover their genuine vocations?

abstract:
  The article describes what constitutes chemical engineering and its branches
are in plain language. It outlines what to expect when one enrolls in a
chemical engineering program as an undergraduate or graduate student. This may
include core subjects to take, skillset to master, and other essential
expertise that might be useful in the workplace. The discussion continues with
career options for enthusiastic chemical engineers and how these young and
early careers graduates could discover their reason for well-being and life
purpose as aspiring chemical engineers.


title: Global entanglement in a topological quantum phase transition

abstract:
  A useful approach to characterize and identify quantum phase transitions lies
in the concept of multipartite entanglement. In this paper, we consider
well-known measures of multipartite (global) entanglement, i.e., average linear
entropy of one-qubit and two-qubit reduced density matrices, in order to study
topological quantum phase transition (TQPT) in the Kitaev Toric code
Hamiltonian with a nonlinear perturbation. We provide an $exact$ mapping from
aforementioned measures in the above model to internal energy and energy-energy
correlations in the classical Ising model. Accordingly, we find that the global
entanglement shows a continuous and sharp transition from a maximum value in
the topological phase to zero in the magnetized phase in a sense that its
first-order derivative diverges at the transition point. In this regard, we
conclude that not only can the global entanglement serve as a reasonable tool
to probe quantum criticality at TQPTs, but it also can reveal the highly
entangled nature of topological phases. Furthermore, we also introduce a
conditional version of global entanglement which becomes maximum at the
critical point. Therefore, regarding a general expectation that multipartite
entanglement reaches maximum value at the critical point of quantum many-body
systems, our result proposes that the conditional global entanglement can be a
good measure of multipartite entanglement in TQPTs.


title: Occlusion-Aware Cost Constructor for Light Field Depth Estimation

abstract:
  Matching cost construction is a key step in light field (LF) depth
estimation, but was rarely studied in the deep learning era. Recent deep
learning-based LF depth estimation methods construct matching cost by
sequentially shifting each sub-aperture image (SAI) with a series of predefined
offsets, which is complex and time-consuming. In this paper, we propose a
simple and fast cost constructor to construct matching cost for LF depth
estimation. Our cost constructor is composed by a series of convolutions with
specifically designed dilation rates. By applying our cost constructor to SAI
arrays, pixels under predefined disparities can be integrated and matching cost
can be constructed without using any shifting operation. More importantly, the
proposed cost constructor is occlusion-aware and can handle occlusions by
dynamically modulating pixels from different views. Based on the proposed cost
constructor, we develop a deep network for LF depth estimation. Our network
ranks first on the commonly used 4D LF benchmark in terms of the mean square
error (MSE), and achieves a faster running time than other state-of-the-art
methods.