title:
Estimating Excess COVID-19 Infections with Nonparametric Self-Exciting
  Point Processes

abstract:
  The COVID-19 pandemic has led to a vast amount of growth for statistical
models and methods which characterize features of disease outbreaks. One class
of models that came to light in this regard has been the use of self-exciting
point processes, wherein infections occur both "at random" and also more
systematically from person-to-person transmission. Beyond the modeling of the
overall COVID-19 outbreak, the pandemic has also motivated research assessing
various policy decisions and event outcomes. One such area of study, addressed
here, relates to the formulation of methods which measure the impact that large
events or gatherings of people had in the local areas where the events were
held. We formulate an alternative approach to traditional causal inference
methods and then apply our method to assessing the impact that then President
Donald Trump's re-election campaign rallies had on COVID-19 infections in areas
where the rallies were hosted. By incorporating several adaptions to
nonparametric self-exciting point process models, we estimate both the excess
number of COVID-19 infections brought on by the rallies and the duration of
time in which these excess infections persisted.


title:
GWCloud: a searchable repository for the creation and curation of
  gravitational-wave inference results

abstract:
  There are at present ${\cal O}(100)$ gravitational-wave candidates from
compact binary mergers reported in the astronomical literature. As detector
sensitivities are improved, the catalog will swell in size: first to ${\cal
O}(1000)$ events in the A+ era and then to ${\cal O}(10^6)$ events in the era
of third-generation observatories like Cosmic Explorer and the Einstein
Telescope. Each event is analyzed using Bayesian inference to determine
properties of the source including component masses, spins, tidal parameters,
and the distance to the source. These inference products are the fodder for
some of the most exciting gravitational-wave science, enabling us to measure
the expansion of the Universe with standard sirens, to characterise the neutron
star equation of state, and to unveil how and where gravitational-wave sources
are assembled. In order to maximize the science from the coming deluge of
detections, we introduce GWCloud, a searchable repository for the creation and
curation of gravitational-wave inference products. It is designed with five
pillars in mind: uniformity of results, reproducibility of results, stability
of results, access to the astronomical community, and efficient use of
computing resources. We describe how to use GWCloud with examples, which
readers can replicate using the companion code to this paper. We describe our
long-term vision for GWCloud.


title: Quintessential Axions from a New Confining Force

abstract:
  Almost massless mesons created at the condensation scale of extra nonabelian
gauge group can be candidates of dark energy source. This can be another
motivation for introducing an additional confining force.


title:
Radioactively-Powered Gamma-Ray Transient Associated with a Kilonova
  from Neutron Star Merger

abstract:
  Association of GW170817/GRB170817A/AT2017gfo provides the first direct
evidence for neutron star mergers as significant sources of $r$-process
nucleosynthesis. A gamma-ray transient (GRT) would be powered by the
radioactive decay of the freshly-synthesized $r$-process elements. By analyzing
the composition and gamma-ray opacity of the kilonova ejecta in details, we
calculate the lightcurve and spectrum of the GRT for a spherically symmetric
merger ejecta with mass $M_{\rm ej}=0.001 \sim 0.05M_{\odot}$ and expansion
velocity $v_{\rm ej}= 0.1\sim 0.4c$. It is found that the peak of the GRT
lightcurve depends on $M_{\rm ej}$ and $v_{\rm ej}$ as $t_{\rm pk} \approx
0.9~{\rm days} ~ (M_{\rm ej}/0.01M_{\odot})^{1/2}(v_{\rm ej}/0.2c)^{-1}$ and
$L_{\rm pk} \approx 7.0\times10^{40} ~{\rm erg~s} ^{-1} (M_{\rm
ej}/0.01M_{\odot})^{1/2}(v_{\rm ej}/0.2c)$. Most radiating photons are in the
$100-3000$ keV band and the spectrum peaks at round 800 keV for different
nuclear physics inputs. The line features are blurred out by the Doppler
broadening effect and the uncertainties of nuclear physics data. Adopting the
ejecta parameters reported in literature, we examine the detection probability
of the possible GRT associated with AT2017gfo. We show that the GRT cannot be
convincingly detected with the proposed missions in the MeV band, such as ETCC
and AMEGO. The low gamma-ray flux, together with the extremely low event rate
at local universe, makes a great challenge for discovery of the GRTs.


title:
On plurisubharmonic defining functions for pseudoconvex domains in
  $\mathbb{C}^2$

abstract:
  We investigate the question of existence of plurisubharmonic defining
functions for smoothly bounded, pseudoconvex domains in $\mathbb{C}^2$. In
particular, we construct a family of simple counterexamples to the existence of
plurisubharmonic smooth local defining functions. Moreover, we give general
criteria equivalent to the existence of plurisubharmonic smooth defining
functions on or near the boundary of the domain. These equivalent
characterizations are then explored for some classes of domains.


title:
Efficient and Accurate Conversion of Spiking Neural Network with Burst
  Spikes

abstract:
  Spiking neural network (SNN), as a brain-inspired energy-efficient neural
network, has attracted the interest of researchers. While the training of
spiking neural networks is still an open problem. One effective way is to map
the weight of trained ANN to SNN to achieve high reasoning ability. However,
the converted spiking neural network often suffers from performance degradation
and a considerable time delay. To speed up the inference process and obtain
higher accuracy, we theoretically analyze the errors in the conversion process
from three perspectives: the differences between IF and ReLU, time dimension,
and pooling operation. We propose a neuron model for releasing burst spikes, a
cheap but highly efficient method to solve residual information. In addition,
Lateral Inhibition Pooling (LIPooling) is proposed to solve the inaccuracy
problem caused by MaxPooling in the conversion process. Experimental results on
CIFAR and ImageNet demonstrate that our algorithm is efficient and accurate.
For example, our method can ensure nearly lossless conversion of SNN and only
use about 1/10 (less than 100) simulation time under 0.693$\times$ energy
consumption of the typical method. Our code is available at
https://github.com/Brain-Inspired-Cognitive-Engine/Conversion_Burst.


title:
Leptonic cascade decays of a heavy Higgs boson through vectorlike
  leptons at the LHC

abstract:
  We demonstrate the potential of fully leptonic cascade decays of a heavy
neutral Higgs boson through vectorlike leptons as a simultaneous probe for
extended Higgs sectors and extra matter particles at the LHC. The processes we
explore are unique in that their event topologies lead to di-boson-like
leptonic final states with a lepton pair which does not reconstruct the mass of
a gauge boson. By recasting existing $2\ell + E_T^{\rm miss}$ and $3/4\ell$
searches channels using run2 data from the LHC we obtain $\textit{model
independent}$ bounds on the masses of heavy scalars and vectorlike leptons and
use these results to explore future prospects at the HL-LHC. Our results can be
directly applied to any kind of new physics scenarios sharing the final states
and the event topology. For concreteness, we apply our results to a benchmark
scenario: a two Higgs doublet model type-II augmented with vectorlike leptons.
Remarkably, even with current data the sensitivity of our analysis shows a
reach for masses of a heavy neutral Higgs and vectorlike leptons up to 2 TeV
and 1.5 TeV, respectively. Even for low $\tan\beta \gtrsim 1,$ the analysis
retains sensitivity to heavy Higgs masses slightly above 1 TeV. The future
sensitivities at the HL-LHC extend the reach for heavy Higgses and new leptons
to 2.7 TeV and 2 TeV, respectively.


title:
Radio bounds on the mixed dark matter scenarios of primordial black
  holes and WIMPs

abstract:
  We study the synchrotron radio emission in the mixed dark matter scenarios
consisting of the primordial black holes (PBHs) and the self-annihilating WIMPs
(weakly interacting massive particles). The WIMPs can form the ultracompact
minihalos around PBHs and the annihilation enhancement from these dense halos
can lead to the efficient synchrotron radiation at the radio frequency in the
presence of galactic magnetic fields. The upper bound of PBH fraction with
respect to the total dark matter abundance is of order $10^{-8}\sim 10^{-5}$
depending on the electroweak scale WIMP mass ($m_{\chi}=10\sim 1000$ GeV) and
the WIMP annihilation channel (e.g. a hadronic $\chi \chi \rightarrow b
\bar{b}$ or a leptonic $\chi \chi \rightarrow e^+ e^-$ channel). The PBH
contribution to the total dark matter abundance is hence negligible when the
other component of dark matter is composed of the conventional electroweak
scale WIMPs.


title:
Magnetic Weyl semimetal in K$_{2}$Mn$_3$(AsO$_{4}$)$_3$ with the minimum
  number of Weyl points

abstract:
  The "Hydrogen atom" of magnetic Weyl semimetals, with the minimum number of
Weyl points, have received growing attention recently due to the possible
presence of Weyl-related phenomena. Here, we report a nontrivial electronic
structure of the ferromagnetic alluaudite-type compound
K$_{2}$Mn$_3$(AsO$_{4}$)$_3$. It exhibits only a pair of Weyl points
constrained in the $z$-direction by the two-fold rotation symmetry, leading to
extremely long Fermi arc surface states. In addition, the study of its
low-energy effective model results in the discovery of various topological
superconducting states, such as the "hydrogen atom" of a Weyl superconductor.
Our work provides a feasible platform to explore the intrinsic properties
related to Weyl points, and the related device applications.


title: Ramification of Tate modules for rank $2$ Drinfeld modules

abstract:
  In this paper, we study the ramification of extensions of a function field
generated by division points of rank $2$ Drinfeld modules. Also conductors of
certain rank $2$ Drinfeld modules are defined as analogues of those for
elliptic curves. A calculation of these conductors allows us to show an
analogue of Szpiro conjecture under a certain limited situation.


title:
Persistent homology analysis of a generalized Aubry-Andr\'{e}-Harper
  model

abstract:
  Observing critical phases in lattice models is challenging due to the need to
analyze the finite time or size scaling of observables. We study how the
computational topology technique of persistent homology can be used to
characterize phases of a generalized Aubry-Andr\'{e}-Harper model. The
persistent entropy and mean squared lifetime of features obtained using
persistent homology behave similarly to conventional measures (Shannon entropy
and inverse participation ratio) and can distinguish localized, extended, and
crticial phases. We find that the persistent entropy also clearly distinguishes
ordered from disordered regimes of the model. The persistent homology approach
can be applied to both the energy eigenstates and the wavepacket propagation
dynamics.


title:
Automatic Detection and Classification of Symbols in Engineering
  Drawings

abstract:
  A method of finding and classifying various components and objects in a
design diagram, drawing, or planning layout is proposed. The method
automatically finds the objects present in a legend table and finds their
position, count and related information with the help of multiple deep neural
networks. The method is pre-trained on several drawings or design templates to
learn the feature set that may help in representing the new templates. For a
template not seen before, it does not require any training with template
dataset. The proposed method may be useful in multiple industry applications
such as design validation, object count, connectivity of components, etc. The
method is generic and domain independent.


title: Sums of Distances on Graphs and Embeddings into Euclidean Space

abstract:
  Let $G=(V,E)$ be a finite, connected graph. We consider a greedy selection of
vertices: given a list of vertices $x_1, \dots, x_k$, take $x_{k+1}$ to be any
vertex maximizing the sum of distances to the existing vertices and iterate: we
keep adding the `most remote' vertex. The frequency with which the vertices of
the graph appear in this sequence converges to a set of probability measures
with nice properties. The support of these measures is, generically, given by a
rather small number of vertices $m \ll |V|$. We prove that this suggests that
the graph $G$ is at most '$m$-dimensional' by exhibiting an explicit
$1-$Lipschitz embedding $\phi: G \rightarrow \ell^1(\mathbb{R}^m)$ with good
properties.


title:
The evolving properties of the corona of GRS 1915+105: A spectral-timing
  perspective through variable-Comptonisation modelling

abstract:
  The inverse Compton process by which soft photons are up-scattered by hot
electrons in a corona plays a fundamental role in shaping the X-ray spectra of
black-hole (BH) low-mass X-ray binaries (LMXBs), particularly in the hard and
hard-intermediate states. In these states, the power-density spectra of these
sources typically show Type-C low-frequency quasi-periodic oscillations (QPOs).
Although several models have been proposed to explain the dynamical origin of
their frequency, only a few of those models predict the spectral-timing
radiative properties of the QPOs. Here we study the physical and geometrical
properties of the corona of the BH-LMXB GRS 1915+105 based on a large sample of
observations available in the RXTE archive. We use a recently-developed
spectral-timing Comptonisation model to fit simultaneously the energy-dependent
fractional rms amplitude and phase-lag spectra of the Type-C QPO in 398
observations. For this, we include spectral information gathered from fitting a
Comptonisation model to the corresponding time-averaged spectra. We analyse the
dependence of the physical and geometrical properties of the corona upon the
QPO frequency and spectral state of the source, the latter characterised by the
hardness ratio. We find consistent trends in the evolution of the corona size,
temperature, and feedback (the fraction of the corona photons that impinge back
onto the disc) that persist for roughly 15~years. By correlating our
observations with simultaneous radio-monitoring of the source at 15 GHz, we
propose a scenario in which the disc-corona interactions connect with the
launching mechanism of the radio jet in this source.


title: Resource-efficient domain adaptive pre-training for medical images

abstract:
  The deep learning-based analysis of medical images suffers from data scarcity
because of high annotation costs and privacy concerns. Researchers in this
domain have used transfer learning to avoid overfitting when using complex
architectures. However, the domain differences between pre-training and
downstream data hamper the performance of the downstream task. Some recent
studies have successfully used domain-adaptive pre-training (DAPT) to address
this issue. In DAPT, models are initialized with the generic dataset
pre-trained weights, and further pre-training is performed using a moderately
sized in-domain dataset (medical images). Although this technique achieved good
results for the downstream tasks in terms of accuracy and robustness, it is
computationally expensive even when the datasets for DAPT are moderately sized.
These compute-intensive techniques and models impact the environment negatively
and create an uneven playing field for researchers with limited resources. This
study proposed computationally efficient DAPT without compromising the
downstream accuracy and robustness. This study proposes three techniques for
this purpose, where the first (partial DAPT) performs DAPT on a subset of
layers. The second one adopts a hybrid strategy (hybrid DAPT) by performing
partial DAPT for a few epochs and then full DAPT for the remaining epochs. The
third technique performs DAPT on simplified variants of the base architecture.
The results showed that compared to the standard DAPT (full DAPT), the hybrid
DAPT technique achieved better performance on the development and external
datasets. In contrast, simplified architectures (after DAPT) achieved the best
robustness while achieving modest performance on the development dataset .


title:
Cyborg Beetle Achieves Efficient Autonomous Navigation Using Feedback
  Control

abstract:
  Terrestrial cyborg insects were long discussed as potential complements for
insect-scale mobile robots. These cyborgs inherit the insects' outstanding
locomotory skills, orchestrated by a sophisticated central nervous system and
various sensory organs, favoring their maneuvers in complex terrains. However,
the autonomous navigation of these cyborgs was not yet comprehensively studied.
The struggle to select optimal stimuli for individual insects hinders reliable
and accurate navigations. This study overcomes this problem and provides a
detailed look at the terrestrial navigation of cyborg insects (darkling beetle)
by implementing a feedback control system. Via a thrust controller for
acceleration and a proportional controller for turning, the system regulates
the stimulation parameters depending on the beetle's instantaneous status.
Adjusting the system's control parameters allows reliable and precise
path-following navigations (i.e., up to ~94% success rate, ~1/2 body length
accuracy). Also, the system's performance can be tuned, providing flexibility
to navigation applications of terrestrial cyborg insects.


title: The Paper Pile at Home: Adopting Personal Electronic Records

abstract:
  Research has found that if respondents do not manage their personal records
such as bills, receipts and tax-related documents efficiently, they risk not
being able to re-find them when needed, resulting in significant problems. A
significant gap in understanding and addressing this problem stems from a lack
of knowledge of the format of these records, particularly in the context of the
COVID-19 pandemic, that may have caused an increase in managing personal
records in an electronic format, rather than by hardcopy. This paper provides
results of quantitative research conducted in 2018, thereby providing a
valuable benchmark for future research on the same and related topics. This
measurement was achieved by means of an online survey distributed via social
media amongst 205 respondents. The results revealed that nearly all respondents
(97%) retained at least some records, and more than 80% kept some of those
records in an electronic format, particularly travel reservations and payslips.
Conversely, only 10% of respondents kept receipts and warranties for appliances
or medical records in an electronic format. The reason for these differences in
propensity of keeping various records in an electronic or hardcopy format will
require further research.


title: Field-Assisted Sub-Terahertz Spin Pumping and Auto-Oscillation in NiO

abstract:
  Spin pumping converting sub-terahertz electromagnetic waves to DC spin
currents has recently been demonstrated in antiferromagnets (AFMs) with
easy-axis magnetic anisotropy. However, easy-plane AFMs such as NiO, which are
easier to prepare experimentally, are considered to be bad candidates for spin
pumping because the N\'{e}el vector oscillation is linearly polarized, placing
a major restriction on the material choice for practical applications. Through
a case study of NiO, we show that an applied magnetic field below the spin-flop
transition can substantially modify the polarization of the resonance
eigenmodes, which enables coherent sub-terahertz spin pumping as strong as that
in easy-axis AFMs. In addition, we find that an applied magnetic field can
significantly reduce the threshold of N\'{e}el vector auto-oscillation
triggered by spin-transfer torques. These prominent field-assisted effects can
greatly facilitate spintronic device engineering in the sub-terahertz frequency
regime.


title:
Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the
  Large-scale BBOB Function Set

abstract:
  This paper investigates the performance of three black-box optimizers
exploiting separability on the 24 large-scale BBOB functions, including the
Hooke-Jeeves method, MTS-LS1, and BSrr. Although BSrr was not specially
designed for large-scale optimization, the results show that BSrr has a
state-of-the-art performance on the five separable large-scale BBOB functions.
The results show that the asymmetry significantly influences the performance of
MTS-LS1. The results also show that the Hooke-Jeeves method performs better
than MTS-LS1 on unimodal separable BBOB functions.


title:
Testing by wave packets and modified scattering in nonlinear dispersive
  pde's

abstract:
  Modified scattering phenomena are encountered in the study of global
properties for nonlinear dispersive partial differential equations in
situations where the decay of solutions at infinity is borderline and
scattering fails just barely. An interesting example is that of problems with
cubic nonlinearities in one space dimension.
  The method of testing by wave packets was introduced by the authors as a tool
to efficiently capture the asymptotic equations associated to such flows, and
thus establish the modified scattering mechanism in a simpler, more efficient
fashion, and at lower regularity. In these expository notes we describe how
this method can be applied to problems with general dispersion relations.


title:
Lightweight Bimodal Network for Single-Image Super-Resolution via
  Symmetric CNN and Recursive Transformer

abstract:
  Single-image super-resolution (SISR) has achieved significant breakthroughs
with the development of deep learning. However, these methods are difficult to
be applied in real-world scenarios since they are inevitably accompanied by the
problems of computational and memory costs caused by the complex operations. To
solve this issue, we propose a Lightweight Bimodal Network (LBNet) for SISR.
Specifically, an effective Symmetric CNN is designed for local feature
extraction and coarse image reconstruction. Meanwhile, we propose a Recursive
Transformer to fully learn the long-term dependence of images thus the global
information can be fully used to further refine texture details. Studies show
that the hybrid of CNN and Transformer can build a more efficient model.
Extensive experiments have proved that our LBNet achieves more prominent
performance than other state-of-the-art methods with a relatively low
computational cost and memory consumption. The code is available at
https://github.com/IVIPLab/LBNet.


title:
Collision Avoidance for Elliptical Agents with Control Barrier Function
  Utilizing Supporting Lines

abstract:
  This paper presents a collision avoidance method for elliptical agents
traveling in a two-dimensional space. We first formulate a separation condition
for two elliptical agents utilizing a signed distance from a supporting line of
an agent to the other agent, which renders a positive value if two ellipses are
separated by the line. Because this signed distance could yield a shorter
length than the actual distance between two ellipses, the supporting line is
rotated so that the signed distance from the line to the other ellipse is
maximized. We prove that this maximization problem renders the signed distance
equivalent to the actual distance between two ellipses, hence not causing the
conservative evasive motion. Then, we propose the collision avoidance method
utilizing novel control barrier functions incorporating a gradient-based update
law of a supporting line. The validity of the proposed methods is evaluated in
the simulations.


title: Local normal forms of em-wavefronts in affine flat coordinates

abstract:
  In our previous work, we have generalized the notion of dually flat or
Hessian manifold to quasi-Hessian manifold; it admits the Hessian metric to be
degenerate but possesses a particular symmetric cubic tensor (generalized
Amari-Centsov tensor). Indeed, it naturally appears as a singular model in
information geometry and related fields. A quasi-Hessian manifold is locally
accompanied with a possibly multi-valued potential and its dual, whose graphs
are called the $e$-wavefront and the $m$-wavefront respectively, together with
coherent tangent bundles endowed with flat connections. In the present paper,
using those connections and the metric, we give coordinate-free criteria for
detecting local diffeomorphic types of $e/m$-wavefronts, and then derive the
local normal forms of those (dual) potential functions for the $e/m$-wavefronts
in affine flat coordinates by means of Malgrange's division theorem. This is
motivated by an early work of Ekeland on non-convex optimization and
Saji-Umehara-Yamada's work on Riemannian geometry of wavefronts. Finally, we
reveal a relation of our geometric criteria with information geometric
quantities of statistical manifolds.


title: Music Enhancement via Image Translation and Vocoding

abstract:
  Consumer-grade music recordings such as those captured by mobile devices
typically contain distortions in the form of background noise, reverb, and
microphone-induced EQ. This paper presents a deep learning approach to enhance
low-quality music recordings by combining (i) an image-to-image translation
model for manipulating audio in its mel-spectrogram representation and (ii) a
music vocoding model for mapping synthetically generated mel-spectrograms to
perceptually realistic waveforms. We find that this approach to music
enhancement outperforms baselines which use classical methods for
mel-spectrogram inversion and an end-to-end approach directly mapping noisy
waveforms to clean waveforms. Additionally, in evaluating the proposed method
with a listening test, we analyze the reliability of common audio enhancement
evaluation metrics when used in the music domain.


title: On the Normalizing Constant of the Continuous Categorical Distribution

abstract:
  Probability distributions supported on the simplex enjoy a wide range of
applications across statistics and machine learning. Recently, a novel family
of such distributions has been discovered: the continuous categorical. This
family enjoys remarkable mathematical simplicity; its density function
resembles that of the Dirichlet distribution, but with a normalizing constant
that can be written in closed form using elementary functions only. In spite of
this mathematical simplicity, our understanding of the normalizing constant
remains far from complete. In this work, we characterize the numerical behavior
of the normalizing constant and we present theoretical and methodological
advances that can, in turn, help to enable broader applications of the
continuous categorical distribution. Our code is available at
https://github.com/cunningham-lab/cb_and_cc/.


title: A Decision Model for Federated Learning Architecture Pattern Selection

abstract:
  Federated learning is growing fast in both academia and industry to resolve
data hungriness and privacy issues in machine learning. A federated learning
system being widely distributed with different components and stakeholders
requires software system design thinking. For instance, multiple patterns and
tactics have been summarised by researchers that cover various aspects, from
client management, training configuration, model deployment, etc. However, the
multitude of patterns leaves the designers confused about when and which
pattern to adopt or adapt. Therefore, in this paper, we present a set of
decision models to assist designers and architects who have limited knowledge
in federated learning, in selecting architectural patterns for federated
learning architecture design. Each decision model maps functional and
non-functional requirements of federated learning systems to a set of patterns.
we also clarify the trade-offs that may be implicit in the patterns. We
evaluated the decision model through a set of interviews with practitioners to
assess the correctness and usefulness in guiding the architecture design
process through various design decision options.


title:
Bifurcation from essential spectrum for an elliptic equation with
  general nonlinearity

abstract:
  In this paper, based on some prior estimates, we show that the essential
spectrum $\lambda=0$ is a bifurcation point for an superlinear elliptic
equation with only local conditions, which generalizes a series of earlier
results on an open problem proposed by C. A. Stuart in 1983 [Lecture Notes in
Mathematics, 1017].


title:
Model Selection, Adaptation, and Combination for Deep Transfer Learning
  through Neural Networks in Renewable Energies

abstract:
  There is recent interest in using model hubs, a collection of pre-trained
models, in computer vision tasks. To utilize the model hub, we first select a
source model and then adapt the model for the target to compensate for
differences. While there is yet limited research on a model selection and
adaption for computer vision tasks, this holds even more for the field of
renewable power. At the same time, it is a crucial challenge to provide
forecasts for the increasing demand for power forecasts based on weather
features from a numerical weather prediction. We close these gaps by conducting
the first thorough experiment for model selection and adaptation for transfer
learning in renewable power forecast, adopting recent results from the field of
computer vision on six datasets. We adopt models based on data from different
seasons and limit the amount of training data. As an extension of the current
state of the art, we utilize a Bayesian linear regression for forecasting the
response based on features extracted from a neural network. This approach
outperforms the baseline with only seven days of training data. We further show
how combining multiple models through ensembles can significantly improve the
model selection and adaptation approach. In fact, with more than 30 days of
training data, both proposed model combination techniques achieve similar
results to those models trained with a full year of training data.


title:
Multipolar Quantum Electrodynamics of Localized Charge-Current
  Distributions: Theory and Renormalization

abstract:
  We formulate a non-relativistic quantum field theory to model interactions
between quantized electromagnetic fields and localized charge-current
distributions. The electronic degrees of freedom are encoded in microscopic
polarization and magnetization field operators whose moments are identified
with the multipole moments of the charge-current distribution. The multipolar
Hamiltonian is obtained from the minimal coupling Hamiltonian through a unitary
transformation corresponding to the canonical PZW transformation in classical
or semi-classical theory; we renormalize this Hamiltonian at leading-order in
perturbation theory, the result of which is used to compute the shift of the
electronic energy levels due to interactions between the electrons and quantum
vacuum fluctuations in the electromagnetic field. Our renormalized energy shift
constitutes a generalization of the Lamb shift in atomic hydrogen, valid for
general localized assemblies of atoms and molecules, possibly with net charge
but absent free current. By expanding the fields in a series of multipole
moments, we study contributions to this energy shift coming from specific
multipole moments.


title:
A novel machine learning enabled hybrid optimization framework for
  efficient and transferable coarse-graining of a model polymer

abstract:
  This work presents a novel framework governing the development of an
efficient, accurate, and transferable coarse-grained (CG) model of a polyether
material. The proposed framework combines the two fundamentally different
classical optimization approaches for the development of coarse-grained model
parameters; namely bottom-up and top-down approaches. This is achieved through
integrating the optimization algorithms into a machine learning (ML) model,
trained using molecular dynamics (MD) simulation data. In the bottom-up
approach, bonded interactions of the CG model are optimized using deep neural
networks (DNN), where atomistic bonded distributions are matched. The atomistic
distributions emulate the local chain structure. In the top-down approach,
optimization of nonbonded potentials is accomplished by reproducing the
temperature-dependent experimental density. We demonstrate that CG model
parameters achieved through our machine-learning enabled hybrid optimization
framework fulfills the thermodynamic consistency and transferability issues
associated with the classical approaches to coarse-graining model polymers. We
demonstrate the efficiency, accuracy, and transferability of the developed CG
model, using our novel framework through accurate predictions of chain size as
well as chain dynamics, including the limiting behavior of the glass transition
temperature, diffusion, and stress relaxation spectrum, where none were
included in the potential parameterization process. The accuracy of the
predicted properties are evaluated in the context of molecular theories and
available experimental data.


title: The Role of Magnetic Fields in Triggered Star Formation of RCW 120

abstract:
  We report on the near-IR polarimetric observations of RCW 120 with the 1.4 m
IRSF telescope. The starlight polarization of the background stars reveal for
the first time the magnetic field of RCW 120. The global magnetic field of RCW
120 is along the direction of $20^\circ$, parallel to the galactic plane. The
field strength on the plane of the sky is $100\pm26\,\mu$G. The magnetic field
around the eastern shell shows evidence of compression by the HII region. The
external pressure (turbulent pressure + magnetic pressure) and the gas density
of the ambient cloud are minimum along the direction where RCW 120 breaks out,
which explains the observed elongation of RCW 120. The dynamical age of RCW
120, depending on the magnetic field strength, is $\sim\,1.6\,\mathrm{Myr}$ for
field strength of $100\,\mu$G, older than the hydrodynamic estimates. In
direction perpendicular to the magnetic field, the density contrast of the
western shell is greatly reduced by the strong magnetic field. The strong
magnetic field in general reduces the efficiency of triggered star formation,
in comparison with the hydrodynamic estimates. Triggered star formation via the
"collect and collapse" mechanism could occur in the direction along the
magnetic field. Core formation efficiency (CFE) is found to be higher in the
southern and eastern shells of RCW 120 than in the infrared dark cloud
receiving little influence from the HII region, suggesting increase in the CFE
related to triggering from ionization feedback.


title: Strong Medvedev reducibilities and the KL-randomness problem

abstract:
  While it is not known whether each real that is Kolmogorov-Loveland random is
Martin-L\"of random, i.e., whether $\mathrm{KLR}\subseteq\mathrm{MLR}$,
Kjos-Hanssen and Webb (2021) showed that $\mathrm{MLR}$ is truth-table Medvedev
reducible ($\le_{s,tt}$) to $\mathrm{KLR}$. They did this by studying a natural
class Either(MLR) and showing that
$\mathrm{MLR}\le_{s,tt}\mathrm{Either(MLR)}\supseteq\mathrm{KLR}$. We show that
Degtev's stronger reducibilities (positive and linear) do not suffice for the
reduction of MLR to Either(MLR), and some related results.


title:
PDM Klein-Gordon particles in G\"odel-type Som-Raychaudhuri cosmic
  string spacetime background

abstract:
  In G\"odel-type Som-Raychaudhuri (SR) cosmic string spacetime background, we
re-cycle the Klein-Gordon (KG) oscillators and report their correct exact
solutions. We argue that the mathematical collapse of the KG-equation into the
2-dimensional radial Schr\"odinger-like oscillator does not yield that the
parametric characterizations of one is inherited by the other. The angular
frequency (positive) in the Schr\"odinger case is replaced by an irrational
frequency-like (positive and negative) in the KG-case. Inheriting the
Schr\"odinger oscillator's parametric characterizations implies that at least
half of the spectra (the negative part) is lost in the process. We also
introduce KG-oscillators in pseudo-G\"odel SR-type spacetime that admit
invariance and isospectrality with those in G\"odel SR-type spacetime
background. We introduce position-dependent mass (PDM) settings to KG-particles
in 4-vector and scalar Lorentz potentials in magnetic field in the G\"odel
SR-type spacetime background. Four illustrative examples of fundamental nature
are discussed and their exact or conditionally exact solvability are reported.
Amongst are, a PDM KG-Coulombic particle in 4-vector and scalar Coulombic
Lorentz potentials in magnetic field, a PDM KG-Coulombic particle in 4-vector
and scalar Coulombic Lorentz potentials in magnetic field at zero vorticity, a
PDM KG-Coulombic particle in equally mixed 4-vector and scalar Coulombic
Lorentz potentials in magnetic field, and a quasi-free PDM KG-oscillator. We
also emphasis that the biconfluent Heun polynomial approach, to the effective
oscillator plus Cornell type potential, yields conditionally exact solvability
that paralyzes the solution from collapsing into that of pure Coulombic one.


title:
On the Convergence of Momentum-Based Algorithms for Federated Stochastic
  Bilevel Optimization Problems

abstract:
  In this paper, we studied the federated stochastic bilevel optimization
problem. In particular, we developed two momentum-based algorithms for
optimizing this kind of problem. In addition, we established the convergence
rate of these two algorithms, providing their sample and communication
complexities. To the best of our knowledge, this is the first work achieving
such favorable theoretical results.


title:
Universal relations for dilute systems with two-body decays in reduced
  dimensions

abstract:
  Physical systems in reduced dimensions exhibit intriguing properties. For
instance, the dependences of two-body and many-body physics on scattering
lengths are distinct from their counterparts in three dimensions. Whereas many
studies of ultracold atoms and molecules in reduced dimensions have been
focusing on closed systems, two-body losses may occur in such systems. Here, we
show that the two-body inelastic loss rate in reduced dimensions can be
expressed in universal relations that are governed by contacts. These universal
relations correlate the two-body decay rate with other physical observables at
arbitrary temperatures and interaction strengths. Our results will provide
experimentalists with a new protocol to study inelastic scatterings in both
few- and many-body systems in reduced dimensions.


title:
Enhance Ambiguous Community Structure via Multi-strategy Community
  Related Link Prediction Method with Evolutionary Process

abstract:
  Most real-world networks suffer from incompleteness or incorrectness, which
is an inherent attribute to real-world datasets. As a consequence, those
downstream machine learning tasks in complex network like community detection
methods may yield less satisfactory results, i.e., a proper preprocessing
measure is required here. To address this issue, in this paper, we design a new
community attribute based link prediction strategy HAP and propose a two-step
community enhancement algorithm with automatic evolution process based on HAP.
This paper aims at providing a community enhancement measure through adding
links to clarify ambiguous community structures. The HAP method takes the
neighbourhood uncertainty and Shannon entropy to identify boundary nodes, and
establishes links by considering the nodes' community attributes and community
size at the same time. The experimental results on twelve real-world datasets
with ground truth community indicate that the proposed link prediction method
outperforms other baseline methods and the enhancement of community follows the
expected evolution process.


title: Interplay of the disorder and strain in gallium oxide

abstract:
  We observed an interesting interplay between the disorder and strain in
gallium oxide by comparing atomic and cluster ion irradiations as well as
atomic ions co-implants. Firstly, we demonstrated that the disorder
accumulation in gallium oxide exhibits superlinear behavior as a function of
the collision cascade density. Moreover, the level of strain correlated with
the amplitude of the bulk disorder peak can be engineered by changing the
disorder conditions in the near surface layer. Such interplay between the
disorder and strain provides an additional degree of freedom to maintain
desirable strain in gallium oxide, potentially applicable to modify the rate of
the polymorphic transitions in this material.


title: Evolution of magnetic fields in cosmic string wakes

abstract:
  We study the evolution of magnetic fields in cosmic string wakes in a plasma
with a low resistivity. The initial magnetic field in the wake is modelled on
the magnetic fields that are generated by the motion of particles around cosmic
strings. The plasma is characterized by a high beta value. We find multiple
shock like structures developing in the wake of the string. We study the
detailed structure of the shocks formed and the evolution of the magnetic field
in the shock using a 2-D magnetohydrodynamic simulation. As expected, the
development of the magnetic field does not depend on the $\beta$ value. Our
results show that instead of a singe uniform shock forming behind the cosmic
string we have multiple shocks forming at short time intervals behind the
string. The presence of multiple shocks will definitely affect the
observational signatures of cosmic string wakes as these signatures depend upon
the temperature fluctuations generated by the shock. We also find that as the
shock moves away, the residual magnetic field left behind reconnects and
dissipates rapidly. The magnetic field around the string is thus very
localized. We find that magnetic field reconnections take place in cosmic
string wakes. This leads to the decrease of the magnetic field in the post
shock region.


title:
Programming Matrices as Staged Sparse Rows to Generate Efficient
  Matrix-free Differential Equation Solver

abstract:
  Solving differential equations is a critical task in scientific computing.
Domain-specific languages (DSLs) have been a promising direction in achieving
performance and productivity, but the current state of the art only supports
stencil computation, leaving solvers requiring loop-carried dependencies aside.
Alternatively, sparse matrices can represent such equation solvers and are more
general than existing DSLs, but the performance is sacrificed.
  This paper points out that sparse matrices can be represented as programs
instead of data, having both the generality from the matrix-based
representation and the performance from program optimizations. Based on the
idea, we propose the Staged Sparse Row (SSR) sparse matrix representation that
can efficiently cover applications on structured grids. With SSR
representation, users can intuitively define SSR matrices using generator
functions and use SSR matrices through a concise object-oriented interface. SSR
matrices can then be chained and applied to construct the algorithm, including
those with loop-carried dependences. We then apply a set of dedicated
optimizations, and ultimately simplify the SSR matrix-based codes into
straightforward matrix-free ones, which are efficient and friendly for further
analysis.
  Implementing BT pseudo application in the NAS Parallel Benchmark, with less
than $10\%$ lines of code compared with the matrix-free reference FORTRAN
implementation, we achieved up to $92.8\%$ performance. Implementing a
matrix-free variant for the High-Performance Conjugate Gradient benchmark, we
achieve $3.29\times$ performance compared with the reference implementation,
while our implementation shares the same algorithm on the same programming
abstraction, which is sparse matrices.


title:
The Effect of Preferences in Abstract Argumentation Under a
  Claim-Centric View

abstract:
  In this paper, we study the effect of preferences in abstract argumentation
under a claim-centric perspective. Recent work has revealed that semantical and
computational properties can change when reasoning is performed on claim-level
rather than on the argument-level, while under certain natural restrictions
(arguments with the same claims have the same outgoing attacks) these
properties are conserved. We now investigate these effects when, in addition,
preferences have to be taken into account and consider four prominent
reductions to handle preferences between arguments. As we shall see, these
reductions give rise to different classes of claim-augmented argumentation
frameworks, and behave differently in terms of semantic properties and
computational complexity. This strengthens the view that the actual choice for
handling preferences has to be taken with care.


title:
Almost reducibility of quasiperiodic $sl(2,\mathbb{R})$-cocycles in
  ultradifferentiable classes, and an application to the regularity of the
  Lyapunov exponent

abstract:
  Given a quasiperiodic cocycle in $sl(2,\mathbb{R})$ sufficiently close to a
constant, we prove that it is almost-reducible in ultradifferentiable class
under an adapted arithmetic condition on the frequency vector. We also give a
corollary on the H{\"o}lder regularity of the Lyapunov exponent.


title: AlphaZero-Inspired General Board Game Learning and Playing

abstract:
  Recently, the seminal algorithms AlphaGo and AlphaZero have started a new era
in game learning and deep reinforcement learning. While the achievements of
AlphaGo and AlphaZero - playing Go and other complex games at super human level
- are truly impressive, these architectures have the drawback that they are
very complex and require high computational resources. Many researchers are
looking for methods that are similar to AlphaZero, but have lower computational
demands and are thus more easily reproducible. In this paper, we pick an
important element of AlphaZero - the Monte Carlo Tree Search (MCTS) planning
stage - and combine it with reinforcement learning (RL) agents. We wrap MCTS
for the first time around RL n-tuple networks to create versatile agents that
keep at the same time the computational demands low. We apply this new
architecture to several complex games (Othello, ConnectFour, Rubik's Cube) and
show the advantages achieved with this AlphaZero-inspired MCTS wrapper. In
particular, we present results that this AlphaZero-inspired agent is the first
one trained on standard hardware (no GPU or TPU) to beat the very strong
Othello program Edax up to and including level 7 (where most other algorithms
could only defeat Edax up to level 2).


title: The $SL(K+3,\mathbb{C})$ symmetry of string scatterings from D-branes

abstract:
  By using the solvability of Lauricella function $F_{D}^{(K)}\left(
\alpha;\beta_{1},...,\beta_{K};\gamma;x_{1},...,x_{K}\right) $ with nonpositive
integer $\beta_{J}$, we show that each scattering or decay process of string
and D-brane states at \textit{arbitrary} mass levels can be expressed in terms
of a single Lauricella function. This result extends the previous exact
$SL(K+3,\mathbb{C})$ symmetry of tree-level open bosonic string theory to
include the D-brane.


title:
Improving robustness of language models from a geometry-aware
  perspective

abstract:
  Recent studies have found that removing the norm-bounded projection and
increasing search steps in adversarial training can significantly improve
robustness. However, we observe that a too large number of search steps can
hurt accuracy. We aim to obtain strong robustness efficiently using fewer
steps. Through a toy experiment, we find that perturbing the clean data to the
decision boundary but not crossing it does not degrade the test accuracy.
Inspired by this, we propose friendly adversarial data augmentation (FADA) to
generate friendly adversarial data. On top of FADA, we propose geometry-aware
adversarial training (GAT) to perform adversarial training on friendly
adversarial data so that we can save a large number of search steps.
Comprehensive experiments across two widely used datasets and three pre-trained
language models demonstrate that GAT can obtain stronger robustness via fewer
steps. In addition, we provide extensive empirical results and in-depth
analyses on robustness to facilitate future studies.


title: Direct Formation of Planetary Embryos in Self-Gravitating Disks

abstract:
  Giant planets have been discovered at large separations from the central
star. Moreover, a striking number of young circumstellar disks have gas and/or
dust gaps at large orbital separations, potentially driven by embedded
planetary objects. To form massive planets at large orbital separations through
core accretion within disk lifetime, however, an early solid body to seed
pebble and gas accretion is desirable. Young protoplanetary disks are likely
self-gravitating, and these gravitoturbulent disks may efficiently concentrate
solid material at the midplane driven by spiral waves. We run 3D local
hydrodynamical simulations of gravitoturbulent disks with Lagrangian dust
particles to determine whether particle and gas self-gravity can lead to the
formation of dense solid bodies, seeding later planet formation. When
self-gravity between dust particles is included, solids of size $\mathrm{St} =
0.1$ to $1$ concentrate within the gravitoturbulent spiral features and
collapse under their own self-gravity into dense clumps up to several
$M_{\oplus}$ in mass at wide orbits. Simulations with dust that drift most
efficiently, $\mathrm{St}=1$, form the most massive clouds of particles, while
simulations with smaller dust particles, $\mathrm{St}=0.1$, have clumps with
masses an order of magnitude lower. When the effect of dust backreaction onto
the gas is included, dust clumps become smaller by a factor of a few but more
numerous. The existence of large solid bodies at an early stage of the disk can
accelerate the planet formation process, particularly at wide orbital
separations, and potentially explain planets distant from the central stars and
young protoplanetary disks with substructures.


title:
The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of
  Danish Texts

abstract:
  Eye movement recordings from reading are one of the richest signals of human
language processing. Corpora of eye movements during reading of contextualized
running text is a way of making such records available for natural language
processing purposes. Such corpora already exist in some languages. We present
CopCo, the Copenhagen Corpus of eye tracking recordings from natural reading of
Danish texts. It is the first eye tracking corpus of its kind for the Danish
language. CopCo includes 1,832 sentences with 34,897 tokens of Danish text
extracted from a collection of speech manuscripts. This first release of the
corpus contains eye tracking data from 22 participants. It will be extended
continuously with more participants and texts from other genres. We assess the
data quality of the recorded eye movements and find that the extracted features
are in line with related research. The dataset available here:
https://osf.io/ud8s5/.


title: Observation of intrinsic crystal phase in bare CrI3 ferromagnetism

abstract:
  Intrinsic structural phase is a crucial foundation for the fundamental
physical properties, and for creating innovative devices with unprecedented
performances and unique functionalities. Long-range ferromagnetic orders of van
der Waals CrI3 are strongly tied with interlayer stacking orders. However, the
intrinsic structure of few-layer CrI3 still remains elusive; the predicted
monoclinic phase has not yet been experimentally detected in bare few-layer
CrI3. Here we uncover the intrinsic structure of few-layer CrI3 with interlayer
antiferromagnetic coupling, which unambiguously show monoclinic stacking in
both bare and hBN-encapsulated bilayer and tri-five-layer CrI3 throughout an
entire temperature range from 300 to 10 K. An exotic spring damping effect from
hBN encapsulation layers is experimentally observed in hBN/CrI3/hBN
heterostructures, which partly hinders interlayer sliding of CrI3. This work
demonstrates the intrinsic monoclinic crystal phase of few-layer CrI3 and
associated correlation with magnetic orders, opening up numerous opportunities
for creating magnetic texture by stacking design.


title:
The equivariant coarse Baum-Connes conjecture for actions by a-T-menable
  groups

abstract:
  The equivariant coarse Baum-Connes conjecture was firstly introduced by Roe
[29] as a unified way to approach both the Baum-Connes conjecture and its
coarse counterpart. In this paper, we prove that if an a-T-menable group
$\Gamma$ acts properly and isometrically on a bounded geometry metric space $X$
with controlled distortion such that the quotient space $X/\Gamma$ is coarsely
embeddable, then the equivariant coarse Baum-Connes conjecture holds for this
action. This answers a question posed in [8] affirmatively.


title:
Region-level Contrastive and Consistency Learning for Semi-Supervised
  Semantic Segmentation

abstract:
  Current semi-supervised semantic segmentation methods mainly focus on
designing pixel-level consistency and contrastive regularization. However,
pixel-level regularization is sensitive to noise from pixels with incorrect
predictions, and pixel-level contrastive regularization has memory and
computational cost with O(pixel_num^2). To address the issues, we propose a
novel region-level contrastive and consistency learning framework (RC^2L) for
semi-supervised semantic segmentation. Specifically, we first propose a Region
Mask Contrastive (RMC) loss and a Region Feature Contrastive (RFC) loss to
accomplish region-level contrastive property. Furthermore, Region Class
Consistency (RCC) loss and Semantic Mask Consistency (SMC) loss are proposed
for achieving region-level consistency. Based on the proposed region-level
contrastive and consistency regularization, we develop a region-level
contrastive and consistency learning framework (RC^2L) for semi-supervised
semantic segmentation, and evaluate our RC$^2$L on two challenging benchmarks
(PASCAL VOC 2012 and Cityscapes), outperforming the state-of-the-art.


title: The Casimir effect in graphene systems: Experiment and theory

abstract:
  The Casimir effect in graphene systems is reviewed with emphasis made on the
large thermal correction to the Casimir force predicted at short separations
between the test bodies. The computational results for the Casimir pressure and
for the thermal correction are presented for both pristine graphene and real
graphene sheets, which possess nonzero energy gap and chemical potential,
obtained by means of exact polarization tensor. Two experiments on measuring
the gradient of the Casimir force between an Au-coated sphere and graphene-
coated substrates performed by using a modified atomic force microscope
cantilever-based technique are described. It is shown that the measurement data
of both experiments are in agreement with theoretical predictions of the
Lifshitz theory using the polarization tensor. Additionally, several important
improvements made in the second experiment, allowed to demonstrate the
predicted large thermal effect in the Casimir interaction at short separations.
Possible implications of this result to resolution of long-term problems of
Casimir physics are discussed.


title: $\Sigma_c\Sigma_c$ interactions in chiral effective field theory

abstract:
  We study the interactions of the $\Sigma_c\Sigma_c$ system in the framework
of chiral effective theory. We consider the contact, one-pion and two-pion
exchange interactions and bridge the low energy constants of the
$\Sigma_c\Sigma_c$ system to those of the $\Sigma_c^{(*)}\bar{D}^{(*)}$ systems
through the quark-level ansatz for the contact interaction. We explore the
influence of intermediate channels in the two-pion exchange diagrams of the
$\Sigma_c\Sigma_c$ system. We obtain a deep bound state
$[\Sigma_c\Sigma_c]_{J=0}^{I=0}$ and a shallow bound state
$[\Sigma_c\Sigma_c]_{J=1}^{I=1}$. As a byproduct, we further investigate the
interactions of the $\Lambda_c\Lambda_c$ and $\Lambda_c\Sigma_c$ systems.


title: MMRotate: A Rotated Object Detection Benchmark using Pytorch

abstract:
  We present an open-source toolbox, named MMRotate, which provides a coherent
algorithm framework of training, inferring, and evaluation for the popular
rotated object detection algorithm based on deep learning. MMRotate implements
18 state-of-the-art algorithms and supports the three most frequently used
angle definition methods. To facilitate future research and industrial
applications of rotated object detection-related problems, we also provide a
large number of trained models and detailed benchmarks to give insights into
the performance of rotated object detection. MMRotate is publicly released at
https://github.com/open-mmlab/mmrotate.


title:
Multi-wavelength magnetic coding of helical luminescence in
  ferromagnetic 2D layered CrI3

abstract:
  Two-dimensional (2D) van der Waals (vdW) ferromagnets have opened new avenues
for manipulating spin at the limits of single or few atomic layers, and for
creating unique magneto-exciton devices through the coupling of long-range
ferromagnetic (FM) orders and excitons. However, 2D vdW ferromagnets explored
so far have rarely possessed exciton behaviors; to date, FM CrI3 have been
recently revealed to show ligand-field photoluminescence correlated with FM
ordering, but typically with a broad emission peak. Alternatively, many-body
excitons have been observed in antiferromagnetic (AFM) NiPS3, but the coupling
of excitons with AFM orders is exponentially more difficult, owing to extremely
high coercivity. Here, we report a straightforward approach to realize strong
coupling of narrow helical emission and FM orders at a low magnetic field in
CrI3 through a relatively simple microsphere cavity. We show that the resonant
whispering-gallery-modes (WGM) of SiO2 microspheres give rising to a series of
strong oscillation helical emissions with a full width at half-maximum (FWHM)
of ~5 nm under continuous wave excitation. Reversible magnetic control and
coding of helical luminescence with multiwavelength is realized in the range of
950-1100 nm. This work enables plenty of opportunities for creating magnetic
encoding lasing for photonic integrated chips.


title: Control-Aware Prediction Objectives for Autonomous Driving

abstract:
  Autonomous vehicle software is typically structured as a modular pipeline of
individual components (e.g., perception, prediction, and planning) to help
separate concerns into interpretable sub-tasks. Even when end-to-end training
is possible, each module has its own set of objectives used for safety
assurance, sample efficiency, regularization, or interpretability. However,
intermediate objectives do not always align with overall system performance.
For example, optimizing the likelihood of a trajectory prediction module might
focus more on easy-to-predict agents than safety-critical or rare behaviors
(e.g., jaywalking). In this paper, we present control-aware prediction
objectives (CAPOs), to evaluate the downstream effect of predictions on control
without requiring the planner be differentiable. We propose two types of
importance weights that weight the predictive likelihood: one using an
attention model between agents, and another based on control variation when
exchanging predicted trajectories for ground truth trajectories.
Experimentally, we show our objectives improve overall system performance in
suburban driving scenarios using the CARLA simulator.


title:
The low-lying hidden- and double-charm tetraquark states in a
  constituent quark model with Instanton-induced Interaction

abstract:
  Spectrum of the low-lying hidden- and double-charm tetraquark states are
investigated in a nonrelativistic quark potential model, where the
Instanton-induced interaction is taken as the residual spin-dependent hyperfine
interaction between quarks. The model parameters are fixed by fitting the
spectrum of the ground hadron states. Our numerical results show that masses of
several presently studied tetraquark states are close to those of the
experimentally observed candidates of exotic meson, which indicates that the
corresponding compact tetraquark components may take considerable probabilities
in those observed exotic states.


title: Epistasis and Adaptation on Fitness Landscapes

abstract:
  Epistasis occurs when the effect of a mutation depends on its carrier's
genetic background. Despite increasing evidence that epistasis for fitness is
common, its role during evolution is contentious. Fitness landscapes, mappings
of genotype or phenotype to fitness, capture the full extent and complexity of
epistasis. Fitness landscape theory has shown how epistasis affects the course
and the outcome of evolution. Moreover, by measuring the competitive fitness of
sets of tens to thousands of connected genotypes, empirical fitness landscapes
have shown that epistasis is frequent and depends on the fitness measure, the
choice of mutations for the landscape, and the environment in which it was
measured. Here, I review fitness landscape theory and experiments and their
implications for the role of epistasis in adaptation. I discuss theoretical
expectations in the light of empirical fitness landscapes and highlight open
challenges and future directions towards integrating theory and data, and
incorporating ecological factors.


title: Two Decades of Colorization and Decolorization for Images and Videos

abstract:
  Colorization is a computer-aided process, which aims to give color to a gray
image or video. It can be used to enhance black-and-white images, including
black-and-white photos, old-fashioned films, and scientific imaging results. On
the contrary, decolorization is to convert a color image or video into a
grayscale one. A grayscale image or video refers to an image or video with only
brightness information without color information. It is the basis of some
downstream image processing applications such as pattern recognition, image
segmentation, and image enhancement. Different from image decolorization, video
decolorization should not only consider the image contrast preservation in each
video frame, but also respect the temporal and spatial consistency between
video frames. Researchers were devoted to develop decolorization methods by
balancing spatial-temporal consistency and algorithm efficiency. With the
prevalance of the digital cameras and mobile phones, image and video
colorization and decolorization have been paid more and more attention by
researchers. This paper gives an overview of the progress of image and video
colorization and decolorization methods in the last two decades.


title:
Discriminative-Region Attention and Orthogonal-View Generation Model for
  Vehicle Re-Identification

abstract:
  Vehicle re-identification (Re-ID) is urgently demanded to alleviate
thepressure caused by the increasingly onerous task of urban traffic
management. Multiple challenges hamper the applications of vision-based vehicle
Re-ID methods: (1) The appearances of different vehicles of the same
brand/model are often similar; However, (2) the appearances of the same vehicle
differ significantly from different viewpoints. Previous methods mainly use
manually annotated multi-attribute datasets to assist the network in getting
detailed cues and in inferencing multi-view to improve the vehicle Re-ID
performance. However, finely labeled vehicle datasets are usually unattainable
in real application scenarios. Hence, we propose a Discriminative-Region
Attention and Orthogonal-View Generation (DRA-OVG) model, which only requires
identity (ID) labels to conquer the multiple challenges of vehicle Re-ID.The
proposed DRA model can automatically extract the discriminative region
features, which can distinguish similar vehicles. And the OVG model can
generate multi-view features based on the input view features to reduce the
impact of viewpoint mismatches. Finally, the distance between vehicle
appearances is presented by the discriminative region features and multi-view
features together. Therefore, the significance of pairwise distance measure
between vehicles is enhanced in acomplete feature space. Extensive experiments
substantiate the effectiveness of each proposed ingredient, and experimental
results indicate that our approach achieves remarkable improvements over the
state- of-the-art vehicle Re-ID methods on VehicleID and VeRi-776 datasets.


title: Controllable Image Captioning

abstract:
  State-of-the-art image captioners can generate accurate sentences to describe
images in a sequence to sequence manner without considering the controllability
and interpretability. This, however, is far from making image captioning widely
used as an image can be interpreted in infinite ways depending on the target
and the context at hand. Achieving controllability is important especially when
the image captioner is used by different people with different way of
interpreting the images. In this paper, we introduce a novel framework for
image captioning which can generate diverse descriptions by capturing the
co-dependence between Part-Of-Speech tags and semantics. Our model decouples
direct dependence between successive variables. In this way, it allows the
decoder to exhaustively search through the latent Part-Of-Speech choices, while
keeping decoding speed proportional to the size of the POS vocabulary. Given a
control signal in the form of a sequence of Part-Of-Speech tags, we propose a
method to generate captions through a Transformer network, which predicts words
based on the input Part-Of-Speech tag sequences. Experiments on publicly
available datasets show that our model significantly outperforms
state-of-the-art methods on generating diverse image captions with high
qualities.


title:
Weak solutions to an initial-boundary value problem for a continuum
  equation of motion of grain boundaries

abstract:
  We investigate an initial-(periodic-)boundary value problem for a continuum
equation, which is a model for motion of grain boundaries based on the
underlying microscopic mechanisms of line defects (disconnections) and
integrated the effects of a diverse range of thermodynamic driving forces. We
first prove the global-in-time existence and uniqueness of weak solution to
this initial-boundary value problem in the case with positive equilibrium
disconnection density parameter B, and then investigate the asymptotic behavior
of the solutions as B goes to zero. The main difficulties in the proof of main
theorems are due to the degeneracy of B=0, a non-local term with singularity,
and a non-smooth coefficient of the highest derivative associated with the
gradient of the unknown. The key ingredients in the proof are the energy
method, an estimate for a singular integral of the Hilbert type, and a
compactness lemma.


title:
Towards Flexible Inference in Sequential Decision Problems via
  Bidirectional Transformers

abstract:
  Randomly masking and predicting word tokens has been a successful approach in
pre-training language models for a variety of downstream tasks. In this work,
we observe that the same idea also applies naturally to sequential decision
making, where many well-studied tasks like behavior cloning, offline RL,
inverse dynamics, and waypoint conditioning correspond to different sequence
maskings over a sequence of states, actions, and returns. We introduce the
FlexiBiT framework, which provides a unified way to specify models which can be
trained on many different sequential decision making tasks. We show that a
single FlexiBiT model is simultaneously capable of carrying out many tasks with
performance similar to or better than specialized models. Additionally, we show
that performance can be further improved by fine-tuning our general model on
specific tasks of interest.


title:
Perfect nonradiating modes in dielectric nanofiber with elliptical
  cross-section

abstract:
  The existence of an infinite number of perfect nonradiating modes in
elliptical nanofibers is demonstrated. Dispersion laws are found for TM and TE
perfect modes in circular and elliptical waveguides with an arbitrary
eccentricity. Numerical simulations in Comsol Multiphysics have shown that
these modes can be excited by plane waves of a certain parity and have
extremely low radiative losses and extremely high quality factors. The found
modes can be used to create highly sensitive nanosensors and other optical
nanodevices where radiation losses should be minimal.


title: A weak-type expression of the Orlicz modular

abstract:
  An equivalent expression of Orlicz modulars in terms of measure of level sets
of difference quotients is established. The result in a sense complements the
famous Maz'ya-Shaposhnikova formula for the fractional Gagliardo-Slobodeckij
seminorm and its recent extension to the setting of Orlicz functions.


title:
Refining Diagnosis Paths for Medical Diagnosis based on an Augmented
  Knowledge Graph

abstract:
  Medical diagnosis is the process of making a prediction of the disease a
patient is likely to have, given a set of symptoms and observations. This
requires extensive expert knowledge, in particular when covering a large
variety of diseases. Such knowledge can be coded in a knowledge graph --
encompassing diseases, symptoms, and diagnosis paths. Since both the knowledge
itself and its encoding can be incomplete, refining the knowledge graph with
additional information helps physicians making better predictions. At the same
time, for deployment in a hospital, the diagnosis must be explainable and
transparent. In this paper, we present an approach using diagnosis paths in a
medical knowledge graph. We show that those graphs can be refined using latent
representations with RDF2vec, while the final diagnosis is still made in an
explainable way. Using both an intrinsic as well as an expert-based evaluation,
we show that the embedding-based prediction approach is beneficial for refining
the graph with additional valid conditions.


title:
A review of Lorentzian synthetic theory of timelike Ricci curvature
  bounds

abstract:
  The scope of this survey is to give a self-contained introduction to
synthetic timelike Ricci curvature bounds for (possibly non-smooth) Lorentzian
spaces via optimal transport $\&$ entropy tools, including a synthetic version
of Hawking's singularity theorem and a synthetic characterisation of Einstein's
vacuum equations. We will also discuss some motivations arising from the smooth
world and some possible directions for future research.


title: Robots: the Century Past and the Century Ahead

abstract:
  Let us reflect on the state of robotics. This year marks the $101$-st
anniversary of R.U.R., a play by the writer Karel \v{C}apek, often credited
with introducing the word "robot". The word used to refer to feudal forced
labourers in Slavic languages. Indeed, it points to one key characteristic of
robotic systems: they are mere slaves, have no rights, and execute our wills
instruction by instruction, without asking anything in return. The relationship
with us humans is commensalism; in biology, commensalism subsists between two
symbiotic species when one species benefits from it (robots boost productivity
for humans), while the other species neither benefits nor is harmed (can you
really argue that robots benefit from simply functioning?).
  We then distinguish robots from "living machines", that is, machines infused
with life. If living machines should ever become a reality, we would need to
shift our relationship with them from commensalism to mutualism. The
distinction is not subtle: we experience it every day with domesticated
animals, that exchange serfdom for forage and protection. This is because life
has evolved to resist any attempt at enslaving it; it is stubborn.
  In the path towards living machines, let us ask: what has been achieved by
robotics in the last $100$ years? What is left to accomplish in the next $100$
years? For us, the answers boil down to three words: juice, need (or death),
and embodiment, as we shall see in the following.


title: Automorphisms of Formal Matrix Rings

abstract:
  We study automorphism groups of formal matrix algebras. We also consider
automorphisms of ordinary matrix algebras (in particular, triangular matrix
algebras).


title:
Discrete Packet Management: Analysis of Age of Information of Discrete
  Time Status Updating Systems

abstract:
  In this paper, we consider performing packet managements in discrete time
status updating system, focusing on determining the stationary AoI-distribution
of the system. Firstly, let the queue model be Ber/G/1/1, we obtain the
AoI-distribution by introducing a two-dimensional AoI-stochastic process and
solving its steady state, which describes the random evolutions of AoI and age
of packet in system simultaneously. In this case, actually we analyze a more
general queue called probabilistic preemption Ber/G/1/1, where the packet
service is allowed to be preempted with certain probabilities. As a special
case, stationary AoI-distribution for the system with Ber/Geo/1/1 queue is
obtained either. For the system having size 2, two specific queues are
considered, i.e., the Ber/Geo/1/2 and Ber/Geo/1/2* queues. The core idea to
find the stationary AoI-distribution is that the random transitions of
three-dimensional vector including AoI at the receiver, the packet age in
service, and the age of waiting packet can be fully described, such that a
three-dimensional AoI process is constituted. The stationary distribution of
three-dimensional process then gives the stationary AoI distribution as one of
its marginal distributions. For both cases, the explicit expressions of
AoI-distribution are derived, thus giving the complete description of the
steady state AoI for the system. For all the cases, since the steady state of a
larger-dimensional AoI process is solved, so that except the AoI-distribution,
we obtain more. For instance, the distributions of packet system time and
waiting time for size-two updating system, and the so-called violation
probabilities that AoI exceeds certain threshold.


title:
Dynamics of hydrogen in silicon at finite temperatures from
  first-principles

abstract:
  Hydrogen point defects in silicon still hold unsolved problems, whose
disclosure is fundamental for future advances in Si technologies. Among the
open issues is the mechanism for the condensation of atomic hydrogen into
molecules in Si quenched from above $T\sim700\,^{\circ}$C to room temperature.
Based on first-principles calculations, we investigated the thermodynamics of
hydrogen monomers and dimers at finite temperatures within the harmonic
approximation. The free energies of formation indicate that the population of
H$^{-}$ cannot be neglected when compared that of H$^{+}$ at high temperatures.
The results allow us to propose that the formation of molecules occurs during
cooling processes, in the temperature window $T\sim700\textrm{-}500\,$K, above
which the molecules collide with Si-Si bonds and dissociate, and below which
the fraction of H$^{-}$ becomes negligible. The formation of H$^{-}$ and most
notably of a fast-diffusing neutral species could also provide an explanation
for the apparent \emph{accelerated} diffusivity of atomic hydrogen at elevated
temperatures in comparison to the figures extrapolated from measurements
carried out at cryogenic temperatures. We finally show that the observed
diffusivity of the molecules is better described upon the assumption that they
are nearly free rotors, all along the minimum energy path, including at the
transition state.


title:
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with
  Anomaly-Aware Bidirectional GANs

abstract:
  The goal of anomaly detection is to identify anomalous samples from normal
ones. In this paper, a small number of anomalies are assumed to be available at
the training stage, but they are assumed to be collected only from several
anomaly types, leaving the majority of anomaly types not represented in the
collected anomaly dataset at all. To effectively leverage this kind of
incomplete anomalous knowledge represented by the collected anomalies, we
propose to learn a probability distribution that can not only model the normal
samples, but also guarantee to assign low density values for the collected
anomalies. To this end, an anomaly-aware generative adversarial network (GAN)
is developed, which, in addition to modeling the normal samples as most GANs
do, is able to explicitly avoid assigning probabilities for collected anomalous
samples. Moreover, to facilitate the computation of anomaly detection criteria
like reconstruction error, the proposed anomaly-aware GAN is designed to be
bidirectional, attaching an encoder for the generator. Extensive experimental
results demonstrate that our proposed method is able to effectively make use of
the incomplete anomalous information, leading to significant performance gains
compared to existing methods.


title:
Human Motion Control of Quadrupedal Robots using Deep Reinforcement
  Learning

abstract:
  A motion-based control interface promises flexible robot operations in
dangerous environments by combining user intuitions with the robot's motor
capabilities. However, designing a motion interface for non-humanoid robots,
such as quadrupeds or hexapods, is not straightforward because different
dynamics and control strategies govern their movements. We propose a novel
motion control system that allows a human user to operate various motor tasks
seamlessly on a quadrupedal robot. We first retarget the captured human motion
into the corresponding robot motion with proper semantics using supervised
learning and post-processing techniques. Then we apply the motion imitation
learning with curriculum learning to develop a control policy that can track
the given retargeted reference. We further improve the performance of both
motion retargeting and motion imitation by training a set of experts. As we
demonstrate, a user can execute various motor tasks using our system, including
standing, sitting, tilting, manipulating, walking, and turning, on simulated
and real quadrupeds. We also conduct a set of studies to analyze the
performance gain induced by each component.


title:
All bi-unitary perfect polynomials over $\F_2$ only divisible by $x$,
  $x+1$ and by Mersenne primes

abstract:
  We give all non splitting bi-unitary perfect polynomials over the prime field
of two elements, which have only Mersenne polynomials as odd irreducible
divisors.


title:
Policy Gradient Stock GAN for Realistic Discrete Order Data Generation
  in Financial Markets

abstract:
  This study proposes a new generative adversarial network (GAN) for generating
realistic orders in financial markets. In some previous works, GANs for
financial markets generated fake orders in continuous spaces because of GAN
architectures' learning limitations. However, in reality, the orders are
discrete, such as order prices, which has minimum order price unit, or order
types. Thus, we change the generation method to place the generated fake orders
into discrete spaces in this study. Because this change disabled the ordinary
GAN learning algorithm, this study employed a policy gradient, frequently used
in reinforcement learning, for the learning algorithm. Through our experiments,
we show that our proposed model outperforms previous models in generated order
distribution. As an additional benefit of introducing the policy gradient, the
entropy of the generated policy can be used to check GAN's learning status. In
the future, higher performance GANs, better evaluation methods, or the
applications of our GANs can be addressed.


title: An Overview of Color Transfer and Style Transfer for Images and Videos

abstract:
  Image or video appearance features (e.g., color, texture, tone, illumination,
and so on) reflect one's visual perception and direct impression of an image or
video. Given a source image (video) and a target image (video), the image
(video) color transfer technique aims to process the color of the source image
or video (note that the source image or video is also referred to the reference
image or video in some literature) to make it look like that of the target
image or video, i.e., transferring the appearance of the target image or video
to that of the source image or video, which can thereby change one's perception
of the source image or video. As an extension of color transfer, style transfer
refers to rendering the content of a target image or video in the style of an
artist with either a style sample or a set of images through a style transfer
model. As an emerging field, the study of style transfer has attracted the
attention of a large number of researchers. After decades of development, it
has become a highly interdisciplinary research with a variety of artistic
expression styles can be achieved. This paper provides an overview of color
transfer and style transfer methods over the past years.


title: Temporal Progressive Attention for Early Action Prediction

abstract:
  Early action prediction deals with inferring the ongoing action from
partially-observed videos, typically at the outset of the video. We propose a
bottleneck-based attention model that captures the evolution of the action,
through progressive sampling over fine-to-coarse scales. Our proposed Temporal
Progressive (TemPr) model is composed of multiple attention towers, one for
each scale. The predicted action label is based on the collective agreement
considering confidences of these attention towers. Extensive experiments over
three video datasets showcase state-of-the-art performance on the task of Early
Action Prediction across a range of backbone architectures. We demonstrate the
effectiveness and consistency of TemPr through detailed ablations.


title: A robust Bayesian analysis of variable selection under prior ignorance

abstract:
  We propose a cautious Bayesian variable selection routine by investigating
the sensitivity of a hierarchical model, where the regression coefficients are
specified by spike and slab priors. We exploit the use of latent variables to
understand the importance of the co-variates. These latent variables also allow
us to obtain the size of the model space which is an important aspect of high
dimensional problems. In our approach, instead of fixing a single prior, we
adopt a specific type of robust Bayesian analysis, where we consider a set of
priors within the same parametric family to specify the selection probabilities
of these latent variables. We achieve that by considering a set of expected
prior selection probabilities, which allows us to perform a sensitivity
analysis to understand the effect of prior elicitation on the variable
selection. The sensitivity analysis provides us sets of posteriors for the
regression coefficients as well as the selection indicators and we show that
the posterior odds of the model selection probabilities are monotone with
respect to the prior expectations of the selection probabilities. We also
analyse synthetic and real life datasets to illustrate our cautious variable
selection method and compare it with other well known methods.


title:
BAGNet: Bidirectional Aware Guidance Network for Malignant Breast
  lesions Segmentation

abstract:
  Breast lesions segmentation is an important step of computer-aided diagnosis
system, and it has attracted much attention. However, accurate segmentation of
malignant breast lesions is a challenging task due to the effects of
heterogeneous structure and similar intensity distributions. In this paper, a
novel bidirectional aware guidance network (BAGNet) is proposed to segment the
malignant lesion from breast ultrasound images. Specifically, the bidirectional
aware guidance network is used to capture the context between global
(low-level) and local (high-level) features from the input coarse saliency map.
The introduction of the global feature map can reduce the interference of
surrounding tissue (background) on the lesion regions. To evaluate the
segmentation performance of the network, we compared with several
state-of-the-art medical image segmentation methods on the public breast
ultrasound dataset using six commonly used evaluation metrics. Extensive
experimental results indicate that our method achieves the most competitive
segmentation results on malignant breast ultrasound images.


title:
Actor-Critic Scheduling for Path-Aware Air-to-Ground Multipath
  Multimedia Delivery

abstract:
  Reinforcement Learning (RL) has recently found wide applications in network
traffic management and control because some of its variants do not require
prior knowledge of network models. In this paper, we present a novel scheduler
for real-time multimedia delivery in multipath systems based on an Actor-Critic
(AC) RL algorithm. We focus on a challenging scenario of real-time video
streaming from an Unmanned Aerial Vehicle (UAV) using multiple wireless paths.
The scheduler acting as an RL agent learns in real-time the optimal policy for
path selection, path rate allocation and redundancy estimation for flow
protection. The scheduler, implemented as a module of the GStreamer framework,
can be used in real or simulated settings. The simulation results show that our
scheduler can target a very low loss rate at the receiver by dynamically
adapting in real-time the scheduling policy to the path conditions without
performing training or relying on prior knowledge of network channel models.


title:
Kinetic theory of the non-local electrodynamic response in anisotropic
  metals: skin effect in 2D systems

abstract:
  The electrodynamic response of ultra-pure materials at low temperatures
becomes spatially non-local. This non-locality gives rise to phenomena such as
hydrodynamic flow in transport and the anomalous skin effect in optics. In
systems characterized by an anisotropic electronic dispersion, the non-local
dynamics becomes dependent on the relative orientation of the sample with
respect to the applied field, in ways that go beyond the usual, homogeneous
response. Such orientational dependence should manifest itself not only in
transport experiments, as recently observed, but also in optical spectroscopy.
In this paper we develop a kinetic theory for the distribution function and the
transverse conductivity of two- and three-dimensional Fermi systems with
anisotropic electronic dispersion. By expanding the collision integral into the
eigenbasis of a collision operator, we include momentum-relaxing scattering as
well as momentum-conserving collisions. We examine the isotropic 2D case as a
reference, as well as anisotropic hexagonal and square Fermi-surface shapes. We
apply our theory to the quantitative calculation of the skin depth and the
surface impedance, in all regimes of skin effect. We find qualitative
differences between the frequency dependence of the impedance in isotropic and
anisotropic systems. In the latter case, we study the orientational dependence
of skin effect, thus providing guidance for the experimental study of non-local
optical effects.


title:
The management, treatment and study of skeletal pain harnessing tissue
  engineering models

abstract:
  Bone pain typically occurs immediately following skeletal damage with
mechanical distortion or rupture of nociceptive fibres. The pain mechanism is
also associated with chronic pain conditions where the healing process is
impaired. Any load impacting on the area of the fractured bone will increase
the intraosseous pressure, consequently stimulating the nociceptive response,
necessitating rapid clinical intervention to relieve pain associated with the
bone damage and appropriate mitigation of any processes involved with the loss
of bone mass, muscle, mobility and, to prevent death. The following review has
examined the mechanisms of pain associated with trauma or cancer-related
skeletal damage focusing on new approaches for the development of innovative
therapeutic interventions. In particular, the current review highlights tissue
engineering approaches that offer considerable promise in the application of
functional biomimetic fabrication of bone and nerve tissues, resembling the
pathological micro-environment. The strategic combination of bone and nerve
tissue engineered models provides significant potential to develop a new class
of in vitro platforms, capable of replacing in vivo models and testing the
safety and efficacy of novel drug treatments aimed at the resolution of
bone-associated pain. To date, the field of bone pain research has centred on
animal models, with accompanying disparity within models often noted and a
paucity of data correlating to the human physiological response. This review
explores the evident gap in pain drug development research and suggests a step
change in approach to harness tissue engineering technologies to recapitulate
the complex pathophysiological environment of the damaged bone tissue enabling
evaluation of the associated pain-mimicking mechanism with, ultimately,
significant therapeutic potential therein for improved patient quality of life.


title: UniTE: Unified Translation Evaluation

abstract:
  Translation quality evaluation plays a crucial role in machine translation.
According to the input format, it is mainly separated into three tasks, i.e.,
reference-only, source-only and source-reference-combined. Recent methods,
despite their promising results, are specifically designed and optimized on one
of them. This limits the convenience of these methods, and overlooks the
commonalities among tasks. In this paper, we propose UniTE, which is the first
unified framework engaged with abilities to handle all three evaluation tasks.
Concretely, we propose monotonic regional attention to control the interaction
among input segments, and unified pretraining to better adapt multi-task
learning. We testify our framework on WMT 2019 Metrics and WMT 2020 Quality
Estimation benchmarks. Extensive analyses show that our \textit{single model}
can universally surpass various state-of-the-art or winner methods across
tasks. Both source code and associated models are available at
https://github.com/NLP2CT/UniTE.


title: A Closer Look at Branch Classifiers of Multi-exit Architectures

abstract:
  Multi-exit architectures consist of a backbone and branch classifiers that
offer shortened inference pathways to reduce the run-time of deep neural
networks. In this paper, we analyze different branching patterns that vary in
their allocation of computational complexity for the branch classifiers.
Constant-complexity branching keeps all branches the same, while
complexity-increasing and complexity-decreasing branching place more complex
branches later or earlier in the backbone respectively. Through extensive
experimentation on multiple backbones and datasets, we find that
complexity-decreasing branches are more effective than constant-complexity or
complexity-increasing branches, which achieve the best accuracy-cost trade-off.
We investigate a cause by using knowledge consistency to probe the effect of
adding branches onto a backbone. Our findings show that complexity-decreasing
branching yields the least disruption to the feature abstraction hierarchy of
the backbone, which explains the effectiveness of the branching patterns.


title: Deep Generalized Unfolding Networks for Image Restoration

abstract:
  Deep neural networks (DNN) have achieved great success in image restoration.
However, most DNN methods are designed as a black box, lacking transparency and
interpretability. Although some methods are proposed to combine traditional
optimization algorithms with DNN, they usually demand pre-defined degradation
processes or handcrafted assumptions, making it difficult to deal with complex
and real-world applications. In this paper, we propose a Deep Generalized
Unfolding Network (DGUNet) for image restoration. Concretely, without loss of
interpretability, we integrate a gradient estimation strategy into the gradient
descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to
deal with complex and real-world image degradation. In addition, we design
inter-stage information pathways across proximal mapping in different PGD
iterations to rectify the intrinsic information loss in most deep unfolding
networks (DUN) through a multi-scale and spatial-adaptive way. By integrating
the flexible gradient descent and informative proximal mapping, we unfold the
iterative PGD algorithm into a trainable DNN. Extensive experiments on various
image restoration tasks demonstrate the superiority of our method in terms of
state-of-the-art performance, interpretability, and generalizability. The
source code is available at
https://github.com/MC-E/Deep-Generalized-Unfolding-Networks-for-Image-Restoration.


title:
Continual Learning with Bayesian Model based on a Fixed Pre-trained
  Feature Extractor

abstract:
  Deep learning has shown its human-level performance in various applications.
However, current deep learning models are characterised by catastrophic
forgetting of old knowledge when learning new classes. This poses a challenge
particularly in intelligent diagnosis systems where initially only training
data of a limited number of diseases are available. In this case, updating the
intelligent system with data of new diseases would inevitably downgrade its
performance on previously learned diseases. Inspired by the process of learning
new knowledge in human brains, we propose a Bayesian generative model for
continual learning built on a fixed pre-trained feature extractor. In this
model, knowledge of each old class can be compactly represented by a collection
of statistical distributions, e.g. with Gaussian mixture models, and naturally
kept from forgetting in continual learning over time. Unlike existing
class-incremental learning methods, the proposed approach is not sensitive to
the continual learning process and can be additionally well applied to the
data-incremental learning scenario. Experiments on multiple medical and natural
image classification tasks showed that the proposed approach outperforms
state-of-the-art approaches which even keep some images of old classes during
continual learning of new classes.


title:
Energy levels for $\mathcal{PT}$-symmetric deformation of the Mathieu
  equation

abstract:
  We propose a non-Hermitian deformation of the Mathieu equation that preserves
$\mathcal{PT}$ symmetry and study its spectrum and the transition from
$\mathcal{PT}$-unbroken to $\mathcal{PT}$-broken phases. We show that our model
not only reproduces behaviors expected by the literature but also indicates the
existence of a richer structure for the spectrum. We also discuss the influence
of the boundary condition and the model parameters in the exceptional line that
marks the $\mathcal{PT}$ breaking.


title: $Z_n$ symmetry in the vortex muon decay

abstract:
  Polarization of a vortex state fermion has rich structure due to the
nontrivial momentum distribution of wave function. This larger freedom provides
an unique opportunity to prepare fermions in exotic polarized states, which do
not exist for plane-wave state fermions. Based on the so called spin-orbit
state which was studied both theoretically and experimentally, we put forward a
peculiar vortex muon whose polarization exhibits $Z_n$ symmetry and study its
decay. We investigate the azimuthal distribution of the emitted electrons and
find that it exhibits the same symmetry ($Z_n$) as the initial state.


title: RoBLEURT Submission for the WMT2021 Metrics Task

abstract:
  In this paper, we present our submission to Shared Metrics Task: RoBLEURT
(Robustly Optimizing the training of BLEURT). After investigating the recent
advances of trainable metrics, we conclude several aspects of vital importance
to obtain a well-performed metric model by: 1) jointly leveraging the
advantages of source-included model and reference-only model, 2) continuously
pre-training the model with massive synthetic data pairs, and 3) fine-tuning
the model with data denoising strategy. Experimental results show that our
model reaching state-of-the-art correlations with the WMT2020 human annotations
upon 8 out of 10 to-English language pairs.


title: Attention Mechanism with Energy-Friendly Operations

abstract:
  Attention mechanism has become the dominant module in natural language
processing models. It is computationally intensive and depends on massive
power-hungry multiplications. In this paper, we rethink variants of attention
mechanism from the energy consumption aspects. After reaching the conclusion
that the energy costs of several energy-friendly operations are far less than
their multiplication counterparts, we build a novel attention model by
replacing multiplications with either selective operations or additions.
Empirical results on three machine translation tasks demonstrate that the
proposed model, against the vanilla one, achieves competitable accuracy while
saving 99\% and 66\% energy during alignment calculation and the whole
attention procedure. Code is available at: https://github.com/NLP2CT/E-Att.


title:
Protecting coherence from environment via Stark many-body localization
  in a Quantum-Dot Simulator

abstract:
  Semiconductor platforms are emerging as a promising architecture for storing
and processing quantum information e.g., in \emph{ quantum dot spin qubits}.
These setups have relatively low nuclear noise, but charge noise coming from
many body interactions between the electrons is a major limiting factor with
scalability for a useful quantum computer. Here we show how an electric field
gradient, readily obtainable with semiconductor quantum dots, can be engineered
to induce local quantum coherent dynamical $\ell-$bits that have potential to
be used as logical qubits. We take into account the full (interacting) electron
and phonon dynamics, and \emph{analytically}, from first principles without any
approximation, show that these dynamical $\ell-$bits are protected from
\emph{all} sources noise for exponentially long times provided only that the
electron-phonon interaction is not completely non-local. Our work opens a new
venue for passive quantum error correction and scaling of semiconductor quantum
computers.