title:
Theoretical Analysis of Deep Neural Networks in Physical Layer
  Communication

abstract:
  Recently, deep neural network (DNN)-based physical layer communication
techniques have attracted considerable interest. Although their potential to
enhance communication systems and superb performance have been validated by
simulation experiments, little attention has been paid to the theoretical
analysis. Specifically, most studies in the physical layer have tended to focus
on the application of DNN models to wireless communication problems but not to
theoretically understand how does a DNN work in a communication system. In this
paper, we aim to quantitatively analyze why DNNs can achieve comparable
performance in the physical layer comparing with traditional techniques, and
also drive their cost in terms of computational complexity. To achieve this
goal, we first analyze the encoding performance of a DNN-based transmitter and
compare it to a traditional one. And then, we theoretically analyze the
performance of DNN-based estimator and compare it with traditional estimators.
Third, we investigate and validate how information is flown in a DNN-based
communication system under the information theoretic concepts. Our analysis
develops a concise way to open the "black box" of DNNs in physical layer
communication, which can be applied to support the design of DNN-based
intelligent communication techniques and help to provide explainable
performance assessment.


title: StyleBERT: Chinese pretraining by font style information

abstract:
  With the success of down streaming task using English pre-trained language
model, the pre-trained Chinese language model is also necessary to get a better
performance of Chinese NLP task. Unlike the English language, Chinese has its
special characters such as glyph information. So in this article, we propose
the Chinese pre-trained language model StyleBERT which incorporate the
following embedding information to enhance the savvy of language model, such as
word, pinyin, five stroke and chaizi. The experiments show that the model
achieves well performances on a wide range of Chinese NLP tasks.


title:
Curve shortening flows on rotational surfaces generated by monotone
  convex functions

abstract:
  In this paper, we study curve shortening flows on rotational surfaces in
$\mathbb{R}^3$. We assume that the surfaces have negative Gauss curvatures and
that some condition related to the Gauss curvature and the curvature of
embedded curve holds on them. Under these assumptions, we prove that the curve
remains a graph over the parallels of the rotational surface along the flow.
Also, we prove the comparison principle and the long-time existence of the
flow.


title: Scale symmetry and composition of compact star matter

abstract:
  The dense compact star matter is studied by using the skyrmion crystal
approach. The chiral effective theory used includes the lightest scalar meson,
the lowest-lying vector mesons as well as pions. Consistency with the vector
manifestation and the dilaton limit fixed point at high density constrains the
anomalous dimension of the gluon field $1.0 \lesssim |\gamma_{G^2}| \lesssim
2.0$ and leads to the significance of the scale symmetry breaking in the
intrinsic parity-odd part of the effective theory. The speed of sound $v_s^2
\simeq 1/3$ and the polytropic index $\gamma \simeq 1$ -- both satisfy the
conformal limits -- after the dilaton limit fixed point at high density but the
matter is still in the hadronic phase. This means that neither the conformal
speed of sound nor the smallness of the polytropic index can be used as a
criterion of the onset of quark matter. These conclusions are significant for
constructing the equation of state of nuclear matter.


title:
A novel, computationally tractable algorithm flags in big matrices every
  column associated in any way with others or a dependent variable, with much
  higher power when columns are linked like mutations in chromosomes

abstract:
  Scanning exhaustively a big data matrix DM for subsets of independent
variables IVs that are associated with a dependent variable DV is
computationally tractable only for 1- and 2-IV effects. I present a highly
computationally tractable Participation-In-Association Score (PAS) that in a DM
with markers flags every column that is strongly associated with others. PAS
examines no column subsets and its computational cost grows linearly with DM
columns, remaining reasonable even in million-column DMs. PAS exploits how
associations of markers in DM rows cause matches associations in the rows'
pairwise comparisons. For every such comparison with a match at a tested
column, PAS computes the other matches by modifying the comparison's total
matches (scored once per DM), yielding a distribution of conditional matches
that is perturbed by associations of the tested column. Equally tractable is
dvPAS that flags DV-associated IVs by permuting the markers in the DV. P values
are obtained by permutation and Sidak-corrected for multiple tests, bypassing
model selection. Simulations show that i) PAS and dvPAS generate
uniform-(0,1)-distributed type I error in null DMs and ii) detect randomly
encountered binary and trinary models of significant n-column association and
n-IV association with a binary DV, respectively, with power in the order of
magnitude of exhaustive evaluation's and false positives that are
uniform-(0,1)-distributed or straightforwardly tuned to be so. Power to detect
2-way DV-associated 100-marker+ runs is non-parametrically ultimate but that to
detect pure n-column associations and pure n-IV DV associations sinks
exponentially as n increases. Power increases about twofold in trinary vs.
binary DMs and in a major way when there are background associations like
between mutations in chromosomes, specially in trinary DMs where dvPAS filters
said background most effectively.


title: Application and issues in abstract convexity

abstract:
  The theory of abstract convexity, also known as convexity without linearity,
is an extension of the classical convex analysis. There are a number of
remarkable results, mostly concerning duality, and some numerical methods,
however, this area has not found many practical applications yet. In this paper
we study the application of abstract convexity to function approximation.
Another important research direction addressed in this paper is the connection
with the so-called axiomatic convexity.


title:
A Novel Distributed Database Architectural Model for Mobile Cloud
  Computing

abstract:
  Cloud computing is the way by which we connect to servers, large systems into
a distributed secure manner without worrying about local memory limits. Here
this paper we proposed a Novel Distributed Database Architectural Model for
Mobile Cloud Computing (NDDAMMCC). Due to the exponential growth of wireless
technologies and internet which are following Nielsen's Law of Internet
Bandwidth, we are in the new era of cloud computing. In the recent
technological era, smart mobile devices play a big role in all sort of
day-by-day human needs. The applicability is so huge that the number of apps
install on a mobile system becomes a hazard due to local memory limitations for
mobile phone users and demands an alternative approach to solve this local
memory problems. Mobile Cloud Computing (MCC) is the ultimate solution to this
issue and our model presents a promising path in this new kind of cloud
computing technology.


title:
Time-dependent stellar-mass binary black hole mergers in AGN disks: Mass
  distribution of hierarchical mergers

abstract:
  There is much debate about the channels for astrophysical origins of the
stellar-mass binary black hole (BBH) mergers detected by LIGO and Virgo. Active
galactic nuclei (AGNs) are promising sites for the efficient formation and
rapid mergers of BBHs due to migration traps in high-density gas disks within
an inner radii. In this paper, we carry out Monte Carlo simulations to explore
the mass properties of mergers over time and hierarchical mergers -- with one
of the black holes (BHs) being the remnant of a previous merger. We find that
the predicted merger rate is $\sim 27-37~{\rm Gpc^{-3}~yr^{-1}}$ and the
detection rate of LIGO/Virgo accompanying with extreme mass-ratio mergers will
increase in the late stage of AGNs. The fraction of hierarchical mergers is
$\sim 24\%$, and its mass-ratio peak is $\sim 0.15-0.35$. Compared with the
low-generation mergers in hierarchical mergers, the mass ratio of
high-generation mergers has a flatter distribution around its peak. These
reveal that the BBH mergers detected by LIGO/Virgo with the extreme mass ratio
or heavy component masses can be well explained in AGN channel.


title: The Thirty Millimeter Telescope

abstract:
  A near-infrared telescope with an effective aperture diameter of thirty
millimeters has been developed. The primary objective of the development is to
observe northern bright stars in the $J$, $H$, and $K_{\rm s}$ bands and
provide accurate photometric data on those stars. The second objective is to
repeatedly observe a belt-like region along the northern Galactic plane ($|b|
\le 5^\circ$ and $\delta \ge -30^\circ$) to monitor bright variable stars
there. The telescope has been in use since December 2016. The purpose of this
paper is to describe the design and operational performances of the telescope,
photometric calibration methods, and our scientific goals. We show that the
telescope has the ability to provide photometry with an uncertainty of less
than 5\% for stars brighter than 7, 6.5, and 6~mag in the $J$, $H$, and $K_{\rm
s}$ bands, respectively. The repeatability of the photometric measurements for
the same star is better than 1\% for bright stars. Our observations will
provide accurate photometry on bright stars that are lacking in the Two Micron
Sky Survey and the Two Micron All-Sky Survey. Repeated observations at a good
cadence will also reveal their nature of the variability in the near-infrared.


title: Anisotropy of the magnetic and transport properties in EuZn$_2$As$_2$

abstract:
  Several recent studies have shown that the anisotropy in the magnetic
structure of \ECA\ plays a significant role in stabilizing the Weyl nodes. To
investigate the relationship between magnetic anisotropy and Weyl physics, we
present a comparative study between EuZn$_2$As$_2$ and EuCd$_2$As$_2$ that are
isostructural but with different magnetic anisotropy. We performed structural
analysis, electronic transport, and magnetization experiments on
millimeter-sized single crystals of EuZn$_2$As$_2$, and compared the results to
those of EuCd$_2$As$_2$. By combining the first principle calculations and
neutron diffraction experiment, we identify the magnetic ground state of
EuZn$_2$As$_2$ as A-type antiferromagnetic order with a transition temperature
($T_\mathrm{N}$ = 19.6 K) twice that of EuCd$_2$As$_2$. Like EuCd$_2$As$_2$,
the negative magnetoresistance of EuZn$_2$As$_2$ is observed after suppressing
the resistivity peak at $T_\mathrm{N}$ with increasing fields. However, the
anisotropy in both transport and magnetization are much reduced in
EuZn$_2$As$_2$. The difference could be ascribed to the weaker spin-orbit
coupling, more localized $d$-orbitals, and a larger contribution from the Eu
$s$-orbitals in the zinc compound, as suggested by the electronic band
calculations. The same band structure effect could be also responsible for the
observation of a smaller non-linear anomalous Hall effect in EuZn$_2$As$_2$
compared to EuCd$_2$As$_2$.


title:
Photometric Redshift Estimation with Convolutional Neural Networks and
  Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods

abstract:
  Deep Learning models have been increasingly exploited in astrophysical
studies, yet such data-driven algorithms are prone to producing biased outputs
detrimental for subsequent analyses. In this work, we investigate two major
forms of biases, i.e., class-dependent residuals and mode collapse, in a case
study of estimating photometric redshifts as a classification problem using
Convolutional Neural Networks (CNNs) and galaxy images with spectroscopic
redshifts. We focus on point estimates and propose a set of consecutive steps
for resolving the two biases based on CNN models, involving representation
learning with multi-channel outputs, balancing the training data and leveraging
soft labels. The residuals can be viewed as a function of spectroscopic
redshifts or photometric redshifts, and the biases with respect to these two
definitions are incompatible and should be treated in a split way. We suggest
that resolving biases in the spectroscopic space is a prerequisite for
resolving biases in the photometric space. Experiments show that our methods
possess a better capability in controlling biases compared to benchmark
methods, and exhibit robustness under varying implementing and training
conditions provided with high-quality data. Our methods have promises for
future cosmological surveys that require a good constraint of biases, and may
be applied to regression problems and other studies that make use of
data-driven models. Nonetheless, the bias-variance trade-off and the demand on
sufficient statistics suggest the need for developing better methodologies and
optimizing data usage strategies.


title:
Chiral superconductivity in cuprates mediated by spin-orbit coupling to
  spinon superfluidity

abstract:
  We utilize the Hubbard model to demonstrate that doping of the
antiferromagnetic parent compounds of cuprate superconductors stabilizes a spin
liquid state. Superconductivity in such a state emerges due to the spin-orbit
coupling between charge current and superfluidity of spinon condensate,
resulting in a chiral relation between the order parameter phase gradient and
supercurrent. We propose simple experimental tests for the presented mechanism.

categories: cond-mat.supr-con cond-mat.mtrl-sci cond-mat.str-el quant-ph


title:
A New Magnetic Parameter of Active Regions Distinguishing Large Eruptive
  and Confined Solar Flares

abstract:
  With the aim of investigating how the magnetic field in solar active regions
(ARs) controls flare activity, i.e., whether a confined or eruptive flare
occurs, we analyze 106 flares of Geostationary Operational Environmental
Satellite (GOES) class $\geq$M1.0 during 2010$-$2019. We calculate mean
characteristic twist parameters $\alpha$$_{FPIL}$ within the "flaring polarity
inversion line" region and $\alpha$$_\mathrm{HFED}$ within the area of high
photospheric magnetic free energy density, which both provide measures of the
nonpotentiality of AR core region. Magnetic twist is thought to be related to
the driving force of electric current-driven instabilities, such as the helical
kink instability. We also calculate total unsigned magnetic flux
($\Phi$$_\mathrm{AR}$) of ARs producing the flare, which describes the strength
of the background field confinement. By considering both the constraining
effect of background magnetic fields and the magnetic non-potentiality of ARs,
we propose a new parameter $\alpha$/$\Phi$$_\mathrm{AR}$ to measure the
probability for a large flare to be associated with a coronal mass ejection
(CME). We find that in about 90\% of eruptive flares,
$\alpha$$_\mathrm{FPIL}$/$\Phi$$_\mathrm{AR}$ and
$\alpha$$_\mathrm{HFED}$/$\Phi$$_\mathrm{AR}$ are beyond critical values
(2.2$\times$$10^{-24}$ and 3.2$\times$$10^{-24}$ Mm$^{-1}$ Mx$^{-1}$), whereas
they are less than critical values in $\sim$ 80\% of confined flares. This
indicates that the new parameter $\alpha$/$\Phi$$_\mathrm{AR}$ is well able to
distinguish eruptive flares from confined flares. Our investigation suggests
that the relative measure of magnetic nonpotentiality within the AR core over
the restriction of the background field largely controls the capability of ARs
to produce eruptive flares.


title:
A Deep Learning Model for Forecasting Global Monthly Mean Sea Surface
  Temperature Anomalies

abstract:
  Sea surface temperature (SST) variability plays a key role in the global
weather and climate system, with phenomena such as El Ni\~{n}o-Southern
Oscillation regarded as a major source of interannual climate variability at
the global scale. The ability to be able to make long-range forecasts of sea
surface temperature anomalies, especially those associated with extreme marine
heatwave events, has potentially significant economic and societal benefits. We
have developed a deep learning time series prediction model (Unet-LSTM) based
on more than 70 years (1950-2021) of ECMWF ERA5 monthly mean sea surface
temperature and 2-metre air temperature data. The Unet-LSTM model is able to
learn the underlying physics driving the temporal evolution of the
2-dimensional global sea surface temperatures. The model accurately predicts
sea surface temperatures over a 24 month period with a root mean square error
remaining below 0.75$^\circ$C for all predicted months. We have also
investigated the ability of the model to predict sea surface temperature
anomalies in the Ni\~{n}o3.4 region, as well as a number of marine heatwave hot
spots over the past decade. Model predictions of the Ni\~{n}o3.4 index allow us
to capture the strong 2010-11 La Ni\~{n}a, 2009-10 El Nino and the 2015-16
extreme El Ni\~{n}o up to 24 months in advance. It also shows long lead
prediction skills for the northeast Pacific marine heatwave, the Blob. However,
the prediction of the marine heatwaves in the southeast Indian Ocean, the
Ningaloo Ni\~{n}o, shows limited skill. These results indicate the significant
potential of data driven methods to yield long-range predictions of sea surface
temperature anomalies.


title:
Cutting feedback and modularized analyses in generalized Bayesian
  inference

abstract:
  Even in relatively simple settings, model misspecification can make the
application and interpretation of Bayesian inference difficult. One approach to
make Bayesian analyses fit-for-purpose in the presence of model
misspecification is the use of cutting feedback methods. These methods modify
conventional Bayesian inference by limiting the influence of one part of the
model by "cutting" the link between certain components. We examine cutting
feedback methods in the context of generalized posterior distributions, i.e.,
posteriors built from arbitrary loss functions, and provide novel results on
their behaviour. A direct product of our results are diagnostic tools that
allow for the quick, and easy, analysis of two key features of cut posterior
distributions: one, how uncertainty about the model unknowns in one component
impacts inferences about unknowns in other components; two, how the
incorporation of additional information impacts the cut posterior distribution.


title:
Sensing as A Service in 6G Perceptive Networks: A Unified Framework for
  ISAC Resource Allocation

abstract:
  In the upcoming next-generation (5G-Advanced and 6G) wireless networks,
sensing as a service will play a more important role than ever before.
Recently, the concept of perceptive network is proposed as a paradigm shift
that provides sensing and communication (S&C) services simultaneously. This
type of technology is typically referred to as Integrated Sensing and
Communications (ISAC). In this paper, we propose the concept of sensing quality
of service (QoS) in terms of diverse applications. Specifically, the
probability of detection, the Cramer-Rao bound (CRB) for parameter estimation
and the posterior CRB for moving target indication are employed to measure the
sensing QoS for detection, localization, and tracking, respectively. Then, we
establish a unified framework for ISAC resource allocation, where the fairness
and the comprehensiveness optimization criteria are considered for the
aforementioned sensing services. The proposed schemes can flexibly allocate the
limited power and bandwidth resources according to both S&C QoSs. Finally, we
study the performance trade-off between S&C services in different resource
allocation schemes by numerical simulations.


title: Extremal Dependence in Australian Electricity Markets

abstract:
  Electricity markets are significantly more volatile than other comparable
financial or commodity markets. Extreme price outcomes and their transmission
between regions pose significant risks for market participants. We examine the
dependence between extreme spot price outcomes in the Australian National
Electricity Market (NEM). We investigate extremal dependence both in a
univariate and multivariate setting, applying the extremogram developed by
Davis and Mikosch (2009) and Davis et al. (2011, 2012). We measure the
persistence of extreme prices within individual regional markets and the
transmission of extreme prices across different regions. With both 5-minute and
30-minute price data, we find that extreme prices are more persistent in the
market with a higher share of intermittent renewable energy. We also find that
persistence of extreme prices is more prevalent in more concentrated markets.
We also show significant extremal price dependence between different regions,
which is typically stronger between physically interconnected markets. The
dependence structure of extreme prices shows asymmetric and time-dependent
patterns. Applying the extremograms, we further show the effectiveness of the
Australian Energy Market Commission's 2016 rebidding rule with respect to
reducing the share of isolated price spikes that are often considered as an
indication of strategic bidding. Our results provide important information for
hedging decisions of market participants and for policy makers who aim to
reduce market volatility and extreme price outcomes through effective
regulations which guide the trading behaviour of market participants as well as
improved network interconnections.


title: Deep Feature based Cross-slide Registration

abstract:
  Cross-slide image analysis provides additional information by analysing the
expression of different biomarkers as compared to a single slide analysis.
These biomarker stained slides are analysed side by side, revealing unknown
relations between them. During the slide preparation, a tissue section may be
placed at an arbitrary orientation as compared to other sections of the same
tissue block. The problem is compounded by the fact that tissue contents are
likely to change from one section to the next and there may be unique artefacts
on some of the slides. This makes registration of each section to a reference
section of the same tissue block an important pre-requisite task before any
cross-slide analysis. We propose a deep feature based registration (DFBR)
method which utilises data-driven features to estimate the rigid
transformation. We adopted a multi-stage strategy for improving the quality of
registration. We also developed a visualisation tool to view registered pairs
of WSIs at different magnifications. With the help of this tool, one can apply
a transformation on the fly without the need to generate transformed source WSI
in a pyramidal form. We compared the performance of data-driven features with
that of hand-crafted features on the COMET dataset. Our approach can align the
images with low registration errors. Generally, the success of non-rigid
registration is dependent on the quality of rigid registration. To evaluate the
efficacy of the DFBR method, the first two steps of the ANHIR winner's
framework are replaced with our DFBR to register challenge provided image
pairs. The modified framework produces comparable results to that of challenge
winning team.


title: Towards fully two-dimensional spintronic devices

abstract:
  Within the field of spintronics major efforts are directed towards developing
applications for spin-based transport devices made fully out of two-dimensional
(2D) materials. In this work we present an experimental realization of a
spin-valve device where the generation of the spin signal is exclusively
attributed to the spin-dependent conductivity of the magnetic graphene
resulting from the proximity of an interlayer antiferromagnet, CrSBr. We
clearly demonstrate that the usage of the conventional 3D magnetic contacts,
that are commonly air-sensitive and incompatible with practical technologies,
can be fully avoided when graphene/CrSBr heterostructures are employed.
Moreover, apart from providing exceptionally long spin relaxation length, the
usage of graphene for both generation and transport of the spin allows to
automatically avoid the conductivity mismatch between the source and the
channel circuits that has to be considered when using conventional
low-resistive contacts. Our results address a necessary step in the engineering
of spintronic circuitry out of layered materials and precede further
developments in the area of complex spin-logic devices. Moreover, we introduce
a fabrication procedure where we designed and implemented a recipe for the
preparation of electrodes via a damage-free technique that offers an immediate
advantage in the fields of air-sensitive and delicate organic materials.


title:
Fuzzy Cluster Analysis: Application to Determining Metallicities for
  Very Metal-poor Stars

abstract:
  This work presents a first attempt to apply fuzzy cluster analysis (FCA) to
analyzing stellar spectra. FCA is adopted to categorize line indices measured
from LAMOST low-resolution spectra, and automatically remove the least
metallicity-sensitive indices. The FCA-processed indices are then transferred
to the artificial neural network (ANN) to derive metallicities for 147 very
metal-poor (VMP) stars that have been analyzed by high-resolution spectroscopy.
The FCA-ANN method could derive robust metallicities for VMP stars, with a
precision of 0.2 dex compared with high-resolution analysis. The recommended
FCA threshold value \lambda for this test is between 0.9965 and 0.9975. After
reducing the dimension of the line indices through FCA, the derived
metallicities are still robust, with no loss of accuracy, and the FCA-ANN
method performs stably for different spectral quality from [Fe/H] from -1.8
down to -3.5. Compared with traditional classification methods, FCA considers
ambiguity in groupings and noncontinuity of data, and is thus more suitable for
observational data analysis. Though this early test uses FCA to analyze
low-resolution spectra, and feeds the input to the ANN method to derive
metallicities, FCA should be able to, in the large data era, also analyze
slitless spectroscopy and multiband photometry, and prepare the input for
methods not limited to ANN, in the field of stellar physics for other studies,
e.g., stellar classification, identification of peculiar objects. The
literature-collected high-resolution sample can help improve pipelines to
derive stellar metallicities, and systematic offsets in metallicities for VMP
stars for three published LAMOST catalogs have been discussed.


title: A shifted Mahler measure identity for Boyd's family

abstract:
  Recently the second author and Qin numerically verified some Mahler measure
identities of genus 2 and 3 polynomial families. In this paper, we use the
elliptic regulator to prove an identity invoving shifted Mahler measure for
Boyd's family.


title:
Prospects for Distinguishing Supernova Models Using a Future Neutrino
  Signal

abstract:
  The next Galactic core-collapse supernova (SN) should yield a large number of
observed neutrinos. Using Bayesian techniques, we show that with an SN at a
known distance up to 25 kpc, the neutrino events in a water Cherenkov detector
similar to Super-Kamiokande (SK) could be used to distinguish between seven
one-dimensional neutrino emission models assuming no flavor oscillations or the
standard Mikheyev-Smirnov-Wolfenstein effect. Some of these models could still
be differentiated with an SN at a known distance of 50 kpc. We also consider
just the relative distributions of neutrino energy and arrival time predicted
by the models and find that a detector like SK meets the requirement to
distinguish between these distributions with an SN at an unknown distance up to
$\sim 10$ kpc.


title:
Goal-directed Planning and Goal Understanding by Active Inference:
  Evaluation Through Simulated and Physical Robot Experiments

abstract:
  We show that goal-directed action planning and generation in a teleological
framework can be formulated using the free energy principle. The proposed
model, which is built on a variational recurrent neural network model, is
characterized by three essential features. These are that (1) goals can be
specified for both static sensory states, e.g., for goal images to be reached
and dynamic processes, e.g., for moving around an object, (2) the model can not
only generate goal-directed action plans, but can also understand goals by
sensory observation, and (3) the model generates future action plans for given
goals based on the best estimate of the current state, inferred using past
sensory observations. The proposed model is evaluated by conducting experiments
on a simulated mobile agent as well as on a real humanoid robot performing
object manipulation.


title: RTGNN: A Novel Approach to Model Stochastic Traffic Dynamics

abstract:
  Modeling stochastic traffic dynamics is critical to developing self-driving
cars. Because it is difficult to develop first principle models of cars driven
by humans, there is great potential for using data driven approaches in
developing traffic dynamical models. While there is extensive literature on
this subject, previous works mainly address the prediction accuracy of
data-driven models. Moreover, it is often difficult to apply these models to
common planning frameworks since they fail to meet the assumptions therein. In
this work, we propose a new stochastic traffic model, Recurrent Traffic Graph
Neural Network (RTGNN), by enforcing additional structures on the model so that
the proposed model can be seamlessly integrated with existing motion planning
algorithms. RTGNN is a Markovian model and is able to infer future traffic
states conditioned on the motion of the ego vehicle. Specifically, RTGNN uses a
definition of the traffic state that includes the state of all players in a
local region and is therefore able to make joint predictions for all agents of
interest. Meanwhile, we explicitly model the hidden states of agents,
"intentions," as part of the traffic state to reflect the inherent partial
observability of traffic dynamics. The above mentioned properties are critical
for integrating RTGNN with motion planning algorithms coupling prediction and
decision making. Despite the additional structures, we show that RTGNN is able
to achieve state-of-the-art accuracy through comparisons with other similar
works.


title: QCD improved top-quark decay at next-to-next-to-leading order

abstract:
  We analyse the top-quark decay at the next-to-next-to-leading order (NNLO) in
QCD by using the Principle of Maximum Conformality (PMC) which provides a
systematic way to eliminate renormalization scheme and scale ambiguities in
perturbative QCD predictions. The PMC renormalization scales of the coupling
constant $\alpha_s$ are determined by absorbing the non-conformal $\beta$ terms
that govern the behavior of the running coupling by using the Renormalization
Group Equation (RGE). We obtain the PMC scale $Q_\star=15.7$ GeV for the
top-quark decay, which is an order of magnitude smaller than the conventional
choice $\mu_r=m_t$. Our analyses reveal that the effective momentum flow for
the top-quark decay is not the conventionally suggested $\mu_r=m_t$, it should
be $\mu_r\ll m_t$. Moreover, due to the non-conformal $\beta$ terms disappear
in the pQCD series, there is no renormalon divergence and the convergence of
the pQCD series for the top-quark decay is greatly improved. By further
including the next-to-leading (NLO) electroweak corrections, the finite $W$
boson width and the finite bottom quark mass, we obtain the top-quark total
decay width $\Gamma^{\rm tot}_t|_{\rm PMC}=1.3383^{+0.0276}_{-0.0273}$ GeV,
where the error is the squared averages of the top-quark mass $\Delta m_t=\pm1$
GeV, the coupling constant $\Delta \alpha_s(M_Z)=\pm0.0009$ and the estimation
of unknown higher-order terms. The PMC improved predictions for the top-quark
decay are complementary to the previous PMC calculations for top-quark pair
production and helpful for detailed studies of properties of the top-quark.


title:
Audio Visual Scene-Aware Dialog Generation with Transformer-based Video
  Representations

abstract:
  There have been many attempts to build multimodal dialog systems that can
respond to a question about given audio-visual information, and the
representative task for such systems is the Audio Visual Scene-Aware Dialog
(AVSD). Most conventional AVSD models adopt the Convolutional Neural Network
(CNN)-based video feature extractor to understand visual information. While a
CNN tends to obtain both temporally and spatially local information, global
information is also crucial for boosting video understanding because AVSD
requires long-term temporal visual dependency and whole visual information. In
this study, we apply the Transformer-based video feature that can capture both
temporally and spatially global representations more efficiently than the
CNN-based feature. Our AVSD model with its Transformer-based feature attains
higher objective performance scores for answer generation. In addition, our
model achieves a subjective score close to that of human answers in DSTC10. We
observed that the Transformer-based visual feature is beneficial for the AVSD
task because our model tends to correctly answer the questions that need a
temporally and spatially broad range of visual information.


title:
Quantum Multi-Parameter Adaptive Bayesian Estimation and Application to
  Super-Resolution Imaging

abstract:
  In Bayesian estimation theory, the estimator ${\hat \theta} = E[\theta|l]$
attains the minimum mean squared error (MMSE) for estimating a scalar parameter
of interest $\theta$ from the observation of $l$ through a noisy channel
$P_{l|\theta}$, given a prior $P_\theta$ on $\theta$. In quantum sensing tasks,
the user gets $\rho_\theta$, the quantum state that encodes $\theta$. They
choose a measurement, a positive-operator valued measure (POVM) $\Pi_l$, which
induces the channel $P_{l|\theta} = {\rm Tr}(\rho_\theta \Pi_l)$ to the
measurement outcome $l$, on which the aforesaid classical MMSE estimator is
employed. Personick found the optimum POVM $\Pi_l$ that minimizes the MMSE over
all possible measurements, and that MMSE. This result from 1971 is less-widely
known than the quantum Fisher information (QFI), which lower bounds the
variance of an unbiased estimator over all measurements, when $P_\theta$ is
unavailable. For multi-parameter estimation, i.e., when $\theta$ is a vector,
in Fisher quantum estimation theory, the inverse of the QFI matrix provides an
operator lower bound to the covariance of an unbiased estimator. However, there
has been little work on quantifying quantum limits and measurement designs, for
multi-parameter quantum estimation in the {\em Bayesian} setting. In this
paper, we build upon Personick's result to construct a Bayesian adaptive
measurement scheme for multi-parameter estimation when $N$ copies of
$\rho_\theta$ are available. We illustrate an application to localizing a
cluster of point emitters in a highly sub-Rayleigh angular field-of-view, an
important problem in fluorescence microscopy and astronomy. Our algorithm
translates to a multi-spatial-mode transformation prior to a photon-detection
array, with electro-optic feedback to adapt the mode sorter. We show that this
receiver performs far superior to quantum-noise-limited focal-plane direct
imaging.


title:
Berman Codes: A Generalization of Reed-Muller Codes that Achieve BEC
  Capacity

abstract:
  We identify a family of binary codes whose structure is similar to
Reed-Muller (RM) codes and which include RM codes as a strict subclass. The
codes in this family are denoted as $C_n(r,m)$, and their duals are denoted as
$B_n(r,m)$. The length of these codes is $n^m$, where $n \geq 2$, and $r$ is
their `order'. When $n=2$, $C_n(r,m)$ is the RM code of order $r$ and length
$2^m$. The special case of these codes corresponding to $n$ being an odd prime
was studied by Berman (1967) and Blackmore and Norton (2001). Following the
terminology introduced by Blackmore and Norton, we refer to $B_n(r,m)$ as the
Berman code and $C_n(r,m)$ as the dual Berman code. We identify these codes
using a recursive Plotkin-like construction, and we show that these codes have
a rich automorphism group, they are generated by the minimum weight codewords,
and that they can be decoded up to half the minimum distance efficiently. Using
a result of Kumar et al. (2016), we show that these codes achieve the capacity
of the binary erasure channel (BEC) under bit-MAP decoding. Furthermore, except
double transitivity, they satisfy all the code properties used by Reeves and
Pfister to show that RM codes achieve the capacity of binary-input memoryless
symmetric channels. Finally, when $n$ is odd, we identify a large class of
abelian codes that includes $B_n(r,m)$ and $C_n(r,m)$ and which achieves BEC
capacity.


title:
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for
  Visual Reinforcement Learning

abstract:
  One of the key challenges in visual Reinforcement Learning (RL) is to learn
policies that can generalize to unseen environments. Recently, data
augmentation techniques aiming at enhancing data diversity have demonstrated
proven performance in improving the generalization ability of learned policies.
However, due to the sensitivity of RL training, naively applying data
augmentation, which transforms each pixel in a task-agnostic manner, may suffer
from instability and damage the sample efficiency, thus further exacerbating
the generalization performance. At the heart of this phenomenon is the diverged
action distribution and high-variance value estimation in the face of augmented
images. To alleviate this issue, we propose Task-aware Lipschitz Data
Augmentation (TLDA) for visual RL, which explicitly identifies the
task-correlated pixels with large Lipschitz constants, and only augments the
task-irrelevant pixels. To verify the effectiveness of TLDA, we conduct
extensive experiments on DeepMind Control suite, CARLA and DeepMind
Manipulation tasks, showing that TLDA improves both sample efficiency in
training time and generalization in test time. It outperforms previous
state-of-the-art methods across the 3 different visual control benchmarks.


title: Chaos for foliated spaces and pseudogroups

abstract:
  We generalize "sensitivity to initial conditions" to foliated spaces and
pseudogroups, offering a definition of Devaney chaos in this setting. In
contrast to the case of group actions, where sensitivity follows from the other
two conditions of Devaney chaos, we show that this is true only for compact
foliated spaces, exhibiting a counterexample in the non-compact case. Finally,
we obtain an analogue of the Auslander-Yorke dichotomy for compact foliated
spaces and compactly generated pseudogroups.


title:
Experimental signatures of versatile Weyl semimetal in pyrochlore
  iridate with spin-ice like magnetic orders

abstract:
  We report experimental signatures of topological transitions among the Weyl
semimetal states of pyrochlore Pr2Ir2O7, where the Kondo coupling between the
Ir topological electrons and the spin-ice like orders of Pr moments plays a
decisive role. The magnetic-field dependence of resistivity and the Hall
conductivity exhibits a plateau and a sharp jump associated with a
magnetic-field hysteresis, similar to a liquid-gas-like transition in dipolar
spin ice system. Furthermore, the Kondo coupling is controlled by the
hydrostatic pressure, revealing that the field-induced displacement of Weyl
points in the momentum space strongly depends on the respective electronic
state as well as on the Kondo coupling strength. These observations pave a
route toward the engineering of band topology in hybrid quantum materials with
relativistic conduction electrons and localized magnetic moments.


title: Monopoly, Product Quality, and Flexible Learning

abstract:
  A seller offers a buyer a schedule of transfers and associated product
qualities, as in Mussa and Rosen (1978). After observing this schedule, the
buyer chooses a flexible costly signal about his type. We show it is without
loss to focus on a class of mechanisms that compensate the buyer for his
learning costs. Using these mechanisms, we prove the quality always lies
strictly below the efficient level. This strict downward distortion holds even
if the buyer acquires no information or when the buyer's posterior type is the
highest possible given his signal, reversing the "no distortion at the top"
feature that holds when information is exogenous.


title:
Isogeometric Analysis of Bound States of a Quantum Three-Body Problem in
  1D

abstract:
  In this paper, we initiate the study of isogeometric analysis (IGA) of a
quantum three-body problem that has been well-known to be difficult to solve.
In the IGA setting, we represent the wavefunctions by linear combinations of
B-spline basis functions and solve the problem as a matrix eigenvalue problem.
The eigenvalue gives the eigenstate energy while the eigenvector gives the
coefficients of the B-splines that lead to the eigenstate. The major difficulty
of isogeometric or other finite-element-method-based analyses lies in the lack
of boundary conditions and a large number of degrees of freedom for accuracy.
For a typical many-body problem with attractive interaction, there are bound
and scattering states where bound states have negative eigenvalues. We focus on
bound states and start with the analysis for a two-body problem. We demonstrate
through various numerical experiments that IGA provides a promising technique
to solve the three-body problem.


title:
Immersed Virtual Element Methods for Maxwell Interface Problems in Three
  Dimensions

abstract:
  Finite element methods for Maxwell's equations are highly sensitive to the
conformity of approximation spaces, and non-conforming methods may cause loss
of convergence. This fact leads to an essential obstacle for almost all the
interface-unfitted mesh methods in the literature regarding the application to
Maxwell interface problems, as they are based on non-conforming spaces. In this
work, a novel immersed virtual element method for solving a 3D Maxwell
interface problems is developed, and the motivation is to combine the
conformity of virtual element spaces and robust approximation capabilities of
immersed finite element spaces. The proposed method is able to achieve optimal
convergence for a 3D Maxwell interface problem. To develop a systematic
framework, the $H^1$, $\mathbf{H}(\mathrm{curl})$ and
$\mathbf{H}(\mathrm{div})$ interface problems and their corresponding
problem-orientated immersed virtual element spaces are considered all together.
In addition, the de Rham complex will be established based on which the HX
preconditioner can be used to develop a fast solver for the
$\mathbf{H}(\mathrm{curl})$ interface problem.


title: Outlier-based Autism Detection using Longitudinal Structural MRI

abstract:
  Diagnosis of Autism Spectrum Disorder (ASD) using clinical evaluation
(cognitive tests) is challenging due to wide variations amongst individuals.
Since no effective treatment exists, prompt and reliable ASD diagnosis can
enable the effective preparation of treatment regimens. This paper proposes
structural Magnetic Resonance Imaging (sMRI)-based ASD diagnosis via an outlier
detection approach. To learn Spatio-temporal patterns in structural brain
connectivity, a Generative Adversarial Network (GAN) is trained exclusively
with sMRI scans of healthy subjects. Given a stack of three adjacent slices as
input, the GAN generator reconstructs the next three adjacent slices; the GAN
discriminator then identifies ASD sMRI scan reconstructions as outliers. This
model is compared against two other baselines -- a simpler UNet and a
sophisticated Self-Attention GAN. Axial, Coronal, and Sagittal sMRI slices from
the multi-site ABIDE II dataset are used for evaluation. Extensive experiments
reveal that our ASD detection framework performs comparably with the
state-of-the-art with far fewer training data. Furthermore, longitudinal data
(two scans per subject over time) achieve 17-28% higher accuracy than
cross-sectional data (one scan per subject). Among other findings, metrics
employed for model training as well as reconstruction loss computation impact
detection performance, and the coronal modality is found to best encode
structural information for ASD detection.


title: Learning Low Degree Hypergraphs

abstract:
  We study the problem of learning a hypergraph via edge detecting queries. In
this problem, a learner queries subsets of vertices of a hidden hypergraph and
observes whether these subsets contain an edge or not. In general, learning a
hypergraph with $m$ edges of maximum size $d$ requires $\Omega((2m/d)^{d/2})$
queries. In this paper, we aim to identify families of hypergraphs that can be
learned without suffering from a query complexity that grows exponentially in
the size of the edges.
  We show that hypermatchings and low-degree near-uniform hypergraphs with $n$
vertices are learnable with poly$(n)$ queries. For learning hypermatchings
(hypergraphs of maximum degree $ 1$), we give an $O(\log^3 n)$-round algorithm
with $O(n \log^5 n)$ queries. We complement this upper bound by showing that
there are no algorithms with poly$(n)$ queries that learn hypermatchings in
$o(\log \log n)$ adaptive rounds. For hypergraphs with maximum degree $\Delta$
and edge size ratio $\rho$, we give a non-adaptive algorithm with $O((2n)^{\rho
\Delta+1}\log^2 n)$ queries. To the best of our knowledge, these are the first
algorithms with poly$(n, m)$ query complexity for learning non-trivial families
of hypergraphs that have a super-constant number of edges of super-constant
size.


title:
Correlation-enhanced spin-orbit coupling and quantum anomalous Hall
  insulator with large band gap and stable ferromagnetism in monolayer
  $\mathrm{Fe_2Br_2}$

abstract:
  Nontrivial band topology combined with magnetic ordering can produce quantum
anomalous Hall insulator (QAHI), which may lead to advances in device concepts.
Here, through first-principles calculations, stable monolayer
$\mathrm{Fe_2Br_2}$ is predicted as a room-temperature large-gap
high-Chern-number QAHI by using generalized gradient approximation plus $U$
(GGA+$U$) approach. The large gap is due to correlation-enhanced spin-orbit
coupling (SOC) effect of Fe atoms, which equates with artificially increasing
the strength of SOC without electronic correlation. Out-of-plane magnetic
anisotropy is very key to produce quantum anomalous Hall (QAH) state because
in-plane magneitization will destroy nontrivial band topology. In the absence
of SOC, $\mathrm{Fe_2Br_2}$ is a half Dirac semimetal state protected by mirror
symmetry, and the electronic correlation along with SOC effect creates QAH
state with a sizable gap and two chiral edge modes. It is found that the QAH
state is robust against biaxial strain ($a/a_0$: 0.96 to 1.04) in monolayer
$\mathrm{Fe_2Br_2}$ with stable ferromagnetic (FM) ordering and out-of-plane
magnetic anisotropy. Calculated results show that Curie temperature is
sensitive to correlation strength and strain. The reduced correlation and
compressive strain are in favour of high Curie temperature. These analysis and
results can be readily extended to other monolayer $\mathrm{Fe_2XY}$ (X/Y=Cl,
Br and I), which possesses the same Fe-dominated low-energy states with a
$\mathrm{Fe_2Br_2}$ monolayer. These findings open new opportunities to design
new high-temperature topological quantum devices.


title: Online Spanners in Metric Spaces

abstract:
  Given a metric space $\mathcal{M}=(X,\delta)$, a weighted graph $G$ over $X$
is a metric $t$-spanner of $\mathcal{M}$ if for every $u,v \in X$,
$\delta(u,v)\le d_G(u,v)\le t\cdot \delta(u,v)$, where $d_G$ is the shortest
path metric in $G$. In this paper, we construct spanners for finite sets in
metric spaces in the online setting. Here, we are given a sequence of points
$(s_1, \ldots, s_n)$, where the points are presented one at a time (i.e., after
$i$ steps, we saw $S_i = \{s_1, \ldots , s_i\}$). The algorithm is allowed to
add edges to the spanner when a new point arrives, however, it is not allowed
to remove any edge from the spanner. The goal is to maintain a $t$-spanner
$G_i$ for $S_i$ for all $i$, while minimizing the number of edges, and their
total weight.
  We construct online $(1+\varepsilon)$-spanners in Euclidean $d$-space,
$(2k-1)(1+\varepsilon)$-spanners for general metrics, and
$(2+\varepsilon)$-spanners for ultrametrics. Most notably, in Euclidean plane,
we construct a $(1+\varepsilon)$-spanner with competitive ratio
$O(\varepsilon^{-3/2}\log\varepsilon^{-1}\log n)$, bypassing the classic lower
bound $\Omega(\varepsilon^{-2})$ for lightness, which compares the weight of
the spanner, to that of the MST.


title: On fibration stability after Dervan-Sektnan and singularities

abstract:
  We introduce $\mathfrak{f}$-stability, a modification of fibration stability
of Dervan-Sektnan [12], and show that $\mathfrak{f}$-semistable fibrations have
only semi log canonical singularities. Moreover, $\mathfrak{f}$-stability puts
restrictions on semi log canonical centers on Fano fibrations.


title:
Weakly informative priors and prior-data conflict checking for
  likelihood-free inference

abstract:
  Bayesian likelihood-free inference, which is used to perform Bayesian
inference when the likelihood is intractable, enjoys an increasing number of
important scientific applications. However, many aspects of a Bayesian analysis
become more challenging in the likelihood-free setting. One example of this is
prior-data conflict checking, where the goal is to assess whether the
information in the data and the prior are inconsistent. Conflicts of this kind
are important to detect, since they may reveal problems in an investigator's
understanding of what are relevant values of the parameters, and can result in
sensitivity of Bayesian inferences to the prior. Here we consider methods for
prior-data conflict checking which are applicable regardless of whether the
likelihood is tractable or not. In constructing our checks, we consider
checking statistics based on prior-to-posterior Kullback-Leibler divergences.
The checks are implemented using mixture approximations to the posterior
distribution and closed-form approximations to Kullback-Leibler divergences for
mixtures, which make Monte Carlo approximation of reference distributions for
calibration computationally feasible. When prior-data conflicts occur, it is
useful to consider weakly informative prior specifications in alternative
analyses as part of a sensitivity analysis. As a main application of our
methodology, we develop a technique for searching for weakly informative priors
in likelihood-free inference, where the notion of a weakly informative prior is
formalized using prior-data conflict checks. The methods are demonstrated in
three examples.


title:
Transferring Adversarial Robustness Through Robust Representation
  Matching

abstract:
  With the widespread use of machine learning, concerns over its security and
reliability have become prevalent. As such, many have developed defenses to
harden neural networks against adversarial examples, imperceptibly perturbed
inputs that are reliably misclassified. Adversarial training in which
adversarial examples are generated and used during training is one of the few
known defenses able to reliably withstand such attacks against neural networks.
However, adversarial training imposes a significant training overhead and
scales poorly with model complexity and input dimension. In this paper, we
propose Robust Representation Matching (RRM), a low-cost method to transfer the
robustness of an adversarially trained model to a new model being trained for
the same task irrespective of architectural differences. Inspired by
student-teacher learning, our method introduces a novel training loss that
encourages the student to learn the teacher's robust representations. Compared
to prior works, RRM is superior with respect to both model performance and
adversarial training time. On CIFAR-10, RRM trains a robust model $\sim
1.8\times$ faster than the state-of-the-art. Furthermore, RRM remains effective
on higher-dimensional datasets. On Restricted-ImageNet, RRM trains a ResNet50
model $\sim 18\times$ faster than standard adversarial training.


title: L-SpEx: Localized Target Speaker Extraction

abstract:
  Speaker extraction aims to extract the target speaker's voice from a
multi-talker speech mixture given an auxiliary reference utterance. Recent
studies show that speaker extraction benefits from the location or direction of
the target speaker. However, these studies assume that the target speaker's
location is known in advance or detected by an extra visual cue, e.g., face
image or video. In this paper, we propose an end-to-end localized target
speaker extraction on pure speech cues, that is called L-SpEx. Specifically, we
design a speaker localizer driven by the target speaker's embedding to extract
the spatial features, including direction-of-arrival (DOA) of the target
speaker and beamforming output. Then, the spatial cues and target speaker's
embedding are both used to form a top-down auditory attention to the target
speaker. Experiments on the multi-channel reverberant dataset called
MC-Libri2Mix show that our L-SpEx approach significantly outperforms the
baseline system.


title:
Integrated Fault Diagnosis and Control Design for DER Inverters using
  Machine Learning Methods

abstract:
  This paper employs a supervised machine learning (ML) algorithm to propose an
integrated fault detection and diagnosis (FDD) and fault-tolerant control (FTC)
strategy to detect, diagnose, and classify the grid faults and correct the
input voltage before affecting the grid-connected distributed energy resources
(DER) inverters. This controller can mitigate the impact of grid faults on
inverters by predicting and modifying the time series of their input voltage.
Simulation results show the effectiveness of the proposed controller and
evaluate its operating performance.


title:
Differentiation operator in the Beurling space of ultradifferentiable
  functions of normal type on an interval

abstract:
  In this paper we study closed subspaces of ultradifferentiable functions
which are invariant under the differentiation operator. We propose a version of
spectral synthesis which takes into account the presence of non-trivial
differentiation invariant subspaces containing no exponential monomials. As an
application, we describe the sets of solutions of finite and infinite systems
of <<local>> homogeneous convolution equations.


title: Approximate Symmetries in $d=4$ CFTs with an Einstein Gravity Dual

abstract:
  By applying the stress-tensor-scalar operator product expansion (OPE) twice,
we search for algebraic structures in $d=4$ conformal field theories (CFTs)
with a pure Einstein gravity dual. We find that a rescaled mode operator
defined by an integral of the stress tensor $T^{++}$ on a $d=2$ plane satisfies
a Virasoro-like algebra when the dimension of the scalar is large. The
structure is enhanced to include a Kac-Moody-type algebra if we incorporate the
$T^{--}$ component. In our scheme, the central terms are finite. It remains
challenging to directly compute the stress-tensor sector of $d=4$ scalar
four-point functions at large central charge, which, based on holography and
bootstrap methods, were recently shown to have a Virasoro/${\cal W}$-algebra
vacuum block-like structure.


title:
Spectral Lag Transition of 32 Fermi Gamma-ray Bursts and their
  Application on Constraining Lorentz Invariance Violation

abstract:
  The positive-to-negative transition of spectral lag is an uncommon feature
reported in a small number of GRBs. An application of such a feature has been
made to constrain the critical quantum gravity energy ($E_{\rm QG}$) of the
light photons under the hypothesis that the Lorentz invariance might be
violated. Motivated by previous case studies, this paper systematically
examined the up-to-date Fermi/GBM GRB sample for the lag transition feature to
establish a comprehensive physical limit on the Lorentz Invariance Violation
(LIV). This search resulted in 32 GRBs with redshift available, which exhibit
the lag-transition phenomenon. We first fit each of the lag-E relations of the
32 GRBs with an empirical smoothly broken power law function, and found that
the lag transition occurs typically at about 400 keV. We then implemented the
LIV effect into the fit, which enabled us to constrain the lower limit of the
linear and quadratic values of $E_{\rm QG}$, which are typically distributed at
$1.5\times 10^{14}$ GeV and $8\times 10^{5}$ GeV, respectively.


title: Domain-Augmented Domain Adaptation

abstract:
  Unsupervised domain adaptation (UDA) enables knowledge transfer from the
labelled source domain to the unlabeled target domain by reducing the
cross-domain discrepancy. However, most of the studies were based on direct
adaptation from the source domain to the target domain and have suffered from
large domain discrepancies. To overcome this challenge, in this paper, we
propose the domain-augmented domain adaptation (DADA) to generate pseudo
domains that have smaller discrepancies with the target domain, to enhance the
knowledge transfer process by minimizing the discrepancy between the target
domain and pseudo domains. Furthermore, we design a pseudo-labeling method for
DADA by projecting representations from the target domain to multiple pseudo
domains and taking the averaged predictions on the classification from the
pseudo domains as the pseudo labels. We conduct extensive experiments with the
state-of-the-art domain adaptation methods on four benchmark datasets: Office
Home, Office-31, VisDA2017, and Digital datasets. The results demonstrate the
superiority of our model.


title:
Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive
  Coding for Multivariate Time-Series Anomaly Detection

abstract:
  As large-scale time-series data can easily be found in real-world
applications, multivariate time-series anomaly detection has played an
essential role in diverse industries. It enables productivity improvement and
maintenance cost reduction by preventing malfunctions and detecting anomalies
based on time-series data. However, multivariate time-series anomaly detection
is challenging because real-world time-series data exhibit complex temporal
dependencies. For this task, it is crucial to learn a rich representation that
effectively contains the nonlinear temporal dynamics of normal behavior. In
this study, we propose an unsupervised multivariate time-series anomaly
detection model named RAE-MEPC which learns informative normal representations
based on multi-resolution ensemble and predictive coding. We introduce
multi-resolution ensemble encoding to capture the multi-scale dependency from
the input time series. The encoder hierarchically aggregates the temporal
features extracted from the sub-encoders with different encoding lengths. From
these encoded features, the reconstruction decoder reconstructs the input time
series based on multi-resolution ensemble decoding where lower-resolution
information helps to decode sub-decoders with higher-resolution outputs.
Predictive coding is further introduced to encourage the model to learn the
temporal dependencies of the time series. Experiments on real-world benchmark
datasets show that the proposed model outperforms the benchmark models for
multivariate time-series anomaly detection.


title:
Vision-based Autonomous Driving for Unstructured Environments Using
  Imitation Learning

abstract:
  Unstructured environments are difficult for autonomous driving. This is
because various unknown obstacles are lied in drivable space without lanes, and
its width and curvature change widely. In such complex environments, searching
for a path in real-time is difficult. Also, inaccurate localization data reduce
the path tracking accuracy, increasing the risk of collision. Instead of
searching and tracking the path, an alternative approach has been proposed that
reactively avoids obstacles in real-time. Some methods are available for
tracking global path while avoiding obstacles using the candidate paths and the
artificial potential field. However, these methods require heuristics to find
specific parameters for handling various complex environments. In addition, it
is difficult to track the global path accurately in practice because of
inaccurate localization data. If the drivable space is not accurately
recognized (i.e., noisy state), the vehicle may not smoothly drive or may
collide with obstacles. In this study, a method in which the vehicle drives
toward drivable space only using a vision-based occupancy grid map is proposed.
The proposed method uses imitation learning, where a deep neural network is
trained with expert driving data. The network can learn driving patterns suited
for various complex and noisy situations because these situations are contained
in the training data. Experiments with a vehicle in actual parking lots
demonstrated the limitations of general model-based methods and the
effectiveness of the proposed imitation learning method.


title:
Sender-controlled measurement-device-independent multiparty quantum
  communication

abstract:
  Multiparty quantum communication is an important branch of quantum networks.
It enables private information transmission with information-theoretic security
among legitimate parties. We propose a sender-controlled
measurement-device-independent multiparty quantum communication protocol. The
sender Alice divides a private message into several parts and delivers them to
different receivers for secret sharing with imperfect measurement devices and
untrusted ancillary nodes. Furthermore, Alice acts as an active controller and
checks the security of quantum channels and the reliability of each receiver
before she encodes her private message for secret sharing, which makes the
protocol convenient for multiparity quantum communication.


title:
Multiplicity of non-contractible closed geodesics on Finsler compact
  space forms

abstract:
  Let $M=S^n/ \Gamma$ and $h$ be a nontrivial element of finite order $p$ in
$\pi_1(M)$, where the integer $n, p\geq2$, $\Gamma$ is a finite abelian group
which acts freely and isometrically on the $n$-sphere and therefore $M$ is
diffeomorphic to a compact space form. In this paper, we prove that for every
irreversible Finsler compact space form $(M,F)$ with reversibility $\lambda$
and flag curvature $K$ satisfying \[ \frac{4p^2}{(p+1)^2}
\big(\frac{\lambda}{\lambda+1} \big)^2 < K \leq 1,\;\;\lambda< \frac{p+1}{p-1},
\] there exist at least $n-1$ non-contractible closed geodesics of class $[h]$.
In addition, if the metric $F$ is bumpy and \[ (\frac{4p}{2p+1})^2
(\frac{\lambda}{\lambda+1})^2 < K \leq 1,\;\;\lambda<\frac{2p+1}{2p-1}, \] then
there exist at least $2[\frac{n+1}{2}]$ non-contractible closed geodesics of
class $[h]$, which is the optimal lower bound due to Katok's example. For
$C^4$-generic Finsler metrics, there are infinitely many non-contractible
closed geodesics of class $[h]$ on $(M, F)$ if $\frac{\lambda^2}{(\lambda+1)^2}
< K \leq 1$ with $n$ being odd, or
$\frac{\lambda^2}{(\lambda+1)^2}\frac{4}{(n-1)^2} < K \leq 1$ with $n$ being
even.


title: On Grid Codes

abstract:
  If $A_{i}$ is finite alphabet for $i=1,...,n$, the Manhattan distance is
defined in $\prod_{i=1}^{n}A_{i}$. A grid code is introduced as a subset of
$\prod_{i=1}^{n}A_{i}$. Alternative versions of the Hamming and
Gilbert-Varshamov bounds are presented for grid codes. If $A_{i}$ is a cyclic
group for $i=1,...,n$, some bounds for the minimum Manhattan distance of codes
that are cyclic subgroups of $\prod_{i=1}^{n}A_{i}$ are determined in terms of
their minimum Hamming and Lee distances. Examples illustrating the main results
are provided.


title: Anyonic braiding via quench dynamics in fractional quantum Hall liquids

abstract:
  In a Laughlin fractional quantum Hall state, one- and two-quasihole states
can be obtained by diagonalizing the many-body Hamiltonian with a trapping
potential or, for larger systems, from the linear combination of the edge Jack
polynomials. The quasihole states live entirely in the subspace of the
lowest-energy branch in the energy spectrum with a fixed number of orbits, or a
hard-wall confinement. The reduction in the Hilbert space dimension facilitates
the study of time evolution of the quasihole states after, say, the removal of
the trapping potential. We explore the quench dynamics under a harmonic
external potential, which rotates the quasiholes in the droplet, and discuss
the effect of long-range interaction and more realistic confinement. Accurate
evaluation of the mutual statistics phase of anyons for a wide range of anyon
separation can be achieved from the Berry-phase calculation.


title:
Statistical Inference for Genetic Relatedness Based on High-Dimensional
  Logistic Regression

abstract:
  This paper studies the problem of statistical inference for genetic
relatedness between binary traits based on individual-level genome-wide
association data. Specifically, under the high-dimensional logistic regression
model, we define parameters characterizing the cross-trait genetic correlation,
the genetic covariance and the trait-specific genetic variance. A novel
weighted debiasing method is developed for the logistic Lasso estimator and
computationally efficient debiased estimators are proposed. The rates of
convergence for these estimators are studied and their asymptotic normality is
established under mild conditions. Moreover, we construct confidence intervals
and statistical tests for these parameters, and provide theoretical
justifications for the methods, including the coverage probability and expected
length of the confidence intervals, as well as the size and power of the
proposed tests. Numerical studies are conducted under both model generated data
and simulated genetic data to show the superiority of the proposed methods and
their applicability to the analysis of real genetic data. Finally, by analyzing
a real data set on autoimmune diseases, we demonstrate the ability to obtain
novel insights about the shared genetic architecture between ten pediatric
autoimmune diseases.


title: Double Thompson Sampling in Finite stochastic Games

abstract:
  We consider the trade-off problem between exploration and exploitation under
finite discounted Markov Decision Process, where the state transition matrix of
the underlying environment stays unknown. We propose a double Thompson sampling
reinforcement learning algorithm(DTS) to solve this kind of problem. This
algorithm achieves a total regret bound of $\tilde{\mathcal{O}}(D\sqrt{SAT})$in
time horizon $T$ with $S$ states, $A$ actions and diameter $D$. DTS consists of
two parts, the first part is the traditional part where we apply the posterior
sampling method on transition matrix based on prior distribution. In the second
part, we employ a count-based posterior update method to balance between the
local optimal action and the long-term optimal action in order to find the
global optimal game value. We established a regret bound of
$\tilde{\mathcal{O}}(\sqrt{T}/S^{2})$. Which is by far the best regret bound
for finite discounted Markov Decision Process to our knowledge. Numerical
results proves the efficiency and superiority of our approach.


title: Relative Maltsev Definability of some Commutator Properties

abstract:
  We show that, when restricted to the class of varieties that have a Taylor
term, several commutator properties are definable by Maltsev conditions.


title:
Phenomenological studies on neutral $B$-meson decays into $J/\psi f_1$
  and $\eta_c f_1$

abstract:
  The neutral $B$-meson decays into $J/\psi f_1$ and $\eta_c f_1$ are analyzed
phenomenologically in the perturbative QCD (PQCD) approach at the known
next-to-leading order accuracy, where $f_1$ denotes the axial-vector mesons
$f_{1}(1285)$ and $f_{1}(1420)$, the mixture of flavor $f_n$ and $f_s$ states
in the quark-flavor basis. With the extracted mixing angle $\varphi^{\rm Theo}
\sim 15^\circ$ and $\varphi^{\rm Exp} \sim 24^\circ$, it is of great interest
to find that (a) the $B \to J/\psi f_1$ and $\eta_c f_1$ decays have large
branching ratios(${\cal B}$) that are measurable at Large Hadron Collider
beauty and/or Belle-II experiments in the near future. The consistent results
within errors in two referenced angles indicate that much more constraints on
$\varphi$ from various aspects are still urgently demanded; (b) the large
${\cal B}(B_s^0 \to (J/\psi, \eta_c) f_1)$ through $f_1(1285) \to 2\pi^+
2\pi^-$, $f_1(1285) \to \eta \pi^+ \pi^-$ and $f_1(1420) \to K_S^0 K^+ \pi^-$
may be experimentally probed more easily. Several interesting ratios among the
${\cal B}(B \to (J/\psi, \eta_c) f_1)$ are presemted for future exploration on
the determination of $|\varphi|$ and/or on the estimation of SU(3)$_{\rm F}$
symmetry breaking; (c) the direct CP violations in the $B_d^0 \to (J/\psi,
\eta_c) f_1$ decays are generally larger than those in the corresponding
$B_s^0$ channels with a factor around 20. It suggests that the $B_s^0 \to
(J/\psi, \eta_c) f_1$ decays could provide helpful information promisingly on
constraining $|\varphi|$ due to the highly small penguin contaminations. All
the results and predictions will be confronted with the future examinations.


title:
Physical observations of the transient evolution of the porosity
  distribution during internal erosion using spatial time domain reflectometry

abstract:
  A purpose-built permeameter was used to explore the transient evolution of
porosity during the mixing process in filtration experiments. The experiments
considered upward seepage flow and explored the influence of base and filter
particle sizes, along with different hydraulic conditions. The permeameter
acted as a coaxial transmission line enabling electromagnetic measurements
based on spatial time domain reflectometry, from which the porosity profile was
obtained using an inversion technique. Quantitative characteristics of the
onset and progression of the mixing process were extracted from a porosity
field map. The limiting onset condition was influenced by geometric and
hydraulic factors, with the critical flow rate exhibiting a strong dependence
on the base particle size, while the critical hydraulic gradient exhibited a
stronger dependence on filter particle size. The progression of the mixing
process was characterised by both the transport of base particles into the
filter layer, as well as the settlement of the filter particles into the base
layer due to the reduction of the effective stress at the base-filter interface
leading to partial bearing failure. The rate of development of the mixture zone
was strongly dependent on the hydraulic loading condition and the base particle
size, but the final height of the sample after complete mixing was independent
of the hydraulic loading path.


title:
Controller Manipulation Attack on Reconfigurable Intelligent Surface
  Aided Wireless Communication

abstract:
  In this paper, we introduce a new attack called controller manipulation
attack (CMA) on a Reconfigurable Intelligent Surface (RIS) assisted
communication system between a transmitter and a receiver. An attacker has the
potential to manipulate the RIS controller and modify the phase shift induced
by the RIS elements. The goal of the attacker is to minimize the data rate at
the receiver, subject to a constraint on the attack detection probability at
the receiver. We consider two different attack detection models: (i) composite
hypothesis testing based attack detection in a given fading block for known
channel gains, and (ii) SNR moment based detection over possibly multiple
fading blocks. In the first case, a simple energy detector turns out to be
uniformly most powerful (UMP) and the attack against this energy detector is
designed via a novel optimization formulation and a semidefinite relaxation
based solution. In the second case, we consider threshold detection using
moments of SNR; various SNR moments under no attack are obtained analytically
for large RIS and then used to formulate the attack design problem as a linear
program. Finally, numerical results illustrate the performance and trade-offs
associated with the attack schemes, and also demonstrate their efficacy.


title: Some remarks on anti-topological spaces

abstract:
  This paper is devoted to a general presentation of anti-topological spaces.
These structures have been initially proposed by \c{S}ahin, Karg{\i}n and M.
Y\"{u}cel in 2021. We analyse their basic definition, showing some of its
subtleties and implications. The framework thus obtained is used to investigate
anti-topological interpretation of some basic topological notions. For example,
we discuss the idea of interior and closure and we show some results on door
spaces. Moreover, we introduce two non-equivalent types of continuity. Finally,
we investigate the idea of density and nowhere density. Finally, we give some
preliminary suggestions concerning the modal logic of anti-topological spaces.
It is noteworthy that the paper contains some additional remarks on
infra-topological and weak spaces. They may be considered as a clarification or
correction of some earlier results present in literature.


title:
Using Illustrations to Communicate Differential Privacy Trust Models: An
  Investigation of Users' Comprehension, Perception, and Data Sharing Decision

abstract:
  Proper communication is key to the adoption and implementation of
differential privacy (DP). However, a prior study found that laypeople did not
understand the data perturbation processes of DP and how DP noise protects
their sensitive personal information. Consequently, they distrusted the
techniques and chose to opt out of participating. In this project, we designed
explanative illustrations of three DP models (Central DP, Local DP, Shuffler
DP) to help laypeople conceptualize how random noise is added to protect
individuals' privacy and preserve group utility. Following pilot surveys and
interview studies, we conducted two online experiments (N = 595) examining
participants' comprehension, privacy and utility perception, and data-sharing
decisions across the three DP models. Besides the comparisons across the three
models, we varied the noise levels of each model. We found that the
illustrations can be effective in communicating DP to the participants. Given
an adequate comprehension of DP, participants preferred strong privacy
protection for a certain type of data usage scenarios (i.e., commercial
interests) at both the model level and the noise level. We also obtained
empirical evidence showing participants' acceptance of the Shuffler DP model
for data privacy protection. Our findings have implications for multiple
stakeholders for user-centered deployments of differential privacy, including
app developers, DP model developers, data curators, and online users.


title: AI/ML Algorithms and Applications in VLSI Design and Technology

abstract:
  An evident challenge ahead for the integrated circuit (IC) industry in the
nanometer regime is the investigation and development of methods that can
reduce the design complexity ensuing from growing process variations and
curtail the turnaround time of chip manufacturing. Conventional methodologies
employed for such tasks are largely manual; thus, time-consuming and
resource-intensive. In contrast, the unique learning strategies of artificial
intelligence (AI) provide numerous exciting automated approaches for handling
complex and data-intensive tasks in very-large-scale integration (VLSI) design
and testing. Employing AI and machine learning (ML) algorithms in VLSI design
and manufacturing reduces the time and effort for understanding and processing
the data within and across different abstraction levels via automated learning
algorithms. It, in turn, improves the IC yield and reduces the manufacturing
turnaround time. This paper thoroughly reviews the AI/ML automated approaches
introduced in the past towards VLSI design and manufacturing. Moreover, we
discuss the scope of AI/ML applications in the future at various abstraction
levels to revolutionize the field of VLSI design, aiming for high-speed, highly
intelligent, and efficient implementations.


title:
Role of Tensor Interaction as Salvation of Cluster Structure in
  $^{44}$Ti

abstract:
  Background: The $^{44}$Ti nucleus has been known to have a $^{40}$Ca+$\alpha$
cluster structure, and inversion doublet structure has been observed; however,
$\alpha$ cluster structure tends to be washed out when the breaking of the
$\alpha$ cluster is allowed due to the spin-orbit interaction. Nevertheless,
$\alpha$ clustering in medium-heavy nuclei is quite a hot subject recently.
  Purpose: The tensor interaction has been known to play an essential role in
the strong binding of the $^4$He nucleus, which induces the
two-particle-two-hole (2p2h) excitation. Since this excitation is blocked when
another nucleus approaches, it is worthwhile to show whether the tensor effect
works to keep the distance between $^4$He and $^{40}$Ca and becomes the
salvation of the clustering in $^{44}$Ti.
  Methods: The spin-orbit effect is included in the cluster model by using the
antisymmetrized quasi cluster model (AQCM) developed by the authors. We have
also developed an improved version of the simplified method to include the
tensor contribution ($i$SMT), which allows us to estimate the tensor effect
within the cluster model. The competition of these two is investigated in the
medium-heavy mass region for the first time.
  Results: According to AQCM, the spin-orbit interaction completely breaks the
$\alpha$ cluster and restores the symmetry of $jj$-coupling shell model when
the $\alpha$ cluster approaches the $^{40}$Ca core. On the other hand, $i$SMT
gives a large distance between $\alpha$ and $^{40}$Ca due to the tensor effect.
  Conclusions: In $^{44}$Ti, because of the strong spin-orbit and tensor
contributions, two completely different configurations ($jj$-coupling shell
model and cluster states) almost degenerate, and their mixing becomes
important.


title:
The PCG-AIID System for L3DAS22 Challenge: MIMO and MISO convolutional
  recurrent Network for Multi Channel Speech Enhancement and Speech Recognition

abstract:
  This paper described the PCG-AIID system for L3DAS22 challenge in Task 1: 3D
speech enhancement in office reverberant environment. We proposed a two-stage
framework to address multi-channel speech denoising and dereverberation. In the
first stage, a multiple input and multiple output (MIMO) network is applied to
remove background noise while maintaining the spatial characteristics of
multi-channel signals. In the second stage, a multiple input and single output
(MISO) network is applied to enhance the speech from desired direction and
post-filtering. As a result, our system ranked 3rd place in ICASSP2022 L3DAS22
challenge and significantly outperforms the baseline system, while achieving
3.2% WER and 0.972 STOI on the blind test-set.


title:
Secret Key-based Authentication With Passive Eavesdropper for Scalar
  Gaussian Sources

abstract:
  We analyze the fundamental trade-off of secret key-based authentication
systems in the presence of an eavesdropper for correlated Gaussian sources. A
complete characterization of trade-off among secret-key, storage, and
privacy-leakage rates of both generated and chosen secret models is provided.
One of the main contributions is revealing that unlike the known results for
discrete sources, there is no need for the second auxiliary random variable in
characterizing the capacity regions for the Gaussian cases. In addition, it is
shown that the strong secrecy for secrecy-leakage of the systems can be
achieved by an information-spectrum approach, and the parametric expressions
(computable forms) of the capacity regions are also derived.


title: Autonomous Warehouse Robot using Deep Q-Learning

abstract:
  In warehouses, specialized agents need to navigate, avoid obstacles and
maximize the use of space in the warehouse environment. Due to the
unpredictability of these environments, reinforcement learning approaches can
be applied to complete these tasks. In this paper, we propose using Deep
Reinforcement Learning (DRL) to address the robot navigation and obstacle
avoidance problem and traditional Q-learning with minor variations to maximize
the use of space for product placement. We first investigate the problem for
the single robot case. Next, based on the single robot model, we extend our
system to the multi-robot case. We use a strategic variation of Q-tables to
perform multi-agent Q-learning. We successfully test the performance of our
model in a 2D simulation environment for both the single and multi-robot cases.


title:
AVQVC: One-shot Voice Conversion by Vector Quantization with applying
  contrastive learning

abstract:
  Voice Conversion(VC) refers to changing the timbre of a speech while
retaining the discourse content. Recently, many works have focused on
disentangle-based learning techniques to separate the timbre and the linguistic
content information from a speech signal. Once successful, voice conversion
will be feasible and straightforward. This paper proposed a novel one-shot
voice conversion framework based on vector quantization voice conversion (VQVC)
and AutoVC, called AVQVC. A new training method is applied to VQVC to separate
content and timbre information from speech more effectively. The result shows
that this approach has better performance than VQVC in separating content and
timbre to improve the sound quality of generated speech.


title:
Frontal effective connectivity increases with task demands and time on
  task: a DCM study on electrocorticogram in macaque monkeys

abstract:
  In this paper, we provide a computational account of changes in synaptic
connectivity within two regions of the fronto-parietal network, the
dorsolateral prefrontal cortex and the pre-supplementary motor area, applying
Dynamic Causal Models to electrocorticogram recordings from two macaque monkeys
performing a problem-solving task that engages working memory, and induces
time-on-task effects. We find that forward connections between the two regions
increased in strength when task demands were increased, and as the experimental
session progressed. Similarities in the effects of task demands and time on
task allow us to interpret changes in frontal connectivity in terms of
increased effort allocation that compensates cognitive fatigue.


title: Hardware Obfuscation of Digital FIR Filters

abstract:
  A finite impulse response (FIR) filter is a ubiquitous block in digital
signal processing applications. Its characteristics are determined by its
coefficients, which are the intellectual property (IP) for its designer.
However, in a hardware efficient realization, its coefficients become
vulnerable to reverse engineering. This paper presents a filter design
technique that can protect this IP, taking into account hardware complexity and
ensuring that the filter behaves as specified only when a secret key is
provided. To do so, coefficients are hidden among decoys, which are selected
beyond possible values of coefficients using three alternative methods. As an
attack scenario, an adversary at an untrusted foundry is considered. A reverse
engineering technique is developed to find the chosen decoy selection method
and explore the potential leakage of coefficients through decoys. An
oracle-less attack is also used to find the secret key. Experimental results
show that the proposed technique can lead to filter designs with competitive
hardware complexity and higher resiliency to attacks with respect to previously
proposed methods.


title: Optical characterization of deuterated silicon-rich nitride waveguides

abstract:
  Chemical vapor deposition-based growth techniques allow flexible design of
CMOS-compatible materials. Here, we report the deuterated silicon-rich nitride
films grown using plasma-enhanced chemical vapor deposition. The linear and
nonlinear properties of the films are characterized. We compare the absorption
at 1550nm wavelength region for films grown with $SiH_4$ and $SiD_4$, and
experimentally confirm that the silicon-rich nitride films grown with $SiD_4$
eliminates Si-H related absorption. Waveguides fabricated on the films are
further shown to possess a linear and nonlinear refractive index of 2.46 and
$9.8$ X $10^{-18} m^2 W^{-1}$ respectively.


title: TESS discovery of a sub-Neptune orbiting a mid-M dwarf TOI-2136

abstract:
  We present the discovery of TOI-2136b, a sub-Neptune planet transiting every
7.85 days a nearby M4.5V-type star, identified through photometric measurements
from the TESS mission. The host star is located $33$ pc away with a radius of
$R_{\ast} = 0.34\pm0.02\ R_{\odot}$, a mass of $0.34\pm0.02\ M_{\odot}$ and an
effective temperature of $\rm 3342\pm100\ K$. We estimate its stellar rotation
period to be $75\pm5$ days based on archival long-term photometry. We confirm
and characterize the planet based on a series of ground-based multi-wavelength
photometry, high-angular-resolution imaging observations, and precise radial
velocities from CFHT/SPIRou. Our joint analysis reveals that the planet has a
radius of $2.19\pm0.17\ R_{\oplus}$, and a mass measurement of $6.4\pm2.4\
M_{\oplus}$. The mass and radius of TOI2136b is consistent with a broad range
of compositions, from water-ice to gas-dominated worlds. TOI-2136b falls close
to the radius valley for low-mass stars predicted by the thermally driven
atmospheric mass loss models, making it an interesting target for future
studies of its interior structure and atmospheric properties.


title: CCDD: A Tractable Representation for Model Counting and Uniform Sampling

abstract:
  Knowledge compilation concerns with the compilation of representation
languages to target languages supporting a wide range of tractable operations
arising from diverse areas of computer science. Tractable target compilation
languages are usually achieved by restrictions on the internal nodes of the
NNF. In this paper, we propose a new representation language CCDD, which
introduces new restrictions on conjunction nodes to capture equivalent
literals. We show that CCDD supports two key queries, model counting and
uniform samping, in polytime. We present algorithms and a compiler to compile
propositional formulas expressed in CNF into CCDD. Experiments over a large set
of benchmarks show that our compilation times are better with smaller
representation than state-of-art Decision-DNNF, SDD and OBDD[AND] compilers. We
apply our techniques to model counting and uniform sampling, and develop model
counter and uniform sampler on CNF. Our empirical evaluation demonstrates the
following significant improvements: our model counter can solve 885 instances
while the prior state of the art solved only 843 instances, representing an
improvement of 43 instances; and our uniform sampler can solve 780 instances
while the prior state of the art solved only 648 instances, representing an
improvement of 132 instances.


title:
Detecting deep axisymmetric toroidal magnetic fields in stars. The
  traditional approximation of rotation for differentially rotating deep
  spherical shells with a general azimutal magnetic field

abstract:
  Asteroseismology has revealed small core-to-the-surface rotation contrasts in
stars in the whole HR diagram. This is the signature of strong transport of
angular momentum (AM) in stellar interiors. One of the plausible candidates to
efficiently carry AM is magnetic fields with various topologies that could be
present in stellar radiative zones. Among them, strong axisymmetric azimuthal
magnetic fields have received a lot of interest. Indeed, if they are subject to
the so-called Tayler instability, the accompanying triggered Maxwell stresses
can transport AM efficiently. In addition, the electromotive force induced by
the fluctuations of magnetic and velocity fields could potentially sustain a
dynamo action that leads to the regeneration of the initial strong axisymmetric
azimuthal magnetic field. The key question we aim to answer is: can we detect
signatures of these deep strong azimuthal magnetic fields? The only way to
answer this question is asteroseismology and the best laboratories of study are
intermediate-mass and massive stars. Most of these are rapid rotators during
their main-sequence. Therefore, we have to study stellar pulsations propagating
in stably stratified, rotating, and potentially strongly magnetised radiative
zones. We generalise the traditional approximation of rotation by taking
simultaneously general axisymmetric differential rotation and azimuthal
magnetic fields into account in a non-perturbative way. Using this new
formalism, we derive the asymptotic properties of magneto-gravito-inertial
(MGI) waves and their period spacings. We find that toroidal magnetic fields
induce a shift in the period spacings of MGI modes. An equatorial azimuthal
magnetic field with an amplitude of the order of $10^5\,\rm G$ leads to
signatures that can be detectable thanks to modern space photometry. More
complex hemispheric configurations are more difficult to observe.

categories: astro-ph.SR astro-ph.HE physics.ao-ph physics.flu-dyn


title: Toward more generalized Malicious URL Detection Models

abstract:
  This paper reveals a data bias issue that can severely affect the performance
while conducting a machine learning model for malicious URL detection. We
describe how such bias can be identified using interpretable machine learning
techniques, and further argue that such biases naturally exist in the real
world security data for training a classification model. We then propose a
debiased training strategy that can be applied to most deep-learning based
models to alleviate the negative effects from the biased features. The solution
is based on the technique of self-supervised adversarial training to train deep
neural networks learning invariant embedding from biased data. We conduct a
wide range of experiments to demonstrate that the proposed strategy can lead to
significantly better generalization capability for both CNN-based and RNN-based
detection models.


title: Obtaining Approximately Optimal and Diverse Solutions via Dispersion

abstract:
  There has been a long-standing interest in computing diverse solutions to
optimization problems. Motivated by reallocation of governmental institutions
in Sweden, in 1995 J. Krarup posed the problem of finding $k$ edge-disjoint
Hamiltonian Circuits of minimum total weight, called the peripatetic salesman
problem (PSP). Since then researchers have investigated the complexity of
finding diverse solutions to spanning trees, paths, vertex covers, matchings,
and more. Unlike the PSP that has a constraint on the total weight of the
solutions, recent work has involved finding diverse solutions that are all
optimal.
  However, sometimes the space of exact solutions may be too small to achieve
sufficient diversity. Motivated by this, we initiate the study of obtaining
sufficiently-diverse, yet approximately-optimal solutions to optimization
problems. Formally, given an integer $k$, an approximation factor $c$, and an
instance $I$ of an optimization problem, we aim to obtain a set of $k$
solutions to $I$ that a) are all $c$ approximately-optimal for $I$ and b)
maximize the diversity of the $k$ solutions. Finding such solutions, therefore,
requires a better understanding of the global landscape of the optimization
function.
  We show that, given any metric on the space of solutions, and the diversity
measure as the sum of pairwise distances between solutions, this problem can be
solved by combining ideas from dispersion and multicriteria optimization. We
first provide a general reduction to an associated budget-constrained
optimization (BCO) problem, where one objective function is to be maximized
(minimized) subject to a bound on the second objective function. We then prove
that bi-approximations to the BCO can be used to give bi-approximations to the
diverse approximately optimal solutions problem with a little overhead.


title:
Jerk Constrained Velocity Planning for an Autonomous Vehicle: Linear
  Programming Approach

abstract:
  Velocity Planning for self-driving vehicles in a complex environment is one
of the most challenging tasks. It must satisfy the following three
requirements: safety with regards to collisions; respect of the maximum
velocity limits defined by the traffic rules; comfort of the passengers. In
order to achieve these goals, the jerk and dynamic objects should be
considered, however, it makes the problem as complex as a non-convex
optimization problem. In this paper, we propose a linear programming (LP) based
velocity planning method with jerk limit and obstacle avoidance constraints for
an autonomous driving system. To confirm the efficiency of the proposed method,
a comparison is made with several optimization-based approaches, and we show
that our method can generate a velocity profile which satisfies the
aforementioned requirements more efficiently than the compared methods. In
addition, we tested our algorithm on a real vehicle at a test field to validate
the effectiveness of the proposed method.


title: Optimality in Multivariate Tie-breaker Designs

abstract:
  Tie-breaker designs (TBDs), in which subjects with extreme values are
assigned treatment deterministically and those in the middle are randomized,
are intermediate between regression discontinuity designs (RDDs) and randomized
controlled trials (RCTs). TBDs thus provide a convenient mechanism by which to
trade off between the treatment benefit of an RDD and the statistical
efficiency gains of an RCT. We study a model where the expected response is one
multivariate regression for treated subjects and another one for control
subjects. For a given set of subject data we show how to use convex
optimization to choose treatment probabilities that optimize a prospective
$D$-optimality condition (expected information gain) adapted from Bayesian
optimal design. We can incorporate economically motivated linear constraints on
those treatment probabilities as well as monotonicity constraints that have a
strong ethical motivation. Our condition can be used in two scenarios: known
covariates with random treatments, and random covariates with random
treatments. We find that optimality for the treatment effect coincides with
optimality for the whole regression, and that the RCT satisfies moment
conditions for optimality. For Gaussian data we can find optimal linear
scorings of subjects, one for statistical efficiency and another for short term
treatment benefit.
  We apply the convex optimization solution to some real emergency triage data
from MIMIC.


title:
Detecting and Monitoring Tidal Dissipation of Hot Jupiters in the Era of
  SiTian

abstract:
  Transit Timing Variation (TTV) of hot Jupiters provides direct observational
evidence of planet tidal dissipation. Detecting tidal dissipation through TTV
needs high precision transit timings and long timing baselines. In this work,
we predict and discuss the potential scientific contribution of SiTian Survey
in detecting and analyzing exoplanet TTV. We develop a tidal dissipation
detection pipeline for SiTian Survey that aims at time-domain astronomy with 72
1-meter optical telescopes. The pipeline includes the modules of light curve
deblending, transit timing obtaining, and TTV modeling. SiTian is capable to
detect more than 25,000 exoplanets among which we expect $\sim$50 sources
showing evidence of tidal dissipation. We present detection and analysis of
tidal dissipating targets, based on simulated SiTian light curves of XO-3b and
WASP-161b. The transit light curve modeling gives consistent results within
1$\sigma$ to input values of simulated light curves. Also, the parameter
uncertainties predicted by Monte-Carlo Markov Chain are consistent with the
distribution obtained from simulating and modeling the light curve 1000 times.
The timing precision of SiTian observations is $\sim$ 0.5 minutes with one
transit visit. We show that differences between TTV origins, e.g., tidal
dissipation, apsidal precession, multiple planets, would be significant,
considering the timing precision and baseline. The detection rate of tidal
dissipating hot Jupiters would answer a crucial question of whether the planet
migrates at an early formation stage or random stages due to perturbations,
e.g., planet scattering, secular interaction. SiTian identified targets would
be constructive given that the sample would extend tenfold.


title:
Domain-level Pairwise Semantic Interaction for Aspect-Based Sentiment
  Classification

abstract:
  Aspect-based sentiment classification (ABSC) is a very challenging subtask of
sentiment analysis (SA) and suffers badly from the class-imbalance. Existing
methods only process sentences independently, without considering the
domain-level relationship between sentences, and fail to provide effective
solutions to the problem of class-imbalance. From an intuitive point of view,
sentences in the same domain often have high-level semantic connections. The
interaction of their high-level semantic features can force the model to
produce better semantic representations, and find the similarities and nuances
between sentences better. Driven by this idea, we propose a plug-and-play
Pairwise Semantic Interaction (PSI) module, which takes pairwise sentences as
input, and obtains interactive information by learning the semantic vectors of
the two sentences. Subsequently, different gates are generated to effectively
highlight the key semantic features of each sentence. Finally, the adversarial
interaction between the vectors is used to make the semantic representation of
two sentences more distinguishable. Experimental results on four ABSC datasets
show that, in most cases, PSI is superior to many competitive state-of-the-art
baselines and can significantly alleviate the problem of class-imbalance.