title: Towards Robust Online Dialogue Response Generation

abstract:
  Although pre-trained sequence-to-sequence models have achieved great success
in dialogue response generation, chatbots still suffer from generating
inconsistent responses in real-world practice, especially in multi-turn
settings. We argue that this can be caused by a discrepancy between training
and real-world testing. At training time, chatbot generates the response with
the golden context, while it has to generate based on the context consisting of
both user utterances and the model predicted utterances during real-world
testing. With the growth of the number of utterances, this discrepancy becomes
more serious in the multi-turn settings. In this paper, we propose a
hierarchical sampling-based method consisting of both utterance-level sampling
and semi-utterance-level sampling, to alleviate the discrepancy, which
implicitly increases the dialogue coherence. We further adopt reinforcement
learning and re-ranking methods to explicitly optimize the dialogue coherence
during training and inference, respectively. Empirical experiments show the
effectiveness of the proposed methods for improving the robustness of chatbots
in real practice.


title: IOLLVM: enhance version of OLLVM

abstract:
  Code obfuscation increases the difficulty of understanding programs, improves
software security, and, in particular, OLLVM offers the possibility of
cross-platform code obfuscation. For OLLVM, we provide enhanced solutions for
control flow obfuscation and identifier obfuscation. First, we propose the
nested switch obfuscation scheme and the in-degree obfuscation for bogus blocks
in the control flow obfuscation. Secondly, the identifier obfuscation scheme is
presented in the LLVM layer to fill the gap of OLLVM at this level. Finally, we
experimentally verify the enhancement effect of the control flow method and the
identifier obfuscation effect and prove that the program's security can be
further improved with less overhead, providing higher software security.


title:
A Comprehensive Study of Bright Fermi-GBM Short Gamma-Ray Bursts: I.
  Multi-Pulse Lightcurves and Multi-Component Spectra

abstract:
  Sorted by the photon fluences of short Gamma-ray Bursts (SGRBs) detected by
the Fermi- Gamma Ray Burst Monitor (GBM), nine brightest bursts are selected to
perform a comprehensive analysis. All GRB lightcurves are fitted well by 1 to 3
pulses that are modelled by fast-rising exponential decay profile (FRED),
within which the resultant rising time is strongly positive-correlated with the
full time width at half maxima (FWHM). A photon spectral model involving a
cutoff power-law function and a standard blackbody function (CPL + BB) could
reproduce the spectral energy distributions of these SGRBs well in the bursting
phase. The CPL's peak energy is found strongly positive-correlated with the
BB's temperature, which indicates they might be from the same physical origin.
Possible physical origins are discussed to account for these correlations.


title: Assessment of Microbial Habitability Across Solar System Targets

abstract:
  With a fleet of exploratory space missions on the horizon, the study of
target specific biospheres is crucial for accurately determining the
probability of the existence of microbial life on various planetary bodies and
prioritising targets accordingly. Although previous studies have compared the
potential habitability of objects in our solar system by bulk characteristics,
it is less common that precise qualitative methods are developed for ranking
candidates hospitable to microbial life on a local environment basis. In this
review we create a planetary environmental database and use it to motivate a
list of primary habitability candidates and essential criteria for microbial
survival. We then propose a new method, the Microbial Habitability Index (MHI)
which uses a metric of microbial survival factor values in target environments
compared with appropriate Earth analogues to assess their potential for life.
We arrive at a selection of eight primary candidates and from this set conclude
that Europa, Mars, and Enceladus have the highest potential for facilitating
microbial survival.


title:
Human-State-Aware Controller for a Tethered Aerial Robot Guiding a Human
  by Physical Interaction

abstract:
  With the rapid development of Aerial Physical Interaction, the possibility to
have aerial robots physically interacting with humans is attracting a growing
interest. In one of our previous works, we considered one of the first systems
in which a human is physically connected to an aerial vehicle by a cable.
There, we developed a compliant controller that allows the robot to pull the
human toward a desired position using forces only as an indirect
communication-channel. However, this controller is based on the robot-state
only, which makes the system not adaptable to the human behavior, and in
particular to their walking speed. This reduces the effectiveness and comfort
of the guidance when the human is still far from the desired point. In this
paper, we formally analyze the problem and propose a human-state-aware
controller that includes a human`s velocity feedback. We theoretically prove
and experimentally show that this method provides a more consistent guiding
force which enhances the guiding experience.


title:
Multiplicative order compact operators between vector lattices and
  $l$-algebras

abstract:
  In the present paper, we introduce and investigate the multiplicative order
compact operators from vector lattices to $l$-algebras. A linear operator $T$
from a vector lattice $X$ to an $l$-algebra $E$ is said to be
$\mathbb{omo}$-compact if every order bounded net $x_\alpha$ in $X$ possesses a
subnet $x_{\alpha_\beta}$ such that $Tx_{\alpha_\beta}\moc y$ for some $y\in
E$. We also introduce and study $\mathbb{omo}$-$M$- and
$\mathbb{omo}$-$L$-weakly compact operators from vector lattices to
$l$-algebras.


title:
Gravitational-wave echoes from numerical-relativity waveforms via
  space-time construction near merging compact objects

abstract:
  We propose a new approach toward reconstructing the late-time near-horizon
geometry of merging binary black holes, and toward computing gravitational-wave
echoes from exotic compact objects. A binary black-hole merger spacetime can be
divided by a time-like hypersurface into a Black-Hole Perturbation (BHP)
region, in which the space-time geometry can be approximated by homogeneous
linear perturbations of the final Kerr black hole, and a nonlinear region. At
late times, the boundary between the two regions is an infalling shell. The BHP
region contains late-time gravitational-waves emitted toward the future
horizon, as well as those emitted toward future null infinity. In this region,
by imposing no-ingoing wave conditions at past null infinity, and matching
out-going waves at future null infinity with waveforms computed from numerical
relativity, we can obtain waves that travel toward the future horizon. In
particular, the Newman-Penrose $\psi_0$ associated with the in-going wave on
the horizon is related to tidal deformations measured by fiducial observers
floating above the horizon. We further determine the boundary of the BHP region
on the future horizon by imposing that $\psi_0$ inside the BHP region can be
faithfully represented by quasi-normal modes. Using a physically-motivated way
to impose boundary conditions near the horizon, and applying the so-called
Boltzmann reflectivity, we compute the quasi-normal modes of non-rotating ECOs,
as well as gravitational-wave echoes. We also investigate the detectability of
these echoes in current and future detectors, and prospects for parameter
estimation.


title: The Dangerous Combo: Fileless Malware and Cryptojacking

abstract:
  Fileless malware and cryptojacking attacks have appeared independently as the
new alarming threats in 2017. After 2020, fileless attacks have been
devastating for victim organizations with low-observable characteristics. Also,
the amount of unauthorized cryptocurrency mining has increased after 2019.
Adversaries have started to merge these two different cyberattacks to gain more
invisibility and profit under "Fileless Cryptojacking." This paper aims to
provide a literature review in academic papers and industry reports for this
new threat. Additionally, we present a new threat hunting-oriented DFIR
approach with the best practices derived from field experience as well as the
literature. Last, this paper reviews the fundamentals of the fileless threat
that can also help ransomware researchers examine similar patterns.


title:
Reconfigurable Intelligent Surfaces for Wireless Communications:
  Overview of Hardware Designs, Channel Models, and Estimation Techniques

abstract:
  The demanding objectives for the future sixth generation (6G) of wireless
communication networks have spurred recent research efforts on novel materials
and radio-frequency front-end architectures for wireless connectivity, as well
as revolutionary communication and computing paradigms. Among the pioneering
candidate technologies for 6G belong the reconfigurable intelligent surfaces
(RISs), which are artificial planar structures with integrated electronic
circuits that can be programmed to manipulate the incoming electromagnetic
field in a wide variety of functionalities. Incorporating RISs in wireless
networks has been recently advocated as a revolutionary means to transform any
wireless signal propagation environment to a dynamically programmable one,
intended for various networking objectives, such as coverage extension and
capacity boosting, spatiotemporal focusing with benefits in energy efficiency
and secrecy, and low electromagnetic field exposure. Motivated by the recent
increasing interests in the field of RISs and the consequent pioneering concept
of the RIS-enabled smart wireless environments, in this paper, we overview and
taxonomize the latest advances in RIS hardware architectures as well as the
most recent developments in the modeling of RIS unit elements and RIS-empowered
wireless signal propagation. We also present a thorough overview of the channel
estimation approaches for RIS-empowered communications systems, which
constitute a prerequisite step for the optimized incorporation of RISs in
future wireless networks. Finally, we discuss the relevance of the RIS
technology in the latest wireless communication standards, and highlight the
current and future standardization activities for the RIS technology and the
consequent RIS-empowered wireless networking approaches.


title:
Towards 6DoF Bilateral Teleoperation of an Omnidirectional Aerial
  Vehicle for Aerial Physical Interaction

abstract:
  Bilateral teleoperation offers an intriguing solution towards shared autonomy
with aerial vehicles in contact-based inspection and manipulation tasks.
Omnidirectional aerial robots allow for full pose operations, making them
particularly attractive in such tasks. Naturally, the question arises whether
standard bilateral teleoperation methodologies are suitable for use with these
vehicles. In this work, a fully decoupled 6DoF bilateral teleoperation
framework for aerial physical interaction is designed and tested for the first
time. The method is based on the well established rate control, recentering and
interaction force feedback policy. However, practical experiments evince the
difficulty of performing decoupled motions in a single axis only. As such, this
work shows that the trivial extension of standard methods is insufficient for
omnidirectional teleoperation, due to the operators physical inability to
properly decouple all input DoFs. This suggests that further studies on
enhanced haptic feedback are necessary.


title: The improved Amati correlations from Gaussian copula

abstract:
  In this paper, we obtain two improved Amati correlations of the Gamma-Ray
burst (GRB) data via a powerful statistical tool called copula. After
calibrating, with the low-redshift GRB data, the improved Amati correlations
based on a fiducial $\Lambda$CDM model with $\Omega_\mathrm{m0}=0.3$ and
$H_0=70~\mathrm{km~s^{-1}Mpc^{-1}}$, and extrapolating the results to the
high-redshift GRB data, we obtain the Hubble diagram of GRB data points.
Applying these GRB data to constrain the $\Lambda$CDM model, we find that the
improved Amati correlation from copula can give a result well consistent with
$\Omega_\mathrm{m0}=0.3$, while the standard Amati and extended Amati
correlations do not. This results suggest that when the improved Amati
correlation from copula is used in the low-redshift calibration method, the GRB
data can be regarded as a viable cosmological explorer. However, the Bayesian
information criterion indicates that the standard Amati correlation remains to
be favored mildly since it has the least model parameters. Furthermore, once
the simultaneous fitting method rather than the low-redshift calibration one is
used, there is no apparent evidence that the improved Amati correlation is
better than the standard one. Thus, more works need to be done in the future in
order to compare different Amati correlations.


title:
Detecting data-driven robust statistical arbitrage strategies with deep
  neural networks

abstract:
  We present an approach, based on deep neural networks, that allows
identifying robust statistical arbitrage strategies in financial markets.
Robust statistical arbitrage strategies refer to self-financing trading
strategies that enable profitable trading under model ambiguity. The presented
novel methodology does not suffer from the curse of dimensionality nor does it
depend on the identification of cointegrated pairs of assets and is therefore
applicable even on high-dimensional financial markets or in markets where
classical pairs trading approaches fail. Moreover, we provide a method to build
an ambiguity set of admissible probability measures that can be derived from
observed market data. Thus, the approach can be considered as being model-free
and entirely data-driven. We showcase the applicability of our method by
providing empirical investigations with highly profitable trading performances
even in 50 dimensions, during financial crises, and when the cointegration
relationship between asset pairs stops to persist.

categories: q-fin.CP cs.LG q-fin.MF q-fin.ST q-fin.TR


title:
Safe Learning-Based Feedback Linearization Tracking Control for
  Nonlinear System with Event-Triggered Model Update

abstract:
  Learning-based methods are powerful in handling complex scenarios. However,
it is still challenging to use learning-based methods under uncertain
environments while stability, safety, and real-time performance of the system
are desired to guarantee. In this paper, we propose a learning-based tracking
control scheme based on a feedback linearization controller in which uncertain
disturbances are approximated online using Gaussian Processes (GPs). Using the
predicted distribution of disturbances given by GPs, a Control Lyapunov
Function (CLF) and Control Barrier Function (CBF) based Quadratic Program is
applied, with which probabilistic stability and safety are guaranteed. In
addition, the trajectory is optimized first by Model Predictive Control (MPC)
based on the linearized dynamics systems to further reduce the tracking error.
We also design an event trigger for GPs updates to improve efficiency while
stability and safety of the system are still guaranteed. The effectiveness of
the proposed tracking control strategy is illustrated in numerical simulations.


title:
Dynamic Template Selection Through Change Detection for Adaptive Siamese
  Tracking

abstract:
  Deep Siamese trackers have recently gained much attention in recent years
since they can track visual objects at high speeds. Additionally, adaptive
tracking methods, where target samples collected by the tracker are employed
for online learning, have achieved state-of-the-art accuracy. However, single
object tracking (SOT) remains a challenging task in real-world application due
to changes and deformations in a target object's appearance. Learning on all
the collected samples may lead to catastrophic forgetting, and thereby corrupt
the tracking model.
  In this paper, SOT is formulated as an online incremental learning problem. A
new method is proposed for dynamic sample selection and memory replay,
preventing template corruption. In particular, we propose a change detection
mechanism to detect gradual changes in object appearance and select the
corresponding samples for online adaption. In addition, an entropy-based sample
selection strategy is introduced to maintain a diversified auxiliary buffer for
memory replay. Our proposed method can be integrated into any object tracking
algorithm that leverages online learning for model adaptation.
  Extensive experiments conducted on the OTB-100, LaSOT, UAV123, and
TrackingNet datasets highlight the cost-effectiveness of our method, along with
the contribution of its key components. Results indicate that integrating our
proposed method into state-of-art adaptive Siamese trackers can increase the
potential benefits of a template update strategy, and significantly improve
performance.


title:
CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road
  Scene

abstract:
  Sensor-based environmental perception is a crucial part of the autonomous
driving system. In order to get an excellent perception of the surrounding
environment, an intelligent system would configure multiple LiDARs (3D Light
Detection and Ranging) to cover the distant and near space of the car. The
precision of perception relies on the quality of sensor calibration. This
research aims at developing an accurate, automatic, and robust calibration
strategy for multiple LiDAR systems in the general road scene. We thus propose
CROON (automatiC multi-LiDAR CalibratiOn and Refinement method in rOad sceNe),
a two-stage method including rough and refinement calibration. The first stage
can calibrate the sensor from an arbitrary initial pose, and the second stage
is able to precisely calibrate the sensor iteratively. Specifically, CROON
utilize the nature characteristics of road scene so that it is independent and
easy to apply in large-scale conditions. Experimental results on real-world and
simulated data sets demonstrate the reliability and accuracy of our method. All
the related data sets and codes are open-sourced on the Github website
https://github.com/OpenCalib/LiDAR2LiDAR.


title: Find a Way Forward: a Language-Guided Semantic Map Navigator

abstract:
  This paper attacks the problem of language-guided navigation in a new
perspective by using novel semantic navigation maps, which enables robots to
carry out natural language instructions and move to a target position based on
the map observations. We break down this problem into parts and introduce three
different modules to solve the corresponding subproblems. Our approach
leverages map information to provide Deterministic Path Candidate Proposals to
reduce the solution space. Different from traditional methods that predict
robots' movements toward the target step-by-step, we design an attention-based
Language Driven Discriminator to evaluate path candidates and determine the
best path as the final result. To represent the map observations along a path
for a better modality alignment, a novel Path Feature Encoding scheme tailored
for semantic navigation maps is proposed. Unlike traditional methods that tend
to produce cumulative errors or be stuck in local decisions, our method which
plans paths based on global information can greatly alleviate these problems.
The proposed approach has noticeable performance gains, especially in
long-distance navigation cases. Also, its training efficiency is significantly
higher than of other methods.


title:
Two-dimensional XY-type Magnetic Properties of Locally
  Noncentrosymmetric Superconductor CeRh$_2$As$_2$

abstract:
  We performed $^{75}$As-NMR measurements to investigate the normal-state
magnetic properties of CeRh$_2$As$_2$, a recently-discovered heavy-fermion
superconductor. The magnitude and temperature dependence of the Knight shift at
the As(2) site indicate easy-plane-type magnetic anisotropy in CeRh$_2$As$_2$.
With regard to spin fluctuations, the temperature dependence of the nuclear
spin-lattice relaxation rate $1/T_1$ arising from the 4$f$ electrons decreases
from high-temperature constant behavior on cooling at $\sim$ 40~K, which is
typical behavior of heavy-fermion systems. In addition, $1/T_1$ becomes
constant at low temperatures, suggesting spatially two-dimensional
antiferromagnetic fluctuations. Two-dimensional magnetic correlations in the
real space are quite rare among heavy-fermion superconductors, and they may be
a key factor in the unique superconducting multi-phase in CeRh$_2$As$_2$.


title:
Covariate-Balancing-Aware Interpretable Deep Learning models for
  Treatment Effect Estimation

abstract:
  Estimating treatment effects is of great importance for many biomedical
applications with observational data. Particularly, interpretability of the
treatment effects is preferable for many biomedical researchers. In this paper,
we first give a theoretical analysis and propose an upper bound for the bias of
average treatment effect estimation under the strong ignorability assumption.
The proposed upper bound consists of two parts: training error for factual
outcomes, and the distance between treated and control distributions. We use
the Weighted Energy Distance (WED) to measure the distance between two
distributions. Motivated by the theoretical analysis, we implement this upper
bound as an objective function being minimized by leveraging a novel additive
neural network architecture, which combines the expressivity of deep neural
network, the interpretability of generalized additive model, the sufficiency of
the balancing score for estimation adjustment, and covariate balancing
properties of treated and control distributions, for estimating average
treatment effects from observational data. Furthermore, we impose a so-called
weighted regularization procedure based on non-parametric theory, to obtain
some desirable asymptotic properties. The proposed method is illustrated by
re-examining the benchmark datasets for causal inference, and it outperforms
the state-of-art.


title:
Bandits Corrupted by Nature: Lower Bounds on Regret and Robust
  Optimistic Algorithm

abstract:
  In this paper, we study the stochastic bandits problem with $k$ unknown
heavy-tailed and corrupted reward distributions or arms with time-invariant
corruption distributions. At each iteration, the player chooses an arm. Given
the arm, the environment returns an uncorrupted reward with probability
$1-\varepsilon$ and an arbitrarily corrupted reward with probability
$\varepsilon$. In our setting, the uncorrupted reward might be heavy-tailed and
the corrupted reward might be unbounded. We prove a lower bound on the regret
indicating that the corrupted and heavy-tailed bandits are strictly harder than
uncorrupted or light-tailed bandits. We observe that the environments can be
categorised into hardness regimes depending on the suboptimality gap $\Delta$,
variance $\sigma$, and corruption proportion $\epsilon$. Following this, we
design a UCB-type algorithm, namely HuberUCB, that leverages Huber's estimator
for robust mean estimation. HuberUCB leads to tight upper bounds on regret in
the proposed corrupted and heavy-tailed setting. To derive the upper bound, we
prove a novel concentration inequality for Huber's estimator, which might be of
independent interest.


title: Knowledge Amalgamation for Object Detection with Transformers

abstract:
  Knowledge amalgamation (KA) is a novel deep model reusing task aiming to
transfer knowledge from several well-trained teachers to a multi-talented and
compact student. Currently, most of these approaches are tailored for
convolutional neural networks (CNNs). However, there is a tendency that
transformers, with a completely different architecture, are starting to
challenge the domination of CNNs in many computer vision tasks. Nevertheless,
directly applying the previous KA methods to transformers leads to severe
performance degradation. In this work, we explore a more effective KA scheme
for transformer-based object detection models. Specifically, considering the
architecture characteristics of transformers, we propose to dissolve the KA
into two aspects: sequence-level amalgamation (SA) and task-level amalgamation
(TA). In particular, a hint is generated within the sequence-level amalgamation
by concatenating teacher sequences instead of redundantly aggregating them to a
fixed-size one as previous KA works. Besides, the student learns heterogeneous
detection tasks through soft targets with efficiency in the task-level
amalgamation. Extensive experiments on PASCAL VOC and COCO have unfolded that
the sequence-level amalgamation significantly boosts the performance of
students, while the previous methods impair the students. Moreover, the
transformer-based students excel in learning amalgamated knowledge, as they
have mastered heterogeneous detection tasks rapidly and achieved superior or at
least comparable performance to those of the teachers in their specializations.


title:
Convergence in law for the capacity of the range of a critical branching
  random walk

abstract:
  Let $R_n$ be the range of a critical branching random walk with $n$ particles
on $\mathbb Z^d$, which is the set of sites visited by a random walk indexed by
a critical Galton--Watson tree conditioned on having exactly $n$ vertices. For
$d\in\{3, 4, 5\}$, we prove that $n^{-\frac{d-2}4} \mathtt{cap}^{(d)}(R_n)$,
the renormalized capacity of $R_n$, converges in law to the capacity of the
support of the integrated super-Brownian excursion. The proof relies on a study
of the intersection probabilities between the critical branching random walk
and an independent simple random walk on $\mathbb Z^d$.


title:
A comparative study of several ADPCM schemes with linear and nonlinear
  prediction

abstract:
  In this paper we compare several ADPCM schemes with nonlinear prediction
based on neural nets with the classical ADPCM schemes based on several linear
prediction schemes. Main studied variations of the ADPCM scheme with adaptive
quantization (2 to 5 bits) are: -forward vs backward -sample adaptive vs block
adaptive


title:
Speaker recognition by means of a combination of linear and nonlinear
  predictive models

abstract:
  This paper deals the combination of nonlinear predictive models with
classical LPCC parameterization for speaker recognition. It is shown that the
combination of both a measure defined over LPCC coefficients and a measure
defined over predictive analysis residual signal gives rise to an improvement
over the classical method that considers only the LPCC coefficients. If the
residual signal is obtained from a linear prediction analysis, the improvement
is 2.63% (error rate drops from 6.31% to 3.68%) and if it is computed through a
nonlinear predictive neural nets based model, the improvement is 3.68%. An
efficient algorithm for reducing the computational burden is also proposed.


title:
Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with
  Articulatory Features

abstract:
  While neural text-to-speech systems perform remarkably well in high-resource
scenarios, they cannot be applied to the majority of the over 6,000 spoken
languages in the world due to a lack of appropriate training data. In this
work, we use embeddings derived from articulatory vectors rather than
embeddings derived from phoneme identities to learn phoneme representations
that hold across languages. In conjunction with language agnostic meta
learning, this enables us to fine-tune a high-quality text-to-speech model on
just 30 minutes of data in a previously unseen language spoken by a previously
unseen speaker.


title: Dynamic Pricing for Client Recruitment in Federated Learning

abstract:
  Though federated learning (FL) well preserves clients' data privacy, many
clients are still reluctant to join FL given the communication cost and energy
consumption in their mobile devices. It is important to design pricing
compensations to motivate enough clients to join FL and distributively train
the global model. Prior pricing mechanisms for FL are static and cannot adapt
to clients' random arrival pattern over time. We propose a new dynamic pricing
solution in closed-form by constructing the Hamiltonian function to optimally
balance the client recruitment time and the model training time, without
knowing clients' actual arrivals or training costs. During the client
recruitment phase, we offer time-dependent monetary rewards per client arrival
to trade-off between the total payment and the FL model's accuracy loss. Such
reward gradually increases when we approach to the recruitment deadline or have
greater data aging, and we also extend the deadline if the clients' training
time per iteration becomes shorter. Further, we extend to consider
heterogeneous client types in training data size and training time per
iteration. We successfully extend our dynamic pricing solution and develop an
optimal algorithm of linear complexity to monotonically select client types for
FL. Finally, we also show the robustness of our solution against estimation
error of clients' data sizes and run numerical experiments to validate our
conclusion.


title: Convex analysis on Hadamard spaces and scaling problems

abstract:
  In this paper, we address the bounded/unbounded determination of geodesically
convex optimization on Hadamard spaces. In Euclidean convex optimization, the
recession function is a basic tool to study the unboundedness, and provides the
domain of the Legendre-Fenchel conjugate of the objective function. In a
Hadamard space, the asymptotic slope function (Kapovich, Leeb, and Millson
2009), which is a function on the boundary at infinity, plays a role of the
recession function. We extend this notion by means of convex analysis and
optimization, and develop a convex analysis foundation for the unbounded
determination of geodesically convex optimization on Hadamard spaces,
particularly on symmetric spaces of nonpositive curvature. We explain how our
developed theory is applied to operator scaling and related optimization on
group orbits, which are our motivation.


title: Uncovering the true periods of the young sub-Neptunes orbiting TOI-2076

abstract:
  Context: TOI-2076 is a transiting three-planet system of sub-Neptunes
orbiting a bright (G = 8.9 mag), young ($340\pm80$ Myr) K-type star. Although a
validated planetary system, the orbits of the two outer planets were
unconstrained as only two non-consecutive transits were seen in TESS
photometry. This left 11 and 7 possible period aliases for each.
  Aims: To reveal the true orbits of these two long-period planets, precise
photometry targeted on the highest-probability period aliases is required.
Long-term monitoring of transits in multi-planet systems can also help
constrain planetary masses through TTV measurements.
  Methods: We used the MonoTools package to determine which aliases to follow,
and then performed space-based and ground-based photometric follow-up of
TOI-2076 c and d with CHEOPS, SAINT-EX, and LCO telescopes.
  Results: CHEOPS observations revealed a clear detection for TOI-2076 c at
$P=21.01538^{+0.00084}_{-0.00074}$ d, and allowed us to rule out three of the
most likely period aliases for TOI-2076 d. Ground-based photometry further
enabled us to rule out remaining aliases and confirm the $P=35.12537\pm0.00067$
d alias. These observations also improved the radius precision of all three
sub-Neptunes to $2.518\pm0.036$, $3.497\pm0.043$, and $3.232\pm0.063$
$R_\oplus$. Our observations also revealed a clear anti-correlated TTV signal
between planets b and c likely caused by their proximity to the 2:1 resonance,
while planets c and d appear close to a 5:3 period commensurability, although
model degeneracy meant we were unable to retrieve robust TTV masses. Their
inflated radii, likely due to extended H-He atmospheres, combined with low
insolation makes all three planets excellent candidates for future comparative
transmission spectroscopy with JWST.


title:
Unpaired Image Captioning by Image-level Weakly-Supervised Visual
  Concept Recognition

abstract:
  The goal of unpaired image captioning (UIC) is to describe images without
using image-caption pairs in the training phase. Although challenging, we
except the task can be accomplished by leveraging a training set of images
aligned with visual concepts. Most existing studies use off-the-shelf
algorithms to obtain the visual concepts because the Bounding Box (BBox) labels
or relationship-triplet labels used for the training are expensive to acquire.
In order to resolve the problem in expensive annotations, we propose a novel
approach to achieve cost-effective UIC. Specifically, we adopt image-level
labels for the optimization of the UIC model in a weakly-supervised manner. For
each image, we assume that only the image-level labels are available without
specific locations and numbers. The image-level labels are utilized to train a
weakly-supervised object recognition model to extract object information (e.g.,
instance) in an image, and the extracted instances are adopted to infer the
relationships among different objects based on an enhanced graph neural network
(GNN). The proposed approach achieves comparable or even better performance
compared with previous methods without the expensive cost of annotations.
Furthermore, we design an unrecognized object (UnO) loss combined with a visual
concept reward to improve the alignment of the inferred object and relationship
information with the images. It can effectively alleviate the issue encountered
by existing UIC models about generating sentences with nonexistent objects. To
the best of our knowledge, this is the first attempt to solve the problem of
Weakly-Supervised visual concept recognition for UIC (WS-UIC) based only on
image-level labels. Extensive experiments have been carried out to demonstrate
that the proposed WS-UIC model achieves inspiring results on the COCO dataset
while significantly reducing the cost of labeling.


title:
Undersampled MRI Reconstruction with Side Information-Guided
  Normalisation

abstract:
  Magnetic resonance (MR) images exhibit various contrasts and appearances
based on factors such as different acquisition protocols, views, manufacturers,
scanning parameters, etc. This generally accessible appearance-related side
information affects deep learning-based undersampled magnetic resonance imaging
(MRI) reconstruction frameworks, but has been overlooked in the majority of
current works. In this paper, we investigate the use of such side information
as normalisation parameters in a convolutional neural network (CNN) to improve
undersampled MRI reconstruction. Specifically, a Side Information-Guided
Normalisation (SIGN) module, containing only few layers, is proposed to
efficiently encode the side information and output the normalisation
parameters. We examine the effectiveness of such a module on two popular
reconstruction architectures, D5C5 and OUCR. The experimental results on both
brain and knee images under various acceleration rates demonstrate that the
proposed method improves on its corresponding baseline architectures with a
significant margin.


title:
SQUAB I: The first release of Strange QUasar candidates with ABnormal
  astrometric characteristics from Gaia EDR3 and SDSS

abstract:
  Given their extremely large distances and small apparent sizes, quasars are
generally considered as objects with near-zero parallax and proper motion.
However, some special quasars may have abnormal astrometric characteristics,
such as quasar pairs, lensed quasars, AGNs with bright parsec-scale optical
jets, which are scientifically interesting objects, such as binary black holes.
These quasars may come with astrometric jitter detectable with Gaia data, or
significant changes in the position at different wavelengths. In this work, we
aim to find these quasar candidates from Gaia EDR3 astrometric data combining
with Sloan Digital Sky Survey (SDSS) spectroscopic data to provide a candidate
catalog to the science community. We propose a series of criteria for selecting
abnormal quasars based on Gaia astrometric data. We obtain two catalogs
containing 155 sources and 44 sources, respectively. They are potential
candidates of quasar pairs.


title: Farthest-point Voronoi diagrams in the presence of rectangular obstacles

abstract:
  We present an algorithm to compute the geodesic $L_1$ farthest-point Voronoi
diagram of $m$ point sites in the presence of $n$ rectangular obstacles in the
plane. It takes $O(nm+n \log n + m\log m)$ construction time using $O(nm)$
space. This is the first optimal algorithm for constructing the farthest-point
Voronoi diagram in the presence of obstacles. We can construct a data structure
in the same construction time and space that answers a farthest-neighbor query
in $O(\log(n+m))$ time.


title: Flexoelectric control of a ferromagnetic metal

abstract:
  Electric fields have played a key role in discovering and controlling exotic
electronic states of condensed matter. However, electric fields usually do not
work in metals as free carriers tend to screen electrostatic fields. While a
pseudo-electric field generated by inhomogeneous lattice strain, namely a
flexoelectric field, can in principle work in all classes of materials, it
remains experimentally unexplored in metals. Here, using heteroepitaxy and
atomic-scale imaging, we show that flexoelectric fields can polarize a metallic
oxide SrRuO3 with unexpectedly large Ru off-center displacements. We also
observe that the flexoelectrically induced polar state of SrRuO3 leads to
sizable lattice expansion, similar to the electrostrictive expansion caused by
ionic displacements in dielectrics under an external electric field. We further
suggest that flexoelectrically driven Ru off-centering promotes strong coupling
between lattice and electronic degrees of freedom, possibly enhancing the
ferromagnetism of SrRuO3. Beyond conventional electric fields, flexoelectric
fields may universally engender novel electronic states and their control via
pure atomic displacements in a nondestructive and fast manner.


title:
On the homotopy fixed points of Maurer-Cartan spaces with finite group
  actions

abstract:
  We develop the basic theory of Maurer-Cartan simplicial sets associated to
(shifted complete) $L_\infty$ algebras equipped with the action of a finite
group. Our main result asserts that the inclusion of the fixed points of this
equivariant simplicial set into the homotopy fixed points is a homotopy
equivalence of Kan complexes, provided the $L_\infty$ algebra is concentrated
in non-negative degrees. As an application, and under certain connectivity
assumptions, we provide rational algebraic models of the fixed and homotopy
fixed points of mapping spaces equipped with the action of a finite group.


title: MS2MP: A Min-Sum Message Passing Algorithm for Motion Planning

abstract:
  Gaussian Process (GP) formulation of continuoustime trajectory offers a fast
solution to the motion planning problem via probabilistic inference on factor
graph. However, often the solution converges to in-feasible local minima and
the planned trajectory is not collision-free. We propose a message passing
algorithm that is more sensitive to obstacles with fast convergence time. We
leverage the utility of min-sum message passing algorithm that performs local
computations at each node to solve the inference problem on factor graph. We
first introduce the notion of compound factor node to transform the factor
graph to a linearly structured graph. We next develop an algorithm denoted as
Min-sum Message Passing algorithm for Motion Planning (MS2MP) that combines
numerical optimization with message passing to find collision-free
trajectories. MS2MP performs numerical optimization to solve non-linear least
square minimization problem at each compound factor node and then exploits the
linear structure of factor graph to compute the maximum a posteriori (MAP)
estimation of complete graph by passing messages among graph nodes. The
decentralized optimization approach of each compound node increases sensitivity
towards avoiding obstacles for harder planning problems. We evaluate our
algorithm by performing extensive experiments for exemplary motion planning
tasks for a robot manipulator. Our evaluation reveals that MS2MP improves
existing work in convergence time and success rate.


title: Orthogonal Stability

abstract:
  An ordinary character or a Brauer character $\chi $ of a finite group is
called orthogonally stable, if all its indicator $+$ constituents have even
degree and there is a representation affording $\chi $ that fixes a
non-degenerate quadratic form. In this case all non-degenerate quadratic forms
in any representation affording $\chi $ have the same discriminant.
  This square class disc$ (\chi ) \in K^{\times }/(K^{\times })^2$ of the
character field
  $K$ of $\chi $ is called the orthogonal discriminant of $\chi $.
  The paper develops some character theoretic methods to compute orthogonal
discriminants.


title:
Constraints on self-dual black hole in loop quantum gravity with S0-2
  star in the Galactic Center

abstract:
  One of remarkable features of loop quantum gravity (LQG) is that it can
provide elegant resolutions to both the black hole and big bang singularities.
In the mini-superspace approach based on the polymerization procedure in LQG, a
self-dual black hole metric which is effective and regular is constructed. It
modifies the Schwarzschild spacetime by the polymeric function $P$, purely due
to the geometric quantum effects from LQG. In this paper, we consider its
effects on the orbital signatures of S0-2 star orbiting Sgr A* in the central
region of our Milky Way, and compare it with the publicly available astrometric
and spectroscopic data, including the astrometric positions, the radial
velocities, and the orbital procession for the S0-2 star. We perform a Monte
Carlo Markov Chain (MCMC) simulation to probe the possible LQG effects on the
orbit of S0-2 star. We do not find any significant evidence of the self-dual
spacetime arsing from LQG and thus place an upper bound on the polymeric
function $P < 0.039$ at 95\% confidence level. This bound leads to a constraint
on the polymeric parameter $\delta$ of LQG to be $\delta< 1.7$.


title:
Piloting Diversity and Inclusion Workshops in Artificial Intelligence
  and Robotics for Children

abstract:
  In this paper, we present preliminary work from a pilot workshop that aimed
to promote diversity and inclusion for fundamentals of Artificial Intelligence
and Robotics for Children (air4children) in the context of developing
countries. Considering the scarcity of funding and the little to none
availability of specialised professionals to teach AI and robotics in
developing countries, we present resources based on free open-source hardware
and software, open educational resources, and alternative education programs.
That said, the contribution of this work is the pilot workshop of four lessons
that promote diversity and inclusion on teaching AI and Robotics for children
to a small gender-balanced sample of 14 children of an average age of 7.64
years old. We conclude that participant, instructors, coordinators and parents
engaged well in the pilot workshop noting the various challenges of having the
right resources for the workshops in developing countries and posing future
work. The resources to reproduce this work are available at
https://github.com/air4children/hri2022.


title: Foliated real hypersurfaces

abstract:
  This paper deals with a limiting case motivated by contact geometry. The
limiting case of a tensorial characterization of contact hypersurfaces in
Kahler manifolds leads to Hopf hypersurfaces whose maximal complex subbundle of
the tangent bundle is integrable. It is known that in non-flat complex space
forms and in complex quadrics such real hypersurfaces do not exist, but the
existence problem in other irreducible Kahler manifolds is open. In this paper
we construct explicitly a one-parameter family of homogeneous Hopf
hypersurfaces, whose maximal complex subbundle of the tangent bundle is
integrable, in a Hermitian symmetric space of non-compact type and rank two.
These are the first known examples of such real hypersurfaces in irreducible
Kahler manifolds.


title:
Three-dimensional geometry and dust/gas ratios in massive star forming
  regions over the entire LMC as revealed by IRSF/SIRIUS survey

abstract:
  We derive the entire dust extinction (Av) map for the Large Magellanic Cloud
(LMC) estimated from the color excess at near-infrared wavelengths. Using the
percentile method we recently adopted to evaluate Av distribution along the
line of sight, we derive the three-dimensional(3D)Av maps of the three massive
star forming regions of N44, N79 and N11 based on the IRSF/SIRIUS point source
catalog. The 3D AV maps are compared with the hydrogen column densities N(H) of
three different velocity components where one is of the LMC disk velocity and
the other two are of velocities lower than the disk velocity. As a result, we
obtain 3D dust geometry suggesting that gas collision is on-going between the
different velocity components. We also find difference in the timing of the gas
collision between the massive star forming regions, which indicates that the
gas collision in N44, N79 and N11 occurred later than that in 30 Doradus. In
addition, difference by a factor of two in Av/N(H) is found between the
velocity components for N44, while significant difference is not found for N79
and N11. From the 3D geometry and Av/N(H) in each star forming region, we
suggest that the massive star formation in N44 was induced by an external
trigger of tidal interaction between the LMC and the SMC, while that in N79 and
N11 is likely to have been induced by internal triggers such as gas converging
from the galactic spiral arm and expansion of a supershell although the
possibility of tidal interaction cannot be ruled out.


title:
Stochastic Aperiodic Control of Networked Systems with i.i.d.
  Time-Varying Communication Delays

abstract:
  This paper studies stochastic aperiodic stabilization of a networked control
system (NCS) consisting of a continuous-time plant and a discrete-time
controller. The plant and the controller are assumed to be connected by
communication channels with i.i.d. time-varying delays. The delays are
theoretically not required to be bounded even when the plant is unstable in the
deterministic sense. In our NCS, the sampling interval is supposed to be
determined directly by such communication delays. A necessary and sufficient
inequality condition is presented for designing a state-feedback controller
stabilizing the NCS at sampling points in a stochastic sense. The results are
also illustrated numerically.


title: Trajectory Test-Train Overlap in Next-Location Prediction Datasets

abstract:
  Next-location prediction, consisting of forecasting a user's location given
their historical trajectories, has important implications in several fields,
such as urban planning, geo-marketing, and disease spreading. Several
predictors have been proposed in the last few years to address it, including
last-generation ones based on deep learning. This paper tests the
generalization capability of these predictors on public mobility datasets,
stratifying the datasets by whether the trajectories in the test set also
appear fully or partially in the training set. We consistently discover a
severe problem of trajectory overlapping in all analyzed datasets, highlighting
that predictors memorize trajectories while having limited generalization
capacities. We thus propose a methodology to rerank the outputs of the
next-location predictors based on spatial mobility patterns. With these
techniques, we significantly improve the predictors' generalization capability,
with a relative improvement on the accuracy up to 96.15% on the trajectories
that cannot be memorized (i.e., low overlap with the training set).


title: CompOSE Reference Manual

abstract:
  CompOSE (CompStar Online Supernovae Equations of State) is an online
repository of equations of state (EoS) for use in nuclear physics and
astrophysics, e.g., in the description of compact stars or the simulation of
core-collapse supernovae and neutron-star mergers, see http://compose.obspm.fr.
The main services, offered via the website, are: a collection of data tables in
a flexible and easily extendable data format for different EoS types and
related physical quantities with extensive documentation and referencing;
software for download to extract and to interpolate these data and to calculate
additional quantities; webtools to generate EoS tables that are customized to
the needs of the users and to illustrate dependencies of various EoS quantities
in graphical form. This manual is an update of previous versions that are
available on the CompOSE website, at arXiv:1307.5715 [astro-ph.SR], and that
was originally published in the journal "Physics of Particles and Nuclei" with
doi:10.1134/S1063779615040061. It contains a detailed description of the
service, containing a general introduction as well as instructions for
potential contributors and for users. Short versions of the manual for EoS
users and providers will also be available as separate publications.


title: Demonstration of a droplet electrohydrodynamic blower in aerosols

abstract:
  Despite a rigorous analysis of nonlinear electrohydrodynamics of a liquid
droplet deformation in air, its influence on the surrounding gas phase has
received less attention. We observed, air circulations created due to large
deformation oscillations of a pendant water droplet subject to a resonating
electric field act like a micro or mini blower in the air around the droplet.
This is demonstrated through the deterioration of a strong electrostatic
airborne particulate capture onto the droplet, when it is placed in an aerosol.
Such strong local aerodynamic influence on the surrounding aerosol particulate,
heat and mass transfer in the two phase system provoke further interest in
fundamental and technological research.


title: Charm Physics in the High-Luminosity Super $\tau$-Charm Factory

abstract:
  This is a white paper on the STCF charm physics contributing to the Snowmass
2021 proceedings in the RF01 working group.


title: Maximizing Conditional Independence for Unsupervised Domain Adaptation

abstract:
  Unsupervised domain adaptation studies how to transfer a learner from a
labeled source domain to an unlabeled target domain with different
distributions. Existing methods mainly focus on matching the marginal
distributions of the source and target domains, which probably lead a
misalignment of samples from the same class but different domains. In this
paper, we deal with this misalignment by achieving the class-conditioned
transferring from a new perspective. We aim to maximize the conditional
independence of feature and domain given class in the reproducing kernel
Hilbert space. The optimization of the conditional independence measure can be
viewed as minimizing a surrogate of a certain mutual information between
feature and domain. An interpretable empirical estimation of the conditional
dependence is deduced and connected with the unconditional case. Besides, we
provide an upper bound on the target error by taking the class-conditional
distribution into account, which provides a new theoretical insight for most
class-conditioned transferring methods. In addition to unsupervised domain
adaptation, we extend our method to the multi-source scenario in a natural and
elegant way. Extensive experiments on four benchmarks validate the
effectiveness of the proposed models in both unsupervised domain adaptation and
multiple source domain adaptation.


title:
Probing Magnetic Fields in Protoplanetary Disk Atmospheres through
  Polarized Optical/IR Light Scattered by Aligned Grains

abstract:
  The magnetic field plays an essential role in the evolution and dynamics of
protoplanetary disks. The magnetic field in the disk atmosphere is of
particular interest, as it is directly connected to the wind-launching
mechanism. In this work, we study the polarization of the light scattered off
magnetically aligned grains in the disk atmosphere, focusing on the deviation
of the polarization orientation from the canonical azimuthal direction, which
may be detectable in optical/IR with instruments such as VLT/SPHERE. We first
study the polarization in the grain's frame (GF), finding that the angle
difference, the deviation from azimuthal orientation, increases with an
increasing inclination angle (in GF) and dust aspect ratio, and can easily
reach $\sim 10^\circ$. We show with a simple disk model that the polarization
can even be oriented along the radial (rather than azimuthal) direction,
especially in a highly inclined disk with a toroidally dominated magnetic
field. This polarization reversal is caused by the anistropy in the
polarizibility of aligned grains and is thus a telltale sign of such grains. We
show that the optical/IR light is scattered mostly by grains of $\mu$m sizes or
smaller at the $\tau=1$ surface and such grains can be magnetically aligned if
they contain superparamagnetic inclusions. For comparison with polarimetric
observations, we generate synthetic maps of the ratios of Stokes parameters
$U_\phi/I$ and $Q_\phi/I$, which can be used to infer the existence of
(magnetically) aligned grains through a negative $Q_\phi$ (polarization
reversal) and/or a significant level of $U_\phi/I$.


title: Observation of novel topological states in hyperbolic lattices

abstract:
  The discovery of novel topological states has served as a major branch in
physics and material science. However, to date, most of the established
topological states of matter have been employed in Euclidean systems, where the
interplay between unique geometrical characteristics of curved spaces and
exotic topological phases is less explored, especially on the experimental
perspective. Recently, the experimental realization of the hyperbolic lattice,
which is the regular tessellation in non-Euclidean spaces with a constant
negative curvature, has attracted much attention in the field of simulating
exotic phenomena from quantum physics in curved spaces to the general
relativity. The question is whether there are novel topological states in such
a non-Euclidean system without analogues in Euclidean spaces. Here, we
demonstrate both in theory and experiment that novel topological states
possessing unique properties compared with their Euclidean counterparts can
exist in engineered hyperbolic lattices. Specially, based on the extended
Haldane model, the boundary-dominated first-order Chern edge state with a
nontrivial real-space Chern number is achieved, and the associated one-way
propagation is proven. Furthermore, we show that fractal-like midgap
higher-order zero modes appear in deformed hyperbolic lattices, where the
number of zero modes increases exponentially with the increase of lattice size.
These novel topological states are observed in designed hyperbolic circuit
networks by measuring site-resolved impendence responses and dynamics of
voltage packets. Our findings suggest a novel platform to study topological
phases beyond Euclidean space and may have potential applications in the field
of designing high-efficient topological devices, such as topological lasers,
with extremely fewer trivial regions.


title: Interacting Emergent Yang-Mills theory

abstract:
  In this article, subleading (in $1/N$) corrections to the action of the one
loop dilatation operator in the su(3) sector of $\mathcal{N}=4$ super
Yang-Mills theory are studied. We focus on the system of operators dual to two
giant graviton systems, which have a bare dimension $\sim\mathcal{O}(N)$ and
are a linear combination of restricted Schur polynomials with $p=2$ long
columns. At the leading order the dilatation operator gives rise to the free
part of an emergent Yang-Mills theory, arising from the open string excitations
of the giant gravitons. We verify that the terms we study describe interactions
between these open string excitations. The interactions have the
U(1)$\times$U(1) gauge invariance expected for a pair of separated branes.


title:
USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration
  Network for Multilingual Complex Named Entity Recognition

abstract:
  This paper describes the system developed by the USTC-NELSLIP team for
SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition
(MultiCoNER). We propose a gazetteer-adapted integration network (GAIN) to
improve the performance of language models for recognizing complex named
entities. The method first adapts the representations of gazetteer networks to
those of language models by minimizing the KL divergence between them. After
adaptation, these two networks are then integrated for backend supervised named
entity recognition (NER) training. The proposed method is applied to several
state-of-the-art Transformer-based NER models with a gazetteer built from
Wikidata, and shows great generalization ability across them. The final
predictions are derived from an ensemble of these trained models. Experimental
results and detailed analysis verify the effectiveness of the proposed method.
The official results show that our system ranked 1st on three tracks (Chinese,
Code-mixed and Bangla) and 2nd on the other ten tracks in this task.


title: An Elementary Proof of the Signature of Satellite Knots

abstract:
  We present a proof of Litherland's formula for the Tristram-Levine signature
of a satellite knot in terms of its constituents. Litherland's original proof
used more advanced algebraic techniques, while ours uses only linear algebra
and some basic results in knot theory.


title:
Enhance Language Identification using Dual-mode Model with Knowledge
  Distillation

abstract:
  In this paper, we propose to employ a dual-mode framework on the x-vector
self-attention (XSA-LID) model with knowledge distillation (KD) to enhance its
language identification (LID) performance for both long and short utterances.
The dual-mode XSA-LID model is trained by jointly optimizing both the full and
short modes with their respective inputs being the full-length speech and its
short clip extracted by a specific Boolean mask, and KD is applied to further
boost the performance on short utterances. In addition, we investigate the
impact of clip-wise linguistic variability and lexical integrity for LID by
analyzing the variation of LID performance in terms of the lengths and
positions of the mimicked speech clips. We evaluated our approach on the MLS14
data from the NIST 2017 LRE. With the 3~s random-location Boolean mask, our
proposed method achieved 19.23%, 21.52% and 8.37% relative improvement in
average cost compared with the XSA-LID model on 3s, 10s, and 30s speech,
respectively.


title:
$L^p$ estimate for positive harmonic functions near singularities and
  B\^{o}cher type theorems

abstract:
  In this paper, positive solutions to the Laplace equation with 1-dimensional
circular singularities are investigated. First, we establish $L^p$
integrability estimates for such solutions $u$ near the singularities, in
comparison with classical $L^1$ estimates. Then we characterize $-\Delta u$
around the circle. The method we developed may also be adapted to higher
dimensional as well as more generalized cases.


title:
On the error rate of importance sampling with randomized quasi-Monte
  Carlo

abstract:
  Importance sampling (IS) is valuable in reducing the variance of Monte Carlo
sampling for many areas, including finance, rare event simulation, and Bayesian
inference. It is natural and obvious to combine quasi-Monte Carlo (QMC) methods
with IS to achieve a faster rate of convergence. However, a naive replacement
of Monte Carlo with QMC may not work well. This paper investigates the
convergence rates of randomized QMC-based IS for estimating integrals with
respect to a Gaussian measure, in which the IS measure is also a Gaussian. We
prove that if the target function satisfies the so-called boundary growth
condition and the covariance matrix of the IS density has eigenvalues no
smaller than 1, then randomized QMC has a mean error of $O(N^{-1+\epsilon})$
for arbitrarily small $\epsilon>0$. These sufficient conditions help to assess
the effectiveness of IS in QMC. For some particular applications, we find that
the Laplace IS, a very general approach to approximate the target function by a
quadratic Taylor approximation around its mode, has eigenvalues smaller than 1,
making the resulting integrand less favorable for QMC. From this point of view,
a change of measure via Laplace IS may transform a favorable integrand into
unfavorable one for QMC although the variance of Monte Carlo sampling is
reduced. We also study the effect of positivization trick on the error rate
when the integrand has mixed sign. If the smooth positivization proposed by
Owen and Zhou (2000) is used, the rate $O(N^{-1+\epsilon})$ is retained. This
is not the case if taking the positive and negative parts of the integrand. We
also give some examples to verify our propositions and warn against naive
replacement of MC with QMC under IS proposals.


title: Generalized Spectral Clustering for Directed and Undirected Graphs

abstract:
  Spectral clustering is a popular approach for clustering undirected graphs,
but its extension to directed graphs (digraphs) is much more challenging. A
typical workaround is to naively symmetrize the adjacency matrix of the
directed graph, which can however lead to discarding valuable information
carried by edge directionality. In this paper, we present a generalized
spectral clustering framework that can address both directed and undirected
graphs. Our approach is based on the spectral relaxation of a new functional
that we introduce as the generalized Dirichlet energy of a graph function, with
respect to an arbitrary positive regularizing measure on the graph edges. We
also propose a practical parametrization of the regularizing measure
constructed from the iterated powers of the natural random walk on the graph.
We present theoretical arguments to explain the efficiency of our framework in
the challenging setting of unbalanced classes. Experiments using directed K-NN
graphs constructed from real datasets show that our graph partitioning method
performs consistently well in all cases, while outperforming existing
approaches in most of them.


title: Quantum computation with cat qubits

abstract:
  These are the lecture notes from the 2019 Les Houches Summer School on
"Quantum Information Machines". After a brief introduction to quantum error
correction and bosonic codes, we focus on the case of cat qubits stabilized by
a nonlinear multi-photon driven dissipation process. We argue that such a
system can be seen as a self-correcting qubit where bit-flip errors are
robustly and exponentially suppressed. Next, we provide some experimental
directions to engineer such a multi-photon driven dissipation process with
superconducting circuits. Finally, we analyze various logical gates that can be
implemented without re-introducing bit-flip errors. This set of bias-preserving
gates pave the way towards a hardware-efficient and fault-tolerant quantum
processor.


title: Timelike orbits around accelerating black holes

abstract:
  We study the geodesics of massive particles around accelerating Schwarzschild
black hole. We show that the radius of the innermost stable circular orbit and
the angular momentum of particle at this orbit decrease by increasing the
acceleration. Apart from quantitative influence, the acceleration qualitatively
changes the physics. We show that in accelerating black hole spacetime there
exist an outermost stable circular orbit in flat, de Sitter, and anti de Sitter
background. Investigations of radial geodesics show that the acceleration acts
like a repulsive force in the sense that test particles around accelerating
black holes can move radially outward, unless there exist a large negative
value of cosmological constant in the background to compensate the repulsive
force. We also investigate the precession of perihelion of orbits around
accelerating black holes. The precession would be larger compared to the
non-accelerating case. It is also shown that the precession in anti de Sitter
background could be opposite to the particle motion.


title:
A Fast Approach to Minimum Curvature Raceline Planning via Probabilistic
  Inference

abstract:
  Finding a racing path that allows minimum lap time is the first and foremost
step in overall autonomous race car planning. Minimum curvature path is the
raceline that offers the highest cornering speed resulting in improved lap time
for a given racetrack. Unfortunately, solving the geometrical optimization
problem for finding a raceline is computationally expensive. In a competitive
race, a few seconds difference in raceline computation could prove to be
decisive. This paper presents a novel approach for finding the minimum
curvature raceline via probabilistic inference. We leverage the tangential
geometry and structure in the minimum curvature planning problem to formulate
it on a factor graph, which is then solved as sparse non-linear least squares
leading to a much faster algorithm. The proposed framework is evaluated for
different racetracks and benchmark results are presented highlighting the
computational efficiency, lap time and resulting raceline. Initial results
indicate the viability of the proposed approach as it offers comparable lap
time achievement with approximately four times faster computation time.


title:
Ultrafast optical observation of spin-pumping induced dynamic exchange
  coupling in ferromagnetic semiconductor/metal bilayer

abstract:
  Spin angular momentum transfer in magnetic bilayers offers the possibility of
ultrafast and low-loss operation for next-generation spintronic devices. We
report the field- and temperature- dependent measurements on the magnetization
precessions in Co$_2$FeAl/(Ga,Mn)As by time-resolved magneto-optical Kerr
effect (TRMOKE). Analysis of the effective Gilbert damping and phase shift
indicates a clear signature of an enhanced dynamic exchange coupling between
the two ferromagnetic (FM) layers due to the reinforced spin pumping at
resonance. The temperature dependence of the dynamic exchange-coupling reveals
a primary contribution from the ferromagnetism in (Ga,Mn)As.


title:
Signature and Log-signature for the Study of Empirical Distributions
  Generated with GANs

abstract:
  In this paper, we develop a new and systematic method to explore and analyze
samples taken by NASA Perseverance on the surface of the planet Mars. A novel
in this context PCA adaptive t-SNE is proposed, as well as the introduction of
statistical measures to study the goodness of fit of the sample distribution.
We go beyond visualization by generating synthetic imagery using Stylegan2-ADA
that resemble the original terrain distribution. We also conduct synthetic
image generation using the recently introduced Scored-based Generative
Modeling. We bring forward the use of the recently developed Signature
Transform as a way to measure the similarity between image distributions and
provide detailed acquaintance and extensive evaluations. We are the first to
pioneer RMSE and MAE Signature and log-signature as an alternative to measure
GAN convergence. Insights on state-of-the-art instance segmentation of the
samples by the use of a model DeepLabv3 are also given.


title:
Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware
  Mobility Robustness Optimization

abstract:
  The legacy mobility robustness optimization (MRO) in self-organizing networks
aims at improving handover performance by optimizing cell-specific handover
parameters. However, such solutions cannot satisfy the needs of next-generation
network with network slicing, because it only guarantees the received signal
strength but not the per-slice service quality. To provide the truly seamless
mobility service, we propose a deep reinforcement learning-based slice-aware
mobility robustness optimization (SAMRO) approach, which improves handover
performance with per-slice service assurance by optimizing slice-specific
handover parameters. Moreover, to allow safe and sample efficient online
training, we develop a two-step transfer learning scheme: 1) regularized
offline reinforcement learning, and 2) effective online fine-tuning with mixed
experience replay. System-level simulations show that compared against the
legacy MRO algorithms, SAMRO significantly improves slice-aware service
continuation while optimizing the handover performance.


title: Correlation between ordering and shear thinning in confined liquids

abstract:
  Despite the extensive research that has been conducted for decades on the
behavior of confined liquids, detailed knowledge of this phenomenon,
particularly in the mixed/boundary lubrication regime, remains limited. This
can be attributed to several factors including the difficulty of direct
experimental observations of the behavior of lubricant molecules under
non-equilibrium conditions, the high computational cost of molecular
simulations to reach steady state, and the low signal-to-noise ratio at
extremely low shear rates corresponding to actual operating conditions. To this
end, we studied the correlation between the structure formation and shear
viscosity of octamethylcyclotetrasiloxane confined between two mica surfaces in
a mixed/boundary lubrication regime. Three different surface separations
corresponding to two-, three-, and five-layered structures were considered to
analyze the effect of confinement. The orientational distributions with one
specific peak for $n=2$ and two distributions, including a parallel orientation
with the surface normal for $n>2$, were observed at rest. The confined liquids
exhibited a distinct shear-thinning behavior independent of surface separations
for a relatively low sliding velocity, $V_{\rm x}\lesssim 10^{-1}\,{\rm m/s}$.
However, the shear viscosities at $V_{\rm x}\lesssim 10^{-1}\,{\rm m/s}$
depended on the number of layered structures. Newtonian behavior was observed
with a further increase in the sliding velocity. Furthermore, we found a strong
correlation between the degree of molecular orientation and the shear viscosity
of the confined liquids. The magnitude of the shear viscosity of the confined
liquids can primarily be determined by the degree of molecular orientation, and
shear-thinning originates from the vanishing of specific orientational
distributions with increasing sliding velocity.


title: Distributed distance domination in graphs with no $K_{2,t}$-minor

abstract:
  We prove that a simple distributed algorithm finds a constant approximation
of an optimal distance-$k$ dominating set in graphs with no $K_{2,t}$-minor.
The algorithm runs in a constant number of rounds. We further show how this
procedure can be used to give a distributed algorithm which given $\epsilon>0$
and $k,t\in \mathbb{Z}^+$ finds in a graph $G=(V,E)$ with no $K_{2,t}$-minor a
distance-$k$ dominating set of size at most $(1+\epsilon)$ of the optimum. The
algorithm runs in $O(\log^*{|V|})$ rounds in the Local model. In particular,
both algorithms work in outerplanar graphs.


title: Hadron Spectroscopy with Lattice QCD

abstract:
  The status and prospects for investigations of exotic and conventional
hadrons with lattice QCD are discussed. The majority of hadrons decay strongly
via one or multiple decay-channels, including most of the experimentally
discovered exotic hadrons. Despite this difficult challenge, the properties of
several hadronic resonances have been determined within lattice QCD. To further
discern the spectroscopic properties of various hadrons and to help resolve
their nature we present our suggestions for future analytic and lattice
studies.


title:
A central limit and Berry-Esseen theorem for continuous-time Markov
  processes conditioned not to be absorbed

abstract:
  This paper aims to establish a central limit and Berry-Esseen-like theorem
for Markov processes conditioned not to be absorbed. First, we prove that a
central limit theorem holds true for the $Q$-process, under general criteria on
its exponential ergodicity. Then, we prove that the Kolmogorov distance between
the conditional distribution of the renormalized centered empirical mean for
the absorbed process and the one for the $Q$-process decays as $1/\sqrt{t}$.


title:
Hidden phases and colossal insulator-metal transition in
  single-crystalline T-Nb2O5 thin films accessed by lithium intercalation

abstract:
  Fast migration of lithium (Li)-ions in oxide materials is fundamental to the
operation of Li-ion batteries. The intercalation of Li-ions into oxides can
further lead to emergent electronic property changes. Some of the fastest
Li-ion conductors are 4d oxides, and of these, the niobium oxide polymorph
T-Nb2O5 is especially interesting with its two-dimensional fast ion migration
channels. However, the growth of single-crystalline T-Nb2O5 films is
challenging due to its stability over only a limited synthesis temperature
window, the existence of many other polymorphs, and its large orthorhombic unit
cell. Here, we first demonstrate the epitaxial growth of single domain T-Nb2O5
thin films, critically with the ion channels oriented perpendicular to the
film's surface. We show that the insertion of just a small amount of Li using
ionic liquids results in conversion of the initially insulating film to a
metallic state with a colossal change in resistivity of almost eleven orders of
magnitude. In situ experiments, in conjunction with theoretical calculations,
reveal a series of transitions between distinct crystal and electronic
structures as the lithium content is systematically increased. These include
hidden phases that have not previously been identified. Furthermore, replacing
the Au electrode with a Li-oxide electrode allows for a significant reduction
of the gate voltage at which metallization takes place. Our study opens a new
path towards the exploration of hidden phases and the development of novel
electrochemically controlled electronic devices.


title:
On the unique solvability of radiative transfer equations with
  polarization

abstract:
  We investigate the well-posedness of the radiative transfer equation with
polarization and varying refractive index. The well-posedness analysis includes
non-homogeneous boundary value problems on bounded spatial domains, which
requires the analysis of suitable trace spaces. Additionally, we discuss
positivity, Hermiticity, and norm-preservation of the matrix-valued solution.
As auxiliary results, we derive new trace inequalities for products of
matrices.


title:
A deep branching solver for fully nonlinear partial differential
  equations

abstract:
  We present a multidimensional deep learning implementation of a stochastic
branching algorithm for the numerical solution of fully nonlinear PDEs. This
approach is designed to tackle functional nonlinearities involving gradient
terms of any orders by combining the use of neural networks with a Monte Carlo
branching algorithm. In comparison with other deep learning PDE solvers, it
also allows us to check the consistency of the learned neural network function.
Numerical experiments presented show that this algorithm can outperform deep
learning approaches based on backward stochastic differential equations or the
Galerkin method, and provide solution estimates that are not obtained by those
methods in fully nonlinear examples.


title: Pre-trained Token-replaced Detection Model as Few-shot Learner

abstract:
  Pre-trained masked language models have demonstrated remarkable ability as
few-shot learners. In this paper, as an alternative, we propose a novel
approach to few-shot learning with pre-trained token-replaced detection models
like ELECTRA. In this approach, we reformulate a classification or a regression
task as a token-replaced detection problem. Specifically, we first define a
template and label description words for each task and put them into the input
to form a natural language prompt. Then, we employ the pre-trained
token-replaced detection model to predict which label description word is the
most original (i.e., least replaced) among all label description words in the
prompt. A systematic evaluation on 16 datasets demonstrates that our approach
outperforms few-shot learners with pre-trained masked language models in both
one-sentence and two-sentence learning tasks.


title: Supersolidity in the second layer of $para$-H$_2$ adsorbed on graphite

abstract:
  We calculated the phase diagram of the second layer of $para$-H$_2$ adsorbed
on graphite using quantum Monte Carlo methods. The second layer shows an
incommensurate triangular crystal structure. By using a symmetric wave
function, that makes possible molecule exchanges, we observed that this nearly
two-dimensional crystal shows a finite superfluid density around a total
density of 0.1650 \AA$^{-2}$. The superfluid fraction of this supersolid phase
was found to be small, 0.41$\pm$0.05 \%, but still experimentally accessible.


title:
Sequential Gaussian approximation for nonstationary time series in high
  dimensions

abstract:
  Gaussian couplings of partial sum processes are derived for the
high-dimensional regime $d=o(n^{1/3})$. The coupling is derived for sums of
independent random vectors and subsequently extended to nonstationary time
series. Our inequalities depend explicitly on the dimension and on a measure of
nonstationarity, and are thus also applicable to arrays of random vectors. To
enable high-dimensional statistical inference, a feasible Gaussian
approximation scheme is proposed. Applications to sequential testing and
change-point detection are described.


title: Semantic Segmentation in Art Paintings

abstract:
  Semantic segmentation is a difficult task even when trained in a supervised
manner on photographs. In this paper, we tackle the problem of semantic
segmentation of artistic paintings, an even more challenging task because of a
much larger diversity in colors, textures, and shapes and because there are no
ground truth annotations available for segmentation. We propose an unsupervised
method for semantic segmentation of paintings using domain adaptation. Our
approach creates a training set of pseudo-paintings in specific artistic styles
by using style-transfer on the PASCAL VOC 2012 dataset, and then applies domain
confusion between PASCAL VOC 2012 and real paintings. These two steps build on
a new dataset we gathered called DRAM (Diverse Realism in Art Movements)
composed of figurative art paintings from four movements, which are highly
diverse in pattern, color, and geometry. To segment new paintings, we present a
composite multi-domain adaptation method that trains on each sub-domain
separately and composes their solutions during inference time. Our method
provides better segmentation results not only on the specific artistic
movements of DRAM, but also on other, unseen ones. We compare our approach to
alternative methods and show applications of semantic segmentation in art
paintings. The code and models for our approach are publicly available at:
https://github.com/Nadavc220/SemanticSegmentationInArtPaintings.


title: Twisted Iwasawa invariants of knots

abstract:
  Let $p$ be a prime number and $m$ an integer coprime to $p$. In the spirit of
arithmetic topology, we introduce the notions of the twisted Iwasawa invariants
$\lambda, \mu, \nu$ of ${\rm GL}_N$-representations and
$\mathbb{Z}/m\mathbb{Z}\times \mathbb{Z}_p$-covers of knots. We prove among
other things that the set of Iwasawa invariants determine the genus and the
fiberedness of a knot, yielding their profinite rigidity. Several intuitive
examples are attached. We further prove the $\mu=0$ theorem for ${\rm
SL}_2$-representations of twist knot groups and give some remarks.


title:
Detection of gamma rays around SNR HB9 and its implication to the
  diffusive shock-acceleration history

abstract:
  We analyze the GeV gamma-ray emission data from the vicinity of the supernova
remnant (SNR) HB9 (G160.9+2.6) from the \textit{Fermi}-LAT 12-year observations
to quantify the evolution of diffusive shock acceleration (DSA) in the SNR. In
the vicinity of HB9, there are molecular clouds whose locations do not coincide
with the SNR shell in the line of sight. We detect significant gamma-ray
emissions above 1~GeV spatially coinciding with the two prominent cloud
regions, as well as a emission from the SNR shell, the latter of which is
consistent with the results of previous studies. The energy spectrum above
1~GeV in each region is fitted with a simple power-law function of $dN/dE
\propto E^{-\Gamma}$. The fitting result indicates harder spectra with
power-law indices of $\Gamma$ = $1.84 \pm 0.18$ and $1.84 \pm 0.14$ than that
at the SNR shell with $\Gamma$= $2.55 \pm 0.10$. The observed spectra from the
cloud regions are found to be consistent with the theoretically expected
gamma-ray emissions originating in the protons that escaped from SNR HB9, where
particles can be accelerated up to higher energies than those at the shell at
present. The resultant diffusion coefficient in the vicinity of the SNR is
comparable to that of the Galactic mean.


title: Magnetoelectric Cavity Magnonics in Skyrmion Crystals

abstract:
  We present a theory of magnetoelectric magnon-photon coupling in cavities
hosting noncentrosymmetric magnets. Analogously to nonreciprocal phenomena in
multiferroics, the magnetoelectric coupling is time-reversal and inversion
asymmetric. This asymmetry establishes a means for exceptional tunability of
magnon-photon coupling, which can be switched on and off by reversing the
magnetization direction. Taking the multiferroic skyrmion-host Cu$_2$OSeO$_3$
as an example, we reveal the electrical activity of skyrmion eigenmodes and
propose it for magnon-photon splitting of "magnetically dark" elliptic modes.
Furthermore, we predict a cavity-induced magnon-magnon coupling between
magnetoelectrically active skyrmion excitations. Our study highlights
magnetoelectric cavity magnonics as a novel platform for realizing
quantum-hybrid systems and the quantum control of topological magnetic
textures.


title: Product formulas for hypergeometric functions over finite fields

abstract:
  Many product formulas are known classically for generalized hypergeometric
functions over the complex numbers. In this paper, we establish some analogous
formulas for generalized hypergeometric functions over finite fields.


title: Choice and Attention Across Time

abstract:
  I study how past choices affect future choices in the framework of attention.
Limited consideration causes a failure of "rationality", where better options
are not chosen because the decision maker has failed to consider them. I
innovate and consider sequences of choices, where past choices are necessarily
considered in future choice problems. This provides a link between two kinds of
rationality violations: those that occur in a cross-section of one-shot
decisions and those that occur within a sequence of realized choices. In my
setting, the former helps identify attention whereas the latter pins down true
preferences. Both types of violations vanish over time and furnish a novel
notion of stability. A series of results shows that attention across time can
act as a manifestation of attention at a given time, and a full
characterization of compatible models is provided.


title:
Precognition in Task-oriented Dialogue Understanding: Posterior
  Regularization by Future Context

abstract:
  Task-oriented dialogue systems have become overwhelmingly popular in recent
researches. Dialogue understanding is widely used to comprehend users' intent,
emotion and dialogue state in task-oriented dialogue systems. Most previous
works on such discriminative tasks only models current query or historical
conversations. Even if in some work the entire dialogue flow was modeled, it is
not suitable for the real-world task-oriented conversations as the future
contexts are not visible in such cases. In this paper, we propose to jointly
model historical and future information through the posterior regularization
method. More specifically, by modeling the current utterance and past contexts
as prior, and the entire dialogue flow as posterior, we optimize the KL
distance between these distributions to regularize our model during training.
And only historical information is used for inference. Extensive experiments on
two dialogue datasets validate the effectiveness of our proposed method,
achieving superior results compared with all baseline models.


title:
Comparison of Spatio-Temporal Models for Human Motion and Pose
  Forecasting in Face-to-Face Interaction Scenarios

abstract:
  Human behavior forecasting during human-human interactions is of utmost
importance to provide robotic or virtual agents with social intelligence. This
problem is especially challenging for scenarios that are highly driven by
interpersonal dynamics. In this work, we present the first systematic
comparison of state-of-the-art approaches for behavior forecasting. To do so,
we leverage whole-body annotations (face, body, and hands) from the very
recently released UDIVA v0.5, which features face-to-face dyadic interactions.
Our best attention-based approaches achieve state-of-the-art performance in
UDIVA v0.5. We show that by autoregressively predicting the future with methods
trained for the short-term future (<400ms), we outperform the baselines even
for a considerably longer-term future (up to 2s). We also show that this
finding holds when highly noisy annotations are used, which opens new horizons
towards the use of weakly-supervised learning. Combined with large-scale
datasets, this may help boost the advances in this field.


title: Genuinely ramified maps and stable vector bundles

abstract:
  Let $f : X \rightarrow Y$ be a separable finite surjective map between
irreducible normal projective varieties defined over an algebraically closed
field, such that the corresponding homomorphism between \'etale fundamental
groups $f_* : \pi_1^{\rm et}(X)\rightarrow\pi_1^{\rm et}(Y)$ is surjective. Fix
a polarization on $Y$ and equip $X$ with the pullback, by $f$, of this
polarization on $Y$. Given a stable vector bundle $E$ on $X$, we prove that
there is a vector bundle $W$ on $Y$ with $f^*W$ isomorphic to $E$ if and only
if the direct image $f_*E$ contains a stable vector bundle $F$ such that $$
\frac{{\rm degree}(F)}{{\rm rank}(F)}= \frac{1}{{\rm degree}(f)}\cdot
\frac{{\rm degree}(E)}{{\rm rank}(E)} $$ We also prove that $f^*V$ is stable
for every stable vector bundle $V$ on $Y$.


title: Adaptive quantum codes: constructions, applications and fault tolerance

abstract:
  A major obstacle towards realizing a practical quantum computer is the noise
that arises due to system-environment interactions. While it is very well known
that quantum error correction (QEC) provides a way to protect against errors
that arise due to the noise affecting the system, a perfect quantum code
requires atleast five physical qubits to observe a noticeable improvement over
the no-QEC scenario. However, in cases where the noise structure in the system
is already known, it might be more useful to consider quantum codes that are
adapted to specific noise models. It is already known in the literature that
such codes are resource efficient and perform on par with the standard codes.
In this spirit, we address the following questions concerning such adaptive
quantum codes in this thesis. (a) Construction: Given a noise model, we propose
a simple and fast numerical optimization algorithm to search for good quantum
codes. (b) Application: As a simple application of noise-adapted codes, we
propose an adaptive QEC protocol that allows transmission of quantum
information from one site to the other over a 1-d spin chain with high
fidelity. (c) Fault-tolerance: Finally, we address the question of whether such
noise-adapted QEC protocols can be made fault-tolerant starting with a [[4,1]]
code and obtain a rigorous lower bound on threshold.


title:
The Production of Singly Charmed Pentaquark $\bar c q qqq$ from Bottom
  Baryon

abstract:
  In the paper, we study the production of pentaquark $\bar{c}q qqq$ from
singly bottom baryon. The tensor representations of pentaquark $\bar{c}q qqq$
are completed at first. Under the light quark symmetry analysis, we decompose
the singly charmed pentaquarks into $6$, $15$ and $15'$ multiple states, and
deduce the representations of these states. Then we construct the production
Hamiltonian of the multiple states in proper order. For completeness, we
systematically study the production channels of pentaquark states, including
the possible production amplitudes and cross section relations between
different channels. Ultimately, we screen out some advantageous channels, which
expected to be fairly valuable supports for future search experiments.


title: The spectrum of a well-generated tensor triangulated category

abstract:
  For a tensor triangulated category and any regular cardinal $\alpha$ we study
the frame of $\alpha$-localizing tensor ideals and its associated space of
points. For a well-generated category and its frame of localizing tensor ideals
we provide conditions such that the associated space is obtained by refining
the topology of the corresponding space for the triangulated subcategory of
$\alpha$-compact objects. This is illustrated by several known examples for
$\alpha=\aleph_0$, and new spaces arise for $\alpha>\aleph_0$.


title:
Cryptographic Security Concerns on Timestamp Sharing via Public Channel
  in Quantum Key Distribution Systems

abstract:
  Quantum key distribution protocols are known to be vulnerable against a side
channel attack that exploits the time difference in detector responses used to
obtain key bits. The recommended solution against this timing side channel
attack is to use a large time bin width instead of high resolution timing
information. Common notion is that using a large bin width reduces the
resolution of detector responses, hence supposedly minimizes the information
leakage to an eavesdropper. We challenge this conventional wisdom, and
demonstrate that increasing the bin width does not monotonically reduce the
mutual information between the key bits and the eavesdropper's observation of
detector responses. Instead of randomly increasing the bin width, it should be
carefully chosen because the mutual information fluctuates with respect to the
bin width. We also examined the effect of full width half maximums (FWHMs) of
the detectors responses on the mutual information and showed that decreasing
the FWHM increases the mutual information. Lastly, the start time of binning is
also shown to be important in binning process and the mutual information
fluctuates periodically with respect to it.


title:
Novel Architecture of Parameterized Quantum Circuit for Graph
  Convolutional Network

abstract:
  Recently, the implementation of quantum neural networks is based on noisy
intermediate-scale quantum (NISQ) devices. Parameterized quantum circuit (PQC)
is such the method, and its current design just can handle linear data
classification. However, data in the real world often shows a topological
structure. In the machine learning field, the classical graph convolutional
layer (GCL)-based graph convolutional network (GCN) can well handle the
topological data. Inspired by the architecture of a classical GCN, in this
paper, to expand the function of the PQC, we design a novel PQC architecture to
realize a quantum GCN (QGCN). More specifically, we first implement an adjacent
matrix based on linear combination unitary and a weight matrix in a quantum
GCL, and then by stacking multiple GCLs, we obtain the QGCN. In addition, we
first achieve gradients decent on quantum circuit following the parameter-shift
rule for a GCL and then for the QGCN. We evaluate the performance of the QGCN
by conducting a node classification task on Cora dataset with topological data.
The numerical simulation result shows that QGCN has the same performance as its
classical counterpart, the GCN, in contrast, requires less tunable parameters.
Compared to a traditional PQC, we also verify that deploying an extra adjacent
matrix can significantly improve the classification performance for quantum
topological data.


title: Quantum Reissner-Nordstr\"om geometry: singularity and Cauchy horizon

abstract:
  We present a quantum description of electrically charged spherically
symmetric black holes given by coherent states of gravitons in which both the
central singularity and the Cauchy horizon are not realised.


title:
Dynamic MLP for Fine-Grained Image Classification by Leveraging
  Geographical and Temporal Information

abstract:
  Fine-grained image classification is a challenging computer vision task where
various species share similar visual appearances, resulting in
misclassification if merely based on visual clues. Therefore, it is helpful to
leverage additional information, e.g., the locations and dates for data
shooting, which can be easily accessible but rarely exploited. In this paper,
we first demonstrate that existing multimodal methods fuse multiple features
only on a single dimension, which essentially has insufficient help in feature
discrimination. To fully explore the potential of multimodal information, we
propose a dynamic MLP on top of the image representation, which interacts with
multimodal features at a higher and broader dimension. The dynamic MLP is an
efficient structure parameterized by the learned embeddings of variable
locations and dates. It can be regarded as an adaptive nonlinear projection for
generating more discriminative image representations in visual tasks. To our
best knowledge, it is the first attempt to explore the idea of dynamic networks
to exploit multimodal information in fine-grained image classification tasks.
Extensive experiments demonstrate the effectiveness of our method. The t-SNE
algorithm visually indicates that our technique improves the recognizability of
image representations that are visually similar but with different categories.
Furthermore, among published works across multiple fine-grained datasets,
dynamic MLP consistently achieves SOTA results
https://paperswithcode.com/dataset/inaturalist and takes third place in the
iNaturalist challenge at FGVC8
https://www.kaggle.com/c/inaturalist-2021/leaderboard. Code is available at
https://github.com/ylingfeng/DynamicMLP.git


title:
Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced
  Human-Robot Interaction and Robotic Interfaces

abstract:
  This paper contributes to a taxonomy of augmented reality and robotics based
on a survey of 460 research papers. Augmented and mixed reality (AR/MR) have
emerged as a new way to enhance human-robot interaction (HRI) and robotic
interfaces (e.g., actuated and shape-changing interfaces). Recently, an
increasing number of studies in HCI, HRI, and robotics have demonstrated how AR
enables better interactions between people and robots. However, often research
remains focused on individual explorations and key design strategies, and
research questions are rarely analyzed systematically. In this paper, we
synthesize and categorize this research field in the following dimensions: 1)
approaches to augmenting reality; 2) characteristics of robots; 3) purposes and
benefits; 4) classification of presented information; 5) design components and
strategies for visual augmentation; 6) interaction techniques and modalities;
7) application domains; and 8) evaluation strategies. We formulate key
challenges and opportunities to guide and inform future research in AR and
robotics.


title: $\Sigma$-semi-compact rings and modules

abstract:
  In this paper several characterizations of semi-compact modules are given.
Among other results, we study rings whose semi-compact modules are injective.
We introduce the property $\Sigma$-semi-compact for modules and we characterize
the modules satisfying this property. In particular, we show that a ring $R$ is
left $\Sigma$-semi-compact if and only if $R$ satisfies the ascending (resp.
descending) chain condition on the left (resp. right) annulets. Moreover, we
prove that every flat left $R$-module is semi-compact if and only if $R$ is
left $\Sigma$-semi-compact. We also show that a ring $R$ is left Noetherian if
and only if every pure projective left $R$-module is semi-compact. Finally, we
consider rings whose flat modules are finitely (singly) projective. For any
commutative arithmetical ring $R$ with quotient ring $Q$, we prove that every
flat $R$-module is semi-compact if and only if every flat $R$-module is
finitely (singly) projective if and only if $Q$ is pure semisimple. A similar
result is obtained for reduced commutative rings $R$ with the space
$\mathrm{Min}\ R$ compact. We also prove that every $(\aleph_{0},1)$-flat left
$R$-module is singly projective if $R$ is left $\Sigma$-semi-compact, and the
converse holds if $R^{\mathbb{N}}$ is an $(\aleph_{0},1)$-flat left $R$-module.


title: Separating Many Localisation Cardinals on the Generalised Baire Space

abstract:
  Given a cofinal cardinal function $h\in{}^\kappa\kappa$ for $\kappa$
inaccessible, we consider the dominating $h$-localisation number, that is, the
least cardinality of a dominating set of $h$-slaloms such that every
$\kappa$-real is localised by a slalom in the dominating set. It was proved in
arXiv:1611.08140 that the dominating localisation numbers can be consistently
different for two functions $h$ (the identity function and the power function).
We will construct a $\kappa$-sized family of functions $h$ and their
corresponding localisation numbers, and use a ${\leq}\kappa$-supported product
of a cofinality-preserving forcing to prove that any simultaneous assignment of
these localisation numbers to cardinals above $\kappa$ is consistent. This
answers an open question from arXiv:1611.08140 .


title: New companions to the generations of the G\"ollnitz-Gordon identities

abstract:
  The G\"ollnitz-Gordon identities were found by G\"ollnitz and Gordon
independently. In 1967, Andrews obtained a combinatorial generalization of the
G\"ollnitz-Gordon identities, called the Andrews-G\"ollnitz-Gordon theorem. In
1980, Bressoud extended the Andrews-G\"ollnitz-Gordon theorem to even moduli,
called the Bressoud-G\"ollnitz-Gordon theorem. Furthermore, Bressoud gave the
generating functions for the generalizations of the G\"ollnitz-Gordon
identities. In this article, we will give new companions to the generalizations
of the G\"ollnitz-Gordon identities.