title: Reduction of Structure to Parabolic Subgroups

abstract:
  Let $G$ be an affine group over a field of characteristic not two. A
$G$-torsor is called isotropic if it admits reduction of structure to a proper
parabolic subgroup of $G$. This definition generalizes isotropy of affine
groups and involutions of central simple algebras. When does $G$ admit
anisotropic torsors? Building on work of J. Tits, we answer this question for
simple groups. We also give an answer for connected and semisimple $G$ under
certain restrictions on its root system.


title:
Probing squeezing for gravitational-wave detectors with an audio-band
  field

abstract:
  Squeezed vacuum states are now employed in gravitational-wave interferometric
detectors, enhancing their sensitivity and thus enabling richer astrophysical
observations. In future observing runs, the detectors will incorporate a filter
cavity to suppress quantum radiation pressure noise using frequency-dependent
squeezing. Interferometers employing internal and external cavities decohere
and degrade squeezing in complex new ways, which must be studied to achieve
increasingly ambitious noise goals. This paper introduces an audio diagnostic
field (ADF) to quickly and accurately characterize the frequency-dependent
response and the transient perturbations of resonant optical systems to
squeezed states. This analysis enables audio field injections to become a
powerful tool to witness and optimize interactions such as inter-cavity mode
matching within gravitational-wave instruments. To demonstrate, we present
experimental results from using the audio field to characterize a 16 m
prototype filter cavity.


title: UniXcoder: Unified Cross-Modal Pre-training for Code Representation

abstract:
  Pre-trained models for programming languages have recently demonstrated great
success on code intelligence. To support both code-related understanding and
generation tasks, recent works attempt to pre-train unified encoder-decoder
models. However, such encoder-decoder framework is sub-optimal for
auto-regressive tasks, especially code completion that requires a decoder-only
manner for efficient inference. In this paper, we present UniXcoder, a unified
cross-modal pre-trained model for programming language. The model utilizes mask
attention matrices with prefix adapters to control the behavior of the model
and leverages cross-modal contents like AST and code comment to enhance code
representation. To encode AST that is represented as a tree in parallel, we
propose a one-to-one mapping method to transform AST in a sequence structure
that retains all structural information from the tree. Furthermore, we propose
to utilize multi-modal contents to learn representation of code fragment with
contrastive learning, and then align representations among programming
languages using a cross-modal generation task. We evaluate UniXcoder on five
code-related tasks over nine datasets. To further evaluate the performance of
code fragment representation, we also construct a dataset for a new task,
called zero-shot code-to-code search. Results show that our model achieves
state-of-the-art performance on most tasks and analysis reveals that comment
and AST can both enhance UniXcoder.


title: Holographic Local Operator Quenches in BCFTs

abstract:
  We present a gravity dual of local operator quench in a two-dimensional CFT
with conformal boundaries. This is given by a massive excitation in a
three-dimensional AdS space with the end of the world brane (EOW brane). Due to
the gravitational backreaction, the EOW brane gets deformed in a nontrivial
way. We show that the energy-momentum tensor and entanglement entropy computed
from the gravity dual and from the BCFT in the large $c$ limit match perfectly.
Interestingly, this comparison avoids the folding of the EOW brane in an
elegant way.


title:
Combined signatures of heavy Higgses and vectorlike fermions at the
  HL-LHC

abstract:
  In extensions of two Higgs doublet models with vectorlike quarks and leptons
the decays of new scalars or fermions can be altered by the presence of the
other, depending on the hierarchy of new physics. If new fermions are heavier
than Higgses, their decays may be dominated by cascade decays through charged
and neutral heavy Higgs bosons. In the opposite hierarchy, the decay of a heavy
Higgs can lead to single production of vectorlike quarks and leptons when
Yukawa couplings to SM fermions are present. We explore the resulting novel
collider signatures in either case and present projected sensitivities of
future runs of the LHC to the masses and branching ratios of new fermions and
heavy Higgses. Importantly, we discuss kinematic challenges of the signatures
and suggest strategic observables which could be adopted in future analyses.
Interpreting the prospective reach of the LHC in the context of a type-II 2HDM,
we find that in the presence of vectorlike fermions heavy Higgs boson masses
slightly above 2 TeV can be explored at the HL-LHC even at low $\tan\beta$. For
heavy Higgs decays through vectorlike leptons, this reach extends to slightly
above 3 TeV for $\tan\beta \gtrsim 10$. Further, these search strategies imply
a reach for vectorlike quark and lepton masses close to 2.4 TeV and 1.5 TeV,
respectively. This article is partly based on Refs. [1-6]


title:
Where Does the Performance Improvement Come From? -- A Reproducibility
  Concern about Image-Text Retrieval

abstract:
  This article aims to provide the information retrieval community with some
reflections on recent advances in retrieval learning by analyzing the
reproducibility of image-text retrieval models. Due to the increase of
multimodal data over the last decade, image-text retrieval has steadily become
a major research direction in the field of information retrieval. Numerous
researchers train and evaluate image-text retrieval algorithms using benchmark
datasets such as MS-COCO and Flickr30k. Research in the past has mostly focused
on performance, with multiple state-of-the-art methodologies being suggested in
a variety of ways. According to their assertions, these techniques provide
improved modality interactions and hence more precise multimodal
representations. In contrast to previous works, we focus on the reproducibility
of the approaches and the examination of the elements that lead to improved
performance by pretrained and nonpretrained models in retrieving images and
text. To be more specific, we first examine the related reproducibility
concerns and explain why our focus is on image-text retrieval tasks. Second, we
systematically summarize the current paradigm of image-text retrieval models
and the stated contributions of those approaches. Third, we analyze various
aspects of the reproduction of pretrained and nonpretrained retrieval models.
To complete this, we conducted ablation experiments and obtained some
influencing factors that affect retrieval recall more than the improvement
claimed in the original paper. Finally, we present some reflections and
challenges that the retrieval community should consider in the future. Our
source code is publicly available at
https://github.com/WangFei-2019/Image-text-Retrieval.


title: Metaverse: Security and Privacy Concerns

abstract:
  The term "metaverse", a three-dimensional virtual universe similar to the
real realm, has always been full of imagination since it was put forward in the
1990s. Recently, it is possible to realize the metaverse with the continuous
emergence and progress of various technologies, and thus it has attracted
extensive attention again. It may bring a lot of benefits to human society such
as reducing discrimination, eliminating individual differences, and
socializing. However, everything has security and privacy concerns, which is no
exception for the metaverse. In this article, we firstly analyze the concept of
the metaverse and propose that it is a super virtual-reality (VR) ecosystem
compared with other VR technologies. Then, we carefully analyze and elaborate
on possible security and privacy concerns from four perspectives: user
information, communication, scenario, and goods, and immediately, the potential
solutions are correspondingly put forward. Meanwhile, we propose the need to
take advantage of the new buckets effect to comprehensively address security
and privacy concerns from a philosophical perspective, which hopefully will
bring some progress to the metaverse community.


title: A new Approach to fully degenerate Bernoulli numbers and polynomials

abstract:
  In this paper, we investigate new class of sequences related to fully
degenerate Bernoulli numbers and polynomials. From those sequences, we derive
some formulae for the degenerate Bernoulli and Euler polynomials.


title:
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for
  Joint Dialog Sentiment Classification and Act Recognition

abstract:
  The task of joint dialog sentiment classification (DSC) and act recognition
(DAR) aims to simultaneously predict the sentiment label and act label for each
utterance in a dialog. In this paper, we put forward a new framework which
models the explicit dependencies via integrating \textit{prediction-level
interactions} other than semantics-level interactions, more consistent with
human intuition. Besides, we propose a speaker-aware temporal graph (SATG) and
a dual-task relational temporal graph (DRTG) to introduce \textit{temporal
relations} into dialog understanding and dual-task reasoning. To implement our
framework, we propose a novel model dubbed DARER, which first generates the
context-, speaker- and temporal-sensitive utterance representations via
modeling SATG, then conducts recurrent dual-task relational reasoning on DRTG,
in which process the estimated label distributions act as key clues in
prediction-level interactions. Experiment results show that DARER outperforms
existing models by large margins while requiring much less computation resource
and costing less training time. Remarkably, on DSC task in Mastodon, DARER
gains a relative improvement of about 25% over previous best model in terms of
F1, with less than 50% parameters and about only 60% required GPU memory.


title: Role of limiting dispersal on metacommunity stability and persistence

abstract:
  The role of dispersal on the stability and synchrony of a metacommunity is a
topic of considerable interest in theoretical ecology. Dispersal is known to
promote both synchrony, which enhances the likelihood of extinction, and
spatial heterogeneity, which favors the persistence of the population. Several
efforts have been made to understand the effect of diverse variants of
dispersal in the spatially distributed ecological community. Despite the
environmental change strongly affect the dispersal, the effects of controlled
dispersal on the metacommunity stability and their persistence remain unknown.
We study the influence of limiting the immigration using two patch
prey-predator metacommunity at both local and spatial scales. We find that the
spread of the inhomogeneous stable steady states (asynchronous states)
decreases monotonically upon limiting the predator dispersal. Nevertheless, at
the local scale, the spread of the inhomogeneous steady states increases up to
a critical value of the limiting factor, favoring the metacommunity
persistence, and then starts decreasing for further decrease in the limiting
factor with varying local interaction. Interestingly, limiting the prey
dispersal promotes inhomogeneous steady states in a large region of the
parameter space, thereby increasing the metacommunity persistence both at
spatial and local scales. Further, we show similar qualitative dynamics in an
entire class of complex networks consisting of a large number of patches. We
also deduce various bifurcation curves and stability condition for the
inhomogeneous steady states, which we find to agree well with the simulation
results. Thus, our findings on the effect of the limiting dispersal can help to
develop conservation measures for ecological community.


title:
Dimension reduction for maximum matchings and the Fastest Mixing Markov
  Chain

abstract:
  Let $G = (V,E)$ be an undirected graph with maximum degree $\Delta$ and
vertex conductance $\Psi^*(G)$. We show that there exists a symmetric,
stochastic matrix $P$, with off-diagonal entries supported on $E$, whose
spectral gap $\gamma^*(P)$ satisfies \[\Psi^*(G)^{2}/\log\Delta \lesssim
\gamma^*(P) \lesssim \Psi^*(G).\] Our bound is optimal under the Small Set
Expansion Hypothesis, and answers a question of Olesker-Taylor and Zanetti, who
obtained such a result with $\log\Delta$ replaced by $\log|V|$.
  In order to obtain our result, we show how to embed a negative-type
semi-metric $d$ defined on $V$ into a negative-type semi-metric $d'$ supported
in $\mathbb{R}^{O(\log\Delta)}$, such that the (fractional) matching number of
the weighted graph $(V,E,d)$ is approximately equal to that of $(V,E,d')$.


title: A New 27 Class Sign Language Dataset Collected from 173 Individuals

abstract:
  After the interviews, it has been comprehended that speech-impaired
individuals who use sign languages have difficulty communicating with other
people who do not know sign language. Due to the communication problems, the
sense of independence of speech-impaired individuals could be damaged and lead
them to socialize less with society. To contribute to the development of
technologies, that can reduce the communication problems of speech-impaired
persons, a new dataset was presented with this paper. The dataset was created
by processing American Sign Language-based photographs collected from 173
volunteers, published as 27 Class Sign Language Dataset on the Kaggle Datasets
web page.


title: Weakly Supervised Semantic Segmentation using Out-of-Distribution Data

abstract:
  Weakly supervised semantic segmentation (WSSS) methods are often built on
pixel-level localization maps obtained from a classifier. However, training on
class labels only, classifiers suffer from the spurious correlation between
foreground and background cues (e.g. train and rail), fundamentally bounding
the performance of WSSS. There have been previous endeavors to address this
issue with additional supervision. We propose a novel source of information to
distinguish foreground from the background: Out-of-Distribution (OoD) data, or
images devoid of foreground object classes. In particular, we utilize the hard
OoDs that the classifier is likely to make false-positive predictions. These
samples typically carry key visual features on the background (e.g. rail) that
the classifiers often confuse as foreground (e.g. train), so these cues let
classifiers correctly suppress spurious background cues. Acquiring such hard
OoDs does not require an extensive amount of annotation efforts; it only incurs
a few additional image-level labeling costs on top of the original efforts to
collect class labels. We propose a method, W-OoD, for utilizing the hard OoDs.
W-OoD achieves state-of-the-art performance on Pascal VOC 2012.


title:
$4D$ AdS Einstein-Gauss-Bonnet black hole with Yang-Mills field and its
  thermodynamics

abstract:
  We derive an exact black hole solution for the Einstein-Gauss-Bonnet gravity
with Yang-Mills field in $4D$ AdS spacetime and investigate its thermodynamic
properties to calculate exact expressions for the black hole mass, temperature,
entropy and heat capacity. The thermodynamic quantities get modification in the
presence of Yang-Mills field, however, entropy remains unaffected by the
Yang-Mills charge. The solution exhibits $P-v$ criticality and belongs to the
universality class of Van der Waals fluid. We study the effect of Gauss-Bonnet
coupling and Yang-Mills charge on the critical behaviour and black hole phase
transition. We observe that the values of critical exponents increase with the
Yang-Mills charge and decrease with the Gauss-Bonnet coupling constant.


title:
Localized Nonlinear Waves on Spatio-Temporally Controllable Backgrounds
  for a (3+1)-Dimensional Kadomtsev-Petviashvili-Boussinesq Model in Water
  Waves

abstract:
  Physics of nonlinear waves on variable backgrounds and the relevant
mathematical analysis continues to be the challenging aspect of the study. In
this work, we consider a (3+1)-dimensional nonlinear model describing the
dynamics of {water waves} and construct nonlinear wave solutions on
spatio-temporally controllable backgrounds for the first time by using a simple
mathematical tool auto-B\"acklund transformation. Mainly, we unravel physically
interesting features to control and manipulate the dynamics of nonlinear waves
through the background. Adapting an exponential function and general polynomial
of degree two as initial seed solutions, we construct single kink-soliton and
rogue wave, respectively. We choose arbitrary periodic, localized and combined
wave backgrounds by incorporating Jacobi elliptic functions and investigate the
modulation of these two nonlinear waves with a clear analysis and graphical
demonstrations. The solutions derived in this work give us sufficient freedom
to generate exotic nonlinear coherent structures on variable backgrounds and
open up an interesting direction to explore the dynamics of various other
nonlinear waves propagating through inhomogeneous media.


title:
Amplitude-Constrained Constellation and Reflection Pattern Designs for
  Directional Backscatter Communications Using Programmable Metasurface

abstract:
  The large-scale reflector array of programmable metasurfaces is capable of
increasing the power efficiency of backscatter communications via passive
beamforming and thus has the potential to revolutionize the low-data-rate
nature of backscatter communications. In this paper, we propose to design the
power-efficient higher-order constellation and reflection pattern under the
amplitude constraint brought by backscatter communications. For constellation
design, we adopt the amplitude and phase-shift keying (APSK) constellation and
optimize the parameters of APSK such as ring number, ring radius, and
inter-ring phase difference. Specifically, we derive closed-form solutions to
the optimal ring radius and inter-ring phase difference for an arbitrary
modulation order. For reflection pattern design, we propose to optimize the
passive beamforming vector by solving a multi-objective optimization problem
that maximizes reflection power and guarantees beam homogenization within the
interested angle range. To solve the problem, we propose a constant-modulus
power iteration method, which is proven to be monotonically increasing, to
maximize the objective function in each iteration. Numerical results show that
the proposed APSK constellation design and reflection pattern design outperform
the existing modulation and beam pattern design in programmable
metasurface-enabled backscatter communications.


title: Quantum Information Masking in Non-Hermitian Systems and Robustness

abstract:
  By studying quantum information masking in non-Hermitian quantum systems, we
show that mutually orthogonal quantum states can be deterministically masked,
while an arbitrary set of quantum states cannot be masked in non-Hermitian
quantum systems. We further demonstrate that a set of linearly independent
states which are mutually $\eta$-orthogonal can be deterministically masked by
a pseudo-unitary operator. Moreover, we study robustness of quantum information
masking against noisy environments. The robustness of deterministic and
probabilistic quantum information masking under different quantum noise
channels is analyzed in detail. Accordingly, we propose and discuss the
$r$-uniform probabilistic quantum information masking in multipartite systems.


title:
A Cauchy-type Hamilton-Jacobi Successive Approximation Scheme For
  Reachable Sets Computation

abstract:
  Motivated by the scalability limitations of Eulerian methods for variational
Hamilton-Jacobi-Isaacs (HJI) formulations that provide a least restrictive
controller in problems that involve state or input constraints under a
worst-possible disturbance, we introduce a second-order, successive sweep
algorithm for computing the zero sublevel sets of a popular reachability value
functional. Under sufficient HJI partial differential equation regularity and
continuity assumption throughout the state space, we show that with state
feedback control under the worst-possible disturbance, we can compute the state
set that are reachable within a prescribed verification time bound.


title:
The application of accumulation tests in Peaks-Over-Threshold modeling
  with Norwegian Fire insurance Data

abstract:
  Modeling excess remains to be an important topic in insurance data modeling.
Among the alternatives of modeling excess, the Peaks Over Threshold (POT)
framework with Generalized Pareto distribution (GPD) is regarded as an
efficient approach due to its flexibility. However, the selection of an
appropriate threshold for such framework is a major difficulty. To address such
difficulty, we applied several accumulation tests along with Anderson-Darling
test to determine an optimal threshold. Based on the selected thresholds, the
fitted GPD with the estimated quantiles can be found. We applied the procedure
to the well-known Norwegian Fire Insurance data and constructed the confidence
intervals for the Value-at-Risks (VaR). The accumulation test approach provides
satisfactory performance in modeling the high quantiles of Norwegian Fire
Insurance data compared to the previous graphical methods.


title: Featured Trajectory Generation for TrackPuzzle

abstract:
  Indoor route graph learning is critically important for autonomous indoor
navigation. A key problem for crowd-sourcing indoor route graph learning is
featured trajectory generation. In this paper, a system is provided to generate
featured trajectories by crowd-sourcing smartphone data. Firstly, we propose a
more accurate PDR algorithm for the generation of trajectory motion data. This
algorithm uses ADAPTIV as the step counting method and uses the step estimation
algorithm o make the trajectory more accurate in length. Next, the barometer is
used to segment the tracks of different floors, and the track floors are
obtained by WiFi feature clustering. Finally, by finding the turning point as
the feature point of the trajectory, the vertices and edges of the trajectory
are extracted to reduce the noise of the long straight trajectory.


title:
Inferring Parsimonious Coupling Statistics in Nonlinear Dynamics with
  Variational Gaussian Processes

abstract:
  Nonparametetric methods of uncovering coupling provides a flexible framework
for researchers to study network configurations and discover causal graphs. For
example, Gaussian Processes have been applied to discover coupling in dynamical
systems under the convergent cross-mapping (GP-CCM) framework. Though GP-CCM is
a nonparametric method, a function of the data, variations in hyperparameters
may impact model results. Maximum marginal likelihood is commonly used to
optimize such hyperparameters, however only point estimates are provided from
such a routine. In this work, we determine a posteriori the hyperparameter
distribution conditioned on the data by the use of variational Bayesian methods
with mean field approximations on the posterior distribution of the
hyperparameters, thus introducing the variational form of Gaussian process
convergent cross-mapping (VGP-CCM). We show that using the a posteriori best
approximate model that maximizes evidence of the data for hyperparameters
sampling in VGP-CCM, in conjunction with a permutation sampling for the null
distribution of uncoupled time series, permits significance statistics that are
more robust than mere permutation sampling of the null GP-CCM distribution. We
perform tests on synthetic data derived from unidirectionally coupled
Lorenz-Rossler systems to demonstrate that the proposed method yields
substantially improved specificity over the original GP-CCM.


title: New Insights into Superintegrability from Unitary Matrix Models

abstract:
  Some eigenvalue matrix models possess an interesting property: one can
manifestly define the basis where all averages can be explicitly calculated.
For example, in the Gaussian Hermitian and rectangular complex models, averages
of the Schur functions are again expressed through the Schur functions.
However, so far this property remains restricted to very particular (e.g.
Gaussian) measures. In this paper, we extend this observation to unitary matrix
integrals, where one could expect that this restriction is easier to lift. We
demonstrate that this is indeed the case, only this time the Schur averages are
linear combinations of the Schur functions. Full factorization to a single item
in the sum appears only on the Miwa locus, where at least one half of the
time-variables is expressed through matrices of the same size. For unitary
integrals, this is a manifestation of the de Wit-t'Hooft anomaly, which
prevents the answer to be fully analytic in the matrix size $N$. Once achieved,
this understanding can be extended back to the Hermitian model, where the
phenomenon looks very similar: beyond Gaussian measures superintegrability
requires an additional summation.


title: Morphological Anti-Aliasing Method for Boundary Slope Prediction

abstract:
  Image pixel aliasing caused by insufficient sampling is a long-standing
problem in the field of computer graphics. It has always been the goal of
researchers to seek anti-aliasing algorithms with high speed and good effect.
Due to the deficiencies in local detection and reconstruction of sloping line
boundaries, a morphological anti-aliasing method for boundary slope prediction
is proposed. This method uses the information of the local line boundary slope
to predict and test the end positions of the line boundary in the global scope,
thereby reconstructing The boundary information more consistent with the actual
boundary is obtained, and a more accurate linear boundary shape is obtained
with only a small increase in the amount of calculation. Compared with the
previous morphological anti-aliasing algorithm, the proposed method is based on
the global morphological boundary. , can reconstruct the straight line boundary
more accurately, and apply it to the anti-aliasing calculation, which can
further improve the color transition of the straight line boundary, make the
inclined straight line boundary have higher continuity, and obtain a better
anti-aliasing effect.


title:
Discriminability-Transferability Trade-Off: An Information-Theoretic
  Perspective

abstract:
  This work simultaneously considers the discriminability and transferability
properties of deep representations in the typical supervised learning task,
i.e., image classification. By a comprehensive temporal analysis, we observe a
trade-off between these two properties. The discriminability keeps increasing
with the training progressing while the transferability intensely diminishes in
the later training period.
  From the perspective of information-bottleneck theory, we reveal that the
incompatibility between discriminability and transferability is attributed to
the over-compression of input information. More importantly, we investigate why
and how the InfoNCE loss can alleviate the over-compression, and further
present a learning framework, named contrastive temporal coding~(CTC), to
counteract the over-compression and alleviate the incompatibility. Extensive
experiments validate that CTC successfully mitigates the incompatibility,
yielding discriminative and transferable representations. Noticeable
improvements are achieved on the image classification task and challenging
transfer learning tasks. We hope that this work will raise the significance of
the transferability property in the conventional supervised learning setting.
Code will be publicly available.


title: Visual anomaly detection in video by variational autoencoder

abstract:
  Video anomalies detection is the intersection of anomaly detection and visual
intelligence. It has commercial applications in surveillance, security,
self-driving cars and crop monitoring. Videos can capture a variety of
anomalies. Due to efforts needed to label training data, unsupervised
approaches to train anomaly detection models for videos is more practical An
autoencoder is a neural network that is trained to recreate its input using
latent representation of input also called a bottleneck layer. Variational
autoencoder uses distribution (mean and variance) as compared to latent vector
as bottleneck layer and can have better regularization effect. In this paper we
have demonstrated comparison between performance of convolutional LSTM versus a
variation convolutional LSTM autoencoder


title:
Solution to a conjecture of Schmidt and Tuller on linear packings and
  coverings

abstract:
  In 2008, Schmidt and Tuller stated a conjecture concerning optimal packing
and covering of integers by translates of a given three-point set. In this
note, we confirm their conjecture and relate it to several other problems in
combinatorics.


title:
Detection and treatment of outliers for multivariate robust loss
  reserving

abstract:
  Traditional techniques for calculating outstanding claim liabilities such as
the chain ladder are notoriously at risk of being distorted by outliers in past
claims data. Unfortunately, the literature in robust methods of reserving is
scant, with notable exceptions such as Verdonck and Debruyne (2011) and
Verdonck and Van Wouwe (2011). In this paper, we put forward two alternative
robust bivariate chain-ladder techniques to extend the approach of Verdonck and
Van Wouwe (2011). The first technique is based on Adjusted Outlyingness (Hubert
and Van der Veeken, 2008) and explicitly incorporates skewness into the
analysis whilst providing a unique measure of outlyingness for each
observation. The second technique is based on bagdistance (Hubert et al., 2016)
which is derived from the bagplot however is able to provide a unique measure
of outlyingness and a means to adjust outlying observations based on this
measure.
  Subsequently, we also extend our robust bivariate chain-ladder approach to an
N-dimensional framework. This is illustrated on a trivariate data set from
Australian general insurers, and results under the different outlier detection
and treatment mechanisms are compared.


title: Occupancy Flow Fields for Motion Forecasting in Autonomous Driving

abstract:
  We propose Occupancy Flow Fields, a new representation for motion forecasting
of multiple agents, an important task in autonomous driving. Our representation
is a spatio-temporal grid with each grid cell containing both the probability
of the cell being occupied by any agent, and a two-dimensional flow vector
representing the direction and magnitude of the motion in that cell. Our method
successfully mitigates shortcomings of the two most commonly-used
representations for motion forecasting: trajectory sets and occupancy grids.
Although occupancy grids efficiently represent the probabilistic location of
many agents jointly, they do not capture agent motion and lose the agent
identities. To this end, we propose a deep learning architecture that generates
Occupancy Flow Fields with the help of a new flow trace loss that establishes
consistency between the occupancy and flow predictions. We demonstrate the
effectiveness of our approach using three metrics on occupancy prediction,
motion estimation, and agent ID recovery. In addition, we introduce the problem
of predicting speculative agents, which are currently-occluded agents that may
appear in the future through dis-occlusion or by entering the field of view. We
report experimental results on a large in-house autonomous driving dataset and
the public INTERACTION dataset, and show that our model outperforms
state-of-the-art models.


title:
High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized
  Nonnegative Matrix Factorization for Community Detection

abstract:
  Community describes the functional mechanism of a network, making community
detection serve as a fundamental graph tool for various real applications like
discovery of social circle. To date, a Symmetric and Non-negative Matrix
Factorization (SNMF) model has been frequently adopted to address this issue
owing to its high interpretability and scalability. However, most existing
SNMF-based community detection methods neglect the high-order connection
patterns in a network. Motivated by this discovery, in this paper, we propose a
High-Order Proximity (HOP)-incorporated, Symmetry and Graph-regularized NMF
(HSGN) model that adopts the following three-fold ideas: a) adopting a weighted
pointwise mutual information (PMI)-based approach to measure the HOP indices
among nodes in a network; b) leveraging an iterative reconstruction scheme to
encode the captured HOP into the network; and c) introducing a symmetry and
graph-regularized NMF algorithm to detect communities accurately. Extensive
empirical studies on eight real-world networks demonstrate that an HSGN-based
community detector significantly outperforms both benchmark and
state-of-the-art community detectors in providing highly-accurate community
detection results.


title:
Effect of surface H$_2$ on molecular hydrogen formation on interstellar
  grains

abstract:
  We investigate how the existence of hydrogen molecules on grain surfaces may
affect H$_2$ formation efficiency in diffuse and translucent clouds. Hydrogen
molecules are able to reduce the desorption energy of H atoms on grain surfaces
in models. The detailed microscopic Monte Carlo method is used to perform model
simulations. We found that the impact of the existence of H$_2$ on H$_2$
formation efficiency strongly depends on the diffusion barriers of H$_2$ on
grain surfaces. Diffuse cloud models that do not consider surface H$_2$ predict
that H atom recombination efficiency is above 0.5 over a grain temperature (T)
range 10 K and 14 K. The adopted H$_2$ diffusion barriers in diffuse cloud
models that consider surface H$_2$ are 80$\%$ H$_2$ desorption energies so that
H$_2$ can be trapped in stronger binding sites. Depending on model parameters,
these diffuse cloud models predict that the recombination efficiency is between
nearly 0 and 0.5 at 10 K $\leq$ T $\leq$ 14 K. Translucent cloud model results
show that H$_2$ formation efficiency is not affected by the existence of
surface H$_2$ if the adopted average H$_2$ diffusion barrier on grain surfaces
is low (194 K) so that H$_2$ can diffuse rapidly on grain surfaces. However,
the recombination efficiency can drop to below 0.002 at T $\geq$ 10 K if higher
average H$_2$ diffusion barrier is used (255 K) in translucent cloud models.


title:
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both
  Language and Vision-and-Language Tasks

abstract:
  The workflow of pretraining and fine-tuning has emerged as a popular paradigm
for solving various NLP and V&L (Vision-and-Language) downstream tasks. With
the capacity of pretrained models growing rapidly, how to perform
parameter-efficient fine-tuning has become fairly important for quick transfer
learning and deployment. In this paper, we design a novel unified
parameter-efficient transfer learning framework that works effectively on both
pure language and V&L tasks. In particular, we use a shared hypernetwork that
takes trainable hyper-embeddings as input, and outputs weights for fine-tuning
different small modules in a pretrained language model, such as tuning the
parameters inserted into multi-head attention blocks (i.e., prefix-tuning) and
feed-forward blocks (i.e., adapter-tuning). We define a set of embeddings
(e.g., layer, block, task and visual embeddings) as the key components to
calculate hyper-embeddings, which thus can support both pure language and V&L
tasks. Our proposed framework adds fewer trainable parameters in multi-task
learning while achieving superior performances and transfer ability compared to
state-of-the-art methods. Empirical results on the GLUE benchmark and multiple
V&L tasks confirm the effectiveness of our framework on both textual and visual
modalities.


title: Structural Transition in the Hidden Ordered Phase of CeCoSi

abstract:
  We have performed X-ray diffraction experiments on a single crystalline
CeCoSi to investigate the unresolved ordered phase below $T_0 \sim 12$ K. We
have discovered that a triclinic lattice distortion takes place below $T_0$,
which is further modified in the subsequent antiferromagnetic ordered phase.
The structural domains can be selected by applying a magnetic field, indicating
that some electronic ordering exists behind and affects the magnetic anisotropy
in the hidden ordered phase below $T_0$. The transition at $T_0$, although the
order parameter is still unknown, is associated with the maximum in the
$c$-axis lattice parameter. In magnetic fields along $[1, 0, 0]$, the
structural transition temperature, named as $T_{\text{s1}}$, deviates from
$T_0$ and decreases with increasing the field, whereas $T_0$ increases. This
shows that the hidden ordered phase without triclinic distortion exists between
$T_{\text{s1}}$ and $T_0$. The results for $H \parallel [1, 1, 0]$ are also
reported.


title:
Integer matrices with a given characteristic polynomial and
  multiplicative dependence of matrices

abstract:
  We consider the set $\mathrm{M}_n(\mathbb{Z}; H))$ of $n\times n$-matrices
with integer elements of size at most $H$ and obtain upper and lower bounds on
the number of $s$-tuples of matrices from $\mathrm{M}_n(\mathbb{Z}; H)$,
satisfying various multiplicative relations, including multiplicative
dependence, commutativity and bounded generation of a subgroup of
$\mathrm{GL}_n(\mathbb{Q})$. These problems generalise those studied in the
scalar case $n=1$ by F. Pappalardi, M. Sha, I. E. Shparlinski and C. L. Stewart
(2018) with an obvious distinction due to the non-commutativity of matrices. As
a part of our method, we obtain a new upper bound on the number of matrices
from $\mathrm{M}_n(\mathbb{Z}; H)$ with a given characteristic polynomial $f
\in\mathbb{Z}[X]$, which is uniform with respect to $f$. This complements the
asymptotic formula of A. Eskin, S. Mozes and N. Shah (1996) in which $f$ has to
be fixed and irreducible.


title:
How ferroelectric BaTiO$_3$ can tune a two-dimensional electron gas at
  the interface of LaInO3 and BaSnO$_3$: a first-principles study

abstract:
  The emerging interest in two-dimensional electron gases (2DEGs), formed at
interfaces between two insulating oxide perovskites poses crucial fundamental
question in view of future electronic devices. In the framework of
density-functional theory, we investigate the possibility to control the
characteristics of the 2DEG formed at the LaInO$_3$/BaSnO$_3$ interface by
including a ferroelectric BaTiO$_3$ layer. To do so, we examine how the
orientation of the ferroelectric polarization impacts density and confinement
of the 2DEG. We find that aligning the ferroelectric polarization toward
(outward) the LaInO$_3$/BaSnO$_3$ interface leads to an accumulation
(depletion) of the interfacial 2DEG. Varying its magnitude, we find a linear
effect on the 2DEG charge density that is confined within the BaSnO$_3$ side.
Analysis of the optimized geometries revels that inclusion of the BaTiO$_3$
block makes structural distortions at the LaInO$_3$/BaSnO$_3$ less pronounced,
which, in turn, enhances the 2DEG density. Thicker ferroelectric layers allow
for reaching higher polarization magnitudes. We discuss the mechanisms behind
all these findings and rationalize how the characteristics of both 2DEG and 2D
hole gases can be controlled in the considered heterostructures. Overall, our
results can be generalized to other combinations of ferroelectric, polar, and
nonpolar materials.


title:
The $N/D$ study on the singularity structure of $\pi N$ scattering
  amplitudes

abstract:
  The $N/D$ method is used to study the $S_{11}$ channel low energy $\pi N$
scattering amplitude. The input of left cuts are obtained from various
phenomenological models. With the aid of the production representation, the
total phase shifts can be decomposed into different contributions, and it
further reveals that the existence of subthreshold resonance $N^*(890)$ doesn't
depend on the details of the dynamical input. Additionally, it is found that
there exist virtual states in partial waves, which are induced by the $u$
channel nucleon exchanges. These virtual states accumulate at the end point of
the $u$ channel segment cut. The end point is hence the essential singularity
of the full amplitude on the second sheet of complex $s$ plane.


title:
Online Dynamic Parameter Estimation of an Alkaline Electrolysis System
  Based on Bayesian Inference

abstract:
  When directly coupled with fluctuating energy sources such as wind and
photovoltage power, the alkaline electrolysis (AEL) in a power-to-hydrogen
(P2H) system is required to operate flexibly by dynamically adjusting its
hydrogen production rate. The flex-ibility characteristics, e.g., loading range
and ramping rate, of an AEL system are significantly influenced by some
parameters re-lated to the dynamic processes of the AEL system. These
parame-ters are usually difficult to measure directly and may even change with
time. To accurately evaluate the flexibility of an AEL system in online
operation, this paper presents a Bayesian Inference-based Markov Chain Monte
Carlo (MCMC) method to estimate these parameters. Meanwhile, posterior joint
probability distribu-tions of the estimated parameters are obtained as a
byproduct, which provides valuable physical insight into the AEL systems.
Experiments on a 25 kW electrolyzer validate the proposed pa-rameter estimation
method.


title: Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

abstract:
  The crux of semi-supervised semantic segmentation is to assign adequate
pseudo-labels to the pixels of unlabeled images. A common practice is to select
the highly confident predictions as the pseudo ground-truth, but it leads to a
problem that most pixels may be left unused due to their unreliability. We
argue that every pixel matters to the model training, even its prediction is
ambiguous. Intuitively, an unreliable prediction may get confused among the top
classes (i.e., those with the highest probabilities), however, it should be
confident about the pixel not belonging to the remaining classes. Hence, such a
pixel can be convincingly treated as a negative sample to those most unlikely
categories. Based on this insight, we develop an effective pipeline to make
sufficient use of unlabeled data. Concretely, we separate reliable and
unreliable pixels via the entropy of predictions, push each unreliable pixel to
a category-wise queue that consists of negative samples, and manage to train
the model with all candidate pixels. Considering the training evolution, where
the prediction becomes more and more accurate, we adaptively adjust the
threshold for the reliable-unreliable partition. Experimental results on
various benchmarks and training settings demonstrate the superiority of our
approach over the state-of-the-art alternatives.


title: Incentivizing Data Contribution in Cross-Silo Federated Learning

abstract:
  In cross-silo federated learning, clients (e.g., organizations) collectively
train a global model using their local data. However, due to business
competitions and privacy concerns, the clients tend to free-ride (i.e., not
contribute enough data points) during training.
  To address this issue, we propose a framework where the profit/benefit
obtained from the global model can be properly allocated to clients to
incentivize data contribution.
  More specifically, we study the game-theoretical interactions among the
clients under three widely used profit allocation mechanisms, i.e., linearly
proportional (LP), leave-one-out (LOO), and Shapley value (SV). We consider two
types of equilibrium structures: symmetric and asymmetric equilibria. We show
that the three mechanisms admit an identical symmetric equilibrium structure.
However, at asymmetric equilibrium, LP outperforms SV and LOO in incentivizing
the clients' average data contribution. We further discuss the impact of
various parameters on the clients' free-riding behaviors.


title:
Boosting Mask R-CNN Performance for Long, Thin Forensic Traces with
  Pre-Segmentation and IoU Region Merging

abstract:
  Mask R-CNN has recently achieved great success in the field of instance
segmentation. However, weaknesses of the algorithm have been repeatedly pointed
out as well, especially in the segmentation of long, sparse objects whose
orientation is not exclusively horizontal or vertical. We present here an
approach that significantly improves the performance of the algorithm by first
pre-segmenting the images with a PSPNet algorithm. To further improve its
prediction, we have developed our own cost functions and heuristics in the form
of training strategies, which can prevent so-called (early) overfitting and
achieve a more targeted convergence. Furthermore, due to the high variance of
the images, especially for PSPNet, we aimed to develop strategies for a high
robustness and generalization, which are also presented here.


title: Optical quantum memory based on electro-optically silenced photon echo

abstract:
  Integrated quantum memories that can synchronize of a large number of quantum
computers are essential to build a quantum network to boost the information
processing capability. Here, we propose to store optical quantum state into
erbium doped lithium niobate ($\rm Er^{3+}:LiNbO_3$) micro-cavity by using
electro-optic effect. The cavity frequency of $\rm Er^{3+}:LiNbO_3$ micro
cavity can be shifted by an external electric field, which can be used to
control the emission of photon echo by changing the resonance condition between
cavity and collective atomic excitation. Our calculation shows that high
efficiency and low noise performance is achievable for large detunings.
Benefiting from the host lithium-niobate thin film, such a device can be
controlled by on-chip electrodes and is easy to be integrated with other
photonic devices of various functionalities, paving way of integrated quantum
chips.


title:
ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via
  Adversarial Rotation

abstract:
  Point cloud classifiers with rotation robustness have been widely discussed
in the 3D deep learning community. Most proposed methods either use rotation
invariant descriptors as inputs or try to design rotation equivariant networks.
However, robust models generated by these methods have limited performance
under clean aligned datasets due to modifications on the original classifiers
or input space. In this study, for the first time, we show that the rotation
robustness of point cloud classifiers can also be acquired via adversarial
training with better performance on both rotated and clean datasets.
Specifically, our proposed framework named ART-Point regards the rotation of
the point cloud as an attack and improves rotation robustness by training the
classifier on inputs with Adversarial RoTations. We contribute an axis-wise
rotation attack that uses back-propagated gradients of the pre-trained model to
effectively find the adversarial rotations. To avoid model over-fitting on
adversarial inputs, we construct rotation pools that leverage the
transferability of adversarial rotations among samples to increase the
diversity of training data. Moreover, we propose a fast one-step optimization
to efficiently reach the final robust model. Experiments show that our proposed
rotation attack achieves a high success rate and ART-Point can be used on most
existing classifiers to improve the rotation robustness while showing better
performance on clean datasets than state-of-the-art methods.


title: Topologically driven Rabi-oscillating interference dislocation

abstract:
  Quantum vortices are the quantized version of classical vortices. Their
center is a phase singularity or vortex core around which the flow of particles
as a whole circulates and is typical in superfluids, condensates and optical
fields. However, the exploration of the motion of the phase singularities in
coherently-coupled systems is still underway. We theoretically analyze the
propagation of an interference dislocation in the regime of strong coupling
between light and matter, with strong mass imbalance, corresponding to the case
of microcavity exciton-polaritons. To this end, we utilize combinations of
vortex and tightly focused Gaussian beams, which are introduced through
resonant pulsed pumping. We show that a dislocation originates from
self-interference fringes, due to the non-parabolic dispersion of polaritons
combined with moving Rabi-oscillating vortices. The morphology of singularities
is analyzed in the Poincar\'{e} space for the pseudospin associated to the
polariton states. The resulting beam carries orbital angular momentum with
decaying oscillations due to the loss of overlap between the normal modes of
the polariton system.


title: ClearPose: Large-scale Transparent Object Dataset and Benchmark

abstract:
  Transparent objects are ubiquitous in household settings and pose distinct
challenges for visual sensing and perception systems. The optical properties of
transparent objects leave conventional 3D sensors alone unreliable for object
depth and pose estimation. These challenges are highlighted by the shortage of
large-scale RGB-Depth datasets focusing on transparent objects in real-world
settings. In this work, we contribute a large-scale real-world RGB-Depth
transparent object dataset named ClearPose to serve as a benchmark dataset for
segmentation, scene-level depth completion and object-centric pose estimation
tasks. The ClearPose dataset contains over 350K labeled real-world RGB-Depth
frames and 4M instance annotations covering 63 household objects. The dataset
includes object categories commonly used in daily life under various lighting
and occluding conditions as well as challenging test scenarios such as cases of
occlusion by opaque or translucent objects, non-planar orientations, presence
of liquids, etc. We benchmark several state-of-the-art depth completion and
object pose estimation deep neural networks on ClearPose.


title:
Heat kernel estimates for non-local operator with multisingular critical
  killing

abstract:
  Given a $C^{1,\beta}$ regular open set $D$ with boundary $\partial D =
\bigcup_{k=1}^d \bigcup_{j=1}^{m_k} \Gamma_{k,j}$, where for any $1\le k\le d$,
$ 1 \le j \le m_k$, $\Gamma_{k,j}$ is a $C^{1,\beta}$ submanifold without
boundary of codimension $k$ and $\{\Gamma_{k,j}\}_{1\le k\le d, 1\le j \le
m_k}$ are disjoint. We show that the heat kernel $p^D(t,x,y)$ of the following
non-local operator with multi-singular critical killing potential
\begin{align*} \big((\Delta|_D)^{\alpha/2} - \kappa\big)(f)(x):=
\text{p.v.}\mathcal{A}(d,-\alpha) \int_D \frac{f(y)-f(x)}{|y-x|^{d+\alpha}}dy -
\sum_{k=1}^d \sum_{j=1}^{m_k} \lambda_{k,j}
\delta_{\Gamma_{k,j}}(x)^{-\alpha}f(x), \end{align*} where $ \lambda_{k,j}>0,
\alpha \in (0,2), \beta \in ((\alpha-1)_+,1]$, has the following estimates: for
any given $T>0$, \begin{align*} p^D(t,x,y) \asymp p(t,x,y) \prod_{k=1}^d
\prod_{j=1}^{m_k} (\frac{\delta_{\Gamma_{k,j}}(x)}{t^{1/\alpha}} \wedge
1)^{p_{k,j}}(\frac{\delta_{\Gamma_{k,j}}(y)}{t^{1/\alpha}} \wedge 1)^{p_{k,j}},
\forall t\in (0,T), x,y\in D, \end{align*} where $p(t,x,y)$ is the heat kernel
of the $\alpha$-stable process on $\mathbb R^d$, and $p_{k,j}$ are determined
by $\lambda_{k,j}$ via a strictly increasing function $\lambda =
C(d,k,\alpha,p)$. Our method is based on the result established in [Cho et al.
Journal de Math\'ematiques Pures et Appliqu\'ees 143(2020): 208-256] and a
detailed analysis of $C^{1,\beta}$ manifolds.


title:
Coarsening and wavelength selection far from equilibrium: a unifying
  framework based on singular perturbation theory

abstract:
  Intracellular protein patterns are described by (nearly) mass-conserving
reaction-diffusion systems. While these patterns initially form out of a
homogeneous steady state due to the well-understood Turing instability, no
general theory exists for the dynamics of fully nonlinear patterns. We develop
a unifying theory for wavelength-selection dynamics in (nearly) mass-conserving
two-component reaction-diffusion systems independent of the specific
mathematical model chosen. This encompasses both the dynamics of the mesa- and
peak-shaped patterns found in these systems. Our analysis uncovers a diffusion-
and a reaction-limited regime of the dynamics, which provides a systematic link
between the dynamics of mass-conserving reaction-diffusion systems and the
Cahn-Hilliard as well as conserved Allen-Cahn equations, respectively. A
stability threshold in the family of stationary patterns with different
wavelengths predicts the wavelength selected for the final stationary pattern.
At short wavelengths, self-amplifying mass transport between single pattern
domains drives coarsening while at large wavelengths weak source terms that
break strict mass conservation lead to an arrest of the coarsening process. The
rate of mass competition between pattern domains is calculated analytically
using singular perturbation theory, and rationalized in terms of the underlying
physical processes. The resulting closed-form analytical expressions enable us
to quantitatively predict the coarsening dynamics and the final pattern
wavelength. We find excellent agreement of these expressions with numerical
results. The systematic understanding of the length-scale dynamics of fully
nonlinear patterns in two-component systems provided here builds the basis to
reveal the mechanisms underlying wavelength selection in multi-component
systems with potentially several conservation laws.


title:
Towards Large-Scale Relative Localization in Multi-Robot Systems with
  Dynamic UWB Role Allocation

abstract:
  Ultra-wideband (UWB) ranging has emerged as a key radio technology for robot
positioning and relative localization in multi-robot systems. Multiple works
are now advancing towards more scalable systems, but challenges still remain.
This paper proposes a novel approach to relative localization in multi-robot
systems where the roles of the UWB nodes are dynamically allocated between
active nodes (using time-of-flight for ranging estimation to other active
nodes) and passive nodes (using time-difference-of-arrival for estimating range
differences with respect to pairs of active nodes). We adaptively update UWB
roles based on the location of the robots with respect to the convex envelope
defined by active nodes, and introducing constraints in the form of
localization frequency and accuracy requirements. We demonstrate the
applicability of the proposed approach and show that the localization errors
remain comparable to fixed-role systems. Then, we show how the navigation of an
autonomous drone is affected by the changes in the localization system,
obtaining significantly better trajectory tracking accuracy than when relying
in passive localization only. Our results pave the way for UWB-based
localization in large-scale multi-robot deployments, for either relative
positioning or for applications in GNSS-denied environments.


title:
Towards Wafer-Scale Production of TwoDimensional Transition Metal
  Chalcogenides

abstract:
  Two-dimensional (2D) Transition Metal Chalcogenides (TMCs) have attracted
tremendous interest from both the scientific and technological communities due
to their variety of properties and superior tunability through layer number,
composition, and interface engineering. Wafer-scale production of 2D TMCs is
critical to the industrial applications of these materials. Extensive efforts
have been bestowed to the large-area growth of 2D TMCs through various
approaches. In this review, recent advances in obtaining large-area 2D TMCs by
different methods such as chemical vapor deposition (CVD), metal-organic CVD,
physical vapor deposition are firstly highlighted and their advantages and
disadvantages are also evaluated. Then strategies for the control of the
grains, morphology, layer number and phase to achieve controllable and uniform
thicknesses and large crystal domains for 2D TMCs are discussed. Applications
of large-area 2D TMCs in electronics, optoelectronics, spintronics etc. are
also introduced. Finally, ideas and prospects for the future developments of
wafer-scale 2D TMCs are provided.


title:
Recent Progress of Heterostructures Based on Two Dimensional Materials
  and Wide Bandgap Semiconductors

abstract:
  Recent progress in the synthesis and assembly of two-dimensional (2D)
materials has laid the foundation for various applications of atomically thin
layer films. These 2D materials possess rich and diverse properties such as
layer-dependent band gaps, interesting spin degrees of freedom, and variable
crystal structures. They exhibit broad application prospects in micro-nano
devices. In the meantime, the wide bandgap semiconductors (WBS) with an
elevated breakdown voltage, high mobility, and high thermal conductivity have
shown important applications in high-frequency microwave devices,
high-temperature and high-power electronic devices. Beyond the study on single
2D materials or WBS materials, the multi-functional 2D/WBS heterostructures can
promote the carrier transport at the interface, potentially providing novel
physical phenomena and applications, and improving the performance of
electronic and optoelectronic devices. In this review, we overview the
advantages of the heterostructures of 2D materials and WBS materials, and
introduce the construction methods of 2D/WBS heterostructures. Then, we present
the diversity and recent progress in the applications of 2D/WBS
heterostructures, including photodetectors, photocatalysis, sensors, and energy
related devices. Finally, we put forward the current challenges of 2D/WBS
heterostructures and propose the promising research directions in the future.


title:
Multi-axis Accelerometry and Rotation Sensing using a Point Source Atom
  Interferometer

abstract:
  A point source atom interferometer (PSI) is a device where atoms are split
and recombined by applying a temporal sequence of Raman pulses during the
expansion of a cloud of cold atoms behaving approximately as a point source.
Unlike a conventional light pulse atom interferometer, the PSI can produce a
signal that corresponds to multi-axis rotation only, independent of
acceleration. In addition, it can be used to measure acceleration along one
direction, independent of rotation. Here, we describe a modified PSI that can
be used to measure multi-axis rotation and multi-axis acceleration.
Specifically, this type of PSI can be used to measure two-axes rotation around
the directions perpendicular to the light pulses, as well as the acceleration
in all three directions, with only one pair of Raman beams. Using two pairs of
Raman beams in orthogonal directions sequentially, such a scheme would enable
the realization of a complete atom interferometric inertial measurement unit.


title: Multi-Modal Mixup for Robust Fine-tuning

abstract:
  Pre-trained large-scale models provide a transferable embedding, and they
show comparable performance on the diverse downstream task. However, the
transferability of multi-modal learning is restricted, and the analysis of
learned embedding has not been explored well. This paper provides a perspective
to understand the multi-modal embedding in terms of uniformity and alignment.
We newly find that the representation learned by multi-modal learning models
such as CLIP has a two separated representation space for each heterogeneous
dataset with less alignment. Besides, there are unexplored large intermediate
areas between two modalities with less uniformity. Less robust embedding might
restrict the transferability of the representation for the downstream task.
This paper provides a new end-to-end fine-tuning method for robust
representation that encourages better uniformity and alignment score. First, we
propose a multi-modal Mixup, $m^{2}$-Mix that mixes the representation of image
and text to generate the hard negative samples. Second, we fine-tune the
multi-modal model on a hard negative sample as well as normal negative and
positive samples with contrastive learning. Our multi-modal Mixup provides a
robust representation, and we validate our methods on classification,
retrieval, and structure-awareness task.


title: Yet another DE-Sinc indefinite integration formula

abstract:
  Based on the Sinc approximation combined with the tanh transformation, Haber
derived an approximation formula for numerical indefinite integration over the
finite interval (-1, 1). The formula, (SE1) uses a special function for the
basis functions. In contrast, Stenger derived another formula (SE2), which does
not use any special function but does include a double sum. Subsequently,
Muhammad and Mori proposed the formula (DE1), which replaces the tanh
transformation with the double-exponential transformation in the formula (SE1).
Almost simultaneously, Tanaka et al. proposed the formula (DE2), which was
based on the same replacement in (SE2). As they reported, the replacement
drastically improves the convergence rate of (SE1) and (SE2). In addition to
the formulas above, Stenger derived yet another indefinite integration formula
(SE3) based on the Sinc approximation combined with the tanh transformation,
which has an elegant matrix-vector form. In this paper, we propose the
replacement of the tanh transformation with the double-exponential
transformation in the formula (SE3). We provide a theoretical analysis as well
as a numerical comparison.


title:
Noisy Low-rank Matrix Optimization: Geometry of Local Minima and
  Convergence Rate

abstract:
  This paper is concerned with low-rank matrix optimization, which has found a
wide range of applications in machine learning. This problem in the special
case of matrix sense has been studied extensively through the notion of
Restricted Isometry Property (RIP), leading to a wealth of results on the
geometric landscape of the problem and the convergence rate of common
algorithms. However, the existing results are not able to handle the problem
with a general objective function subject to noisy data. In this paper, we
address this problem by developing a mathematical framework that can deal with
random corruptions to general objective functions, where the noise model is
arbitrary. We prove that as long as the RIP constant of the noiseless objective
is less than $1/3$, any spurious local solution of the noisy optimization
problem must be close to the ground truth solution. By working through the
strict saddle property, we also show that an approximate solution can be found
in polynomial time. We characterize the geometry of the spurious local minima
of the problem in a local region around the ground truth in the case when the
RIP constant is greater than $1/3$. This paper offers the first set of results
on the global and local optimization landscapes of general low-rank
optimization problems under arbitrary random corruptions.


title: Differential operator approach to $\imath$quantum groups

abstract:
  For a quasi-split Satake diagram, we define a modified $q$-Weyl algebra, and
show that there is an algebra homomorphism between it and the corresponding
$\imath$quantum group. In other words, we provide a differential operator
approach to $\imath$quantum groups. Meanwhile, the Oscillator representations
are obtained. The crystal basis of the irreducible subrepresentations of these
Oscillator representations are constructed.


title:
Persistence of fermionic spin excitations through a genuine Mott
  transition in $\kappa$-type organics

abstract:
  We investigate the continuous variation of electronic states from a Fermi
liquid to a quantum spin liquid induced by chemical substitution in the organic
dimer-Mott system of
$\kappa$-[(BEDSe-TTF)$_x$(BEDT-TTF)$_{1-x}$]$_2$Cu[N(CN)$_2$]Br. Electrical
transport measurements reveal that the mixing of BEDSe-TTF into the BEDT-TTF
layers induces a quantum Mott transition around $x$=0.10. Although a charge gap
disappears at this point, magnetic susceptibility and heat capacity
measurements indicate that fermionic low-energy excitations remain in the
insulating salts, suggesting that fermionic spin excitations persist. We
propose that the transition can be classified into a genuine Mott transition.


title:
Odd entanglement entropy in Galilean conformal field theories and flat
  holography

abstract:
  The odd entanglement entropy (OEE) for bipartite states in a class of
$(1+1)$-dimensional Galilean conformal field theories ($GCFT_{1+1}$) is
obtained through an appropriate replica technique. In this context our results
are compared with the entanglement wedge cross section (EWCS) for
$(2+1)$-dimensional asymptotically flat geometries dual to the $GCFT_{1+1}$ in
the framework of flat holography. We find that our results are consistent with
the duality of the difference between the odd entanglement entropy and the
entanglement entropy of bipartite states, with the bulk EWCS for flat
holographic scenarios.


title:
InstructionNER: A Multi-Task Instruction-Based Generative Framework for
  Few-shot NER

abstract:
  Recently, prompt-based methods have achieved significant performance in
few-shot learning scenarios by bridging the gap between language model
pre-training and fine-tuning for downstream tasks. However, existing prompt
templates are mostly designed for sentence-level tasks and are inappropriate
for sequence labeling objectives. To address the above issue, we propose a
multi-task instruction-based generative framework, named InstructionNER, for
low-resource named entity recognition. Specifically, we reformulate the NER
task as a generation problem, which enriches source sentences with
task-specific instructions and answer options, then inferences the entities and
types in natural language. We further propose two auxiliary tasks, including
entity extraction and entity typing, which enable the model to capture more
boundary information of entities and deepen the understanding of entity type
semantics, respectively. Experimental results show that our method consistently
outperforms other baselines on five datasets in few-shot settings.


title: Nonlinear topological Toda quasicrystal

abstract:
  Topological edge states are known to emerge in certain quasicrystals. We
investigate a topological quasicrystal in the presence of nonlinearity by
generalizing the Toda lattice to include modulated periodic hoppings, where the
period is taken irrational to the original lattice. It is found that
topological edge states in a quasicrystal survive against nonlinearity based on
the quench dynamics. It is also found that an extended-localization transition
is induced by the quasicrystal hopping modulation. The present model is
experimentally realizable by a transmission line with variable capacitance
diodes, where the inductance is modulated.


title: End-to-end system for object detection from sub-sampled radar data

abstract:
  Robust and accurate sensing is of critical importance for advancing
autonomous automotive systems. The need to acquire situational awareness in
complex urban conditions using sensors such as radar has motivated research on
power and latency-efficient signal acquisition methods. In this paper, we
present an end-to-end signal processing pipeline, capable of operating in
extreme weather conditions, that relies on sub-sampled radar data to perform
object detection in vehicular settings. The results of the object detection are
further utilized to sub-sample forthcoming radar data, which stands in contrast
to prior work where the sub-sampling relies on image information. We show
robust detection based on radar data reconstructed using 20% of samples under
extreme weather conditions such as snow or fog, and on low-illuminated nights.
Additionally, we generate 20% sampled radar data in a fine-tuning set and show
1.1% gain in AP50 across scenes and 3% AP50 gain in motorway condition.


title:
Graph Reinforcement Learning for Predictive Power Allocation to Mobile
  Users

abstract:
  Allocating resources with future channels can save resource to ensure
quality-of-service of video streaming. In this paper, we optimize predictive
power allocation to minimize the energy consumed at distributed units (DUs) by
using deep deterministic policy gradient (DDPG) to find optimal policy and
predict average channel gains. To improve training efficiency, we resort to
graph DDPG for exploiting two kinds of relational priors: (a) permutation
equivariant (PE) and permutation invariant (PI) properties of policy function
and action-value function, (b) topology relation among users and DUs. To design
graph DDPG framework more systematically in harnessing the priors, we first
demonstrate how to transform matrix-based DDPG into graph-based DDPG. Then, we
respectively design the actor and critic networks to satisfy the permutation
properties when graph neural networks are used in embedding and end to-end
manners. To avoid destroying the PE/PI properties of the actor and critic
networks, we conceive a batch normalization method. Finally, we show the impact
of leveraging each prior. Simulation results show that the learned predictive
policy performs close to the optimal solution with perfect future information,
and the graph DDPG algorithms converge much faster than existing DDPG
algorithms.


title:
On a Simple Connection Between $\Delta$-modular ILP and LP, and a New
  Bound on the Number of Integer Vertices

abstract:
  Let $A \in Z^{m \times n}$, $rank(A) = n$, $b \in Z^m$, and $P$ be an
$n$-dimensional polyhedron, induced by the system $A x \leq b$.
  It is a known fact that if $F$ is a $k$-face of $P$, then there exist at
least $n-k$ linearly independent inequalities of the system $A x \leq b$ that
become equalities on $F$. In other words, there exists a set of indices $J$,
such that $|J| = n-k$, $rank(A_{JC}) = n-k$, and
  $$
  A_{J} x - b_{J} = 0,\quad \text{for any $x \in F$}.
  $$
  We show that a similar fact holds for the integer polyhedron
  $$
  P_{I} = conv\bigl(P \cap Z^n\bigr)$$ if we additionally suppose that $P$ is
$\Delta$-modular, for some $\Delta \in \{1,2,\dots\}$. More precisely, if $F$
is a $k$-face of $P_{I}$, then there exists a set of indices $J$, such that
$|J| = n-k$, $rank(A_{J}) = n-k$, and
  $$
  \|A_{J} x - b_{J}\|_{\infty} < \Delta,\quad \text{for any $x \in F \cap
Z^n$.}
  $$ In other words, there exist at least $n-k$ linearly independent
inequalities of the system $A x \leq b$ that almost become equalities on $F
\cap Z^n$. When we say almost, we mean that the slacks are at most $\Delta-1$
in the absolute value. Using this fact, we prove the inequality
  $$
  |vert(P_I)| \leq 2 \cdot \binom{m}{n} \cdot \Delta^{n-1},
  $$ for the number of vertices of $P_I$, which is better, than the state of
the art bound for $\Delta = O(n^2)$.


title: DC Signature of snap-through bi-stability in carbon nanotube resonators

abstract:
  Bi-stable arched beams exhibiting Euler-Bernoulli snap-through buckling are
vastly used as electronic devices in various applications, such as memory
devices, energy harvesters, sensors, and actuators. Recently, we reported the
realization of the smallest bi-stable resonator to date, in the form of a
buckled suspended carbon nanotube (CNT), which exhibits a unique
three-dimensional snap-through transition and an extremely large change in
frequency as a result. In this article, we address a unique characteristic of
these devices, in which a significant change in the DC conductance is also
observed at the mechanical snap-through transition. After verifying that the
change in the CNT tension due to the "jump" cannot account for the conductance
difference measured, we attribute the conductance "jump" to the change in
capacitance as a result of the snap-through buckling. However, we show that
quantitative analysis of this phenomenon is not at all trivial, and is enabled
only due to our ability to predict the exact CNT shape before and after the
transition. Understanding this mechanism enables a fast characterization of
fabricated devices and improves our understanding of their behavior, key in
developing this technology further and better design. As an example, we show
how the hysteretic trait of this phenomenon is indicative of the ability to
achieve static latching, useful for RF switches, bi-stable relays, and memory
devices.


title: Nematic state of the FeSe superconductor

abstract:
  We study the crystal structure of the tetragonal iron selenide FeSe and its
nematic phase transition to the low-temperature orthorhombic structure using
synchrotron x-ray and neutron scattering analyzed in both real and reciprocal
space. We show that in the local structure the orthorhombic distortion
associated with the electronically driven nematic order is more pronounced at
short length scales. It also survives up to temperatures above 90 K where
reciprocal-space analysis suggests tetragonal symmetry. Additionally, the
real-space pair distribution function analysis of the synchrotron x-ray
diffraction data reveals a tiny broadening of the peaks corresponding to the
nearest Fe-Fe, nearest Fe-Se, and the next-nearest Fe-Se bond distances as well
as the tetrahedral torsion angles at a short length scale of 20 angstr\"om.
This broadening appears below 20 K and is attributed to a pseudogap. However,
we did not observe any further reduction in local symmetry below orthorhombic
down to 3 K. Our results suggest that the superconducting gap anisotropy in
FeSe is not associated with any symmetry-lowering short-range structural
correlations.


title:
Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced
  Training for Neural Machine Translation

abstract:
  Neural networks tend to gradually forget the previously learned knowledge
when learning multiple tasks sequentially from dynamic data distributions. This
problem is called \textit{catastrophic forgetting}, which is a fundamental
challenge in the continual learning of neural networks. In this work, we
observe that catastrophic forgetting not only occurs in continual learning but
also affects the traditional static training. Neural networks, especially
neural machine translation models, suffer from catastrophic forgetting even if
they learn from a static training set. To be specific, the final model pays
imbalanced attention to training samples, where recently exposed samples
attract more attention than earlier samples. The underlying cause is that
training samples do not get balanced training in each model update, so we name
this problem \textit{imbalanced training}. To alleviate this problem, we
propose Complementary Online Knowledge Distillation (COKD), which uses
dynamically updated teacher models trained on specific data orders to
iteratively provide complementary knowledge to the student model. Experimental
results on multiple machine translation tasks show that our method successfully
alleviates the problem of imbalanced training and achieves substantial
improvements over strong baseline systems.


title:
Language Matters: A Weakly Supervised Pre-training Approach for Scene
  Text Detection and Spotting

abstract:
  Recently, Vision-Language Pre-training (VLP) techniques have greatly
benefited various vision-language tasks by jointly learning visual and textual
representations, which intuitively helps in Optical Character Recognition (OCR)
tasks due to the rich visual and textual information in scene text images.
However, these methods cannot well cope with OCR tasks because of the
difficulty in both instance-level text encoding and image-text pair acquisition
(i.e. images and captured texts in them). This paper presents a weakly
supervised pre-training method that can acquire effective scene text
representations by jointly learning and aligning visual and textual
information. Our network consists of an image encoder and a character-aware
text encoder that extract visual and textual features, respectively, as well as
a visual-textual decoder that models the interaction among textual and visual
features for learning effective scene text representations. With the learning
of textual features, the pre-trained model can attend texts in images well with
character awareness. Besides, these designs enable the learning from weakly
annotated texts (i.e. partial texts in images without text bounding boxes)
which mitigates the data annotation constraint greatly. Experiments over the
weakly annotated images in ICDAR2019-LSVT show that our pre-trained model
improves F-score by +2.5% and +4.8% while transferring its weights to other
text detection and spotting networks, respectively. In addition, the proposed
method outperforms existing pre-training techniques consistently across
multiple public datasets (e.g., +3.2% and +1.3% for Total-Text and CTW1500).


title:
Level-crossing distributions of kinematic variables in multidirectional
  second-order ocean waves

abstract:
  The conditional value of a stationary random process, given the
level-upcrossing of another dependent stationary random process, is considered.
Assuming that both processes are weakly non-Gaussian, an analytical
approximation for the related conditional distribution is derived. It is based
on a trivariate Edgeworth expansion truncated to non-Gaussian terms of lowest
order, to which Rice's formula is then applied. As an application, the effect
of level-upcrossing conditioning in second-order ocean waves is investigated.
Upcrossing events are monitored for the sea surface elevation. The conditional
distributions of different kinematic variables, given upcrossing, are
considered for different sea-state configurations. Predictions from the
analytical model are compared with numerical data obtained from Monte Carlo
experiments. It is found that the analytical approximation provides conditional
mean and variance in good agreement with numerical data, although moderate
discrepancies appear for the sea states with the most severe wave cambers.
Regarding the conditional skewness, given upcrossing, results are mixed, with
significant discrepancies between the analytical approximation and numerical
estimates, in a number of cases. An Edgeworth-type approximation is also
provided for the upcrossing frequency and compared with Monte Carlo estimates;
this analytical estimate is found to be accurate over a wide range of crossing
levels.


title: Where and how does a decay-index profile become saddle-like?

abstract:
  The decay index of solar magnetic fields is known as an important parameter
in regulating solar eruptions from the standpoint of the torus instability. In
particular, a saddle-like profile of decay index, which hosts a local
torus-stable regime at higher altitudes than where the decay index first
exceeds the instability threshold, is found to be associated with some confined
or two-step eruptions. To understand the occurrence of such a profile, we
employed dipoles to emulate different kinds of photospheric flux distributions.
Corroborated by observations of representative active regions (ARs), our major
results are: 1) in bipolar configurations the critical height increases away
from the AR center along the polarity inversion line (PIL) and its average is
roughly half of the centroid distance between opposite polarities; 2) in
quadrupolar configurations saddle-like profiles appear above the PIL when the
two dipoles oriented in the same direction are significantly more separated in
this direction than in the perpendicular direction, and when the two dipoles
are oriented differently or have unequal fluxes; 3) saddle-like profiles in
quadrupolar configurations are associated with magnetic skeletons such as a
null point or a hyperbolic flux tube, and the role of such profiles in
eruptions is anticipated to be double-edged if magnetic reconnection is
involved.


title: Globally-Optimal Event Camera Motion Estimation

abstract:
  Event cameras are bio-inspired sensors that perform well in HDR conditions
and have high temporal resolution. However, different from traditional
frame-based cameras, event cameras measure asynchronous pixel-level brightness
changes and return them in a highly discretised format, hence new algorithms
are needed. The present paper looks at fronto-parallel motion estimation of an
event camera. The flow of the events is modeled by a general homographic
warping in a space-time volume, and the objective is formulated as a
maximisation of contrast within the image of unwarped events. However, in stark
contrast to prior art, we derive a globally optimal solution to this generally
non-convex problem, and thus remove the dependency on a good initial guess. Our
algorithm relies on branch-and-bound optimisation for which we derive novel,
recursive upper and lower bounds for six different contrast estimation
functions. The practical validity of our approach is supported by a highly
successful application to AGV motion estimation with a downward facing event
camera, a challenging scenario in which the sensor experiences fronto-parallel
motion in front of noisy, fast moving textures.


title:
Enhancement of thermal spin pumping by orbital angular momentum of rare
  earth iron garnet

abstract:
  In a bilayer of ferromagnetic and non-magnetic metal, spin pumping can be
generated by a thermal gradient. The spin current generation depends on the
spin mixing conductance of the interface and the magnetic properties of the
ferromagnetic layer. Due to its low intrinsic damping, rare earth iron garnet
is often used for the ferromagnetic layer in the spin Seebeck experiment.
However, it is actually a ferrimagnetic with antiferromagnetically coupled
magnetic lattices and the contribution of rare earth magnetic lattice of rare
earth iron garnet on thermal spin pumping is not well understand. Here we focus
on the effect of magnetic properties of lanthanide and show that the orbital
angular momentum of rare earth iron garnet enhances thermal spin current
generation of lanthanide substituted yttrium iron garnet.


title:
Estimating the average causal effect of intervention in continuous
  variables using machine learning

abstract:
  The most widely discussed methods for estimating the Average Causal
Effect/Average Treatment Effect are those for intervention in discrete binary
variables whose value represents intervention/non-intervention groups. On the
other hand, methods for intervening in continuous variables independent of data
generating models have not been developed. In this study, we give a method for
estimating the average causal effect for intervention in continuous variables
that can be applied to data of any generating models as long as the causal
effect is identifiable. The proposing method is independent of machine learning
algorithms and preserves the identifiability of data.


title: An Analysis of Measure-Valued Derivatives for Policy Gradients

abstract:
  Reinforcement learning methods for robotics are increasingly successful due
to the constant development of better policy gradient techniques. A precise
(low variance) and accurate (low bias) gradient estimator is crucial to face
increasingly complex tasks. Traditional policy gradient algorithms use the
likelihood-ratio trick, which is known to produce unbiased but high variance
estimates. More modern approaches exploit the reparametrization trick, which
gives lower variance gradient estimates but requires differentiable value
function approximators. In this work, we study a different type of stochastic
gradient estimator - the Measure-Valued Derivative. This estimator is unbiased,
has low variance, and can be used with differentiable and non-differentiable
function approximators. We empirically evaluate this estimator in the
actor-critic policy gradient setting and show that it can reach comparable
performance with methods based on the likelihood-ratio or reparametrization
tricks, both in low and high-dimensional action spaces. With this work, we want
to show that the Measure-Valued Derivative estimator can be a useful
alternative to other policy gradient estimators.


title:
Residual Robot Learning for Object-Centric Probabilistic Movement
  Primitives

abstract:
  It is desirable for future robots to quickly learn new tasks and adapt
learned skills to constantly changing environments. To this end, Probabilistic
Movement Primitives (ProMPs) have shown to be a promising framework to learn
generalizable trajectory generators from distributions over demonstrated
trajectories. However, in practical applications that require high precision in
the manipulation of objects, the accuracy of ProMPs is often insufficient, in
particular when they are learned in cartesian space from external observations
and executed with limited controller gains. Therefore, we propose to combine
ProMPs with recently introduced Residual Reinforcement Learning (RRL), to
account for both, corrections in position and orientation during task
execution. In particular, we learn a residual on top of a nominal ProMP
trajectory with Soft-Actor Critic and incorporate the variability in the
demonstrations as a decision variable to reduce the search space for RRL. As a
proof of concept, we evaluate our proposed method on a 3D block insertion task
with a 7-DoF Franka Emika Panda robot. Experimental results show that the robot
successfully learns to complete the insertion which was not possible before
with using basic ProMPs.


title: A Hierarchical Approach to Active Pose Estimation

abstract:
  Creating mobile robots which are able to find and manipulate objects in large
environments is an active topic of research. These robots not only need to be
capable of searching for specific objects but also to estimate their poses
often relying on environment observations, which is even more difficult in the
presence of occlusions. Therefore, to tackle this problem we propose a simple
hierarchical approach to estimate the pose of a desired object. An Active
Visual Search module operating with RGB images first obtains a rough estimation
of the object 2D pose, followed by a more computationally expensive Active Pose
Estimation module using point cloud data. We empirically show that processing
image features to obtain a richer observation speeds up the search and pose
estimation computations, in comparison to a binary decision that indicates
whether the object is or not in the current image.


title:
A second-order perturbation theory for the continuous model of indirect
  reciprocity

abstract:
  Reputation is one of key mechanisms to maintain human cooperation, but its
analysis gets complicated if we consider the possibility that reputation does
not reach consensus because of erroneous assessment. The difficulty is
alleviated if we assume that reputation and cooperation do not take binary
values but have continuous spectra so that disagreement over reputation can be
analysed through a perturbation theory. In this work, we carry out the analysis
by expanding the dynamics of reputation to the second order of perturbation
under the assumption that everyone initially cooperates with good reputation.
The second-order theory clarifies the difference between Image Scoring and
Simple Standing in that punishment for defection against a well-reputed player
should be regarded as good for maintaining cooperation. Moreover, comparison
among the leading eight shows that the stabilizing effect of justified
punishment weakens if cooperation between two ill-reputed players is regarded
as bad. Our analysis thus explains how Simple Standing achieves a high level of
stability by permitting justified punishment and also by disregarding
irrelevant information in assessing cooperation. This observation suggests
which factors affect the stability of a social norm when reputation can be
perturbed by noise.


title:
New construction of strongly regular graphs with parameters of the
  complement of symplectic graph

abstract:
  Symplectic graph Sp(2d, q) is the collinearity graph of the symplectic space
of dimension 2d over a finite field of order q. A k-regular graph on v vertices
is a divisible design graph with parameters (v, k, lambda_1, lambda_2 ,m,n) if
its vertex set can be partitioned into m classes of size n, such that any two
different vertices from the same class have lambda_1 common neighbours, and any
two vertices from different classes have lambda_2 common neighbours whenever it
is not complete or edgeless. In this paper we present a new prolific
construction of strongly regular graphs with parameters of the complement of
symplectic graph using a new prolific construction of one type of divisible
design graphs.


title:
Multiobjective Combinatorial Optimization with Interactive Evolutionary
  Algorithms: the case of facility location problems

abstract:
  We consider multiobjective combinatorial optimization problems handled by
means of preference driven efficient heuristics. They look for the most
preferred part of the Pareto front on the basis of some preferences expressed
by the Decision Maker during the process. In general, what is searched for in
this case is the Pareto set of efficient solutions. This is a problem much more
difficult than optimizing a single objective function. Moreover, obtaining the
Pareto set does not mean that the decision problem is solved since one or some
of the solutions have to be chosen. Indeed, to make a decision, it is necessary
to determine the most preferred solution in the Pareto set, so that it is also
necessary to elicit the preferences of the user. In this perspective, what we
are proposing can be seen as the first structured methodology in facility
location problems to search optimal solutions taking into account preferences
of the user. With this aim, we approach facility location problems using a
recently proposed interactive evolutionary multiobjective optimization
procedure called NEMO-II-Ch. NEMO-II-Ch is applied to a real world
multiobjective location problem with many users and many facilities to be
located. Several simulations considering different fictitious users have been
performed. The results obtained by NEMO-II-Ch are compared with those got by
three algorithms which know the user's true value function that is, instead,
unknown to NEMO-II-Ch. They show that in many cases NEMO-II-Ch finds the best
subset of locations more quickly than the methods knowing, exactly, the whole
user's true preferences.


title:
ROLL: Long-Term Robust LiDAR-based Localization With Temporary Mapping
  in Changing Environments

abstract:
  Long-term scene changes present challenges to localization systems using a
pre-built map. This paper presents a LiDAR-based system that can provide robust
localization against those challenges. Our method starts with activation of a
mapping process temporarily when global matching towards the pre-built map is
unreliable. The temporary map will be merged onto the pre-built map for later
localization runs once reliable matching is obtained again. We further
integrate a LiDAR inertial odometry (LIO) to provide motion-compensated LiDAR
scans and a reliable initial pose guess for the global matching module. To
generate a smooth real-time trajectory for navigation purposes, we fuse poses
from odometry and global matching by solving a pose graph optimization problem.
We evaluate our localization system with extensive experiments on the NCLT
dataset including a variety of changing indoor and outdoor environments, and
the results demonstrate a robust and accurate localization performance for over
a year. The implementations are open sourced on GitHub.


title: The primordial structure from Quantum Cosmological bouncing models

abstract:
  By quantizing the background as well as the perturbations in a simple one
fluid cosmological model, we show that there exists an ambiguity in the choice
of relevant variables, potentially leading to incompatible observational
physical predictions. In a classical inflationary background, the exact same
canonical transformations lead to unique predictions, so the ambiguity we put
forward demands a semiclassical background with a sufficiently strong departure
from classical evolution. The latter condition is clearly satisfied by bouncing
models. We propose coherent states as the tool for introducing the
semiclassical universe. We solve the quantum dynamics of the perturbation modes
both analytically and numerically and investigate the amplitude spectra of the
perturbations. We study the underlying quantum state, the Bunch-Davies vacuum,
from the point of view of late-time observers by means of the Bogolyubov
transformations. In particular, we study the phase space probability
distributions obtained with the standard coherent states built from
instantaneous vacua. We discuss the issue of the temporal phase shift with
which the modes emerge from the bounce as sine waves. Finally, we find that the
model may be fitted to data and shed light on the physical universe,
constraining free parameters of the bouncing universe.


title: Physics Opportunities for the Fermilab Booster Replacement

abstract:
  This white paper presents opportunities afforded by the Fermilab Booster
Replacement and its various options. Its goal is to inform the design process
of the Booster Replacement about the accelerator needs of the various options,
allowing the design to be versatile and enable, or leave the door open to, as
many options as possible. The physics themes covered by the paper include
searches for dark sectors and new opportunities with muons.


title:
Numerical simulation of sound propagation in and around ducts using thin
  boundary elements

abstract:
  Investigating the sound field in and around ducts is an important topic in
acoustics, e.g. when simulating musical instruments or the human vocal tract.
In this paper a method that is based on the boundary element method in 3D
combined with a formulation for infinitely thin elements is presented. The
boundary integral equations for these elements are presented, and numerical
experiments are used to illustrate the behavior of the thin elements. Using the
example of a closed benchmark duct, boundary element solutions for thin
elements and surface elements are compared with the analytic solution, and the
accuracy of the boundary element method as function of element size is
investigated. As already shown for surface elements in the literature, an
accumulation of the error along the duct can also be found for thin elements,
but in contrast to surface elements this effect is not as big and a damping of
the amplitude cannot be seen. In a second experiment, the impedance at the open
end of a half open duct is compared with formulas for the radiation impedance
of an unflanged tube, and a good agreement is shown. Finally, resonance
frequencies of a tube open at both ends are calculated and compared with
measured spectra. For sufficiently small element sizes frequencies for lower
harmonics agree very well, for higher frequencies a difference of a few Hertz
can be observed, which may be explained by the fact that the method does not
consider dampening effects near the duct walls. The numerical experiments also
suggest, that for duct simulations the usual six to eight elements per
wavelength rule is not enough for accurate results.


title:
Quadruped Guidance Robot for the Visually Impaired: A Comfort-Based
  Approach

abstract:
  A quadrupedal guidance robot that can guide people and avoid various
obstacles, could potentially be owned by more visually impaired people at a
fairly low cost. In this paper, we propose a novel guidance robot system with a
comfort-based concept. We design a leash containing an elastic rope and a thin
string, and use a motor to adjust the length of the string to ensure comfort.
We use the force-based human motion model to plan the forces experienced by the
human. Afterward, the direction and magnitude of the force are controlled by
the motion of the robot, and the rotation of the motor, respectively. This
allows humans to be guided safely and more comfortably to the target position
in complex environments. The system has been deployed on Unitree Laikago
quadrupedal platform and validated in real-world scenarios.


title: An APEX search for carbon emission from NGC 1977 proplyds

abstract:
  We used the Atacama Pathfinder Experiment (APEX) telescope to search for CI
1-0 (492.16GHz) emission towards 8 proplyds in NGC 1977, which is an FUV
radiation environment two orders of magnitude weaker than that irradiating the
Orion Nebular Cluster (ONC) proplyds. CI is expected to enable us to probe the
wind launching region of externally photoevaporating discs. Of the 8 targets
observed, no 3$\sigma$ detections of the CI line were made despite reaching
sensitivities deeper than the anticipated requirement for detection from prior
APEX CI observations of nearby discs and models of external photoevaporation of
quite massive discs. By comparing both the proplyd mass loss rates and CI flux
constraints with a large grid of external photoevaporation simulations, we
determine that the non-detections are in fact fully consistent with the models
if the proplyd discs are very low mass. Deeper observations in CI and probes of
the disc mass with other tracers (e.g. in the continuum and CO) can test this.
If such a test finds higher masses, this would imply carbon depletion in the
outer disc, as has been proposed for other discs with surprisingly low CI
fluxes, though more massive discs would also be incompatible with models that
can explain the observed mass loss rates and CI non-detections. The expected
remaining lifetimes of the proplyds are estimated to be similar to those of
proplyds in the ONC at 0.1Myr. Rapid destruction of discs is therefore also a
feature of common, intermediate UV environments.


title:
Quantifying Privacy Risks of Masked Language Models Using Membership
  Inference Attacks

abstract:
  The wide adoption and application of Masked language models~(MLMs) on
sensitive data (from legal to medical) necessitates a thorough quantitative
investigation into their privacy vulnerabilities -- to what extent do MLMs leak
information about their training data? Prior attempts at measuring leakage of
MLMs via membership inference attacks have been inconclusive, implying the
potential robustness of MLMs to privacy attacks. In this work, we posit that
prior attempts were inconclusive because they based their attack solely on the
MLM's model score. We devise a stronger membership inference attack based on
likelihood ratio hypothesis testing that involves an additional reference MLM
to more accurately quantify the privacy risks of memorization in MLMs. We show
that masked language models are extremely susceptible to likelihood ratio
membership inference attacks: Our empirical results, on models trained on
medical notes, show that our attack improves the AUC of prior membership
inference attacks from 0.66 to an alarmingly high 0.90 level, with a
significant improvement in the low-error region: at 1% false positive rate, our
attack is 51X more powerful than prior work.