title:
Halogenation induced transition of superconductor-to-semiconductor in
  MXene-like MOene with direct band gap and long carrier lifetime

abstract:
  Traditional MXenes with intriguing mechanical and electronic properties,
together with the fertilities of elemental compositions and chemical
decorations have aroused much attentions. However, the semiconducting traits
with direc band gap are extremetely rare among reported MXenes. Thus,
broadening the family of MXene beyond carbides and nitrides with unique
behaviors is still an extraordinary and fascinating field.


abstract:
  Various models have been proposed to incorporate knowledge of syntactic
structures into neural language models. However, previous works have relied
heavily on elaborate components for a specific language model, usually
recurrent neural network (RNN), which makes themselves unwieldy in practice to
fit into other neural language models, such as Transformer and GPT-2. In this
paper, we introduce the Dependency-based Mixture Language Models. In detail, we
first train neural language models with a novel dependency modeling objective
to learn the probability distribution of future dependent tokens given context.
We then formulate the next-token probability by mixing the previous dependency
modeling probability distributions with self-attention. Extensive experiments
and human evaluations show that our method can be easily and effectively
applied to different neural language models while improving neural text
generation on various tasks.


title:
Spatiotemporal mode-locking and photonic flywheel in multimode
  microresonators

abstract:
  Dissipative Kerr soliton (DKS) frequency combs - also known as microcombs -
have arguably created a new field in cavity nonlinear photonics, with a strong
cross-fertilization between theoretical, experimental, and technological
research. Spatiotemporal mode-locking (STML) not only add new degrees of
freedom to ultrafast laser technology, but also provide new insights for
implementing analogue computers and heuristic optimizers with photonics. Here,
we combine the principles of DKS and STML for the first time to demonstrate the
STML DKS by developing an unexplored ultrahigh-quality-factor Fabry-Perot
microresonator based on graded index multimode fiber (GRIN-MMF). Using the
intermodal stimulated Brillouin scattering, we can selectively excite either
the eigenmode DKS or the STML DKS. Furthermore, we demonstrate an ultralow
noise microcomb that enhances the photonic flywheel performance in both the
fundamental comb linewidth and DKS timing jitter. The demonstrated fundamental
comb linewidth of 400 mHz and DKS timing jitter of 500 attosecond represent
improvements of 25x and 2.5x, respectively, from the state-of-the-art. Our
results show the potential of GRIN-MMF FP microresonators as an ideal testbed
for high-dimensional nonlinear cavity dynamics and photonic flywheel with
ultrahigh coherence and ultralow timing jitter.


title:
Doubly Robust Collaborative Targeted Learning for Recommendation on Data
  Missing Not at Random

abstract:
  In recommender systems, the feedback data received is always missing not at
random (MNAR), which poses challenges for accurate rating prediction. To
address this issue, many recent studies have been conducted on the doubly
robust (DR) method and its variants to reduce bias. However, theoretical
analysis shows that the DR method has a relatively large variance, while that
of the error imputation-based (EIB) method is smaller. In this paper, we
propose {\bf DR-TMLE} that effectively captures the merits of both EIB and DR,
by leveraging the targeted maximum likelihood estimation (TMLE) technique.
DR-TMLE first obtains an initial EIB estimator and then updates the error
imputation model along with the bias-reduced direction. Furthermore, we propose
a novel RCT-free collaborative targeted learning algorithm for DR-TMLE, called
{\bf DR-TMLE-TL}, which updates the propensity model adaptively to reduce the
bias of imputed errors. Both theoretical analysis and experiments demonstrate
the advantages of the proposed methods compared with existing debiasing
methods.


title: Representation-Agnostic Shape Fields

abstract:
  3D shape analysis has been widely explored in the era of deep learning.
Numerous models have been developed for various 3D data representation formats,
e.g., MeshCNN for meshes, PointNet for point clouds and VoxNet for voxels. In
this study, we present Representation-Agnostic Shape Fields (RASF), a
generalizable and computation-efficient shape embedding module for 3D deep
learning. RASF is implemented with a learnable 3D grid with multiple channels
to store local geometry. Based on RASF, shape embeddings for various 3D shape
representations (point clouds, meshes and voxels) are retrieved by coordinate
indexing. While there are multiple ways to optimize the learnable parameters of
RASF, we provide two effective schemes among all in this paper for RASF
pre-training: shape reconstruction and normal estimation. Once trained, RASF
becomes a plug-and-play performance booster with negligible cost. Extensive
experiments on diverse 3D representation formats, networks and applications,
validate the universal effectiveness of the proposed RASF. Code and pre-trained
models are publicly available https://github.com/seanywang0408/RASF


title:
Spectroscopic and Electric Properties of the TaO$^+$ Molecule Ion for
  the Search of New Physics: A Platform for Identification and State Control

abstract:
  The TaO$^+$ cation is an attractive molecular species to search for parity-
and time-reversal-violating interactions, in particular of hadronic origin. For
the spectroscopic detection and preparation of TaO$^+$ cation in a desired
state detailed knowledge of spectroscopic and electric properties in excited
states is essential information. In this work we present spectroscopy constants
for TaO$^+$ in the electronic ground and 29 excited states calculated with
relativistic configuration interaction theory. The equilibrium bond lengths
($R_\mathrm{e}$), harmonic vibrational frequencies ($\omega_\mathrm{e}$),
transition dipole moments (TDM), vertical excitation energies and static
molecular dipole moments (PDM) are summarized. We include a detailed
characterization of all electronic states in terms of their spinor occupations.
This work supports the realization of experiments using TaO$^+$ ions to search
for new physics beyond the standard model of elementary particles.


title: FaiRR: Faithful and Robust Deductive Reasoning over Natural Language

abstract:
  Transformers have been shown to be able to perform deductive reasoning on a
logical rulebase containing rules and statements written in natural language.
Recent works show that such models can also produce the reasoning steps (i.e.,
the proof graph) that emulate the model's logical reasoning process. Currently,
these black-box models generate both the proof graph and intermediate
inferences within the same model and thus may be unfaithful. In this work, we
frame the deductive logical reasoning task by defining three modular
components: rule selection, fact selection, and knowledge composition. The rule
and fact selection steps select the candidate rule and facts to be used and
then the knowledge composition combines them to generate new inferences. This
ensures model faithfulness by assured causal relation from the proof step to
the inference reasoning. To test our framework, we propose FaiRR (Faithful and
Robust Reasoner) where the above three components are independently modeled by
transformers. We observe that FaiRR is robust to novel language perturbations,
and is faster at inference than previous works on existing reasoning datasets.
Additionally, in contrast to black-box generative models, the errors made by
FaiRR are more interpretable due to the modular approach.


title:
Perturbation Analysis of Randomized SVD and its Applications to
  High-dimensional Statistics

abstract:
  Randomized singular value decomposition (RSVD) is a class of computationally
efficient algorithms for computing the truncated SVD of large data matrices.
Given a $n \times n$ symmetric matrix $\mathbf{M}$, the prototypical RSVD
algorithm outputs an approximation of the $k$ leading singular vectors of
$\mathbf{M}$ by computing the SVD of $\mathbf{M}^{g} \mathbf{G}$; here $g \geq
1$ is an integer and $\mathbf{G} \in \mathbb{R}^{n \times k}$ is a random
Gaussian sketching matrix. In this paper we study the statistical properties of
RSVD under a general "signal-plus-noise" framework, i.e., the observed matrix
$\hat{\mathbf{M}}$ is assumed to be an additive perturbation of some true but
unknown signal matrix $\mathbf{M}$. We first derive upper bounds for the
$\ell_2$ (spectral norm) and $\ell_{2\to\infty}$ (maximum row-wise $\ell_2$
norm) distances between the approximate singular vectors of $\hat{\mathbf{M}}$
and the true singular vectors of the signal matrix $\mathbf{M}$. These upper
bounds depend on the signal-to-noise ratio (SNR) and the number of power
iterations $g$. A phase transition phenomenon is observed in which a smaller
SNR requires larger values of $g$ to guarantee convergence of the $\ell_2$ and
$\ell_{2\to\infty}$ distances. We also show that the thresholds for $g$ where
these phase transitions occur are sharp whenever the noise matrices satisfy a
certain trace growth condition. Finally, we derive normal approximations for
the row-wise fluctuations of the approximate singular vectors and the entrywise
fluctuations of the approximate matrix. We illustrate our theoretical results
by deriving nearly-optimal performance guarantees for RSVD when applied to
three statistical inference problems, namely, community detection, matrix
completion, and principal component analysis with missing data.

categories: math.ST cs.NA math.NA stat.CO stat.ML stat.TH


title:
DiSECt: A Differentiable Simulator for Parameter Inference and Control
  in Robotic Cutting

abstract:
  Robotic cutting of soft materials is critical for applications such as food
processing, household automation, and surgical manipulation. As in other areas
of robotics, simulators can facilitate controller verification, policy
learning, and dataset generation. Moreover, differentiable simulators can
enable gradient-based optimization, which is invaluable for calibrating
simulation parameters and optimizing controllers. In this work, we present
DiSECt: the first differentiable simulator for cutting soft materials. The
simulator augments the finite element method (FEM) with a continuous contact
model based on signed distance fields (SDF), as well as a continuous damage
model that inserts springs on opposite sides of the cutting plane and allows
them to weaken until zero stiffness, enabling crack formation. Through various
experiments, we evaluate the performance of the simulator. We first show that
the simulator can be calibrated to match resultant forces and deformation
fields from a state-of-the-art commercial solver and real-world cutting
datasets, with generality across cutting velocities and object instances. We
then show that Bayesian inference can be performed efficiently by leveraging
the differentiability of the simulator, estimating posteriors over hundreds of
parameters in a fraction of the time of derivative-free methods. Next, we
illustrate that control parameters in the simulation can be optimized to
minimize cutting forces via lateral slicing motions. Finally, we conduct
experiments on a real robot arm equipped with a slicing knife to infer
simulation parameters from force measurements. By optimizing the slicing motion
of the knife, we show on fruit cutting scenarios that the average knife force
can be reduced by more than 40% compared to a vertical cutting motion. We
publish code and additional materials on our project website at
https://diff-cutting-sim.github.io.


title: Assessing Gender Bias in Predictive Algorithms using eXplainable AI

abstract:
  Predictive algorithms have a powerful potential to offer benefits in areas as
varied as medicine or education. However, these algorithms and the data they
use are built by humans, consequently, they can inherit the bias and prejudices
present in humans. The outcomes can systematically repeat errors that create
unfair results, which can even lead to situations of discrimination (e.g.
gender, social or racial). In order to illustrate how important is to count
with a diverse training dataset to avoid bias, we manipulate a well-known
facial expression recognition dataset to explore gender bias and discuss its
implications.


title:
Extreme points of the unit ball of $\mathcal{L}(X)_w^*$ and best
  approximation in $\mathcal{L}(X)_w$

abstract:
  We study the geometry of $\mathcal{L}(X)_w,$ the space of all bounded linear
operators on a Banach space $X,$ endowed with the numerical radius norm,
whenever the numerical radius defines a norm. We obtain the form of the extreme
points of the unit ball of the dual space of $\mathcal{L}(X)_w.$ Using this
structure, we explore Birkhoff-James orthogonality, best approximation and
deduce distance formula in $\mathcal{L}(X)_w.$ A special attention is given to
the case of operators satisfying a notion of smoothness. Finally, we obtain an
equivalence between Birkhoff-James orthogonality in $\mathcal{L}(X)_w$ and that
in $X.$


title: An approximation problem in the space of bounded operators

abstract:
  For Banach spaces $X,Y,$ we consider a distance problem in the space of
bounded linear operators $\mathcal{L}(X,Y).$ Motivated by a recent paper
\cite{RAO21}, we obtain sufficient conditions so that for a compact operator
$T\in\mathcal{L}(X,Y)$ and a closed subspace $Z\subset Y,$ the following
equation holds, which relates global approximation with local approximation:
  \[d(T,\mathcal{L}(X,Z))=\sup\{d(Tx,Z):x\in X,\|x\|=1\}.\]
  In some cases, we show that the supremum is attained at an extreme point of
the corresponding unit ball. Furthermore, we obtain some situations when the
following equivalence holds:
  $$T\perp_B \mathcal{L}(X,Z)\Leftrightarrow T^{**}x_0^{**}\perp_B
Z^{\perp\perp}\Leftrightarrow
T^{**}\perp_B\mathcal{L}(X^{**},Z^{\perp\perp}),$$ for some $x_0^{**}\in
X^{**}$ satisfying $\|T^{**}x_0^{**}\|=\|T^{**}\|\|x_0^{**}\|,$ where $Z^\perp$
is the annihilator of $Z.$ One such situation is when $Z$ is an $L^1-$predual
space and an $M-$ideal in $Y$ and $T$ is a multi-smooth operator of finite
order. Another such situation is when $X$ is an abstract $L_1-$space and $T$ is
a multi-smooth operator of finite order. Finally, as a consequence of the
results, we obtain a sufficient condition for proximinality of a subspace $Z$
in $Y.$


title:
Hybrid Active and Passive Sensing for SLAM in Wireless Communication
  Systems

abstract:
  Integrating sensing functions into future mobile equipment has become an
important trend. Realizing different types of sensing and achieving mutual
enhancement under the existing communication hardware architecture is a crucial
challenge in realizing the deep integration of sensing and communication. In
the 5G New Radio context, active sensing can be performed through uplink beam
sweeping on the user equipment (UE) side to observe the surrounding
environment. In addition, the UE can perform passive sensing through downlink
channel estimation to measure the multipath component (MPC) information. This
study is the first to develop a hybrid simultaneous localization and mapping
(SLAM) mechanism that combines active and passive sensing, in which mutual
enhancement between the two sensing modes is realized in communication systems.
Specifically, we first establish a common feature associated with the
reflective surface to bridge active and passive sensing, thus enabling
information fusion. Based on the common feature, we can attain physical anchor
initialization through MPC with the assistance of active sensing. Then, we
extend the classic probabilistic data association SLAM mechanism to achieve UE
localization and continuously refine the physical anchor and target reflections
through the subsequent passive sensing. Numerical results show that the
proposed hybrid active and passive sensing-based SLAM mechanism can work
successfully in tricky scenarios without any prior information on the floor
plan, anchors, or agents. Moreover, the proposed algorithm demonstrates
significant performance gains compared with active or passive sensing only
mechanisms.


title: Truncated estimation for varying-coefficient functional linear model

abstract:
  Varying-coefficient functional linear models consider the relationship
between a response and a predictor, where the response depends not only the
predictor but also an exogenous variable. It then accounts for the relation of
the predictors and the response varying with the exogenous variable. We
consider the method for truncated estimation for varying-coefficient models.
The proposed method can clarify to what time point the functional predictor
relates to the response at any value of the exogenous variable by investigating
the coefficient function. To obtain the truncated model, we apply the penalized
likelihood method with the sparsity inducing penalty. Simulation studies are
conducted to investigate the effectiveness of the proposed method. We also
report the application of the proposed method to the analysis of crop yield
data to investigate when an environmental factor relates to the crop yield at
any season.


title:
A proposed test of quantum mechanics with three connected atomic clock
  transitions

abstract:
  We consider possible extensions to quantum mechanics proposed by Steven
Weinberg, and re-analyze his prediction of a new test based upon three atomic
clocks in the same atom. We propose two realistic experimental systems where
this hypothesis can be tested. One system already sets limits on any deviations
from quantum mechanics, while the other system would be able to search for new
physics at the limit of sensitivity of the best atomic clocks.


title:
Wavenumber-explicit parametric holomorphy of Helmholtz solutions in the
  context of uncertainty quantification

abstract:
  A crucial role in the theory of uncertainty quantification (UQ) of PDEs is
played by the regularity of the solution with respect to the stochastic
parameters; indeed, a key property one seeks to establish is that the solution
is holomorphic with respect to (the complex extensions of) the parameters. In
the context of UQ for the high-frequency Helmholtz equation, a natural question
is therefore: how does this parametric holomorphy depend on the wavenumber $k$?
  The recent paper [Ganesh, Kuo, Sloan 2021] showed for a particular
nontrapping variable-coefficient Helmholtz problem with affine dependence of
the coefficients on the stochastic parameters that the solution operator can be
analytically continued a distance $\sim k^{-1}$ into the complex plane.
  In this paper, we generalise the result in [Ganesh, Kuo, Sloan 2021] about
$k$-explicit parametric holomorphy to a much wider class of Helmholtz problems
with arbitrary (holomorphic) dependence on the stochastic parameters; we show
that in all cases the region of parametric holomorphy decreases with $k$, and
show how the rate of decrease with $k$ is dictated by whether the unperturbed
Helmholtz problem is trapping or nontrapping. We then give examples of both
trapping and nontrapping problems where these bounds on the rate of decrease
with $k$ of the region of parametric holomorphy are sharp, with the trapping
examples coming from the recent results of [Galkowski, Marchand, Spence 2021].
  An immediate implication of these results is that the $k$-dependent
restrictions imposed on the randomness in the analysis of quasi-Monte Carlo
(QMC) methods in [Ganesh, Kuo, Sloan 2021] arise from a genuine feature of the
Helmholtz equation with $k$ large (and not, for example, a suboptimal bound).


title: On maximal extensions of nilpotent Lie algebras

abstract:
  The maximum extensions of finite-dimensional nilpotent Lie algebras are
considered. In particular, it is proved that in the general case such an
extension is not unique, which refutes one L. Snoble's assumption.


title:
Scaling of finite size effect of $\alpha$-R\'enyi entropy in disjointed
  intervals under dilation

abstract:
  The $\alpha$-R\'enyi entropy in the gapless models have been obtained by the
conformal field theory, which is exact in the thermodynamic limit. However, the
calculation of its finite size effect (FSE) is challenging. So far only the FSE
in a single interval in the XX model has been understood and the FSE in the
other models and in the other conditions are totally unknown. Here we report
the FSE of this entropy in disjointed intervals $A = \cup_i A_i$ under a
uniform dilation $\lambda A$ in the XY model, showing of a universal scaling
law as
  \begin{equation*}
  \Delta_{\lambda A}^\alpha = \Delta_A^\alpha \lambda^{-\eta} \mathcal{B}(A,
\lambda),
  \end{equation*}
  where $|\mathcal{B}(A, \lambda)| \le 1$ is a bounded function and $\eta =
\text{min}(2, 2/\alpha)$ when $\alpha < 10$. We verify this relation in the
phase boundaries of the XY model, in which the different central charges
correspond to the physics of free Fermion and free Boson models. We find that
in the disjointed intervals, two FSEs, termed as extrinsic FSE and intrinsic
FSE, are required to fully account for the FSE of the entropy. Physically, we
find that only the edge modes of the correlation matrix localized at the open
ends $\partial A$ have contribution to the total entropy and its FSE. Our
results provide some incisive insight into the entanglement entropy in the
many-body systems.


title:
Crystalline Electronic Field in Rare-Earth Based Quasicrystal and
  Approximant: Analysis of Quantum Critical Au-Al-Yb Quasicrystal and
  Approximant

abstract:
  On the basis of the point charge model, we formulate the crystalline
electronic field (CEF) Hamiltonian $H_{\rm CEF}$ in the rare-earth based
quasicrystal (QC) and approximant crystal (AC) with ligand ions located at
pseudo 5-fold configurations by using the operator equivalent method. By
setting the total angular momentum $J=7/2$, the CEF in the quantum critical QC
Au$_{51}$Al$_{34}$Yb$_{15}$ and the 1/1 AC Au$_{51}$Al$_{35}$Yb$_{14}$ is
analyzed with consideration for the effect of Al/Au mixed sites. We find that
the ratio of the valences of ligand ions $x=Z_{\rm Al}/Z_{\rm Au}$ plays an
important role in characterizing the CEF ground state. As $x$ decreases from
$x=3$, the 4f wave function of the CEF ground state with the flat shape lying
in the mirror plane is deformed around $x\approx 0.8$ to the flat shape
perpendicular to the pseudo 5-fold axis at $x=0$. The formulated $H_{\rm CEF}$
by $J$ is generally applicable to rare-earth-based QCs and ACs, which is useful
to analyze the CEF.


title:
Exploiting Cross Domain Acoustic-to-articulatory Inverted Features For
  Disordered Speech Recognition

abstract:
  Articulatory features are inherently invariant to acoustic signal distortion
and have been successfully incorporated into automatic speech recognition (ASR)
systems for normal speech. Their practical application to disordered speech
recognition is often limited by the difficulty in collecting such specialist
data from impaired speakers. This paper presents a cross-domain
acoustic-to-articulatory (A2A) inversion approach that utilizes the parallel
acoustic-articulatory data of the 15-hour TORGO corpus in model training before
being cross-domain adapted to the 102.7-hour UASpeech corpus and to produce
articulatory features. Mixture density networks based neural A2A inversion
models were used. A cross-domain feature adaptation network was also used to
reduce the acoustic mismatch between the TORGO and UASpeech data. On both
tasks, incorporating the A2A generated articulatory features consistently
outperformed the baseline hybrid DNN/TDNN, CTC and Conformer based end-to-end
systems constructed using acoustic features only. The best multi-modal system
incorporating video modality and the cross-domain articulatory features as well
as data augmentation and learning hidden unit contributions (LHUC) speaker
adaptation produced the lowest published word error rate (WER) of 24.82% on the
16 dysarthric speakers of the benchmark UASpeech task.


title: Out of equilibrium charge transport in molecular electronic devices

abstract:
  Using the Lindblad equation approach, we study the nonequilibrium stationary
state of a benzene ring connected to two reservoirs in the large bias regime, a
prototype of a generic molecular electronic device. We show the emergence of an
optimal working point (corresponding to a change in the monotonicity of the
stationary current, as a function of the applied bias) and its robustness
against chemical potential and bond disorder.


title:
Epidemic Propagation under Evolutionary Behavioral Dynamics: Stability
  and Bifurcation Analysis

abstract:
  We consider the class of SIS epidemic models in which a large population of
individuals chooses whether to adopt protection or to remain unprotected as the
epidemic evolves. For a susceptible individual, adopting protection reduces the
probability of becoming infected but it comes with a cost that is weighed with
the instantaneous risk of becoming infected. An infected individual adopting
protection transmits a new infection with a smaller probability compared to an
unprotected infected individual. We focus on the replicator evolutionary
dynamics to model the evolution of protection decisions by susceptible and
infected subpopulations. We completely characterize the existence and local
stability of the equilibria of the resulting coupled epidemic and replicator
dynamics. We further show how the stability of different equilibrium points
gets exchanged as certain parameters change. Finally, we investigate the system
behavior under timescale separation between the epidemic and the evolutionary
dynamics.


title: Entanglement distribution in fermion model with long-range interaction

abstract:
  How two-party entanglement (TPE) is distributed in the many-body systems?
This is a fundamental issue because the total TPE between one party with all
the other parties, $\mathcal{C}^N$, is upper bounded by the Coffman, Kundu and
Wootters (CKW) monogamy inequality, from which $\mathcal{C}^N \le \sqrt{N-1}$
can be proved by the geometric inequality. Here we explore the total
entanglement $\mathcal{C}^\infty$ and the associated total tangle $\tau^\infty$
in a $p$-wave free fermion model with long-range interaction, showing that
$\mathcal{C}^\infty \sim \mathcal{O}(1)$ and $\tau^\infty$ may become vanishing
small with the increasing of long-range interaction. However, we always find
$\mathcal{C}^\infty \sim 2\xi \tau^\infty$, where $\xi$ is the truncation
length of entanglement, beyond which the TPE is quickly vanished, hence
$\tau^\infty \sim 1/\xi$.
  This relation is a direct consequence of the exponential decay of the TPE
induced by the long-range interaction. These results unify the results in the
Lipkin-Meshkov-Glick (LMG) model and Dicke model and generalize the Koashi,
Buzek and Imono bound to the quantum many-body models, with much broader
applicability.


title:
Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and
  Semi-Supervised Semantic Segmentation

abstract:
  Semantic segmentation with limited annotations, such as weakly supervised
semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS),
is a challenging task that has attracted much attention recently. Most leading
WSSS methods employ a sophisticated multi-stage training strategy to estimate
pseudo-labels as precise as possible, but they suffer from high model
complexity. In contrast, there exists another research line that trains a
single network with image-level labels in one training cycle. However, such a
single-stage strategy often performs poorly because of the compounding effect
caused by inaccurate pseudo-label estimation. To address this issue, this paper
presents a Self-supervised Low-Rank Network (SLRNet) for single-stage WSSS and
SSSS. The SLRNet uses cross-view self-supervision, that is, it simultaneously
predicts several complementary attentive LR representations from different
views of an image to learn precise pseudo-labels. Specifically, we reformulate
the LR representation learning as a collective matrix factorization problem and
optimize it jointly with the network learning in an end-to-end manner. The
resulting LR representation deprecates noisy information while capturing stable
semantics across different views, making it robust to the input variations,
thereby reducing overfitting to self-supervision errors. The SLRNet can provide
a unified single-stage framework for various label-efficient semantic
segmentation settings: 1) WSSS with image-level labeled data, 2) SSSS with a
few pixel-level labeled data, and 3) SSSS with a few pixel-level labeled data
and many image-level labeled data. Extensive experiments on the Pascal VOC
2012, COCO, and L2ID datasets demonstrate that our SLRNet outperforms both
state-of-the-art WSSS and SSSS methods with a variety of different settings,
proving its good generalizability and efficacy.


title: Sub-additive unstable topological pressure on saturated subsets

abstract:
  In this paper, we continue our investigation on sub-additive pressures for
$C^1$-smooth partially hyperbolic diffeomorphisms. Under the assumption of
unstable almost product property, we show that the unstable Bowen topological
pressure on the saturated set of a given non-empty compact connected set of
invariant measures equals the infimum of the summation of unstable metric
entropy and the corresponding \emph{Lyapunov exponent}, where the infimum is
taken over all invariant measures inside the compact connected set above.
Moreover, we also show that the unstable topological capacity pressure on the
saturated sets above coincide with the unstable topological pressure of the
whole system.


title:
Meta-Weight Graph Neural Network: Push the Limits Beyond Global
  Homophily

abstract:
  Graph Neural Networks (GNNs) show strong expressive power on graph data
mining, by aggregating information from neighbors and using the integrated
representation in the downstream tasks. The same aggregation methods and
parameters for each node in a graph are used to enable the GNNs to utilize the
homophily relational data. However, not all graphs are homophilic, even in the
same graph, the distributions may vary significantly. Using the same
convolution over all nodes may lead to the ignorance of various graph patterns.
Furthermore, many existing GNNs integrate node features and structure
identically, which ignores the distributions of nodes and further limits the
expressive power of GNNs. To solve these problems, we propose Meta Weight Graph
Neural Network (MWGNN) to adaptively construct graph convolution layers for
different nodes. First, we model the Node Local Distribution (NLD) from node
feature, topological structure and positional identity aspects with the
Meta-Weight. Then, based on the Meta-Weight, we generate the adaptive graph
convolutions to perform a node-specific weighted aggregation and boost the node
representations. Finally, we design extensive experiments on real-world and
synthetic benchmarks to evaluate the effectiveness of MWGNN. These experiments
show the excellent expressive power of MWGNN in dealing with graph data with
various distributions.


title:
Min-Max Latency Optimization Based on Sensed Position State Information
  in Internet of Vehicles

abstract:
  The dual-function radar communication (DFRC) is an essential technology in
Internet of Vehicles (IoV). Consider that the road-side unit (RSU) employs the
DFRC signals to sense the vehicles' position state information (PSI), and
communicates with the vehicles based on PSI. The objective of this paper is to
minimize the maximum communication delay among all vehicles by considering the
estimation accuracy constraint of the vehicles' PSI and the transmit power
constraint of RSU. By leveraging convex optimization theory, two iterative
power allocation algorithms are proposed with different complexities and
applicable scenarios. Simulation results indicate that the proposed power
allocation algorithm converges and can significantly reduce the maximum
transmit delay among vehicles compared with other schemes.


title: Clickbait Spoiling via Question Answering and Passage Retrieval

abstract:
  We introduce and study the task of clickbait spoiling: generating a short
text that satisfies the curiosity induced by a clickbait post. Clickbait links
to a web page and advertises its contents by arousing curiosity instead of
providing an informative summary. Our contributions are approaches to classify
the type of spoiler needed (i.e., a phrase or a passage), and to generate
appropriate spoilers. A large-scale evaluation and error analysis on a new
corpus of 5,000 manually spoiled clickbait posts -- the Webis Clickbait
Spoiling Corpus 2022 -- shows that our spoiler type classifier achieves an
accuracy of 80%, while the question answering model DeBERTa-large outperforms
all others in generating spoilers for both types.


title: Indirect Control of the $\rm {}^{29}SiV^{-}$ Nuclear Spin in Diamond

abstract:
  Coherent control and optical readout of the electron spin of the
$^{29}$SiV$^{-}$ center in diamond has been demonstrated in literature, with
exciting prospects for implementations as memory nodes and spin qubits. Nuclear
spins may be even better suited for many applications in quantum information
processing due to their long coherence times. Control of the $^{29}$SiV$^{-}$
nuclear spin using conventional NMR techniques is feasible, albeit at slow
kilohertz rates due to the nuclear spin's low gyromagnetic ratio. In this work
we theoretically demonstrate how indirect control using the electron spin-orbit
effect can be employed for high-speed, megahertz control of the $^{29}$Si
nuclear spin. We discuss the impact of the nuclear spin precession frequency on
gate times and the exciting possibility of all optical nuclear spin control.


title:
On the coexistence of divergence and convergence phenomena for the
  Fourier-Haar series for non-negative functions

abstract:
  Let $\{H_{n,m}\}_{n,m\in \mathbb{N}}$ be the two dimensional Haar system and
$S_{n,m}f$ be the rectangular partial sums of its Fourier series with respect
to some $f\in L^1([0,1)^2)$. Let $\mathcal{N}, \mathcal{M}\subset \mathbb{N}$
be two disjoint subsets of indices. We give a necessary and sufficient
condition on the sets $\mathcal{N}, \mathcal{M}$ so that for some $f \in
L^1([0,1)^2)$, $f \geq 0$ one has for almost every $z\in [0,1)^2$ that $$
\lim_{n,m \rightarrow \infty;n,m \in \mathcal{N}}S_{n,m}f(z)=f(z)\quad \text{
and }\quad \limsup_{n,m \rightarrow \infty;n,m \in
\mathcal{M}}|S_{n,m}f(z)|=\infty. $$ The proof uses some constructions from the
theory of low-discrepancy sequences such as the van der Corput sequence and an
associated tiling of the plane. This extends some earlier results.


title:
An Efficient Approach to Move Elements in a Distributed Geo-Replicated
  Tree

abstract:
  Replicated tree data structures are extensively used in collaborative
applications and distributed file systems, where clients often perform move
operations. Local move operations at different replicas may be safe. However,
remote move operations may not be safe. When clients perform arbitrary move
operations concurrently on different replicas, it could result in various bugs,
making this operation challenging to implement. Previous work has revealed bugs
such as data duplication and cycling in replicated trees. In this paper, we
present an efficient algorithm to perform move operations on the distributed
replicated tree while ensuring eventual consistency. The proposed technique is
primarily concerned with resolving conflicts efficiently, requires no
interaction between replicas, and works well with network partitions. We use
the last write win semantics for conflict resolution based on globally unique
timestamps of operations. The proposed solution requires only one compensation
operation to avoid cycles being formed when move operations are applied. The
proposed approach achieves an effective speedup of 68.19$\times$ over the
state-of-the-art approach in a geo-replicated setting on Microsoft Azure
standard instances at three different continents.


title:
Multi-channel CNN to classify nepali covid-19 related tweets using
  hybrid features

abstract:
  Because of the current COVID-19 pandemic with its increasing fears among
people, it has triggered several health complications such as depression and
anxiety. Such complications have not only affected the developed countries but
also developing countries such as Nepal. These complications can be understood
from peoples' tweets/comments posted online after their proper analysis and
sentiment classification. Nevertheless, owing to the limited number of
tokens/words in each tweet, it is always crucial to capture multiple
information associated with them for their better understanding. In this study,
we, first, represent each tweet by combining both syntactic and semantic
information, called hybrid features. The syntactic information is generated
from the bag of words method, whereas the semantic information is generated
from the combination of the fastText-based (ft) and domain-specific (ds)
methods. Second, we design a novel multi-channel convolutional neural network
(MCNN), which ensembles the multiple CNNs, to capture multi-scale information
for better classification. Last, we evaluate the efficacy of both the proposed
feature extraction method and the MCNN model classifying tweets into three
sentiment classes (positive, neutral and negative) on NepCOV19Tweets dataset,
which is the only public COVID-19 tweets dataset in Nepali language. The
evaluation results show that the proposed hybrid features outperform individual
feature extraction methods with the highest classification accuracy of 69.7%
and the MCNN model outperforms the existing methods with the highest
classification accuracy of 71.3% during classification.


title: Computing Galois groups of Ehrhart polynomials in OSCAR

abstract:
  We report on an implementation of Galois groups in the new computer algebra
system OSCAR. As an application we compute Galois groups of Ehrhart polynomials
of lattice polytope


title:
Numerical Study on Droplet Evaporation and Propagation Stability in
  Normal-temperature Two-phase Rotating Detonation System

abstract:
  A numerical study is carried out on the droplet-laden two-phase rotating
detonation wave (RDW) of kerosene/oxygen-enriched air at normal temperature.
Two types of combustors without and with the inlet mixing section (IMS) are
constructed to illustrate the effect of IMS on the combustion characteristics
of two-phase RDW. The important role of the preheating zone in the IMS after
the back-propagation shock on the droplet evaporation is analyzed. The
parameter sensitivity of RDW propagation stability to the average droplet
diameter d0 is further discussed. Results show that the droplets mainly
evaporate after the detonation front in the combustor without IMS, and the
reaction heat release is completed in a short distance, which propels
continuous propagation of the detonation wave. When d0 gradually increases, the
droplet evaporation distance increases, and the coupling between the incident
shock and reaction is continuously weakened, finally resulting in the
detonation quenching. In the combustor with IMS, a preheating zone is induced
close to the contact surface by the back-propagation shock of the RDW. A large
number of droplets evaporate in this zone, and generate sufficient mixture of
fuel vapor and oxidizer in front of detonation wave to maintain the detonation
propagation. Priority to the combustor without IMS, the droplet evaporation
relies less on the inlet high-temperature airflow with the assistance of
preheating zone, and thus the wave propagation stability can be enhanced and
the RDW can sustain for a wider range of d0. The present analysis provides a
new understanding of two-phase rotating detonation systems.


title: METL: a modern ETL pipeline with a dynamic mapping matrix

abstract:
  Modern ETL streaming pipelines extract data from various sources and forward
it to multiple consumers, such as data warehouses (DW) and analytical systems
that leverage machine learning (ML). However, the increasing number of systems
that are connected to such pipelines requires new solutions for data
integration. The canonical (or common) data model (CDM) offers such an
integration. It is particular useful for integrating microservice systems into
ETL pipelines. (Villaca et al 2020, Oliveira et al 2019) However, a mapping to
a CDM is complex. (Lemcke et al 2012) There are three complexity problems,
namely the size of the required mapping matrix, the automation of updates of
the matrix in response to changes in the extraction sources and the time
efficiency of the mapping. In this paper, we present a new solution for these
problems. More precisely, we present a new dynamic mapping matrix (DMM), which
is based on permutation matrices that are obtained by block-partitioning the
full mapping matrix. We show that the DMM can be used for automated updates in
response to schema changes, for parallel computation in near real-time and for
highly efficient compacting. For the solution, we draw on research into matrix
partitioning (Quinn 2004) and dynamic networks (Haase et al 2021). The DMM has
been implemented into an app called Message ETL (METL). METL is the key part of
a new ETL streaming pipeline at EOS that conducts the transformation to a CDM.
The ETL pipeline is based on Kafka-streams. It extracts data from more than 80
microservices with log-based Change Data Capture (CDC) with Debezium and loads
the data to a DW and an ML platform. EOS is part of the Otto-Group, the
second-largest e-commerce provider in Europe.


title: Adversarial Defense via Image Denoising with Chaotic Encryption

abstract:
  In the literature on adversarial examples, white box and black box attacks
have received the most attention. The adversary is assumed to have either full
(white) or no (black) access to the defender's model. In this work, we focus on
the equally practical gray box setting, assuming an attacker has partial
information. We propose a novel defense that assumes everything but a private
key will be made available to the attacker. Our framework uses an image
denoising procedure coupled with encryption via a discretized Baker map.
Extensive testing against adversarial images (e.g. FGSM, PGD) crafted using
various gradients shows that our defense achieves significantly better results
on CIFAR-10 and CIFAR-100 than the state-of-the-art gray box defenses in both
natural and adversarial accuracy.


title:
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame
  Interpolation

abstract:
  For video frame interpolation (VFI), existing deep-learning-based approaches
strongly rely on the ground-truth (GT) intermediate frames, which sometimes
ignore the non-unique nature of motion judging from the given adjacent frames.
As a result, these methods tend to produce averaged solutions that are not
clear enough. To alleviate this issue, we propose to relax the requirement of
reconstructing an intermediate frame as close to the GT as possible. Towards
this end, we develop a texture consistency loss (TCL) upon the assumption that
the interpolated content should maintain similar structures with their
counterparts in the given frames. Predictions satisfying this constraint are
encouraged, though they may differ from the pre-defined GT. Without the bells
and whistles, our plug-and-play TCL is capable of improving the performance of
existing VFI frameworks. On the other hand, previous methods usually adopt the
cost volume or correlation map to achieve more accurate image/feature warping.
However, the O(N^2) ({N refers to the pixel count}) computational complexity
makes it infeasible for high-resolution cases. In this work, we design a
simple, efficient (O(N)) yet powerful cross-scale pyramid alignment (CSPA)
module, where multi-scale information is highly exploited. Extensive
experiments justify the efficiency and effectiveness of the proposed strategy.


title: Quantum Neural Networks -- Computational Field Theory and Dynamics

abstract:
  To address Quantum Artificial Neural Networks as quantum dynamical computing
systems, a formalization of quantum artificial neural networks as dynamical
systems is developed, expanding the concept of unitary map to the neural
computation setting and introducing a quantum computing field theory on the
network. The formalism is illustrated in a simulation of a quantum recurrent
neural network and the resulting field dynamics is researched upon, showing
emergent neural waves with excitation and relaxation cycles at the level of the
quantum neural activity field, as well as edge of chaos signatures, with the
local neurons operating as far-from-equilibrium open quantum systems,
exhibiting entropy fluctuations with complex dynamics including complex
quasiperiodic patterns and power law signatures. The implications for quantum
computer science, quantum complexity research, quantum technologies and
neuroscience are also addressed.


title: A note on the Canonical Ramsey Theorem and Ramsey ultrafilters

abstract:
  We give a characterizations of Ramsey ultrafilters on $\mathscr P(\omega)$ in
terms of functions $f:\omega^n\to\omega$ and their ultrafilter extensions. To
do this, we prove that for any partition $\mathcal P$ of $[\omega]^n$ there is
a finite partition~$\mathcal Q$ of $[\omega]^{2n}$ such that any set
$X\subseteq \omega$ that is homogeneous for $\mathcal Q$ is a finite union of
sets that are canonical for $\mathcal P$.


title:
From meaning to perception -- exploring the space between word and odor
  perception embeddings

abstract:
  In this paper we propose the use of the Word2vec algorithm in order to obtain
odor perception embeddings (or smell embeddings), only using publicly available
perfume descriptions. Besides showing meaningful similarity relationships among
each other, these embeddings also demonstrate to possess some shared
information with their respective word embeddings. The meaningfulness of these
embeddings suggests that aesthetics might provide enough constraints for using
algorithms motivated by distributional semantics on non-randomly combined data.
Furthermore, they provide possibilities for new ways of classifying odors and
analyzing perfumes. We have also employed the embeddings in an attempt to
understand the aesthetic nature of perfumes, based on the difference between
real and randomly generated perfumes. In an additional tentative experiment we
explore the possibility of a mapping between the word embedding space and the
odor perception embedding space by fitting a regressor on the shared vocabulary
and then predict the odor perception embeddings of words without an a priori
associated smell, such as night or sky.


title:
Optical transitions, exciton radiative decay, and valley coherence in
  lead chalcogenide quantum dots

abstract:
  We propose the concept of valley coherence and superradiance in the
reciprocal space and show that it leads to an $N$-fold decrease of the bright
exciton radiative lifetime in quantum dots (QDs) of an $N$-valley
semiconductor. Next we explain why, despite this, the exciton radiative
lifetimes in PbX (X = S, Se, Te) QDs, measured from the photoluminescence
decay, are in the microsecond range. We also address peculiarities of the
light-matter interaction in nanostructures made of narrow-gap materials with
strong inter-band coupling.


title: Soft Pomeron in the Colour Glass Condensate approach

abstract:
  In this paper we suggest a new approach to the structure of the soft Pomeron:
based on the $t$-channel unitarity, we expressed the exchange of the soft
Pomeron through the interaction of the dipole of small size of the order of
$1/Q_s(Y)$ ($Q_s(Y)$ is the saturation momentum) with the hadrons. Therefore,
it is shown that the typical distances in soft processes are small $r \sim
1/Q_s\Lb \h Y \Rb $, where $Y \,=\,ln s$. The saturation momentum, which
determines the energy dependence of the scattering amplitude is proportional to
$ Q^2_s\Lb \h Y \Rb \propto\,\exp\Lb\h \lambda\,Y\Rb$, with $\lambda
\approx\,0.2$, and this behaviour is in perfect agreement with phenomenological
Donnachie-Landshoff Pomeron. We demonstrate that the saturation models could
describe the experimental data for $\sigma_{tot},\sigma_{el},\sigma_{diff} $
and $B_{el}$. Hence our approach is a good first approximation to start
discussion of the soft processes in CGC approach on the solid theoretical
basis.


title: Incremental Few-Shot Learning via Implanting and Compressing

abstract:
  This work focuses on tackling the challenging but realistic visual task of
Incremental Few-Shot Learning (IFSL), which requires a model to continually
learn novel classes from only a few examples while not forgetting the base
classes on which it was pre-trained. Our study reveals that the challenges of
IFSL lie in both inter-class separation and novel-class representation. Dur to
intra-class variation, a novel class may implicitly leverage the knowledge from
multiple base classes to construct its feature representation. Hence, simply
reusing the pre-trained embedding space could lead to a scattered feature
distribution and result in category confusion. To address such issues, we
propose a two-step learning strategy referred to as \textbf{Im}planting and
\textbf{Co}mpressing (\textbf{IMCO}), which optimizes both feature space
partition and novel class reconstruction in a systematic manner. Specifically,
in the \textbf{Implanting} step, we propose to mimic the data distribution of
novel classes with the assistance of data-abundant base set, so that a model
could learn semantically-rich features that are beneficial for discriminating
between the base and other unseen classes. In the \textbf{Compressing} step, we
adapt the feature extractor to precisely represent each novel class for
enhancing intra-class compactness, together with a regularized parameter
updating rule for preventing aggressive model updating. Finally, we demonstrate
that IMCO outperforms competing baselines with a significant margin, both in
image classification task and more challenging object detection task.


title:
Multi-Wideband Terahertz Communications via Tunable Graphene-based
  Metasurfaces in 6G Networks

abstract:
  The next generation of wireless networks is expected to tap into the
terahertz (0.1--10 THz) band to satisfy the extreme latency and bandwidth
density requirements of future applications. However, the development of
systems in this band is challenging as THz waves confront severe spreading and
penetration losses, as well as molecular absorption, which leads to strong
line-of-sight requirements through highly directive antennas. Recently,
reconfigurable intelligent surfaces (RISs) have been proposed to address issues
derived from non-line-of-sight propagation, among other impairments, by
redirecting the incident wave toward the receiver and implementing
virtual-line-of-sight communications. However, the benefits provided by a RIS
may be lost if the network operates at multiple bands. In this position paper,
the suitability of the RIS paradigm in indoor THz scenarios for 6G is assessed
grounded on the analysis of a tunable graphene-based RIS that can operate in
multiple wideband transparency windows. A possible implementation of such a RIS
is provided and numerically evaluated at 0.65/0.85/1.05 THz separately,
demonstrating that beam steering and other relevant functionalities are
realizable with excellent performance. Finally, the challenges associated with
the design and fabrication of multi-wideband graphene-based RISs are discussed,
paving the way to the concurrent control of multiple THz bands in the context
of 6G networks.


title:
Neural Machine Translation with Phrase-Level Universal Visual
  Representations

abstract:
  Multimodal machine translation (MMT) aims to improve neural machine
translation (NMT) with additional visual information, but most existing MMT
methods require paired input of source sentence and image, which makes them
suffer from shortage of sentence-image pairs. In this paper, we propose a
phrase-level retrieval-based method for MMT to get visual information for the
source input from existing sentence-image data sets so that MMT can break the
limitation of paired sentence-image input. Our method performs retrieval at the
phrase level and hence learns visual information from pairs of source phrase
and grounded region, which can mitigate data sparsity. Furthermore, our method
employs the conditional variational auto-encoder to learn visual
representations which can filter redundant visual information and only retain
visual information related to the phrase. Experiments show that the proposed
method significantly outperforms strong baselines on multiple MMT datasets,
especially when the textual context is limited.


title:
Analyzing speaker verification embedding extractors and back-ends under
  language and channel mismatch

abstract:
  In this paper, we analyze the behavior and performance of speaker embeddings
and the back-end scoring model under domain and language mismatch. We present
our findings regarding ResNet-based speaker embedding architectures and show
that reduced temporal stride yields improved performance. We then consider a
PLDA back-end and show how a combination of small speaker subspace,
language-dependent PLDA mixture, and nuisance-attribute projection can have a
drastic impact on the performance of the system. Besides, we present an
efficient way of scoring and fusing class posterior logit vectors recently
shown to perform well for speaker verification task. The experiments are
performed using the NIST SRE 2021 setup.


title:
Exploring the impact of spatiotemporal granularity on the demand
  prediction of dynamic ride-hailing

abstract:
  Dynamic demand prediction is a key issue in ride-hailing dispatching. Many
methods have been developed to improve the demand prediction accuracy of an
increase in demand-responsive, ride-hailing transport services. However, the
uncertainties in predicting ride-hailing demands due to multiscale
spatiotemporal granularity, as well as the resulting statistical errors, are
seldom explored. This paper attempts to fill this gap and to examine the
spatiotemporal granularity effects on ride-hailing demand prediction accuracy
by using empirical data for Chengdu, China. A convolutional, long short-term
memory model combined with a hexagonal convolution operation (H-ConvLSTM) is
proposed to explore the complex spatial and temporal relations. Experimental
analysis results show that the proposed approach outperforms conventional
methods in terms of prediction accuracy. A comparison of 36 spatiotemporal
granularities with both departure demands and arrival demands shows that the
combination of a hexagonal spatial partition with an 800 m side length and a 30
min time interval achieves the best comprehensive prediction accuracy. However,
the departure demands and arrival demands reveal different variation trends in
the prediction errors for various spatiotemporal granularities.


title: When regression coefficients change over time: A proposal

abstract:
  A common approach in forecasting problems is to estimate a least-squares
regression (or other statistical learning models) from past data, which is then
applied to predict future outcomes. An underlying assumption is that the same
correlations that were observed in the past still hold for the future. We
propose a model for situations when this assumption is not met: adopting
methods from the state space literature, we model how regression coefficients
change over time. Our approach can shed light on the large uncertainties
associated with forecasting the future, and how much of this is due to changing
dynamics of the past. Our simulation study shows that accurate estimates are
obtained when the outcome is continuous, but the procedure fails for binary
outcomes.


title:
Multi-wavelength and Multi-CO View of The Minor Merger Driven Star
  Formation in the Nearby LIRG NGC 3110

abstract:
  We present Atacama Large Millimeter/submillimeter Array observations of
multiple CO(1-0), $^{13}$CO(1-0), and C$^{18}$O(1-0) lines and 2.9 mm and 1.3
mm continuum emission toward the nearby interacting luminous infrared galaxy
NGC 3110, supplemented with similar spatial resolution H$\alpha$, 1.4GHz
continuum, and $K$-band data. We estimate the typical CO-to-H$_2$ conversion
factor of 1.7 $M_{\odot}$ (K km s$^{-1}$ pc$^2$)$^{-1}$ within the disk using
LTE-based and dust-based H$_2$ column densities, and measure the 1-kpc scale
surface densities of star formation rate ($\Sigma_{\rm SFR}$), super star
clusters ($\Sigma_{\rm SSC}$), molecular gas mass, and star formation
efficiency (SFE) toward the entire gas disk. These parameters show a peak at
the southern part of the southern spiral arm (SFE $\sim$ 10$^{-8.2}$ yr$^{-1}$,
$\Sigma_{\rm SFR}$ $\sim$ 10$^{-0.6}$ $M_{\odot}$ kpc$^{-2}$ yr$^{-1}$,
$\Sigma_{\rm SSC}$ $\sim$ 6.0 kpc$^{-2}$), which is likely attributed to the
on-going tidal interaction with the companion galaxy MCG-01-26-013, as well as
toward the circumnuclear region. We also find that thermal free-free emission
contributes to a significant fraction of the millimeter continuum emission at
the southern peak position. Those measurements imply that the peak of the
southern arm is an active and young star-forming region, whereas the central
part of NGC 3110 is a site of long-continued star formation. We suggest that,
during the early stage of the galaxy-galaxy interaction with large mass ratio
that in NGC 3110, fragmentation along the main galaxy's arms is an important
driver of merger-induced star formation and massive gas inflow results in dusty
nuclear starbursts.


title: PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs

abstract:
  Optimization of directed acyclic graph (DAG) structures has many
applications, such as neural architecture search (NAS) and probabilistic
graphical model learning. Encoding DAGs into real vectors is a dominant
component in most neural-network-based DAG optimization frameworks. Currently,
most DAG encoders use an asynchronous message passing scheme which sequentially
processes nodes according to the dependency between nodes in a DAG. That is, a
node must not be processed until all its predecessors are processed. As a
result, they are inherently not parallelizable. In this work, we propose a
Parallelizable Attention-based Computation structure Encoder (PACE) that
processes nodes simultaneously and encodes DAGs in parallel. We demonstrate the
superiority of PACE through encoder-dependent optimization subroutines that
search the optimal DAG structure based on the learned DAG embeddings.
Experiments show that PACE not only improves the effectiveness over previous
sequential DAG encoders with a significantly boosted training and inference
speed, but also generates smooth latent (DAG encoding) spaces that are
beneficial to downstream optimization subroutines. Our source code is available
at \url{https://github.com/zehaodong/PACE}


title:
Keeping up with "The Joneses": reference dependent choice with social
  comparisons

abstract:
  Keeping up with "The Joneses" matters. This paper examines a model of
reference dependent choice where reference points are determined by social
comparisons. An increase in the strength of social comparisons, even by only a
few agents, increases consumption and decreases welfare for everyone.
Strikingly, a higher marginal cost of consumption can increase welfare. In a
labour market, social comparisons with co-workers create a big fish in a small
pond effect, inducing incomplete labour market sorting. Further, it is the
skilled workers with the weakest social networks who are induced to give up
income to become the big fish.


title:
Model-free Continuation of Periodic Orbits in Certain Nonlinear Systems
  Using Continuous-Time Adaptive Control

abstract:
  This paper generalizes recent results by the authors on noninvasive
model-reference adaptive control designs for control-based continuation of
periodic orbits in periodically excited linear systems with matched
uncertainties to a larger class of periodically excited nonlinear systems with
matched uncertainties and known structure. A candidate adaptive feedback design
is also proposed in the case of scalar problems with unmodeled nonlinearities.
In the former case, rigorous analysis shows guaranteed performance bounds for
the associated prediction and estimation errors. Together with an assumption of
persistent excitation, there follows asymptotic convergence to periodic
responses determined uniquely by an a priori unknown periodic reference input
and independent of initial conditions, as required by the control-based
continuation paradigm. In particular, when the reference input equals the
sought periodic response, the steady-state control input vanishes. Identical
conclusions follow for the case of scalar dynamics with unmodeled
nonlinearities, albeit with slow rates of convergence. Numerical simulations
validate the theoretical predictions for individual parameter values.
Integration with the software package COCO demonstrate successful continuation
along families of stable and unstable periodic orbits with a minimum of
parameter tuning. The results expand the envelope of known noninvasive feedback
strategies for use in experimental model validation and engineering design.


title:
Modelling nonlinear dependencies in the latent space of inverse
  scattering

abstract:
  The problem of inverse scattering proposed by Angles and Mallat in 2018,
concerns training a deep neural network to invert the scattering transform
applied to an image. After such a network is trained, it can be used as a
generative model given that we can sample from the distribution of principal
components of scattering coefficients. For this purpose, Angles and Mallat
simply use samples from independent Gaussians. However, as shown in this paper,
the distribution of interest can actually be very far from normal and
non-negligible dependencies might exist between different coefficients. This
motivates using models for this distribution that allow for non-linear
dependencies between variables. Within this paper, two such models are
explored, namely a Variational AutoEncoder and a Generative Adversarial
Network. We demonstrate the results obtained can be extremely realistic on some
datasets and look better than those produced by Angles and Mallat. The
conducted meta-analysis also shows a clear practical advantage of such
constructed generative models in terms of the efficiency of their training
process compared to existing generative models for images.


title: Thermodynamic selection: mechanisms and scenarios

abstract:
  Thermodynamic selection is an indirect competition between agents feeding on
the same energy resource and obeying the laws of thermodynamics. We examine
scenarios of this selection, where the agent is modeled as a heat-engine
coupled to two thermal baths and extracting work from the high-temperature
bath. The agents can apply different work-extracting, game-theoretical
strategies, e.g. the maximum power or the maximum efficiency. They can also
have a fixed structure or be adaptive. Depending on whether the resource (i.e.
the high-temperature bath) is infinite or finite, the fitness of the agent
relates to the work-power or the total extracted work. These two selection
scenarios lead to increasing or decreasing efficiencies of the work-extraction,
respectively. The scenarios are illustrated via plant competition for sunlight,
and the competition between different ATP production pathways. We also show
that certain general concepts of game-theory and ecology--the prisoner's
dilemma and the maximal power principle--emerge from the thermodynamics of
competing agents. We emphasize the role of adaptation in developing efficient
work-extraction mechanisms.


title:
Scalable, Highly Crystalline, 2D Semiconductor Atomic Layer Deposition
  Process for High Performance Electronic Applications

abstract:
  This work demonstrates a large area process for atomically thin 2D
semiconductors to unlock the technological upscale required for their
commercial uptake. The new atomic layer deposition (ALD) and conversion
technique yields large area performance uniformity and tunability. Like
graphene, 2D Transition Metal Dichalcogenides (TMDCs) are prone to upscaling
challenges limiting their commercial uptake. They are challenging to grow
uniformly on large substrates and to transfer on alternative substrates while
they often lack in large area electrical performance uniformity. The scalable
ALD process of this work enables uniform growth of 2D TMDCs on large area with
independent control of layer thickness, stoichiometry and crystallinity while
allowing chemical free transfers to application substrates. Field effect
transistors (FETs) fabricated on flexible substrates using the process present
a field effect mobility of up to 55 cm^2/Vs, subthreshold slope down to 80
mV/dec and on/off ratios of 10^7. Additionally, non-volatile memory transistors
using ferroelectric FETs (FeFETs) operating at +-5 V with on/off ratio of 107
and a memory window of 3.25 V are demonstrated. These FeFETs demonstrate
state-of-the-art performance with multiple state switching, suitable for
one-transistor non-volatile memory and for synaptic transistors revealing the
applicability of the process to flexible neuromorphic applications.


title: Homotopy type of the nilpotent orbits in classical Lie algebras

abstract:
  In the paper "The Second cohomology of nilpotent orbits in classical Lie
algebras, Kyoto J. Math. 60 (2020), no. 2, 717-799" by I. Biswas, P. Chatterjee
and C. Maity homotopy types of nilpotent orbits are explicitly described in the
case of real simple classical Lie algebras for which any maximal compact
subgroup in the associated adjoint group is not semisimple. In this paper we
extend the above description of homotopy type of nilpotent orbits to the
remaining cases of real simple classical Lie algebras for which any maximal
compact subgroup in the associated adjoint group is semisimple.


title: Duality of Topological Edge States in a Mechanical Kitaev Chain

abstract:
  We theoretically investigate and experimentally demonstrate the existence of
topological edge states in a mechanical analog of the Kitaev chain with a
non-zero chemical potential. Our system is a one-dimensional monomer system
involving two coupled degrees of freedom, i.e., transverse displacement and
rotation of elastic elements. Due to the particle-hole symmetry, a
topologically nontrivial bulk leads to the emergence of edge states in a finite
chain with fixed boundaries. In contrast, a topologically trivial bulk also
leads to the emergence of edge states in a finite chain, but with free
boundaries. We unravel a duality in our system that predicts the existence of
the latter edge states. This duality involves the iso-spectrality of a subspace
for finite chains, and as a consequence, a free chain with topologically
trivial bulk maps to a fixed chain with a nontrivial bulk. Lastly, we provide
the conditions under which the system can exhibit perfectly degenerate in-gap
modes, akin to Majorana zero modes. These findings suggest that mechanical
systems with fine-tuned degrees of freedom can be fertile testbeds for
exploring the intricacies of Majorana physics.


title: On fractional harmonic functions

abstract:
  Our concern in this paper is to study the qualitative properties for harmonic
functions related to the fractional Laplacian. Firstly we classify the
polynomials in the whole space and in the half space for the fractional
Laplacian defined in a principle value sense at infinity. Secondly, we study
the fractional harmonic functions in half space with singularities on the
boundary and the related distributional identities.


title:
Characterizations of smooth projective horospherical varieties of Picard
  number one

abstract:
  Let $X$ be a smooth projective horospherical variety of Picard number one, of
type $B_m$ for $m \geq 3$ or of type $F_4$. We show that any uniruled
projective manifold with Picard number one is biholomorphic to $X$ if the
variety of minimal rational tangents at a general point is projectively
equivalent to that of $X$.


title:
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from
  Point Clouds

abstract:
  Transformer has demonstrated promising performance in many 2D vision tasks.
However, it is cumbersome to compute the self-attention on large-scale point
cloud data because point cloud is a long sequence and unevenly distributed in
3D space. To solve this issue, existing methods usually compute self-attention
locally by grouping the points into clusters of the same size, or perform
convolutional self-attention on a discretized representation. However, the
former results in stochastic point dropout, while the latter typically has
narrow attention fields. In this paper, we propose a novel voxel-based
architecture, namely Voxel Set Transformer (VoxSeT), to detect 3D objects from
point clouds by means of set-to-set translation. VoxSeT is built upon a
voxel-based set attention (VSA) module, which reduces the self-attention in
each voxel by two cross-attentions and models features in a hidden space
induced by a group of latent codes. With the VSA module, VoxSeT can manage
voxelized point clusters with arbitrary size in a wide range, and process them
in parallel with linear complexity. The proposed VoxSeT integrates the high
performance of transformer with the efficiency of voxel-based model, which can
be used as a good alternative to the convolutional and point-based backbones.
VoxSeT reports competitive results on the KITTI and Waymo detection benchmarks.
The source codes can be found at \url{https://github.com/skyhehe123/VoxSeT}.


title:
Bridging Pre-trained Language Models and Hand-crafted Features for
  Unsupervised POS Tagging

abstract:
  In recent years, large-scale pre-trained language models (PLMs) have made
extraordinary progress in most NLP tasks. But, in the unsupervised POS tagging
task, works utilizing PLMs are few and fail to achieve state-of-the-art (SOTA)
performance. The recent SOTA performance is yielded by a Guassian HMM variant
proposed by He et al. (2018). However, as a generative model, HMM makes very
strong independence assumptions, making it very challenging to incorporate
contexualized word representations from PLMs. In this work, we for the first
time propose a neural conditional random field autoencoder (CRF-AE) model for
unsupervised POS tagging. The discriminative encoder of CRF-AE can
straightforwardly incorporate ELMo word representations. Moreover, inspired by
feature-rich HMM, we reintroduce hand-crafted features into the decoder of
CRF-AE. Finally, experiments clearly show that our model outperforms previous
state-of-the-art models by a large margin on Penn Treebank and multilingual
Universal Dependencies treebank v2.0.


title:
Learning to Reason Deductively: Math Word Problem Solving as Complex
  Relation Extraction

abstract:
  Solving math word problems requires deductive reasoning over the quantities
in the text. Various recent research efforts mostly relied on
sequence-to-sequence or sequence-to-tree models to generate mathematical
expressions without explicitly performing relational reasoning between
quantities in the given context. While empirically effective, such approaches
typically do not provide explanations for the generated expressions. In this
work, we view the task as a complex relation extraction problem, proposing a
novel approach that presents explainable deductive reasoning steps to
iteratively construct target expressions, where each step involves a primitive
operation over two quantities defining their relation. Through extensive
experiments on four benchmark datasets, we show that the proposed model
significantly outperforms existing strong baselines. We further demonstrate
that the deductive procedure not only presents more explainable steps but also
enables us to make more accurate predictions on questions that require more
complex reasoning.


title: Practical Recommendations for Replay-based Continual Learning Methods

abstract:
  Continual Learning requires the model to learn from a stream of dynamic,
non-stationary data without forgetting previous knowledge. Several approaches
have been developed in the literature to tackle the Continual Learning
challenge. Among them, Replay approaches have empirically proved to be the most
effective ones. Replay operates by saving some samples in memory which are then
used to rehearse knowledge during training in subsequent tasks. However, an
extensive comparison and deeper understanding of different replay
implementation subtleties is still missing in the literature. The aim of this
work is to compare and analyze existing replay-based strategies and provide
practical recommendations on developing efficient, effective and generally
applicable replay-based strategies. In particular, we investigate the role of
the memory size value, different weighting policies and discuss about the
impact of data augmentation, which allows reaching better performance with
lower memory sizes.


title:
Exact rate analysis for quantum repeaters with imperfect memories and
  entanglement swapping as soon as possible

abstract:
  We present an exact rate analysis for a secret key that can be shared among
two parties employing a linear quantum repeater chain. One of our main
motivations is to address the question whether simply placing quantum memories
along a quantum communication channel can be beneficial in a realistic setting.
The underlying model assumes deterministic entanglement swapping of single-spin
quantum memories and it excludes probabilistic entanglement distillation, and
thus two-way classical communication, on higher nesting levels. Within this
framework, we identify the essential properties of any optimal repeater scheme:
entanglement distribution in parallel, entanglement swapping as soon and
parallel quantum storage as little as possible. While these features are
obvious or trivial for the simplest repeater with one middle station, for more
stations they cannot always be combined. We propose an optimal scheme including
channel loss and memory dephasing, proving its optimality for the case of two
stations and conjecturing it for the general case. In an even more realistic
setting, we consider additional tools and parameters such as memory cut-offs,
multiplexing, initial state and swapping gate fidelities, and finite link
coupling efficiencies in order to identify potential regimes in memory-assisted
quantum key distribution beyond one middle station that exceed the rates of the
smallest quantum repeaters as well as those obtainable in all-optical schemes
unassisted by stationary memory qubits and two-way classical communication.


title: Constraint Dissolving Approaches for Riemannian Optimization

abstract:
  In this paper, we propose a class of constraint dissolving approaches for
optimization problems over closed Riemannian manifolds. In these proposed
approaches, solving a Riemannian optimization problem is transferred into the
unconstrained minimization of a constraint dissolving function named CDF.
Different from existing exact penalty functions, the exact gradient and Hessian
of CDF are easy to compute. We study the theoretical properties of CDF and
prove that the original problem and CDF have the same first-order and
second-order stationary points, local minimizers, and {\L}ojasiewicz exponents
in a neighborhood of the feasible region. Remarkably, the convergence
properties of our proposed constraint dissolving approaches can be directly
inherited from the existing rich results in unconstrained optimization.
Therefore, the proposed constraint dissolving approaches build up short cuts
from unconstrained optimization to Riemannian optimization. Several
illustrative examples further demonstrate the potential of our proposed
constraint dissolving approaches.


title:
Scalable fast benchmarking for individual quantum gates with local
  twirling

abstract:
  With the development of controllable quantum systems, fast and practical
characterization for multi-qubit gates is essential for building high-fidelity
quantum computing devices. The usual way to fulfill this requirement via
randomized benchmarking asks for complicated implementation of numerous
multi-qubit twirling gates. How to efficiently and reliably estimate the
fidelity of a quantum process remains an open problem. In this work, we propose
a character-cycle benchmarking protocol and a fast benchmarking protocol only
using local twirling gates to estimate the process fidelity of an individual
multi-qubit operation. Our protocols are able to characterize a large class of
quantum gates including and beyond the Clifford group via the local gauge
transformation, which forms a universal gate set for quantum computing. We
numerically demonstrate our protocols for a non-Clifford gate --
controlled-$(TX)$ and a Clifford gate -- five-qubit quantum error-correcting
encoding circuit. The numerical results show that our protocols can efficiently
and reliably characterize the gate process fidelities. Compared with the
cross-entropy benchmarking, the simulation results show that the fast
benchmarking achieves three orders of magnitude improvements in terms of
sampling complexity.


title: Sequence-to-Sequence Knowledge Graph Completion and Question Answering

abstract:
  Knowledge graph embedding (KGE) models represent each entity and relation of
a knowledge graph (KG) with low-dimensional embedding vectors. These methods
have recently been applied to KG link prediction and question answering over
incomplete KGs (KGQA). KGEs typically create an embedding for each entity in
the graph, which results in large model sizes on real-world graphs with
millions of entities. For downstream tasks these atomic entity representations
often need to be integrated into a multi stage pipeline, limiting their
utility. We show that an off-the-shelf encoder-decoder Transformer model can
serve as a scalable and versatile KGE model obtaining state-of-the-art results
for KG link prediction and incomplete KG question answering. We achieve this by
posing KG link prediction as a sequence-to-sequence task and exchange the
triple scoring approach taken by prior KGE methods with autoregressive
decoding. Such a simple but powerful method reduces the model size up to 98%
compared to conventional KGE models while keeping inference time tractable.
After finetuning this model on the task of KGQA over incomplete KGs, our
approach outperforms baselines on multiple large-scale datasets without
extensive hyperparameter tuning.


title:
Guidelines for data-driven approaches to study transitions in multiscale
  systems: the case of Lyapunov vectors

abstract:
  We study in detail the role of covariant Lyapunov vectors and their
respective angles for detecting transitions between metastable states in
dynamical systems, as recently discussed in several atmospheric science
applications. The underlying models are built from data by the dynamical
clustering method, called FEM-BV-VAR, and the Lyapunov vectors are approximated
based on these models. We test this data-based numerical approach at the hand
of three well-understood example systems with increasing dynamical complexity,
identifying crucial properties that allow for a successful application of the
method: in particular, it turns out that the method requires a clear multiple
time scale structure with fast transitions between slow subsystems which can be
dynamically characterized by invariant neutral directions of the linear
approximation model.


title:
Differential Private Discrete Noise Adding Mechanism: Conditions,
  Properties and Optimization

abstract:
  Differential privacy is a standard framework to quantify the privacy loss in
the data anonymization process. To preserve differential privacy, a random
noise adding mechanism is widely adopted, where the trade-off between data
privacy level and data utility is of great concern. The privacy and utility
properties for the continuous noise adding mechanism have been well studied.
However, the related works are insufficient for the discrete random mechanism
on discretely distributed data, e.g., traffic data, health records. This paper
focuses on the discrete random noise adding mechanisms. We study the basic
differential privacy conditions and properties for the general discrete random
mechanisms, as well as the trade-off between data privacy and data utility.
Specifically, we derive a sufficient and necessary condition for discrete
epsilon-differential privacy and a sufficient condition for discrete (epsilon,
delta)-differential privacy, with the numerical estimation of differential
privacy parameters. These conditions can be applied to analyze the differential
privacy properties for the discrete noise adding mechanisms with various kinds
of noises. Then, with the differential privacy guarantees, we propose an
optimal discrete epsilon-differential private noise adding mechanism under the
utility-maximization framework, where the utility is characterized by the
similarity of the statistical properties between the mechanism's input and
output. For this setup, we find that the class of the discrete noise
probability distributions in the optimal mechanism is Staircase-shaped.


title: DevOps Education: An Interview Study of Challenges and Recommendations

abstract:
  Over the last years, the software industry has adopted several DevOps
technologies related to practices such as continuous integration and continuous
delivery. The high demand for DevOps practitioners requires non-trivial
adjustments in traditional software engineering courses and educational
methodologies. This work presents an interview study with 14 DevOps educators
from different universities and countries, aiming to identify the main
challenges and recommendations for DevOps teaching. Our study identified 83
challenges, 185 recommendations, and several association links and conflicts
between them. Our findings can help educators plan, execute and evaluate DevOps
courses. They also highlight several opportunities for researchers to propose
new methods and tools for teaching DevOps.


title: Track Reconstruction in a High-Density Environment with ALICE

abstract:
  ALICE is the dedicated heavy-ion experiment at the CERN Large Hadron Collider
(LHC). Its main tracking and particle-identification detector is a large volume
Time Projection Chamber (TPC). The TPC has been designed to perform well in the
high-track density environment created in high-energy heavy-ion collisions. In
this proceeding, we describe the track reconstruction procedure in ALICE. In
particular, we focus on the two main challenges that were faced during the Run
2 data-taking period (2015--2018) of the LHC, which were the baseline
fluctuations and the local space charge distortions in the TPC. We present the
corresponding solutions in detail and describe the software tools that allowed
us to circumvent these challenges.


title:
Pretraining with Artificial Language: Studying Transferable Knowledge in
  Language Models

abstract:
  We investigate what kind of structural knowledge learned in neural network
encoders is transferable to processing natural language. We design artificial
languages with structural properties that mimic natural language, pretrain
encoders on the data, and see how much performance the encoder exhibits on
downstream tasks in natural language. Our experimental results show that
pretraining with an artificial language with a nesting dependency structure
provides some knowledge transferable to natural language. A follow-up probing
analysis indicates that its success in the transfer is related to the amount of
encoded contextual information and what is transferred is the knowledge of
position-aware context dependence of language. Our results provide insights
into how neural network encoders process human languages and the source of
cross-lingual transferability of recent multilingual language models.


title: Implicit Parameter-free Online Learning with Truncated Linear Models

abstract:
  Parameter-free algorithms are online learning algorithms that do not require
setting learning rates. They achieve optimal regret with respect to the
distance between the initial point and any competitor. Yet, parameter-free
algorithms do not take into account the geometry of the losses. Recently, in
the stochastic optimization literature, it has been proposed to instead use
truncated linear lower bounds, which produce better performance by more closely
modeling the losses. In particular, truncated linear models greatly reduce the
problem of overshooting the minimum of the loss function. Unfortunately,
truncated linear models cannot be used with parameter-free algorithms because
the updates become very expensive to compute. In this paper, we propose new
parameter-free algorithms that can take advantage of truncated linear models
through a new update that has an "implicit" flavor. Based on a novel
decomposition of the regret, the new update is efficient, requires only one
gradient at each step, never overshoots the minimum of the truncated model, and
retains the favorable parameter-free properties. We also conduct an empirical
study demonstrating the practical utility of our algorithms.


title:
Funnel MPC with feasibility constraints for nonlinear systems with
  arbitrary relative degree

abstract:
  We study tracking control for nonlinear systems with known relative degree
and stable internal dynamics by the recently introduced technique of Funnel
MPC. The objective is to achieve the evolution of the tracking error within a
prescribed performance funnel. We propose a novel stage cost for Funnel MPC,
extending earlier designs to the case of arbitrary relative degree, and show
that the control objective as well as initial and recursive feasibility are
always achieved - without requiring any terminal conditions or a sufficiently
long prediction horizon. We only impose an additional feasibility constraint in
the optimal control problem.


title:
Desirable Companion for Vertical Federated Learning: New Zeroth-Order
  Gradient Based Algorithm

abstract:
  Vertical federated learning (VFL) attracts increasing attention due to the
emerging demands of multi-party collaborative modeling and concerns of privacy
leakage. A complete list of metrics to evaluate VFL algorithms should include
model applicability, privacy security, communication cost, and computation
efficiency, where privacy security is especially important to VFL. However, to
the best of our knowledge, there does not exist a VFL algorithm satisfying all
these criteria very well. To address this challenging problem, in this paper,
we reveal that zeroth-order optimization (ZOO) is a desirable companion for
VFL. Specifically, ZOO can 1) improve the model applicability of VFL framework,
2) prevent VFL framework from privacy leakage under curious, colluding, and
malicious threat models, 3) support inexpensive communication and efficient
computation. Based on that, we propose a novel and practical VFL framework with
black-box models, which is inseparably interconnected to the promising
properties of ZOO. We believe that it takes one stride towards designing a
practical VFL framework matching all the criteria. Under this framework, we
raise two novel {\bf asy}nchronous ze{\bf r}oth-ord{\bf e}r algorithms for {\bf
v}ertical f{\bf e}derated {\bf l}earning (AsyREVEL) with different smoothing
techniques. We theoretically drive the convergence rates of AsyREVEL algorithms
under nonconvex condition. More importantly, we prove the privacy security of
our proposed framework under existing VFL attacks on different levels.
Extensive experiments on benchmark datasets demonstrate the favorable model
applicability, satisfied privacy security, inexpensive communication, efficient
computation, scalability and losslessness of our framework.


title: Bounds on Unique-Neighbor Codes

abstract:
  Let $A$ be an $m\times n$ parity check matrix of a binary code of length $n$,
rate $R$, and distance $\delta n$. This means that for every $\delta n >k>0$,
every $m\times k$ submatrix of $A$ has a row of odd weight. Message-passing
decoding algorithms require the stronger unique neighbor property. Namely, that
every such submatrix has a row of weight $1$. It is well known that if
$\delta\ge\frac 12$, then $R=o_n(1)$ whereas for every $\frac 12>\delta$ there
exist linear codes of length $n\to\infty$ and distance $> \delta n$ with $R$
bounded away from zero. We believe that the unique neighbor property entails
sharper upper bounds on the rate. Concretely, we conjecture that for a proper
choice of $f(n) = o(n)$ and some constant $\epsilon_0 > 0$, every
$n\times(n+f(n))$ binary matrix has an $n\times k$ submatrix with $(\frac 12
-\epsilon_0)n\ge k >0$ where no row weighs $1$. We make several contributions
to the study of this conjecture.


title:
The Relative Deligne Tensor Product over Pointed Braided Fusion
  Categories

abstract:
  We give a formula for the relative Deligne tensor product of two
indecomposable finite semisimple module categories over a pointed braided
fusion category over an algebraically closed field.


title:
Domain Adaptation Meets Zero-Shot Learning: An Annotation-Efficient
  Approach to Multi-Modality Medical Image Segmentation

abstract:
  Due to the lack of properly annotated medical data, exploring the
generalization capability of the deep model is becoming a public concern.
Zero-shot learning (ZSL) has emerged in recent years to equip the deep model
with the ability to recognize unseen classes. However, existing studies mainly
focus on natural images, which utilize linguistic models to extract auxiliary
information for ZSL. It is impractical to apply the natural image ZSL solutions
directly to medical images, since the medical terminology is very
domain-specific, and it is not easy to acquire linguistic models for the
medical terminology. In this work, we propose a new paradigm of ZSL
specifically for medical images utilizing cross-modality information. We make
three main contributions with the proposed paradigm. First, we extract the
prior knowledge about the segmentation targets, called relation prototypes,
from the prior model and then propose a cross-modality adaptation module to
inherit the prototypes to the zero-shot model. Second, we propose a relation
prototype awareness module to make the zero-shot model aware of information
contained in the prototypes. Last but not least, we develop an inheritance
attention module to recalibrate the relation prototypes to enhance the
inheritance process. The proposed framework is evaluated on two public
cross-modality datasets including a cardiac dataset and an abdominal dataset.
Extensive experiments show that the proposed framework significantly
outperforms the state of the arts.


title:
Cool molecular highly charged ions for precision tests of fundamental
  physics

abstract:
  Molecules and atomic highly charged ions provide powerful low-energy probes
of the fundamental laws of physics: Polar molecules possess internal fields
suitable to enhance fundamental symmetry violation by several orders of
magnitudes, whereas atoms in high charge states can feature large relativistic
effects and compressed level structures, ideally posed for high sensitivity to
variations of fundamental constants. Polar, highly charged molecules could
benefit from both: large internal fields and large relativistic effects.
However, a high charge dramatically weakens chemical bonding and drives systems
to the edge of Coulomb explosion. Herein, we propose multiply-charged polar
molecules, that contain actinides, as promising candidates for precision tests
of physics beyond the standard model. Explicitly, we predict PaF$^{3+}$ to be
thermodynamically stable, coolable and well-suited for precision spectroscopy.
The proposed class of compounds, especially with short-lived actinide isotopes
from the territory of pear-shaped nuclei, has potential to advance our
understanding of molecules under extreme conditions, to provide a window into
unknown properties of atomic nuclei, and to boost developments in molecular
precision spectroscopy in various areas, such as optical clocks and searches
for new physics.


title: Poincar\'e type inequality for hypersurfaces and rigidity results

abstract:
  In this paper, we deal with general divergence formulas involving symmetric
endomorphisms. Using mild constraints in the sectional curvature and such
divergence formulas we deduce a very general Poincar\'e type inequality. We
apply such general inequality for higher-order mean curvature, in space forms
and Einstein manifolds, to obtain several isoperimetric inequalities, as well
as rigidity results for complete $r$-minimal hypersurfaces satisfying a
suitable decay of the second fundamental form at infinity. Furthermore, using
these techniques, we prove the flatness and non existence results for
self-similar solutions of a large class of fully nonlinear curvature flows.


title:
TO-FLOW: Efficient Continuous Normalizing Flows with Temporal
  Optimization adjoint with Moving Speed

abstract:
  Continuous normalizing flows (CNFs) construct invertible mappings between an
arbitrary complex distribution and an isotropic Gaussian distribution using
Neural Ordinary Differential Equations (neural ODEs). It has not been tractable
on large datasets due to the incremental complexity of the neural ODE training.
Optimal Transport theory has been applied to regularize the dynamics of the ODE
to speed up training in recent works. In this paper, a temporal optimization is
proposed by optimizing the evolutionary time for forward propagation of the
neural ODE training. In this appoach, we optimize the network weights of the
CNF alternately with evolutionary time by coordinate descent. Further with
temporal regularization, stability of the evolution is ensured. This approach
can be used in conjunction with the original regularization approach. We have
experimentally demonstrated that the proposed approach can significantly
accelerate training without sacrifying performance over baseline models.


title:
First application of Markov Chain Monte Carlo-based Bayesian data
  analysis to the Doppler-Shift Attenuation Method

abstract:
  Motivated primarily by the large uncertainties in the thermonuclear rate of
the $^{30}$P$(p,\gamma)^{31}$S reaction that limit our understanding of
classical novae, we carried out lifetime measurements of $^{31}$S excited
states using the Doppler Shift Lifetimes (DSL) facility at the TRIUMF Isotope
Separator and Accelerator (ISAC-II) facility. The $^{31}$S excited states were
populated by the $^{3}$He$(^{32}$S$,\alpha)^{31}$S reaction. The deexcitation
$\gamma$ rays were detected by a clover-type high-purity germanium detector in
coincidence with the $\alpha$ particles detected by a silicon detector
telescope. We have applied modern Markov chain Monte Carlo-based Bayesian
methods to perform lineshape analyses of Doppler-Shift Attenuation Method
$\gamma$-ray data for the first time. We have determined the lifetimes for the
two lowest-lying $^{31}$S excited states. Upper limits on the previously
unknown lifetimes of four higher-lying states have been obtained. The
experimental results were compared to the shell-model calculations using five
universal $sd$-shell Hamiltonians. The $\gamma$ rays originating from the
astrophysically important $J^\pi=3/2^+$, 260-keV $^{30}$P$(p,\gamma)^{31}$S
resonance have also been observed, but the lifetime is expected to be better
constrained with more statistics at the upgraded DSL2 facility.


title: On Structural Aspects of Friends-And-Strangers Graphs

abstract:
  Given two graphs $X$ and $Y$ with the same number of vertices, the
friends-and-strangers graph $\mathsf{FS}(X, Y)$ has as its vertices all $n!$
bijections from $V(X)$ to $V(Y)$, with bijections $\sigma, \tau$ adjacent if
and only if they differ on two elements of $V(X)$, whose mappings are adjacent
in $Y$. In this article, we study necessary and sufficient conditions for
$\mathsf{FS}(X, Y)$ to be connected for all graphs $X$ from some set. In the
setting that we take $X$ to be drawn from the set of all biconnected graphs, we
prove that $\mathsf{FS}(X, Y)$ is connected for all biconnected $X$ if and only
if $\overline{Y}$ is a forest with trees of jointly coprime size; this resolves
a conjecture of Defant and Kravitz. We also initiate and make significant
progress toward determining the girth of $\mathsf{FS}(X, \text{Star}_n)$ for
connected graphs $X$, and in particular focus on the necessary trajectories
that the central vertex of $\text{Star}_n$ takes around all such graphs $X$ to
achieve the girth.


title: Understanding COVID-19 News Coverage using Medical NLP

abstract:
  Being a global pandemic, the COVID-19 outbreak received global media
attention. In this study, we analyze news publications from CNN and The
Guardian - two of the world's most influential media organizations. The dataset
includes more than 36,000 articles, analyzed using the clinical and biomedical
Natural Language Processing (NLP) models from the Spark NLP for Healthcare
library, which enables a deeper analysis of medical concepts than previously
achieved. The analysis covers key entities and phrases, observed biases, and
change over time in news coverage by correlating mined medical symptoms,
procedures, drugs, and guidance with commonly mentioned demographic and
occupational groups. Another analysis is of extracted Adverse Drug Events about
drug and vaccine manufacturers, which when reported by major news outlets has
an impact on vaccine hesitancy.