title:
A Masked Image Reconstruction Network for Document-level Relation
  Extraction

abstract:
  Document-level relation extraction aims to extract relations among entities
within a document. Compared with its sentence-level counterpart, Document-level
relation extraction requires inference over multiple sentences to extract
complex relational triples. Previous research normally complete reasoning
through information propagation on the mention-level or entity-level
document-graphs, regardless of the correlations between the relationships. In
this paper, we propose a novel Document-level Relation Extraction model based
on a Masked Image Reconstruction network (DRE-MIR), which models inference as a
masked image reconstruction problem to capture the correlations between
relationships. Specifically, we first leverage an encoder module to get the
features of entities and construct the entity-pair matrix based on the
features. After that, we look on the entity-pair matrix as an image and then
randomly mask it and restore it through an inference module to capture the
correlations between the relationships. We evaluate our model on three public
document-level relation extraction datasets, i.e. DocRED, CDR, and GDA.
Experimental results demonstrate that our model achieves state-of-the-art
performance on these three datasets and has excellent robustness against the
noises during the inference process.


title: The Risks of Machine Learning Systems

abstract:
  The speed and scale at which machine learning (ML) systems are deployed are
accelerating even as an increasing number of studies highlight their potential
for negative impact. There is a clear need for companies and regulators to
manage the risk from proposed ML systems before they harm people. To achieve
this, private and public sector actors first need to identify the risks posed
by a proposed ML system. A system's overall risk is influenced by its direct
and indirect effects. However, existing frameworks for ML risk/impact
assessment often address an abstract notion of risk or do not concretize this
dependence.
  We propose to address this gap with a context-sensitive framework for
identifying ML system risks comprising two components: a taxonomy of the first-
and second-order risks posed by ML systems, and their contributing factors.
First-order risks stem from aspects of the ML system, while second-order risks
stem from the consequences of first-order risks. These consequences are system
failures that result from design and development choices. We explore how
different risks may manifest in various types of ML systems, the factors that
affect each risk, and how first-order risks may lead to second-order effects
when the system interacts with the real world.
  Throughout the paper, we show how real events and prior research fit into our
Machine Learning System Risk framework (MLSR). MLSR operates on ML systems
rather than technologies or domains, recognizing that a system's design,
implementation, and use case all contribute to its risk. In doing so, it
unifies the risks that are commonly discussed in the ethical AI community
(e.g., ethical/human rights risks) with system-level risks (e.g., application,
design, control risks), paving the way for holistic risk assessments of ML
systems.


title: The Cheeger constants of random Belyi surfaces

abstract:
  Brooks and Makover developed a combinatorial model of random hyperbolic
surfaces by gluing certain hyperbolic ideal triangles. In this paper we show
that for any $\epsilon>0$, as the number of ideal triangles goes to infinity, a
generic hyperbolic surface in Brooks-Makover's model has Cheeger constant less
than $\frac{2}{3}+\epsilon$.


title:
Self-Supervised Learning to Guide Scientifically Relevant Categorization
  of Martian Terrain Images

abstract:
  Automatic terrain recognition in Mars rover images is an important problem
not just for navigation, but for scientists interested in studying rock types,
and by extension, conditions of the ancient Martian paleoclimate and
habitability. Existing approaches to label Martian terrain either involve the
use of non-expert annotators producing taxonomies of limited granularity (e.g.
soil, sand, bedrock, float rock, etc.), or rely on generic class discovery
approaches that tend to produce perceptual classes such as rover parts and
landscape, which are irrelevant to geologic analysis. Expert-labeled datasets
containing granular geological/geomorphological terrain categories are rare or
inaccessible to public, and sometimes require the extraction of relevant
categorical information from complex annotations. In order to facilitate the
creation of a dataset with detailed terrain categories, we present a
self-supervised method that can cluster sedimentary textures in images captured
from the Mast camera onboard the Curiosity rover (Mars Science Laboratory). We
then present a qualitative analysis of these clusters and describe their
geologic significance via the creation of a set of granular terrain categories.
The precision and geologic validation of these automatically discovered
clusters suggest that our methods are promising for the rapid classification of
important geologic features and will therefore facilitate our long-term goal of
producing a large, granular, and publicly available dataset for Mars terrain
recognition.


title: On the non-existence of trapped surfaces under low-regularity bounds

abstract:
  The emergence of trapped surfaces in solutions to the Einstein field
equations is intimately tied to the well-posedness properties of the
corresponding Cauchy problem in the low regularity regime. In this paper, we
study the question of existence of trapped surfaces already at the level of the
initial hypersurface when the scale invariant size of the Cauchy data is
assumed to be bounded. Our main theorem states that no trapped surfaces can
exist initially when the Cauchy data are close to the data induced on a
spacelike hypersurface of Minkowski spacetime (not necessarily a flat
hyperplane) in the Besov $B^{3/2}_{2,1}$ norm. We also discuss the question of
extending the above result to the case when merely smallness in $H^{3/2}$ is
assumed.


title: Magnetic Couplings in Edge-Sharing $d^7$ Compounds

abstract:
  High-spin $d^7$ Co(II) compounds have recently been identified as possible
platforms for realising highly anisotropic and bond-dependent couplings
featured in quantum-compass models such as the celebrated Kitaev model. In
order to evaluate this potential, we consider all symmetry-allowed
contributions to the magnetic exchange for ideal edge-sharing bonds. Though a
combination of ab-initio and cluster many-body calculations we conclude that
bond-dependent couplings are generally suppressed in favor of Heisenberg
exchange for realistic materials. Consequences for several prominent materials
including Na$_2$Co$_2$TeO$_6$ and BaCo$_2$(AsO$_4$)$_2$ are discussed.


title:
Linear and nonlinear analysis of the Rayleigh-Taylor system with
  Navier-slip boundary conditions

abstract:
  In this paper, we are interested in the linear and the nonlinear Rayleigh
instability for the incompressible Navier-Stokes equations with Navier-slip
boundary conditions around a laminar smooth density profile $\rho_0(x_2)$ being
increasing in an infinite slab $2\pi L\mathbb{T} \times (-1,1)$ ($L>0$,
$\mathbb{T}$ is the usual 1D torus). The linear instability study of the
viscous Rayleigh-Taylor model amounts to the study of the following ordinary
differential equation of the vertical component of perturbed velocity on the
finite interval $(-1,1)$, \begin{equation} -\lambda^2 [ \rho_0 k^2 \phi -
(\rho_0 \phi')'] = \lambda \mu (\phi^{(4)} - 2k^2 \phi'' + k^4 \phi) - gk^2
\rho_0'\phi, \qquad(0.1)\end{equation} with the boundary conditions
\begin{equation}\label{4thBound} \begin{cases} \phi(-1)=\phi(1)=0,\\ \mu
\phi''(1) = \xi_+ \phi'(1), \\ \mu \phi''(-1) =- \xi_- \phi'(-1), \end{cases}
\qquad(0.2)\end{equation} where $g>0$ is the gravity constant, $\lambda$ is the
growth rate in time, $k$ is the wave number transverse to the density profile
and two Navier-slip coefficients $\xi_{\pm}$ are nonnegative constants. For
each $k\in L^{-1}\mathbb{Z}\setminus\{0\}$, we define a threshold of viscosity
coefficient $\mu_c(k,\Xi)$ for linear instability. we provide a spectral
analysis adapting the operator method of Lafitte-Nguyen in \cite{LN20} and then
prove that there are infinite solutions of \eqref{EqMain}-\eqref{4thBound}.
Based on infinitely unstable modes of the linearized problem, we consider a
wide class of initial data to the nonlinear perturbation problem, extending
Grenier's framework \cite{Gre00}, to prove nonlinear Rayleigh-Taylor
instability in a high regime of viscosity coefficient, namely $\mu >3\sup_{k\in
L^{-1}\mathbb{Z}\setminus\{0\}}\mu_c(k,\Xi)$.


title:
Ultraviolet Finiteness or Asymptotic Safety in Higher Derivative
  Gravitational Theories

abstract:
  We present and discuss well known conditions for ultraviolet finiteness and
asymptotic safety. The requirements for complete absence of ultraviolet
divergences in quantum field theories and existence of a non-trivial fixed
point for renormalization group flow in the ultraviolet regime are compared
based on the example of a six-derivative quantum gravitational theory in $d=4$
spacetime dimensions. In this model, it is possible for the first time to have
fully UV-finite quantum theory without adding matter or special symmetry, but
by inclusion of additional terms cubic in curvatures. We comment on
similarities and some apparent differences between the two approaches, but we
show that they are both compatible to each other. Finally, we motivate the
claim that actually asymptotic safety needs UV-finite models for providing
explicit form of the ultraviolet limit of Wilsonian effective actions
describing special situations at fixed points.

categories: hep-th gr-qc hep-ph math-ph math.MP quant-ph


title: Purcell enhancement of erbium ions in TiO$_{2}$ on silicon nanocavities

abstract:
  Isolated solid-state atomic defects with telecom optical transitions are
ideal quantum photon emitters and spin qubits for applications in long-distance
quantum communication networks. Prototypical telecom defects such as erbium
suffer from poor photon emission rates, requiring photonic enhancement using
resonant optical cavities. Many of the traditional hosts for erbium ions are
not amenable to direct incorporation with existing integrated photonics
platforms, limiting scalable fabrication of qubit-based devices. Here we
present a scalable approach towards CMOS-compatible telecom qubits by using
erbium-doped titanium dioxide thin films grown atop silicon-on-insulator
substrates. From this heterostructure, we have fabricated one-dimensional
photonic crystal cavities demonstrating quality factors in excess of
$5\times10^{4}$ and corresponding Purcell-enhanced optical emission rates of
the erbium ensembles in excess of 200. This easily fabricated materials
platform represents an important step towards realizing telecom quantum
memories in a scalable qubit architecture compatible with mature silicon
technologies.


title:
Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and
  Local Information

abstract:
  Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become
an urgent research hotspot due to its ability of enabling fast and flexible
information extraction on remote sensing (RS) images. However, current RSCTIR
methods mainly focus on global features of RS images, which leads to the
neglect of local features that reflect target relationships and saliency. In
this article, we first propose a novel RSCTIR framework based on global and
local information (GaLR), and design a multi-level information dynamic fusion
(MIDF) module to efficaciously integrate features of different levels. MIDF
leverages local information to correct global information, utilizes global
information to supplement local information, and uses the dynamic addition of
the two to generate prominent visual representation. To alleviate the pressure
of the redundant targets on the graph convolution network (GCN) and to improve
the model s attention on salient instances during modeling local features, the
de-noised representation matrix and the enhanced adjacency matrix (DREA) are
devised to assist GCN in producing superior local representations. DREA not
only filters out redundant features with high similarity, but also obtains more
powerful local features by enhancing the features of prominent objects.
Finally, to make full use of the information in the similarity matrix during
inference, we come up with a plug-and-play multivariate rerank (MR) algorithm.
The algorithm utilizes the k nearest neighbors of the retrieval results to
perform a reverse search, and improves the performance by combining multiple
components of bidirectional retrieval. Extensive experiments on public datasets
strongly demonstrate the state-of-the-art performance of GaLR methods on the
RSCTIR task. The code of GaLR method, MR algorithm, and corresponding files
have been made available at https://github.com/xiaoyuan1996/GaLR .


title: Dual $r$-Rank Decomposition and Its Applications

abstract:
  In this paper, we introduce the dual $r$-rank decomposition of dual matrix,
get its existence condition and equivalent form of the decomposition, as well
as derive some characterizations of dual Moore-Penrose generalized
inverse(DMPGI). Based on DMPGI, we introduce one special dual matrix(dual EP
matrix). By applying the dual $r$-rank decomposition we derive several
characterizations of dual EP matrix, dual idempotent matrix, dual generalized
inverses, and the relationships among dual Penrose equations.


title:
The $\theta$-augmented model for Bayesian semiparametric inference on
  functional parameters

abstract:
  Semiparametric Bayesian inference has so far relied on models for the
observable that partition into two parts, one being parametric and the other
nonparametric, with the target parameter being dependent on the parametric
component. While a partitioned structure makes specification of the marginal
prior on the target parameter simple to perform, it often arises from
conditional modelling which is subject to misspecification and ultimately a
lack of consistency. We introduce a new type of semiparametric model to allow
easy prior specification for a parameter that is defined as a functional of the
distribution for the observable. Our semiparametric model is obtained as an
extension of nonparametric models that are consistent under very general
conditions. This type of Bayesian semiparametric model can be used to obtain
Bayesian versions of Frequentist estimators that are defined as functionals of
the empirical distribution. This gives us new opportunities to conduct Bayesian
analysis in problems where Frequentist estimators exist but not well-accepted
likelihoods.


title:
Optimal design of exchange water networks with control inputs in
  Eco-Industrial Parks

abstract:
  Industrial water conservation is an important adaptation to preserve the
environment. Eco-Industrial Parks (EIPs) have been designed to encourage the
establishment of water exchange networks between enterprises in order to
minimize freshwater consumption and wastewater discharge by maximizing
wastewater reuse. This control-input model presents a mathematical programming
formulation for designing and optimizing industrial water networks in EIPs,
formulating and solving it as a Single- Leader Multi-Follower (SLMF) game
problem. Enterprises (followers) aim to minimize their operating costs by
reusing wastewater from other enterprises, while the designer (leader) aims to
minimize the consumption of natural resources within the ecopark. Moreover,
when participating in the ecopark, enterprises can control all their input
fluxes and the designer guarantees a minimal relative improvement in comparison
with the stand-alone operation of each enterprise. The SLMF game is transformed
into a single mixed-integer optimization problem. The obtained results are
compared with the results of the blind-input model [D. Salas, Cao Van Kien, D.
Aussel, L. Montastruc, Optimal design of exchange networks with blind inputs
and its application to Eco-Industrial parks, Computers \& Chemical Engineering
143 (2020)].


title: Using SGX for Meta-Transactions Support in Ethereum DApps

abstract:
  Decentralized applications (DApps) gained traction in the context of the
blockchain technology. Ethereum is currently the public blockchain that backs
the largest amount of the existing DApps. Onboarding new users to Ethereum
DApps is a notoriously hard issue to solve. This is mainly caused by lack of
cryptocurrency ownership, needed for transaction fees. Several meta-transaction
patterns emerged for decoupling users from paying these fees. However, such
solutions are mostly offered via off-chain, often paid relayer services and do
not fully address the security issues present in the meta-transaction path. In
this paper, we introduce a new meta-transaction architecture that makes use of
the Intel Software Guard Extensions (SGX). Unlike other solutions, our approach
would offer the possibility to deploy a fee-free Ethereum DApp on a web server
that can directly relay meta-transactions to the Ethereum network while having
essential security guarantees integrated by design.


title:
Detection of roton and phonon excitations in a spin-orbit coupled
  Bose-Einstein condensate with a moving barrier

abstract:
  We propose to detect phonon and roton excitations in a two-dimensional
Bose-Einstein condensate with Raman-induced spin-orbit coupling by perturbing
the atomic cloud with a weak barrier. The two excitation modes can be observed
by moving the barrier along different directions in appropriate parameter
regimes. Phonon excitations are identified by the appearance of solitary waves,
while roton excitations lead to distinctive spatial density modulations. We
show that this method can also be used to determine the anisotropic critical
velocities of superfluid.


title: Pixel2Mesh++: 3D Mesh Generation and Refinement from Multi-View Images

abstract:
  We study the problem of shape generation in 3D mesh representation from a
small number of color images with or without camera poses. While many previous
works learn to hallucinate the shape directly from priors, we adopt to further
improve the shape quality by leveraging cross-view information with a graph
convolution network. Instead of building a direct mapping function from images
to 3D shape, our model learns to predict series of deformations to improve a
coarse shape iteratively. Inspired by traditional multiple view geometry
methods, our network samples nearby area around the initial mesh's vertex
locations and reasons an optimal deformation using perceptual feature
statistics built from multiple input images. Extensive experiments show that
our model produces accurate 3D shapes that are not only visually plausible from
the input perspectives, but also well aligned to arbitrary viewpoints. With the
help of physically driven architecture, our model also exhibits generalization
capability across different semantic categories, and the number of input
images. Model analysis experiments show that our model is robust to the quality
of the initial mesh and the error of camera pose, and can be combined with a
differentiable renderer for test-time optimization.


title:
A Model-Agnostic Data Manipulation Method for Persona-based Dialogue
  Generation

abstract:
  Towards building intelligent dialogue agents, there has been a growing
interest in introducing explicit personas in generation models. However, with
limited persona-based dialogue data at hand, it may be difficult to train a
dialogue generation model well. We point out that the data challenges of this
generation task lie in two aspects: first, it is expensive to scale up current
persona-based dialogue datasets; second, each data sample in this task is more
complex to learn with than conventional dialogue data. To alleviate the above
data issues, we propose a data manipulation method, which is model-agnostic to
be packed with any persona-based dialogue generation model to improve its
performance. The original training samples will first be distilled and thus
expected to be fitted more easily. Next, we show various effective ways that
can diversify such easier distilled data. A given base model will then be
trained via the constructed data curricula, i.e. first on augmented distilled
samples and then on original ones. Experiments illustrate the superiority of
our method with two strong base dialogue models (Transformer encoder-decoder
and GPT2).


title:
Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote
  Sensing Image Retrieval

abstract:
  Remote sensing (RS) cross-modal text-image retrieval has attracted extensive
attention for its advantages of flexible input and efficient query. However,
traditional methods ignore the characteristics of multi-scale and redundant
targets in RS image, leading to the degradation of retrieval accuracy. To cope
with the problem of multi-scale scarcity and target redundancy in RS multimodal
retrieval task, we come up with a novel asymmetric multimodal feature matching
network (AMFMN). Our model adapts to multi-scale feature inputs, favors
multi-source retrieval methods, and can dynamically filter redundant features.
AMFMN employs the multi-scale visual self-attention (MVSA) module to extract
the salient features of RS image and utilizes visual features to guide the text
representation. Furthermore, to alleviate the positive samples ambiguity caused
by the strong intraclass similarity in RS image, we propose a triplet loss
function with dynamic variable margin based on prior similarity of sample
pairs. Finally, unlike the traditional RS image-text dataset with coarse text
and higher intraclass similarity, we construct a fine-grained and more
challenging Remote sensing Image-Text Match dataset (RSITMD), which supports RS
image retrieval through keywords and sentence separately and jointly.
Experiments on four RS text-image datasets demonstrate that the proposed model
can achieve state-of-the-art performance in cross-modal RS text-image retrieval
task.


title:
Relaxed constant positive linear dependence constraint qualification for
  disjunctive programs

abstract:
  The disjunctive program is a class of optimization problems in which the
constraint involves a disjunctive set which is the union of finitely many
polyhedral convex sets. In this paper, we introduce a notion of the relaxed
constant positive linear dependence constraint qualification (RCPLD) for the
disjunctive program. Our notion is weaker than the one we introduced for a
nonsmooth system which includes an abstract set constraint recently (J. Glob.
Optim. 2020) and is still a constraint qualification for a Mordukhovich
stationarity condition for the disjunctive program. To obtain the error bound
property for the disjunctive program, we introduce the piecewise RCPLD under
which the error bound property holds if all inequality constraint functions are
subdifferentially regular and the rest of the constraint functions are smooth.
We then specialize our results to the ortho-disjunctive program, which includes
the mathematical program with equilibrium constraints (MPEC), the mathematical
program with vanishing constraints (MPVC) and the mathematical program with
switching constraints (MPSC) as special cases. For MPEC, we recover MPEC-RCPLD,
an MPEC variant of RCPLD and propose the MPEC piecewise RCPLD to obtain the
{error bound property}. For MPVC, we introduce MPVC-RCPLD as a constraint
qualification and the piecewise RCPLD as a sufficient condition for the error
bound property. For MPSC, we show that both RCPLD and the piecewise RCPLD
coincide and hence it is not only a constraint qualification, but also a
sufficient condition for the error bound property.


title: Signed spectral Tura\'{n} type theorems

abstract:
  A signed graph $\Sigma = (G, \sigma)$ is a graph where the function $\sigma$
assigns either $1$ or $-1$ to each edge of the simple graph $G$. The adjacency
matrix of $\Sigma$, denoted by $A(\Sigma)$, is defined canonically. In a recent
paper, Wang et al. extended the eigenvalue bounds of Hoffman and Cvetkovi\'{c}
for the signed graphs. They proposed an open problem related to the balanced
clique number and the largest eigenvalue of a signed graph. We solve a
strengthened version of this open problem. As a byproduct, we give alternate
proofs for some of the known classical bounds for the least eigenvalues of the
unsigned graphs. We extend the Tur\'{a}n's inequality for the signed graphs.
Besides, we study the Bollob\'{a}s and Nikiforov conjecture for the signed
graphs and show that the conjecture need not be true for the signed graphs.
Nevertheless, the conjecture holds for signed graphs under some assumptions.
Finally, we study some of the relationships between the number of signed walks
and the largest eigenvalue of a signed graph.


title:
Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for
  Task-Specific Imaging

abstract:
  Cameras were originally designed using physics-based heuristics to capture
aesthetic images. In recent years, there has been a transformation in camera
design from being purely physics-driven to increasingly data-driven and
task-specific. In this paper, we present a framework to understand the building
blocks of this nascent field of end-to-end design of camera hardware and
algorithms. As part of this framework, we show how methods that exploit both
physics and data have become prevalent in imaging and computer vision,
underscoring a key trend that will continue to dominate the future of
task-specific camera design. Finally, we share current barriers to progress in
end-to-end design, and hypothesize how these barriers can be overcome.


title: Reciprocal distance energy of complete multipartite graphs

abstract:
  In this paper, first we compute the energy of a special partitioned matrix
under some cases. As a consequence, we obtain the reciprocal distance energy of
the complete multipartite graph and also we give various other energies of
complete multipartite graphs. Next, we show that among all complete k-partite
graphs on n vertices, the complete split graph has minimum reciprocal distance
energy and the reciprocal distance energy is maximum for the Turan graph. At
last, it is shown that the reciprocal distance energy of the complete bipartite
graph km,m decreases under deletion of an edge if 2\le m\le 7, whereas the
reciprocal distance energy increases if 8 \le m. Also, we show that the
reciprocal distance energy of the complete tripartite graph does not increase
under edge deletion


title:
Gaussian Processes for real-time 3D motion and uncertainty estimation
  during MR-guided radiotherapy

abstract:
  Respiratory motion during radiotherapy causes uncertainty in the tumor's
location, which is typically addressed by an increased radiation area and a
decreased dose. As a result, the treatments' efficacy is reduced. The recently
proposed hybrid MR-linac scanner holds the promise to efficiently deal with
such respiratory motion through real-time adaptive MR-guided radiotherapy
(MRgRT). For MRgRT, motion-fields should be estimated from MR-data and the
radiotherapy plan should be adapted in real-time according to the estimated
motion-fields. All of this should be performed with a total latency of
maximally 200 ms, including data acquisition and reconstruction. A measure of
confidence in such estimated motion-fields is highly desirable, for instance to
ensure the patient's safety in case of unexpected and undesirable motion. In
this work, we propose a framework based on Gaussian Processes to infer 3D
motion-fields and uncertainty maps in real-time from only three readouts of
MR-data. We demonstrated an inference frame rate up to 69 Hz including data
acquisition and reconstruction, thereby exploiting the limited amount of
required MR-data. Additionally, we designed a rejection criterion based on the
motion-field uncertainty maps to demonstrate the framework's potential for
quality assurance. The framework was validated in silico and in vivo on healthy
volunteer data (n=5) acquired using an MR-linac, thereby taking into account
different breathing patterns and controlled bulk motion. Results indicate
end-point-errors with a 75th percentile below 1mm in silico, and a correct
detection of erroneous motion estimates with the rejection criterion.
Altogether, the results show the potential of the framework for application in
real-time MR-guided radiotherapy with an MR-linac.


title:
Is Neural Topic Modelling Better than Clustering? An Empirical Study on
  Clustering with Contextual Embeddings for Topics

abstract:
  Recent work incorporates pre-trained word embeddings such as BERT embeddings
into Neural Topic Models (NTMs), generating highly coherent topics. However,
with high-quality contextualized document representations, do we really need
sophisticated neural models to obtain coherent and interpretable topics? In
this paper, we conduct thorough experiments showing that directly clustering
high-quality sentence embeddings with an appropriate word selecting method can
generate more coherent and diverse topics than NTMs, achieving also higher
efficiency and simplicity.


title: Persistent-Transient Duality in Human Behavior Modeling

abstract:
  We propose to model the persistent-transient duality in human behavior using
a parent-child multi-channel neural network, which features a parent persistent
channel that manages the global dynamics and children transient channels that
are initiated and terminated on-demand to handle detailed interactive actions.
The short-lived transient sessions are managed by a proposed Transient Switch.
The neural framework is trained to discover the structure of the duality
automatically. Our model shows superior performances in human-object
interaction motion prediction.


title:
On scattering and damping of Toroidal Alfven eigenmode by drift wave
  turbulence

abstract:
  We demonstrate analytically that, in toroidal plasmas, scattering by drift
wave turbulence could lead to appreciable damping of toroidal Alfven eigenmodes
via generation of short-wavelength electron Landau damped kinetic Alfven waves.
A corresponding analytic expression of the damping rate is derived, and found
to be, typically, comparable to the linear drive by energetic particles. The
implications of this novel mechanism on the transport and heating processes in
burning plasmas are also discussed.


title: Non-autoregressive Model for Full-line Code Completion

abstract:
  Code completion tools are frequently used by software developers to
accelerate software development by suggesting the following code elements.
Completing a sequence of code tokens (e.g., a full line of code) has been
proved more efficient than predicting a single token at a time. To complete the
code sequence, researchers are employing AutoRegressive (AR) decoders to
generate tokens in a left-to-right, token-by-token fashion. Consequently, the
prediction of the next token depends on all previously generated tokens, which
leads to high latency in inference. To improve the efficiency and accuracy of
full-line code completion, in this paper, we propose a Non-AutoRegressive (NAR)
model for code completion boosted by a syntax-aware sampling strategy. Our
experimental results on two widely used datasets suggest that our model
outperforms both AR and NAR baselines on full-line code completion, and it is
faster than the AR model with up to 9 times speed-up.


title: Computably totally disconnected locally compact groups

abstract:
  We study totally disconnected, locally compact (t.d.l.c.) groups from an
algorithmic perspective. We give various approaches to defining computable
presentations of t.d.l.c.\ groups, and show their equivalence. In the process,
we obtain an algorithmic Stone-type duality between t.d.l.c.~groups and certain
countable ordered groupoids given by the compact open cosets. We exploit the
flexibility given by these different approaches to show that several natural
groups, such as $\Aut(T_d)$ and $\SL_n(\QQ_p)$, have computable presentations.
We show that many construction leading from t.d.l.c.\ groups to new t.d.l.c.\
groups have algorithmic versions that stay within the class of computably
presented t.d.l.c.\ groups. This leads to further examples, such as
$\PGL_n(\QQ_p)$. We study whether objects associated with computably t.d.l.c.\
groups are computable: the modular function, the scale function, and
Cayley-Abels graphs in the compactly generated case. We give a criterion when
computable presentations of t.d.l.c.~groups are unique up to computable
isomorphism, and apply it to $\QQ_p$ as an additive group, and the semidirect
product $\ZZ\ltimes \QQ_p$.


title: Evolution and Consequences of Coronal Mass Ejections in the Heliosphere

abstract:
  Investigating the heliospheric evolution and consequences of Coronal mass
ejections (CMEs) is critical to understanding the solar-terrestrial
relationship. For the first time, Heliospheric Imagers (HIs) onboard STEREO,
providing multiple views of CMEs in the heliosphere, observed the vast and
crucial observational gap between the Sun and the Earth. Using J-maps
constructed from coronagraphs (CORs) and HIs observations, we continuously
tracked different density enhanced features of CMEs. We implemented several
reconstruction methods to estimate the three-dimensional (3D) kinematics of
CMEs during their evolution from the Sun to Earth. Our study provides evidence
that the 3D speeds of CMEs near the Sun are not reasonably sufficient for
understanding the propagation and accurate forecasting of the arrival time at
the Earth of a majority of CMEs. This finding can be due to many factors that
significantly change the CME kinematics beyond the COR field of view, such as
the interaction/collision of two or more CMEs or the interaction of CMEs with
the ambient solar wind medium. We attempted to understand the evolution and
consequences of the interacting/colliding CMEs in the heliosphere using
STEREO/HI, WIND, and ACE observations. The study found a significant change in
the dynamics of the CMEs after their collision and interaction. The in situ
observations show the signatures of CME-CME interaction as heating and
compression, formation of magnetic holes (MHs), and interaction region (IR). We
also noticed that long-lasting IR, formed at the rear edge of preceding CME, is
responsible for large geomagnetic perturbations. Our study highlights the
significance of using HIs observations in studying heliospheric evolution of
CMEs, CME-CME collision, identifying and associating the three-part structure
of CMEs in their remote and in situ observations, and hence for improved space
weather forecasting.


title:
Helical magnetic fields and semi-classical asymptotics of the lowest
  eigenvalue

abstract:
  We study the 3D Neuman magnetic Laplacian in the presence of a semi-classical
parameter and a non-uniform magnetic field with constant intensity. We
determine a sharp two term asymptotics for the lowest eigenvalue, where the
second term involves a quantity related to the magnetic field and the geometry
of the domain. In the special case of the unit ball and a helical magnetic
field, the concentration takes place on two symmetric points of the unit
sphere.


title: CNLL: A Semi-supervised Approach For Continual Noisy Label Learning

abstract:
  The task of continual learning requires careful design of algorithms that can
tackle catastrophic forgetting. However, the noisy label, which is inevitable
in a real-world scenario, seems to exacerbate the situation. While very few
studies have addressed the issue of continual learning under noisy labels, long
training time and complicated training schemes limit their applications in most
cases. In contrast, we propose a simple purification technique to effectively
cleanse the online data stream that is both cost-effective and more accurate.
After purification, we perform fine-tuning in a semi-supervised fashion that
ensures the participation of all available samples. Training in this fashion
helps us learn a better representation that results in state-of-the-art (SOTA)
performance. Through extensive experimentation on 3 benchmark datasets, MNIST,
CIFAR10 and CIFAR100, we show the effectiveness of our proposed approach. We
achieve a 24.8% performance gain for CIFAR10 with 20% noise over previous SOTA
methods. Our code is publicly available.


title: Color Invariant Skin Segmentation

abstract:
  This paper addresses the problem of automatically detecting human skin in
images without reliance on color information. A primary motivation of the work
has been to achieve results that are consistent across the full range of skin
tones, even while using a training dataset that is significantly biased toward
lighter skin tones. Previous skin-detection methods have used color cues almost
exclusively, and we present a new approach that performs well in the absence of
such information. A key aspect of the work is dataset repair through
augmentation that is applied strategically during training, with the goal of
color invariant feature learning to enhance generalization. We have
demonstrated the concept using two architectures, and experimental results show
improvements in both precision and recall for most Fitzpatrick skin tones in
the benchmark ECU dataset. We further tested the system with the RFW dataset to
show that the proposed method performs much more consistently across different
ethnicities, thereby reducing the chance of bias based on skin color. To
demonstrate the effectiveness of our work, extensive experiments were performed
on grayscale images as well as images obtained under unconstrained illumination
and with artificial filters. Source code:
https://github.com/HanXuMartin/Color-Invariant-Skin-Segmentation


title:
Layer-wise Fast Adaptation for End-to-End Multi-Accent Speech
  Recognition

abstract:
  Accent variability has posed a huge challenge to automatic speech
recognition~(ASR) modeling. Although one-hot accent vector based adaptation
systems are commonly used, they require prior knowledge about the target accent
and cannot handle unseen accents. Furthermore, simply concatenating accent
embeddings does not make good use of accent knowledge, which has limited
improvements. In this work, we aim to tackle these problems with a novel
layer-wise adaptation structure injected into the E2E ASR model encoder. The
adapter layer encodes an arbitrary accent in the accent space and assists the
ASR model in recognizing accented speech. Given an utterance, the adaptation
structure extracts the corresponding accent information and transforms the
input acoustic feature into an accent-related feature through the linear
combination of all accent bases. We further explore the injection position of
the adaptation layer, the number of accent bases, and different types of accent
bases to achieve better accent adaptation. Experimental results show that the
proposed adaptation structure brings 12\% and 10\% relative word error
rate~(WER) reduction on the AESRC2020 accent dataset and the Librispeech
dataset, respectively, compared to the baseline.


title:
The maximum spectral radius of non-bipartite graphs forbidding short odd
  cycles

abstract:
  It is well-known that eigenvalues of graphs can be used to describe
structural properties and parameters of graphs. A theorem of Nosal states that
if $G$ is a triangle-free graph with $m$ edges, then $\lambda (G)\le \sqrt{m}$,
equality holds if and only if $G$ is a complete bipartite graph. Recently, Lin,
Ning and Wu [Combin. Probab. Comput. 30 (2021)] proved a generalization for
non-bipartite triangle-free graphs. Moreover, Zhai and Shu [Discrete Math. 345
(2022)] presented a further improvement. In this paper, we present an
alternative method for proving the improvement by Zhai and Shu. Furthermore,
the method can allow us to give a refinement on the result of Zhai and Shu for
non-bipartite graphs without short odd cycles.


title:
An Attention-Based Model for Predicting Contextual Informativeness and
  Curriculum Learning Applications

abstract:
  Both humans and machines learn the meaning of unknown words through
contextual information in a sentence, but not all contexts are equally helpful
for learning. We introduce an effective method for capturing the level of
contextual informativeness with respect to a given target word. Our study makes
three main contributions. First, we develop models for estimating contextual
informativeness, focusing on the instructional aspect of sentences. Our
attention-based approach using pre-trained embeddings demonstrates
state-of-the-art performance on our single-context dataset and an existing
multi-sentence context dataset. Second, we show how our model identifies key
contextual elements in a sentence that are likely to contribute most to a
reader's understanding of the target word. Third, we examine how our contextual
informativeness model, originally developed for vocabulary learning
applications for students, can be used for developing better training curricula
for word embedding models in batch learning and few-shot machine learning
settings. We believe our results open new possibilities for applications that
support language learning for both human and machine learners


title: On the slice genus of quasipositive knots in indefinite 4-manifolds

abstract:
  Let $X$ be a closed indefinite $4$-manifold with $b_+(X) = 3 \; ({\rm mod} \;
4)$ and with non-vanishing mod $2$ Seiberg--Witten invariants. We prove a new
lower bound on the genus of a properly embedded surface in $X \setminus B^4$
representing a given homology class and with boundary a quasipositive knot $K
\subset S^3$. In the null-homologous case our inequality implies that the
minimal genus of such a surface is equal to the slice genus of $K$. If $X$ is
symplectic then our lower bound differs from the minimal genus by at most $1$
for any homology class that can be represented by a symplectic surface. Along
the way, we also prove an extension of the adjunction inequality for closed
$4$-manifolds to classes of negative self-intersection without requiring $X$ to
be of simple type.


title:
Two General Series Identities Involving Modified Bessel Functions and a
  Class of Arithmetical Functions

abstract:
  We consider two sequences $a(n)$ and $b(n)$, $1\leq n<\infty$, generated by
Dirichlet series
  $$\sum_{n=1}^{\infty}\frac{a(n)}{\lambda_n^{s}}\qquad\text{and}\qquad
  \sum_{n=1}^{\infty}\frac{b(n)}{\mu_n^{s}},$$ satisfying a familiar functional
equation involving the gamma function $\Gamma(s)$. Two general identities are
established. The first involves the modified Bessel function $K_{\mu}(z)$, and
can be thought of as a 'modular' or 'theta' relation wherein modified Bessel
functions, instead of exponential functions, appear. Appearing in the second
identity are $K_{\mu}(z)$, the Bessel functions of imaginary argument
$I_{\mu}(z)$, and ordinary hypergeometric functions ${_2F_1}(a,b;c;z)$.
Although certain special cases appear in the literature, the general identities
are new. The arithmetical functions appearing in the identities include
Ramanujan's arithmetical function $\tau(n)$; the number of representations of
$n$ as a sum of $k$ squares $r_k(n)$; and primitive Dirichlet characters
$\chi(n)$.


title: Fairness in Graph Mining: A Survey

abstract:
  Graph mining algorithms have been playing a significant role in myriad fields
over the years. However, despite their promising performance on various graph
analytical tasks, most of these algorithms lack fairness considerations. As a
consequence, they could lead to discrimination towards certain populations when
exploited in human-centered applications. Recently, algorithmic fairness has
been extensively studied in graph-based applications. In contrast to
algorithmic fairness on independent and identically distributed (i.i.d.) data,
fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling
techniques. In this survey, we provide a comprehensive and up-to-date
introduction of existing literature under the context of fair graph mining.
Specifically, we propose a novel taxonomy of fairness notions on graphs, which
sheds light on their connections and differences. We further present an
organized summary of existing techniques that promote fairness in graph mining.
Finally, we summarize the widely used datasets in this emerging research field
and provide insights on current research challenges and open questions, aiming
at encouraging cross-breeding ideas and further advances.


title: Inducing Gaussian Process Networks

abstract:
  Gaussian processes (GPs) are powerful but computationally expensive machine
learning models, requiring an estimate of the kernel covariance matrix for
every prediction. In large and complex domains, such as graphs, sets, or
images, the choice of suitable kernel can also be non-trivial to determine,
providing an additional obstacle to the learning task. Over the last decade,
these challenges have resulted in significant advances being made in terms of
scalability and expressivity, exemplified by, e.g., the use of inducing points
and neural network kernel approximations. In this paper, we propose inducing
Gaussian process networks (IGN), a simple framework for simultaneously learning
the feature space as well as the inducing points. The inducing points, in
particular, are learned directly in the feature space, enabling a seamless
representation of complex structured domains while also facilitating scalable
gradient-based learning methods. We consider both regression and (binary)
classification tasks and report on experimental results for real-world data
sets showing that IGNs provide significant advances over state-of-the-art
methods. We also demonstrate how IGNs can be used to effectively model complex
domains using neural network architectures.


title: Spurious Correlations in Reference-Free Evaluation of Text Generation

abstract:
  Model-based, reference-free evaluation metrics have been proposed as a fast
and cost-effective approach to evaluate Natural Language Generation (NLG)
systems. Despite promising recent results, we find evidence that reference-free
evaluation metrics of summarization and dialog generation may be relying on
spurious correlations with measures such as word overlap, perplexity, and
length. We further observe that for text summarization, these metrics have high
error rates when ranking current state-of-the-art abstractive summarization
systems. We demonstrate that these errors can be mitigated by explicitly
designing evaluation metrics to avoid spurious features in reference-free
evaluation.


title:
Naked Singularities for the Einstein Vacuum Equations: The Interior
  Solution

abstract:
  In a previous work [I. Rodnianski and Y. Shlapentokh-Rothman, Naked
Singularities for the Einstein Vacuum Equations: The Exterior Solution,
arXiv:1912.08478] we constructed solutions to the Einstein vacuum equations in
3+1 dimensions which corresponded to the exterior region of a naked
singularity. In this work we construct solutions which correspond to the
interior region and show that the two solutions may be glued together to
produce a naked singularity. Fundamental to our construction is the novel type
of self-similarity for the Einstein vacuum equations that we introduced in our
previous work and also the study of a new class of quasilinear PDE's of mixed
degenerate elliptic-hyperbolic type.


title:
Running Newton Coupling, Scale Identification and Black Hole
  Thermodynamics

abstract:
  We discuss the quantum improvement of black hole solutions in the context of
asymptotic safety. The Newton coupling in this formulation depends on an energy
scale, which must be identified with some length scale in order to study
physical consequences to black holes. However, no physical principle has so far
been known for the identification. Here we propose that the consistency of the
first law of thermodynamics is the principle that should determine physically
sensible scale identification, at least close to the horizon. We show that this
leads to a natural solution that the Newton coupling should be a function of
the horizon area and find a universal formula for the quantum entropy, which
agrees with the standard Bekenstein-Hawking entropy for constant Newton
coupling, for Kerr black holes and other higher-dimensional black holes. This
suggests that the Newton coupling is a function of the area near the horizon,
and also away to infinity, where the quantum effects may not be so important.


title:
MAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and
  Plasticity into Bio-Plausible Spiking Neural Networks

abstract:
  Spiking Neural Network (SNN) is considered more biologically realistic and
power-efficient as it imitates the fundamental mechanism of the human brain.
Recently, backpropagation (BP) based SNN learning algorithms that utilize deep
learning frameworks have achieved good performance. However,
bio-interpretability is partially neglected in those BP-based algorithms.
Toward bio-plausible BP-based SNNs, we consider three properties in modeling
spike activities: Multiplicity, Adaptability, and Plasticity (MAP). In terms of
multiplicity, we propose a Multiple-Spike Pattern (MSP) with multiple spike
transmission to strengthen model robustness in discrete time-iteration. To
realize adaptability, we adopt Spike Frequency Adaption (SFA) under MSP to
decrease spike activities for improved efficiency. For plasticity, we propose a
trainable convolutional synapse that models spike response current to enhance
the diversity of spiking neurons for temporal feature extraction. The proposed
SNN model achieves competitive performances on neuromorphic datasets: N-MNIST
and SHD. Furthermore, experimental results demonstrate that the proposed three
aspects are significant to iterative robustness, spike efficiency, and temporal
feature extraction capability of spike activities. In summary, this work
proposes a feasible scheme for bio-inspired spike activities with MAP, offering
a new neuromorphic perspective to embed biological characteristics into spiking
neural networks.


title: The Bounded Euler Class and the Symplectic Rotation Number

abstract:
  Ghys established the relationship between the bounded Euler class in
$H_{b}^{2}(\mathrm{Homeo}_{+}(S^{1});\mathbb{Z})$ and the Poincar\'{e} rotation
number, that is, he proved that the pullback of the bounded Euler class under a
homomorphism $\varphi \colon \mathbb{Z} \to \mathrm{Homeo}_{+}(S^{1})$
coincides with the Poincar\'{e} rotation number of $\varphi(1)$. In this paper,
we extend the above result to the symplectic group in some sense, and clarify
the relationship between the bounded Euler class in
$H_{b}^{2}(Sp(2n;\mathbb{R});\mathbb{Z})$ and the symplectic rotation number
investigated by Barge and Ghys.


title:
Non-linear excitation of low frequency modes by overstable convective
  modes in rotating stars

abstract:
  We discuss non-linear excitation and amplitude saturation of $g$-modes,
$r$-modes and overstable convective (OsC) modes in early type main sequence
stars, taking account of the effects of three-mode couplings on amplitude
evolutions. OsC modes are rotationally stabilized convective modes in the
convective core and they resonantly excite low frequency $g$-modes to obtain
large amplitudes in the envelope when the rotation rate of the core is larger
than critical rates. We use, for a network of three-mode couplings, amplitude
equations governing the time evolution of the mode amplitudes where each of
three-mode couplings is assumed to occur between two stable modes and one
unstable mode. Assuming that the unstable modes in the couplings are OsC modes
in the core and the stable modes are $g$- and $r$-modes in the envelope, we
integrate the amplitude equations to see how the $g$- and $r$-modes are
non-linearly excited by the OsC modes and whether or not the amplitude
evolutions tend toward a state of finite amplitudes. We find that the
non-linear three-mode couplings do excite low frequency $g$- and $r$-modes but
they are not necessarily effective to achieve amplitude saturation since the
three-mode couplings between the OsC modes with large growth rates and $g$- and
$r$-modes with small damping rates tend to destabilize amplitude evolutions.


title: {A direct construction of the Wiener measure on $\textbf{C}[0, \infty)$

abstract:
  Our construction of the Wiener measure on $\textbf{C}=\textbf{C}[0, \infty)$
consists in first defining a set function $\varphi$\ on the class of all
compact sets based on certain $n$-dimensional normal distributions, $n = 1,\
2,\ldots$\ using the structural relation at (\ref{E1.2}) below. This structural
relation, discovered by the first author, is recorded in his book (2013) on
page 130. We then define a measure $\mu$ on the Borel $\sigma$-field of subsets
of $\textbf{C}$ which is the Wiener measure. This is done via a similar
construction of the Wiener measure on $\textbf{C}_a=\textbf{C}[0, a)$ where $a
> 0$ is an arbitrary real number.
  The traditional way is to first construct the Brownian Motion process (BMP)
and then, by proving it is a measurable mapping into $(\textbf{C},\
\mathscr{C}_\infty)$, call the measure induced by the BMP on $\textbf{C}$\ the
Wiener measure. In the present paper, we define the Wiener measure directly.


title: Two-particle coalescence conditions revisited

abstract:
  The notion of the n-th order local energy, generated by the n-th power of the
Hamiltonian, has been introduced. The n-th order two-particle coalescence
conditions have been derived from the requirements that the n-th order local
energy at the coalescence point is non-singular and equal to the n-th power of
the Hamiltonian eigenvalue. The first condition leads to energy-independent
constraints. The second one is state-specific. The analysis has been done using
a radial, one-dimensional, model Hamiltonian. The model is valid in the
asymptotic region of r ~ 0. The coalescence conditions set the relations
between the expansion coefficients of the radial wave function into a power
series with respect to r.


title: Functional Horseshoe Smoothing for Functional Trend Estimation

abstract:
  Due to developments in instruments and computers, functional observations are
increasingly popular. However, effective methodologies for flexibly estimating
the underlying trends with valid uncertainty quantification for a sequence of
functional data (e.g. functional time series) are still scarce. In this work,
we develop a locally adaptive smoothing method, called functional horseshoe
smoothing, by introducing a shrinkage prior to the general order of differences
of functional variables. This allows us to capture abrupt changes by taking
advantage of the shrinkage capability and also to assess uncertainty by
Bayesian inference. The fully Bayesian framework also allows the selection of
the number of basis functions via the posterior predictive loss. Also, by
taking advantage of the nature of functional data, this method is able to
handle heterogeneously observed data without data augmentation. We show the
theoretical properties of the proposed prior distribution and the posterior
mean, and finally demonstrate them through simulation studies and applications
to a real-world dataset.


title: Multiple-photon bundle emission in the $n$-photon Jaynes-Cummings model

abstract:
  We study the multiple-photon bundle emission in the $n$-photon
Jaynes-Cummings model composed of a two-level system coupled to a single-mode
optical field via the $n$-photon exciting process. Here, the two-level system
is strongly driven by a near-resonant monochromatic field, and hence the system
can work in the Mollow regime, in which a super-Rabi oscillation between the
zero-photon state and the $n$-photon state can take place under proper resonant
conditions. We calculate the photon number populations and the standard
equal-time high-order correlation functions, and find that the multiple-photon
bundle emission can occur in this system. The multiple-photon bundle emission
is also confirmed by investigating the quantum trajectories of the state
populations and both the standard and generalized time-delay second-order
correlation functions for multiple-photon bundle. Our work paves the way
towards the study of multiple-photon quantum coherent devices, with potential
application in quantum information sciences and technologies.


title:
Unsupervised Video Interpolation by Learning Multilayered 2.5D Motion
  Fields

abstract:
  The problem of video frame interpolation is to increase the temporal
resolution of a low frame-rate video, by interpolating novel frames between
existing temporally sparse frames. This paper presents a self-supervised
approach to video frame interpolation that requires only a single video. We
pose the video as a set of layers. Each layer is parameterized by two implicit
neural networks -- one for learning a static frame and the other for a
time-varying motion field corresponding to video dynamics. Together they
represent an occlusion-free subset of the scene with a pseudo-depth channel. To
model inter-layer occlusions, all layers are lifted to the 2.5D space so that
the frontal layer occludes distant layers. This is done by assigning each layer
a depth channel, which we call `pseudo-depth', whose partial order defines the
occlusion between layers. The pseudo-depths are converted to visibility values
through a fully differentiable SoftMin function so that closer layers are more
visible than layers in a distance. On the other hand, we parameterize the video
motions by solving an ordinary differentiable equation (ODE) defined on a
time-varying neural velocity field that guarantees valid motions. This implicit
neural representation learns the video as a space-time continuum, allowing
frame interpolation at any temporal resolution. We demonstrate the
effectiveness of our method on real-world datasets, where our method achieves
comparable performance to state-of-the-arts that require ground truth labels
for training.


title:
Joint Trajectory Design and User Scheduling of Aerial Cognitive Radio
  Networks

abstract:
  Unmanned aerial vehicles (UAVs) have been widely employed to enhance the
end-to-end performance of wireless communications since the links between UAVs
and terrestrial nodes are line-of-sight (LoS) with high probability. However,
the broadcast characteristics of signal propagation in LoS links make it
vulnerable to being wiretapped by malicious eavesdroppers, which poses a
considerable challenge to the security of wireless communications. This paper
investigates the security of aerial cognitive radio networks (CRNs). An
airborne base station transmits confidential messages to secondary users
utilizing the same spectrum as the primary network. An aerial base station
transmits jamming signals to suppress the eavesdropper to enhance secrecy
performance. The uncertainty of eavesdropping node locations is considered, and
the average secrecy rate of the cognitive user is maximized by optimizing
multiple users' scheduling, the UAVs' trajectory, and transmit power. To solve
the non-convex optimization problem with mixed multiple integers variable
problem, we propose an iterative algorithm based on block coordinate descent
and successive convex approximation. Numerical results verify the effectiveness
of our proposed algorithm and demonstrate that our scheme is beneficial to
improving the secrecy performance of aerial CRNs.


title:
Transition operator approach for the description of spontaneous decay in
  a multi-qubit system

abstract:
  In this paper we discuss the use of the transition operator method for the
theoretical description of a multi-qubit system in a one-dimensional waveguide.
A general calculation has been performed for the N-qubit system, which was then
applied to the case of spontaneous decay for one and two qubits. The
probabilities of transitions in such systems, as well as the emission spectra,
are investigated in detail.


title:
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot
  Segmentation

abstract:
  Few-shot segmentation, which aims to segment unseen-class objects given only
a handful of densely labeled samples, has received widespread attention from
the community. Existing approaches typically follow the prototype learning
paradigm to perform meta-inference, which fails to fully exploit the underlying
information from support image-mask pairs, resulting in various segmentation
failures, e.g., incomplete objects, ambiguous boundaries, and distractor
activation. To this end, we propose a simple yet versatile framework in the
spirit of divide-and-conquer. Specifically, a novel self-reasoning scheme is
first implemented on the annotated support image, and then the coarse
segmentation mask is divided into multiple regions with different properties.
Leveraging effective masked average pooling operations, a series of
support-induced proxies are thus derived, each playing a specific role in
conquering the above challenges. Moreover, we devise a unique parallel decoder
structure that integrates proxies with similar attributes to boost the
discrimination power. Our proposed approach, named divide-and-conquer proxies
(DCP), allows for the development of appropriate and reliable information as a
guide at the "episode" level, not just about the object cues themselves.
Extensive experiments on PASCAL-5i and COCO-20i demonstrate the superiority of
DCP over conventional prototype-based approaches (up to 5~10% on average),
which also establishes a new state-of-the-art. Code is available at
github.com/chunbolang/DCP.


title:
Infographics Wizard: Flexible Infographics Authoring and Design
  Exploration

abstract:
  Infographics are an aesthetic visual representation of information following
specific design principles of human perception. Designing infographics can be a
tedious process for non-experts and time-consuming, even for professional
designers. With the help of designers, we propose a semi-automated infographic
framework for general structured and flow-based infographic design generation.
For novice designers, our framework automatically creates and ranks infographic
designs for a user-provided text with no requirement for design input. However,
expert designers can still provide custom design inputs to customize the
infographics. We will also contribute an individual visual group (VG) designs
dataset (in SVG), along with a 1k complete infographic image dataset with
segmented VGs in this work. Evaluation results confirm that by using our
framework, designers from all expertise levels can generate generic infographic
designs faster than existing methods while maintaining the same quality as
hand-designed infographics templates.


title:
Liouville quantum gravity weighted by conformal loop ensemble nesting
  statistics

abstract:
  We study Liouville quantum gravity (LQG) surfaces whose law has been
reweighted according to nesting statistics for a conformal loop ensemble (CLE)
relative to $n\in \{0,1,2,\dots\}$ marked points $z_1,\dots,z_n$. The idea is
to consider a reweighting by $\prod_{B\subseteq [n]} e^{\sigma_B N_B}$, where
$\sigma_B$ are real constants and $N_B$ is the number of CLE loops surrounding
the points $z_i$ for $i\in B$. This is made precise via an approximation
procedure where as part of the proof we derive strong spatial independence
results for CLE. The reweighting induces logarithmic singularities for the
Liouville field at $z_1,\dots,z_n$ with a magnitude depending explicitly on
$\sigma_1,\dots,\sigma_n$. We define the partition function of the surface and
explain its relationship to Liouville conformal field theory (CFT) correlation
functions and a potential relationship with a CFT for CLE. In the case of
$n\in\{0,1\}$ points we derive an explicit formula for the partition function.
Furthermore, we obtain a recursive formula for the partition functions where we
express the $n$ point partition function in terms of the partition function for
disks with $k<n$ marked points, and we use this to partially determine for
which values of $(\sigma_B\colon B\subseteq [n])$ the partition function is
finite. The recursive formula is derived via a continuum counterpart of the
peeling process on planar maps, which was earlier studied in works of Miller,
Sheffield, and Werner in the setting of $n=0$ marked points. We also find an
explicit formula for the generator of the Markov process describing the LQG
boundary lengths in the continuum peeling process. Via an explicit calculation
for this Markov process for $n=0$ we give a new proof for the law of the
conformal radius of the outermost CLE loop in the unit disk around 0, which was
earlier established by Schramm, Sheffield, and Wilson.


title: The Self-energy of Nucleon for the Pion-nucleon Scattering Parameter

abstract:
  The scattering amplitude of the pion-nucleon elastic scattering is calculated
by the lowest-order approximation of the perturbative expansion with the
non-perturbative term. The self-energy of nucleon is determined so as to give
the scattering parameters of the $S$-wave. It is shown that the scattering
process mediated by the $\sigma$-meson plays a decisive role together with the
pseudovector pion interaction.


title:
Vibrationally-excited Lines of HC$_{3}$N Associated with the Molecular
  Disk around the G24.78+0.08 A1 Hyper-compact H$_{\rm {II}}$ Region

abstract:
  We have analyzed Atacama Large Millimeter/submillimeter Array Band 6 data of
the hyper-compact H$_{\rm {II}}$ region G24.78+0.08 A1 (G24 HC H$_{\rm {II}}$)
and report the detection of vibrationally-excited lines of HC$_{3}$N
($v_{7}=2$, $J=24-23$). The spatial distribution and kinematics of a
vibrationally-excited line of HC$_{3}$N ($v_{7}=2$, $J=24-23$, $l=2e$) are
found to be similar to the CH$_{3}$CN vibrationally-excited line ($v_{8}=1$),
which indicates that the HC$_{3}$N emission is tracing the disk around the G24
HC H$_{\rm {II}}$ region previously identified by the CH$_{3}$CN lines. We
derive the $^{13}$CH$_{3}$CN/HC$^{13}$CCN abundance ratios around G24 and
compare them to the CH$_{3}$CN/HC$_{3}$N abundance ratios in disks around
Herbig Ae and T Tauri stars. The $^{13}$CH$_{3}$CN/HC$^{13}$CCN ratios around
G24 ($\sim 3.0-3.5$) are higher than the CH$_{3}$CN/HC$_{3}$N ratios in the
other disks ($\sim 0.03-0.11$) by more than one order of magnitude. The higher
CH$_{3}$CN/HC$_{3}$N ratios around G24 suggest that the thermal desorption of
CH$_{3}$CN in the hot dense gas and efficient destruction of HC$_{3}$N in the
region irradiated by the strong UV radiation are occurring. Our results
indicate that the vibrationally-excited HC$_{3}$N lines can be used as a disk
tracer of massive protostars at the HC H$_{\rm {II}}$ region stage, and the
combination of these nitrile species will provide information of not only
chemistry but also physical conditions of the disk structures.


title:
XMM-Newton Observations of the Narrow-Line Seyfert 1 Galaxy IRAS
  13224$-$3809 : X-ray Spectral Analysis II

abstract:
  Previously, we modelled the X-ray spectra of the narrow-line Seyfert 1 galaxy
IRAS 13224$-$3809 using a disc reflection model with a fixed electron density
of $10^{15}$ cm$^{-3}$. An additional blackbody component was required to fit
the soft X-ray excess below 2 keV. In this work, we analyse simultaneously five
flux-resolved XMM-Newton spectra of this source comprising data collected over
2 Ms. A disc reflection model with an electron density of $n_{\rm
e}\approx10^{20}$ cm$^{-3}$ and an iron abundance of $Z_{\rm
Fe}=3.2\pm0.5Z_{\odot}$ is used to fit the broad-band spectra of this source.
No additional component is required to fit the soft excess. Our best-fit model
provides consistent measurements of black hole spin and disc inclination angle
as in previous models where a low disc density was assumed. In the end, we
calculate the average illumination distance between the corona and the
reflection region in the disc of IRAS 13224$-$3809 based on best-fit density
and ionisation parameters, which changes from 0.43$\sqrt{f_{\rm AD}/f_{\rm
INF}}$ $r_{\rm g}$ in the lowest flux state to 1.71$\sqrt{f_{\rm AD}/f_{\rm
INF}}$ $r_{\rm g}$ in the highest flux state assuming a black hole mass of
$2\times10^{6}M_{\odot}$. $f_{\rm AD}/f_{\rm INF}$ is the ratio between the
flux of the coronal emission that reaches the accretion disc and infinity. This
ratio depends on the geometry of the coronal region in IRAS 13224$-$3809. So we
only discuss its value based on the simple `lamp-post' model, although detailed
modelling of the disc emissivity profile of IRAS 13224$-$3809 is required in
future to reveal the exact geometry of the corona.


title:
An Efficient End-to-End Deep Neural Network for Interstitial Lung
  Disease Recognition and Classification

abstract:
  The automated Interstitial Lung Diseases (ILDs) classification technique is
essential for assisting clinicians during the diagnosis process. Detecting and
classifying ILDs patterns is a challenging problem. This paper introduces an
end-to-end deep convolution neural network (CNN) for classifying ILDs patterns.
The proposed model comprises four convolutional layers with different kernel
sizes and Rectified Linear Unit (ReLU) activation function, followed by batch
normalization and max-pooling with a size equal to the final feature map size
well as four dense layers. We used the ADAM optimizer to minimize categorical
cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with
five classes is taken to train and assess the proposed model. A comparison
study showed that the presented model outperformed pre-trained CNNs and
five-fold cross-validation on the same dataset. For ILDs pattern
classification, the proposed approach achieved the accuracy scores of 99.09%
and the average F score of 97.9%, outperforming three pre-trained CNNs. These
outcomes show that the proposed model is relatively state-of-the-art in
precision, recall, f score, and accuracy.


title:
Some remarks and conjectures about Hankel determinants of polynomials
  which are related to Motzkin paths

abstract:
  This note collects some results and conjectures for the generating functions
of the Hankel determinants of certain polynomials which are related to Motzkin
paths.


title: STFT-Domain Neural Speech Enhancement with Very Low Algorithmic Latency

abstract:
  Deep learning based speech enhancement in the short-term Fourier transform
(STFT) domain typically uses a large window length such as 32 ms. A larger
window contains more samples and the frequency resolution can be higher for
potentially better enhancement. This however incurs an algorithmic latency of
32 ms in an online setup, because the overlap-add algorithm used in the inverse
STFT (iSTFT) is also performed based on the same 32 ms window size. To reduce
this inherent latency, we adapt a conventional dual window size approach, where
a regular input window size is used for STFT but a shorter output window is
used for the overlap-add in the iSTFT, for STFT-domain deep learning based
frame-online speech enhancement. Based on this STFT and iSTFT configuration, we
employ single- or multi-microphone complex spectral mapping for frame-online
enhancement, where a deep neural network (DNN) is trained to predict the real
and imaginary (RI) components of target speech from the mixture RI components.
In addition, we use the RI components predicted by the DNN to conduct
frame-online beamforming, the results of which are then used as extra features
for a second DNN to perform frame-online post-filtering. The frequency-domain
beamforming in between the two DNNs can be easily integrated with complex
spectral mapping and is designed to not incur any algorithmic latency.
Additionally, we propose a future-frame prediction technique to further reduce
the algorithmic latency. Evaluation results on a noisy-reverberant speech
enhancement task demonstrate the effectiveness of the proposed algorithms.
Compared with Conv-TasNet, our STFT-domain system can achieve better
enhancement performance for a comparable amount of computation, or comparable
performance with less computation, maintaining strong performance at an
algorithmic latency as low as 2 ms.


title:
Interstellar detection and chemical modeling of iso-propanol and its
  normal isomer

abstract:
  The detection of a branched alkyl molecule in the high-mass star forming
protocluster Sgr B2(N) permitted by the advent of ALMA revealed a new dimension
of interstellar chemistry. Astrochemical simulations subsequently predicted
that beyond a certain degree of molecular complexity, branched molecules could
even dominate over their straight-chain isomers. More generally, we aim at
probing further the presence in the ISM of complex organic molecules with the
capacity to exhibit both a normal and iso form, via the attachment of a
functional group to either a primary or secondary carbon atom. We used the
imaging spectral line survey survey ReMoCA performed with ALMA and the results
of a recent spectroscopic study of propanol to search for the iso and normal
isomers of this molecule in the hot molecular core Sgr B2(N2). We expanded the
network of the astrochemical model MAGICKAL to explore the formation routes of
propanol. We report the first interstellar detection of iso-propanol toward a
position of Sgr B2(N2) that shows narrow linewidths. We also report the first
secure detection of normal-propanol in a hot core. i-Propanol is found to be
nearly as abundant as n-propanol, with an abundance ratio of 0.6 similar to the
ratio of 0.4 that we obtained previously for i- and n-propyl cyanide in Sgr
B2(N2). The results are in good agreement with the outcomes of our
astrochemical models, which indicate that OH-radical addition to propylene in
dust-grain ice mantles, driven by water photodissociation, can produce
appropriate quantities of n- and i-propanol. The n-to-i ratio in Sgr B2(N2) may
be a direct inheritance of the branching ratio of this reaction process. The
detection of n- and i-propanol and their ratio indicate that the modest
preference toward the normal form of propyl cyanide determined previously may
be a more general feature among similarly sized interstellar molecules.
[abridged]


title: A note on commutators in compact semisimple Lie algebras

abstract:
  Given two elements $A,B$ in a compact semisimple Lie algebra, we show that
there is a regular element $X$ and elements $Y,Z$ with $A=[X,Y]$ and $B=[X,Z]$.
In the course of the proof we show also that every linear subspace $V$ of
codimension at most 2 in the Lie algebra contains a CSA.


title:
CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic
  Segmentation

abstract:
  LiDAR semantic segmentation essential for advanced autonomous driving is
required to be accurate, fast, and easy-deployed on mobile platforms. Previous
point-based or sparse voxel-based methods are far away from real-time
applications since time-consuming neighbor searching or sparse 3D convolution
are employed. Recent 2D projection-based methods, including range view and
multi-view fusion, can run in real time, but suffer from lower accuracy due to
information loss during the 2D projection. Besides, to improve the performance,
previous methods usually adopt test time augmentation (TTA), which further
slows down the inference process. To achieve a better speed-accuracy trade-off,
we propose Cascade Point-Grid Fusion Network (CPGNet), which ensures both
effectiveness and efficiency mainly by the following two techniques: 1) the
novel Point-Grid (PG) fusion block extracts semantic features mainly on the 2D
projected grid for efficiency, while summarizes both 2D and 3D features on 3D
point for minimal information loss; 2) the proposed transformation consistency
loss narrows the gap between the single-time model inference and TTA. The
experiments on the SemanticKITTI and nuScenes benchmarks demonstrate that the
CPGNet without ensemble models or TTA is comparable with the state-of-the-art
RPVNet, while it runs 4.7 times faster.


title:
Do Human Mobility Network Analyses Produced from Different
  Location-based Data Sources Yield Similar Results across Scales?

abstract:
  The burgeoning availability of sensing technology and location-based data is
driving the expansion of analysis of human mobility networks in science and
engineering research, as well as in epidemic forecasting and mitigation, urban
planning, traffic engineering, emergency response, and business development.
However, studies employ datasets provided by different location-based data
providers, and the extent to which the human mobility measures and results
obtained from different datasets are comparable is not known. To address this
gap, in this study, we examined three prominent location-based data sources:
Spectus, X-Mode, and Veraset to analyze human mobility networks across
metropolitan areas at different scales: global, sub-structure, and microscopic.
Dissimilar results were obtained from the three datasets, suggesting the
sensitivity of network models and measures to datasets. This finding has
important implications for building generalized theories of human mobility and
urban dynamics based on different datasets. The findings also highlighted the
need for ground-truthed human movement datasets to serve as the benchmark for
testing the representativeness of human mobility datasets. Researchers and
decision-makers across different fields of science and technology should
recognize the sensitivity of human mobility results to dataset choice and
develop procedures for ground-truthing the selected datasets in terms of
representativeness of data points and transferability of results.


title:
The Higgs boson as a self-similar system: A new solution to the
  hierarchy problem

abstract:
  We propose a new solution to the hierarchy (naturalness) problem, concerning
quantum corrections of the Higgs mass. Suggesting the Higgs boson as a system
with a self-similar internal structure, we calculate its two-point function and
find that the quadratic divergence is replaced by a logarithmic one. It is
shown that the partonic-like distribution follows the Tsallis statistics and
also high energy physics experimental data for the Higgs transverse momentum
distribution can be described by the self-similar statistical model.


title:
SinTra: Learning an inspiration model from a single multi-track music
  segment

abstract:
  In this paper, we propose SinTra, an auto-regressive sequential generative
model that can learn from a single multi-track music segment, to generate
coherent, aesthetic, and variable polyphonic music of multi-instruments with an
arbitrary length of bar. For this task, to ensure the relevance of generated
samples and training music, we present a novel pitch-group representation.
SinTra, consisting of a pyramid of Transformer-XL with a multi-scale training
strategy, can learn both the musical structure and the relative positional
relationship between notes of the single training music segment. Additionally,
for maintaining the inter-track correlation, we use the convolution operation
to process multi-track music, and when decoding, the tracks are independent to
each other to prevent interference. We evaluate SinTra with both subjective
study and objective metrics. The comparison results show that our framework can
learn information from a single music segment more sufficiently than Music
Transformer. Also the comparison between SinTra and its variant, i.e., the
single-stage SinTra with the first stage only, shows that the pyramid structure
can effectively suppress overly-fragmented notes.


title: MRAM-based Analog Sigmoid Function for In-memory Computing

abstract:
  We propose an analog implementation of the transcendental activation function
leveraging two spin-orbit torque magnetoresistive random-access memory
(SOT-MRAM) devices and a CMOS inverter. The proposed analog neuron circuit
consumes 1.8-27x less power, and occupies 2.5-4931x smaller area, compared to
the state-of-the-art analog and digital implementations. Moreover, the
developed neuron can be readily integrated with memristive crossbars without
requiring any intermediate signal conversion units. The architecture-level
analyses show that a fully-analog in-memory computing (IMC) circuit that use
our SOT-MRAM neuron along with an SOT-MRAM based crossbar can achieve more than
1.1x, 12x, and 13.3x reduction in power, latency, and energy, respectively,
compared to a mixed-signal implementation with analog memristive crossbars and
digital neurons. Finally, through cross-layer analyses, we provide a guide on
how varying the device-level parameters in our neuron can affect the accuracy
of multilayer perceptron (MLP) for MNIST classification.


title:
Sonic Interactions in Virtual Environments: the Egocentric Audio
  Perspective of the Digital Twin

abstract:
  The relationships between the listener, physical world and virtual
environment (VE) should not only inspire the design of natural multimodal
interfaces but should be discovered to make sense of the mediating action of VR
technologies. This chapter aims to transform an archipelago of studies related
to sonic interactions in virtual environments (SIVE) into a research field
equipped with a first theoretical framework with an inclusive vision of the
challenges to come: the egocentric perspective of the auditory digital twin. In
a VE with immersive audio technologies implemented, the role of VR simulations
must be enacted by a participatory exploration of sense-making in a network of
human and non-human agents, called actors. The guardian of such locus of agency
is the auditory digital twin that fosters intra-actions between humans and
technology, dynamically and fluidly redefining all those configurations that
are crucial for an immersive and coherent experience. The idea of entanglement
theory is here mainly declined in an egocentric-spatial perspective related to
emerging knowledge of the listener's perceptual capabilities. This is an
actively transformative relation with the digital twin potentials to create
movement, transparency, and provocative activities in VEs. The chapter contains
an original theoretical perspective complemented by several bibliographical
references and links to the other book chapters that have contributed
significantly to the proposal presented here.


title: Perception Visualization: Seeing Through the Eyes of a DNN

abstract:
  Artificial intelligence (AI) systems power the world we live in. Deep neural
networks (DNNs) are able to solve tasks in an ever-expanding landscape of
scenarios, but our eagerness to apply these powerful models leads us to focus
on their performance and deprioritises our ability to understand them. Current
research in the field of explainable AI tries to bridge this gap by developing
various perturbation or gradient-based explanation techniques. For images,
these techniques fail to fully capture and convey the semantic information
needed to elucidate why the model makes the predictions it does. In this work,
we develop a new form of explanation that is radically different in nature from
current explanation methods, such as Grad-CAM. Perception visualization
provides a visual representation of what the DNN perceives in the input image
by depicting what visual patterns the latent representation corresponds to.
Visualizations are obtained through a reconstruction model that inverts the
encoded features, such that the parameters and predictions of the original
models are not modified. Results of our user study demonstrate that humans can
better understand and predict the system's decisions when perception
visualizations are available, thus easing the debugging and deployment of deep
models as trusted systems.


title: Measurement of the effective weak mixing angle at the CEPC

abstract:
  We present a study of the measurement of the effective weak mixing angle
parameter ($\sin^2\theta_{\text{eff}}^{\text{$\ell$}}$) at the Circular
Electron Positron Collider (CEPC). As a fundamental physics parameter,
$\sin^2\theta_{\text{eff}}^{\text{$\ell$}}$ plays a key role not only in the
global test of the standard model electroweak sector, but also in constraining
the potential beyond standard model new physics at high energy frontier. CEPC
proposes a two year running period around the $Z$ boson mass pole at high
instantaneous luminosity. It is demonstrated that with one month data taken,
the uncertainty of $\sin^2\theta_{\text{eff}}^{\text{$\ell$}}$ at $Z$ pole can
be reduced to 0.00001 both in the lepton and heavy quark final states, which is
much smaller than the theoretical uncertainty of the calculation with two-loop
radiative corrections. It will not only improve the precision with respect to
the measurements from previous colliders including the LEP, SLC, Tevatron and
LHC, but also provide direct comparison between different final states. In this
paper, we also study the measurement of
$\sin^2\theta_{\text{eff}}^{\text{$\ell$}}$ at high mass region. With one month
data taken, the precision of $\sin^2\theta_{\text{eff}}^{\text{$\ell$}}$
measured at 130 GeV from $b$ quark final state is 0.00007, which will be an
important experimental observation on the energy-running effect of
$\sin^2\theta_{\text{eff}}^{\text{$\ell$}}$.


title:
Information flow in one-dimensional non-unitary quantum cellular
  automata

abstract:
  The information flow in a quantum system is a fundamental feature of its
dynamics. An important class of dynamics are quantum cellular automata (QCA),
systems with discrete updates invariant in time and space, for which an index
theory has been proposed for the quantification of the net flow of quantum
information across a boundary. While the index is rigid in the sense of begin
invariant under finite-depth local circuits, it is not defined when the system
is coupled to an environment, i.e. for non-unitary time evolution of open
quantum systems. We propose a new measure of information flow for non-unitary
QCA denoted the information current which is not rigid, but can be computed
locally based on the matrix-product operator representation of the map.


title:
Transport theory for topological Josephson junctions with a Majorana
  qubit

abstract:
  We construct a semiclassical theory for the transport of topological
junctions starting from a microscopic Hamiltonian that comprehensively includes
the interplay among the Majorana qubit, the Josephson phase, and the
dissipation process. With the path integral approach, we derive a set of
semiclassical equations of motion that can be used to calculate the time
evolution of the Josephson phase and the Majorana qubit. In the equations we
reveal rich dynamical phenomena such as the qubit induced charge pumping, the
effective spin-orbit torque, and the Gilbert damping. We demonstrate the
influence of these dynamical phenomena on the transport signatures of the
junction. We apply the theory to study the Shapiro steps of the junction, and
find the suppression of the first Shapiro step due to the dynamical feedback of
the Majorana qubit.


title: Progressive Training of A Two-Stage Framework for Video Restoration

abstract:
  As a widely studied task, video restoration aims to enhance the quality of
the videos with multiple potential degradations, such as noises, blurs and
compression artifacts. Among video restorations, compressed video quality
enhancement and video super-resolution are two of the main tacks with
significant values in practical scenarios. Recently, recurrent neural networks
and transformers attract increasing research interests in this field, due to
their impressive capability in sequence-to-sequence modeling. However, the
training of these models is not only costly but also relatively hard to
converge, with gradient exploding and vanishing problems. To cope with these
problems, we proposed a two-stage framework including a multi-frame recurrent
network and a single-frame transformer. Besides, multiple training strategies,
such as transfer learning and progressive training, are developed to shorten
the training time and improve the model performance. Benefiting from the above
technical contributions, our solution wins two champions and a runner-up in the
NTIRE 2022 super-resolution and quality enhancement of compressed video
challenges.


title: Honing in on a topological zero-bias conductance peak

abstract:
  A popular signature of Majorana bound states in topological superconductors
is the zero-energy conductance peak with a height of $2e^2/h$. However, a
similar zero energy conductance peak with almost the same height can also arise
due to non-topological reasons. Here we show that these trivial and topological
zero energy conductance peaks can be distinguished via the zero energy local
density of states and local magnetization density of states. We find that the
zero-energy local density of states exhibits oscillations with a finite period
for a trivial zero-bias conductance peak. In contrast, these oscillations
disappear for the topological zero-bias conductance peak. On the other hand,
zero energy local magnetization density of states shows a periodic oscillation
for trivial zero-bias conductance peak, while for topological ZBCP, they
vanish. Our results suggest that zero-energy local density of states and local
magnetization density of states can be used as an experimental probe to
distinguish trivial zero energy conductance peak from topological zero energy
conductance peak.

categories: cond-mat.mes-hall hep-th physics.app-ph quant-ph


title: Power structures of directed spaces

abstract:
  Powerdomains in domain theory plays an important role in modeling the
semantics of nondeterministic functional programming languages.\ In this
paper,\ we extend the notion of powerdomain to the category of directed
spaces,\ which is equivalent to the notion of the\ $T_0$\ monotone-determined
space\ \cite{EN2009}.\ We define the notion of upper,\ lower and convex
powerspace of a directed space by the way of free algebras.\ We show that the
upper,\ lower and convex powerspace over any directed space exist and give
their concrete structures.\ Generally,\ the upper,\ lower and convex
powerspaces of a directed spaces are different from the upper,\ lower and
convex powerdomains of a dcpos endowed with the Scott topology and the
observationally-induced upper and lower powerspaces introduced by Battenfeld
and Sch\"{o}der in 2015. Keywords: powerdomain,\ directed lower powerspace of
directed spaces,\ directed upper powerspace of directed spaces,\ directed
convex powerspace of directed spaces,\ observationally-induced lower
powerspace,\ observationally-induced lower powerspace


title:
Partial compactification of metabelian Lie groups with prescribed
  varieties of minimal rational tangents

abstract:
  We study minimal rational curves on a complex manifold that are tangent to a
distribution. In this setting, the variety of minimal rational tangents (VMRT)
has to be isotropic with respect to the Levi tensor of the distribution. Our
main result is a converse of this: any smooth projective variety isotropic with
respect to a vector-valued anti-symmetric form can be realized as VMRT of
minimal rational curves tangent to a distribution on a complex manifold. The
complex manifold is constructed as a partial equivariant compactification of a
metabelian group, which is a result of independent interest.


title:
Bures-Wasserstein minimizing geodesics between covariance matrices of
  different ranks

abstract:
  The set of covariance matrices equipped with the Bures-Wasserstein distance
is the orbit space of the smooth, proper and isometric action of the orthogonal
group on the Euclidean space of square matrices. This construction induces a
natural orbit stratification on covariance matrices, which is exactly the
stratification by the rank. Thus, the strata are the manifolds of symmetric
positive semi-definite (PSD) matrices of fixed rank endowed with the
Bures-Wasserstein Riemannian metric. In this work, we study the geodesics of
the Bures-Wasserstein distance. Firstly, we complete the literature on
geodesics in each stratum by clarifying the set of preimages of the exponential
map and by specifying the injection domain. We also give explicit formulae of
the horizontal lift, the exponential map and the Riemannian logarithms that
were kept implicit in previous works. Secondly, we give the expression of all
the minimizing geodesic segments joining two covariance matrices of any rank.
More precisely, we show that the set of all minimizing geodesics between two
covariance matrices $\Sigma$ and $\Lambda$ is parametrized by the closed unit
ball of $\mathbb{R}^{(k-r)\times(l-r)}$ for the spectral norm, where $k, l, r$
are the respective ranks of $\Sigma$, $\Lambda$, $\Sigma$$\Lambda$. In
particular, the minimizing geodesic is unique if and only if $r = \min(k, l)$.
Otherwise, there are infinitely many.


title:
Normalization procedure for obtaining the local density of states from
  high-bias scanning tunneling spectroscopy

abstract:
  Differential conductance spectroscopy performed in the high bias regime -- in
which the applied voltage exceeds the sample work function -- is a poor measure
of the local density of states due to the effects of the changing tunnel
barrier. Additionally, the large applied voltage oftentimes makes
constant-height measurement experimentally impractical, lending
constant-current spectroscopy an advantageous edge; but the differential
conductance in that case is even further removed from the local density of
states due to the changing tip height. Here, we present a normalization scheme
for extracting the local density of states from high bias scanning tunneling
spectroscopy, obtained in either constant-current or constant-height mode. We
extend this model to account for the effects of the in-plane momentum of the
probed states to the overall current. We demonstrate the validity of the
proposed scheme by applying it to laterally confined field-emission resonances,
which appear as peak-shaped spectroscopic features with a well-defined in-plane
momentum.


title:
Multi-task recommendation system for scientific papers with high-way
  networks

abstract:
  Finding and selecting the most relevant scientific papers from a large number
of papers written in a research community is one of the key challenges for
researchers these days. As we know, much information around research interest
for scholars and academicians belongs to papers they read. Analysis and
extracting contextual features from these papers could help us to suggest the
most related paper to them. In this paper, we present a multi-task
recommendation system (RS) that predicts a paper recommendation and generates
its meta-data such as keywords. The system is implemented as a three-stage deep
neural network encoder that tries to maps longer sequences of text to an
embedding vector and learns simultaneously to predict the recommendation rate
for a particular user and the paper's keywords. The motivation behind this
approach is that the paper's topics expressed as keywords are a useful
predictor of preferences of researchers. To achieve this goal, we use a system
combination of RNNs, Highway and Convolutional Neural Networks to train
end-to-end a context-aware collaborative matrix. Our application uses Highway
networks to train the system very deep, combine the benefits of RNN and CNN to
find the most important factor and make latent representation. Highway Networks
allow us to enhance the traditional RNN and CNN pipeline by learning more
sophisticated semantic structural representations. Using this method we can
also overcome the cold start problem and learn latent features over large
sequences of text.


title:
Multi-scale Knowledge Distillation for Unsupervised Person
  Re-Identification

abstract:
  Unsupervised person re-identification is a challenging and promising task in
the computer vision. Nowadays unsupervised person re-identification methods
have achieved great improvements by training with pseudo labels. However, the
appearance and label noise are less explicitly studied in the unsupervised
manner. To relieve the effects of appearance noise the global features
involved, we also take into account the features from two local views and
produce multi-scale features. We explore the knowledge distillation to filter
label noise, Specifically, we first train a teacher model from noisy pseudo
labels in a iterative way, and then use the teacher model to guide the learning
of our student model. In our setting, the student model could converge fast in
the supervision of the teacher model thus reduce the interference of noisy
labels as the teacher model greatly suffered. After carefully handling the
noises in the feature learning, Our multi-scale knowledge distillation are
proven to be very effective in the unsupervised re-identification. Extensive
experiments on three popular person re-identification datasets demonstrate the
superiority of our method. Especially, our approach achieves a state-of-the-art
accuracy 85.7% @mAP or 94.3% @Rank-1 on the challenging Market-1501 benchmark
with ResNet-50 under the fully unsupervised setting.


title:
Topological contribution to magnetism in the Kane-Mele model: An
  explicit wave function approach

abstract:
  In our previous publication [S. Ozaki and M. Ogata, Phys. Rev. Research 3,
013058], the quantization of the orbital-Zeeman (OZ) cross term in the magnetic
susceptibility, or the cross term of spin Zeeman and orbital effect, was shown
for the Kane-Mele model using the expansion around the Dirac points. In the
present study, we accurately evaluate the orbital, spin-Zeeman, and OZ cross
term of the Kane-Mele model using a recently developed formulation. This
formula is written in terms of the explicit Bloch wave functions, and enables
us to evaluate each contribution taking account of the integration over the
whole Brillouin zone and the summation over all the bands. As a result,
additional contributions such as core-electron diamagnetism are found.
Furthermore, our evaluation confirms the quantization of the OZ cross term and
reveals its behavior including the metallic case. The possibility of
experimental detection of the quantization is discussed.


title:
A flow method for a generalization of $L_{p}$ Christofell-Minkowski
  problem

abstract:
  In this paper, a generalization of the $L_{p}$-Christoffel-Minkowski problem
is studied. We consider an anisotropic curvature flow and derive the long-time
existence of the flow. Then under some initial data, we obtain the existence of
smooth solutions to this problem for $c=1$.