title: Ethics and Efficacy of Unsolicited Anti-Trafficking SMS Outreach

abstract:
  The sex industry exists on a continuum based on the degree of work autonomy
present in labor conditions: a high degree exists on one side of the continuum
where independent sex workers have a great deal of agency, while much less
autonomy exists on the other side, where sex is traded under conditions of
human trafficking. Organizations across North America perform outreach to sex
industry workers to offer assistance in the form of services (e.g., healthcare,
financial assistance, housing), prayer, and intervention. Increasingly,
technology is used to look for trafficking victims or facilitate the provision
of assistance or services, for example through scraping and parsing sex
industry workers' advertisements into a database of contact information that
can be used by outreach organizations. However, little is known about the
efficacy of anti-trafficking outreach technology, nor the potential risks of
using it to identify and contact the highly stigmatized and marginalized
population of those working in the sex industry.
  In this work, we investigate the use, context, benefits, and harms of an
anti-trafficking technology platform via qualitative interviews with multiple
stakeholders: the technology developers (n=6), organizations that use the
technology (n=17), and sex industry workers who have been contacted or wish to
be contacted (n=24). Our findings illustrate misalignment between developers,
users of the platform, and sex industry workers they are attempting to assist.
In their current state, anti-trafficking outreach tools such as the one we
investigate are ineffective and, at best, serve as a mechanism for spam and, at
worst, scale and exacerbate harm against the population they aim to serve. We
conclude with a discussion of best practices for technology-facilitated
outreach efforts to minimize risk or harm to sex industry workers while
efficiently providing needed services.


title: Faltings' annihilator theorem and almost Cohen-Macaulay rings

abstract:
  Faltings' annihilator theorem is an important result in local cohomology
theory. Recently, Doustimehr and Naghipour generalized the Falitings'
annihilator theorem. They proved that if $R$ is a homomorphic image of a
Gorenstein ring, then $f_\mathfrak{a}^\mathfrak{b}(M)_n =
\lambda_\mathfrak{a}^\mathfrak{b}(M)_n$, where
$f_\mathfrak{a}^\mathfrak{b}(M)_n := \inf\{i \in \mathbb{N} \mid
\operatorname{dim}{\operatorname{Supp}(\mathfrak{b}^t H_\mathfrak{a}^i(M))}
\geq n \text{ for all } t\in \mathbb{N}\}$ and
$\lambda_\mathfrak{a}^\mathfrak{b}(M)_n := \inf\{\lambda_{\mathfrak{a}
R_\mathfrak{p}}^{\mathfrak{b} R_\mathfrak{p}}(M_\mathfrak{p}) \mid
\mathfrak{p}\in\operatorname{Spec}{R} \text{ with }
\operatorname{dim}{R/\mathfrak{p}} \geq n\}$. In this paper, we study the
relation between $f_\mathfrak{a}^\mathfrak{b}(M)_n$ and
$\lambda_\mathfrak{a}^\mathfrak{b}(M)_n$, and prove that if $R$ is an almost
Cohen-Macaulay ring, then $f_\mathfrak{a}^\mathfrak{b}(M)_n \geq
\lambda_\mathfrak{a}^\mathfrak{b}(M)_n - \operatorname{cmd}{R}$. Using this
result, we prove that if $R$ is a homomorphic image of a Cohen-Macaulay ring,
then $f_\mathfrak{a}^\mathfrak{b}(M)_n =
\lambda_\mathfrak{a}^\mathfrak{b}(M)_n$.


title:
LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and
  Zero-Resource Children's Dialects

abstract:
  This paper proposes a novel linear prediction coding-based data aug-mentation
method for children's low and zero resource dialect ASR. The data augmentation
procedure consists of perturbing the formant peaks of the LPC spectrum during
LPC analysis and reconstruction. The method is evaluated on two novel
children's speech datasets with one containing California English from the
Southern CaliforniaArea and the other containing a mix of Southern American
English and African American English from the Atlanta, Georgia area. We test
the proposed method in training both an HMM-DNN system and an end-to-end system
to show model-robustness and demonstrate that the algorithm improves ASR
performance, especially for zero resource dialect children's task, as compared
to common data augmentation methods such as VTLP, Speed Perturbation, and
SpecAugment.


title:
Plasmonic Band-stop MIM Waveguide Filter Based on Bilateral Asymmetric
  Equilateral Triangular Ring

abstract:
  In this paper, a bilateral asymmetric equilateral triangular ring (ETR)
band-stop filter based on metal-insulator-metal (MIM) waveguide is proposed.
The transmission spectrum and electric field distribution of the filter are
simulated and theoretically investigated by finite difference time domain
(FDTD) and coupled mode theory (CMT) method. The results show that changing the
number and position of resonant cavities can adjust the transmission
characteristics of the filter. The minimum transmission and sensitivity of this
filter is 0.19% and 1149 nm/RIU, respectively. The filters have broad prospects
in the highly integrated optical circuits and refractive index sensor.


title:
Can Social Robots Effectively Elicit Curiosity in STEM Topics from K-1
  Students During Oral Assessments?

abstract:
  This paper presents the results of a pilot study that introduces social
robots into kindergarten and first-grade classroom tasks. This study aims to
understand 1) how effective social robots are in administering educational
activities and assessments, and 2) if these interactions with social robots can
serve as a gateway into learning about robotics and STEM for young children. We
administered a commonly-used assessment (GFTA3) of speech production using a
social robot and compared the quality of recorded responses to those obtained
with a human assessor. In a comparison done between 40 children, we found no
significant differences in the student responses between the two conditions
over the three metrics used: word repetition accuracy, number of times
additional help was needed, and similarity of prosody to the assessor. We also
found that interactions with the robot were successfully able to stimulate
curiosity in robotics, and therefore STEM, from a large number of the 164
student participants.


title: Short-range Atomic Topology of Ab initio Generated Amorphous PdSi Alloys

abstract:
  Since the pioneering efforts of Duwez and coworkers in 1965, when a solid
amorphous phase of Pd-Si ($a$-PdSi) was obtained in the vicinity of the
eutectic concentration, much work has been done. However, some points related
to the atomic structures remain to be systematized. In this work, 8 amorphous
Pd$_{100-c}$Si$_{c}$ alloys (c = 2.5, 5, 10, 13.34, 15, 17.5, 20, and 22 at %)
were generated by molecular dynamics $ab$ $initio$ simulations; the short-range
structure is analyzed using several correlation functions, like Pair
Distribution Functions, reduced Pair Distribution Functions, Plane Angle
Distribution Functions. Other related properties, like nearest-neighbors and
some Frank-Kasper polyhedra are reported. The generated samples correctly
reproduce the scarce experimental pair correlation functions that are reported
in the literature. An unexpected outcome is the appearance of structural
changes in the neighborhood of Pd$_{86.66}$Si$_{13.34}$, which may be related
to the magnetic changes reported in the liquid and amorphous Pd-Si alloys
previously reported. The explicit amorphous topologies are reported.


title: C2N: Practical Generative Noise Modeling for Real-World Denoising

abstract:
  Learning-based image denoising methods have been bounded to situations where
well-aligned noisy and clean images are given, or samples are synthesized from
predetermined noise models, e.g., Gaussian. While recent generative noise
modeling methods aim to simulate the unknown distribution of real-world noise,
several limitations still exist. In a practical scenario, a noise generator
should learn to simulate the general and complex noise distribution without
using paired noisy and clean images. However, since existing methods are
constructed on the unrealistic assumption of real-world noise, they tend to
generate implausible patterns and cannot express complicated noise maps.
Therefore, we introduce a Clean-to-Noisy image generation framework, namely
C2N, to imitate complex real-world noise without using any paired examples. We
construct the noise generator in C2N accordingly with each component of
real-world noise characteristics to express a wide range of noise accurately.
Combined with our C2N, conventional denoising CNNs can be trained to outperform
existing unsupervised methods on challenging real-world benchmarks by a large
margin.


title: Bayesian Quantile Trend Filtering on Graphs using Shrinkage Priors

abstract:
  Quantiles are useful characteristics of random variables that can provide
substantial information of distributions compared with commonly used summary
statistics such as means. In this paper, we propose a Bayesian quantile trend
filtering method to estimate non-stationary trend of quantiles on graphs. We
introduce general shrinkage priors for graph differences to induce locally
adaptive Bayesian inference on trends. Introducing so-called shadow priors with
multivariate truncated distribution for local scale parameters and mixture
representation of the asymmetric Laplace distribution, we provide a simple
Gibbs sampling algorithm to generate posterior samples. We also develop
variational Bayes approximation to quickly compute point estimates (e.g.
posterior means). The numerical performance of the proposed method is
demonstrated through simulation study with time series data, application of
quantile regression and robust spatial quantile smoothing.


title:
Optimal Regularity and Uhlenbeck Compactness for General Relativity and
  Yang-Mills Theory

abstract:
  We discuss the discovery and the significance of the RT-equations, a novel
system of elliptic partial differential equations foundational to geometric
analysis in General Relativity and Mathematical Physics: Solutions of the
RT-equations furnish coordinate and gauge transformations which give a
non-optimal connection a gain of one derivative over its own Riemann curvature
tensor, i.e., to optimal regularity. The RT-equations are elliptic regardless
of metric or metric signature, so the elliptic RT-equations regularize
singularities in solutions of the hyperbolic Einstein equations. Our existence
theory for the RT-equations resolves the problem of optimal regularity for
affine connections on arbitrary manifolds, including Lorentzian manifolds, and
for connections on vector bundles over such manifolds. As an application we
prove that singularities at GR shock waves are removable, implying geodesic
curves, locally inertial coordinates and the Newtonian limit all exist. By use
of the extra derivative of regularity for connections, we extend Uhlenbeck
compactness from Uhlenbeck's setting of vector bundles with compact gauge
groups over Riemannian manifolds, to the case of compact and non-compact gauge
groups over general manifolds, including the Lorentzian manifolds of General
Relativity. Compactness is the essential tool of mathematical analysis for
establishing the validity of approximation schemes. Uhlenbeck compactness is an
improvement of the Div-Curl Lemma which is intrinsic to nonlinear problems in
Mathematical Physics because it provides existence of strongly convergent
sequences of connections from bounds on curvature alone.


title:
Controlling degeneracy and magnetization switching in an artificial spin
  ice system of peanut-shaped nanomagnets

abstract:
  Using extensive numerical simulations, we probe the magnetization switching
in two-dimensional artificial spin ice (ASI) system consisting of peanut-shaped
nanomagnets. We also investigated the effect of the external magnetic field on
the degeneracy of the magnetic states in such a system. The switching field is
found to be one order smaller in the proposed ASI system with peanut-shaped
nanomagnets as compared to the conventionally used highly-anisotropic
nanoisland such as elliptically shaped nanomagnets. The metastable 2-in/2-out
(Type II) magnetic state is robust at the remanence. We are also able to access
the other possible microstates corresponding to Type II magnetic configurations
by carefully varying the external magnetic field. It implies that one can
control the degeneracy of the magnetic state by the application of a suitable
magnetic field. Interestingly, the magnetic charge neutrality breaks due to the
defects induced by removing nanomagnets. In such a case, the system also
appears to have a 1-out/3-in or 3-out/1-in (Type III) spin state, reminiscent
of a magnetic monopole. We believe that our study is highly desirable in the
context of developing the next-generation spintronics-based devices for future
technologies.


title:
Electron beam splitting at topological insulator surface states and a
  proposal for electronic Goos-Hanchen shift measurement

abstract:
  The hexagonal warping effect on transport properties and Goos-H\"anchen (GH)
lateral shift of electrons on the surface of a topological insulator with a
potential barrier is investigated theoretically. Due to the warped Fermi
surface for incident electron beams, we can expect two propagating transmitted
beams corresponding to the occurrence of double refraction. The transmitted
beams have spin orientations locked to their momenta so one of the spin
directions rotates compared to the incident spin direction. Based on a
low-energy Hamiltonian near the Dirac point and considering Gaussian beams, we
derive expressions for calculating lateral shifts in the presence of warping
effect. We study the dependence of transmission probabilities and GH shifts of
transmitted beams on system parameters in detail by giving an explanation for
the appearance of large peaks in the lateral shifts corresponding to their
transmission peaks. It is shown that the separation between two transmitted
beams through their different GH shifts can be as large as a few micrometers,
which is large enough to be observed experimentally. Finally, we propose a
method to measure the GH shift of electron beams based on the transverse
magnetic focusing technique in which, by tuning an applied magnetic field, a
detectable resonant path for electrons can be induced.


title:
Improving the Level of Autism Discrimination through GraphRNN Link
  Prediction

abstract:
  Dataset is the key of deep learning in Autism disease research. However, due
to the few quantity and heterogeneity of samples in current dataset, for
example ABIDE (Autism Brain Imaging Data Exchange), the recognition research is
not effective enough. Previous studies mostly focused on optimizing feature
selection methods and data reinforcement to improve accuracy. This paper is
based on the latter technique, which learns the edge distribution of real brain
network through GraphRNN, and generates the synthetic data which has incentive
effect on the discriminant model. The experimental results show that the
combination of original and synthetic data greatly improves the discrimination
of the neural network. For instance, the most significant effect is the
50-layer ResNet, and the best generation model is GraphRNN, which improves the
accuracy by 32.51% compared with the model reference experiment without
generation data reinforcement. Because the generated data comes from the
learned edge connection distribution of Autism patients and typical controls
functional connectivity, but it has better effect than the original data, which
has constructive significance for further understanding of disease mechanism
and development.


title:
Configuration of magnetotail current sheet prior to magnetic
  reconnection onset

abstract:
  The magnetotail current sheet configuration determines magnetic reconnection
properties that control the substorm onset, one of the most energetic phenomena
in the Earth's magnetosphere. The quiet-time current sheet is often
approximated as a two-dimensional (2D) magnetic field configuration balanced by
isotropic plasma pressure gradients. However, reconnection onset is preceded by
the current sheet thinning and the formation of a nearly one-dimensional (1D)
magnetic field configuration. In this study, using particle-in-cell
simulations, we investigate the force balance of such thin current sheets when
they are driven by plasma inflow. We demonstrate that the magnetic field
configuration transitions from 2D to 1D thanks to the formation of plasma
pressure nongyrotropy and reveal its origin in the nongyrotropic terms of the
ion distributions. We show that substorm onset may be controlled by the
instability and dynamics of such nongyrotropic current sheets, having
properties much different from the most commonly investigated 2D isotropic
configuration.


title: On Higher Order Weierstrass Points on $X_0(N)$

abstract:
  Let $\Gamma$ be the Fuchsian group of the first kind. For an even integer
$m\ge 4$, we describe the space $H^{m/2}\left(\mathfrak R_\Gamma\right)$ of
$m/2$--holomorphic differentials in terms of a subspace $S_m^H(\Gamma)$ of the
space of (holomorphic) cuspidal modular forms $S_m(\Gamma)$. This generalizes
classical isomorphism $S_2(\Gamma)\simeq H^{1}\left(\mathfrak R_\Gamma\right)$.
We study the properties of $S_m^H(\Gamma)$. As an application, we describe the
algorithm implemented in SAGE for testing if a cusp at $\infty$ for
non-hyperelliptic $X_0(N)$ is a $\frac{m}{2}$-Weierstrass point.


title:
BP-Triplet Net for Unsupervised Domain Adaptation: A Bayesian
  Perspective

abstract:
  Triplet loss, one of the deep metric learning (DML) methods, is to learn the
embeddings where examples from the same class are closer than examples from
different classes. Motivated by DML, we propose an effective BP-Triplet Loss
for unsupervised domain adaption (UDA) from the perspective of Bayesian
learning and we name the model as BP-Triplet Net. In previous metric learning
based methods for UDA, sample pairs across domains are treated equally, which
is not appropriate due to the domain bias. In our work, considering the
different importance of pair-wise samples for both feature learning and domain
alignment, we deduce our BP-Triplet loss for effective UDA from the perspective
of Bayesian learning. Our BP-Triplet loss adjusts the weights of pair-wise
samples in intra domain and inter domain. Especially, it can self attend to the
hard pairs (including hard positive pair and hard negative pair). Together with
the commonly used adversarial loss for domain alignment, the quality of target
pseudo labels is progressively improved. Our method achieved low joint error of
the ideal source and target hypothesis. The expected target error can then be
upper bounded following Ben-David s theorem. Comprehensive evaluations on five
benchmark datasets, handwritten digits, Office31, ImageCLEF-DA, Office-Home and
VisDA-2017 demonstrate the effectiveness of the proposed approach for UDA.


title: Meromorphic quasi-modular forms and their $L$-functions

abstract:
  We investigate the meromorphic quasi-modular forms and their $L$-functions.
We study the space of meromorphic quasi-modular forms. Then we define their
$L$-functions by using the technique of regularized integral. Moreover, we give
an explicit formula for the $L$-functions. As an application, we obtain some
vanishing results of special $L$-values of meromorphic quasi-modular forms.


title: Volume of a rotating black hole in 2+1 dimensions

abstract:
  In this article we apply the technique for maximal volume estimation of a
black hole developed by Christodoulou and Rovelli for Schwarzchild blackhole
and by Zhang et al for non rotating BTZ black hole, to the case of a rotating
black hole in 2+1 dimensions. We derive the equation of the maximal
hypersurface for the rotating BTZ blackhole using the Lagrangian formulation
demonstrated by Christodoulou and Rovelli . Further we use maximization
technique illustrated earlier by Bengtsson et al for Kerr black hole to arrive
at the similar result for our case. We argue that the maximum contribution to
the volume of the hypersurface comes from what we call the steady state radius,
which we show depends on mass M and the AdS length scale. We demonstrate that
this steady state radius can be arrived at using independent considerations of
vanishing extrinsic curvature. We show that the volume of this segment of the
maximal hypersurface, the CR volume, depends on mass, AdS length scale and
angular momentum J. We further compute the entropy of a scalar field living on
the maximal hypersurface for a near extremal black hole and show that it is
proportional to the horizon entropy of the black hole.


title:
Joint inversion of receiver function and surface wave dispersion based
  on Innocent unscented Kalman Methodology

abstract:
  The Bayesian methodology is widely used for inversion analysis and
uncertainty analysis. In this work, we focus on the joint inversion of receiver
function and surface wave dispersion. Based on unscented Kalman methodology, we
present a novel joint inversion framework(Innocent unscented Kalman
Inversion,IUKI), which assumes the thickness and S-wave velocity at every
artificial layer are both Gaussian random variables and can adaptively adjust
their means and variances until convergence. This approach is derivative-free
and can efficiently provide uncertainty estimations of models with noisy data.
Furthermore, our method can also be easily extended as a generalized joint
inversion framework of multimodal geophysical data. With comprehensive
experiments, the proposed framework demonstrates superior performance in terms
of accuracy and stability.


title:
Going Deeper into Recognizing Actions in Dark Environments: A
  Comprehensive Benchmark Study

abstract:
  While action recognition (AR) has gained large improvements with the
introduction of large-scale video datasets and the development of deep neural
networks, AR models robust to challenging environments in real-world scenarios
are still under-explored. We focus on the task of action recognition in dark
environments, which can be applied to fields such as surveillance and
autonomous driving at night. Intuitively, current deep networks along with
visual enhancement techniques should be able to handle AR in dark environments,
however, it is observed that this is not always the case in practice. To dive
deeper into exploring solutions for AR in dark environments, we launched the
UG2+ Challenge Track 2 (UG2-2) in IEEE CVPR 2021, with a goal of evaluating and
advancing the robustness of AR models in dark environments. The challenge
builds and expands on top of a novel ARID dataset, the first dataset for the
task of dark video AR, and guides models to tackle such a task in both fully
and semi-supervised manners. Baseline results utilizing current AR models and
enhancement methods are reported, justifying the challenging nature of this
task with substantial room for improvements. Thanks to the active participation
from the research community, notable advances have been made in participants'
solutions, while analysis of these solutions helped better identify possible
directions to tackle the challenge of AR in dark environments.


title:
Existence and uniqueness of solutions to Bogomol'nyi-Prased-Sommerfeld
  equations on graphs

abstract:
  Let $G=(V,E)$ be a connected finite graph. We investigate two
Bogomol'nyi-Prased-Sommerfeld equations on $G$. We establish necessary and
sufficient conditions for the existence and uniqueness of solutions to the two
BPS equations.


title:
A Link Mixture Model for Spatio-temporal Infection Data, with
  Applications to the COVID Epidemic

abstract:
  Spatio-temporal models for infection counts generally follow themes of the
broader disease mapping literature, but may need to address specific features
of spatio-temporal infection data including considerable time fluctuations
(with epidemic phases) and spatial diffusion. Low order autoregression is a
feature of several recent spatio-temporal studies of infection data, possibly
with lags on both within area infections and on infections in adjacent areas.
Many epidemic time series show a period of relatively stable infection levels
(possibly characterized as endemicity), followed by a sudden sharp phase of
increasing infection levels. After the epidemic peak there is a period of
descending rates and return to stability. Hence one may seek to adapt the
autoregressive scheme to these pronounced fluctuations, with temporary
departures from stationarity, but returning to stationarity as rates descend
and infections resume endemic levels. We consider a mixture link model for
infection counts that allows adaptivity to both explosive phases and static
endemicity. Two case study applications involve COVID area-time data, one for
32 London boroughs since the start of the COVID epidemic, the other focusing on
the epidemic phase in 144 area of South East England associated with the Delta
variant.


title:
On the singularity structure of a discrete modified-Korteweg-deVries
  equation

abstract:
  We study the singularities of a modified lattice Korteweg-deVries (KdV)
equation and show that it admits three families of singularities, with
analogous properties to those found in the lattice KdV equation. The first
family consists of localised singularities which can occupy an arbitrarily
large domain but which are, nevertheless, always confined. The second family
consists of one or more lines extending all the way from the south-west to the
north-east on the plane, involving a single finite value that depends on the
parameter that appears in the equation. We argue that the infinite extent of
this singularity is not incompatible with the confinement property or with the
integrability of the equation. The third family consists of horizontal strips
in which the product of values on vertically adjacent lattice sites is equal to
1. In the case of the lattice KdV equation this type of singularity was dubbed
`taishi'. The taishi for the modified lattice KdV equation can interact with
singularities of the other two families, giving rise to very rich {and quite
intricate} singularity structures. Nonetheless, these interactions can be
described in a compact way through the formulation of a symbolic representation
of the dynamics that is similar to, but in a sense, simpler than that for the
KdV case. We give an interpretation of this symbolic representation in terms of
a Box&Ball system related to the ultradiscrete mKdV equation. These results
show that taishi-type singularities are not limited to the lattice KdV
equation, but might very well be a general feature of integrable lattice
equations with deep connections to other facets of their integrability.


title:
Learning to Detect Slip with Barometric Tactile Sensors and a Temporal
  Convolutional Neural Network

abstract:
  The ability to perceive object slip via tactile feedback enables humans to
accomplish complex manipulation tasks including maintaining a stable grasp.
Despite the utility of tactile information for many applications, tactile
sensors have yet to be widely deployed in industrial robotics settings; part of
the challenge lies in identifying slip and other events from the tactile data
stream. In this paper, we present a learning-based method to detect slip using
barometric tactile sensors. These sensors have many desirable properties
including high durability and reliability, and are built from inexpensive,
off-the-shelf components. We train a temporal convolution neural network to
detect slip, achieving high detection accuracies while displaying robustness to
the speed and direction of the slip motion. Further, we test our detector on
two manipulation tasks involving a variety of common objects and demonstrate
successful generalization to real-world scenarios not seen during training. We
argue that barometric tactile sensing technology, combined with data-driven
learning, is suitable for many manipulation tasks such as slip compensation.


title: student dangerous behavior detection in school

abstract:
  Video surveillance systems have been installed to ensure the student safety
in schools. However, discovering dangerous behaviors, such as fighting and
falling down, usually depends on untimely human observations. In this paper, we
focus on detecting dangerous behaviors of students automatically, which faces
numerous challenges, such as insufficient datasets, confusing postures,
keyframes detection and prompt response. To address these challenges, we first
build a danger behavior dataset with locations and labels from surveillance
videos, and transform action recognition of long videos to an object detection
task that avoids keyframes detection. Then, we propose a novel end-to-end
dangerous behavior detection method, named DangerDet, that combines multi-scale
body features and keypoints-based pose features. We could improve the accuracy
of behavior classification due to the highly correlation between pose and
behavior. On our dataset, DangerDet achieves 71.0\% mAP with about 11 FPS. It
keeps a better balance between the accuracy and time cost.


title: Implementing Boolean Functions with switching lattice networks

abstract:
  Four terminal switching network is an alternative structure to realize the
logic functions in electronic circuit modeling. This network can be used to
implement a Boolean function with less number of switches than the two terminal
based CMOS switch. Each switch of the network is driven by a Boolean literal.
Any switch is connected to its four neighbors if a literal takes the value 1 ,
else it is disconnected. In our work, we aimed to develop a technique by which
we can find out if any Boolean function can be implemented with a given
four-terminal network. It is done using the path of any given lattice network.
First, we developed a synthesis tool by which we can create a library of
Boolean functions with a given four-terminal switching network and random
Boolean literals. This tool can be used to check the output of any lattice
network which can also function as a lattice network solver. In the next step,
we used the library functions to develop and test our MAPPING tool where the
functions were given as input and from the output, we can get the implemented
function in four terminal lattice network. Finally, we have proposed a
systematic procedure to implement any Boolean function with a efficient way by
any given one type of lattice network.


title: A survey on making skylines more flexible

abstract:
  Top-$k$ queries and skylines are the two most common approaches to finding
the most interesting entries in a homogeneous multi-dimensional dataset.
However, both of these strategies have some shortcomings. Top-$k$ queries are
very challenging to specify precisely and skylines are not customizable to
specific scenarios, on top of having unpredictable output cardinalities. We
describe some alternative methods aimed at addressing the shortcomings of
top-$k$ queries and skylines and compare all approaches to illustrate which of
the desired properties each of them possesses.


title: Holistic Attention-Fusion Adversarial Network for Single Image Defogging

abstract:
  Adversarial learning-based image defogging methods have been extensively
studied in computer vision due to their remarkable performance. However, most
existing methods have limited defogging capabilities for real cases because
they are trained on the paired clear and synthesized foggy images of the same
scenes. In addition, they have limitations in preserving vivid color and rich
textual details in defogging. To address these issues, we develop a novel
generative adversarial network, called holistic attention-fusion adversarial
network (HAAN), for single image defogging. HAAN consists of a Fog2Fogfree
block and a Fogfree2Fog block. In each block, there are three learning-based
modules, namely, fog removal, color-texture recovery, and fog synthetic, that
are constrained each other to generate high quality images. HAAN is designed to
exploit the self-similarity of texture and structure information by learning
the holistic channel-spatial feature correlations between the foggy image with
its several derived images. Moreover, in the fog synthetic module, we utilize
the atmospheric scattering model to guide it to improve the generative quality
by focusing on an atmospheric light optimization with a novel sky segmentation
network. Extensive experiments on both synthetic and real-world datasets show
that HAAN outperforms state-of-the-art defogging methods in terms of
quantitative accuracy and subjective visual quality.


title: SODA: Site Object Detection dAtaset for Deep Learning in Construction

abstract:
  Computer vision-based deep learning object detection algorithms have been
developed sufficiently powerful to support the ability to recognize various
objects. Although there are currently general datasets for object detection,
there is still a lack of large-scale, open-source dataset for the construction
industry, which limits the developments of object detection algorithms as they
tend to be data-hungry. Therefore, this paper develops a new large-scale image
dataset specifically collected and annotated for the construction site, called
Site Object Detection dAtaset (SODA), which contains 15 kinds of object classes
categorized by workers, materials, machines, and layout. Firstly, more than
20,000 images were collected from multiple construction sites in different site
conditions, weather conditions, and construction phases, which covered
different angles and perspectives. After careful screening and processing,
19,846 images including 286,201 objects were then obtained and annotated with
labels in accordance with predefined categories. Statistical analysis shows
that the developed dataset is advantageous in terms of diversity and volume.
Further evaluation with two widely-adopted object detection algorithms based on
deep learning (YOLO v3/ YOLO v4) also illustrates the feasibility of the
dataset for typical construction scenarios, achieving a maximum mAP of 81.47%.
In this manner, this research contributes a large-scale image dataset for the
development of deep learning-based object detection methods in the construction
industry and sets up a performance benchmark for further evaluation of
corresponding algorithms in this area.


title: A Probabilistic Programming Idiom for Active Knowledge Search

abstract:
  In this paper, we derive and implement a probabilistic programming idiom for
the problem of acquiring new knowledge about an environment. The idiom is
implemented utilizing a modern probabilistic programming language. We
demonstrate the utility of this idiom by implementing an algorithm for the
specific problem of active mapping and robot exploration. Finally, we evaluate
the functionality of the implementation through an extensive simulation study
utilizing the HouseExpo dataset.


title:
HDAM: Heuristic Difference Attention Module for Convolutional Neural
  Networks

abstract:
  The attention mechanism is one of the most important priori knowledge to
enhance convolutional neural networks. Most attention mechanisms are bound to
the convolutional layer and use local or global contextual information to
recalibrate the input. This is a popular attention strategy design method.
Global contextual information helps the network to consider the overall
distribution, while local contextual information is more general. The
contextual information makes the network pay attention to the mean or maximum
value of a particular receptive field. Different from the most attention
mechanism, this article proposes a novel attention mechanism with the heuristic
difference attention module, HDAM. HDAM's input recalibration is based on the
difference between the local and global contextual information instead of the
mean and maximum values. At the same time, to make different layers have a more
suitable local receptive field size and increase the exibility of the local
receptive field design, we use genetic algorithm to heuristically produce local
receptive fields. First, HDAM extracts the mean value of the global and local
receptive fields as the corresponding contextual information. Then the
difference between the global and local contextual information is calculated.
Finally HDAM uses this difference to recalibrate the input. In addition, we use
the heuristic ability of genetic algorithm to search for the local receptive
field size of each layer. Our experiments on CIFAR-10 and CIFAR-100 show that
HDAM can use fewer parameters than other attention mechanisms to achieve higher
accuracy. We implement HDAM with the Python library, Pytorch, and the code and
models will be publicly available.


title: Safe Control Synthesis with Uncertain Dynamics and Constraints

abstract:
  This paper considers safe control synthesis for dynamical systems in the
presence of uncertainty in the dynamics model and the safety constraints that
the system must satisfy. Our approach captures probabilistic and worst-case
model errors and their effect on control Lyapunov function (CLF) and control
barrier function (CBF) constraints in the control-synthesis optimization
problem. We show that both the probabilistic and robust formulations lead to
second-order cone programs (SOCPs), enabling safe and stable control synthesis
that can be performed efficiently online. We evaluate our approach in PyBullet
simulations of an autonomous robot navigating in unknown environments and
compare the performance with a baseline CLF-CBF quadratic programming approach.


title:
The appearance of particle tracks in detectors -- II: the semi-classical
  realm

abstract:
  The appearance of tracks, close to classical orbits, left by charged quantum
particles propagating inside a detector, such as a cavity periodically
illuminated by light pulses, is studied for a family of idealized models. In
the semi-classical regime, which is reached when one considers highly energetic
particles, we present a detailed, mathematically rigorous analysis of this
phenomenon. If the Hamiltonian of the particles is quadratic in position- and
momentum operators, as in the examples of a freely moving particle or a
particle in a homogeneous external magnetic field, we show how symmetries, such
as spherical symmetry, of the initial state of a particle are broken by tracks
consisting of infinitely many approximately measured particle positions and
how, in the classical limit, the initial position and velocity of a classical
particle trajectory can be reconstructed from the observed particle track.


title:
Priming Cross-Session Motor Imagery Classification with A Universal Deep
  Domain Adaptation Framework

abstract:
  Motor imagery (MI) is a common brain computer interface (BCI) paradigm. EEG
is non-stationary with low signal-to-noise, classifying motor imagery tasks of
the same participant from different EEG recording sessions is generally
challenging, as EEG data distribution may vary tremendously among different
acquisition sessions. Although it is intuitive to consider the cross-session MI
classification as a domain adaptation problem, the rationale and feasible
approach is not elucidated. In this paper, we propose a Siamese deep domain
adaptation (SDDA) framework for cross-session MI classification based on
mathematical models in domain adaptation theory. The proposed framework can be
easily applied to most existing artificial neural networks without altering the
network structure, which facilitates our method with great flexibility and
transferability. In the proposed framework, domain invariants were firstly
constructed jointly with channel normalization and Euclidean alignment. Then,
embedding features from source and target domain were mapped into the
Reproducing Kernel Hilbert Space (RKHS) and aligned accordingly. A cosine-based
center loss was also integrated into the framework to improve the
generalizability of the SDDA. The proposed framework was validated with two
classic and popular convolutional neural networks from BCI research field
(EEGNet and ConvNet) in two MI-EEG public datasets (BCI Competition IV IIA,
IIB). Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework
was able to boost the MI classification accuracy by 15.2%, 10.2% respectively
in IIA dataset, and 5.5%, 4.2% in IIB dataset. The final MI classification
accuracy reached 82.01% in IIA dataset and 87.52% in IIB, which outperformed
the state-of-the-art methods in the literature.


title: Additive rule of real and reciprocal space topologies at disclinations

abstract:
  Topological materials are renowned for their ability to harbor states
localized at their peripheries, such as surfaces, edges, and corners.
Accompanying these states, fractional charges appear on peripheral unit cells.
Recently, topologically bound states and fractional charges at disclinations of
crystalline defects have been theoretically predicted. This so-called
bulk-disclination correspondence has been experimentally confirmed in
artificial crystalline structures, such as microwave-circuit arrays and
photonic crystals. Here, we demonstrate an additive rule between the real-space
topological invariant $\mathbf{s}$ (related to the Burgers vector $\mathbf{B}$)
and the reciprocal-space topological invariant $\mathbf{p}$ (vectored Zak's
phase of bulk wave functions). The bound states and fractional charges concur
at a disclination center only if $\mathbf{s}+\mathbf{p}/2\pi$ is topologically
nontrivial; otherwise, no bound state forms even if fractional charges are
trapped. Besides the dissociation of fractional charges from bound states, the
additive rule also dictates the existence of half-bound states extending over
only half of a sample and ultra-stable bound states protected by both
real-space and reciprocal-space topologies. Our results add another dimension
to the ongoing study of topological matter and may germinate interesting
applications.


title:
Characterizing the aerosol atmosphere above the Observatorio del Roque
  de los Muchachos by analyzing seven years of data taken with an GaAsP
  HPD-readout, absolutely calibrated elastic LIDAR

abstract:
  We present a new elastic LIDAR concept, based on a bi-axially mounted Nd:YAG
laser and a telescope with HPD readout, combined with fast FADC signal
digitization and offline pulse analysis. The LIDAR return signals have been
extensively quality checked and absolutely calibrated. We analyze seven years
of quasi-continuous LIDAR data taken during those nights when the MAGIC
telescopes were operating. Characterization of the nocturnal ground layer
yields zenith and azimuth angle dependent aerosol extinction scale heights for
clear nights. We derive aerosol transmission statistics for light emitted from
various altitudes throughout the year and separated by seasons. We find further
seasonal dependencies of cloud base and top altitudes, but none for the LIDAR
ratios of clouds. Finally, the night sky background light is characterized
using the LIDAR photon backgrounds. abstract.txt


title:
Numerical optimization of single-mode fiber-coupled single-photon
  sources based on semiconductor quantum dots

abstract:
  We perform extended numerical studies to maximize the overall photon coupling
efficiency of fiber-coupled quantum dot single-photon sources emitting in the
near-infrared and telecom regime. Using the finite element method, we optimize
the photon extraction and fiber-coupling efficiency of quantum dot
single-photon sources based on micromesas, microlenses, circular Bragg grating
cavities and micropillars. The numerical simulations which consider the entire
system consisting of the quantum dot source itself, the coupling lens, and the
single-mode fiber yield overall photon coupling efficiencies of up to 83%. Our
work provides objectified comparability of different fiber-coupled
single-photon sources and proposes optimized geometries for the realization of
practical and highly efficient quantum dot single-photon sources.


title: On the solubilizer of an element in a finite group

abstract:
  The solubility graph $\Gamma_S(G)$ associated with a finite group $G$ is a
simple graph whose vertices are the elements of $G$, and there is an edge
between two distinct vertices if and only if they generate a soluble subgroup.
In this paper, we focus on the set of neighbors of a vertex $x$ which we call
it the solubilizer of $x$ in G, investigating both arithmetic and structural
properties of this set.


title:
Control and tunability of magnetic bubble states in multilayers with
  strong perpendicular magnetic anisotropy at ambient conditions

abstract:
  The reversal of magnetic bubble helicity through topologically trivial
transient states provides an additional degree of freedom that promises the
development of multidimensional magnetic memories. A key requirement for this
concept is the stabilization of bubble states at ambient conditions on
application-compatible substrates. In the present work we demonstrate a
stabilization routine for remanent bubble states in high perpendicular magnetic
anisotropy [(Co(0.44\,nm)/Pt(0.7\,nm)]$_X$, X = 48, 100, 150 multilayers on
Si/SiO$_2$ substrates by exploring the effect of external magnetic fields
($H_\mathrm{m}$) of different strength and angles ($\theta$) with respect to
the film surface normal. By systematic variation of these two parameters, we
demonstrate that remanent bubble density and mean bubble diameter can be
carefully tuned and optimized for each sample. Our protocol based on
magnetometry only reveals the densest remanent bubble states at $H_\mathrm{m} =
0.87 H_\mathrm{s}$ ($H_\mathrm{s}$ is the magnetic saturation field) and
$\theta=60^\circ - 75^\circ$ for all $X$ with a maximum of 3736 domains/100
$\mu$m$^2$ for the X = 48 sample. The experimental observations are supported
by micromagnetic simulations taking into account the nanoscale lateral grain
structure of multilayers synthesized by magnetron sputter deposition, and thus
helping understand the different density of the bubble states found in these
systems.


title: Methods of Retrieving Large-Variable Exponents

abstract:
  Methods of determining, from small-variable asymptotic expansions, the
characteristic exponents for variables tending to infinity are analyzed. The
following methods are considered: diff-log Pad\'e summation, self-similar
factor approximation, self-similar diff-log summation, self-similar Borel
summation, and self-similar Borel-Leroy summation. Several typical problems are
treated. The comparison of the results shows that all these methods provide
close estimates for the large-variable exponents. The reliable estimates are
obtained when different methods of summation are compatible with each other.

categories: cond-mat.stat-mech hep-ph math-ph math.MP


title: Objective Flow Measures Based on Few Trajectories

abstract:
  Sparse trajectory data consist of a low number of trajectories such that the
reconstruction of an underlying velocity field is not possible. Recently,
approaches have been introduced to analyze flow behavior based on a single
trajectory only: trajectory stretching exponent (TSE) to detect hyperbolic
(stretching) behavior, and trajectory angular velocity (TRA) to detect elliptic
(rotation) behavior. In this paper, we analyze these approaches and in
particular show that they are -- contrary to what is claimed in the literature
-- not objective in the extended phase space. Furthermore, we introduce the
first objective measure of rotation behavior that is based on only few
trajectories: at least 3 in 2D, and at least 4 in 3D. For this measure --
called trajectory vorticity (TRV) -- we show that it is objective and that it
can be introduced in two independent ways: by approaches for unsteadiness
minimization and by considering the relative spin tensor. We apply TRV to a
number of constructed and real trajectory data sets, including drifting buoys
in the Atlantic, midge swarm tracking data, and a simulated vortex street.


title: Resilience of critical structures, infrastructures and communities

abstract:
  In recent years, the concept of resilience has been introduced to the field
of engineering as it relates to disaster mitigation and management. However,
the built environment is only one element that supports community
functionality. Maintaining community functionality during and after a disaster,
defined as resilience, is influenced by multiple components. This report
summarizes the research activities of the first two years of an ongoing
collaboration between the Politecnico di Torino and the University of
California, Berkeley, in the field of disaster resilience. Chapter 1 focuses on
the economic dimension of disaster resilience with an application to the San
Francisco Bay Area; Chapter 2 analyzes the option of using base-isolation
systems to improve the resilience of hospitals and school buildings; Chapter 3
investigates the possibility to adopt discrete event simulation models and a
meta-model to measure the resilience of the emergency department of a hospital;
Chapter 4 applies the meta-model developed in Chapter 3 to the hospital network
in the San Francisco Bay Area, showing the potential of the model for design
purposes Chapter 5 uses a questionnaire combined with factorial analysis to
evaluate the resilience of a hospital; Chapter 6 applies the concept of
agent-based models to analyze the performance of socio-technical networks
during an emergency. Two applications are shown: a museum and a train station;
Chapter 7 defines restoration fragility functions as tools to measure
uncertainties in the restoration process; and Chapter 8 focuses on modeling
infrastructure interdependencies using temporal networks at different spatial
scales.


title:
Data-driven approximation and reduction from noisy data in matrix pencil
  frameworks

abstract:
  This work aims at tackling the problem of learning surrogate models from
noisy time-domain data by means of matrix pencil-based techniques, namely the
Hankel and Loewner frameworks. A data-driven approach to obtain reduced order
state-space models from time-domain input-output measurements for linear
time-invariant (LTI) systems is proposed. This is accomplished by combining the
aforementioned model order reduction (MOR) techniques with the signal matrix
model (SMM) approach. The proposed method is illustrated by a numerical
benchmark example consisting of a building model.


title: Maxima of the $Q$-index: Graphs with no $K_{1,t}$-minor

abstract:
  A graph is said to be \textit{$H$-minor free} if it does not contain $H$ as a
minor. In this paper, we characteristic the unique extremal graph with maximal
$Q$-index among all $n$-vertex $K_{1,t}$-minor free graphs ($t\ge3$).


title:
An explicit Hopf bifurcation criterion of fractional-order systems with
  order 1 < {\alpha} < 2

abstract:
  A Hopf bifurcation criterion of fractional-order systems with order 1 <
{\alpha} < 2 is established in this paper, in which all conditions are
explicitly expressed by parameters without solving the roots of the relevant
characteristic polynomial of Hopf bifurcation conditions. It avoids the problem
that existing methods may fail due to the computational complexity in the
multi-parameter situation. The bifurcation hyper-surface of multi-parameter can
be obtained directly.


title: Bit-wise Training of Neural Network Weights

abstract:
  We introduce an algorithm where the individual bits representing the weights
of a neural network are learned. This method allows training weights with
integer values on arbitrary bit-depths and naturally uncovers sparse networks,
without additional constraints or regularization techniques. We show better
results than the standard training technique with fully connected networks and
similar performance as compared to standard training for convolutional and
residual networks. By training bits in a selective manner we found that the
biggest contribution to achieving high accuracy is given by the first three
most significant bits, while the rest provide an intrinsic regularization. As a
consequence more than 90\% of a network can be used to store arbitrary codes
without affecting its accuracy. These codes may be random noise, binary files
or even the weights of previously trained networks.


title:
Cracking predictions of lithium-ion battery electrodes by X-ray computed
  tomography and modelling

abstract:
  Fracture of lithium-ion battery electrodes is found to contribute to capacity
fade and reduce the lifespan of a battery. Traditional fracture models for
batteries are restricted to consideration of a single, idealised particle;
here, advanced X-ray computed tomography (CT) imaging, an
electro-chemo-mechanical model and a phase field fracture framework are
combined to predict the void-driven fracture in the electrode particles of a
realistic battery electrode microstructure. The electrode is shown to exhibit a
highly heterogeneous electrochemical and fracture response that depends on the
particle size and distance from the separator/current collector. The model
enables prediction of increased cracking due to enlarged cycling voltage
windows, cracking susceptibility as a function of electrode thickness, and
damage sensitivity to discharge rate. This framework provides a platform that
facilitates a deeper understanding of electrode fracture and enables the design
of next-generation electrodes with higher capacities and improved degradation
characteristics.


title: Diversity aware image generation

abstract:
  The machine learning generative algorithms such as GAN and VAE show
impressive results in practice when constructing images similar to those in a
training set. However, the generation of new images builds mainly on the
understanding of the hidden structure of the training database followed by a
mere sampling from a multi-dimensional normal variable. In particular each
sample is independent from the other ones and can repeatedly propose same type
of images. To cure this drawback we propose a kernel-based measure
representation method that can produce new objects from a given target measure
by approximating the measure as a whole and even staying away from objects
already drawn from that distribution. This ensures a better variety of the
produced images. The method is tested on some classic machine learning
benchmarks.\end{abstract}


title:
Training Robots without Robots: Deep Imitation Learning for
  Master-to-Robot Policy Transfer

abstract:
  Deep imitation learning is a promising method for dexterous robot
manipulation because it only requires demonstration samples for learning
manipulation skills. In this paper, deep imitation learning is applied to tasks
that require force feedback, such as bottle opening. However, simple visual
feedback systems, such as teleoperation, cannot be applied because they do not
provide force feedback to operators. Bilateral teleoperation has been used for
demonstration with force feedback; however, this requires an expensive and
complex bilateral robot system. In this paper, a new master-to-robot (M2R)
transfer learning system is presented that does not require robots but can
still teach dexterous force feedback-based manipulation tasks to robots. The
human directly demonstrates a task using a low-cost controller that resembles
the kinematic parameters of the robot arm. Using this controller, the operator
can naturally feel the force feedback without any expensive bilateral system.
Furthermore, the M2R transfer system can overcome domain gaps between the
master and robot using the gaze-based imitation learning framework and a simple
calibration method. To demonstrate this, the proposed system was evaluated on a
bottle-cap-opening task that requires force feedback only for the master
demonstration.


title: Quadratic decomposition of bivariate orthogonal polynomials

abstract:
  We describe bivariate polynomial sequences orthogonal to a symmetric weight
function in terms of several bivariate polynomial sequences orthogonal with
respect to Christoffel transformations of the initial weight under a quadratic
transformation. We analyze the construction of a symmetric bivariate orthogonal
polynomial sequence from a given one, orthogonal to a weight function defined
on the positive plane. In this description plays an important role a sort of
Backlund type matrix transformations for the involved three term matrix
coefficients. We take as a case study relations between symmetric orthogonal
polynomials defined on the ball and on the simplex.


title:
Routh-Hurwitz criterion of stability and robust stability for
  fractional-order systems with order {\alpha}{\in}[1,2)

abstract:
  Based on the generalized Routh-Hurwitz criterion, we propose a sufficient and
necessary criterion for testing the stability of fractional-order linear
systems with order {\alpha}{\in}[1,2), called the fractional-order
Routh-Hurwitz criterion. Compared with the existing criterion, our one involves
fewer and simpler expressions, which is significant for analyzing robust
stability of fractional-order uncertain systems. All these expressions are
explicit ones about the coefficients of the characteristic polynomial of system
matrix, so the stable parameter region of fractional-order uncertain systems
can be described directly. Some examples show the effectiveness of our method.


title:
A simple and low-cost setup for part per billion level frequency
  stabilization and characterization of red He-Ne laser

abstract:
  This work describes the frequency stabilization of a dual longitudinal mode,
red (632.8 nm) He-Ne laser, implemented using a low-cost microcontroller and
its performance characterization using a simple interferometric method. Our
studies demonstrate that frequency stability up to 3.0 MHz (3$\sigma$, 16
hours) can be achieved using this set up. This simple and low-cost system can
serve as an excellent part per billion level frequency reference for several
high resolution spectroscopy based applications.


title:
Non-perturbative Lee-Wick gauge theory: BRST, confinement & RGE with
  strong couplings

abstract:
  We consider non-Abelian non-local Lee-Wick gauge theory and prove
Becchi-Rouet-Stora-Tyutin (BRST) invariance. It contains fourth-order
derivative as extensions of the kinetic term, leading to massive ghosts in the
theory upon quantization. We particularly focus on the confinement conditions
in strongly-coupled regimes, using the Kugo-Ojima approach, and obtain the
exact $\beta-$functions in the non-perturbative regimes. This is achieved using
a set of exact solutions of the corresponding local theory in terms of Jacobi
elliptical functions. We obtain an identical $\beta-$function just as for the
local Yang-Mills theory but the main difference is that now, the cut-off arises
naturally from the Lee-Wick heavy mass scales (M). We show that the fate of the
ghosts are fixed in these regimes: they are no more the propagating degrees of
freedom in the infrared (IR)-limit. In the limit $M \rightarrow \infty $, one
recovers the standard results for the local non-Abelian Yang-Mills theory.


title: Tripartite: Tackle Noisy Labels by a More Precise Partition

abstract:
  Samples in large-scale datasets may be mislabeled due to various reasons, and
Deep Neural Networks can easily over-fit to the noisy label data. To tackle
this problem, the key point is to alleviate the harm of these noisy labels.
Many existing methods try to divide training data into clean and noisy subsets
in terms of loss values, and then process the noisy label data varied. One of
the reasons hindering a better performance is the hard samples. As hard samples
always have relatively large losses whether their labels are clean or noisy,
these methods could not divide them precisely. Instead, we propose a Tripartite
solution to partition training data more precisely into three subsets: hard,
noisy, and clean. The partition criteria are based on the inconsistent
predictions of two networks, and the inconsistency between the prediction of a
network and the given label. To minimize the harm of noisy labels but maximize
the value of noisy label data, we apply a low-weight learning on hard data and
a self-supervised learning on noisy label data without using the given labels.
Extensive experiments demonstrate that Tripartite can filter out noisy label
data more precisely, and outperforms most state-of-the-art methods on five
benchmark datasets, especially on real-world datasets.


title: Image-to-Graph Transformers for Chemical Structure Recognition

abstract:
  For several decades, chemical knowledge has been published in written text,
and there have been many attempts to make it accessible, for example, by
transforming such natural language text to a structured format. Although the
discovered chemical itself commonly represented in an image is the most
important part, the correct recognition of the molecular structure from the
image in literature still remains a hard problem since they are often
abbreviated to reduce the complexity and drawn in many different styles. In
this paper, we present a deep learning model to extract molecular structures
from images. The proposed model is designed to transform the molecular image
directly into the corresponding graph, which makes it capable of handling
non-atomic symbols for abbreviations. Also, by end-to-end learning approach it
can fully utilize many open image-molecule pair data from various sources, and
hence it is more robust to image style variation than other tools. The
experimental results show that the proposed model outperforms the existing
models with 17.1 % and 12.8 % relative improvement for well-known benchmark
datasets and large molecular images that we collected from literature,
respectively.


title: Infinitesimal time reparametrisation and its applications

abstract:
  A geometric approach to Sundman infinitesimal time-reparametrisation is given
and some of its applications are used to illustrate the general theory. Special
emphasis is put on geodesic motions and systems described by mechanical type
Lagrangians.
  The Jacobi metric appears as a particular case of a Sundman transformation.


title:
Key graph properties affecting transport efficiency of flip-flop Grover
  percolated quantum walks

abstract:
  Quantum walks exhibit properties without classical analogues. One of those is
the phenomenon of asymptotic trapping -- there can be non-zero probability of
the quantum walker being localised in a finite part of the underlying graph
indefinitely even though locally all directions of movement are assigned
non-zero amplitudes at each step. We study quantum walks with the flip-flop
shift operator and the Grover coin, where this effect has been identified
previously. For the version of the walk further modified by a random dynamical
disruption of the graph (percolated quantum walks) we provide a recipe for the
construction of a complete basis of the subspace of trapped states allowing to
determine the asymptotic probability of trapping for arbitrary finite connected
simple graphs, thus significantly generalizing the previously known result
restricted to planar 3-regular graphs. We show how the position of the source
and sink together with the graph geometry and its modifications affect the
excitation transport. This gives us a deep insight into processes where
elongation or addition of dead-end subgraphs may surprisingly result in
enhanced transport and we design graphs exhibiting this pronounced behavior. In
some cases this even provides closed-form formulas for the asymptotic transport
probability in dependence on some structure parameters of the graphs.


title: Models and Datasets for Cross-Lingual Summarisation

abstract:
  We present a cross-lingual summarisation corpus with long documents in a
source language associated with multi-sentence summaries in a target language.
The corpus covers twelve language pairs and directions for four European
languages, namely Czech, English, French and German, and the methodology for
its creation can be applied to several other languages. We derive cross-lingual
document-summary instances from Wikipedia by combining lead paragraphs and
articles' bodies from language aligned Wikipedia titles. We analyse the
proposed cross-lingual summarisation task with automatic metrics and validate
it with a human study. To illustrate the utility of our dataset we report
experiments with multi-lingual pre-trained models in supervised, zero- and
few-shot, and out-of-domain scenarios.


title: Annihilators and dimensions of the singularity category

abstract:
  Let R be a commutative noetherian ring. We prove that if R is either an
equidimensional finitely generated algebra over a perfect field, or an
equidimensional equicharacteristic complete local ring with a perfect residue
field, then the annihilator of the singularity category of R coincides with the
Jacobian ideal of R up to radical. We establish a relation between the
annihilator of the singularity category of R and the cohomological annihilator
of R under some mild assumptions. Finally, we give an upper bound for the
dimension of the singularity category of an equicharacteristic excellent local
ring with isolated singularity. This extends a result of Dao and Takahashi to
non Cohen-Macaulay rings.


title: Characteristic Polynomials in Coupled Matrix Models

abstract:
  We study correlation functions of the characteristic polynomials in coupled
matrix models based on the Schur polynomial expansion, which manifests their
determinantal structure.


title:
Atomistic simulations of the deformation behavior of an Nb nanowire
  embedded in a NiTi shape memory alloy

abstract:
  The influence of pre-strain and temperature on the superior properties
exhibited by an Nb nanowire embedded in a NiTi shape memory alloy (SMA) are
investigated via molecular dynamics simulations. To this end, a new Nb-Ni-Ti
ternary interatomic potential based on the second nearest-neighbor modified
embedded-atom method (2NN MEAM) is developed and employed. The origin of the
unique phenomena of quasi-linear elasticity, slim hysteresis, and reduction in
Young's modulus observed for pre-strained nanowire-SMA composites is uncovered.
The results demonstrate the importance of plastic deformation in the embedded
Nb nanowires and reveal how the deformation facilitates the just-mentioned,
unprecedented phenomena. A simple and straightforwardly obtainable descriptor
to correlate and monitor Young's modulus evolution during pre-straining is
proposed. Furthermore, our simulations suggest that the desired Young's modulus
can be obtained for a wide range of application temperatures through
appropriate pre-straining.


title:
An Evaluation of Open-source Tools for the Provision of Differential
  Privacy

abstract:
  The concept of differential privacy has widely penetrated academia and
industry, with its formal guarantee on individual privacy that leads to
compliances with privacy legislation, e.g., GDPR. However, there is a lack of
understanding on tools capable of achieving differential privacy, and it is not
clear what to expect from existing differential privacy tools when implementing
privacy protection. Such an obstacle limits private applications' further
prosperity. This paper reviews and evaluates the state-of-the-art open-source
differential privacy tools of different domains using various estimating
categories and privacy settings. Particularly, we look into the performances of
three differential privacy tools for machine learning, two for statistical
query, and four for synthetic data generation. We test all the tools on both
continuous and categorical data and quantify their performance under different
privacy budget and data size w.r.t. utility loss and system overhead. The
accumulated evaluation results reveal several patterns that users can follow to
optimally configure the tools, and provide preliminary guidelines on tool
selection under different criteria. Finally, we openly release our evaluation
coding repository, a framework that users can reuse to further evaluate the
studied tools and beyond. We anticipate this work to provide a comprehensive
insight into the performances of the existing dominant privacy tools, and a
concrete reference for a potentially large developer community on private
applications, thus narrowing the gap between conceptual differential privacy
and private functionality development.


title: Soft colloids for complex interfacial assemblies

abstract:
  The design of complex materials and the formation of specific patterns often
arise from the properties of the individual building blocks. In this respect,
colloidal systems offer a unique opportunity because nowadays they can be
synthesized in the laboratory with many different shapes and features. Hence,
an appropriate choice of the particle characteristics makes it possible to
generate macroscopic structures with desired properties. The versatility of
colloids can also be explored in two dimensions, using liquid-liquid or
air-liquid interfaces as privileged substrates where they can adsorb and
self-assemble. Besides being innovative model systems for fundamental studies,
the great interest of the scientific community is also technological and
applicative, since colloidal-scale surface patterns are very promising for
example in photonics or biosensing. In a recent study published in PNAS, Menath
and coworkers combine these elements and exploit core-shell colloids,
consisting of a silica core and a soft, non-crosslinked polymer shell, to make
an important step forward in controlling the assembly of complex structures at
an interface.


title:
Calibration requirements for Epoch of Reionization 21-cm signal
  observations -- II. Analytical estimation of the bias and variance with
  time-correlated residual gains

abstract:
  Observation of redshifted 21-cm signals from neutral hydrogen holds the key
to understanding the structure formation and its evolution during the
reionization and post-reionization era. Apart from the presence of orders of
magnitude larger foregrounds in the observed frequency range, the instrumental
effects of the interferometers combined with the ionospheric effects present a
considerable challenge in the extraction of 21-cm signals from strong
foregrounds. The systematic effects of time and frequency correlated residual
gain errors originating from the measurement process introduce a bias and
enhance the variance of the power spectrum measurements. In this work, we study
the effect of time-correlated residual gain errors in the presence of strong
foreground. We present a method to produce analytic estimates of the bias and
vari ance in the power spectrum. We use simulated observations to confirm the
efficacy of this method and then use it to understand various effects of the
gain errors. We find that as the standard deviation in the residual gain errors
increases, the bias in the estimation supersedes the variance. It is observed
that an optimal choice of the time over which the gain solutions are estimated
minimizes the risk. We also find that the interferometers with higher baseline
densities are preferred instruments for these studies.


title: The cost of symmetry in connected graphs

abstract:
  The paper answers the question posed in a joint paper by A. A. Klyachko and
N. M. Luneva about the optimality of the estimate for the cost of symmetry in
graphs. The original estimate says that if 2021 vertices can be removed from a
connected graph so that there is no connected subgraph of isomorphic $\Gamma$
left in it, then at most $2021|V(\Gamma)|$ vertices that form a set invariant
under all automorphisms of the graph so that the graph does not contain a
subgraph isomorphic to $\Gamma$. We will prove that there exists a graph
$\Gamma$ for which this estimate is not optimal.


title: Persistent homology of semi-algebraic sets

abstract:
  We give an algorithm with singly exponential complexity for computing the
barcodes up to dimension $\ell$ (for any fixed $\ell \geq 0$) of the filtration
of a given semi-algebraic set by the sub-level sets of a given polynomial. Our
algorithm is the first algorithm for this problem with singly exponential
complexity, and generalizes the corresponding results for computing the Betti
numbers up to dimension $\ell$ of semi-algebraic sets with no filtration
present.


title:
Nuclear quantum effects on the vibrational dynamics of the water-air
  interface

abstract:
  We have applied path integral molecular dynamics simulations to investigate
nuclear quantum effects on the vibrational dynamics of water molecules at the
water-air interface. The instantaneous fluctuations in the frequencies of the
O-H stretch modes are calculated using the wavelet method of time series
analysis, while the time scales of vibrational spectral diffusion are
determined from frequency-time correlation functions and joint probability
distributions, as well as the hydrogen bond number correlation functions. We
find that the inclusion of nuclear quantum effects leads not only to a redshift
in the vibrational frequency distribution by about 120~cm$^{-1}$ for both the
bulk and interfacial water molecules, but also to an acceleration of the
vibrational dynamics on the water-air interface by as much as 60$\%$. In
addition, a blueshift of about 30 ~cm$^{-1}$ is seen in the vibrational
frequency distribution of interfacial water molecules compared to that of the
bulk. Furthermore, the dynamics of water molecules beyond the topmost two
molecular layers was found to be essentially similar to that of bulk water.

categories: physics.chem-ph cond-mat.soft cond-mat.stat-mech physics.comp-ph


title:
Phase and amplitude evolution in the network of triadic interactions of
  the Hasegawa-Wakatani system

abstract:
  Hasegawa-Wakatani system, commonly used as a toy model of dissipative drift
waves in fusion devices is revisited with considerations of phase and amplitude
dynamics of its triadic interactions. It is observed that a single resonant
triad can saturate via three way phase locking where the phase differences
between dominant modes converge to constant values as individual phases
increase in time. This allows the system to have approximately constant
amplitude solutions. Non-resonant triads show similar behavior only when one of
its legs is a zonal wave number. However when an additional triad, which is a
reflection of the original one with respect to the $y$ axis is included, the
behavior of the resulting triad pair is shown to be more complex. In
particular, it is found that triads involving small radial wave numbers (large
scale zonal flows) end up transferring their energy to the subdominant mode
which keeps growing exponentially, while those involving larger radial wave
numbers (small scale zonal flows) tend to find steady chaotic or limit cycle
states (or decay to zero). In order to study the dynamics in a connected
network of triads, a network formulation is considered including a pump mode,
and a number of zonal and non-zonal subdominant modes as a dynamical system. It
was observed that the zonal modes become clearly dominant only when a large
number of triads are connected. When the zonal flow becomes dominant as a
'collective mean field', individual interactions between modes become less
important, which is consistent with the inhomogeneous wave-kinetic picture.
Finally, the results of direct numerical simulation is discussed for the same
parameters and various forms of the order parameter are computed. It is
observed that nonlinear phase dynamics results in a flattening of the large
scale phase velocity as a function of scale in direct numerical simulations.


title: Independent domination of graphs with bounded maximum degree

abstract:
  An independent dominating set of a graph, also known as a maximal independent
set, is a set $S$ of pairwise non-adjacent vertices such that every vertex not
in $S$ is adjacent to some vertex in $S$. We prove that for $\Delta=4$ or
$\Delta\ge 6$, every connected $n$-vertex graph of maximum degree at most
$\Delta$ has an independent dominating set of size at most
$(1-\frac{\Delta}{\lfloor\Delta^2/4\rfloor+\Delta})(n-1)+1$. In addition, we
characterize all connected graphs having the equality and we show that other
connected graphs have an independent dominating set of size at most
$(1-\frac{\Delta}{\lfloor\Delta^2/4\rfloor+\Delta})n$.


title: Innovative semantic communication system

abstract:
  Traditional communication systems focus on the transmission process, and the
context-dependent meaning has been ignored. The fact that 5G system has
approached Shannon limit and the increasing amount of data will cause
communication bottleneck, such as the increased delay problems. Inspired by the
ability of artificial intelligence to understand semantics, we propose a new
communication paradigm, which integrates artificial intelligence and
communication, the semantic communication system. Semantic communication is at
the second level of communication based on Shannon and Weaver\cite{6197583},
which retains the semantic features of the transmitted information and recovers
the signal at the receiver, thus compressing the communication traffic without
losing important information. Different from other semantic communication
systems, the proposed system not only transmits semantic information but also
transmits semantic decoder. In addition, a general semantic metrics is proposed
to measure the quality of semantic communication system. In particular, the
semantic communication system for image, namely AESC-I, is designed to verify
the feasibility of the new paradigm. Simulations are conducted on our system
with the additive white Gaussian noise (AWGN) and the multipath fading channel
using MNIST and Cifar10 datasets. The experimental results show that DeepSC-I
can effectively extract semantic information and reconstruct images at a
relatively low SNR.


title:
All-order correlation of single excitons in nanocrystals using a
  $\mathbf{k}\cdot\mathbf{p}$ envelope-function approach: application to
  lead-halide perovskites

abstract:
  We discuss a variety of many-body approaches, within effective-mass and
$\mathbf{k}\cdot\mathbf{p}$ envelope-function formalisms, for calculating
correlated single excitons in semiconductor nanocrystals (NCs) to all orders in
the electron-hole Coulomb interaction. These approaches are applied to NCs of
the lead-halide perovskite CsPbBr$_{3}$, which typically present excitons in
intermediate confinement with physical observables often strongly renormalized
by correlation (e.g., radiative decay rate enhanced by a factor of about 7
relative to a mean-field approach, for a NC of edge length 11 nm). The
many-body methods considered include the particle-hole Bethe-Salpeter equation,
configuration interaction with single excitations, and the random-phase
approximation with exchange (RPAE), which are shown to be closely related to
each other but to treat $\mathbf{k}\cdot\mathbf{p}$ corrections differently,
with RPAE being the most complete method. The methods are applied to calculate
the correlation energy, the radiative lifetime, and the long-range Coulomb
contribution to the fine structure of the ground-state exciton. In the limit of
large NC sizes, the numerical results are shown to agree well with analytical
results for this limit, where these are known. Correlated excited states of the
single exciton are used to calculate the one-photon absorption cross section;
the shape of the resulting cross-section curve (versus laser wavelength) at
threshold and up to an excitation energy of about 1 eV is in good agreement
with experimental cross sections. The equations for the methods are explicitly
adapted to spherical symmetry (involving radial integrals and angular factors)
and in this form permit a rapid computation for systems in intermediate
confinement.


title: STAR-RIS-NOMA Networks: An Error Performance Perspective

abstract:
  This letter investigates the bit error rate (BER) performance of simultaneous
transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) in
non-orthogonal multiple access (NOMA) networks. In the investigated network, a
STAR-RIS serves two non-orthogonal users located on either side of the surface
by utilizing the mode switching protocol. We derive the closed-form and upper
bound BER expressions in perfect and imperfect successive interference
cancellation cases. Furthermore, asymptotic analyses are also conducted to
provide further insights into the BER behavior in the high signal-to-noise
ratio region. Finally, the accuracy of our theoretical analysis is validated
through Monte Carlo simulations. The obtained results reveal that the BER
performance of STAR-RIS-NOMA outperforms that of the classical NOMA system, and
STAR-RIS might be a promising NOMA 2.0 solution.


title: Achieving passive daytime radiative cooling via TiO$_2$/PDMS coating

abstract:
  The exponential growth in population and the increasing global temperature
trickles down to an explosive demand in cooling and refrigeration. The vicious
cycle of carbon footprint generation by these cooling devices can be broken by
a mechanism of passive cooling. This study outlines research undertaken with
the aim to design such materials - exhibiting high reflectance in the solar
spectrum and high emission in the atmospheric transparency window of 8-13
$\mu$m. The Monte Carlo (MC) method is used to simulate light propagation in a
composite material aiding the design of metamaterials with these specific
thermo-optical properties. A TiO$_2$/PDMS coating is fabricated to obtain > 91
\% solar reflectivity and > 75 \% emissivity in the atmospheric transparency
window. This translates to cooling the coated body by 4-9 $^\circ$C below the
ambient under peak solar irradiation in Mumbai, India. The facile fabrication
process supplemented with the potential versatility of this coating shows
promise to attain passive daytime radiative cooling on a commercial scale.


title:
Numerical study of the logarithmic Schrodinger equation with repulsive
  harmonic potential

abstract:
  We consider the Schrodinger equation with a logarithmic nonlinearity and a
repulsive harmonic potential. Depending on the parameters of the equation, the
solution may or may not be dispersive. When dispersion occurs, it does with an
exponential rate in time. To control this, we change the unknown function
through a generalized lens transform. This approach neutralizes the possible
boundary effects, and could be used in the case of the Schrodinger equation
without potential. We then employ standard splitting methods on the new
equation via a nonuniform grid, after the logarithmic nonlinearity has been
regularized. We also discuss the case of a power nonlinearity and give some
results concerning the error estimates of the first-order Lie-Trotter splitting
method for both cases of nonlinearities. Finally extensive numerical
experiments are reported to investigate the dynamics of the equations.


title: Equivariant Vector Bundles on Varieties with Codimension-one Orbits

abstract:
  Let $G$ be an algebraic group and let $X$ be a smooth $G$-variety with two
orbits: an open orbit and a a closed orbit of codimension $1$. We give an
algebraic description of the category of $G$-equivariant vector bundles on $X$
under a mild technical hypothesis. We deduce simpler classifications in the
special cases of line bundles and vector bundles which are generically local
systems. We apply our results to the study of admissible representations of
semisimple Lie groups. Our main result gives a new set of constraints on the
associated cycles of unipotent representations.


title:
Frequency-dependent polarization of repeating fast radio
  bursts-implications for their origin

abstract:
  The polarization of fast radio bursts (FRBs), bright astronomical transients,
contains crucial information about their environments. We report polarization
measurements of five repeating FRBs, the abundant signals of which enable
wide-band observations with two telescopes. A clear trend of lower polarization
at lower frequencies was found, which can be well characterized by a single
parameter rotation-measure-scatter ({\sigma}RM) and modeled by multi-path
scatter. Sources with higher {\sigma}RM have higher RM magnitude and scattering
timescales. The two sources with the most substantial {\sigma}RM, FRB 20121102A
and FRB 20190520B, are associated with a compact persistent radio source. These
properties indicate a complex environment near the repeating FRBs, such as a
supernova remnant or a pulsar wind nebula, consistent with their arising from
young populations.


title:
Temporal Scattering, Depolarization, and Persistent Radio Emission from
  Magnetized Inhomogeneous Environments Near Repeating Fast Radio Burst Sources

abstract:
  Some repeating fast radio burst (FRB) sources exhibit complex polarization
behaviors, including frequency-dependent depolarization, variation of rotation
measure (RM), and oscillating spectral structures of polarized components. Very
recently, Feng et al. (2022) reported that active repeaters exhibit conspicuous
frequency-dependent depolarization and a strong correlation between RM scatter
($\sigma_{\rm RM}$) and the temporal scattering time ($\tau_{\rm s}$),
$\sigma_{\rm RM}\propto\tau_{\rm s}^{1.0\pm0.2}$, both of which can be well
described by multi-path propagation through a magnetized inhomogeneous plasma
screen. This observation strongly suggests that the temporal scattering and RM
scatter originate from the same region. Besides, a particular finding of note
in Feng et al. (2022) is that the FRBs with compact persistent radio sources
(PRS) tend to have extreme $\sigma_{\rm RM}$. In this work, we focus on some
theoretical predictions on the relations among temporal scattering,
depolarization by RM scatter, and PRS contributed by the magnetized plasma
environment close to a repeating FRB source. The behaviors of the RM scatter
imply that the magnetized plasma environment is consistent with a supernova
remnant or a pulsar wind nebula, and the predicted $\sigma_{\rm RM}$-$\tau_{\rm
s}$ relation is $\sigma_{\rm RM}\propto\tau_{\rm s}^{(0.54-0.83)}$ for
different astrophysical scenarios. We further make a general discussion on PRS
that does not depend on specific astrophysical scenarios. We show that the
specific luminosity of a PRS should have a positive correlation with the RM
contributed by the plasma screen. This is consistent with the observations of
FRB 121102 and FRB 190520B.


title: Device Identification in Blockchain-Based Internet of Things

abstract:
  In recent years blockchain technology has received tremendous attention.
Blockchain users are known by a changeable Public Key (PK) that introduces a
level of anonymity, however, studies have shown that anonymized transactions
can be linked to deanonymize the users. Most of the existing studies on user
de-anonymization focus on monetary applications, however, blockchain has
received extensive attention in non-monetary applications like IoT. In this
paper we study the impact of de-anonymization on IoT-based blockchain. We
populate a blockchain with data of smart home devices and then apply machine
learning algorithms in an attempt to classify transactions to a particular
device that in turn risks the privacy of the users. Two types of attack models
are defined: (i) informed attacks: where attackers know the type of devices
installed in a smart home, and (ii) blind attacks: where attackers do not have
this information. We show that machine learning algorithms can successfully
classify the transactions with 90% accuracy. To enhance the anonymity of the
users, we introduce multiple obfuscation methods which include combining
multiple packets into a transaction, merging ledgers of multiple devices, and
delaying transactions. The implementation results show that these obfuscation
methods significantly reduce the attack success rates to 20% to 30% and thus
enhance user privacy.


title:
Calculating the Raman Signal Beyond Perturbation Theory for a Diatomic
  Molecular Crystal

abstract:
  We calculate the eigenstates of a diatomic molecule in a range of model
mean-field potentials, and evaluate the evolution of their associated Raman
spectra with field strength. We demonstrate that dramatic changes in the
appearance of the Raman spectrum for a diatomic molecule occur without any
associated change in the symmetry of the surrounding potential. The limiting
cases of the quantum eigenstates correspond, in the classical sense, to free
rotation, and libration of well-oriented molecules. However, there are also
many mixed modes which are neither rotons nor librons. The consequence for
Raman spectroscopy is a series of complications - the non-harmonic potential
splits the Raman active modes, and breaks the selection rules on forbidden
modes. The mass-dependence of the various states is different - rotors,
oscillators and reorientations have $1/m$, $1/\sqrt{m}$ and weaker mass
dependence respectively. This may allow one to identify the character of the
mode with isotope spectroscopy. However it is complicated by mixed modes and
transitions between two different eigenstates with different character. We
conclude that significant changes in the Raman spectrum of molecular systems
are insufficient to demonstrate a phase transition since such changes can also
occur in a fixed symmetry potential upon increasing field strength.


title: On the best lattice quantizers

abstract:
  A lattice quantizer approximates an arbitrary real-valued source vector with
a vector taken from a specific discrete lattice. The quantization error is the
difference between the source vector and the lattice vector. In a classic 1996
paper, Zamir and Feder show that the globally optimal lattice quantizer (which
minimizes the mean square error) has white quantization noise: for a uniformly
distributed source, the covariance of the error is the identity matrix,
multiplied by a positive real factor. We generalize the theorem, showing that
the same property holds (i) for any locally optimal lattice quantizer and (ii)
for an optimal product lattice, if the component lattices are themselves
locally optimal. We derive an upper bound on the normalized second moment (NSM)
of the optimal lattice in any dimension, by proving that any lower- or
upper-triangular modification to the generator matrix of a product lattice
reduces the NSM. Using these tools and employing the best currently known
lattice quantizers to build product lattices, we construct improved lattice
quantizers in dimensions 13 to 15, 17 to 23, and 25 to 48. In some dimensions,
these are the first reported lattices with normalized second moments below the
Zador upper bound.

categories: cs.IT astro-ph.IM gr-qc math-ph math.IT math.MG math.MP


title: MixKG: Mixing for harder negative samples in knowledge graph

abstract:
  Knowledge graph embedding~(KGE) aims to represent entities and relations into
low-dimensional vectors for many real-world applications. The representations
of entities and relations are learned via contrasting the positive and negative
triplets. Thus, high-quality negative samples are extremely important in KGE.
However, the present KGE models either rely on simple negative sampling
methods, which makes it difficult to obtain informative negative triplets; or
employ complex adversarial methods, which requires more training data and
strategies. In addition, these methods can only construct negative triplets
using the existing entities, which limits the potential to explore harder
negative triplets. To address these issues, we adopt mixing operation in
generating harder negative samples for knowledge graphs and introduce an
inexpensive but effective method called MixKG. Technically, MixKG first
proposes two kinds of criteria to filter hard negative triplets among the
sampled negatives: based on scoring function and based on correct entity
similarity. Then, MixKG synthesizes harder negative samples via the convex
combinations of the paired selected hard negatives. Experiments on two public
datasets and four classical KGE methods show MixKG is superior to previous
negative sampling algorithms.


title:
Report on a pre-earthquake signal detection by enhanced E\"otv\"os
  torsion balance

abstract:
  More than 30 minutes before the earthquake event in Florina, Greece at
21:43:47(UTC) on 9th January 2022 an enhanced E\"otv\"os torsion balance
registered unidentified signals in the J\'anossy Underground Research
Laboratory in Budapest, Hungary. These signals were not visible on the
seismograms, although seismic noises are continuously recorded by a
side-by-side broadband seismometer. Moreover, seismological stations did not
detect anything unusual, they presented a negative confirmation of the events.
Our observation suggests that torsion balances might effectively detect
precursory earthquake signals from a considerable distance. Such a finding
could trigger the development of new observational devices and networks and can
provide novel knowledge about the origin and mechanism of earthquake phenomena.


title: Radial velocity variability and evolution of hot subdwarf stars

abstract:
  Hot subdwarf stars represent a late and peculiar stage in the evolution of
low-mass stars, because they are likely formed by close binary interactions.
Here we performed a radial velocity (RV) variability study of a sample of 646
hot subdwarfs with multi-epoch radial velocities from Sloan Digital Sky Survey
(SDSS) and Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST)
spectra [...] As diagnostics we used the fraction of RV-variable stars and the
distribution of the maximum RV variations $\Delta RV_{\rm max}$. Both
indicators turned out to be quite inhomogeneous across the studied parameter
ranges. A striking feature is the completely different behaviour of He-poor and
He-rich hot subdwarfs. While the former have a high fraction of close binaries,
almost no significant RV variations could be detected for the latter. This led
us to the conclusion that there likely is no evolutionary connection between
these subtypes. Intermediate He-rich- and extreme He-rich sdOB/Os on the other
hand are likely related. We conclude further that the vast majority of this
population is formed via one or several binary merger channels. Hot subdwarfs
with temperatures cooler than $\sim24\,000\,{\rm K}$ tend to show less and
smaller RV-variations. These objects might constitute a new subpopulation of
binaries with longer periods and late-type or compact companions. The
RV-variability properties of the extreme horizontal branch (EHB) and
corresponding post-EHB populations of the He-poor hot subdwarfs match and
confirm the predicted evolutionary connection between them. Stars found below
the canonical EHB at somewhat higher surface gravities show large RV-variations
and a high RV-variability fraction, which is consistent with most of them being
low-mass EHB stars or progenitors of low-mass helium white dwarfs in close
binaries.


title:
A Lightweight Dual-Domain Attention Framework for Sparse-View CT
  Reconstruction

abstract:
  Computed Tomography (CT) plays an essential role in clinical diagnosis. Due
to the adverse effects of radiation on patients, the radiation dose is expected
to be reduced as low as possible. Sparse sampling is an effective way, but it
will lead to severe artifacts on the reconstructed CT image, thus sparse-view
CT image reconstruction has been a prevailing and challenging research area.
With the popularity of mobile devices, the requirements for lightweight and
real-time networks are increasing rapidly. In this paper, we design a novel
lightweight network called CAGAN, and propose a dual-domain reconstruction
pipeline for parallel beam sparse-view CT. CAGAN is an adversarial
auto-encoder, combining the Coordinate Attention unit, which preserves the
spatial information of features. Also, the application of Shuffle Blocks
reduces the parameters by a quarter without sacrificing its performance. In the
Radon domain, the CAGAN learns the mapping between the interpolated data and
fringe-free projection data. After the restored Radon data is reconstructed to
an image, the image is sent into the second CAGAN trained for recovering the
details, so that a high-quality image is obtained. Experiments indicate that
the CAGAN strikes an excellent balance between model complexity and
performance, and our pipeline outperforms the DD-Net and the DuDoNet.


title:
The Linear Convergence Analysis of The Linearized Version of The
  Generalized Alternating Direction Method of Multipliers for Convex
  Optimization Problems

abstract:
  To solve the separable convex optimization problem with linear constraints,
Eckstein and Bertsekas introduced the generalized alternating direction method
of multipliers (in short, GADMM), which is an efficient and simple acceleration
scheme of the aternating direction method of multipliers. Recently,
\textbf{Fang et. al} proposed the linearized version of generalized alternating
direction method of multipliers (in short, L-GADMM), where one of its
subproblems is approximated by a linearization strategy, and proved its
worst-case $\mathcal{O}(1/t)$ convergence rate measured by the iteration
complexity in both ergodic and nonergodic senses. In this paper, we introduce
the doubly linearized version of generalized alternating direction method of
multipliers (in short, DL-GADMM), where both the $x$-subproblem and
$y$-subproblem are approximated by linearization strategies. Based on the error
bound approach, we establish the linear convergence rate of both L-GADMM and
DL-GADMM for separable convex optimization problem that the subdifferentials of
the underlying functions are piecewise linear multifunctions. The results in
this paper extend, generalize and improve some known results in the literature.