title: BatchHL: Answering Distance Queries on Batch-Dynamic Networks at Scale

abstract:
  Many real-world applications operate on dynamic graphs that undergo rapid
changes in their topological structure over time. However, it is challenging to
design dynamic algorithms that are capable of supporting such graph changes
efficiently. To circumvent the challenge, we propose a batch-dynamic framework
for answering distance queries, which combines offline labelling and online
searching to leverage the advantages from both sides - accelerating query
processing through a partial distance labelling that is of limited size but
provides a good approximation to bound online searches. We devise batch-dynamic
algorithms to dynamize a distance labelling efficiently in order to reflect
batch updates on the underlying graph. In addition to providing theoretical
analysis for the correctness, labelling minimality, and computational
complexity, we have conducted experiments on 14 real-world networks to
empirically verify the efficiency and scalability of the proposed algorithms.


title:
The relationship of the neutron-skin thickness to the symmetry energy
  and its slope

abstract:
  The neutron-skin thickness of asymmetric semi-infinite nuclear matter is
shown to be a function of Coulomb energy, the asymmetry-energy
coefficient($J$), the slope($L$) of the asymmetry energy, and the
incompressibility coefficient, in addition to he Fermi momentum and the
asymmetry parameter. The relational formula is derived on the basis of the
Hugenholtz-Van Hove theorem in the mean-field approximation for nuclear matter.
Using the formula as a guide, the neutron-skin thickness($\delta R$) in
$^{208}$Pb is examined. The linear correlation between $L$ and $\delta R$
appears as a kind of spurious ones through the model-dependent correlation of
$L$ with $J$ which is included in the main components of the formula.


title:
Discriminative Feature Learning Framework with Gradient Preference for
  Anomaly Detection

abstract:
  Unsupervised representation learning has been extensively employed in anomaly
detection, achieving impressive performance. Extracting valuable feature
vectors that can remarkably improve the performance of anomaly detection are
essential in unsupervised representation learning. To this end, we propose a
novel discriminative feature learning framework with gradient preference for
anomaly detection. Specifically, we firstly design a gradient preference based
selector to store powerful feature points in space and then construct a feature
repository, which alleviate the interference of redundant feature vectors and
improve inference efficiency. To overcome the looseness of feature vectors,
secondly, we present a discriminative feature learning with center constrain to
map the feature repository to a compact subspace, so that the anomalous samples
are more distinguishable from the normal ones. Moreover, our method can be
easily extended to anomaly localization. Extensive experiments on popular
industrial and medical anomaly detection datasets demonstrate our proposed
framework can achieve competitive results in both anomaly detection and
localization. More important, our method outperforms the state-of-the-art in
few shot anomaly detection.


title:
Surface Reconstruction from Point Clouds by Learning Predictive Context
  Priors

abstract:
  Surface reconstruction from point clouds is vital for 3D computer vision.
State-of-the-art methods leverage large datasets to first learn local context
priors that are represented as neural network-based signed distance functions
(SDFs) with some parameters encoding the local contexts. To reconstruct a
surface at a specific query location at inference time, these methods then
match the local reconstruction target by searching for the best match in the
local prior space (by optimizing the parameters encoding the local context) at
the given query location. However, this requires the local context prior to
generalize to a wide variety of unseen target regions, which is hard to
achieve. To resolve this issue, we introduce Predictive Context Priors by
learning Predictive Queries for each specific point cloud at inference time.
Specifically, we first train a local context prior using a large point cloud
dataset similar to previous techniques. For surface reconstruction at inference
time, however, we specialize the local context prior into our Predictive
Context Prior by learning Predictive Queries, which predict adjusted spatial
query locations as displacements of the original locations. This leads to a
global SDF that fits the specific point cloud the best. Intuitively, the query
prediction enables us to flexibly search the learned local context prior over
the entire prior space, rather than being restricted to the fixed query
locations, and this improves the generalizability. Our method does not require
ground truth signed distances, normals, or any additional procedure of signed
distance fusion across overlapping regions. Our experimental results in surface
reconstruction for single shapes or complex scenes show significant
improvements over the state-of-the-art under widely used benchmarks.


title: Existence of Vortices for Nonlinear Schr\"{o}dinger Equations

abstract:
  In this paper, we study the existence of vortices for two kinds of nonlinear
Schr\"{o}dinger equations arising from the Bose-Einstein condensates and
geometric optics arguments, respectively. For the Gross-Pitaevskii equation
from Bose-Einstein condensates arguments, we introduce the weighted Sobolev
space on which the corresponding functional is coercive. By using the
variational methods, we prove the existence of positive and radially symmetric
solutions under different types of boundary condition. And we study another
equation arising from geometric optics arguments by constrained minimization
method. Furthermore some explicit estimates for the bound of the wave
propagation constant are also derived.


title: Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets

abstract:
  Federated learning allows clients to collaboratively train models on datasets
that are acquired in different locations and that cannot be exchanged because
of their size or regulations. Such collected data is increasingly
non-independent and non-identically distributed (non-IID), negatively affecting
training accuracy. Previous works tried to mitigate the effects of non-IID
datasets on training accuracy, focusing mainly on non-IID labels, however
practical datasets often also contain non-IID features. To address both non-IID
labels and features, we propose FedGMCC, a novel framework where a central
server aggregates client models that it can cluster together. FedGMCC
clustering relies on a Monte Carlo procedure that samples the output space of
client models, infers their position in the weight space on a loss manifold and
computes their geometric connection via an affine curve parametrization.
FedGMCC aggregates connected models along their path connectivity to produce a
richer global model, incorporating knowledge of all connected client models.
FedGMCC outperforms FedAvg and FedProx in terms of convergence rates on the
EMNIST62 and a genomic sequence classification datasets (by up to +63%).
FedGMCC yields an improved accuracy (+4%) on the genomic dataset with respect
to CFL, in high non-IID feature space settings and label incongruency.


title:
Exploring Negatives in Contrastive Learning for Unpaired Image-to-Image
  Translation

abstract:
  Unpaired image-to-image translation aims to find a mapping between the source
domain and the target domain. To alleviate the problem of the lack of
supervised labels for the source images, cycle-consistency based methods have
been proposed for image structure preservation by assuming a reversible
relationship between unpaired images. However, this assumption only uses
limited correspondence between image pairs. Recently, contrastive learning (CL)
has been used to further investigate the image correspondence in unpaired image
translation by using patch-based positive/negative learning. Patch-based
contrastive routines obtain the positives by self-similarity computation and
recognize the rest patches as negatives. This flexible learning paradigm
obtains auxiliary contextualized information at a low cost. As the negatives
own an impressive sample number, with curiosity, we make an investigation based
on a question: are all negatives necessary for feature contrastive learning?
Unlike previous CL approaches that use negatives as much as possible, in this
paper, we study the negatives from an information-theoretic perspective and
introduce a new negative Pruning technology for Unpaired image-to-image
Translation (PUT) by sparsifying and ranking the patches. The proposed
algorithm is efficient, flexible and enables the model to learn essential
information between corresponding patches stably. By putting quality over
quantity, only a few negative patches are required to achieve better results.
Lastly, we validate the superiority, stability, and versatility of our model
through comparative experiments.


title:
Beam steering with quasi-mosaic bent silicon single crystals -- Computer
  simulations for 855 MeV and 6.3 GeV electrons and comparison with experiments

abstract:
  Monte Carlo simulations have been performed for 855 MeV and 6.3 GeV electrons
channeling in silicon single crystals at circular bent (111) planes. The aim
was to identify critical experimental parameters which effect the
volume-deflection and volume-capture characteristics. To these belongs the
angular alignment of the crystal with respect to the nominal beam direction.
The continuum potential picture has been utilized. The simulation results were
compared with experiments. It turns out that the assumption of an anticlastic
bending of the crystal, bent on the principle of the quasi-mosaic effect, is
not required to reproduce the experimental observations.


title:
Indoor simultaneous localization and mapping based on fringe projection
  profilometry

abstract:
  Simultaneous Localization and Mapping (SLAM) plays an important role in
outdoor and indoor applications ranging from autonomous driving to indoor
robotics. Outdoor SLAM has been widely used with the assistance of LiDAR or
GPS. For indoor applications, the LiDAR technique does not satisfy the accuracy
requirement and the GPS signals will be lost. An accurate and efficient scene
sensing technique is required for indoor SLAM. As the most promising 3D sensing
technique, the opportunities for indoor SLAM with fringe projection
profilometry (FPP) systems are obvious, but methods to date have not fully
leveraged the accuracy and speed of sensing that such systems offer. In this
paper, we propose a novel FPP-based indoor SLAM method based on the coordinate
transformation relationship of FPP, where the 2D-to-3D descriptor-assisted is
used for mapping and localization. The correspondences generated by matching
descriptors are used for fast and accurate mapping, and the transform
estimation between the 2D and 3D descriptors is used to localize the sensor.
The provided experimental results demonstrate that the proposed indoor SLAM can
achieve the localization and mapping accuracy around one millimeter.


title:
The generalized noncommutative residue and the Kastler-Kalau-Walze type
  theorem

abstract:
  In this paper, we define the generalized noncommutative residue of the Dirac
operator. And we give the proof of Kastler-Kalau-Walze type theorems for the
generalized noncommutative residue on 4-dimensional and 6-dimensional compact
manifolds with (resp.without) boundary.


title: Towards Data-Free Model Stealing in a Hard Label Setting

abstract:
  Machine learning models deployed as a service (MLaaS) are susceptible to
model stealing attacks, where an adversary attempts to steal the model within a
restricted access framework. While existing attacks demonstrate near-perfect
clone-model performance using softmax predictions of the classification
network, most of the APIs allow access to only the top-1 labels. In this work,
we show that it is indeed possible to steal Machine Learning models by
accessing only top-1 predictions (Hard Label setting) as well, without access
to model gradients (Black-Box setting) or even the training dataset (Data-Free
setting) within a low query budget. We propose a novel GAN-based framework that
trains the student and generator in tandem to steal the model effectively while
overcoming the challenge of the hard label setting by utilizing gradients of
the clone network as a proxy to the victim's gradients. We propose to overcome
the large query costs associated with a typical Data-Free setting by utilizing
publicly available (potentially unrelated) datasets as a weak image prior. We
additionally show that even in the absence of such data, it is possible to
achieve state-of-the-art results within a low query budget using synthetically
crafted samples. We are the first to demonstrate the scalability of Model
Stealing in a restricted access setting on a 100 class dataset as well.


title: Unboundedness for Recursion Schemes: A Simpler Type System

abstract:
  Decidability of the problems of unboundedness and simultaneous unboundedness
(aka. the diagonal problem) for higher-order recursion schemes was established
by Clemente, Parys, Salvati, and Walukiewicz (2016). Then a procedure of
optimal complexity was presented by Parys (2017); this procedure used a
complicated type system, involving multiple flags and markers. We present here
a simpler and much more intuitive type system serving the same purpose. We
prove that this type system allows to solve the unboundedness problem for a
widely considered subclass of recursion schemes, called safe schemes. For
unsafe recursion schemes we only have soundness of the type system: if one can
establish a type derivation claiming that a recursion scheme is unbounded then
it is indeed unbounded. Completeness of the type system for unsafe recursion
schemes is left as an open question. Going further, we discuss an extension of
the type system that allows to handle the simultaneous unboundedness problem.
  We also design and implement an algorithm that fully automatically checks
unboundedness of a given recursion scheme, completing in a short time for a
wide variety of inputs.


title:
VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame
  Filtration for Automatic Retail Checkout

abstract:
  Multi-class product counting and recognition identifies product items from
images or videos for automated retail checkout. The task is challenging due to
the real-world scenario of occlusions where product items overlap, fast
movement in the conveyor belt, large similarity in overall appearance of the
items being scanned, novel products, and the negative impact of misidentifying
items. Further, there is a domain bias between training and test sets,
specifically, the provided training dataset consists of synthetic images and
the test set videos consist of foreign objects such as hands and tray. To
address these aforementioned issues, we propose to segment and classify
individual frames from a video sequence. The segmentation method consists of a
unified single product item- and hand-segmentation followed by entropy masking
to address the domain bias problem. The multi-class classification method is
based on Vision Transformers (ViT). To identify the frames with target objects,
we utilize several image processing methods and propose a custom metric to
discard frames not having any product items. Combining all these mechanisms,
our best system achieves 3rd place in the AI City Challenge 2022 Track 4 with
an F1 score of 0.4545. Code will be available at


title:
GAMORRA: An API-Level Workload Model for Rasterization-based Graphics
  Pipeline Architecture

abstract:
  The performance of applications that require frame rendering time estimation
or dynamic frequency scaling, rely on the accuracy of the workload model that
is utilized within these applications. Existing models lack sufficient accuracy
in their core model. Hence, they require changes to the target application or
the hardware to produce accurate results. This paper introduces a mathematical
workload model for a rasterization-based graphics Application Programming
Interface (API) pipeline, named GAMORRA, which works based on the load and
complexity of each stage of the pipeline. Firstly, GAMORRA models each stage of
the pipeline based on their operation complexity and the input data size. Then,
the calculated workloads of the stages are fed to a Multiple Linear Regression
(MLR) model as explanatory variables. A hybrid offline/online training scheme
is proposed as well to train the model. A suite of benchmarks is also designed
to tune the model parameters based on the performance of the target system. The
experiments were performed on Direct3D 11 and on two different rendering
platforms comparing GAMORRA to an AutoRegressive (AR) model, a Frame Complexity
Model (FCM) and a frequency-based (FRQ) model. The experiments show an average
of 1.27 ms frame rendering time estimation error (9.45%) compared to an average
of 1.87 ms error (13.23%) for FCM which is the best method among the three
chosen methods. However, this comes at the cost of 0.54 ms (4.58%) increase in
time complexity compared to FCM. Furthermore, GAMMORA improves frametime
underestimations by 1.1% compared to FCM.


title:
Bioinformatic analysis for structure and function of Glutamine
  synthetase(GS)

abstract:
  Objective: To predict structure and function of Glutamine synthetase (GS)
from Pseudoalteromonas sp. by bioinformatics technology, and to provide a
theoretical basis for further study. Methods: Open reading frame (ORF) of GS
sequence from Pseudoalteromonas sp. was obtained by ORF finder and was
translated into amino acid residue. The structure domain was analyzed by Blast.
By the method of analysis tools: Protparam, ProtScale, SignalP-4.0, TMHMM,
SOPMA, SWISS-MODEL, NCBI SMART-BLAST and MAGA 7.0, the structure and function
of the protein were predicted and analyzed. Results: The results showed that
the sequence was GS with 468 amino acid residues, theoretical molecular weight
was 51986.64 Da. The protein has the closest evolutionary status with
Shewanella oneidensis. Then it had no signal peptide site and transmembrane
domain. Secondary structure of GS contained 35.04% alpha-helix, 16.67% Extended
chain, 5.34% beta-turn, 42.95% RandomCoil. Conclusions: This GU was a variety
of biological functions of protein that may be used as a molecular samples of
microbial nitrogen metabolism in extreme environments.


title: Quantum affine algebras, graded limits, and flags

abstract:
  In this survey, we review some of the recent connections between the
representation theory of (untwisted) quantum affine algebras and the
representation theory of current algebras. We mainly focus on the
finite-dimensional representations of these algebras. This connection arises
via the notion of the graded and classical limit of finite-dimensional
representations of quantum affine algebras. We explain how this study has led
to interesting connections with Macdonald polynomials and discuss a BGG-type
reciprocity result. We also discuss the role of Demazure modules in this theory
and several recent results on the presentation, structure, and combinatorics of
Demazure modules.


title: Reinforced Causal Explainer for Graph Neural Networks

abstract:
  Explainability is crucial for probing graph neural networks (GNNs), answering
questions like "Why the GNN model makes a certain prediction?". Feature
attribution is a prevalent technique of highlighting the explanatory subgraph
in the input graph, which plausibly leads the GNN model to make its prediction.
Various attribution methods exploit gradient-like or attention scores as the
attributions of edges, then select the salient edges with top attribution
scores as the explanation. However, most of these works make an untenable
assumption - the selected edges are linearly independent - thus leaving the
dependencies among edges largely unexplored, especially their coalition effect.
We demonstrate unambiguous drawbacks of this assumption - making the
explanatory subgraph unfaithful and verbose. To address this challenge, we
propose a reinforcement learning agent, Reinforced Causal Explainer
(RC-Explainer). It frames the explanation task as a sequential decision process
- an explanatory subgraph is successively constructed by adding a salient edge
to connect the previously selected subgraph. Technically, its policy network
predicts the action of edge addition, and gets a reward that quantifies the
action's causal effect on the prediction. Such reward accounts for the
dependency of the newly-added edge and the previously-added edges, thus
reflecting whether they collaborate together and form a coalition to pursue
better explanations. As such, RC-Explainer is able to generate faithful and
concise explanations, and has a better generalization power to unseen graphs.
When explaining different GNNs on three graph classification datasets,
RC-Explainer achieves better or comparable performance to SOTA approaches
w.r.t. predictive accuracy and contrastivity, and safely passes sanity checks
and visual inspections. Codes are available at
https://github.com/xiangwang1223/reinforced_causal_explainer.


title:
Anisotropic terahertz optostriction in group-IV monochalcogenide
  compounds

abstract:
  Terahertz (THz) technology is a cutting-edge scheme with various promising
applications, such as next generation telecommunication, non-destructive
evaluation, security check, and in-depth characterization, owing to their
sensitivity to material geometric change and good transparency. Even though
tremendous progresses have been made during the past decade, exploration the
mechanisms of THz-matter interaction microscopically is still in its infancy.
In this work, we use thermodynamic theory to show how THz illumination deforms
materials and use group-IV monochalcogenide compounds to illustrate it.
According to our first-principles density functional theory calculations, THz
light with intermediate intensity (~109 W/cm2) could yield elastic deformations
on the order of ~0.1%, depending on laser polarization direction. Large
anisotropic opto-mechanical responses are also revealed. Finally, we show that
such strain can be detected via measuring the layer-resolved shift current
under a probe light irradiation.


title: Improving Self-Supervised Learning-based MOS Prediction Networks

abstract:
  MOS (Mean Opinion Score) is a subjective method used for the evaluation of a
system's quality. Telecommunications (for voice and video), and speech
synthesis systems (for generated speech) are a few of the many applications of
the method. While MOS tests are widely accepted, they are time-consuming and
costly since human input is required. In addition, since the systems and
subjects of the tests differ, the results are not really comparable. On the
other hand, a large number of previous tests allow us to train machine learning
models that are capable of predicting MOS value. By automatically predicting
MOS values, both the aforementioned issues can be resolved.
  The present work introduces data-, training- and post-training specific
improvements to a previous self-supervised learning-based MOS prediction model.
We used a wav2vec 2.0 model pre-trained on LibriSpeech, extended with LSTM and
non-linear dense layers. We introduced transfer learning, target data
preprocessing a two- and three-phase training method with different batch
formulations, dropout accumulation (for larger batch sizes) and quantization of
the predictions.
  The methods are evaluated using the shared synthetic speech dataset of the
first Voice MOS challenge.


title:
Investigation of Deformation and Fracture Mechanisms in Two-dimensional
  Gallium Telluride Multilayers Using Nanoindentation

abstract:
  Two-dimensional (2D) materials possess great potential for flexible devices,
ascribing to their outstanding electrical, optical, and mechanical properties.
However, their mechanical deformation property and fracture mechanism, which
are inescapable in many applications like flexible optoelectronics, are still
unclear or not thoroughly investigated due methodology limitations. In light of
this, such mechanical properties and mechanisms are explored on example of
gallium telluride (GaTe), a promising optoelectronic candidate with an
ultrahigh photo-responsibility and a high plasticity within 2D family.
Considering the driving force insufficient in atomic force microscopy
(AFM)-based nanoindentation method, here the mechanical properties of both
substrate-supported and suspended GaTe multilayers were systematically
investigated through full-scale Berkovich-tip nanoindentation, micro-Raman
spectroscopy, AFM, and scanning electron microscopy. An unusual concurrence of
multiple pop-in and load-drop events in loading curve was observed. By further
correlating to molecular dynamics calculations, this concurrence was unveiled
originating from the interlayer sliding mediated layers-by-layers fracture
mechanism within GaTe multilayers. The van der Waals force between GaTe
multilayers and substrates was revealed much stronger than that between GaTe
interlayers, resulting in the easy sliding and fracture of multilayers within
GaTe. This work provides new insights into the deformation and fracture
mechanisms of GaTe and other similar 2D multilayers in flexible applications.


title:
Heterogeneous Separation Consistency Training for Adaptation of
  Unsupervised Speech Separation

abstract:
  Recently, supervised speech separation has made great progress. However,
limited by the nature of supervised training, most existing separation methods
require ground-truth sources and are trained on synthetic datasets. This
ground-truth reliance is problematic, because the ground-truth signals are
usually unavailable in real conditions. Moreover, in many industry scenarios,
the real acoustic characteristics deviate far from the ones in simulated
datasets. Therefore, the performance usually degrades significantly when
applying the supervised speech separation models to real applications. To
address these problems, in this study, we propose a novel separation
consistency training, termed SCT, to exploit the real-world unlabeled mixtures
for improving cross-domain unsupervised speech separation in an iterative
manner, by leveraging upon the complementary information obtained from
heterogeneous (structurally distinct but behaviorally complementary) models.
SCT follows a framework using two heterogeneous neural networks (HNNs) to
produce high confidence pseudo labels of unlabeled real speech mixtures. These
labels are then updated, and used to refine the HNNs to produce more reliable
consistent separation results for real mixture pseudo-labeling. To maximally
utilize the large complementary information between different separation
networks, a cross-knowledge adaptation is further proposed. Together with
simulated dataset, those real mixtures with high confidence pseudo labels are
then used to update the HNN separation models iteratively. In addition, we find
that combing the heterogeneous separation outputs by a simple linear fusion can
further slightly improve the final system performance.


title: Transitive irreducible Lie superalgebras of vector fields

abstract:
  Let $\mathfrak{d}$ be the Lie superalgebra of superderivations of the sheaf
of sections of the exterior algebra of the homogeneous vector bundle $E$ over
the flag variety $G/P$, where $G$ is a simple finite-dimensional complex Lie
group and $P$ its parabolic subgroup. Then, $\mathfrak{d}$ is transitive and
irreducible whenever $E$ is defined by an irreducible $P$-module $V$ such that
the highest weight of $V^*$ is dominant. Moreover, $\mathfrak{d}$ is simple; it
is isomorphic to the Lie superalgebra of vector fields on the superpoint, i.e.,
on a $0|n$-dimensional supervariety.


title:
Compact star merger events with stars composed of interacting strange
  quark matter

abstract:
  We investigate the properties of stars participating in double compact star
merger events considering interacting model of stable strange quark matter. We
model the matter making it compatible with the recent astrophysical
observations of compact star mass-radius and gravitational wave events. In this
context we consider modified MIT bag model and vector bag model with and
without self interaction. We find new upper bound on tidal deformability of
$1.4~M_\odot$ strange star corresponding to the upper bound of effective tidal
deformability inferred from gravitational wave event. Range of compactness of
$1.4~M_\odot$ strange star is obtained as ${0.175}\leq{C_{1.4}}\leq{0.199}$.
Radius range of $1.5M_\odot$ primary star is deduced to be
${10.57}\leq{R_{1.5}}\leq{12.04}$ km, following stringent GW170817 constraints.
GW190425 constraints provide with upper limit on radius of $1.7$ solar mass
strange star that it should be less than $13.41$ $\text{km}$.


title:
How to Approximate any Objective Function via Quadratic Unconstrained
  Binary Optimization

abstract:
  Quadratic unconstrained binary optimization (QUBO) has become the standard
format for optimization using quantum computers, i.e., for both the quantum
approximate optimization algorithm (QAOA) and quantum annealing (QA). We
present a toolkit of methods to transform almost arbitrary problems to QUBO by
(i) approximating them as a polynomial and then (ii) translating any polynomial
to QUBO. We showcase the usage of our approaches on two example problems (ratio
cut and logistic regression).


title: Action of vectorial Lie superalgebras on some split supermanifolds

abstract:
  The "curved" super Grassmannian is the supervariety of subsupervarieties of
purely odd dimension $k$ in a~supervariety of purely odd dimension $n$, unlike
the "usual" super Grassmannian which is the supervariety of linear
subsuperspacies of purely odd dimension $k$ in a~superspace of purely odd
dimension $n$. The Lie superalgebras of all and Hamiltonian vector fields on
the superpoint are realized as Lie superalgebras of derivations of the
structure sheaves of certain "curved" super Grassmannians,


title: Monotone ODEs with Discontinuous Vector Fields in Sequence Spaces

abstract:
  We consider a system of ODE in a Fr\'echet space with unconditional Schauder
basis. The right side of the ODE is a discontinuous function. Under certain
monotonicity conditions we prove an existence theorem for the corresponding
initial value problem.


title:
Dimension free non-asymptotic bounds on the accuracy of high dimensional
  Laplace approximation

abstract:
  This note attempts to revisit the classical results on Laplace approximation
in a modern non-asymptotic and dimension free form. Such an extension is
motivated by applications to high dimensional statistical and optimization
problems. The established results provide explicit non-asymptotic bounds on the
quality of a Gaussian approximation of the posterior distribution in total
variation distance in terms of the so called \emph{effective dimension} \(
\dimL \). This value is defined as interplay between information contained in
the data and in the prior distribution. In the contrary to prominent Bernstein
- von Mises results, the impact of the prior is not negligible and it allows to
keep the effective dimension small or moderate even if the true parameter
dimension is huge or infinite. We also address the issue of using a Gaussian
approximation with inexact parameters with the focus on replacing the Maximum
Aposteriori Probability (MAP) value by the posterior mean and design the
algorithm of Bayesian optimization based on Laplace iterations.


title:
Industry-Academia Research Collaboration in Software Engineering: The
  Certus Model

abstract:
  Context: Research collaborations between software engineering industry and
academia can provide significant benefits to both sides, including improved
innovation capacity for industry, and real-world environment for motivating and
validating research ideas. However, building scalable and effective research
collaborations in software engineering is known to be challenging. While such
challenges can be varied and many, in this paper we focus on the challenges of
achieving participative knowledge creation supported by active dialog between
industry and academia and continuous commitment to joint problem solving.
Objective: This paper aims to understand what are the elements of a successful
industry-academia collaboration that enable the culture of participative
knowledge creation. Method: We conducted participant observation collecting
qualitative data spanning 8 years of collaborative research between a software
engineering research group on software V&V and the Norwegian IT sector. The
collected data was analyzed and synthesized into a practical collaboration
model, named the Certus Model. Results: The model is structured in seven
phases, describing activities from setting up research projects to the
exploitation of research results. As such, the Certus model advances other
collaborations models from literature by delineating different phases covering
the complete life cycle of participative research knowledge creation.
Conclusion: The Certus model describes the elements of a research collaboration
process between researchers and practitioners in software engineering, grounded
on the principles of research knowledge co-creation and continuous commitment
to joint problem solving. The model can be applied and tested in other contexts
where it may be adapted to the local context through experimentation.


title: Long-Range ICN for the IoT: Exploring a LoRa System Design

abstract:
  This paper presents LoRa-ICN, a comprehensive IoT networking system based on
a common long-range communication layer (LoRa) combined with
Information-Centric Networking (ICN) principles. We have replaced the LoRaWAN
MAC layer with an IEEE 802.15.4 Deterministic and Synchronous Multi-Channel
Extension (DSME). This multifaceted MAC layer allows for different mappings of
ICN message semantics, which we explore to enable new LoRa cenarios.
  We designed LoRa-ICN from the ground-up to improve reliability and to reduce
dependency on centralized components in LoRa IoT scenarios. We have implemented
a feature-complete prototype in a common network simulator to validate our
approach. Our results show design trade-offs of different mapping alternatives
in terms of robustness and efficiency.


title:
Learning by Erasing: Conditional Entropy based Transferable
  Out-Of-Distribution Detection

abstract:
  Out-of-distribution (OOD) detection is essential to handle the distribution
shifts between training and test scenarios. For a new in-distribution (ID)
dataset, existing methods require retraining to capture the dataset-specific
feature representation or data distribution. In this paper, we propose a deep
generative models (DGM) based transferable OOD detection method, which is
unnecessary to retrain on a new ID dataset. We design an image erasing strategy
to equip exclusive conditional entropy distribution for each ID dataset, which
determines the discrepancy of DGM's posteriori ucertainty distribution on
different ID datasets. Owing to the powerful representation capacity of
convolutional neural networks, the proposed model trained on complex dataset
can capture the above discrepancy between ID datasets without retraining and
thus achieve transferable OOD detection. We validate the proposed method on
five datasets and verity that ours achieves comparable performance to the
state-of-the-art group based OOD detection methods that need to be retrained to
deploy on new ID datasets. Our code is available at
https://github.com/oOHCIOo/CETOOD.


title:
Simple Quantum State Encodings for Hybrid Programming of Quantum
  Simulators

abstract:
  Especially sparse quantum states can be efficiently encoded with simple
classical data structures. We show the admissibility of using a classical
database to encode quantum states for a few practical examples and argue in
favor of further optimizations for quantum simulation targeting simpler, only
'semi-quantum' circuits.


title:
Room-temperature superconductivity in heavy rare earth metal substituted
  sodalite-like clathrate hexahydrides under moderate pressure

abstract:
  Room temperature superconductivity is a dream that mankind has been chasing
for a century. In recent years, the synthesis of H3S, LaH10 and C-S-H system
has gradually made this dream a reality. But the extreme pressures required for
the metallization of hydrogen-based superconductors limit their applications.
In this work, we design a series of high temperature superconductors that can
be stable at moderate pressures by incorporating heavy rare earth elements
Yb/Lu into sodalite-like clathrate hydrides. In particular, the critical
temperatures of Y3LuH24, YLuH12 and YLu3H24 are 283 K, 275 K and 288 K,
respectively, which are close to or have reached room temperature, and the
required pressure for stabilization of these hydrides is about 120 GPa which is
significantly lower than that of reported room temperature superconductors. Our
work provides an effective method for the rational design of low-pressure
stabilized hydrogen-based superconductors with high-Tc and will stimulate
further experimental exploration.


title:
Fiber-optic multimode interference sensing: comprehensive
  characterization and its potential for strain-insensitive temperature sensing

abstract:
  A strain-insensitive temperature sensor based on multimode interference using
standard multimode fibers (MMFs) is proposed according to the comprehensive
study of the characteristics of the MMFs. The temperature and strain
dependences on the core diameter, numerical aperture (NA), and the length of
the MMF section in the single-mode--multimode--single-mode (SMS) fiber
structure are investigated experimentally. The results indicate that the larger
core diameter of the MMF leads to higher temperature sensitivity but lower
strain sensitivity (absolute values); the higher NA does not influence the
temperature sensitivity but results in higher absolute value of strain
sensitivity; the longer MMF section brings lower temperature sensitivity but
does not have an impact on strain sensitivity. These findings also contribute
to the theoretical analysis of the length dependence in the SMS fiber sensors.
Besides, the results of the characterization study show that the strain
sensitivity is relatively low, which brings a possibility to develop a
strain-insensitive temperature sensor. The proposed sensor is used for
temperature sensing while the strain is constantly applied from 0 to 1100
$\mu\epsilon$ with steps of 100 $\mu\epsilon$. The measured results are
consistent with the comprehensive study. The mean temperature sensitivity is
6.14 pm/$^{\circ}$C with a standard deviation of 0.39 pm/$^{\circ}$C, which
proves that the proposed temperature sensor exhibits good stability and is
insensitive to strain. We expect that these results will provide a profound
guideline to fiber sensors based on multimode interference.


title: Investigating Neural Architectures by Synthetic Dataset Design

abstract:
  Recent years have seen the emergence of many new neural network structures
(architectures and layers). To solve a given task, a network requires a certain
set of abilities reflected in its structure. The required abilities depend on
each task. There is so far no systematic study of the real capacities of the
proposed neural structures. The question of what each structure can and cannot
achieve is only partially answered by its performance on common benchmarks.
Indeed, natural data contain complex unknown statistical cues. It is therefore
impossible to know what cues a given neural structure is taking advantage of in
such data. In this work, we sketch a methodology to measure the effect of each
structure on a network's ability, by designing ad hoc synthetic datasets. Each
dataset is tailored to assess a given ability and is reduced to its simplest
form: each input contains exactly the amount of information needed to solve the
task. We illustrate our methodology by building three datasets to evaluate each
of the three following network properties: a) the ability to link local cues to
distant inferences, b) the translation covariance and c) the ability to group
pixels with the same characteristics and share information among them. Using a
first simplified depth estimation dataset, we pinpoint a serious nonlocal
deficit of the U-Net. We then evaluate how to resolve this limitation by
embedding its structure with nonlocal layers, which allow computing complex
features with long-range dependencies. Using a second dataset, we compare
different positional encoding methods and use the results to further improve
the U-Net on the depth estimation task. The third introduced dataset serves to
demonstrate the need for self-attention-like mechanisms for resolving more
realistic depth estimation tasks.


abstract:
  Side information fusion for sequential recommendation (SR) aims to
effectively leverage various side information to enhance the performance of
next-item prediction. Most state-of-the-art methods build on self-attention
networks and focus on exploring various solutions to integrate the item
embedding and side information embeddings before the attention layer. However,
our analysis shows that the early integration of various types of embeddings
limits the expressiveness of attention matrices due to a rank bottleneck and
constrains the flexibility of gradients. Also, it involves mixed correlations
among the different heterogeneous information resources, which brings extra
disturbance to attention calculation. Motivated by this, we propose Decoupled
Side Information Fusion for Sequential Recommendation (DIF-SR), which moves the
side information from the input to the attention layer and decouples the
attention calculation of various side information and item representation. We
theoretically and empirically show that the proposed solution allows
higher-rank attention matrices and flexible gradients to enhance the modeling
capacity of side information fusion. Also, auxiliary attribute predictors are
proposed to further activate the beneficial interaction between side
information and item representation learning. Extensive experiments on four
real-world datasets demonstrate that our proposed solution stably outperforms
state-of-the-art SR models. Further studies show that our proposed solution can
be readily incorporated into current attention-based SR models and
significantly boost performance. Our source code is available at
https://github.com/AIM-SE/DIF-SR.


title: The Moore-Penrose Inverses of Clifford Algebra $C\ell_{1,2}$

abstract:
  In this paper, we introduce a ring isomorphism between the Clifford algebra
$C\ell_{1,2}$ and a ring of matrices. By such a ring isomorphism, we introduce
the concept of the Moore-Penrose inverse in Clifford algebra $C\ell_{1,2}$.
Using the Moore-Penrose inverse, we solve the linear equation $axb=d$ in
$C\ell_{1,2}$. We also obtain necessary and sufficient conditions for two
numbers in $C\ell_{1,2}$ to be similar.


title: Class Balanced PixelNet for Neurological Image Segmentation

abstract:
  In this paper, we propose an automatic brain tumor segmentation approach
(e.g., PixelNet) using a pixel-level convolutional neural network (CNN). The
model extracts feature from multiple convolutional layers and concatenate them
to form a hyper-column where samples a modest number of pixels for
optimization. Hyper-column ensures both local and global contextual information
for pixel-wise predictors. The model confirms the statistical efficiency by
sampling a few pixels in the training phase where spatial redundancy limits the
information learning among the neighboring pixels in conventional pixel-level
semantic segmentation approaches. Besides, label skewness in training data
leads the convolutional model often converge to certain classes which is a
common problem in the medical dataset. We deal with this problem by selecting
an equal number of pixels for all the classes in sampling time. The proposed
model has achieved promising results in brain tumor and ischemic stroke lesion
segmentation datasets.


title: LAB: A Leader-Advocate-Believer Based Optimization Algorithm

abstract:
  This manuscript introduces a new socio-inspired metaheuristic technique
referred to as Leader-Advocate-Believer based optimization algorithm (LAB) for
engineering and global optimization problems. The proposed algorithm is
inspired by the AI-based competitive behaviour exhibited by the individuals in
a group while simultaneously improving themselves and establishing a role
(Leader, Advocate, Believer). LAB performance in computational time and
function evaluations are benchmarked using other metaheuristic algorithms.
Besides benchmark problems, the LAB algorithm was applied for solving
challenging engineering problems, including abrasive water jet machining,
electric discharge machining, micro-machining processes, and process parameter
optimization for turning titanium alloy in a minimum quantity lubrication
environment. The results were superior to the other algorithms compared such as
Firefly Algorithm, Variations of Co-hort Intelligence, Genetic Algorithm,
Simulated Annealing, Particle Swarm Optimisation, and Multi-Cohort
Intelligence. The results from this study highlighted that the LAB outperforms
the other algorithms in terms of function evaluations and computational time.
The prominent features of the LAB algorithm along with its limitations are also
discussed.


title: Chaos in 2-d Bohmian Trajectories

abstract:
  We make a short review of the most general mechanism for the generation of
chaos in 2-d Bohmian trajectories, the so called `nodal point-X-point complex'
(NPXPC) mechanism. The presentation is based on numerical calculations made
with Maple and is enriched with new results on the details of the generation of
chaos, and the form of the potential around the NPXPC.


title:
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian
  Optimization

abstract:
  Bayesian optimization (BO) has become an established framework and popular
tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms.
While known for its sample-efficiency, vanilla BO can not utilize readily
available prior beliefs the practitioner has on the potential location of the
optimum. Thus, BO disregards a valuable source of information, reducing its
appeal to ML practitioners. To address this issue, we propose $\pi$BO, an
acquisition function generalization which incorporates prior beliefs about the
location of the optimum in the form of a probability distribution, provided by
the user. In contrast to previous approaches, $\pi$BO is conceptually simple
and can easily be integrated with existing libraries and many acquisition
functions. We provide regret bounds when $\pi$BO is applied to the common
Expected Improvement acquisition function and prove convergence at regular
rates independently of the prior. Further, our experiments show that $\pi$BO
outperforms competing approaches across a wide suite of benchmarks and prior
characteristics. We also demonstrate that $\pi$BO improves on the
state-of-the-art performance for a popular deep learning task, with a 12.5
$\times$ time-to-accuracy speedup over prominent BO approaches.


title: On some resultants formulas of Schur type

abstract:
  Let $(r_{A,n}(x))_{n \in \mathbb{N}}$ be a sequence of polynomials with
coefficients from a field $K$ satisfying the recurrence relation $r_{A,n}(x)=
\sum_{|\alpha|\leq m} t_{\alpha,n}(x)\textbf{r}_{A,n}^\alpha(x)$ of order $d+1
\in \mathbb{N}_{+}$, where $t_{\alpha,n} \in K[x]$, $m \in \mathbb{N}_{+}$ are
fixed, $\alpha \in \mathbb{N}^{d+1}$, $|\alpha| = \alpha_0 + \ldots+\alpha_d$
and
$\textbf{r}_{A,n}^\alpha(x)=r_{A,n-1}^{\alpha_0}(x)r_{A,n-2}^{\alpha_1}(x)\cdots
r_{A,n-d-1}^{\alpha_d}(x).$ We show that under mild assumptions on the initial
polynomials $r_{A,0}, \ldots, r_{A,d}$ and the coefficients $t_{\alpha,n}$, we
can give the expression for the resultant $\text{Res}(r_{A,n}, r_{A,n-1})$. Our
results generalize recent result of Ulas concerning the case $m=1$ and $d=1$.


title:
Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial
  Expression Recognition

abstract:
  High-quality annotated images are significant to deep facial expression
recognition (FER) methods. However, uncertain labels, mostly existing in
large-scale public datasets, often mislead the training process. In this paper,
we achieve uncertain label correction of facial expressions using auxiliary
action unit (AU) graphs, called ULC-AG. Specifically, a weighted regularization
module is introduced to highlight valid samples and suppress category imbalance
in every batch. Based on the latent dependency between emotions and AUs, an
auxiliary branch using graph convolutional layers is added to extract the
semantic information from graph topologies. Finally, a re-labeling strategy
corrects the ambiguous annotations by comparing their feature similarities with
semantic templates. Experiments show that our ULC-AG achieves 89.31% and 61.57%
accuracy on RAF-DB and AffectNet datasets, respectively, outperforming the
baseline and state-of-the-art methods.


title:
MLP-Hash: Protecting Face Templates via Hashing of Randomized
  Multi-Layer Perceptron

abstract:
  Applications of face recognition systems for authentication purposes are
growing rapidly. Although state-of-the-art (SOTA) face recognition systems have
high recognition performance, the features which are extracted for each user
and are stored in the system's database contain privacy-sensitive information.
Accordingly, compromising this data would jeopardize users' privacy. In this
paper, we propose a new cancelable template protection method, dubbed MLP-hash,
which generates protected templates by passing the extracted features through a
user-specific randomly-weighted multi-layer perceptron (MLP) and binarizing the
MLP output. We evaluated the unlinkability, irreversibility, and recognition
performance of our proposed biometric template protection method to fulfill the
ISO/IEC 30136 standard requirements. Our experiments with SOTA face recognition
systems on the MOBIO and LFW datasets show that our method has competitive
performance with the BioHashing and IoM Hashing (IoM-GRP and IoM-URP) template
protection algorithms. We provide an open-source implementation of all the
experiments presented in this paper so that other researchers can verify our
findings and build upon our work.


title:
Varieties of aperiodic monoids with central idempotents whose subvariety
  lattice is distributive

abstract:
  We completely classify all varieties of aperiodic monoids with central
idempotents whose subvariety lattice is distributive.


title:
Transient behavior of three SU UMa-type dwarf novae; AR Pic, QW Ser and
  V521 Peg

abstract:
  Changes in the supercycle lengths of some SU UMa-type dwarf novae have been
detected by other studies, and indicate that the mass transfer rates noticeably
decrease over time. We investigated the supercycle lengths of three SU UMa-type
dwarf novae: AR Pic, QW Ser and V521 Peg, to determine if they have detectable
changes in their supercycles. We present the results of optical spectroscopic
and photometric observations of these sources. Our observations were conducted
in 2016 and 2017 at the Boyden Observatory and the Sutherland station of the
South African Astronomical Observatory. The quiescent results indicated that
all three sources are typical SU UMa-type dwarf novae. We also present results
of AR Pic and QW Ser in outburst and of V521 Peg during a precursor outburst
and superoutburst. Light curves were supplemented by the Catalina Real-Time
Transient Survey, the ASAS-3 and ASAS-SN archives, and the AAVSO International
database in order to investigate the long-term behavior of these sources. Our
results combined with catalogued properties for all short-period dwarf novae,
shows a possible relationship between the supercycle time in SU UMa systems and
their orbital periods, which is interpreted as the decline in the mass transfer
rate as systems evolve toward and away from the 'period minimum'. At the
shortest orbital periods, SU UMa systems are almost indistinguishable from WZ
Sge systems. However, we propose that the scaleheight between the secondary's
photosphere and L1 may be a factor that distinguish the SU UMa subclasses.


title: Theory of inertial spin dynamics in anisotropic ferromagnets

abstract:
  Recent experimental observation of inertial spin dynamics calls upon holistic
reevaluation of the theoretical framework of magnetic resonance in
ferromagnets. Here, we derive the secular equation of an inertial spin system
in analogy to the ubiquitous Smit-Beljers formalism. We find that the frequency
of precessional ferromagnetic resonances is decreased as compared to
non-inertial case. We also find that the frequency of nutational resonances is
generally increased due to the presence of magnetic anisotropy and applied
magnetic field. We obtain exact solutions of the secular equation and
approximations that employ the terminology of non-inertial theory and thus
allow for convenient estimates of the inertial effects.


title:
Precision measurements of decay parameters and $CP$ asymmetry in
  $\Lambda$ decays

abstract:
  Based on 10 billion $J/\psi$ events collected in the BESIII experiment,the
phase angle $\Delta\Phi$ associated with hyperon polarization in
$\Lambda/\bar{\Lambda}$ production, and the decay parameters $\alpha_{-}$ for
$\Lambda \rightarrow p\pi^-$ and $\alpha_{+}$ for $\bar\Lambda \rightarrow
\bar{p}\pi^+$ are studied in the process $J/\psi \rightarrow (\Lambda
\rightarrow p\pi^-)(\bar\Lambda \rightarrow \bar{p}\pi^+)$. The free parameters
of the joint angular distribution are determined by means of a maximum
likelihood fit, and the results are: $\Delta\Phi = 0.7521 \pm 0.0042 \pm
0.0080$, $\alpha_{-} = 0.7519 \pm 0.0036 \pm 0.0019$, and $\alpha_{+} = -0.7559
\pm 0.0036 \pm 0.0029$. The average value of the $\Lambda$ decay parameter is
$\alpha_{\rm{avg}} = 0.7542 \pm 0.0010 \pm 0.0020$. The CP asymmetry observable
$A_{CP}=(\alpha_{-}+\alpha_{+})/(\alpha_{-}-\alpha_{+})$ is determined to be
$-0.0025 \pm 0.0046 \pm 0.0011$, which is the most precise measurement in the
strange baryon sector.


title: Optical Flare Search on the RS CVn-type flare stars AR Lacertae

abstract:
  We present the results of fast spectrophotometry of flares on the RS CVn-type
star AR Lac with a time resolution of 34 s and a spectroscopic resolution R
$\sim $ 1300. The observations were performed on July 21-22, 2021 with the 2.0
m Karl Zeiss telescope at the Terskol Observatory. During the flares, an
additional emission appeared in the spectrum of AR Lac at wavelengths nearby Ca
II H\&K ($\lambda $ = 3933, 3968 \AA), nearby Si IV lines ($\lambda $ = 4089,
4116 \AA). Variations in these lines range from 2 to 5\%. We have estimated the
UBV magnitudes from spectrograms through a mathematical convolution of the
spectra with the filter transmission curves. The flare amplitudes in the UBV
band up to 0.8 magnitudes were discovered. A detailed colorimetric analysis has
allowed important parameters of the flares on AR Lac to be estimated: the
temperatures at maximum light and their sizes. The color-color $(U-B)$ -
$(B-V)$ diagrams confirm that all the flares at maximum light radiate as a
blackbody. The temperatures at maximum light were up to 12000 $\pm $ 300 K.
Based on our colorimetric analysis, we have estimated the linear sizes of the
flares at maximum light. The linear size of the flares at the maximum
luminosity is approximately 2\% of the radius of the star.


title: Dimension Reduction for time series with Variational AutoEncoders

abstract:
  In this work, we explore dimensionality reduction techniques for univariate
and multivariate time series data. We especially conduct a comparison between
wavelet decomposition and convolutional variational autoencoders for dimension
reduction. We show that variational autoencoders are a good option for reducing
the dimension of high dimensional data like ECG. We make these comparisons on a
real world, publicly available, ECG dataset that has lots of variability and
use the reconstruction error as the metric. We then explore the robustness of
these models with noisy data whether for training or inference. These tests are
intended to reflect the problems that exist in real-world time series data and
the VAE was robust to both tests.


title: Calculation of Integrals in MathPartner

abstract:
  We present the possibilities provided by the MathPartner service of
calculating definite and indefinite integrals. MathPartner contains software
implementation of the Risch algorithm and provides users with the ability to
compute antiderivatives for elementary functions. Certain integrals, including
improper integrals, can be calculated using numerical algorithms. In this case,
every user has the ability to indicate the required accuracy with which he
needs to know the numerical value of the integral. We highlight special
functions allowing us to calculate complete elliptic integrals. These include
functions for calculating the arithmetic-geometric mean and the
geometric-harmonic mean, which allow us to calculate the complete elliptic
integrals of the first kind. The set also includes the modified
arithmetic-geometric mean, proposed by Semjon Adlaj, which allows us to
calculate the complete elliptic integrals of the second kind as well as the
circumference of an ellipse. The Lagutinski algorithm is of particular
interest. For given differentiation in the field of bivariate rational
functions, one can decide whether there exists a rational integral. The
algorithm is based on calculating the Lagutinski determinant. Mikhail
Lagutinski (1871--1915) had worked at Kharkiv (Ukraine). This year we are
celebrating his 150th anniversary.


title: Selective clustering ensemble based on kappa and F-score

abstract:
  Clustering ensemble has an impressive performance in improving the accuracy
and robustness of partition results and has received much attention in recent
years. Selective clustering ensemble (SCE) can further improve the ensemble
performance by selecting base partitions or clusters in according to diversity
and stability. However, there is a conflict between diversity and stability,
and how to make the trade-off between the two is challenging. The key here is
how to evaluate the quality of the base partitions and clusters. In this paper,
we propose a new evaluation method for partitions and clusters using kappa and
F-score, leading to a new SCE method, which uses kappa to select informative
base partitions and uses F-score to weight clusters based on stability. The
effectiveness and efficiency of the proposed method is empirically validated
over real datasets.


title: Bell-type inequalities for systems of relativistic vector bosons

abstract:
  We perform a detailed analysis of the possible violation of various Bell-type
inequalities for systems of vector boson-antiboson pairs. Considering the
general case of an overall scalar state of the bipartite system, we identify
two distinct classes of such states, and determine the joint probabilities of
spin measurement outcomes for each them. We calculate the expectation values of
the CHSH, Mermin and CGLMP inequalities and find that while the generalised
CHSH inequality is not expected to be violated for any of the scalar states, in
the case of the Mermin and CGLMP inequalities the situation is different --
these inequalities can be violated in certain scalar states while they cannot
be violated in others. Moreover, the degree of violation depends on the
relative speed of the two particles.


title: Searching Extra-tidal Features around the Globular Cluster Whiting 1

abstract:
  Whiting 1 is a faint and young globular cluster in the halo of the Milky Way,
and was suggested to have originated in the Sagittarius spherical dwarf galaxy
(Sgr dSph). In this paper, we use the deep DESI Legacy Imaging Surveys to
explore tentative spatial connection between Whiting 1 and the Sgr dSph. We
redetermine the fundamental parameters of Whiting 1 and use the best-fitting
isochrone (age $\tau$=6.5 Gyr, metalicity Z=0.005 and $\rm d_{\odot}$=26.9 kpc)
to construct a theoretical matched filter for the extra-tidal features
searching. Without any smooth technique to the matched filter density map, we
detect a round-shape feature with possible leading and trailing tails on either
side of the cluster. This raw image is not totally new compared to old
discoveries, but confirms that no more large-scale features can be detected
under a depth of r<=22.5 mag. In our results, the whole feature stretches
0.1-0.2 degree along the orbit of Whiting 1, which gives a much larger area
than the cluster core. The tails on both sides of the cluster align along the
orbital direction of the Sgr dSph as well as the cluster itself, which implies
that these debris are probably stripped remnants of Whiting 1 by the Milky Way.


title:
A stochastic three-block splitting algorithm and its application to
  quantized deep neural networks

abstract:
  Deep neural networks (DNNs) have made great progress in various fields. In
particular, the quantized neural network is a promising technique making DNNs
compatible on resource-limited devices for memory and computation saving. In
this paper, we mainly consider a non-convex minimization model with three
blocks to train quantized DNNs and propose a new stochastic three-block
alternating minimization (STAM) algorithm to solve it. We develop a convergence
theory for the STAM algorithm and obtain an $\epsilon$-stationary point with
optimal convergence rate $\mathcal{O}(\epsilon^{-4})$. Furthermore, we apply
our STAM algorithm to train DNNs with relaxed binary weights. The experiments
are carried out on three different network structures, namely VGG-11, VGG-16
and ResNet-18. These DNNs are trained using two different data sets, CIFAR-10
and CIFAR-100, respectively. We compare our STAM algorithm with some classical
efficient algorithms for training quantized neural networks. The test accuracy
indicates the effectiveness of STAM algorithm for training relaxed binary
quantization DNNs.


title:
Transformation Invariant Cancerous Tissue Classification Using Spatially
  Transformed DenseNet

abstract:
  In this work, we introduce a spatially transformed DenseNet architecture for
transformation invariant classification of cancer tissue. Our architecture
increases the accuracy of the base DenseNet architecture while adding the
ability to operate in a transformation invariant way while simultaneously being
simpler than other models that try to provide some form of invariance.


title:
CORE: Simple and Effective Session-based Recommendation within
  Consistent Representation Space

abstract:
  Session-based Recommendation (SBR) refers to the task of predicting the next
item based on short-term user behaviors within an anonymous session. However,
session embedding learned by a non-linear encoder is usually not in the same
representation space as item embeddings, resulting in the inconsistent
prediction issue while recommending items. To address this issue, we propose a
simple and effective framework named CORE, which can unify the representation
space for both the encoding and decoding processes. Firstly, we design a
representation-consistent encoder that takes the linear combination of input
item embeddings as session embedding, guaranteeing that sessions and items are
in the same representation space. Besides, we propose a robust distance
measuring method to prevent overfitting of embeddings in the consistent
representation space. Extensive experiments conducted on five public real-world
datasets demonstrate the effectiveness and efficiency of the proposed method.
The code is available at: https://github.com/RUCAIBox/CORE.


title:
Integer Forcing Interference Management for the MIMO Interference
  Channel

abstract:
  A new interference management scheme based on integer forcing (IF) receivers
is studied for the two-user multiple-input and multiple-output (MIMO)
interference channel. The proposed scheme employs a message splitting method
that divides each data stream into common and private sub-streams, in which the
private stream is recovered by the dedicated receiver only while the common
stream is required to be recovered by both receivers. Specifically, to enable
IF sum decoding at the receiver side, all streams are encoded using the same
lattice code. Additionally, the number of common and private streams of each
user is carefully determined by considering the number of antennas at
transmitters and receivers, the channel matrices, and the effective
signal-to-noise ratio (SNR) at each receiver to maximize the achievable rate.
Furthermore, we consider various assumptions of channel state information at
the transmitter side (CSIT) and propose low-complexity linear transmit
beamforming suitable for each CSIT assumption. The achievable sum rate and rate
region are analytically derived and extensively evaluated by simulation for
various environments, demonstrating that the proposed interference management
scheme strictly outperforms the previous benchmark schemes in a wide range of
channel parameters due to the gain from IF sum decoding.


title: Kyle's Model with Stochastic Liquidity

abstract:
  We construct an equilibrium for the continuous time Kyle's model with
stochastic liquidity, a general distribution of the fundamental price, and
correlated stock and volatility dynamics. For distributions with positive
support, our equilibrium allows us to study the impact of the stochastic
volatility of noise trading on the volatility of the asset. In particular, when
the fundamental price is log-normally distributed, informed trading forces the
log-return up to maturity to be Gaussian for any choice of noise-trading
volatility even though the price process itself comes with stochastic
volatility. Surprisingly, we find that in equilibrium both Kyle's Lambda and
its inverse (the market depth) are submartingales.


title: Approximating triangular meshes by implicit, multi-sided surfaces

abstract:
  The I-patch is a multi-sided surface representation, defined as a combination
of implicit ribbon and bounding surfaces, whose pairwise intersections
determine the natural boundaries of the patch. Our goal is to show how a
collection of smoothly connected I-patches can be used to approximate
triangular meshes. We start from a coarse, user-defined vertex graph which
specifies an initial subdivision of the surface. Based on this, we create
ribbons that tightly fit the mesh along its edges in both positional and
tangential sense, then we optimize the free parameters of the patch to better
approximate the interior. If the surfaces are not sufficiently accurate, the
network needs to be refined; here we exploit that the I-patch construction
naturally supports T-nodes. We also describe a normalization method that nicely
approximates the Euclidean distance field, and can be efficiently evaluated.
The capabilities and limitations of the approach are analyzed through several
examples.


title:
Intelligent Reflecting Surface Enabled Sensing: Cram\'er-Rao Lower Bound
  Optimization

abstract:
  This paper investigates intelligent reflecting surface (IRS) enabled
non-line-of-sight (NLoS) wireless sensing, in which an IRS is dedicatedly
deployed to assist an access point (AP) to sense a target at its NLoS region.
It is assumed that the AP is equipped with multiple antennas and the IRS is
equipped with a uniform linear array. The AP aims to estimate the target's
direction-of-arrival (DoA) with respect to the IRS based on the echo signal
from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the
transmit beamforming at the AP and the reflective beamforming at the IRS to
minimize the DoA estimation error in terms of Cram\'er-Rao lower bound (CRLB).
Towards this end, we first obtain the closed-form expression of CRLB for DoA
estimation. Next, we optimize the joint beamforming design to minimize the
obtained CRLB, via alternating optimization, semi-definite relaxation, and
successive convex approximation. Finally, numerical results show that the
proposed design based on CRLB minimization achieves improved sensing
performance in terms of lower estimation mean squared error (MSE), as compared
to the traditional schemes with signal-to-noise ratio maximization and separate
beamforming designs.


title: The speed of invasion in an advancing population

abstract:
  We derive rigorous estimates on the speed of invasion of an advantageous
trait in a spatially advancing population in the context of a system of
one-dimensional F-KPP equations. The model was introduced and studied
heuristically and numerically in a paper by Venegas-Ortiz et al. In that paper,
it was noted that the speed of invasion by the mutant trait is faster faster
when the resident population ist expanding in space compared to the speed when
the resident population is already present everywhere. We use the Feynman-Kac
representation to provide rigorous estimates that confirm these predictions.


title: Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps

abstract:
  Transformer-based language models significantly advanced the state-of-the-art
in many linguistic tasks. As this revolution continues, the ability to explain
model predictions has become a major area of interest for the NLP community. In
this work, we present Gradient Self-Attention Maps (Grad-SAM) - a novel
gradient-based method that analyzes self-attention units and identifies the
input elements that explain the model's prediction the best. Extensive
evaluations on various benchmarks show that Grad-SAM obtains significant
improvements over state-of-the-art alternatives.


title: Grothendieck's Dessins d'Enfants in a Web of Dualities. III

abstract:
  We identify the dessin partition function with the partition function of the
Laguerre unitary ensemble (LUE). Combined with the result due to Cunden et al
on the relationship between the LUE correlators and strictly monotone Hurwitz
numbers introduced by Goulden et al, we then establish connection of dessin
counting to strictly monotone Hurwitz numbers. We also introduce a correction
factor for the dessin/LUE partition function, which plays an important role in
showing that the corrected dessin/LUE partition function is a tau-function of
the Toda lattice hierarchy. As an application, we use the approach of Dubrovin
and Zhang for the computation of the dessin correlators. In physicists'
terminology, we establish dualities among dessin counting, generalized Penner
model, and $\mathbb{P}^1$-topological sigma model.


title: Smart App Attack: Hacking Deep Learning Models in Android Apps

abstract:
  On-device deep learning is rapidly gaining popularity in mobile applications.
Compared to offloading deep learning from smartphones to the cloud, on-device
deep learning enables offline model inference while preserving user privacy.
However, such mechanisms inevitably store models on users' smartphones and may
invite adversarial attacks as they are accessible to attackers. Due to the
characteristic of the on-device model, most existing adversarial attacks cannot
be directly applied for on-device models. In this paper, we introduce a
grey-box adversarial attack framework to hack on-device models by crafting
highly similar binary classification models based on identified transfer
learning approaches and pre-trained models from TensorFlow Hub. We evaluate the
attack effectiveness and generality in terms of four different settings
including pre-trained models, datasets, transfer learning approaches and
adversarial attack algorithms. The results demonstrate that the proposed
attacks remain effective regardless of different settings, and significantly
outperform state-of-the-art baselines. We further conduct an empirical study on
real-world deep learning mobile apps collected from Google Play. Among 53 apps
adopting transfer learning, we find that 71.7\% of them can be successfully
attacked, which includes popular ones in medicine, automation, and finance
categories with critical usage scenarios. The results call for the awareness
and actions of deep learning mobile app developers to secure the on-device
models. The code of this work is available at
https://github.com/Jinxhy/SmartAppAttack


title:
Turning the Hunted into the Hunter via Threat Hunting: Life Cycle,
  Ecosystem, Challenges and the Great Promise of AI

abstract:
  The threat hunting lifecycle is a complex atmosphere that requires special
attention from professionals to maintain security. This paper is a collection
of recent work that gives a holistic view of the threat hunting ecosystem,
identifies challenges, and discusses the future with the integration of
artificial intelligence (AI). We specifically establish a life cycle and
ecosystem for privacy-threat hunting in addition to identifying the related
challenges. We also discovered how critical the use of AI is in threat hunting.
This work paves the way for future work in this area as it provides the
foundational knowledge to make meaningful advancements for threat hunting.


title:
Orbital dynamics and extreme scattering event properties from long-term
  scintillation observations of PSR J1603-7202

abstract:
  We model long-term variations in the scintillation of binary pulsar PSR
J1603$-$7202, observed by the 64 m Parkes radio telescope (Murriyang) between
2004 and 2016. We find that the time variation in the scintillation arc
curvature is well-modelled by scattering from an anisotropic thin screen of
plasma between the Earth and the pulsar. Using our scintillation model, we
measure the inclination angle and longitude of ascending node of the orbit,
yielding a significant improvement over the constraints from pulsar timing.
From our measurement of the inclination angle, we place a lower bound on the
mass of J1603$-$7202's companion of $\gtrsim 0.5\,\text{M}_\odot$ assuming a
pulsar mass of $\gtrsim1.2\,\text{M}_\odot$. We find that the scintillation
arcs are most pronounced when the electron column density along the line of
sight is increased, and that arcs are present during a known extreme scattering
event. We measure the distance to the interstellar plasma and its velocity, and
we discuss some structures seen in individual scintillation arcs within the
context of our model.


title:
Giant spectral renormalization and complex hybridization physics in a
  Kondo lattice system, CeCuSb2

abstract:
  We investigate the electronic structure of a Kondo lattice system, CeCuSb2
exhibiting significant mass enhancement and Kondo-type behavior. We observe
multiple features in the hard x-ray photoemission spectra of Ce core levels due
to strong final-state effects. The depth-resolved data exhibit a significant
change in relative intensity of the features with the surface sensitivity of
the probe. The extracted surface and bulk spectral functions are different and
exhibit a Kondo-like feature at higher binding energies in addition to the well
and poorly screened features. The core-level spectra of Sb exhibit huge and
complex changes as a function of the surface sensitivity of the technique. The
analysis of the experimental data suggests that the two non-equivalent Sb sites
possess different electronic structures and in each category, the Sb layers
close to the surface are different from the bulk ones. An increase in
temperature influences the Ce-Sb hybridization significantly. The
plasmon-excitation-induced loss features are also observed in all core level
spectra. All these results reveal the importance of Ce-Sb hybridizations and
indicate that the complex renormalization of Ce-Sb hybridization may be the
reason for the exotic electronic properties of this system.


title: Decoding the double heavy tetraquark state $T^+_{cc}$

abstract:
  We analyse the deuteron-like $T^+_{cc}$ state observed by the LHCb
Collaboration from the perspective of the quark clustering. The $T^+_{cc}$
state implies the possible existence of other compact doubly heavy tetraquark
states.


title: Fast Computation of Zigzag Persistence

abstract:
  Zigzag persistence is a powerful extension of the standard persistence which
allows deletions of simplices besides insertions. However, computing zigzag
persistence usually takes considerably more time than the standard persistence.
This paper sheds light on narrowing this efficiency gap. Our main result is
that an input simplex-wise zigzag filtration can be converted to a cell-wise
non-zigzag filtration of a $\Delta$-complex with the same length, where the
cells are copies of the input simplices. This conversion step incurs very
little cost. Furthermore, the barcode of the original filtration can be easily
read from the barcode of the new cell-wise filtration because the conversion
embodies a series of diamond switches known in topological data analysis. This
seemingly simple observation opens up the vast possibilities for improving the
computation of zigzag persistence because any efficient algorithm/software for
standard persistence can now be applied to computing zigzag persistence. Our
experiment shows that this indeed achieves substantial performance gain over
the existing state-of-the-art softwares.


title: A Theory for Colors of Strongly Correlated Electronic Systems

abstract:
  Many strongly correlated transition metal insulators are colored, even though
they have large fundamental band gaps and no quasi-particle excitations in the
visible range. Why such insulators possess the colors they do poses a serious
challenge for any many-body theory to reliably pick up the interactions
responsible for the color. We pick two archetypal cases as examples: NiO with
green color and MnF$_{2}$ with pink color. To explain the origin of color in
these systems, we employ two kinds of advanced \emph{ab initio} many body
Green's function theories to investigate both optical and spin
susceptibilities. The first, a perturbative theory based on low-order
extensions of the $GW$ approximation, is able to explain the color in NiO, and
indeed well describe the dielectric response over the entire frequency
spectrum, while the same theory is unable to explain why MnF\textsubscript{2}
is pink. We show its color originates from higher order spin-flip transitions
that modify the optical response. This phenomenon is not captured by low-order
perturbation theory, but it is contained in dynamical mean-field theory (DMFT),
which has a dynamical spin-flip vertex that contributes to the charge
susceptibility. By combining DMFT with an extension of the quasiparticle
self-consistent \emph{GW} approximation to provide a high-fidelity framework
for the one-body Green's function, we are able to well describe the peaks in
subgap charge susceptibilities in both NiO and MnF\textsubscript{2}. As a
secondary outcome of this work, we establish that the one-particle properties
of paramagnetic NiO and MnF\textsubscript{2} are both well described by an
adequate single Slater-determinant theory and do not require a dynamical
vertex.


title: On the semantics of big Earth observation data for land classification

abstract:
  This paper discusses the challenges of using big Earth observation data for
land classification. The approach taken is to consider pure data-driven methods
to be insufficient to represent continuous change. We argue for sound theories
when working with big data. After revising existing classification schemes such
as FAO's Land Cover Classification System (LCCS), we conclude that LCCS and
similar proposals cannot capture the complexity of landscape dynamics. We then
investigate concepts that are being used for analyzing satellite image time
series; we show these concepts to be instances of events. Therefore, for
continuous monitoring of land change, event recognition needs to replace object
identification as the prevailing paradigm. The paper concludes by showing how
event semantics can improve data-driven methods to fulfil the potential of big
data.


title: Blockchain-Oriented Software Variant Forks: A Preliminary Study

abstract:
  In collaborative social development platforms such as GitHub, forking a
repository is a common activity. A variant fork wants to split the development
from the original repository and grow towards a different direction. In this
preliminary exploratory research, we analyze the possible reasons for creating
a variant fork in blockchain-oriented software. By collecting repositories in
GitHub, we created a dataset with repositories and their variants, from which
we manually analyzed 86 variants. Based on the variants we studied, the main
reason to create a variant in blockchain-oriented software is to support a
different blockchain platform (65%).


title: Decompositions of functions defined on finite sets in $\mathbb{R}^d$

abstract:
  A finite subset $M \subset \mathbb{R}^d$ is basic, if for any function $f
\colon M \to \mathbb{R}$ there exists a collection of functions $f_1, \ldots,
f_d \colon \mathbb{R} \to \mathbb{R}$ such that for each element $(x_1, \ldots,
x_d)\in M$ we have $f(x_1, \ldots, x_d) = f_1(x_1) + \ldots + f_d(x_d)$. For
certain finite sets, we prove a criterion for a set to be basic, and we show
that it cannot be extended to the general case. In addition, we interpret the
above criterion in terms of doubly-weighted graphs and give an estimation for
the number of elements in certain basic and non-basic subsets.


title:
Negative reflection and negative refraction in biaxial van der Waals
  materials

abstract:
  Negative reflection and negative refraction are exotic phenomena that can be
achieved by platforms such as double-negative metamaterial, hyperbolic
metamaterial, and phase-discontinuity metasurface. Recently, natural biaxial
van der Waals (vdW) materials, which support extremely anisotropic, low-loss,
and highly confined polaritons from infrared to visible regime, are emerging as
promising candidates for planar reflective and refractive optics. Here, we
introduce three degrees of freedom, namely interface, crystal direction, and
electric tunability to manipulate the reflection and refraction of the
polaritons. With broken in-plane symmetry contributed by the interface and
crystal direction, distinguished reflection and refraction such as negative and
backward reflection, positive and negative refraction could exist
simultaneously and exhibit high tunability. The numerical simulations show good
consistency with the theoretical analysis. Our findings provide a robust recipe
for the realization of negative reflection and refraction in biaxial vdW
materials, paving the way for the polaritonics and interface nano-optics.


title:
Emerging population of gap-opening planets around type-A stars --
  Long-term evolution of the forming planets around HD 163296

abstract:
  The presence of forming planets embedded in their protoplanetary disks has
been inferred from the detection of multiring structures in such disks. Most of
these suspected planets are undetectable by direct imaging observations at
current measurement sensitivities. Inward migration and accretion might make
these putative planets accessible to the Doppler method, but the actual extent
of growth and orbital evolution remains unconstrained.
  Under the premise that the gaps in the disk around HD 163296 originate from
new-born planets, we investigate if and under which circumstances the
gap-opening planets could represent progenitors of the exoplanet population
detected around A-type stars. In particular, we study the dependence of final
planetary masses and orbital parameters on the viscosity of the disk. The
evolution of the embedded planets was simulated throughout the disk lifetime
and up to 100 Myr after the dispersal of the disk, taking the evolving disk
structure and a likely range of disk lifetimes into account.
  We find that the final configuration of the planets is largely determined by
the $\alpha$ viscosity parameter of the disk and less dependent on the choice
for the disk lifetime and the initial planetary parameters. If we assume that
planets such as those in HD 163296 evolve to form the observed exoplanet
population of A-type stars, a $\alpha$ parameter on the order of $3.16 \times
10^{-4} \lesssim \alpha \lesssim 10^{-3}$ is required for the disks to induce
sufficiently high migration rates. Depending on whether or not future direct
imaging surveys will uncover a larger number of planets with $m_\mathrm{pl}
\lesssim 3 M_\mathrm{Jup}$ and $a_\mathrm{pl} \gtrsim 10 \mathrm{AU}$ we expect
the $\alpha$ parameter to be at the lower or upper end of this range, always
under the assumption that such disks indeed harbor wide orbit planets.


title: LitMind Dictionary: An Open-Source Online Dictionary

abstract:
  Dictionaries can help language learners to learn vocabulary by providing
definitions of words. Since traditional dictionaries present word senses as
discrete items in predefined inventories, they fall short of flexibility, which
is required in providing specific meanings of words in particular contexts. In
this paper, we introduce the LitMind Dictionary
(https://dictionary.litmind.ink), an open-source online generative dictionary
that takes a word and context containing the word as input and automatically
generates a definition as output. Incorporating state-of-the-art definition
generation models, it supports not only Chinese and English, but also
Chinese-English cross-lingual queries. Moreover, it has a user-friendly
front-end design that can help users understand the query words quickly and
easily. All the code and data are available at
https://github.com/blcuicall/litmind-dictionary.


title: Trade Facilitation and Economic Growth Among Middle-Income Countries

abstract:
  This study examined the relationship between trade facilitation and economic
growth among the middle-income countries from 2010 to 2020 using 94 countries
made up of 48 lower-middle-income countries and 46 upper-middle-income
countries. The study utilized both difference and system Generalised Method of
Moments (GMM) since the cross-sections (N) were greater than the periods (T).
The study found that container port traffic, quality of trade and
transport-related infrastructure have a strong influence on imports and exports
of goods and national income while trade tariff hurts the growth of the
countries. The study also found that most of the trade facilitation indicators
indicated a weak positive influence on trade flows and economic growth. Based
on these findings, the study recommends that reforms aimed at significantly
lowering the costs of trading across borders among middle-income countries
should be highly prioritized in policy formulations, with a focus on the export
side by reducing at-the-border documentation, time, and real costs of trading
across borders while the international organizations should continue to report
the set of Trade Facilitation Indicators (TFIs) that identify areas for action
and enable the potential impact of reforms to be assessed.


title: Transcendental Julia sets of positive finite Lebesgue measure

abstract:
  We show that there exists a transcendental entire function whose Julia set
has positive finite Lebesgue measure.


title:
Learning Shape Priors by Pairwise Comparison for Robust Semantic
  Segmentation

abstract:
  Semantic segmentation is important in medical image analysis. Inspired by the
strong ability of traditional image analysis techniques in capturing shape
priors and inter-subject similarity, many deep learning (DL) models have been
recently proposed to exploit such prior information and achieved robust
performance. However, these two types of important prior information are
usually studied separately in existing models. In this paper, we propose a
novel DL model to model both type of priors within a single framework.
Specifically, we introduce an extra encoder into the classic encoder-decoder
structure to form a Siamese structure for the encoders, where one of them takes
a target image as input (the image-encoder), and the other concatenates a
template image and its foreground regions as input (the template-encoder). The
template-encoder encodes the shape priors and appearance characteristics of
each foreground class in the template image. A cosine similarity based
attention module is proposed to fuse the information from both encoders, to
utilize both types of prior information encoded by the template-encoder and
model the inter-subject similarity for each foreground class. Extensive
experiments on two public datasets demonstrate that our proposed method can
produce superior performance to competing methods.


title:
On-Device Next-Item Recommendation with Self-Supervised Knowledge
  Distillation

abstract:
  Modern recommender systems operate in a fully server-based fashion. To cater
to millions of users, the frequent model maintaining and the high-speed
processing for concurrent user requests are required, which comes at the cost
of a huge carbon footprint. Meanwhile, users need to upload their behavior data
even including the immediate environmental context to the server, raising the
public concern about privacy. On-device recommender systems circumvent these
two issues with cost-conscious settings and local inference. However, due to
the limited memory and computing resources, on-device recommender systems are
confronted with two fundamental challenges: (1) how to reduce the size of
regular models to fit edge devices? (2) how to retain the original capacity?
Previous research mostly adopts tensor decomposition techniques to compress the
regular recommendation model with limited compression ratio so as to avoid
drastic performance degradation. In this paper, we explore ultra-compact models
for next-item recommendation, by loosing the constraint of dimensionality
consistency in tensor decomposition. Meanwhile, to compensate for the capacity
loss caused by compression, we develop a self-supervised knowledge distillation
framework which enables the compressed model (student) to distill the essential
information lying in the raw data, and improves the long-tail item
recommendation through an embedding-recombination strategy with the original
model (teacher). The extensive experiments on two benchmarks demonstrate that,
with 30x model size reduction, the compressed model almost comes with no
accuracy loss, and even outperforms its uncompressed counterpart in most cases.


title:
The effects of a magnetar engine on the gamma-ray burst-associated
  supernovae: Application to double-peaked SN 2006aj

abstract:
  A millisecond magnetar engine has been widely suggested to exist in gamma-ray
burst (GRB) phenomena, in view of its substantial influences on the GRB
afterglow emission. In this paper, we investigate the effects of the magnetar
engine on the supernova (SN) emission which is associated with long GRBs and,
specifically, confront the model with the observational data of SN 2006aj/GRB
060218. SN 2006aj is featured by its remarkable double-peaked
ultraviolet-optical (UV-opt) light curves. By fitting these light curves, we
demonstrate that the first peak can be well accounted for by the breakout
emission of the shock driven by the magnetar wind, while the primary supernova
emission is also partly powered by the energy injection from the magnetar. The
magnetic field strength of the magnetar is constrained to be $\sim 10^{15}$ G,
which is in good agreement with the common results inferred from the afterglow
emission of long GRBs. In more detail, it is further suggested that the UV
excess in the late emission of the supernova could also be due to the leakage
of the non-thermal emission of the pulsar wind nebula (PWN), if some special
conditions can be satisfied. The consistency between the model and the SN
2006aj observation indicates that the magnetar engine is likely to be
ubiquitous in the GRB phenomena and even further intensify their connection
with the phenomena of superluminous supernovae.


title:
Nonreciprocal light propagation induced by a subwavelength spinning
  cylinder

abstract:
  Nonreciprocal optical devices have broad applications in light manipulations
for communications and sensing. Non-magnetic mechanisms of optical
nonreciprocity are highly desired for high-frequency on-chip applications.
Here, we investigate the nonreciprocal properties of light propagation in a
dielectric waveguide induced by a subwavelength spinning cylinder. We find that
the chiral modes of the cylinder can give rise to unidirectional coupling with
the waveguide via the transverse spin-orbit interaction, leading to different
transmissions for guided wave propagating in opposite directions and thus
optical isolation. We reveal the dependence of the nonreciprocal properties on
various system parameters including mode order, spinning speed, and coupling
distance. The results show that higher-order chiral modes and larger spinning
speed generally give rise to stronger nonreciprocity, and there exists an
optimal cylinder-waveguide coupling distance where the optical isolation
reaches the maximum. Our work contributes to the understanding of
nonreciprocity in subwavelength moving structures and can find applications in
integrated photonic circuits, topological photonics, and novel metasurfaces.


title:
Efficiently recognizing graphs with equal independence and annihilation
  numbers

abstract:
  The annihilation number $a(G)$ of a graph $G$ is an efficiently computable
upper bound on the independence number $\alpha(G)$ of $G$. Recently, Hiller
observed that a characterization of the graphs $G$ with $\alpha(G)=a(G)$ due to
Larson and Pepper is false. Since the known efficient algorithm recognizing
these graphs was based on this characterization, the complexity of recognizing
graphs $G$ with $\alpha(G)=a(G)$ was once again open. We show that these graphs
can indeed be recognized efficiently. More generally, we show that recognizing
graphs $G$ with $\alpha(G)\geq a(G)-\ell$ is fixed parameter tractable using
$\ell$ as parameter.


title:
EdgeKeeper: Resilient and Lightweight Coordination for Mobile Edge
  Computing Systems

abstract:
  Mobile Edge Computing (MEC) has been gaining significant interest from first
responders and tactical teams, primarily because they can employ handheld
mobile devices to form a computing cluster (for computing tasks like face/scene
recognition, virtual assistance) when connectivity to the cloud is not present
or it is limited. High user mobility in first responder or tactical
environments makes MEC challenging, as wireless links observe substantial
fluctuations. Typical cloud-based coordination (e.g., ZooKeeper-based service
discovery and coordination, device naming, security) needed by edge computing
tasks cannot work in these environments. Driven by the need for a resilient and
lightweight coordination service, in this paper, we design and implement \ek to
provide cloud-like coordination for MEC systems. It provides naming, network
management, application coordination, and security to distributed edge
computing applications. It maintains an edge cluster among devices and
intelligently stores its data on a group of replicas to guard against node
failure and disconnections. We provide a full-system implementation of
EdgeKeeper for Android and Linux platforms. We have integrated EdgeKeeper with
existing MEC applications and performed real-world performance evaluations in a
wide-area search and rescue operation conducted by first responders, which
proves it to be lightweight and suitable for mobile devices.


title: On light surfaces in black hole thermodynamics

abstract:
  We discuss the fundamentals of classical black hole (BH) thermodynamics in a
new framework determined by light surfaces and their frequencies. This new
approach allows us to study BH transitions inside the Kerr geometry. In the
case of BHs, we introduce a new parametrization of the metric in terms of the
maximum extractable rotational energy or, correspondingly, the irreducible
mass, which is an alternative to the spin parametrization.
  It turns out that BH spacetimes with spins $a/M= \sqrt {8/9}$ and
$a/M=1/\sqrt{2}$ show anomalies in the rotational energy extraction and surface
gravity whereas the case $a/M=\sqrt{3}/2$ is of particular relevance to study
the variations of the horizon area. We find the general conditions under which
BH transitions can occur and express them in terms of the masses of the initial
and final states. This shows that BH transitions in the Kerr geometry are not
arbitrary but depend on the relationship between the mass and spin of the
initial and final states. From an observational point of view, we argue that
near the BH poles it is possible to detect photon orbits with frequencies that
characterize the light surfaces analyzed in this work.


title:
The SCORE normalization, especially for highly heterogeneous network and
  text data

abstract:
  SCORE was introduced as a spectral approach to network community detection.
Since many networks have severe degree heterogeneity, the ordinary spectral
clustering (OSC) approach to community detection may perform unsatisfactorily.
SCORE alleviates the effect of degree heterogeneity by introducing a new
normalization idea in the spectral domain and makes OSC more effective. SCORE
is easy to use and computationally fast. It adapts easily to new directions and
sees an increasing interest in practice. In this paper, we review the basics of
SCORE, the adaption of SCORE to network mixed membership estimation and topic
modeling, and the application of SCORE in real data, including two datasets on
the publications of statisticians. We also review the theoretical 'ideology'
underlying SCORE. We show that in the spectral domain, SCORE converts a
simplicial cone to a simplex, and provides a simple and direct link between the
simplex and network memberships. SCORE attains an exponential rate and a sharp
phase transition in community detection, and achieves optimal rates in mixed
membership estimation and topic modeling.


title: Multi-sensor Suboptimal Fusion Student's $t$ Filter

abstract:
  A multi-sensor fusion Student's $t$ filter is proposed for time-series
recursive estimation in the presence of heavy-tailed process and measurement
noises. Driven from an information-theoretic optimization, the approach extends
the single sensor Student's $t$ Kalman filter based on the suboptimal
arithmetic average (AA) fusion approach. To ensure computationally efficient,
closed-form $t$ density recursion, reasonable approximation has been used in
both local-sensor filtering and inter-sensor fusion calculation. The overall
framework accommodates any Gaussian-oriented fusion approach such as the
covariance intersection (CI). Simulation demonstrates the effectiveness of the
proposed multi-sensor AA fusion-based $t$ filter in dealing with outliers as
compared with the classic Gaussian estimator, and the advantage of the AA
fusion in comparison with the CI approach and the augmented measurement fusion.