title:
Thermodynamics of the Bardeen Black Hole with quintessence matter on the
  EGUP framework

abstract:
  In this paper, we have used the extended generalized uncertainty principle to
investigate the thermodynamics of the Bardeen Black Hole with quintessence
matter on the EGUP framework. We started with a brief perusal of the Extended
generalized uncertainty principle. Subsequently, the EGUP-modified Hawking
temperature, heat capacity, and entropy functions of the Bardeen Black Hole are
obtained, which show that the modified uncertainty principles and normalization
factor restrict the lower bound value of horizon radius to affect the Hawking
temperature. Moreover, we compare and discuss the findings within the
generalized uncertainty principle (GUP) and extended uncertainty principle
(EUP). In addition, we examine the equation of state associated with the
pressure and the volume.


title: Centralized Hierarchical Coded Caching Scheme Over Two-Layer Networks

abstract:
  This paper considers a hierarchical caching system where a server connects
with multiple mirror sites, each connecting with a distinct set of users, and
both the mirror sites and users are equipped with caching memories. Although
there already exist works studying this setup and proposing coded caching
scheme to reduce transmission loads, two main problems are remained to address:
1) the optimal communication load under the uncoded placement for the first
hop, denoted by $R_1$, is still unknown. 2) the previous schemes are based on
Maddah-Ali and Niesen's data placement and delivery, which requires high
subpacketization level. How to achieve the well tradeoff between transmission
loads and subpacketization level for the hierarchical caching system is
unclear. In this paper, we aim to address these two problems. We first propose
a new combination structure named hierarchical placement delivery array (HPDA),
which characterizes the data placement and delivery for any hierarchical
caching system. Then we construct two classes of HPDAs, where the first class
leads to a scheme achieving the optimal $R_1$ for some cases, and the second
class requires a smaller subpacketization level at the cost of slightly
increasing transmission loads.


title: Characteristic properties of $\sigma$-A-nuclei of a quasigroup

abstract:
  In this paper, we investigate the properties of $\sigma$-A-nuclei of a
quasigroup including relations between them and relations between their
respective component sets, where $\sigma \in S_3$. We also find connections
between components of $\sigma$-A-nuclei of a quasigroup and components of
$\sigma$-A-nuclei of the isostrophic images. Further, we investigate the
properties of various inverse quasigroups using the derived connections. These
properties will not only make the study of $\sigma$-A-nuclei of a quasigroup
simple but also reduce the time required in the computation of
$\sigma$-A-nuclei of a quasigroup for different values of $\sigma$.


title: To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers

abstract:
  Current pre-trained language model approaches to information retrieval can be
broadly divided into two categories: sparse retrievers (to which belong also
non-neural approaches such as bag-of-words methods, e.g., BM25) and dense
retrievers. Each of these categories appears to capture different
characteristics of relevance. Previous work has investigated how relevance
signals from sparse retrievers could be combined with those from dense
retrievers via interpolation. Such interpolation would generally lead to higher
retrieval effectiveness. In this paper we consider the problem of combining the
relevance signals from sparse and dense retrievers in the context of Pseudo
Relevance Feedback (PRF). This context poses two key challenges: (1) When
should interpolation occur: before, after, or both before and after the PRF
process? (2) Which sparse representation should be considered: a zero-shot
bag-of-words model (BM25), or a learnt sparse representation? To answer these
questions we perform a thorough empirical evaluation considering an effective
and scalable neural PRF approach (Vector-PRF), three effective dense retrievers
(ANCE, TCTv2, DistillBERT), and one state-of-the-art learnt sparse retriever
(uniCOIL). The empirical findings from our experiments suggest that, regardless
of sparse representation and dense retriever, interpolation both before and
after PRF achieves the highest effectiveness across most datasets and metrics.


title:
Proportional Allocation of Indivisible Goods up to the Least Valued Good
  on Average

abstract:
  We study the problem of fairly allocating a set of indivisible goods to
multiple agents and focus on the proportionality, which is one of the classical
fairness notions. Since proportional allocations do not always exist when goods
are indivisible, approximate concepts of proportionality have been considered
in the previous work. Among them, proportionality up to the maximin good
(PROPm) has been the best approximate notion of proportionality that can be
achieved for all instances. In this paper, we introduce the notion of
proportionality up to the least valued good on average (PROPavg), which is a
stronger notion than PROPm, and show that a PROPavg allocation always exists.
Our results establish PROPavg as a notable non-trivial fairness notion that can
be achieved for all instances. Our proof is constructive, and based on a new
technique that generalizes the cut-and-choose protocol.


title:
On Dynamic Ray Tracing and Anticipative Channel Prediction for Dynamic
  Environments

abstract:
  Ray tracing algorithms, that can simulate multipath radio propagation in
presence of geometric obstacles such as buildings, objects or vehicles, are
becoming quite popular, due to the increasing availability of digital
environment databases and high-performance computation platforms, such as
multicore computers and cloud computing services. When objects or vehicles are
moving, which is the case of industrial or vehicular environments, multiple
successive representations of the environment ("snapshots") and multiple ray
tracing runs are often necessary, which require a great human effort and a
great deal of computation resources. Recently, the Dynamic Ray Tracing (DRT)
approach has been proposed to predict the multipath evolution within a given
time lapse on the base of the current multipath geometry, assuming constant
speeds and/or accelerations for moving objects, using analytical extrapolation
formulas. This is done without re-running a full ray tracing for every
"snapshot" of the environment, therefore with a great computation time saving.
When DRT is embedded in a mobile radio system and used in real-time,
ahead-of-time (or anticipative) field prediction is possible that opens the way
to interesting applications. In the present work, a full-3D DRT algorithm is
presented that allows to account for multiple reflections, edge diffraction and
diffuse scattering for the general case where moving objects can translate and
rotate. For the purpose of validation, the model is first applied to some ideal
cases and then to realistic cases where results are compared with conventional
ray tracing simulation and measurements available in the literature.


title:
Quantum Gravitational Corrections to the Geometry of Charged AdS Black
  Holes

abstract:
  We study the quantum gravitational corrections to the geometry of a
four-dimensionalcharged (Reissner-Nordstr\"om) Anti de Sitter black hole
starting from an effective field theory approach to quantum gravity. We use the
expression of the modified horizon radius to compute the quantum corrected Wald
entropy, whose expression reproduces the logarithmic behaviour found by other
methods. We perform a thermodynamics analysis and compute the quantum
gravitational corrections to the temperature, pressure, specific heat and
Helmholtz free energy. All these quantities are renormalisation group
invariant. We find that a quantum charged AdS black hole can exist only for a
bounded range of masses and that it can undergo a second order phase transition
as it moves from a state with positive specific heat to a negative one.


title:
$\textit{Ab initio}$ Materials Design of Superconductivity in $d^9$
  Nickelates

abstract:
  Motivated by the recent theoretical materials design of superconducting $d^9$
nickelates for which the charge transfer from the NiO$_2$ to the block layer is
completely suppressed [M. Hirayama $\textit{et al.}$, Phys. Rev. B
$\textbf{101}$, 075107 (2020)], we perform a calculation based on the dynamical
vertex approximation and obtain the phase diagram of RbCa$_2$NiO$_3$ and
$A_2$NiO$_2$Br$_2$ where $A$ is a cation with a valence of 2.5+. We show that
the phase diagram of these nickelates exhibits the same essential features as
those found in cuprates. Namely, superconductivity appears upon hole-doping
into an antiferromagnetic Mott insulator, and the superconducting transition
temperature shows a dome-like shape. This demonstrates that the electron
correlations play an essential role in nickelate superconductors and we can
control them by changing block layers.


title:
The Conley-Zehnder index of a minimal orbit and existence of a positive
  hyperbolic orbit

abstract:
  As a refinement of the Weinstein conjecture, it is a natural question whether
a Reeb orbit of particular types exists. D. Cristofaro-Gardiner, M. Hutchings
and D. Pomerleano showed that every nondegenerate closed contact three manifold
with $b_{1}>0$ has at least one positive hyperbolic orbit by directly using the
isomorphism between ECH and Seiberg-Witten Floer (co)homology. In the same
paper, they also asked whether the case of $b_{1}=0$ does. Suppose that
$(S^{3},\lambda)$ is non-degenerate contact three sphere with infinity many
orbits. In the present paper, we prove the existence of a simple positive
hyperbolic orbit on $(S^{3},\lambda)$ under the condition that the
Conley-Zehnder index of a minimal periodic orbit induced by the trivialization
of a bounding disc is larger than or equal to 3. As an immediate corollary, we
have the existence of a simple positive hyperbolic orbit on a non-degenerate
dynamically convex contact three sphere $(S^{3},\lambda)$ with infinity many
simple orbits. In particulr, this implies that a $C^{\infty}$ generic compact
strictly convex energy hypersurface in $\mathbb{R}^{4}$ carries a positive
hyperbolic simple orbit.


title:
A Two-Stream AMR-enhanced Model for Document-level Event Argument
  Extraction

abstract:
  Most previous studies aim at extracting events from a single sentence, while
document-level event extraction still remains under-explored. In this paper, we
focus on extracting event arguments from an entire document, which mainly faces
two critical problems: a) the long-distance dependency between trigger and
arguments over sentences; b) the distracting context towards an event in the
document. To address these issues, we propose a Two-Stream Abstract meaning
Representation enhanced extraction model (TSAR). TSAR encodes the document from
different perspectives by a two-stream encoding module, to utilize local and
global information and lower the impact of distracting context. Besides, TSAR
introduces an AMR-guided interaction module to capture both intra-sentential
and inter-sentential features, based on the locally and globally constructed
AMR semantic graphs. An auxiliary boundary loss is introduced to enhance the
boundary information for text spans explicitly. Extensive experiments
illustrate that TSAR outperforms previous state-of-the-art by a large margin,
with 2.54 F1 and 5.13 F1 performance gain on the public RAMS and WikiEvents
datasets respectively, showing the superiority in the cross-sentence arguments
extraction. We release our code in https://github.com/ PKUnlp-icler/TSAR.


title:
Approximating Permutations with Neural Network Components for Travelling
  Photographer Problem

abstract:
  Many of current inference techniques rely upon Bayesian inference on
Probabilistic Graphical Models of observations, and does prediction and
classification on observations rather well. However, little has been done to
facilitate of the mining of relationship between observations, and build models
of relationship between sets of observations or within the scope of
observations. Event understanding of machines with observation inputs needs to
deal with understanding of the relationship between observations, and thus
there is a crucial need to build models and come up with effective data
structures to accumulate and organize relationships between observations. Given
a set of states probabilisitcally-related with observations, this paper
attempts to fit a permutation of states to a sequence of observation tokens
(The Travelling Photographer Problem). We have devised a machine learning
inspired architecture for randomized approximation of state permutation,
facilitating parallelization of heuristic search of permutations. Our algorithm
is able to solve The Travelling Photographer Problem with very small error. We
demonstrate that by mimicking components of machine learning such as
normalization, dropout, lambda layer with randomized algorithm, we are able to
devise an architecture which solves TPP, a permutation NP-Hard problem. Other
than TPP, we are also able to provide a 2-Local improvement heuristic for the
Travelling Salesman Problem (TSP) with similar ideas.


title:
Local Topology Inference of Mobile Robotic Networks under Formation
  Control

abstract:
  The interaction topology is critical for efficient cooperation of mobile
robotic networks (MRNs). We focus on the local topology inference problem of
MRNs under formation control, where an inference robot with limited observation
range can manoeuvre among the formation robots. This problem faces new
challenges brought by the highly coupled influence of unobservable formation
robots, inaccessible formation inputs, and unknown interaction range. The novel
idea here is to advocate a range-shrink strategy to perfectly avoid the
influence of unobservable robots while filtering the input. To that end, we
develop consecutive algorithms to determine a feasible constant robot subset
from the changing robot set within the observation range, and estimate the
formation input and the interaction range. Then, an ordinary least squares
based local topology estimator is designed with the previously inferred
information. Resorting to the concentration measure, we prove the convergence
rate and accuracy of the proposed estimator, taking the estimation errors of
previous steps into account. Extensions on nonidentical observation slots and
more complicated scenarios are also analyzed. Comprehensive simulation tests
and method comparisons corroborate the theoretical findings.


title:
Auroral Morphological Changes to the Formation of Auroral Spiral during
  the Late Substorm Recovery Phase: Polar UVI and Ground All-Sky Camera
  Observations

abstract:
  Polar UVI and an all-sky camera at Longyearbyen contemporaneously detected an
auroral vortex structure (so-called "auroral spiral"). Particularly, from
space, the auroral spiral was observed as a "small spot" in the poleward region
of the main auroral oval near midnight, which was formed while the
substorm-associated auroral bulge was subsiding and several poleward elongated
auroral streaks appeared during the late substorm recovery phase. To pursue the
spiral source region in the magnetotail, we trace each UVI image along the
field line to the magnetic equatorial plane of nightside magnetosphere using an
empirical magnetic field model. Interestingly, the magnetotail region
corresponding to the auroral spiral broadly covered from Xgsm ~ -40 to -70 RE
at Ygsm ~ 8 to 12 RE. Appearance of this auroral spiral may suggest that
plasmas are extensively and actively transported from the magnetotail to the
ionosphere even during the late substorm recovery phase.


title: Craig interpolation theorem fails in bi-intuitionistic predicate logic

abstract:
  In this article we show that bi-intuitionistic predicate logic lacks the
Craig Interpolation Property. We proceed by adapting the counterexample given
by Mints, Olkhovikov and Urquhart for intuitionistic predicate logic with
constant domains (G. Mints, G. K. Olkhovikov and A. Urquhart. Failure of
Interpolation in Constant Domain Intuitionistic Logic. Journal of Symbolic
Logic, 78: 937--950 (2013)). More precisely, we show that there is a valid
implication $\phi \rightarrow \psi$ with no interpolant (i.e. a formula
$\theta$ in the intersection of the vocabularies of $\phi$ and $\psi$ such that
both $\phi \rightarrow \theta$ and $\theta \rightarrow \psi$ are valid).
Importantly, this result does not contradict the unfortunately named `Craig
interpolation' theorem established by Rauszer in (Cecylia Rauszer. Craig
Interpolation Theorem for an Extention of Intuitionistic Logic. Bull. Ac. Pol.
Sc., 25(4), 337--341 (1977)) since that article is about the property more
correctly named `deductive interpolation' (see Galatos, Jipsen, Kowalski and
Ono's use of this term in N. Galatos, P. Jipsen, T. Kowalski, \& H. Ono.
Residuated Lattices: An Algebraic Glimpse at Substructural Logics. Studies in
Logic and the Foundations of Mathematics, Vol. 151. Amsterdam: Elsevier B. V.
(2007)). Given that the deduction theorem fails for bi-intuitionistic logic,
the two formulations of the property are not equivalent, in contrast to
classical logic.


title:
Development of an electron impact ion source with high ionization
  efficiency for future planetary missions

abstract:
  Ion sources using electron impact ionization (EI) methods have been widely
accepted in mass spectrometry for planetary exploration missions because of
their simplicity. Previous space-borne mass spectrometers were primarily
designed with the EI method using rhenium tungsten alloy filaments, enabling up
to 200 uA emission in typical cases. The emission level is desired to be
enhanced because the sensitivity of mass spectrometers is a critical
requirement for the future in situ mass spectrometry related to the measurement
of trace components in planetary samples. In this study, we developed a new
high-emission EI ion source using a Y2O3-coated iridium filament, which has a
lower work function than rhenium tungsten alloy. The size of the ion source was
30 mm * 26 mm * 70 mm, and its weight was 70 g. We confirmed that when
consuming 3.0 W power, the ion source emits more than 2 mA electrons, which is
10 times greater than the previous models electron emission level. Ionization
efficiency of the EI ion source is proportional to the amount of electron
emission, which implies our new model increased the ionization efficiency 10
times. We conducted performance tests on the prototype with the 3.0 W heating
condition, confirming a high ionization efficiency (10^4 nA/Pa). In addition,
we conducted endurance tests of the ion source and demonstrated the persistence
of the ionization efficiency for 30 min * 100 cycles.


title:
WKB approximation under Killingbeck potential and its application to
  double heavy meson mass spectrum

abstract:
  As we all known that non-relativistic or semi-relativistic constituent quark
models can describe a large number of the meson sand baryon properties with
surprising accuracy. In this work, we studied Killingbeck potential by using
WKB approximation in the constituent quark model. The implicit approximate
solution of the energy eigenvalue of the Schr\"{o}dinger equation is obtained
by using appropriate quantization conditions. Under the Killingbeck potential
with the WKB approximation, we studied the spectra of double heavy mesons and
predict the masses of high excited states in $c\bar{c}$, $b\bar{b}$ and
$b\bar{c}$ families. Finally, we discuss the application scope of double heavy
mesons in Killingbeck potential.


title:
Regularity theory for a new class of fractional parabolic stochastic
  evolution equations

abstract:
  A new class of fractional-order stochastic evolution equations of the form
$(\partial_t + A)^\gamma X(t) = \dot{W}^Q(t)$, $t\in[0,T]$, $\gamma \in
(0,\infty)$, is introduced, where $-A$ generates a $C_0$-semigroup on a
separable Hilbert space $H$ and the spatiotemporal driving noise $\dot{W}^Q$ is
an $H$-valued cylindrical $Q$-Wiener process. Mild and weak solutions are
defined; these concepts are shown to be equivalent and to lead to well-posed
problems. Temporal and spatial regularity of the solution process $X$ are
investigated, the former being measured by mean-square or pathwise smoothness
and the latter by using domains of fractional powers of $A$. In addition, the
covariance of $X$ and its long-time behavior are analyzed. These abstract
results are applied to the cases when $A := L^\beta$ and
$Q:=\widetilde{L}^{-\alpha}$ are fractional powers of symmetric, strongly
elliptic second-order differential operators defined on (i) bounded Euclidean
domains or (ii) smooth, compact surfaces. In these cases, the Gaussian solution
processes can be seen as generalizations of merely spatial (Whittle-)Mat\'ern
fields to space-time.


title:
The ion acoustic instability of the cylindrical inhomogeneous helicon
  discharge plasma with rotating electrons

abstract:
  The kinetic theory for the cylindrical plasma, produced by the cylindrical
azimuthally symmetric (azimuthal mode number $m_{0}=0$) helicon wave, is
developed with accounting for the radial inhomogeneity of the helicon wave
field and of a plasma in cylindrical geometry. This theory reveals macroscale
effect of the steady azimuthal rotation of electrons with a radially
inhomogeneous angular velocity, caused by radially inhomogeneous electric field
of the helicon wave. It is found that this sheared rotation of electrons
relative to ions and a plasma inhomogeneity are responsible for the development
of the high frequency ion acoustic instability of the inhomogeneous cylindrical
helicon plasma


title: Not every countable complete lattice is sober

abstract:
  The study of the sobriety of Scott spaces has got an relative long history in
domain theory. Lawson and Hoffmann independently proved that the Scott space of
every continuous directed complete poset (usually called domain) is sober.
Johnstone constructed the first directed complete poset whose Scott space is
non-sober. Not long after, Isbell gave a complete lattice with non-sober Scott
space. Based on Isbell's example, Xu, Xi and Zhao showed that there is even a
complete Heyting algebra whose Scott space is non-sober. Achim Jung then asked
whether every countable complete lattice has a sober Scott space.
  Let $\Sigma P$ be the Scott space of poset $P$. In this paper, we first prove
that the topology of the product space $\Sigma P\times \Sigma Q$ coincides with
the Scott topology on the product poset $P\times Q$ if the set $Id(P)$ and
$Id(Q)$ of all non-trivial ideals of posets $P$ and $Q$ are both countable.
Based on this result, we deduce that a directed complete poset $P$ has a sober
Scott space, if $Id(P)$ is countable and the space $\Sigma P$ is coherent and
well-filtered. Thus a complete lattice $L$ with $Id(L)$ countable has a sober
Scott space. Making use the obtained results, we then construct a countable
complete lattice whose Scott space is non-sober and thus give a negative answer
to Jung's problem.


title:
Synchronous Multistep Predictive Spectral Control of the Switching
  Distortion in DC--DC Converters

abstract:
  In automotive power electronics, distortion and electromagnetic interference
(EMI) generated by the switching action of power semiconductors can be a
significant challenge for the design of a compact, lightweight vehicle. As
semiconductor switching frequencies increase, e.g., through the introduction of
new materials, such as gallium nitride and silicon carbide, this problem
becomes more severe. We present a control scheme for an automotive dc-to-dc
converter that reduces the EMI generated by shaping switching distortion
predictively at the run time. The multistep model-predictive control scheme
chooses the subsequent switching state that optimizes the output spectrum
according to predefined criteria. To achieve real-time operation, it evaluates
the possible switching state candidates for the next modulation step without
explicitly solving a single Fourier transform. In addition, the switching rate
and voltage ripple are controlled in a single unified control law. We present
and experimentally validate that the control scheme can indeed run at real time
already with currently available mid-range hardware. The results demonstrate
that the largest spectral peak of the switching distortion can be decreased by
48 dB compared to conventional pulse-width modulation. Furthermore, spectral
gaps can be implemented in the output distortion and altered in real-time,
allowing certain frequency bands -- e.g., bands used by other sensitive
electronics such as sensors, communication busses, or tuners -- to be kept free
of EMI from the converter's switching actions.


title: Invariant subspaces of powers of some unicellular operators

abstract:
  In this paper we study subspaces which are invariant under squares and cubes
(separately as well as jointly) of unicellular backward weighted shift
operators on a separable Hilbert space. The finite-dimensional subspaces are
characterized for all weights and the infinite-dimensional subspaces are
characterized for two classes of weights.


title: On the greatest common divisor of integer parts of polynomials

abstract:
  Motivated by a question of V. Bergelson and F. K. Richter (2017), we obtain
asymptotic formulas for the number of relatively prime tuples composed of
positive integers $n\le N$ and integer parts of polynomials evaluated at $n$.
The error terms in our formulas depend on the Diophantine properties of the
leading coefficients of these polynomials.


title: PGD: A Large-scale Professional Go Dataset for Data-driven Analytics

abstract:
  Lee Sedol is on a winning streak--does this legend rise again after the
competition with AlphaGo? Ke Jie is invincible in the world championship--can
he still win the title this time? Go is one of the most popular board games in
East Asia, with a stable professional sports system that has lasted for decades
in China, Japan, and Korea. There are mature data-driven analysis technologies
for many sports, such as soccer, basketball, and esports. However, developing
such technology for Go remains nontrivial and challenging due to the lack of
datasets, meta-information, and in-game statistics. This paper creates the
Professional Go Dataset (PGD), containing 98,043 games played by 2,148
professional players from 1950 to 2021. After manual cleaning and labeling, we
provide detailed meta-information for each player, game, and tournament.
Moreover, the dataset includes analysis results for each move in the match
evaluated by advanced AlphaZero-based AI. To establish a benchmark for PGD, we
further analyze the data and extract meaningful in-game features based on prior
knowledge related to Go that can indicate the game status. With the help of
complete meta-information and constructed in-game features, our results
prediction system achieves an accuracy of 75.30%, much higher than several
state-of-the-art approaches (64%-65%). As far as we know, PGD is the first
dataset for data-driven analytics in Go and even in board games. Beyond this
promising result, we provide more examples of tasks that benefit from our
dataset. The ultimate goal of this paper is to bridge this ancient game and the
modern data science community. It will advance research on Go-related analytics
to enhance the fan experience, help players improve their ability, and
facilitate other promising aspects. The dataset will be made publicly
available.


title: Joint Radar and Multicast-Unicast Communication: A NOMA Aided Framework

abstract:
  The novel concept of non-orthogonal multiple access (NOMA) aided joint radar
and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the
same spectrum resource, a multi-input-multi-output (MIMO) dual-functional
radar-communication (DFRC) base station detects the radar-centric user
(R-user), while transmitting mixed multicast-unicast messages both to the
R-user and to the communication-centric user (C-user). In particular, the
multicast information is intended for both the R- and C-users, whereas the
unicast information is only intended for the C-user. More explicitly, NOMA is
employed to facilitate this double spectrum sharing, where the multicast and
unicast signals are superimposed in the power domain and the superimposed
communication signals are also exploited as radar probing waveforms. A
beamformer-based NOMA-aided joint Rad-MU-Com framework is proposed for the
system having a single R-user and a single C-user. Based on this framework, the
unicast rate maximization problem is formulated by optimizing the beamformers
employed, while satisfying the rate requirement of multicast and the predefined
accuracy of the radar beam pattern. The resultant non-convex optimization
problem is solved by a penalty-based iterative algorithm to find a high-quality
near-optimal solution. Finally, our numerical results reveal that significant
performance gains can be achieved by the proposed scheme over the benchmark
schemes.


title:
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally
  Contrastive Learning

abstract:
  Heterogeneous graph neural network (HGNN) is a very popular technique for the
modeling and analysis of heterogeneous graphs. Most existing HGNN-based
approaches are supervised or semi-supervised learning methods requiring graphs
to be annotated, which is costly and time-consuming. Self-supervised
contrastive learning has been proposed to address the problem of requiring
annotated data by mining intrinsic information hidden within the given data.
However, the existing contrastive learning methods are inadequate for
heterogeneous graphs because they construct contrastive views only based on
data perturbation or pre-defined structural properties (e.g., meta-path) in
graph data while ignore the noises that may exist in both node attributes and
graph topologies. We develop for the first time a novel and robust
heterogeneous graph contrastive learning approach, namely HGCL, which
introduces two views on respective guidance of node attributes and graph
topologies and integrates and enhances them by reciprocally contrastive
mechanism to better model heterogeneous graphs. In this new approach, we adopt
distinct but most suitable attribute and topology fusion mechanisms in the two
views, which are conducive to mining relevant information in attributes and
topologies separately. We further use both attribute similarity and topological
correlation to construct high-quality contrastive samples. Extensive
experiments on three large real-world heterogeneous graphs demonstrate the
superiority and robustness of HGCL over state-of-the-art methods.


title:
Unsupervised Visible-light Images Guided Cross-Spectrum Depth Estimation
  from Dual-Modality Cameras

abstract:
  Cross-spectrum depth estimation aims to provide a depth map in all
illumination conditions with a pair of dual-spectrum images. It is valuable for
autonomous vehicle applications when the vehicle is equipped with two cameras
of different modalities. However, images captured by different-modality cameras
can be photometrically quite different. Therefore, cross-spectrum depth
estimation is a very challenging problem. Moreover, the shortage of large-scale
open-source datasets also retards further research in this field. In this
paper, we propose an unsupervised visible-light image guided cross-spectrum
(i.e., thermal and visible-light, TIR-VIS in short) depth estimation framework
given a pair of RGB and thermal images captured from a visible-light camera and
a thermal one. We first adopt a base depth estimation network using RGB-image
pairs. Then we propose a multi-scale feature transfer network to transfer
features from the TIR-VIS domain to the VIS domain at the feature level to fit
the trained depth estimation network. At last, we propose a cross-spectrum
depth cycle consistency to improve the depth result of dual-spectrum image
pairs. Meanwhile, we release a large dual-spectrum depth estimation dataset
with visible-light and far-infrared stereo images captured in different scenes
to the society. The experiment result shows that our method achieves better
performance than the compared existing methods. Our datasets is available at
https://github.com/whitecrow1027/VIS-TIR-Datasets.


title:
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language
  Processing

abstract:
  The success of Pre-Trained Models (PTMs) has reshaped the development of
Natural Language Processing (NLP). Yet, it is not easy to obtain
high-performing models and deploy them online for industrial practitioners. To
bridge this gap, EasyNLP is designed to make it easy to build NLP applications,
which supports a comprehensive suite of NLP algorithms. It further features
knowledge-enhanced pre-training, knowledge distillation and few-shot learning
functionalities for large-scale PTMs, and provides a unified framework of model
training, inference and deployment for real-world applications. Currently,
EasyNLP has powered over ten business units within Alibaba Group and is
seamlessly integrated to the Platform of AI (PAI) products on Alibaba Cloud.
The source code of our EasyNLP toolkit is released at GitHub
(https://github.com/alibaba/EasyNLP).


title: Wrangling multivariate spatio-temporal data with the R package cubble

abstract:
  Multivariate spatio-temporal data refers to multiple measurements taken
across space and time. For many analyses, spatial and time components can be
separately studied: for example, to explore the temporal trend of one variable
for a single spatial location, or to model the spatial distribution of one
variable at a given time. However for some studies, it is important to analyse
different aspects of the spatio-temporal data simultaneouly, like for instance,
temporal trends of multiple variables across locations. In order to facilitate
the study of different portions or combinations of spatio-temporal data, we
introduce a new data structure, cubble, with a suite of functions enabling easy
slicing and dicing on the different components spatio-temporal components. The
proposed cubble structure ensures that all the components of the data are easy
to access and manipulate while providing flexibility for data analysis. In
addition, cubble facilitates visual and numerical explorations of the data
while easing data wrangling and modelling. The cubble structure and the
functions provided in the cubble R package equip users with the capability to
handle hierarchical spatial and temporal structures. The cubble structure and
the tools implemented in the package are illustrated with different examples of
Australian climate data.


title: Optimal Control of Several Motion Models

abstract:
  This paper is devoted to the study of the dynamic optimization of several
controlled crowd motion models in the general planar settings, which is an
application of a class of optimal control problems involving a general
nonconvex sweeping process with perturbations. A set of necessary optimality
conditions for such optimal control problems involving the crowd motion models
with multiple agents and obstacles is obtained and analyzed. Several effective
algorithms based on such necessary optimality conditions are proposed and
various nontrivial illustrative examples together with their simulations are
also presented.


title:
A simple strategy to measure the contact resistance between metals and
  doped organic films

abstract:
  Charge injection from electrodes into doped organic films is a widespread
technology used in the majority of state-of-the-art organic semiconductor
devices. Although such interfaces are commonly considered to form Ohmic
contacts via strong band bending, an experiment that directly measures the
contact resistance has not yet been demonstrated. In this study, we use a
simple metal/doped organic semiconductor/metal stack and study its
voltage-dependent resistance. A transport layer thickness variation proves that
the presented experiment gains direct access to the contact resistance of the
device. We can quantify that for an operating current density of 10mA/cm2 the
investigated material system exhibits a voltage drop over the metal/organic
interface of about 200mV, which can be reduced by more than one order of
magnitude when employing an additional injection layer. The presented
experiment proposes a simple strategy to measure the contact resistance between
any metal and doped organic film without applying numerical tools or elaborate
techniques. Furthermore, the simplistic device architecture allows for very
high, homogeneous, and tunable electric fields within the organic layer, which
enables a clear investigation of the Poole-Frenkel effect.


title:
Dimensionality-controlled evolution of charge-transfer energy in digital
  nickelates superlattices

abstract:
  Fundamental understanding and control of the electronic structure evolution
in rare-earth nickelates is a fascinating and meaningful issue, as well as
being helpful to understand the mechanism of recently discovered
superconductivity. Here we systematically study the dimensionality effect on
the ground electronic state in high-quality (NdNiO3)m/(SrTiO3)1 superlattices
through transport and soft x-ray absorption spectroscopy. The
metal-to-insulator transition temperature decreases with the thickness of the
NdNiO3 slab decreasing from bulk to 7 unit cells, then increases gradually as m
further reduces to 1 unit cell. Spectral evidence demonstrates that the
stabilization of insulating phase can be attributed to the increase of the
charge-transfer energy between O 2p and Ni 3d bands. The prominent multiplet
feature on the Ni L3 edge develops with the decrease of NdNiO3 slab thickness,
suggesting the strengthening of the charge disproportionate state under the
dimensional confinement. Our work provides convincing evidence that
dimensionality is an effective knob to modulate the charge-transfer energy and
thus the collective ground state in nickelates.


title:
Complete Verification via Multi-Neuron Relaxation Guided
  Branch-and-Bound

abstract:
  State-of-the-art neural network verifiers are fundamentally based on one of
two paradigms: either encoding the whole verification problem via tight
multi-neuron convex relaxations or applying a Branch-and-Bound (BaB) procedure
leveraging imprecise but fast bounding methods on a large number of easier
subproblems. The former can capture complex multi-neuron dependencies but
sacrifices completeness due to the inherent limitations of convex relaxations.
The latter enables complete verification but becomes increasingly ineffective
on larger and more challenging networks. In this work, we present a novel
complete verifier which combines the strengths of both paradigms: it leverages
multi-neuron relaxations to drastically reduce the number of subproblems
generated during the BaB process and an efficient GPU-based dual optimizer to
solve the remaining ones. An extensive evaluation demonstrates that our
verifier achieves a new state-of-the-art on both established benchmarks as well
as networks with significantly higher accuracy than previously considered. The
latter result (up to 28% certification gains) indicates meaningful progress
towards creating verifiers that can handle practically relevant networks.


title:
Linear growth of the two-point function for the Unruh state in $1+1$
  dimensional black holes

abstract:
  The symmetric two-point function for a massless, minimally coupled scalar
field in the Unruh state is examined for Schwarzschild-de Sitter spacetime in
two dimensions. This function grows linearly in terms of a time coordinate that
is well-defined on the future black hole and cosmological horizons, when the
points are split in the space direction. This type of behavior also occurs in
two dimensions for other static black hole spacetimes when the field is in the
Unruh state, and at late times it occurs in spacetimes where a black hole forms
from the collapse of a null shell. The generalization to the case of the
symmetric two-point function in two dimensions for a massive scalar field in
Schwarzschild-de Sitter spacetime is discussed.


title:
Designing a Sequential Recommendation System for Heterogeneous
  Interactions Using Transformers

abstract:
  While many production-ready and robust algorithms are available for the task
of recommendation systems, many of these systems do not take the order of
user's consumption into account. The order of consumption can be very useful
and matters in many scenarios. One such scenario is an educational content
recommendation, where users generally follow a progressive path towards more
advanced courses. Researchers have used RNNs to build sequential recommendation
systems and other models that deal with sequences. Sequential Recommendation
systems try to predict the next event for the user by reading their history.
With the massive success of Transformers in Natural Language Processing and
their usage of Attention Mechanism to better deal with sequences, there have
been attempts to use this family of models as a base for a new generation of
sequential recommendation systems. In this work, by converting each user's
interactions with items into a series of events and basing our architecture on
Transformers, we try to enable the use of such a model that takes different
types of events into account. Furthermore, by recognizing that some events have
to occur before some other types of events take place, we try to modify the
architecture to reflect this dependency relationship and enhance the model's
performance.


title: Geometric Koszul complexes, syzygies of K3 surfaces and the Tango bundle

abstract:
  A key result for syzygies of curves is Voisin's proof of Green's conjecture
for the canonical embedding of a general curve of any genus. Her primary tools
were the Lazarsfeld Mukai bundle on a K3 surface and a representation of Koszul
cohomology on the Hilbert scheme of points on the surface. In this note we
construct representations of the Koszul complex on Grassmann varieties;
Voisin's setup arises as the inverse image of one of the maps. Using a
different map, we give a substantially shorter proof of Voisin's result for K3
surfaces of even sectional genus.


title:
Exposing Cross-Lingual Lexical Knowledge from Multilingual Sentence
  Encoders

abstract:
  Pretrained multilingual language models (LMs) can be successfully transformed
into multilingual sentence encoders (SEs; e.g., LaBSE, xMPNET) via additional
fine-tuning or model distillation on parallel data. However, it remains
uncertain how to best leverage their knowledge to represent sub-sentence
lexical items (i.e., words and phrases) in cross-lingual lexical tasks. In this
work, we probe these SEs for the amount of cross-lingual lexical knowledge
stored in their parameters, and compare them against the original multilingual
LMs. We also devise a novel method to expose this knowledge by additionally
fine-tuning multilingual models through inexpensive contrastive learning
procedure, requiring only a small amount of word translation pairs. We evaluate
our method on bilingual lexical induction (BLI), cross-lingual lexical semantic
similarity, and cross-lingual entity linking, and report substantial gains on
standard benchmarks (e.g., +10 Precision@1 points in BLI), validating that the
SEs such as LaBSE can be 'rewired' into effective cross-lingual lexical
encoders. Moreover, we show that resulting representations can be successfully
interpolated with static embeddings from cross-lingual word embedding spaces to
further boost the performance in lexical tasks. In sum, our approach provides
an effective tool for exposing and harnessing multilingual lexical knowledge
'hidden' in multilingual sentence encoders.


title: Performance of the Thresholding Greedy Algorithm with Larger Greedy Sums

abstract:
  The goal of this note is to study the performance of the Thresholding Greedy
Algorithm (TGA) when we increase the size of greedy sums by a constant factor
$\lambda\ge 1$. We introduce the so-called ($\lambda$, partially greedy) bases.
While the case $\lambda = 1$ gives strong partially greedy bases, we show that,
for each $\lambda > 1$, there exists a (Schauder) basis that is ($\lambda$,
partially greedy) but is not strong partially greedy. Furthermore, we
investigate and give examples when a basis is
  1. not $1$-(almost) greedy but the TGA still gives the smallest error from an
$m$-term approximation if we allow greedy sums to be of size $\lceil \lambda
m\rceil$, and
  2. not $1$-strong partially greedy but $1$-($\lambda$, partially greedy) for
some $\lambda > 1$.
  Finally, we prove various equivalences for different greedy-type bases.


title: The configurational entropy of colloidal particles in a confined space

abstract:
  We calculate the configurational entropy of colloidal particles in a confined
geometry interacting as hard disks using Monte Carlo integration method. In
particular, we consider systems with three kinds of boundary conditions: hard,
periodic and spherical. For small to moderate packing fraction $\phi$ values,
we find the entropies per particle for systems with the periodic and spherical
boundary conditions tend to reach a same value with the increase of the
particle number $N$, while that for the system with the hard boundary
conditions still has obvious differences compared to them within the studied
$N$ range. Surprisingly, despite the small system sizes, the estimated
entropies per particle at infinite system size from extrapolations in the
periodic and spherical systems are in reasonable agreement with that calculated
using thermodynamic integration method. Besides, as $N$ increases we find the
pair correlation function begins to exhibit similar features as that of a large
self-assembled system at the same packing fraction. Our findings may contribute
to a better understanding of how the configurational entropy changes with the
system size and the influence of boundary conditions, and provide insights
relevant to engineering particles in confined spaces.


title:
Constraints on the magnetized Ernst black hole spacetime through
  quasiperiodic oscillations

abstract:
  We study the dynamics of test particles around a static and spherically
symmetric magnetized Ernst black hole considering its magnetic field in the
environment surrounding the black hole. We show how its magnetic filed can
influence on the dynamics of particles and epicyclic motions around the black
hole. Based on the analysis we find that the radius of the innermost stable
circular orbit (ISCO) for both neutral and charged test particles and epicyclic
frequencies are strongly affected as a consequence of the impact of magnetic
field. We also show that the ISCO radius of charged particles gets decreased
rapidly. It turns out that the gravitational and the Lorentz forces of the
magnetic field are combined, thus strongly shrinking the values of the ISCO of
charged test particles. Finally, we obtain the generic form for the epicyclic
frequencies and selected three microquasars with known astrophysical
quasiperiodic oscilations (QPOs) data to constrain the magnetic field. We show
that the magnetic field is of the order of magnitude $B\sim 10^{-7}$ Gauss,
taking into account the motion of neutral particles in circular orbit about the
black hole.


title:
Deep Learning-Enabled Semantic Communication Systems with Task-Unaware
  Transmitter and Dynamic Data

abstract:
  Existing deep learning-enabled semantic communication systems often rely on
shared background knowledge between the transmitter and receiver that includes
empirical data and their associated semantic information. In practice, the
semantic information is defined by the pragmatic task of the receiver and
cannot be known to the transmitter. The actual observable data at the
transmitter can also have non-identical distribution with the empirical data in
the shared background knowledge library. To address these practical issues,
this paper proposes a new neural network-based semantic communication system
for image transmission, where the task is unaware at the transmitter and the
data environment is dynamic. The system consists of two main parts, namely the
semantic extraction (SE) network and the data adaptation (DA) network. The SE
network learns how to extract the semantic information using a receiver-leading
training process. By using domain adaptation technique from transfer learning,
the DA network learns how to convert the data observed into a similar form of
the empirical data that the SE network can process without re-training.
Numerical experiments show that the proposed method can be adaptive to
observable datasets while keeping high performance in terms of both data
recovery and task execution. The codes are available on
https://github.com/SJTU-mxtao/Semantic-Communication-Systems.


title:
Improving Visual Grounding with Visual-Linguistic Verification and
  Iterative Reasoning

abstract:
  Visual grounding is a task to locate the target indicated by a natural
language expression. Existing methods extend the generic object detection
framework to this problem. They base the visual grounding on the features from
pre-generated proposals or anchors, and fuse these features with the text
embeddings to locate the target mentioned by the text. However, modeling the
visual features from these predefined locations may fail to fully exploit the
visual context and attribute information in the text query, which limits their
performance. In this paper, we propose a transformer-based framework for
accurate visual grounding by establishing text-conditioned discriminative
features and performing multi-stage cross-modal reasoning. Specifically, we
develop a visual-linguistic verification module to focus the visual features on
regions relevant to the textual descriptions while suppressing the unrelated
areas. A language-guided feature encoder is also devised to aggregate the
visual contexts of the target object to improve the object's distinctiveness.
To retrieve the target from the encoded visual features, we further propose a
multi-stage cross-modal decoder to iteratively speculate on the correlations
between the image and text for accurate target localization. Extensive
experiments on five widely used datasets validate the efficacy of our proposed
components and demonstrate state-of-the-art performance. Our code is public at
https://github.com/yangli18/VLTVG.


title: Algebraic groups with good reduction and the genus problem

abstract:
  We first provide an overview of several results dealing with the genus of a
division algebra and highlight the role of ramification in its analysis. We
then give a survey of recent developments on the genus problem for simple
algebraic groups and its connections to the analysis of groups with good
reduction.


title: Clues Before Answers: Generation-Enhanced Multiple-Choice QA

abstract:
  A trending paradigm for multiple-choice question answering (MCQA) is using a
text-to-text framework. By unifying data in different tasks into a single
text-to-text format, it trains a generative encoder-decoder model which is both
powerful and universal. However, a side effect of twisting a generation target
to fit the classification nature of MCQA is the under-utilization of the
decoder and the knowledge that can be decoded. To exploit the generation
capability and underlying knowledge of a pre-trained encoder-decoder model, in
this paper, we propose a generation-enhanced MCQA model named GenMC. It
generates a clue from the question and then leverages the clue to enhance a
reader for MCQA. It outperforms text-to-text models on multiple MCQA datasets.


title: Dynamic Curriculum Learning for Great Ape Detection in the Wild

abstract:
  We propose a novel end-to-end curriculum learning approach that leverages
large volumes of unlabelled great ape camera trap footage to improve supervised
species detector construction in challenging real-world jungle environments. In
contrast to previous semi-supervised methods, our approach gradually improves
detection quality by steering training towards virtuous self-reinforcement. To
achieve this, we propose integrating pseudo-labelling with dynamic curriculum
learning policies. We show that such dynamics and controls can avoid learning
collapse and gradually tie detector adjustments to higher model quality. We
provide theoretical arguments and ablations, and confirm significant
performance improvements against various state-of-the-art systems when
evaluating on the Extended PanAfrican Dataset holding several thousand camera
trap videos of great apes. We note that system performance is strongest for
smaller labelled ratios, which are common in ecological applications. Our
approach, although designed with wildlife data in mind, also shows competitive
benchmarks for generic object detection in the MS-COCO dataset, indicating
wider applicability of introduced concepts. The code is available at
https://github.com/youshyee/DCL-Detection.


title:
Rate and ellipticity of dilepton production in magnetized quark-gluon
  plasma

abstract:
  Using the Landau-level representation for the imaginary part of the photon
polarization tensor, we derive an explicit expression for the dilepton
production rate from a hot quark-gluon plasma in a quantizing background
magnetic field. We study in detail the dependence of the production rate on the
dilepton invariant mass and the transverse momentum at mid-rapidity. We also
investigate the angular dependence and ellipticity of dilepton emission. By
comparing the result with the zero-field Born approximation, we find that the
magnetic field leads to a strong enhancement of the dilepton rate at small
values of the invariant mass ($M\lesssim\sqrt{|eB|}$). In the same kinematic
region, the dilepton production is characterized by a sizable ellipticity. At
large values of the dilepton invariant mass ($M\gtrsim\sqrt{|eB|}$), the role
of the magnetic fields decreases and the result approaches the isotropic
zero-field Born rate. By investigating the dependence of ellipticity on the
transverse momentum, we argue that the future measurements of dilepton rate in
the region of small invariant masses can constrain the magnetic field produced
in heavy-ion collisions.


title: Chromatic $k$-Nearest Neighbor Queries

abstract:
  Let $P$ be a set of $n$ colored points. We develop efficient data structures
that store $P$ and can answer chromatic $k$-nearest neighbor ($k$-NN) queries.
Such a query consists of a query point $q$ and a number $k$, and asks for the
color that appears most frequently among the $k$ points in $P$ closest to $q$.
Answering such queries efficiently is the key to obtain fast $k$-NN
classifiers. Our main aim is to obtain query times that are independent of $k$
while using near-linear space.
  We show that this is possible using a combination of two data structures. The
first data structure allow us to compute a region containing exactly the
$k$-nearest neighbors of a query point $q$, and the second data structure can
then report the most frequent color in such a region. This leads to linear
space data structures with query times of $O(n^{1 / 2} \log n)$ for points in
$\mathbb{R}^1$, and with query times varying between $O(n^{2/3}\log^{2/3} n)$
and $O(n^{5/6} {\rm polylog} n)$, depending on the distance measure used, for
points in $\mathbb{R}^2$. Since these query times are still fairly large we
also consider approximations. If we are allowed to report a color that appears
at least $(1-\varepsilon)f^*$ times, where $f^*$ is the frequency of the most
frequent color, we obtain a query time of $O(\log n +
\log\log_{\frac{1}{1-\varepsilon}} n)$ in $\mathbb{R}^1$ and expected query
times ranging between $\tilde{O}(n^{1/2}\varepsilon^{-3/2})$ and
$\tilde{O}(n^{1/2}\varepsilon^{-5/2})$ in $\mathbb{R}^2$ using near-linear
space (ignoring polylogarithmic factors).


title: Mentors and Recombinators: Multi-Dimensional Social Learning

abstract:
  We study games in which the set of strategies is multi-dimensional, and new
agents might learn various strategic dimensions from different mentors. We
introduce a new family of dynamics, the recombinator dynamics, which is
characterised by a single parameter, the recombination rate r in [0,1]. The
case of r = 0 coincides with the standard replicator dynamics. The opposite
case of r = 1 corresponds to a setup in which each new agent learns each new
strategic dimension from a different mentor, and combines these dimensions into
her adopted strategy. We fully characterise stationary states and stable states
under these dynamics, and we show that they predict novel behaviour in various
applications.


title:
Distance between closed sets and the solutions to stochastic partial
  differential equations

abstract:
  The goal of this paper is to clarify when the solutions to stochastic partial
differential equations stay close to a given subset of the state space for
starting points which are close as well. This includes results for
deterministic partial differential equations. As an example, we will consider
the situation where the subset is a finite dimensional submanifold with
boundary. We also discuss applications to mathematical finance, namely the
modeling of the evolution of interest rate curves.


title:
Directly wireless communication of human minds via non-invasive
  brain-computer-metasurface platform

abstract:
  Brain-computer interfaces (BCIs), invasive or non-invasive, have projected
unparalleled vision and promise for assisting patients in need to better their
interaction with the surroundings. Inspired by the BCI-based rehabilitation
technologies for nerve-system impairments and amputation, we propose an
electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by
human's cognition by brain signals directly and non-invasively. We
experimentally show that our EBCM platform can translate human's mind from
evoked potentials of P300-based electroencephalography to digital coding
information in the electromagnetic domain non-invasively, which can be further
processed and transported by an information metasurface in automated and
wireless fashions. Directly wireless communications of the human minds are
performed between two EBCM operators with accurate text transmissions.
Moreover, several other proof-of-concept mind-control schemes are presented
using the same EBCM platform, exhibiting flexibly-customized capabilities of
information processing and synthesis like visual-beam scanning, wave
modulations, and pattern encoding.


title:
Understanding the Generalization Performance of Spectral Clustering
  Algorithms

abstract:
  The theoretical analysis of spectral clustering mainly focuses on
consistency, while there is relatively little research on its generalization
performance. In this paper, we study the excess risk bounds of the popular
spectral clustering algorithms: \emph{relaxed} RatioCut and \emph{relaxed}
NCut. Firstly, we show that their excess risk bounds between the empirical
continuous optimal solution and the population-level continuous optimal
solution have a $\mathcal{O}(1/\sqrt{n})$ convergence rate, where $n$ is the
sample size. Secondly, we show the fundamental quantity in influencing the
excess risk between the empirical discrete optimal solution and the
population-level discrete optimal solution. At the empirical level, algorithms
can be designed to reduce this quantity. Based on our theoretical analysis, we
propose two novel algorithms that can not only penalize this quantity, but also
cluster the out-of-sample data without re-eigendecomposition on the overall
sample. Experiments verify the effectiveness of the proposed algorithms.


title:
Law of large numbers for ballistic random walks in dynamic random
  environments under lateral decoupling

abstract:
  We establish a strong law of large numbers for one-dimensional
continuous-time random walks in dynamic random environments under two main
assumptions: the environment is required to satisfy a decoupling inequality
that can be interpreted as a bound on the speed of dependence propagation,
while the random walk is assumed to move ballistically with a speed larger than
this bound. Applications include environments with strong space-time
correlations such as the zero-range process and the asymmetric exclusion
process.


title:
Leveraging Emotion-specific Features to Improve Transformer Performance
  for Emotion Classification

abstract:
  This paper describes the approach to the Emotion Classification shared task
held at WASSA 2022 by team PVGs AI Club. This Track 2 sub-task focuses on
building models which can predict a multi-class emotion label based on essays
from news articles where a person, group or another entity is affected.
Baseline transformer models have been demonstrating good results on sequence
classification tasks, and we aim to improve this performance with the help of
ensembling techniques, and by leveraging two variations of emotion-specific
representations. We observe better results than our baseline models and achieve
an accuracy of 0.619 and a macro F1 score of 0.520 on the emotion
classification task.


title: Band inversion and quasi-nodal spheres

abstract:
  Band inversion is a known feature in a wide range of topological insulators
characterized by a change of orbital type around a high symmetry point close to
the Fermi level. In some cases of band inversion which are due to the
hybridization of the Hamiltonian, the presence of quasi-nodal spheres has been
detected. In order to understand this phenomenon, we develop a local effective
four-fold Hamiltonian which models the band inversion and reproduces the
quasi-nodal sphere. This model shows that the change of orbital type along the
quasi-nodal sphere characterizes the topological nature of the material. Using
K-theoretical methods we show that the parametrized change of orbital type is
equivalent to the strong Fu-Kane-Mele invariant. We corroborate these results
with ab-initio calculations for the materials YH3 and CaTe where in both cases
the signal of the spin Hall conductivity is localized on the quasi-nodal
spheres in momentum space. We conclude that the existence of the quasi-nodal
spheres is enforced in systems with band inversion due to orbital
hybridization.


title: Flexible skyline: overview and applicability

abstract:
  Ranking (or top-k) and skyline queries are the most popular approaches used
to extract interesting data from large datasets. The first one is based on a
scoring function to evaluate and rank tuples. Its computation is fast, but it
is sensitive to the choice of the evaluating function. Skyline queries are
based on the idea of dominance and the result is the set of all non-dominated
tuples. This is a very interesting approach, but it can't allow to control the
cardinality of the output. Recent researches discovered more techniques to
compensate for these drawbacks. In particular, this paper will focus on the
flexible skyline approach.


title:
Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal
  Particles

abstract:
  We construct a reduced, data-driven, parameter dependent effective Stochastic
Differential Equation (eSDE) for electric-field mediated colloidal
crystallization using data obtained from Brownian Dynamics Simulations. We use
Diffusion Maps (a manifold learning algorithm) to identify a set of useful
latent observables. In this latent space we identify an eSDE using a deep
learning architecture inspired by numerical stochastic integrators and compare
it with the traditional Kramers-Moyal expansion estimation. We show that the
obtained variables and the learned dynamics accurately encode the physics of
the Brownian Dynamic Simulations. We further illustrate that our reduced model
captures the dynamics of corresponding experimental data. Our dimension
reduction/reduced model identification approach can be easily ported to a broad
class of particle systems dynamics experiments/models.


title: Assessing Fatigue with Multimodal Wearable Sensors and Machine Learning

abstract:
  Fatigue is a loss in cognitive or physical performance due to various
physiological factors such as insufficient sleep, long work hours, stress, and
physical exertion. It has an adverse effect on the human body and can slow down
reaction times, reduce attention, and limit short-term memory. Hence, there is
a need to monitor a person's state to avoid extreme fatigue conditions that can
result in physiological complications. However, tools to understand and assess
fatigue are minimal. This paper first focuses on building an experimental setup
that induces cognitive fatigue (CF) and physical fatigue (PF) through multiple
cognitive and physical tasks while simultaneously recording physiological data.
Second, self-reported visual analog scores (VAS) from the participants are
reported after each task to confirm fatigue induction. Finally, an evaluation
system is built that utilizes machine learning (ML) models to detect states of
CF and PF from sensor data, thus providing an objective measure. Random Forest
performs the best in detecting PF with an accuracy of 80.5% while correctly
predicting the true PF condition 88% of the time. On the other hand, the long
short-term memory (LSTM) recurrent neural network produces the best results in
detecting CF in the subjects (with 84.1% accuracy, 0.9 recall).


title: Baselines and Protocols for Household Speaker Recognition

abstract:
  Speaker recognition on household devices, such as smart speakers, features
several challenges: (i) robustness across a vast number of heterogeneous
domains (households), (ii) short utterances, (iii) possibly absent speaker
labels of the enrollment data (passive enrollment), and (iv) presence of
unknown persons (guests). While many commercial products exist, there is less
published research and no publicly-available evaluation protocols or
open-source baselines. Our work serves to bridge this gap by providing an
accessible evaluation benchmark derived from public resources (VoxCeleb and
ASVspoof 2019 data) along with a preliminary pool of open-source baselines.
This includes four algorithms for active enrollment (speaker labels available)
and one algorithm for passive enrollment.


title:
Dark matter properties from the Fornax globular cluster timing:
  dynamical friction and cored profiles

abstract:
  I summarize our recent results to use the orbits of globular clusters (GCs)
in the Fornax dwarf spheroidal (dSph) galaxy to learn more about dark matter
(DM) properties. Our focus is on clarifying how dynamical friction (DF) from
the DM halo is modified from the different microscopic properties of DM, which
may alter $both$ the scattering processes responsible of DF and the DM profiles
(in particular generating a core), which also modifies DF. We consider: $(i)$
fermionic degenerate dark matter (DDM), where Pauli blocking should be taken
into account in the dynamical friction computation; $(ii)$ self-interacting
dark matter (SIDM) and $(iii)$ ultralight dark matter (ULDM), for which this
problem has been addressed by a variety of methods in recent literature. We
derive DF with a Fokker-Planck formalism, reproducing previous results for ULDM
and cold DM, while providing new results for DDM. Furthermore, ULDM, DDM and
SIDM may generate cores in dSphs, which suppress dynamical friction and prolong
GC orbits. We conclude that in all these cases the modifications in the DM
modelling does not easily solve the so-called timing `problem' of Fornax GCs.
We finally study this `problem' in terms of the initial conditions,
demonstrating that the observed orbits of Fornax GCs are consistent with this
expectation of a cuspy DM profile with a mild `fine-tuning' at the level of
$\sim25\%$.


title:
Isotopic Production Cross Sections in Proton-$^{12}$C Interactions for
  Energies from 10 MeV/N to 100 GeV/N

abstract:
  Proton interactions with $^{12}$C nuclei are a frequent nuclear interaction
leading to secondary radiation in tissues for space radiation and cancer
therapy with protons or $^{12}$C beams. The fragmentation of $^{12}$C by
protons produces a large number of heavy ion (A>4) target or projectile
fragments often with high ionization density. Here we develop an analytical
model of energy dependent proton-$^{12}$C cross sections for isotopic nuclei
production. Using experimental data and a 2nd order optical model an accurate
formula for the p-$^{12}$C absorption cross section from <10 MeV/n to >10 GeV/N
is obtained. The energy dependence of the elemental and isotopic cross sections
is modeled as multiplicities scaled to absorption cross section with average
isotopic fractions estimated from experimental data. We show that this approach
results in an accurate analytic formula model over the full energy range in
Hadron therapy and space radiation protection studies.


title: Learning Mixed Strategies in Trajectory Games

abstract:
  In multi-agent settings, game theory is a natural framework for describing
the strategic interactions of agents whose objectives depend upon one another's
behavior. Trajectory games capture these complex effects by design. In
competitive settings, this makes them a more faithful interaction model than
traditional "predict then plan" approaches. However, current game-theoretic
planning methods have important limitations. In this work, we propose two main
contributions. First, we introduce an offline training phase which reduces the
online computational burden of solving trajectory games. Second, we formulate a
lifted game which allows players to optimize multiple candidate trajectories in
unison and thereby construct more competitive "mixed" strategies. We validate
our approach on a number of experiments using the pursuit-evasion game "tag."


title:
Dynamic quantum-enhanced sensing without entanglement in central spin
  systems

abstract:
  We propose a dynamic quantum sensing scheme by using a quantum many-spin
system composed of a central spin interacting with many surrounding spins.
Starting from a generalized Ising ring model, we investigate the error
propagation formula of the central spin and it indicates that Heisenberg
scaling can be reached while the probe state only needs to be a product state.
Particularly, we derive an analytical form of the dynamic quantum Fisher
information in a limit case, which explicitly exhibits the Heisenberg scaling.
By comparing with numerical results, we demonstrate that the general case can
be well approximated by the analytical result when the coupling strength among
the surrounding spins is much weaker than the coupling strength between the
central and surrounding spins. This analytic result guides us to find the
appropriate probe state and the proper measurement time, to achieve the
Heisenberg scaling in realistic situations. Furthermore, we investigate various
effects which are important in practical quantum systems, including the central
spin Zeeman term, the anisotropy of the hyperfine interaction and the
inhomogeneity of the hyperfine coupling strength. Our result indicates that the
dynamic quantum-enhanced sensing scheme seems feasible in realistic quantum
central spin systems, like semiconductor quantum dots.


title:
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and
  its Application to Reinforcement Learning

abstract:
  We present a novel procedure for optimization based on the combination of
efficient quantized tensor train representation and a generalized maximum
matrix volume principle. We demonstrate the applicability of the new Tensor
Train Optimizer (TTOpt) method for various tasks, ranging from minimization of
multidimensional functions to reinforcement learning. Our algorithm compares
favorably to popular evolutionary-based methods and outperforms them by the
number of function evaluations or execution time, often by a significant
margin.


title: Hyperaccurate bounds in discrete-state Markovian systems

abstract:
  Generalized empirical currents represent a vast class of thermodynamic
observables of mesoscopic systems. Their fluctuations satisfy the thermodynamic
uncertainty relations (TURs), as they can be bounded by the average entropy
production. Here, we derive a general closed expression for the hyperaccurate
current in discrete-state Markovian systems, i.e., the one with the least
fluctuations, for both discrete- and continuous-time evolution. We show that
its associated hyperaccurate bound is generally much tighter than the one given
by the TURs, and might be crucial to providing a reliable estimation of the
average entropy production. We also show that one-loop systems (rings) exhibit
a hyperaccurate current only for finite times, highlighting the importance of
short-time observations. Additionally, we derive two novel bounds for the
efficiency of work-to-work converters, solely as a function of either the input
or the output power. Finally, our theoretical results are employed to analyze a
6-state model network for kinesin, and a chemical system in a thermal gradient
exhibiting a dissipation-driven selection of states.


title:
Greenhouse Gas Emissions and its Main Drivers: a Panel Assessment for
  EU-27 Member States

abstract:
  This paper assesses the effects of greenhouse gas emissions drivers in EU-27
over the period 2010-2019, using a Panel EGLS model with period fixed effects.
In particular, we focused our research on studying the effects of GDP,
renewable energy, households energy consumption and waste on the greenhouse gas
emissions. In this regard, we found a positive relationship between three
independent variables (real GDP per capita, households final consumption per
capita and waste generation per capita) and greenhouse gas emissions per
capita, while the effect of the share of renewable energy in gross final energy
consumption on the dependent variable proved to be negative, but quite low. In
addition, we demonstrate that the main challenge that affects greenhouse gas
emissions is related to the structure of households energy consumption, which
is generally composed by environmentally harmful fuels. This suggests the need
to make greater efforts to support the shift to a green economy based on a
higher energy efficiency.


title:
Unfolding, higher spins, metaplectic groups and resolution of classical
  singularities

abstract:
  We review and extend some recent results concerning the analysis of spacetime
singularities in four-dimensional higher spin gravity, summarizing how the
coupling of the gravitational field to massless higher spins may provide
resolution mechanisms. We elucidate such mechanisms at the level of curvature
singularities and degenerate metrics in exact as well as linearized solutions
to Vasiliev's equations. As a preamble, we review the underlying higher-spin
algebra and its metaplectic group extensions, after which we detail various
gauge functions encoding the $AdS_4$ vacuum and the non-rotating
Banados--Gomberoff--Martinez (BGM) metric, the four-dimensional lift of the
spinless BTZ black hole, in different coordinate patches related by transition
functions. We then revisit how, within the unfolded formalism, it is natural
extend the BGM black hole through its causal singularity. Finally, we compare
the metric-like and unfolded descriptions of scalar fluctuations over the
(extended) BGM background, showing how the latter description maps
singularities to well-defined metaplectic group elements providing regular
values for the Weyl zero-form master field, which thus admits continuation over
the full extended BGM spacetime.


title:
Bose-Einstein Condensate dark matter models in the presence of baryonic
  matter and random confining potentials

abstract:
  We consider the effects of an uncorrelated random potential on the properties
of Bose-Einstein Condensate (BEC) dark matter halos, which acts as a source of
disorder, and which is added as a new term in the Gross-Pitaevskii equation,
describing the properties of the halo. By using the hydrodynamic representation
we derive the basic equation describing the density distribution of the
galactic dark matter halo, by also taking into account the effects of the
baryonic matter, and of the rotation. The density, mass and tangential velocity
profiles are obtained exactly in spherical symmetry by considering a simple
exponential density profile for the baryonic matter, and a Gaussian type
disorder potential. To test the theoretical model we compare its predictions
with a set of 39 galaxies from the Spitzer Photometry \& Accurate Rotation
Curves (SPARC) database. We obtain estimates of the relevant astrophysical
parameters of the dark matter dominated galaxies, including the baryonic matter
properties, and the parameters of the random potential. The BEC model in the
presence of baryonic matter and a random confining potential gives a good
statistical description of the SPARC data. The presence of the condensate dark
matter could also provide a solution for the core/cusp problem.


title: Space-like Singularities of General Relativity: A Phantom menace?

abstract:
  The big bang and the Schwarzschild singularities are space-like. They are
generally regarded as the "final frontiers" at which space-time ends and
general relativity breaks down. We review the status of such space-like
singularities from three increasingly more general perspectives. They are
provided by (i) A reformulation of classical general relativity motivated by
the Belinskii, Khalatnikov, Lifshitz conjecture on the behavior of the
gravitational field near space-like singularities; (ii) The use of test quantum
fields to probe the nature of these singularities; and, (iii) An analysis of
the fate of these singularities in loop quantum gravity due to quantum geometry
effects. At all three levels singularities turn out to be less menacing than
one might a priori expect from classical general relativity. Our goal is to
present an overview of the emerging conceptual picture and suggest lines for
further work. In line with the \emph{Introduction to Current Research} theme,
we have made an attempt to make it easily accessible to all researchers in
gravitational physics.


title: A Survey of Machine Narrative Reading Comprehension Assessments

abstract:
  As the body of research on machine narrative comprehension grows, there is a
critical need for consideration of performance assessment strategies as well as
the depth and scope of different benchmark tasks. Based on narrative theories,
reading comprehension theories, as well as existing machine narrative reading
comprehension tasks and datasets, we propose a typology that captures the main
similarities and differences among assessment tasks; and discuss the
implications of our typology for new task design and the challenges of
narrative reading comprehension.


title: Piezoelectric ferromagnetism in two dimensional FeCl$_2$

abstract:
  We predict that monolayer FeCl$_2$ is a two-dimensional piezoelectric
ferromagnet (PFM) with easy-axis magnetism and a Curie temperature of 260 K.
Our ab-initio calculations combined with data mining reveal 2H-FeCl$_2$ as the
only easy-axis 2D monolayer PFM, and that its magnetic anisotropy increases
many-fold with moderate hole doping. We develop an analysis based on magnetic
anisotropy energy densities that explain the magnetic and doping-dependent
behavior of FeCl$_2$, as well as VSe$_2$ and CrI$_3$, and can enable the design
of future 2D magnetically ordered materials.


title: ONCE-3DLanes: Building Monocular 3D Lane Detection

abstract:
  We present ONCE-3DLanes, a real-world autonomous driving dataset with lane
layout annotation in 3D space. Conventional 2D lane detection from a monocular
image yields poor performance of following planning and control tasks in
autonomous driving due to the case of uneven road. Predicting the 3D lane
layout is thus necessary and enables effective and safe driving. However,
existing 3D lane detection datasets are either unpublished or synthesized from
a simulated environment, severely hampering the development of this field. In
this paper, we take steps towards addressing these issues. By exploiting the
explicit relationship between point clouds and image pixels, a dataset
annotation pipeline is designed to automatically generate high-quality 3D lane
locations from 2D lane annotations in 211K road scenes. In addition, we present
an extrinsic-free, anchor-free method, called SALAD, regressing the 3D
coordinates of lanes in image view without converting the feature map into the
bird's-eye view (BEV). To facilitate future research on 3D lane detection, we
benchmark the dataset and provide a novel evaluation metric, performing
extensive experiments of both existing approaches and our proposed method. The
aim of our work is to revive the interest of 3D lane detection in a real-world
scenario. We believe our work can lead to the expected and unexpected
innovations in both academia and industry.


title:
SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation
  of Individual Modalities

abstract:
  As deep learning advances, there is an ever-growing demand for models capable
of synthesizing information from multi-modal resources to address the complex
tasks raised from real-life applications. Recently, many large multi-modal
datasets have been collected, on which researchers actively explore different
methods of fusing multi-modal information. However, little attention has been
paid to quantifying the contribution of different modalities within the
proposed models. In this paper, we propose the {\bf SH}apley v{\bf A}lue-based
{\bf PE}rceptual (SHAPE) scores that measure the marginal contribution of
individual modalities and the degree of cooperation across modalities. Using
these scores, we systematically evaluate different fusion methods on different
multi-modal datasets for different tasks. Our experiments suggest that for some
tasks where different modalities are complementary, the multi-modal models
still tend to use the dominant modality alone and ignore the cooperation across
modalities. On the other hand, models learn to exploit cross-modal cooperation
when different modalities are indispensable for the task. In this case, the
scores indicate it is better to fuse different modalities at relatively early
stages. We hope our scores can help improve the understanding of how the
present multi-modal models operate on different modalities and encourage more
sophisticated methods of integrating multiple modalities.


title:
Composition-aware Graphic Layout GAN for Visual-textual Presentation
  Designs

abstract:
  In this paper, we study the graphic layout generation problem of producing
high-quality visual-textual presentation designs for given images. We note that
image compositions, which contain not only global semantics but also spatial
information, would largely affect layout results. Hence, we propose a deep
generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to
synthesize layouts based on the global and spatial visual contents of input
images. To obtain training images from images that already contain manually
designed graphic layout data, previous work suggests masking design elements
(e.g., texts and embellishments) as model inputs, which inevitably leaves hint
of the ground truth. We study the misalignment between the training inputs
(with hint masks) and test inputs (without masks), and design a novel domain
alignment module (DAM) to narrow this gap. For training, we built a large-scale
layout dataset which consists of 60,548 advertising posters with annotated
layout information. To evaluate the generated layouts, we propose three novel
metrics according to aesthetic intuitions. Through both quantitative and
qualitative evaluations, we demonstrate that the proposed model can synthesize
high-quality graphic layouts according to image compositions.


title:
A General Framework For Constructing Locally Self-Normalized
  Multiple-Change-Point Tests

abstract:
  We propose a general framework to construct self-normalized
multiple-change-point tests with time series data. The only building block is a
user-specified one-change-point detecting statistic, which covers a wide class
of popular methods, including cumulative sum process, outlier-robust rank
statistics and order statistics. Neither robust and consistent estimation of
nuisance parameters, selection of bandwidth parameters, nor pre-specification
of the number of change points is required. The finite-sample performance shows
that our proposal is size-accurate, robust against misspecification of the
alternative hypothesis, and more powerful than existing methods. Case studies
of NASDAQ option volume and Shanghai-Hong Kong Stock Connect turnover are
provided.


title:
AdapterBias: Parameter-efficient Token-dependent Representation Shift
  for Adapters in NLP Tasks

abstract:
  Transformer-based pre-trained models with millions of parameters require
large storage. Recent approaches tackle this shortcoming by training adapters,
but these approaches still require a relatively large number of parameters. In
this study, AdapterBias, a surprisingly simple yet effective adapter
architecture, is proposed. AdapterBias adds a token-dependent shift to the
hidden output of transformer layers to adapt to downstream tasks with only a
vector and a linear layer. Extensive experiments are conducted to demonstrate
the effectiveness of AdapterBias. The experiments show that our proposed method
can dramatically reduce the trainable parameters compared to the previous works
with a minimal decrease in task performances compared with fine-tuned
pre-trained models. We further find that AdapterBias automatically learns to
assign more significant representation shifts to the tokens related to the task
in consideration.


title: Cyberinfrastructure Value: a survey on perceived importance and usage

abstract:
  The research landscape in science and engineering is heavily reliant on
computation and data storage. The intensity of computation required for many
research projects illustrates the importance of the availability of high
performance computing (HPC) resources and services. This paper summarizes the
results of a recent study among principal investigators that attempts to
measure the impact of the cyberinfrastructure (CI) resources allocated by the
XSEDE (eXtreme Science and Engineering Discovery Environment) project to
various research activities across the United States. Critical findings from
this paper include: a majority of respondents report that the XSEDE environment
is important or very important in completing their funded work, and two-thirds
of our study's respondents developed products (e.g., datasets, websites,
software, etc.) using XSEDE-allocated resources. With nearly one-third of
respondents citing the importance of XSEDE-allocated resources in securing
research funding, we estimate that respondents of this survey have secured
approximately $3.3B in research funding from various sources, as self-reported
by respondents.


title: Learning to Get Up

abstract:
  Getting up from an arbitrary fallen state is a basic human skill. Existing
methods for learning this skill often generate highly dynamic and erratic
get-up motions, which do not resemble human get-up strategies, or are based on
tracking recorded human get-up motions. In this paper, we present a staged
approach using reinforcement learning, without recourse to motion capture data.
The method first takes advantage of a strong character model, which facilitates
the discovery of solution modes. A second stage then learns to adapt the
control policy to work with progressively weaker versions of the character.
Finally, a third stage learns control policies that can reproduce the weaker
get-up motions at much slower speeds. We show that across multiple runs, the
method can discover a diverse variety of get-up strategies, and execute them at
a variety of speeds. The results usually produce policies that use a final
stand-up strategy that is common to the recovery motions seen from all initial
states. However, we also find policies for which different strategies are seen
for prone and supine initial fallen states. The learned get-up control
strategies often have significant static stability, i.e., they can be paused at
a variety of points during the get-up motion. We further test our method on
novel constrained scenarios, such as having a leg and an arm in a cast.


title: Modeling Political Activism around Gun Debate via Social Media

abstract:
  The United States have some of the highest rates of gun violence among
developed countries. Yet, there is a disagreement about the extent to which
firearms should be regulated. In this study, we employ social media signals to
examine the predictors of offline political activism, at both population and
individual level. We show that it is possible to classify the stance of users
on the gun issue, especially accurately when network information is available.
Alongside socioeconomic variables, network information such as the relative
size of the two sides of the debate is also predictive of state-level gun
policy. On individual level, we build a statistical model using network,
content, and psycho-linguistic features that predicts real-life political
action, and explore the most predictive linguistic features. Thus, we argue
that, alongside demographics and socioeconomic indicators, social media
provides useful signals in the holistic modeling of political engagement around
the gun debate.


title:
Cotangent bundle reduction and Routh reduction for polysymplectic
  manifolds

abstract:
  We discuss Lagrangian and Hamiltonian field theories that are invariant under
a symmetry group. We apply the polysymplectic reduction theorem for both types
of field equations and we investigate aspects of the corresponding
reconstruction process. We identify the polysymplectic structures that lie at
the basis of cotangent bundle reduction and Routh reduction in this setting and
we relate them by means of the Routhian function and its associated Legendre
transformation. We end the paper with examples that illustrate the
applicability of our results.


title: On the spectrum and energy of Seidel matrix for chain graphs

abstract:
  We study various spectral properties of the Seidel matrix $S$ of a connected
chain graph. We prove that $-1$ is always an eigenvalue of $S$ and all other
eigenvalues of $S$ can have multiplicity at most two. We obtain the
multiplicity of the Seidel eigenvalue $-1$, minimum number of distinct
eigenvalues, eigenvalue bounds, characteristic polynomial, lower and upper
bounds of Seidel energy of a chain graph. It is also shown that the energy
bounds obtained here work better than the bounds conjectured by Haemers. We
also obtain the minimal Seidel energy for some special chain graphs of order
$n$. We also give a number of open problems.


title:
A lightning monitoring system for studying transient phenomena in cosmic
  ray observatories

abstract:
  During thunderstorms, the atmospheric electric field can increase above
hundreds of kV/m, causing an acceleration in the charged particles of secondary
cosmic rays. Such an acceleration causes avalanche processes in the atmosphere,
enhancing/reducing the particle flux at ground level depending on the
strength/polarity of the electric field. We present the design and
implementation of a self-triggered and fast-recording lightning monitoring
system used to study the transient electric field atmospheric effect on the
secondary particle flux above cosmic ray observatories. The acquisition device
records lightning electric field at 10 $\mu$s resolution (during 1.2 s per
event), covering a detection range up to 200 km ($I_{peak} >$ 100 kA)


title:
Source Domain Subset Sampling for Semi-Supervised Domain Adaptation in
  Semantic Segmentation

abstract:
  In this paper, we introduce source domain subset sampling (SDSS) as a new
perspective of semi-supervised domain adaptation. We propose domain adaptation
by sampling and exploiting only a meaningful subset from source data for
training. Our key assumption is that the entire source domain data may contain
samples that are unhelpful for the adaptation. Therefore, the domain adaptation
can benefit from a subset of source data composed solely of helpful and
relevant samples. The proposed method effectively subsamples full source data
to generate a small-scale meaningful subset. Therefore, training time is
reduced, and performance is improved with our subsampled source data. To
further verify the scalability of our method, we construct a new dataset called
Ocean Ship, which comprises 500 real and 200K synthetic sample images with
ground-truth labels. The SDSS achieved a state-of-the-art performance when
applied on GTA5 to Cityscapes and SYNTHIA to Cityscapes public benchmark
datasets and a 9.13 mIoU improvement on our Ocean Ship dataset over a baseline
model.


title:
FairSR: Fairness-aware Sequential Recommendation through Multi-Task
  Learning with Preference Graph Embeddings

abstract:
  Sequential recommendation (SR) learns from the temporal dynamics of user-item
interactions to predict the next ones. Fairness-aware recommendation mitigates
a variety of algorithmic biases in the learning of user preferences. This paper
aims at bringing a marriage between SR and algorithmic fairness. We propose a
novel fairness-aware sequential recommendation task, in which a new metric,
interaction fairness, is defined to estimate how recommended items are fairly
interacted by users with different protected attribute groups. We propose a
multi-task learning based deep end-to-end model, FairSR, which consists of two
parts. One is to learn and distill personalized sequential features from the
given user and her item sequence for SR. The other is fairness-aware preference
graph embedding (FPGE). The aim of FPGE is two-fold: incorporating the
knowledge of users' and items' attributes and their correlation into entity
representations, and alleviating the unfair distributions of user attributes on
items. Extensive experiments conducted on three datasets show FairSR can
outperform state-of-the-art SR models in recommendation performance. In
addition, the recommended items by FairSR also exhibit promising interaction
fairness.


title:
Fault friction under thermal pressurization during large coseismic-slip
  Part I: Numerical analyses

abstract:
  In this paper, we study the role of thermal pressurization in the frictional
response of a fault under large coseismic slip. We investigate the role of the
seismic slip velocity, mixture compressibility, characteristic grain size and
viscosity parameter in the frictional response of the coupled
thermo-hydro-mechanical problem, taking into account the fault's
microstructure. Starting from the mass, energy and momentum balance for
Cosserat continua we derive the equations of our model. We complete the
mathematical description using perfect plasticity and Perzyna viscoplasticity
in the material constitutive behavior. We investigate both the rate independent
as well as the rate dependent frictional response and compare with existing
models found in literature, namely the rate and state friction law (Dieterich
(1992),Ruina(1983a)). We show that our model is capable of predicting strain
rate hardening and velocity softening without the assumption of a state
variable. We observe traveling instabilities inside the layer that lead to
oscillations in the fault's frictional response, like in the case of Portevin
Le Chatelier (PLC) effect. This behavior is not captured by existing numerical
analyses presented in Rattez et al. (2018c,b,a) and go beyond the established
models of uniform shear (Lachenbruch (1980)) and shear on a mathematical plane
(Rice (2006a)), which predict a strictly monotonous behavior during shearing.
Recent experimental analyses, which have managed to insulate thermal
pressurization from other weakening mechanisms (Badt et al. (2020)),
corroborate our numerical results.


title: Scrambling in Double-Scaled SYK and De Sitter Space

abstract:
  I want to call attention to a simple previously noted fact about the
double-scaled version of the SYK model which suggests that it may be
holographically dual to de Sitter space.


title:
Fault friction under thermal pressurization during large coseismic-slip
  Part II: Expansion to the model of frictional slip

abstract:
  In Stathas and Stefanou (2022) we presented the frictional response of a
bounded fault gouge under largecoseismic slip. We did so by taking into account
the evolution of the Principal Slip Zone (PSZ) thickness using a Cosserat
micromorphic continuum model for the description of the fault's mechanical
response. The numerical results obtained differ significantly from those
predicted by the established model of thermal pressurization during slip on a
mathematical plane (see Mase and Smith (1987); Rice (2006a); Platt et al.
(2014a) among others). These differences prompt us to reconsider the basic
assumptions of a stationary strain localization on an unbounded domain present
in the original model. We depart from these assumptions, extending the model to
incorporate different strain localization modes, temperature and pore fluid
pressure boundary conditions. The new model allows us to gain significant
understanding of the detailed numerical results of Part I. We investigate the
influence of a traveling strain localization inside the fault gouge considering
isothermal, drained boundary conditions for the bounded and unbounded domain
respectively. Our results establish that when a stationary strain localization
profile is applied on a bounded domain, the boundary conditions lead to a
steady state, where total strength regain is achieved. In the case of a
traveling instability such a steady state is not possible and the fault only
regains part of its frictional strength, depending on the seismic slip velocity
and the traveling velocity of the shear band. In this case frictional
oscillations increasing the frequency content of the earthquake are also
developed. Our results indicate a reappraisal of the role of thermal
pressurization as a frictional weakening mechanism.