title:
Time-Varying Magnetopause Reconnection during Sudden Commencement:
  Global MHD Simulations

abstract:
  In response to a solar wind dynamic pressure enhancement, the compression of
the magnetosphere generates strong ionospheric signatures and a sharp variation
in the ground magnetic field, termed sudden commencement (SC). Whilst such
compressions have also been associated with a contraction of the ionospheric
polar cap due to the triggering of reconnection in the magnetotail, the effect
of any changes in dayside reconnection is less clear and is a key component in
fully understanding the system response. In this study we explore the
time-dependent nature of dayside coupling during SC by performing global
simulations using the Gorgon MHD code, and impact the magnetosphere with a
series of interplanetary shocks with different parameters. We identify the
location and evolution of the reconnection region in each case as the shock
propagates through the magnetosphere, finding strong enhancement in the dayside
reconnection rate and prompt expansion of the dayside polar cap prior to the
eventual triggering of tail reconnection. This effect pervades for a variety of
IMF orientations, and the reconnection rate is most enhanced for events with
higher dynamic pressure. We explain this by repeating the simulations with a
large explicit resistivity, showing that compression of the magnetosheath
plasma near the propagating shock front allows for reconnection of much greater
intensity and at different locations on the dayside magnetopause than during
typical solar wind conditions. The results indicate that the dynamic behaviour
of dayside coupling may render steady models of reconnection inaccurate during
the onset of a severe space weather event.

categories: physics.space-ph astro-ph.EP physics.comp-ph physics.geo-ph


title:
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable
  Model from a Hybrid Dataset

abstract:
  We present FaceVerse, a fine-grained 3D Neural Face Model, which is built
from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K
high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed
to take better advantage of our hybrid dataset. In the coarse module, we
generate a base parametric model from large-scale RGB-D images, which is able
to predict accurate rough 3D face models in different genders, ages, etc. Then
in the fine module, a conditional StyleGAN architecture trained with
high-fidelity scan models is introduced to enrich elaborate facial geometric
and texture details. Note that different from previous methods, our base and
detailed modules are both changeable, which enables an innovative application
of adjusting both the basic attributes and the facial details of 3D face
models. Furthermore, we propose a single-image fitting framework based on
differentiable rendering. Rich experiments show that our method outperforms the
state-of-the-art methods.


title: Constant factor approximations for Lower and Upper bounded Clusterings

abstract:
  Clustering is one of the most fundamental problem in Machine Learning.
Researchers in the field often require a lower bound on the size of the
clusters to maintain anonymity and upper bound for the ease of analysis.
Specifying an optimal cluster size is a problem often faced by scientists. In
this paper, we present a framework to obtain constant factor approximations for
some prominent clustering objectives, with lower and upper bounds on cluster
size. This enables scientists to give an approximate cluster size by specifying
the lower and the upper bounds for it. Our results preserve the lower bounds
but may violate the upper bound a little. %{GroverGD21_LBUBFL_Cocoon} to $2$.
%namely, $k$ Center (LUkC) and $k$ Median (LUkM) problem. We study the problems
when either of the bounds is uniform. We apply our framework to give the first
constant factor approximations for LUkM and its generalization, $k$-facility
location problem (LUkFL), with $\beta+1$ factor violation in upper bounds where
$\beta$ is the violation of upper bounds in solutions of upper bounded
$k$-median and $k$-facility location problems respectively. We also present a
result on LUkC with uniform upper bounds and, its generalization, lower and
(uniform) upper bounded $k$ supplier problem (LUkS). The approach also gives a
result on lower and upper bounded facility location problem (LUFL), improving
upon the upper bound violation of $5/2$ due to Gupta et al.
  We also reduce the violation in upper bounds for a special case when the gap
between the lower and upper bounds is not too small.


title:
Finite presentations of the mapping class groups of once-stabilized
  Heegaard splittings

abstract:
  Let $g \ge 2$ and assume that we are given a genus $g$ Heegaard splitting of
a closed orientable $3$-manifold with the distance greater than $2g+2$. We
prove that the mapping class group of the once-stabilization of such a Heegaard
splitting is finitely presented.


title:
Estimation of Full Sky CMB Angular Power Spectrum from High Resolution
  Partial Sky CMB Map Using Artificial Neural Network

abstract:
  Reliable extraction of cosmological information from observed cosmic
microwave background (CMB) maps requires accurate removal of foreground
components. Since some regions of the sky are inevitably contaminated by strong
foreground signals, it may be desired to excise such strongly contaminated
regions even after a foreground subtraction has been performed. We employ an
artificial neural network (ANN) to predict the full sky CMB angular power
spectrum from the partial sky spectrum obtained from masked CMB anisotropy map,
at a high pixel resolution. We use a simple ANN with one hidden layer
containing $895$ neurons. Using $1.2 \times 10^{5}$ training samples of full
sky and corresponding partial sky spectra at Healpix pixel resolution parameter
$N_{side} = 256$, we show that predicted spectrum by our ANN agrees well with
the target spectrum at each realization for the multipole range $2 \leq l \leq
512$. The predicted spectra are statistically unbiased and they preserve the
cosmic variance accurately. Statistically, the differences between the mean
predicted and underlying theoretical spectra are within $3\sigma$. Moreover,
the probability densities obtained from predicted spectra agree very well with
those obtained from actual full sky spectra for each multipole. Equally
interesting is that the correlation matrix calculated from predicted spectra
does not show any signature of correlation between the spectrum of different
multipoles. Our work shows that the predicted spectra do not need to be binned
even at high resolution to rid of correlations due to mode-mode coupling
introduced on the partial sky since the ANN learns to effectively recover the
lost information due to sky cut. The excellent agreement of statistical
properties between the predicted and the ground-truth demonstrates the
importance of using artificial intelligence systems in cosmological analysis
more widely.


title: Phonon-dominated energy transport in purely metallic heterostructures

abstract:
  We use ultrafast x-ray diffraction to quantify the transport of energy in
laser-excited nanoscale Au/Ni bilayers. Electron transport and efficient
electron-phonon coupling in Ni convert the laser-deposited energy in the
conduction electrons within a few picoseconds into a strong non-equilibrium
between hot Ni and cold Au phonons at the bilayer interface. Modeling of the
subsequent equilibration dynamics within various two-temperature models
confirms that for ultrathin Au films the thermal transport is dominated by
phonons instead of conduction electrons because of the weak electron-phonon
coupling in Au.


title: A high-fidelity quantum matter-link between ion-trap microchip modules

abstract:
  System scalability is fundamental for large-scale quantum computers (QCs) and
is being pursued over a variety of hardware platforms. For QCs based on trapped
ions, architectures such as the quantum charge-coupled device (QCCD) are used
to scale the number of qubits on a single device. However, the number of ions
that can be hosted on a single quantum computing module is limited by the size
of the chip being used. Therefore, a modular approach is of critical importance
and requires quantum connections between individual modules. Here, we present
the demonstration of a quantum matter-link in which ion qubits are transferred
between adjacent QC modules. Ion transport between adjacent modules is realised
at a rate of 2424 s$^{-1}$ and with an ion-transfer fidelity in excess of
99.999993%. Furthermore, we show that the link does not measurably impact the
phase coherence of the qubit. The realisation of the quantum matter-link
demonstrates a novel mechanism for interconnecting QCCD devices. This achieves
a key milestone for the implementation of modular QCs capable of hosting
millions of trapped-ion qubits.


title:
Manifold Principle Component Analysis for Large-Dimensional Matrix
  Elliptical Factor Model

abstract:
  Matrix factor model has been growing popular in scientific fields such as
econometrics, which serves as a two-way dimension reduction tool for matrix
sequences. In this article, we for the first time propose the matrix elliptical
factor model, which can better depict the possible heavy-tailed property of
matrix-valued data especially in finance. Manifold Principle Component Analysis
(MPCA) is for the first time introduced to estimate the row/column loading
spaces. MPCA first performs Singular Value Decomposition (SVD)for each "local"
matrix observation and then averages the local estimated spaces across all
observations, while the existing ones such as 2-dimensional PCA first
integrates data across observations and then does eigenvalue decomposition of
the sample covariance matrices. We propose two versions of MPCA algorithms to
estimate the factor loading matrices robustly, without any moment constraints
on the factors and the idiosyncratic errors. Theoretical convergence rates of
the corresponding estimators of the factor loading matrices, factor score
matrices and common components matrices are derived under mild conditions. We
also propose robust estimators of the row/column factor numbers based on the
eigenvalue-ratio idea, which are proven to be consistent. Numerical studies and
real example on financial returns data check the flexibility of our model and
the validity of our MPCA methods.


title:
BARGAIN-MATCH: A Game Theoretical Approach for Resource Allocation and
  Task Offloading in Vehicular Edge Computing Networks

abstract:
  Vehicular edge computing (VEC) is emerging as a promising architecture of
vehicular networks (VNs) by deploying the cloud computing resources at the edge
of the VNs. This work aims to optimize resource allocation and task offloading
in VEC networks. Specifically, we formulate a game theoretical resource
allocation and task offloading problem (GTRATOP) that aims to maximize the
system performance by jointly considering the incentive for cooperation,
competition among vehicles, heterogeneity between VEC servers and vehicles, and
inherent dynamic of VNs. Since the formulated GTRATOP is NP-hard, we propose an
adaptive approach for resource allocation and task offloading in VEC networks
by incorporating bargaining game and matching game, which is called
BARGAIN-MATCH. First, for resource allocation, a bargaining game-based
incentive is proposed to stimulate the vehicles and VEC servers to negotiate
the optimal resource allocation and pricing decisions. Second, for task
offloading, a many-to-one matching scheme is proposed to decide the optimal
offloading strategies. Third, the dynamic and time-varying features are
considered to adapt the strategies of BARGAIN-MATCH to the real-time VEC
networks. Moreover, the BARGAIN-MATCH is proved to be stable and weak Pareto
optimal. Simulation results demonstrate that the proposed BARGAIN-MATCH
achieves superior system performance and efficiency compared to other methods,
especially when the system workload is heavy.


title:
Neural MoCon: Neural Motion Control for Physically Plausible Human
  Motion Capture

abstract:
  Due to the visual ambiguity, purely kinematic formulations on monocular human
motion capture are often physically incorrect, biomechanically implausible, and
can not reconstruct accurate interactions. In this work, we focus on exploiting
the high-precision and non-differentiable physics simulator to incorporate
dynamical constraints in motion capture. Our key-idea is to use real physical
supervisions to train a target pose distribution prior for sampling-based
motion control to capture physically plausible human motion. To obtain accurate
reference motion with terrain interactions for the sampling, we first introduce
an interaction constraint based on SDF (Signed Distance Field) to enforce
appropriate ground contact modeling. We then design a novel two-branch decoder
to avoid stochastic error from pseudo ground-truth and train a distribution
prior with the non-differentiable physics simulator. Finally, we regress the
sampling distribution from the current state of the physical character with the
trained prior and sample satisfied target poses to track the estimated
reference motion. Qualitative and quantitative results show that we can obtain
physically plausible human motion with complex terrain interactions, human
shape variations, and diverse behaviors. More information can be found
at~\url{https://www.yangangwang.com/papers/HBZ-NM-2022-03.html}


title:
High-order harmonic generation in X-ray range from laser induced noble
  gas multivalent ions

abstract:
  Sub-femtosecond x-ray burst is powerful tool for probing and imaging
electronic and concomitant atomic motion in attosecond physics. For years,
x-ray source (above 2 keV) had mainly been obtained from X-ray free electron
laser (XFEL) or synchrotron radiation, which are high energy consumption, high
cost and huge volume. Here we propose a low-cost and small-size method to
generate X-ray source. We experimentally obtained high photon energy spectrum
(~ 5.2 keV) through both atom and multiple valence state ions using a
near-infrared 1.45 {\mu}m driving laser interacting with krypton gas, according
to our knowledge, which is the highest photon energy generated through
high-order harmonic generation up to now. In our scheme, multi-keV photon
energy can be achieved with a relaxed requirement on experimental conditions,
and make time-resolved studies more accessible to many laboratories that are
capable of producing high energy photon extending to hard x-ray region.
Furthermore, our scheme minimizes the influence of X-ray fluorescence process
on detection, and can also be utilized to study the quantum-optical nature of
high-order harmonic generation.


title:
Rate-Splitting Multiple Access for Dual-Functional Radar-Communication
  Satellite Systems

abstract:
  In this paper, we consider a multi-antenna dual-functional
radar-communication (DFRC) satellite system, where the satellite has a dual
capability to simultaneously communicate with downlink satellite users (SUs)
and probe detection signals to a moving target. To design an appropriate DFRC
waveform, we investigate the rate-splitting multiple access (RSMA)-assisted
DFRC beamfoming, and employ the Cramer-Rao bound (CRB) as a radar performance
metric, which represents a lower bound on the variance of unbiased estimators.
The beamforming is optimized to minimize the CRB subject to quality of service
(QoS) constraints of SUs and a per-feed transmit power budget. Satellite
communication and detecting ground/ sea objects in a bistatic mode are
accomplished simultaneously using the DFRC waveform we designed. Simulation
results demonstrate that the proposed RSMA-assisted DFRC beamforming
outperforms the conventional space-division multiple access (SDMA) strategy in
terms of the communication-sensing trade-off and target estimation performance
in a multibeam satellite system.


title: Nonequilibrium dynamics in a spin valve with non-collinear magnetization

abstract:
  We utilize a hybrid quantum-classical equation of motion approach to
investigate the spin dynamics and spin-transfer torque in a spin valve under
bias voltage. We show that the interplay between localized classical magnetic
moments and conduction electrons induces a complex effective exchange coupling
between the magnetic layers. This leads to a declination of magnetizations from
layer anisotropy axes even in equilibrium. Introducing a finite bias voltage
triggers spin currents and related spin transfer torques which further tilt the
magnetizations and govern the relaxation processes of the spin dynamics.
Analyzing different scenarios of the applied bias voltage, we show that
symmetric and asymmetric voltage drops can lead to relaxation times of the spin
dynamics that differ by several orders of magnitude at comparable charge
currents. In both cases we observe resonant features, where the relaxation is
boosted whenever the chemical potential of the leads matches the maxima in the
density of the states of the spin-valve electrons.


title: Mathematical Elements of Density Functional Theory

abstract:
  We review some of the basic mathematical results about density functional
theory.


title: Exact and Heuristic Algorithms for Energy-Efficient Scheduling

abstract:
  The combined increase of energy demand and environmental pollution at a
global scale is entailing a rethinking of the production models in sustainable
terms. As a consequence, energy suppliers are starting to adopt strategies that
flatten demand peaks in power plants by means of pricing policies that
stimulate a change in the consumption practices of customers. A representative
example is the Time-of-Use (TOU)-based tariffs policy, which encourages
electricity usage at off-peak hours by means of low prices, while penalizing
peak hours with higher prices. The TOU-based tariffs policy induces a
partitioning of the time horizon into a set of time slots, each associated with
a cost that becomes a part of the optimization objective. This thesis focuses
on a representative bi-objective energy-efficient job scheduling problem on
parallel identical machines under TOU-based tariffs by delving into the
description of its inherent properties, mathematical formulations, and solution
approaches. Specifically, the thesis starts by reviewing the flourishing
literature on the subject, and providing a useful framework for theoreticians
and practitioners. Subsequently, it describes the considered problem and
investigates its theoretical properties. In the same chapter, it presents a
first mathematical model for the problem, as well as a possible reformulation
that exploits the structure of the solution space so as to achieve a
considerable increase in compactness. Afterwards, the thesis introduces a
sophisticated heuristic scheme to tackle the inherent hardness of the problem,
and an exact algorithm that exploits the mathematical models. Then, it shows
the computational efficiency of the presented solution approaches on a wide
test benchmark. Finally, it presents a perspective on future research
directions for the class of energy-efficient scheduling problems under
TOU-based tariffs as a whole.


title: RadioLensfit: an HPC Tool for Accurate Galaxy Shape Measurement with SKA

abstract:
  The new generation radio telescopes, such as the Square Kilometre Array
(SKA), are expected to reach sufficient sensitivity and resolution to provide
large number densities of resolved faint sources, and therefore to open weak
gravitational lensing observations to the radio band. In this paper we present
RadioLensfit, an open-source tool for an efficient and fast galaxy shape
measurement for radio weak lensing shear. It performs a single source model
fitting in the Fourier domain, after isolating the source visibilities with a
sky model and a faceting technique. This approach makes real sized radio
datasets accessible to an analysis in this domain, where data is not yet
affected by the systematics introduced by the non-linear imaging process. We
detail the implementation of the code and discuss limitations of the source
extraction algorithm. We describe the hybrid parallelization MPI+OpenMP of the
code, implemented to exploit multi-node HPC infrastructures for accelerating
the computation and dealing with very large datasets that possibly cannot
entirely be stored in the memory of a single processor. Finally, we present
performance results both in terms of measurement accuracy and code scalability
on SKA-MID simulated datasets. In particular, we compare shape measurements of
1000 sources at the expected source density in SKA Phase 1 with the ones
obtained from the same dataset in a previous work by a joint fitting of the raw
visibility data, and show that results are comparable while the computational
time is highly reduced.


title: Learning to Answer Questions in Dynamic Audio-Visual Scenarios

abstract:
  In this paper, we focus on the Audio-Visual Question Answering (AVQA) task,
which aims to answer questions regarding different visual objects, sounds, and
their associations in videos. The problem requires comprehensive multimodal
understanding and spatio-temporal reasoning over audio-visual scenes. To
benchmark this task and facilitate our study, we introduce a large-scale
MUSIC-AVQA dataset, which contains more than 45K question-answer pairs covering
33 different question templates spanning over different modalities and question
types. We develop several baselines and introduce a spatio-temporal grounded
audio-visual network for the AVQA problem. Our results demonstrate that AVQA
benefits from multisensory perception and our model outperforms recent A-, V-,
and AVQA approaches. We believe that our built dataset has the potential to
serve as testbed for evaluating and promoting progress in audio-visual scene
understanding and spatio-temporal reasoning. Code and dataset:
http://gewu-lab.github.io/MUSIC-AVQA/


title: Dust in Clusters of Galaxies

abstract:
  The presence of dust in the intracluster medium (ICM) has been a
long-standing problem that is still awaiting elucidation. Direct observational
diagnostics are rather challenging (though not impossible) either because of a
sparse distribution of dust in the intracluster space that makes extinction
measurements difficult or because of a low surface brightness of infrared
emission from dust. Complex indirect approaches are currently available that
can overcome uncertainties and provide a reasonable understanding of the basic
regulations of the physical state of dust in the ICM. Contrary to the common
opinion that the hot ICM does not allow dust to survive and manifest, many
sparse observational data either directly point out that dust exists in the
intracluster space or its presence is consistent with the data. Highly
divergent data in direct evidence and highly uncertain indirect indicators are
often connected either with dust fragility in a hot environment, the possible
compactness of spatial (clumpy) dust distribution in the ICM, or dynamical
features of dust transport. The source of dust is obviously connected with
galaxies, and it turns out that in most cases, dust is carried from galaxies
into the ICM while being thermally and dynamically shielded against the hostile
influence of high-energy ions. In this review, we briefly discuss related
issues from observational and theoretical points of view, including the
transport of dust into the ICM, and the associated destructive and protective
mechanisms and their characteristic time scales.


title:
V3GAN: Decomposing Background, Foreground and Motion for Video
  Generation

abstract:
  Video generation is a challenging task that requires modeling plausible
spatial and temporal dynamics in a video. Inspired by how humans perceive a
video by grouping a scene into moving and stationary components, we propose a
method that decomposes the task of video generation into the synthesis of
foreground, background and motion. Foreground and background together describe
the appearance, whereas motion specifies how the foreground moves in a video
over time. We propose V3GAN, a novel three-branch generative adversarial
network where two branches model foreground and background information, while
the third branch models the temporal information without any supervision. The
foreground branch is augmented with our novel feature-level masking layer that
aids in learning an accurate mask for foreground and background separation. To
encourage motion consistency, we further propose a shuffling loss for the video
discriminator. Extensive quantitative and qualitative analysis on synthetic as
well as real-world benchmark datasets demonstrates that V3GAN outperforms the
state-of-the-art methods by a significant margin.


title:
Robust heterodimensional cycles in two-parameter unfolding of homoclinic
  tangencies

abstract:
  We consider $C^r$ $(r=3,\dots,\infty,\omega)$ diffeomorphisms with a generic
homoclinic tangency to a hyperbolic periodic point, where this point has at
least one complex (non-real) central multiplier and some explicit assumptions
on central multipliers are satisfied so that the dynamics near the homoclinic
tangency is not effectively one-dimensional. We prove that $C^1$-robust
heterodimensional cycles of co-index one appear in any generic two-parameter
$C^r$-unfolding of such a tangency. These heterodimensional cycles also have
$C^1$-robust homoclinic tangencies.


title: MiSeRTrace: Kernel-level Request Tracing for Microservice Visibility

abstract:
  With the evolution of microservice applications, the underlying architectures
have become increasingly complex compared to their monolith counterparts. This
mainly brings in the challenge of observability. By providing a deeper
understanding into the functioning of distributed applications, observability
enables improving the performance of the system by obtaining a view of the
bottlenecks in the implementation. The observability provided by currently
existing tools that perform dynamic tracing on distributed applications is
limited to the user-space and requires the application to be instrumented to
track request flows. In this paper, we present a new open-source framework
MiSeRTrace that can trace the end-to-end path of requests entering a
microservice application at the kernel space without requiring instrumentation
or modification of the application. Observability at the comprehensiveness of
the kernel space allows breaking down of various steps in activities such as
network transfers and IO tasks, thus enabling root cause based performance
analysis and accurate identification of hotspots. MiSeRTrace supports tracing
user-enabled kernel events provided by frameworks such as bpftrace or ftrace
and isolates kernel activity associated with each application request with
minimal overheads. We then demonstrate the working of the solution with results
on a benchmark microservice application.


title:
Crime and social environments: Differences between misdemeanors and
  felonies

abstract:
  Owing to the growing population density of urban areas, many people are being
increasingly exposed to criminal activity. Increasing crime rates raise the
risk of both physical and psychological injury to law-abiding citizens,
creating anxiety. From the viewpoint of complex systems, crime prevention
through data science can be a solution to such issues. However, previous
studies have focused only on a single aspect of crime, ignoring the complex
interplay between the various characteristics, which must be considered in an
analysis to understand the dynamics underlying criminal activities. In this
study, we examined 12 features that have been identified as correlates of crime
rates using state-level statistics from the USA. We found that the correlates
of misdemeanors and felonies differ. The number of misdemeanors is strongly
associated with the police precinct, whereas felony rates are strongly
correlated with gun possession and happiness. Our findings suggest that the
countermeasures for misdemeanors should be treated differently from those for
felonies.


title:
Computationally efficient joint coordination of multiple electric
  vehicle charging points using reinforcement learning

abstract:
  A major challenge in todays power grid is to manage the increasing load from
electric vehicle (EV) charging. Demand response (DR) solutions aim to exploit
flexibility therein, i.e., the ability to shift EV charging in time and thus
avoid excessive peaks or achieve better balancing. Whereas the majority of
existing research works either focus on control strategies for a single EV
charger, or use a multi-step approach (e.g., a first high level aggregate
control decision step, followed by individual EV control decisions), we rather
propose a single-step solution that jointly coordinates multiple charging
points at once. In this paper, we further refine an initial proposal using
reinforcement learning (RL), specifically addressing computational challenges
that would limit its deployment in practice. More precisely, we design a new
Markov decision process (MDP) formulation of the EV charging coordination
process, exhibiting only linear space and time complexity (as opposed to the
earlier quadratic space complexity). We thus improve upon earlier
state-of-the-art, demonstrating 30% reduction of training time in our case
study using real-world EV charging session data. Yet, we do not sacrifice the
resulting performance in meeting the DR objectives: our new RL solutions still
improve the performance of charging demand coordination by 40-50% compared to a
business-as-usual policy (that charges EV fully upon arrival) and 20-30%
compared to a heuristic policy (that uniformly spreads individual EV charging
over time).


title:
Generalization in Automated Process Discovery: A Framework based on
  Event Log Patterns

abstract:
  The importance of quality measures in process mining has increased. One of
the key quality aspects, generalization, is concerned with measuring the degree
of overfitting of a process model w.r.t. an event log, since the recorded
behavior is just an example of the true behavior of the underlying business
process. Existing generalization measures exhibit several shortcomings that
severely hinder their applicability in practice. For example, they assume the
event log fully fits the discovered process model, and cannot deal with large
real-life event logs and complex process models. More significantly, current
measures neglect generalizations for clear patterns that demand a certain
construct in the model. For example, a repeating sequence in an event log
should be generalized with a loop structure in the model. We address these
shortcomings by proposing a framework of measures that generalize a set of
patterns discovered from an event log with representative traces and check the
corresponding control-flow structures in the process model via their trace
alignment. We instantiate the framework with a generalization measure that uses
tandem repeats to identify repetitive patterns that are compared to the loop
structures and a concurrency oracle to identify concurrent patterns that are
compared to the parallel structures of the process model. In an extensive
qualitative and quantitative evaluation using 74 log-model pairs using against
two baseline generalization measures, we show that the proposed generalization
measure consistently ranks process models that fulfil the observed patterns
with generalizing control-flow structures higher than those which do not, while
the baseline measures disregard those patterns. Further, we show that our
measure can be efficiently computed for datasets two orders of magnitude larger
than the largest dataset the baseline generalization measures can handle.


title:
Remix-cycle-consistent Learning on Adversarially Learned Separator for
  Accurate and Stable Unsupervised Speech Separation

abstract:
  A new learning algorithm for speech separation networks is designed to
explicitly reduce residual noise and artifacts in the separated signal in an
unsupervised manner. Generative adversarial networks are known to be effective
in constructing separation networks when the ground truth for the observed
signal is inaccessible. Still, weak objectives aimed at
distribution-to-distribution mapping make the learning unstable and limit their
performance. This study introduces the remix-cycle-consistency loss as a more
appropriate objective function and uses it to fine-tune adversarially learned
source separation models. The remix-cycle-consistency loss is defined as the
difference between the mixed speech observed at microphones and the
pseudo-mixed speech obtained by alternating the process of separating the mixed
sound and remixing its outputs with another combination. The minimization of
this loss leads to an explicit reduction in the distortions in the output of
the separation network. Experimental comparisons with multichannel speech
separation demonstrated that the proposed method achieved high separation
accuracy and learning stability comparable to supervised learning.


title: Fermion picture for cellular automata

abstract:
  Many cellular automata admit an interpretation in terms of fermionic
particles. Reversible automata on space-lattices with a local updating rule can
be described by a partition function or Grassmann functional integral for
interacting fermions moving in this space. We discuss large classes of automata
that are equivalent to discretized fermionic quantum field theories with
various types of interactions. Two-dimensional models include relativistic
Thirring or Gross-Neveu type models with abelian or non-abelian continuous
global symmetries, a particle in a potential, local gauge symmetries, and
spinor gravity with local Lorentz symmetry as well as diffeomorphism invariance
in the (naive) continuum limit.
  Probabilistic cellular automata are characterized by a probability
distribution over initial bit-configurations. They can be described by the
quantum formalism with wave functions, density matrix and non-commuting
operators associated to observables. This formalism is useful for a discussion
of concepts as vacuum states, spontaneous symmetry breaking, coarse graining
and the continuum limit for probabilistic cellular automata. The formalism of
fermionic quantum field theories opens a new view on the statistical behavior
of automata with a very large number of cells. In the other direction, some
specific many body systems or discrete quantum field theories for fermions can
be understood by solving or simulating the associated probabilistic cellular
automaton. This opens a new access to non-equilibrium quantum field theory.


title: Metropolis-Hastings Data Augmentation for Graph Neural Networks

abstract:
  Graph Neural Networks (GNNs) often suffer from weak-generalization due to
sparsely labeled data despite their promising results on various graph-based
tasks. Data augmentation is a prevalent remedy to improve the generalization
ability of models in many domains. However, due to the non-Euclidean nature of
data space and the dependencies between samples, designing effective
augmentation on graphs is challenging. In this paper, we propose a novel
framework Metropolis-Hastings Data Augmentation (MH-Aug) that draws augmented
graphs from an explicit target distribution for semi-supervised learning.
MH-Aug produces a sequence of augmented graphs from the target distribution
enables flexible control of the strength and diversity of augmentation. Since
the direct sampling from the complex target distribution is challenging, we
adopt the Metropolis-Hastings algorithm to obtain the augmented samples. We
also propose a simple and effective semi-supervised learning strategy with
generated samples from MH-Aug. Our extensive experiments demonstrate that
MH-Aug can generate a sequence of samples according to the target distribution
to significantly improve the performance of GNNs.


title:
Growth windows of epitaxial $\textrm{Nb}_x\textrm{N}$ films on c-plane
  sapphire and their structural and superconducting properties

abstract:
  NbN films are grown on c-plane sapphire substrates by molecular beam epitaxy.
The structural and superconducting properties of the film are characterized to
demonstrate that growth parameters such as substrate temperature and active
nitrogen flux effect the structural phase of films, and thereby the
superconducting critical temperature. Four phases of NbN are identified for
films grown in different conditions. In a novel finding, we demonstrate that
atomically flat and highly crystalline $\beta$-$\textrm{Nb}_2\textrm{N}$ films
can be grown at substrate temperatures of 1100 \degree C or higher, and that
the superconducting critical temperature of phase pure
$\beta$-$\textrm{Nb}_2\textrm{N}$ films is $0.35~K<T_c<0.6~K$, based on
measurements of films grown at different substrate temperatures.


title:
Self-Supervised Point Cloud Representation Learning with Occlusion
  Auto-Encoder

abstract:
  Learning representations for point clouds is an important task in 3D computer
vision, especially without manually annotated supervision. Previous methods
usually take the common aid from auto-encoders to establish the
self-supervision by reconstructing the input itself. However, the existing
self-reconstruction based auto-encoders merely focus on the global shapes, and
ignore the hierarchical context between the local and global geometries, which
is a crucial supervision for 3D representation learning. To resolve this issue,
we present a novel self-supervised point cloud representation learning
framework, named 3D Occlusion Auto-Encoder (3D-OAE). Our key idea is to
randomly occlude some local patches of the input point cloud and establish the
supervision via recovering the occluded patches using the remaining visible
ones. Specifically, we design an encoder for learning the features of visible
local patches, and a decoder for leveraging these features to predict the
occluded patches. In contrast with previous methods, our 3D-OAE can remove a
large proportion of patches and predict them only with a small number of
visible patches, which enable us to significantly accelerate training and yield
a nontrivial self-supervisory performance. The trained encoder can be further
transferred to various downstream tasks. We demonstrate our superior
performances over the state-of-the-art methods in different discriminant and
generative applications under widely used benchmarks.


title: Near-Infrared Depth-Independent Image Dehazing using Haar Wavelets

abstract:
  We propose a fusion algorithm for haze removal that combines color
information from an RGB image and edge information extracted from its
corresponding NIR image using Haar wavelets. The proposed algorithm is based on
the key observation that NIR edge features are more prominent in the hazy
regions of the image than the RGB edge features in those same regions. To
combine the color and edge information, we introduce a haze-weight map which
proportionately distributes the color and edge information during the fusion
process. Because NIR images are, intrinsically, nearly haze-free, our work
makes no assumptions like existing works that rely on a scattering model and
essentially designing a depth-independent method. This helps in minimizing
artifacts and gives a more realistic sense to the restored haze-free image.
Extensive experiments show that the proposed algorithm is both qualitatively
and quantitatively better on several key metrics when compared to existing
state-of-the-art methods.


title:
Getting more from the skyline operator through restricted skylines,
  regret minimizing sets and skyline ordering: a survey on their properties and
  comparison

abstract:
  Given a set of multidimensional points, the skyline operator returns a set of
potentially interesting points from such a dataset. This popular operator
filters out a set of tuples that are not dominated by other ones, reducing the
size of a possibly large initial dataset. However, with the dataset dimensions
growing, the query result of the skyline operator can have a high cardinality
that could overwhelm the final user looking at the result, yielding the very
opposite goal. Moreover, it doesn't allow any user to express their preferences
over the attributes, in order to control the dataset cardinality and to return
a more satisfying result. In this survey, we discuss and compare the more
recent approaches developed to extend the skyline operator's functionalities,
and we discuss their applicability in real-world scenarios. We show how these
frameworks operate on the dataset and how they claim to mitigate the drawbacks
coming from the skyline operator.


title: Design, Status and Physics Potential of JUNO

abstract:
  The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton
multi-purpose liquid scintillator detector currently being built in a dedicated
underground laboratory in Jiangmen (PR China). Data-taking is expected to start
in 2023. JUNO's main physics goal is the determination of the neutrino mass
ordering using electron anti-neutrinos from two nuclear power plants at a
baseline of about 53 km. JUNO aims for an unprecedented energy resolution of 3
$\%$ at 1 MeV for the central detector, which will allow determining the mass
ordering with 3 $\sigma$ significance within six years of operation.
Furthermore, measurements in neutrino physics and astrophysics, such as
estimating the solar oscillation parameters and the atmospheric mass splitting
with an accuracy of 0.5 $\%$ or better, will be performed. In these
proceedings, JUNO's design, the status of its construction, and its physics
potential, will be presented alongside a short excursion into its rich R$\&$D
program.


title: Distributed data analytics

abstract:
  Machine Learning (ML) techniques have begun to dominate data analytics
applications and services. Recommendation systems are a key component of online
service providers. The financial industry has adopted ML to harness large
volumes of data in areas such as fraud detection, risk-management, and
compliance. Deep Learning is the technology behind voice-based personal
assistants, etc. Deployment of ML technologies onto cloud computing
infrastructures has benefited numerous aspects of our daily life. The
advertising and associated online industries in particular have fuelled a rapid
rise the in deployment of personal data collection and analytics tools.
Traditionally, behavioural analytics relies on collecting vast amounts of data
in centralised cloud infrastructure before using it to train machine learning
models that allow user behaviour and preferences to be inferred. A contrasting
approach, distributed data analytics, where code and models for training and
inference are distributed to the places where data is collected, has been
boosted by two recent, ongoing developments: increased processing power and
memory capacity available in user devices at the edge of the network, such as
smartphones and home assistants; and increased sensitivity to the highly
intrusive nature of many of these devices and services and the attendant
demands for improved privacy. Indeed, the potential for increased privacy is
not the only benefit of distributing data analytics to the edges of the
network: reducing the movement of large volumes of data can also improve energy
efficiency, helping to ameliorate the ever increasing carbon footprint of our
digital infrastructure, enabling much lower latency for service interactions
than is possible when services are cloud-hosted. These approaches often
introduce challenges in privacy, utility, and efficiency trade-offs, while
having to ensure fruitful user engagement.


title:
An adaptive residual sub-sampling algorithm for kernel interpolation
  based on maximum likelihood estimations

abstract:
  In this paper we propose an enhanced version of the residual sub-sampling
method (RSM) in [9] for adaptive interpolation by radial basis functions
(RBFs). More precisely, we introduce in the context of sub-sampling methods a
maximum profile likelihood estimation (MPLE) criterion for the optimal
selection of the RBF shape parameter. This choice is completely automatic,
provides highly reliable and accurate results for any RBFs, and, unlike the
original RSM, guarantees that the RBF interpolant exists uniquely. The efficacy
of this new method, called MPLE-RSM, is tested by numerical experiments on some
1D and 2D benchmark target functions.


title:
RadonPy: Automated Physical Property Calculation using All-atom
  Classical Molecular Dynamics Simulations for Polymer Informatics

abstract:
  The rapid growth of data-driven materials research has made it necessary to
develop systematically designed, open databases of material properties.
However, there are few open databases for polymeric materials compared to other
material systems such as inorganic crystals. To this end, we developed RadonPy,
the world-first open-source Python library for fully automated all-atom
classical molecular dynamics (MD) simulations. For a given polymer repeating
unit, the entire process of molecular modeling, equilibrium and nonequilibrium
MD calculations, and property calculations can be conducted fully
automatically. In this study, 15 different properties, including the thermal
conductivity, density, specific heat capacity, thermal expansion coefficients,
and refractive index, were calculated for more than 1,000 unique amorphous
polymers. The calculated properties were compared and validated systematically
with experimental values from PoLyInfo. During the high-throughput data
production, eight amorphous polymers with extremely high thermal
conductivities, exceeding 0.4 W/mK, were identified, including six polymers
with unreported thermal conductivities. These polymers were found to have a
high density of hydrogen bonding units or rigid backbones. A decomposition
analysis of the heat conduction, which is implemented in RadonPy, revealed the
underlying mechanisms that yield a high thermal conductivity of the amorphous
polymers: heat transfer via hydrogen bonds and dipole-dipole interactions
between the polymer chains with their hydrogen bonding units or via the
covalent bonds of the polymer backbone with high rigidity. The creation of
massive amounts of computational property data using RadonPy will facilitate
the development of polymer informatics, similar to how the emergence of the
first-principles computational database for inorganic crystals had
significantly advanced materials informatics.


title: Adaptive LOOCV-based kernel methods for solving time-dependent BVPs

abstract:
  In this paper we propose an adaptive scheme for the solution of
time-dependent boundary value problems (BVPs). To solve numerically these
problems, we consider the kernel-based method of lines that allows us to split
the spatial and time derivatives, dealing with each separately. This adaptive
algorithm is based on a leave-one-out cross validation (LOOCV) technique, which
is employed as an error indicator. By this scheme, we can first detect the
domain areas where the error is estimated to be too large -- generally due to
steep variations or quick changes in the solution -- and then accordingly
enhance the numerical solution by applying a two-point refinement strategy.
Numerical experiments show the efficacy and performance of our adaptive
refinement method.


title:
Towards Visual Affordance Learning: A Benchmark for Affordance
  Segmentation and Recognition

abstract:
  The physical and textural attributes of objects have been widely studied for
recognition, detection and segmentation tasks in computer vision. A number of
datasets, such as large scale ImageNet, have been proposed for feature learning
using data hungry deep neural networks and for hand-crafted feature extraction.
To intelligently interact with objects, robots and intelligent machines need
the ability to infer beyond the traditional physical/textural attributes, and
understand/learn visual cues, called visual affordances, for affordance
recognition, detection and segmentation. To date there is no publicly available
large dataset for visual affordance understanding and learning. In this paper,
we introduce a large scale multi-view RGBD visual affordance learning dataset,
a benchmark of 47210 RGBD images from 37 object categories, annotated with 15
visual affordance categories and 35 cluttered/complex scenes with different
objects and multiple affordances. To the best of our knowledge, this is the
first ever and the largest multi-view RGBD visual affordance learning dataset.
We benchmark the proposed dataset for affordance recognition and segmentation.
To achieve this we propose an Affordance Recognition Network a.k.a ARNet. In
addition, four state-of-the-art deep learning networks are evaluated for
affordance segmentation task. Our experimental results showcase the challenging
nature of the dataset and present definite prospects for new and robust
affordance learning algorithms. The dataset is available at:
https://sites.google.com/view/afaqshah/dataset.


title:
MQDD: Pre-training of Multimodal Question Duplicity Detection for
  Software Engineering Domain

abstract:
  This work proposes a new pipeline for leveraging data collected on the Stack
Overflow website for pre-training a multimodal model for searching duplicates
on question answering websites. Our multimodal model is trained on question
descriptions and source codes in multiple programming languages. We design two
new learning objectives to improve duplicate detection capabilities. The result
of this work is a mature, fine-tuned Multimodal Question Duplicity Detection
(MQDD) model, ready to be integrated into a Stack Overflow search system, where
it can help users find answers for already answered questions. Alongside the
MQDD model, we release two datasets related to the software engineering domain.
The first Stack Overflow Dataset (SOD) represents a massive corpus of paired
questions and answers. The second Stack Overflow Duplicity Dataset (SODD)
contains data for training duplicate detection models.


title:
SlimFL: Federated Learning with Superposition Coding over Slimmable
  Neural Networks

abstract:
  Federated learning (FL) is a key enabler for efficient communication and
computing leveraging devices' distributed computing capabilities. However,
applying FL in practice is challenging due to the local devices' heterogeneous
energy, wireless channel conditions, and non-independently and identically
distributed (non-IID) data distributions. To cope with these issues, this paper
proposes a novel learning framework by integrating FL and width-adjustable
slimmable neural networks (SNN). Integrating FL with SNNs is challenging due to
time-varing channel conditions and data distributions. In addition, existing
multi-width SNN training algorithms are sensitive to the data distributions
across devices, which makes SNN ill-suited for FL. Motivated by this, we
propose a communication and energy-efficient SNN-based FL (named SlimFL) that
jointly utilizes superposition coding (SC) for global model aggregation and
superposition training (ST) for updating local models. By applying SC, SlimFL
exchanges the superposition of multiple width configurations decoded as many
times as possible for a given communication throughput. Leveraging ST, SlimFL
aligns the forward propagation of different width configurations while avoiding
inter-width interference during backpropagation. We formally prove the
convergence of SlimFL. The result reveals that SlimFL is not only
communication-efficient but also deals with the non-IID data distributions and
poor channel conditions, which is also corroborated by data-intensive
simulations.


title:
Investigation of the stability and charge states of vacancy in clusters
  Si$_{29}$ and Si$_{38}$

abstract:
  Stability and charge states of vacancy in Si$_{29}$ and Si$_{38}$ clusters
have been calculated by non-conventional tight-binding method and molecular
dynamics. Based on the theoretical calculations, it was shown that the vacancy
in pure dimerized clusters is unstable, while in hydrogenated Si$_{29}$H$_{24}$
and Si$_{38}$H$_{30}$ clusters it is stable, but leads to a distortion of its
central part with the transition of symmetry from Td to $C_{3v}$ and a change
in the forbidden gap. The charges of cluster atoms in the presence of a vacancy
are distributed so that all silicon atoms acquire a stable negative charge,
which occurs due to the outflow of electrons of the central atom to the
neighboring spheres.


title: Fourier coefficients of Hilbert modular forms at cusps

abstract:
  The aim of this article is to study the fields generated by the Fourier
coefficients of Hilbert newforms at arbitrary cusps. Precisely, given a
cuspidal Hilbert newform $f$ and a matrix $\sigma$ in (a suitable conjugate of)
the Hilbert modular group, we give a cyclotomic extension of the field
generated by the Fourier coefficients at infinity which contains all the
Fourier coefficients of $f||_k\sigma$.


title:
Numerical Investigation of Dynamical and Morphological Trends in
  Relativistic Jets

abstract:
  Active galactic nuclei (AGN) show a range of morphologies and dynamical
properties, which are determined not only by parameters intrinsic to the
central engine but also their interaction with the surrounding environment. We
investigate the connection of kiloparsec scale AGN jet properties to their
intrinsic parameters and surroundings. This is done using a suite of 40
relativistic hydrodynamic simulations spanning a wide range of engine
luminosities and opening angles. We explore AGN jet propagation with different
ambient density profiles, including $r^{-2}$ (self-similar solution) and
$r^{-1}$, which is more relevant for AGN host environments. The Fanaroff-Riley
(FR) morphological dichotomy arises naturally in our models. Jets with low
energy density compared to the ambient medium produce a center-brightened
emissivity distribution, while emissivity from relatively higher energy density
jets is dominated by a terminal bright spot. We observe recollimation shocks in
our simulations that can generate bright spots along the spine of the jet,
providing a possible explanation for "knots" observed in AGN jets. We
additionally find a scaling relation between the number of knots and the
jet-head-to-surroundings energy density ratio. This scaling relation is
generally consistent with the observations of the jets in M87 and Cygnus A. Our
model also correctly predicts M87 as FR I and Cygnus A as FR II. Our model can
be used to relate jet dynamical parameters such as jet head velocity, jet
opening angle, and external pressure to jet power and ambient density
estimates.


title:
Uncertainty-aware Contrastive Distillation for Incremental Semantic
  Segmentation

abstract:
  A fundamental and challenging problem in deep learning is catastrophic
forgetting, i.e. the tendency of neural networks to fail to preserve the
knowledge acquired from old tasks when learning new tasks. This problem has
been widely investigated in the research community and several Incremental
Learning (IL) approaches have been proposed in the past years. While earlier
works in computer vision have mostly focused on image classification and object
detection, more recently some IL approaches for semantic segmentation have been
introduced. These previous works showed that, despite its simplicity, knowledge
distillation can be effectively employed to alleviate catastrophic forgetting.
In this paper, we follow this research direction and, inspired by recent
literature on contrastive learning, we propose a novel distillation framework,
Uncertainty-aware Contrastive Distillation (\method). In a nutshell, \method~is
operated by introducing a novel distillation loss that takes into account all
the images in a mini-batch, enforcing similarity between features associated to
all the pixels from the same classes, and pulling apart those corresponding to
pixels from different classes. In order to mitigate catastrophic forgetting, we
contrast features of the new model with features extracted by a frozen model
learned at the previous incremental step. Our experimental results demonstrate
the advantage of the proposed distillation technique, which can be used in
synergy with previous IL approaches, and leads to state-of-art performance on
three commonly adopted benchmarks for incremental semantic segmentation. The
code is available at \url{https://github.com/ygjwd12345/UCD}.


title: Competition-Based Resilience in Distributed Quadratic Optimization

abstract:
  This paper proposes a novel approach to resilient distributed optimization
with quadratic costs in a networked control system (e.g., wireless sensor
network, power grid, robotic team) prone to external attacks (e.g., hacking,
power outage) that cause agents to misbehave. Departing from classical
filtering strategies proposed in literature, we draw inspiration from a
game-theoretic formulation of the consensus problem and argue that adding
competition to the mix can enhance resilience in the presence of malicious
agents. Our intuition is corroborated by analytical and numerical results
showing that i) our strategy highlights the presence of a nontrivial tradeoff
between blind collaboration and full competition, and ii) such
competition-based approach can outperform state-of-the-art algorithms based on
Mean Subsequence Reduced.


title:
Anomalous phase diagram of the elastic interface with non-local
  hydrodynamic interactions in the presence of quench disorder

abstract:
  We investigate the influence of quenched disorder on the steady states of
driven systems of the elastic interface with non-local hydrodynamic
interactions. The generalized elastic model (GEM), which has been used to
characterize numerous physical systems such as polymers, membranes, single-file
systems, rough interfaces, and fluctuating surfaces, is a standard approach to
studying the dynamics of elastic interfaces with non-local hydrodynamic
interactions. The criticality and phase transition of the quenched generalized
elastic model (qGEM) are investigated numerically, and the results are
presented in a phase diagram spanned by two tuning parameters. We demonstrate
that in 1-d disordered driven GEM, three qualitatively different behavior
regimes are possible with a proper specification of the order parameter (mean
velocity) for this system. In the vanishing order parameter regime, the
steady-state order parameter approaches zero in the thermodynamic limit. A
system with a non-zero mean velocity can be in either the continuous regime,
which is characterized by a second-order phase transition, or the discontinuous
regime, which is characterized by a first-order phase transition. The focus of
this research was to investigate at the critical scaling features near the
pinning-depinning threshold. The behavior of the quenched generalized elastic
model at the critical depinning force is explored. Near the depinning
threshold, the critical exponent obtained numerically.


title: A Roadmap for Big Model

abstract:
  With the rapid development of deep learning, training Big Models (BMs) for
multiple downstream tasks becomes a popular paradigm. Researchers have achieved
various outcomes in the construction of BMs and the BM application in many
fields. At present, there is a lack of research work that sorts out the overall
progress of BMs and guides the follow-up research. In this paper, we cover not
only the BM technologies themselves but also the prerequisites for BM training
and applications with BMs, dividing the BM review into four parts: Resource,
Models, Key Technologies and Application. We introduce 16 specific BM-related
topics in those four parts, they are Data, Knowledge, Computing System,
Parallel Training System, Language Model, Vision Model, Multi-modal Model,
Theory&Interpretability, Commonsense Reasoning, Reliability&Security,
Governance, Evaluation, Machine Translation, Text Generation, Dialogue and
Protein Research. In each topic, we summarize clearly the current studies and
propose some future research directions. At the end of this paper, we conclude
the further development of BMs in a more general view.


title: Influential Observations in Bayesian Regression Tree Models

abstract:
  BCART (Bayesian Classification and Regression Trees) and BART (Bayesian
Additive Regression Trees) are popular Bayesian regression models widely
applicable in modern regression problems. Their popularity is intimately tied
to the ability to flexibly model complex responses depending on
high-dimensional inputs while simultaneously being able to quantify
uncertainties. This ability to quantify uncertainties is key, as it allows
researchers to perform appropriate inferential analyses in settings that have
generally been too difficult to handle using the Bayesian approach. However,
surprisingly little work has been done to evaluate the sensitivity of these
modern regression models to violations of modeling assumptions. In particular,
we will consider influential observations, which one reasonably would imagine
to be common -- or at least a concern -- in the big-data setting. In this
paper, we consider both the problem of detecting influential observations and
adjusting predictions to not be unduly affected by such potentially problematic
data. We consider two detection diagnostics for Bayesian tree models, one an
analogue of Cook's distance and the other taking the form of a divergence
measure, and then propose an importance sampling algorithm to re-weight
previously sampled posterior draws so as to remove the effects of influential
data in a computationally efficient manner. Finally, our methods are
demonstrated on real-world data where blind application of the models can lead
to poor predictions and inference.


title: Lite Unified Modeling for Discriminative Reading Comprehension

abstract:
  As a broad and major category in machine reading comprehension (MRC), the
generalized goal of discriminative MRC is answer prediction from the given
materials. However, the focuses of various discriminative MRC tasks may be
diverse enough: multi-choice MRC requires model to highlight and integrate all
potential critical evidence globally; while extractive MRC focuses on higher
local boundary preciseness for answer extraction. Among previous works, there
lacks a unified design with pertinence for the overall discriminative MRC
tasks. To fill in above gap, we propose a lightweight POS-Enhanced Iterative
Co-Attention Network (POI-Net) as the first attempt of unified modeling with
pertinence, to handle diverse discriminative MRC tasks synchronously. Nearly
without introducing more parameters, our lite unified design brings model
significant improvement with both encoder and decoder components. The
evaluation results on four discriminative MRC benchmarks consistently indicate
the general effectiveness and applicability of our model, and the code is
available at https://github.com/Yilin1111/poi-net.


title:
Bridge-Prompt: Towards Ordinal Action Understanding in Instructional
  Videos

abstract:
  Action recognition models have shown a promising capability to classify human
actions in short video clips. In a real scenario, multiple correlated human
actions commonly occur in particular orders, forming semantically meaningful
human activities. Conventional action recognition approaches focus on analyzing
single actions. However, they fail to fully reason about the contextual
relations between adjacent actions, which provide potential temporal logic for
understanding long videos. In this paper, we propose a prompt-based framework,
Bridge-Prompt (Br-Prompt), to model the semantics across adjacent actions, so
that it simultaneously exploits both out-of-context and contextual information
from a series of ordinal actions in instructional videos. More specifically, we
reformulate the individual action labels as integrated text prompts for
supervision, which bridge the gap between individual action semantics. The
generated text prompts are paired with corresponding video clips, and together
co-train the text encoder and the video encoder via a contrastive approach. The
learned vision encoder has a stronger capability for ordinal-action-related
downstream tasks, e.g. action segmentation and human activity recognition. We
evaluate the performances of our approach on several video datasets: Georgia
Tech Egocentric Activities (GTEA), 50Salads, and the Breakfast dataset.
Br-Prompt achieves state-of-the-art on multiple benchmarks. Code is available
at https://github.com/ttlmh/Bridge-Prompt


title: Homological methods in certain Picard group computations

abstract:
  Let $G$ be a connected complex semisimple Lie group, $\Gamma$ be a cocompact,
irreducible and torsionless lattice in $G$ and $K$ be a maximal compact
subgroup of $G$. Assume $\Gamma$ acts by left multiplication and $K$ acts by
right multiplication on $G$. Let $M_{\Gamma}= \Gamma\backslash G$, $X=G/K$ and
$X_{\Gamma}=\Gamma\backslash X$. In this article we prove that for any
$n\geq0$, the composition $H^{n}(X_{\Gamma},\mathbb{C})\rightarrow
H^{n}(M_{\Gamma},\mathbb{C})\rightarrow
H^{n}(M_{\Gamma},\mathcal{O}_{M_{\Gamma}})$ is an isomorphism. As an
application when $G$ is simply connected, we compute the Picard group of
$M_{\Gamma}$ for the cases rank($G$) $=1,2$. More precisely we show that if
rank($G$) $=1$, $Pic(M_{\Gamma})=(\mathbb{C}^{r}/\mathbb{Z}^{r})\oplus A$ and
if rank($G$) $=2$, then $Pic(M_{\Gamma})\cong A$ via the first Chern class map,
where $A$ is the torsion subgroup of $H^{2}(M_{\Gamma},\mathbb{Z})$ and $r$ is
the rank of $\Gamma/[\Gamma,\Gamma]$.


title:
Equilibrium properties of the lattice fluid with the repulsion between
  the nearest neighbors on the two-level lattice with nonrectangular geometry

abstract:
  The equilibrium properties of the lattice fluid with the repulsion between
the nearest neighbors on the two-level planar triangular lattice are
investigated. The numerical results obtained from the analytical expressions
are compared with the Monte Carlo simulation data. It is shown that the
previously proposed diagrammatic approximation makes it possible to determine
the equilibrium characteristics of the lattice fluid with the repulsion between
the nearest neighbors on a two-level lattice with an accuracy comparable to the
accuracy of modelling the system using the Monte Carlo method in the entire
range of thermodynamic parameters. It was found that, in contrast to a similar
one-level system, a lattice fluid with the repulsion between the nearest
neighbors undergoes a first-order phase transition.


title:
Non-extensive thermodynamics of the radiation in heterogeneous thermal
  plasmas

abstract:
  Thermodynamic characteristics of the radiation of condensed combustion
products presented in the form of agglomerates of metal-oxide nanoparticles
demonstrate deviations from the classical Planck's law. We propose to interpret
these deviations in terms of the non-additive entropy of the photon system
interacting with the heterogeneous combustion products, which makes it possible
to use the non-extensive Tsallis thermodynamics for their description. It is
assumed that the non-additive character of the radiation entropy in
heterogeneous plasma can be explained by the influence of long-range
interactions and non-equilibrium physicochemical processes. An expression is
obtained for the energy-dependent distribution of the photon density, based on
the phenomenological parameter of non-extensiveness $q$ which, in the first
approximation, does not depend on the energy. In this case, the "non-extensive"
Planck's law can be reduced to the "usual" Planck distribution by introducing
the "effective temperature" that exceeds the real temperature. Numerical
modelling has shown that the spectral density of photons, the position and
magnitude of its maximum depend on the value of the parameter $q$, which can be
used for its experimental determination and revelation of its physical nature
and origin.


title:
Electronic structure of short-period ZnSe/ZnTe superlattices based on
  DFT calculations

abstract:
  In the present study we discuss the effect of variation in the number of
monolayers $n$ on the electronic and optical properties of superlattices (SLs)
(ZnSe)$_n$/(ZnTe)$_n$. The total energies were calculated by the full-potential
linear muffin-tin orbital (FP-LMTO) method, and the exchange-correlation energy
was applied in the local density approximation (LDA). First, the calculations
show a decrease in the derivative of bulk modulus and electronic bandgap with
an increase in the number of monolayers $n$. Second, the radiation energies up
to $15$ eV, the dielectric function $\varepsilon(\omega$), the refractive index
$n(\omega)$, and the reflectivity $R(\omega)$ are studied. These calculations
may be beneficial to understand the properties of short-period superlattices
(ZnSe)$_n$/(ZnTe)$_n$.


title: Human-centred home network security

abstract:
  This chapter draws from across the foregoing chapters discussing many core
HDI approaches and disciplinary perspectives to consider the specific
application of HDI in home network security. While much work has considered the
challenges of securing in home IoT devices and their communications, especially
for those with limited power or computational capacity, scant attention has
been paid by the research community to home network security, and its
acceptability and usability, from the viewpoint of ordinary citizens. It will
be clear that we need a radical transformation in our approach to designing
domestic networking infrastructure to guard against widespread cyber-attacks
that threaten to counter the benefits of the IoT. Our aim has to be to defend
against enemies inside the walls, to protect critical functionality in the home
against rogue devices and prevent the proliferation of disruptive wide-scale
IoT DDOS attacks that are already occurring [1].


title:
Correct-By-Construction Design of Adaptive Cruise Control with Control
  Barrier Functions Under Safety and Regulatory Constraints

abstract:
  The safety-critical nature of adaptive cruise control (ACC) systems calls for
systematic design procedures, e.g., based on formal methods or control barrier
functions (CBFs), to provide strong guarantees of safety and performance under
all driving conditions. However, existing approaches have mostly focused on
fully verified solutions under smooth traffic conditions, with the exception of
stop-and-go scenarios. Systematic methods for high-performance ACC design under
safety and regulatory constraints like traffic signals are still elusive. A
challenge for correct-by-construction approaches based on CBFs stems from the
need to capture the constraints imposed by traffic signals, which lead to
candidate time-varying CBFs (TV-CBFs) with finite jump discontinuities in
bounded time intervals.


title: Figure-of-merit for Semi-transparent Solar Cells

abstract:
  Semi-transparent Solar Cells (ST-SCs) has emerged as one of the most
prominent energy harvesting technology that combines the benefits of light
transparency and light-to-electricity conversion. The biggest opportunities for
such technologies lie in their integration as windows and skylights within
energy-sustainable buildings or combining them with other solar cell
technologies in tandem configuration. The performance of ST-SCs is mainly
determined by the trade-off between the competing parameters of the capability
to convert the incident light into electricity while allowing some parts to
transmit providing transparency through the device. Depending on the target
application, the selection of ST-SCs is a tricky affair as some devices might
offer high efficiency but compromises transparency and vice-versa. On the other
way around, this is again not helped by the fact that due to advancements in
materials engineering, processing, and characterization, a vastly different
combination of efficiency and transparency has been reported by research
groups. So, in order to quantify the performance of ST-SCs, we proposed, a
figure-of-merit (FoM) which can be used as a tool that can help in analysing
and comparing the performance among various ST-SCs. The defined FoM focuses on
the power conversion efficiency of the device, bifaciality factor,
transmittance in the desired region, and that corresponding to 550 nm
wavelength. Additionally, in this work, we have been shown how the proposed FoM
can be correlated for tandem and building-integrated photovoltaics
applications. Based on these resultant parameters, FoM is calculated and
compared for different device architectures available in the literature. The
proposed FoM shall serve as a meaningful guiding path to the researchers for
the development of advanced ST-SCs.


title:
Flow rate-pressure drop relation for deformable channels via fluidic and
  elastic reciprocal theorems

abstract:
  Viscous flows through configurations manufactured from soft materials apply
both pressure and shear stress at the solid-liquid interface, leading to
deformation of the cross-section, which affects the flow rate-pressure drop
relation. Conventionally, calculating this flow rate-pressure drop relation
requires solving the complete elastohydrodynamic problem, which couples the
fluid flow and elastic deformation. In this work, we use the reciprocal
theorems for Stokes flow and linear elasticity to derive a closed-form
expression for the flow rate-pressure drop relation in deformable channels,
bypassing the detailed calculation of the solution to the
fluid-structure-interaction problem. For small deformations (under a domain
perturbation scheme), our theory provides the leading-order effect, of the
interplay between the fluid stresses and the compliance of the channel, on the
flow rate-pressure drop relation. Our approach uses solely the fluid flow
solution and the elastic deformation due to the fluid stress distribution in an
undeformed channel, eliminating the need to solve the coupled
elastohydrodynamic problem. Unlike previous theoretical studies that neglected
the presence of lateral sidewalls and considered shallow geometries of
effectively infinite width, our approach allows to determine the influence of
confining sidewalls on the flow rate-pressure drop relation. For the
flow-rate-controlled situation and the plate-bending theory for the elastic
deformation, we show a trade-off between the effect of compliance of the
deforming top wall and the drag due to sidewalls on the pressure drop. While
increased compliance decreases the pressure drop, the effect of the sidewalls
increases it. Our theoretical framework may provide insight into existing
experimental data and pave the way for the design of novel optimized soft
microfluidic configurations of different cross-sectional shapes.


title:
Probabilistic Registration for Gaussian Process 3D shape modelling in
  the presence of extensive missing data

abstract:
  Gaussian Processes are a powerful tool for shape modelling. While the
existing methods on this area prove to work well for the general case of the
human head, when looking at more detailed and deformed data, with a high
prevalence of missing data, such as the ears, the results are not satisfactory.
In order to overcome this, we formulate the shape fitting problem as a
multi-annotator Gaussian Process Regression and establish a parallel with the
standard probabilistic registration. The achieved method GPReg shows better
performance when dealing with extensive areas of missing data when compared to
a state-of-the-art registration method and the current approach for
registration with GP.


title:
Data-driven Stabilization of Discrete-time Control-affine Nonlinear
  Systems: A Koopman Operator Approach

abstract:
  In recent years data-driven analysis of dynamical systems has attracted a lot
of attention and transfer operator techniques, namely, Perron-Frobenius and
Koopman operators are being used almost ubiquitously. Since data is always
obtained in discrete-time, in this paper, we propose a purely data-driven
approach for the design of a stabilizing feedback control law for a general
class of discrete-time control-affine non-linear systems. In particular, we use
the Koopman operator to lift a control-affine system to a higher-dimensional
space, where the control system's evolution is bilinear. We analyze the
controllability of the lifted bilinear system and relate it to the
controllability of the underlying non-linear system. We then leverage the
concept of Control Lyapunov Function (CLF) to design a state feedback law that
stabilizes the origin. Furthermore, we demonstrate the efficacy of the proposed
method to stabilize the origin of the Van der Pol oscillator and the chaotic
Henon map from the time-series data.


title: Recollections of Steven Weinberg

abstract:
  Brief recollections of how the author interacted with Steve Weinberg over
issues in quantum mevhanics


title: Photon cooling: linear vs nonlinear interactions

abstract:
  Linear optics imposes a relation that is more general than the second law of
thermodynamics: For modes undergoing a linear evolution, the full mean
occupation number (i.e. photon number for optical modes) does not decrease,
provided that the evolution starts from a diagonal state. This relation
connects to noise-increasing (or heating), and is akin to the second law,
though for the linear evolution it holds for a wider set of initial states. We
show that this general trend can be reversed via nonlinear interactions between
the modes. They can cool -- i.e. decrease the full mean occupation number and
the related noise -- an equilibrium system of modes provided that their
frequencies are different. Such an effect cannot exist in energy cooling, where
only a part of an equilibrium system is cooled. We describe the cooling set-up
via both efficiency and coefficient of performance, and relate the cooling
effect to the Manley-Rowe theorem in nonlinear optics.


title:
Power Allocation for IRS-aided Two-way Decode-and-Forward Relay Wireless
  Network

abstract:
  In this paper, an intelligent reflecting surface (IRS)-aided two-way
decode-and-forward (DF) relay wireless network is considered, where two users
exchange information via IRS and DF relay. To enhance the sum rate performance,
three power allocation (PA) strategies are proposed. Firstly, a method of
maximizing sum rate (Max-SR) is proposed to jointly optimize the PA factors of
user U1, user U2 and relay station (RS). To further improve the sum rate
performance, two high-performance schemes, namely maximizing minimum sum rate
(Max-Min-SR) and maximizing sum rate with rate constraint (Max-SR-RC), are
presented. Simulation results show that the proposed three methods outperform
the equal power allocation (EPA) method in terms of sum rate performance. In
particular, the highest performance gain achieved by Max-SR-RC method is up to
45.2% over EPA. Furthermore, it is verified that the total power and random
shadow variable X{\sigma} have a substantial impact on the sum rate
performance.


abstract:
  Digital electronics is a technological cornerstone in our modern society
which has covered the increasing demand in computing power during the last
decades thanks to a periodic doubling of transistor density and power
efficiency in integrated circuits. Currently, such scaling laws are reaching
their fundamental limits, leading to the emergence of a large gamut of
applications that cannot be supported by digital electronics, specifically,
those that involve real time analog multi-data processing, e.g., medical
diagnostic imaging, robotic control and remote sensing, among others. In this
scenario, an analog computing approach implemented in a real-time
reconfigurable non-electronic hardware such as programmable integrated
photonics (PIP) can be more efficient than digital electronics to perform these
emerging applications. However, actual analog computing models such as quantum
and neuromorphic computation were not conceived to extract the benefits of PIP
technology (and integrated photonics in general). Here, we present the
foundations of a new computation theory, termed Analog Programmable-Photonic
Computation (APC), explicitly designed to unleash the full potential of PIP.
Interestingly, APC enables overcoming basic theoretical and technological
limitations of existing computational models, can be implemented in other
technologies (e.g. in electronics, acoustics or using metamaterials) and,
consequently, exhibits the potential to spark a ground-breaking impact on our
information society.


title:
The ALMA-PILS survey: First tentative detection of 3-hydroxypropenal
  (HOCHCHCHO) in the interstellar medium and chemical modeling of the
  C$_3$H$_4$O$_2$ isomers

abstract:
  Characterizing the molecular composition of solar-type protostars is useful
for improving our understanding of the physico-chemical conditions under which
the Sun and its planets formed. In this work, we analyzed the Atacama Large
Millimeter/submillimeter Array (ALMA) data of the Protostellar Interferometric
Line Survey (PILS), an unbiased spectral survey of the solar-type protostar
IRAS~16293--2422, and we tentatively detected 3-hydroxypropenal (HOCHCHCHO) for
the first time in the interstellar medium towards source B. Based on the
observed line intensities and assuming local thermodynamic equilibrium, its
column density is constrained to be $\sim$10$^{15}$ cm$^{-2}$, corresponding to
an abundance of 10$^{-4}$ relative to methanol, CH$_3$OH. Additional
spectroscopic studies are needed to constrain the excitation temperature of
this molecule. We included HOCHCHCHO and five of its isomers in the chemical
network presented in Manigand et al. (2021) and we predicted their chemical
evolution with the Nautilus code. The model reproduces the abundance of
HOCHCHCHO within the uncertainties. This species is mainly formed through the
grain surface reaction CH$_2$CHO + HCO $\rightarrow$ HCOCH$_2$CHO, followed by
the tautomerization of HCOCH$_2$CHO into HOCHCHCHO. Two isomers, CH$_3$COCHO
and CH$_2$COHCHO, are predicted to be even more abundant than HOCHCHCHO.
Spectroscopic studies of these molecules are essential in searching for them in
IRAS~16293--2422 and other astrophysical sources.


title: Constructive controllability for incompressible vector fields

abstract:
  We give a constructive proof of a global controllability result for an
autonomous system of ODEs guided by bounded locally Lipschitz and divergence
free (i.e.\ incompressible) vector field, when the phase space is the whole
Euclidean space and the vector field satisfies so-called vanishing mean drift
condition. For the case when the ODE is defined over some smooth compact
connected Riemannian manifold, we significantly strengthen the assertion of the
known controllability theorem in absence of nonholonomic constraints by proving
that one can find a control steering the state vector from one given point to
another by using the observations of only the state vector, i.e., in other
words, by changing slightly the vector field, and such a change can be made
small not only in uniform, but also in Lipschitz (i.e. $C^1$) topology.


title:
Dynamical system analysis for accelerating models in non-metricity
  $f(Q)$ gravity

abstract:
  Two accelerating cosmological models are presented in symmetric teleparallel
$f(Q)$ gravity, $Q$ be the non-metricity. The models are constructed based on
the assumptions of two different functional forms of $f(Q)$ and a dynamically
changing nature of the deceleration parameter that shows transition at
$t=2n\pm\sqrt{\frac{4n^2+1}{3}}$, $n$ being a positive constant. In both the
models, the equation of state parameter for the dark energy in $f(Q)$ gravity
becomes a dynamical quantity and crosses the phantom divide line. The violation
of the strong energy condition and the null energy condition at late times are
also established. In addition, the dynamical system analysis has been performed
and three critical points in each model are identified. In each model, at least
one stable node has been observed. To strengthen further, the stability
analysis using homogeneous linear perturbations has been performed to ensure
the stability of the models.


title: A Runtime Environment for Contract Automata

abstract:
  Realising contract-based applications from formal specifications with formal
guarantees requires to show the adherence of a specification, the contract, to
its implementation. Contract automata have been introduced for specifying
contract-based applications and synthesising their orchestrations as
finite-state automata. This paper introduces CARE, a newly developed library
for implementing applications specified via contract automata, providing a
runtime environment to coordinate services implementing contracts.


title:
Resilient Constrained Consensus over Complete Graphs via Feasibility
  Redundancy

abstract:
  This paper considers a resilient high-dimensional constrained consensus
problem and studies a resilient distributed algorithm for complete graphs. For
convex constrained sets with a singleton intersection, a sufficient condition
on feasibility redundancy and set regularity for reaching a desired consensus
exponentially fast in the presence of Byzantine agents is derived, which can be
directly applied to polyhedral sets. A necessary condition on feasibility
redundancy for the resilient constrained consensus problem to be solvable is
also provided.


title: Feature Selective Transformer for Semantic Image Segmentation

abstract:
  Recently, it has attracted more and more attentions to fuse multi-scale
features for semantic image segmentation. Various works were proposed to employ
progressive local or global fusion, but the feature fusions are not rich enough
for modeling multi-scale context features. In this work, we focus on fusing
multi-scale features from Transformer-based backbones for semantic
segmentation, and propose a Feature Selective Transformer (FeSeFormer), which
aggregates features from all scales (or levels) for each query feature.
Specifically, we first propose a Scale-level Feature Selection (SFS) module,
which can choose an informative subset from the whole multi-scale feature set
for each scale, where those features that are important for the current scale
(or level) are selected and the redundant are discarded. Furthermore, we
propose a Full-scale Feature Fusion (FFF) module, which can adaptively fuse
features of all scales for queries. Based on the proposed SFS and FFF modules,
we develop a Feature Selective Transformer (FeSeFormer), and evaluate our
FeSeFormer on four challenging semantic segmentation benchmarks, including
PASCAL Context, ADE20K, COCO-Stuff 10K, and Cityscapes, outperforming the
state-of-the-art.


title:
Tunable magneto-optical properties in MoS$_2$ via defect-induced exciton
  transitions

abstract:
  The presence of chalcogen vacancies in monolayer transition metal
dichalcogenides (TMDs) leads to excitons with mixed localized-delocalized
character and to reduced valley selectivity. Recent experimental advances in
defect design in TMDs allow for a close examination of such mixed exciton
states as a function of their degree of circular polarization under external
magnetic fields, revealing strongly varying defect-induced magnetic properties.
A theoretical understanding of these observations and their physical origins
demands a predictive, structure-sensitive theory. In this work, we study the
effect of chalcogen vacancies on the exciton magnetic properties in monolayer
MoS$_2$. Using many-body perturbation theory, we show how the complex excitonic
picture associated with the presence of defects -- with reduced valley and spin
selectivity due to hybridized electron-hole transitions -- leads to
structurally-controllable exciton magnetic response. We find a variety of
g-factors with changing magnitudes and sign depending on the exciton energy and
character. Our findings suggest a pathway to tune the nature of the excitons --
and by that their magneto-optical properties -- through defect architecture.


title: Robust No-Regret Learning in Min-Max Stackelberg Games

abstract:
  The behavior of no-regret learning algorithms is well understood in
two-player min-max (i.e, zero-sum) games. In this paper, we investigate the
behavior of no-regret learning in min-max games with dependent strategy sets,
where the strategy of the first player constrains the behavior of the second.
Such games are best understood as sequential, i.e., min-max Stackelberg, games.
We consider two settings, one in which only the first player chooses their
actions using a no-regret algorithm while the second player best responds, and
one in which both players use no-regret algorithms. For the former case, we
show that no-regret dynamics converge to a Stackelberg equilibrium. For the
latter case, we introduce a new type of regret, which we call Lagrangian
regret, and show that if both players minimize their Lagrangian regrets, then
play converges to a Stackelberg equilibrium. We then observe that online mirror
descent (OMD) dynamics in these two settings correspond respectively to a known
nested (i.e., sequential) gradient descent-ascent (GDA) algorithm and a new
simultaneous GDA-like algorithm, thereby establishing convergence of these
algorithms to Stackelberg equilibrium. Finally, we analyze the robustness of
OMD dynamics to perturbations by investigating online min-max Stackelberg
games. We prove that OMD dynamics are robust for a large class of online
min-max games with independent strategy sets. In the dependent case, we
demonstrate the robustness of OMD dynamics experimentally by simulating them in
online Fisher markets, a canonical example of a min-max Stackelberg game with
dependent strategy sets.


title:
Measures and generalized Bratteli diagrams for dynamics of infinite
  alphabet-substitutions

abstract:
  We study substitutions on countably infinite alphabet (without
compactification) as Borel dynamical systems. We construct stationary and
non-stationary generalized Bratteli-Vershik models for a class of such
substitutions, known as left determined. In this setting of Borel dynamics,
using a stationary generalized Bratteli-Vershik model, we provide a new and
canonical construction of shift-invariant measures (both finite and infinite)
for the associated class of subshifts.


title: Automated Thermal Screening for COVID-19 using Machine Learning

abstract:
  In the last two years, millions of lives have been lost due to COVID-19.
Despite the vaccination programmes for a year, hospitalization rates and deaths
are still high due to the new variants of COVID-19. Stringent guidelines and
COVID-19 screening measures such as temperature check and mask check at all
public places are helping reduce the spread of COVID-19. Visual inspections to
ensure these screening measures can be taxing and erroneous. Automated
inspection ensures an effective and accurate screening. Traditional approaches
involve identification of faces and masks from visual camera images followed by
extraction of temperature values from thermal imaging cameras. Use of visual
imaging as a primary modality limits these applications only for good-lighting
conditions. The use of thermal imaging alone for these screening measures makes
the system invariant to illumination. However, lack of open source datasets is
an issue to develop such systems. In this paper, we discuss our work on using
machine learning over thermal video streams for face and mask detection and
subsequent temperature screening in a passive non-invasive way that enables an
effective automated COVID-19 screening method in public places. We open source
our NTIC dataset that was used for training our models and was collected at 8
different locations. Our results show that the use of thermal imaging is as
effective as visual imaging in the presence of high illumination. This
performance stays the same for thermal images even under low-lighting
conditions, whereas the performance with visual trained classifiers show more
than 50% degradation.


title:
Nash, Conley, and Computation: Impossibility and Incompleteness in Game
  Dynamics

abstract:
  Under what conditions do the behaviors of players, who play a game
repeatedly, converge to a Nash equilibrium? If one assumes that the players'
behavior is a discrete-time or continuous-time rule whereby the current mixed
strategy profile is mapped to the next, this becomes a problem in the theory of
dynamical systems. We apply this theory, and in particular the concepts of
chain recurrence, attractors, and Conley index, to prove a general
impossibility result: there exist games for which any dynamics is bound to have
starting points that do not end up at a Nash equilibrium. We also prove a
stronger result for $\epsilon$-approximate Nash equilibria: there are games
such that no game dynamics can converge (in an appropriate sense) to
$\epsilon$-Nash equilibria, and in fact the set of such games has positive
measure. Further numerical results demonstrate that this holds for any
$\epsilon$ between zero and $0.09$. Our results establish that, although the
notions of Nash equilibria (and its computation-inspired approximations) are
universally applicable in all games, they are also fundamentally incomplete as
predictors of long term behavior, regardless of the choice of dynamics.


title: Homeostasis and its energy costs studied via inverted pendulum

abstract:
  Homeostasis is an active, self-regulating process by which an unstable state
is stabilized against external perturbations. Such processes are ubiquitous in
nature, but energetic resources needed for their existence were not studied
systematically. Here we undertake such a study using the mechanical model of
inverted pendulum, where its upper (normally unstable) state is stabilized due
to a fast controlling degree of freedom and due to friction. It is shown that
the stabilization itself does not need constant dissipation of energy. There is
only a transient dissipation of energy related to the relaxation to the stable
state. The stabilization is achieved not due to a constant dissipation of
energy, but due to the energy initially stored in the controlling degree of
freedom. In particular, the stored energy is needed for ensuring stability
against multiple perturbations, a notion of stability that differs from the
usual asymptotic stability against a single perturbation.

categories: cond-mat.stat-mech physics.bio-ph physics.class-ph q-bio.OT


title: On the Galois-Gauss sums of weakly ramified characters

abstract:
  Bley, Burns and Hahn used relative algebraic $K$-theory methods to formulate
a precise conjectural link between the (second Adams-operator twisted)
Galois-Gauss sums of weakly ramified Artin characters and the square root of
the inverse different of finite, odd degree, Galois extensions of number
fields. We provide concrete new evidence for this conjecture in the setting of
extensions of odd prime-power degree by using a refined version of a well-known
result of Ullom.


title:
A comparative analysis of Graph Neural Networks and commonly used
  machine learning algorithms on fake news detection

abstract:
  Fake news on social media is increasingly regarded as one of the most
concerning issues. Low cost, simple accessibility via social platforms, and a
plethora of low-budget online news sources are some of the factors that
contribute to the spread of false news. Most of the existing fake news
detection algorithms are solely focused on the news content only but engaged
users prior posts or social activities provide a wealth of information about
their views on news and have significant ability to improve fake news
identification. Graph Neural Networks are a form of deep learning approach that
conducts prediction on graph-described data. Social media platforms are
followed graph structure in their representation, Graph Neural Network are
special types of neural networks that could be usually applied to graphs,
making it much easier to execute edge, node, and graph-level prediction.
Therefore, in this paper, we present a comparative analysis among some commonly
used machine learning algorithms and Graph Neural Networks for detecting the
spread of false news on social media platforms. In this study, we take the UPFD
dataset and implement several existing machine learning algorithms on text data
only. Besides this, we create different GNN layers for fusing graph-structured
news propagation data and the text data as the node feature in our GNN models.
GNNs provide the best solutions to the dilemma of identifying false news in our
research.


title:
Cluster-permutohedra and submanifolds of flag varieties with torus
  actions

abstract:
  In this paper we describe a relation between the notion of graphicahedron,
introduced by Araujo-Pardoa, Del R\'{\i}o-Francosa, L\'{o}pez-Dudeta,
Oliverosa, and Schulte in 2010, and toric topology of manifolds of sparse
isospectral Hermitian matrices. More precisely, we recall the notion of a
cluster-permutohedron, a certain poset defined for a simple graph $\Gamma$.
This poset is very similar to the graphicahedron of $\Gamma$, in particular,
1-skeleta of both posets are isomorphic to Cayley graphs of the symmetric
group. In general, two posets are related via Galois connection, and
cluster-permutohedron is the core of a graphicahedron. We prove that the face
poset of the natural torus action on the manifold of isospectral
$\Gamma$-shaped Hermitian matrices is isomorphic to the cluster-permutohedron.
Using recent results in toric topology, we show that certain homotopy
properties of graphicahedra may be an obstruction to equivariant formality of
isospectral matrix manifolds. We also introduce a generalization of
cluster-permutohedron, used to describe combinatorial structures of a larger
family of manifolds with torus actions, including Grassmann manifolds and
partial flag manifolds.


title: RGBD Object Tracking: An In-depth Review

abstract:
  RGBD object tracking is gaining momentum in computer vision research thanks
to the development of depth sensors. Although numerous RGBD trackers have been
proposed with promising performance, an in-depth review for comprehensive
understanding of this area is lacking. In this paper, we firstly review RGBD
object trackers from different perspectives, including RGBD fusion, depth
usage, and tracking framework. Then, we summarize the existing datasets and the
evaluation metrics. We benchmark a representative set of RGBD trackers, and
give detailed analyses based on their performances. Particularly, we are the
first to provide depth quality evaluation and analysis of tracking results in
depth-friendly scenarios in RGBD tracking. For long-term settings in most RGBD
tracking videos, we give an analysis of trackers' performance on handling
target disappearance. To enable better understanding of RGBD trackers, we
propose robustness evaluation against input perturbations. Finally, we
summarize the challenges and provide open directions for this community. All
resources are publicly available at
https://github.com/memoryunreal/RGBD-tracking-review.