title:
A review of the use of optimal transport distances for high resolution
  seismic imaging based on the full waveform

abstract:
  We consider the high-resolution seismic imaging method called full-waveform
inversion (FWI). FWI is a data fitting method aimed at inverting for subsurface
mechanical parameters. Despite the large adoption of FWI by the academic and
industrial communities, and many successful results, FWI still suffers from
severe limitations. From a mathematical standpoint, FWI is a large scale
PDE-constrained optimization problem. The misfit function that is used, which
measures the discrepancy between observed seismic data and data calculated
through the solution of a wave propagation problem, is non-convex. After
discretization, the size of the FWI problem requires the use of local
optimization solvers, which are prone to converge towards local minima. Thus
the success of FWI strongly depends on the choice of the initial model to
ensure the convergence towards the global minimum of the misfit function.
  This limitation has been the motivation for a large variety of strategies.
Among the different methods that have been investigated, the use of optimal
transport (OT) distances-based misfit functions has been recently promoted. The
leading idea is to benefit from the inherent convexity of OT distances with
respect to dilation and translation to render the FWI problem more convex.
However, the application of OT distances in the framework of FWI is not
straightforward, as seismic data is signed, while OT has been developed for the
comparison of probability measures.
  The purpose of this study is to review two methods that were developed to
overcome this difficulty. Both have been successfully applied to field data in
an industrial framework. Both make it possible to better exploit the seismic
data, alleviating the sensitivity to the initial model and to various
conventional workflow steps, and reducing the uncertainty attached to the
subsurface mechanical parameters inversion.


title: Multi-dimensional extensions of the Hegselmann-Krause model

abstract:
  In this paper, we consider two multi-dimensional Hagselmann-Krause (HK)
models for opinion dynamics. The two models describe how individuals adjust
their opinions on multiple topics, based on the influence of their peers. The
models differ in the criterion according to which individuals decide whom they
want to be influenced from. In the average-based model, individuals compare
their average opinions on the various topics with those of the other
individuals and interact only with those individuals whose average opinions lie
within a confidence interval. For this model, we provide an alternative proof
for the contractivity of the range of opinions and show that the agents'
opinions reach consensus/clustering if and only if their average opinions do
so. In the uniform affinity model agents compare their opinions on every single
topic and influence each other only if, topic-wise, such opinions do not differ
more than a given tolerance. We identify conditions under which the uniform
affinity model enjoys the order-preservation property topic-wise and we prove
that the global range of opinions (and hence the range of opinions on every
single topic) are nonincreasing.


title:
LwHBench: A low-level hardware component benchmark and dataset for
  Single Board Computers

abstract:
  In today's computing environment, where Artificial Intelligence (AI) and data
processing are moving toward the Internet of Things (IoT) and the Edge
computing paradigm, benchmarking resource-constrained devices is a critical
task to evaluate their suitability and performance. The literature has
extensively explored the performance of IoT devices when running high-level
benchmarks specialized in particular application scenarios, such as AI or
medical applications. However, lower-level benchmarking applications and
datasets that analyze the hardware components of each device are needed. This
low-level device understanding enables new AI solutions for network, system and
service management based on device performance, such as individual device
identification, so it is an area worth exploring more in detail. In this paper,
we present LwHBench, a low-level hardware benchmarking application for
Single-Board Computers that measures the performance of CPU, GPU, Memory and
Storage taking into account the component constraints in these types of
devices. LwHBench has been implemented for Raspberry Pi devices and run for 100
days on a set of 45 devices to generate an extensive dataset that allows the
usage of AI techniques in different application scenarios. Finally, to
demonstrate the inter-scenario capability of the created dataset, a series of
AI-enabled use cases about device identification and context impact on
performance are presented as examples and exploration of the published data.


title:
Norm Preserving Extensions of Holomorphic Functions Defined on Varieties
  in ${\mathbb C}^n$

abstract:
  If $V$ is an analytic set in a pseudoconvex domain $\Omega$, we show there is
always a pseudoconvex domain $G \subseteq \Omega$ that contains $V$ and has the
property that every bounded holomorphic function on $V$ extends to a bounded
holomorphic function on $G$ with the same norm. We find such a $G$ for some
particular analytic sets.
  When $\Omega$ is an operhedron we show there is a norm on holomorphic
functions on $V$ that can always be preserved by extensions to $\Omega$.


title:
Detecting High-Energy Neutrino Minibursts from Local Supernovae with
  Multiple Neutrino Observatories

abstract:
  Growing evidence from multiwavelength observations of extragalactic
supernovae (SNe) has established the presence of dense circumstellar material
in Type II SNe. Interaction between the SN ejecta and the circumstellar
material should lead to the acceleration of cosmic rays and associated
high-energy emission. Observation of high-energy neutrinos along with the MeV
neutrinos from SNe will provide unprecedented opportunities to understand
unanswered questions in cosmic-ray and neutrino physics. We show that current
and future neutrino detectors can identify high-energy neutrinos from an
extragalactic SN in the neighborhood of the Milky Way. We present the prospects
for detecting high-energy neutrino minibursts from SNe in known local galaxies,
and demonstrate how the future high-energy neutrino network will extend the
edge for identification of SN neutrinos.


title: Modeling the Cooperative Process of Learning a Task

abstract:
  In this paper, we propose a mathematical model for a Transactive Memory
System (TMS) involved in the cooperative process of learning a task. The model
is based on an intertwined dynamics involving both the individuals level of
expertise and the interaction network among the cooperators. The model shows
that if all the agents are non-stubborn, then all of them are able to acquire
the competence of the most expert members of the group, asymptotically reaching
their level of proficiency. Conversely, when dealing with all stubborn agents,
the capability to pass on the task depends on the connectedness properties of
the interaction graph.


title: Module tensorizing maps on $ C^*$-algebras

abstract:
  For $ C^*$-algebras $ \mathfrak{A}, A$ and $ B $ where $ A $ and $ B $ are $
\mathfrak{A} $-bimodules with compatible actions, we consider amalgamated $
\mathfrak{A} $-module tensor product of $ A $ and $ B $ and study its relation
with the C*-tensor product of $A$ and $B$ for the min and max norms. We
introduce and study the notions of module tensorizing maps, module exactness,
and module nuclear pairs of $ C^*$-algebras in this setting. We give concrete
examples of $C^*$-algebras on inverse semigroups.


title: Complete Norm Preserving Extensions of Holomorphic Functions

abstract:
  We show that for every connected analytic subvariety $V$ there is a
pseudoconvex set $\Omega$ such that every bounded matrix-valued holomorphic
function on $V$ extends isometrically to $\Omega$. We prove that if $V$ is two
analytic disks intersecting at one point, if every bounded scalar valued
holomorphic function extends isometrically to $\Omega$, then so does every
matrix-valued function. In the special case that $\Omega$ is the symmetrized
bidisk, we show that this cannot be done by finding a linear isometric
extension from the functions that vanish at one point.


title: Photonic interface for long-distance entanglement of logical-qubits

abstract:
  A scalable fault-tolerant quantum-computer hardware with current noisy
intermediate-scale quantum (NISQ) devices requires the juxtaposition of
different types of quantum systems. In this sense, long-distance entanglement
of stationary error-corrected logical qubits requires a photonic bus
facilitating inter/intra-connection among the cores of quantum processors, the
units of quantum memories, and the worldwide quantum internet. This article
proposes a photonic interface for 4 and 6-qubit encoding of surface-code
logical-qubits in an atomic-lattice platform. Accommodating the lattice inside
a cavity, the gate emits photonic-qubits that are entangled by the
logical-qubits. The entangling mechanism is provided by the Fermi scattering of
a Rydberg electron from the plaquette atoms trapped in a qubit-dependent
lattice. Therefore, different arrangements of logical-qubits derive the central
atom over distinguished eigenstates, featuring photon emission at the early or
late times distinguished by quantum interference. Finally, entanglement
swapping of two emitted photons would make the far separated plaquettes
entangled in the logical basis.

categories: quant-ph physics.app-ph physics.atm-clus physics.atom-ph physics.optics


title: Active-learning-based non-intrusive Model Order Reduction

abstract:
  The Model Order Reduction (MOR) technique can provide compact numerical
models for fast simulation. Different from the intrusive MOR methods, the
non-intrusive MOR does not require access to the Full Order Models (FOMs),
especially system matrices. Since the non-intrusive MOR methods strongly rely
on the snapshots of the FOMs, constructing good snapshot sets becomes crucial.
In this work, we propose a new active learning approach with two novelties. A
novel idea with our approach is the use of single-time step snapshots from the
system states taken from an estimation of the reduced-state space. These states
are selected using a greedy strategy supported by an error estimator based
Gaussian Process Regression (GPR). Additionally, we introduce a use
case-independent validation strategy based on Probably Approximately Correct
(PAC) learning. In this work, we use Artificial Neural Networks (ANNs) to
identify the Reduced Order Model (ROM), however the method could be similarly
applied to other ROM identification methods. The performance of the whole
workflow is tested by a 2-D thermal conduction and a 3-D vacuum furnace model.
With little required user interaction and a training strategy independent to a
specific use case, the proposed method offers a huge potential for industrial
usage to create so-called executable Digital Twins (DTs).


title:
So2Sat POP -- A Curated Benchmark Data Set for Population Estimation
  from Space on a Continental Scale

abstract:
  Obtaining a dynamic population distribution is key to many decision-making
processes such as urban planning, disaster management and most importantly
helping the government to better allocate socio-technical supply. For the
aspiration of these objectives, good population data is essential. The
traditional method of collecting population data through the census is
expensive and tedious. In recent years, machine learning methods have been
developed to estimate the population distribution. Most of the methods use data
sets that are either developed on a small scale or not publicly available yet.
Thus, the development and evaluation of the new methods become challenging. We
fill this gap by providing a comprehensive data set for population estimation
in 98 European cities. The data set comprises digital elevation model, local
climate zone, land use classifications, nighttime lights in combination with
multi-spectral Sentinel-2 imagery, and data from the Open Street Map
initiative. We anticipate that it would be a valuable addition to the research
community for the development of sophisticated machine learning-based
approaches in the field of population estimation.


title: Bayesian interpolation for power laws in neural data analysis

abstract:
  Power laws arise in a variety of phenomena ranging from matter undergoing
phase transition to the distribution of word frequencies in the English
language. Usually, their presence is only apparent when data is abundant, and
accurately determining their exponents often requires even larger amounts of
data. As the scale of recordings in neuroscience becomes larger, an increasing
number of studies attempt to characterise potential power-law relationships in
neural data. In this paper, we aim to discuss the potential pitfalls that one
faces in such efforts and to promote a Bayesian interpolation framework for
this purpose. We apply this framework to synthetic data and to data from a
recent study of large-scale recordings in mouse primary visual cortex (V1),
where the exponent of a power-law scaling in the data played an important role:
its value was argued to determine whether the population's stimulus-response
relationship is smooth, and experimental data was provided to confirm that this
is indeed so. Our analysis shows that with such data types and sizes as we
consider here, the best-fit values found for the parameters of the power law
and the uncertainty for these estimates are heavily dependent on the noise
model assumed for the estimation, the range of the data chosen, and (with all
other things being equal) the particular recordings. It is thus challenging to
offer a reliable statement about the exponents of the power law. Our analysis,
however, shows that this does not affect the conclusions regarding the
smoothness of the population response to low-dimensional stimuli but casts
doubt on those to natural images. We discuss the implications of this result
for the neural code in the V1 and offer the approach discussed here as a
framework that future studies, perhaps exploring larger ranges of data, can
employ as their starting point to examine power-law scalings in neural data.


title:
Impact of Cyber Failures on Operation and Adequacy of Multi-Microgrid
  Distribution Systems

abstract:
  Large-scale successful integration of microgeneration, together with active
loads, energy storage devices, and energy scheduling strategies, requires the
extensive adoption of advanced information and communication technologies
(ICTs) at the distribution network level; this brings so-called Cyber-Physical
Multi-MicroGrid (CPMMG) systems into the picture. However, as such ICTs are not
failure-free, their integration affects the system's operation and adequacy. To
quantify how cyber failures influence a CPMMG system, this study proposes an
adequacy framework based on sequential Monte Carlo Simulation (MCS). In the
proposed framework, the typical structure of a CPMMG is exemplified, and the
consequences of various cyber failures in this system are explained. Possible
operation modes -- normal, island, joint, and shutdown modes -- are then
explained and modeled. Finally, the adequacy index Expected Energy Not Served
(EENS) is computed based on the proposed methodology, and two new adequacy
indices are proposed: Interrupted but Gained Compensation (IbGC) and Supplied
by Expensive Resources (SbER). A comprehensive case study is conducted to
reveal the salient features of the proposed framework.


title:
A simple mathematical model of cyclic hypoxia and hypofractionated
  radiotherapy

abstract:
  There is now substantial evidence that the population of cells that
experience fluctuating oxygen levels ("cyclic" hypoxia) are more radioresistant
than chronically hypoxic ones and hence, this population likely determines
radiotherapy (RT) response, in particular for hypofractionated RT, where
reoxygenation may not be as prominent. A considerable effort has been devoted
to examining the impact of hypoxia on hypofractionated RT; however, much less
attention has been paid to cyclic hypoxia specifically and the role its
kinetics may play in determining the efficacy of these treatments. Here, a
simple model of cyclic hypoxia and fractionation effects was worked out to
quantify this. Cancer clonogen survival was estimated using the linear
quadratic model, modified to account for oxygen enhancement effects. An
analytic approximation for oxygen transport away from a random network of
capillaries with fluctuating oxygen levels was used to model inter-fraction
tissue oxygen kinetics. Using relevant literature parameter values,
inter-fraction fluctuations in oxygenation were found to result in an added 1-2
logs of clonogen survival fraction in going from five fractions to one for the
same nominal biologically effective dose (i.e., excluding the effects of oxygen
levels on radiosensitivity). Although significant, the loss of cell-killing
with increasing hypofractionation is not nearly as large as previous estimates
based on the assumption of complete reoxygenation between fractions. Most
ultra-hypofractionated regimens currently in place offer sufficiently high
doses to counter this loss of cell killing, although care should be taken in
implementing single-fraction regimens.


title: An Optimal Time Variable Learning Framework for Deep Neural Networks

abstract:
  Feature propagation in Deep Neural Networks (DNNs) can be associated to
nonlinear discrete dynamical systems. The novelty, in this paper, lies in
letting the discretization parameter (time step-size) vary from layer to layer,
which needs to be learned, in an optimization framework. The proposed framework
can be applied to any of the existing networks such as ResNet, DenseNet or
Fractional-DNN. This framework is shown to help overcome the vanishing and
exploding gradient issues. Stability of some of the existing continuous DNNs
such as Fractional-DNN is also studied. The proposed approach is applied to an
ill-posed 3D-Maxwell's equation.


title:
Improving Information Cascade Modeling by Social Topology and Dual Role
  User Dependency

abstract:
  In the last decade, information diffusion (also known as information cascade)
on social networks has been massively investigated due to its application
values in many fields. In recent years, many sequential models including those
models based on recurrent neural networks have been broadly employed to predict
information cascade. However, the user dependencies in a cascade sequence
captured by sequential models are generally unidirectional and inconsistent
with diffusion trees. For example, the true trigger of a successor may be a
non-immediate predecessor rather than the immediate predecessor in the
sequence. To capture user dependencies more sufficiently which are crucial to
precise cascade modeling, we propose a non-sequential information cascade model
named as TAN-DRUD (Topology-aware Attention Networks with Dual Role User
Dependency). TAN-DRUD obtains satisfactory performance on information cascade
modeling through capturing the dual role user dependencies of information
sender and receiver, which is inspired by the classic communication theory.
Furthermore, TANDRUD incorporates social topology into two-level attention
networks for enhanced information diffusion prediction. Our extensive
experiments on three cascade datasets demonstrate that our model is not only
superior to the state-of-the-art cascade models, but also capable of exploiting
topology information and inferring diffusion trees.


title: Twitter Dataset on the Russo-Ukrainian War

abstract:
  On 24 February 2022, Russia invaded Ukraine, also known now as
Russo-Ukrainian War. We have initiated an ongoing dataset acquisition from
Twitter API. Until the day this paper was written the dataset has reached the
amount of 57.3 million tweets, originating from 7.7 million users. We apply an
initial volume and sentiment analysis, while the dataset can be used to further
exploratory investigation towards topic analysis, hate speech, propaganda
recognition, or even show potential malicious entities like botnets.


title: Conformally mapped black hole effect in elastic curved continuum

abstract:
  We present a black hole effect by strategically leveraging a conformal
mapping in elastic continuum with curved-space framework, which is less
stringent compared to a Schwarzschild model transformed to isotropic refractive
index profiles. In the conformal map approach, the 2D point singularity
associated to the black hole effect is accomplished by physical plates with
near-to-zero thickness. The analog gravity around the singularity results in
highly confined energy and lagged timings within a branch cut of the conformal
map. These effects are quantified both numerically and experimentally in
reference to control trials in which the thickness is not modulated. The
findings would deepen our understanding of the elastic analog in mimicking
gravitational phenomena, as well as establish the elastic continuum framework
for developing a generic design recipe in the presence of the index
singularity. Geometric landscapes with elastically curved surfaces would be
applicable in a variety of applications such as sensing, imaging, vibration
isolation, and energy harvesting.


title: Dress Code: High-Resolution Multi-Category Virtual Try-On

abstract:
  Image-based virtual try-on strives to transfer the appearance of a clothing
item onto the image of a target person. Prior work focuses mainly on upper-body
clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body
items. This shortcoming arises from a main factor: current publicly available
datasets for image-based virtual try-on do not account for this variety, thus
limiting progress in the field. To address this deficiency, we introduce Dress
Code, which contains images of multi-category clothes. Dress Code is more than
3x larger than publicly available datasets for image-based virtual try-on and
features high-resolution paired images (1024 x 768) with front-view, full-body
reference models. To generate HD try-on images with high visual quality and
rich in details, we propose to learn fine-grained discriminating features.
Specifically, we leverage a semantic-aware discriminator that makes predictions
at pixel-level instead of image- or patch-level. Extensive experimental
evaluation demonstrates that the proposed approach surpasses the baselines and
state-of-the-art competitors in terms of visual quality and quantitative
results. The Dress Code dataset is publicly available at
https://github.com/aimagelab/dress-code.


title: Utilizing the null stream of Einstein Telescope

abstract:
  Among third-generation ground-based gravitational-wave detectors proposed for
the next decade, Einstein Telescope provides a unique kind of null stream
$\unicode{x2014}$ the signal-free linear combination of data $\unicode{x2014}$
that enables otherwise inaccessible tests of the noise models. We project and
showcase challenges in modeling the noise in the 2030-s and how it will affect
the performance of third-generation detectors. We find that the null stream of
Einstein Telescope is capable of entirely eliminating transient detector
glitches that are known to limit current gravitational-wave searches. The
techniques we discuss are computationally efficient and do not require a-priori
knowledge about glitch models. Furthermore, we show how the null stream can be
used to provide an unbiased estimation of the noise power spectrum necessary
for online and offline data analyses even with multiple loud signals in band.
We overview other approaches to utilizing the null stream. Finally, we comment
on the limitations and future challenges of null stream analyses for Einstein
Telescope and arbitrary detector networks.


title:
Tilted Dirac cone effects in superconducting phase transitions in planar
  four-fermion models

abstract:
  The chiral and superconducting gaps are studied in the context of a planar
fermion model with four-fermion interactions. The effect of the tilt of the
Dirac cone on both gaps is shown and discussed. The changes caused by the
presence of a non-null chemical potential are also analyzed. Our results point
to two different behaviors exhibited by planar fermionic systems. For values of
the effective tilt parameter $|{\bf \tilde{t}}| < \tilde{t}^*$, where
$\tilde{t}^* \approx 0.55$, the superconducting phase persists for negative
values of the superconducting coupling constant. For positive values of the
superconducting coupling constant, the induction of a superconducting gap by a
chemical potential exists and which is similar as seen in graphene-like
systems. For $|{\bf
  \tilde{t}}| > \tilde{t}^*$ and for a positive superconducting coupling
constant, the superconducting phase can be present, but it is restricted to a
smaller area in the phase portrait. Finally, our analysis shows that, in the
case of positive values for the superconducting coupling constant, the
induction of a superconducting gap in the presence of a chemical potential is
ruled out and the increase of the chemical potential works in favor of the
manifestation of a metallic phase.


title: Imagination-Augmented Natural Language Understanding

abstract:
  Human brains integrate linguistic and perceptual information simultaneously
to understand natural language, and hold the critical ability to render
imaginations. Such abilities enable us to construct new abstract concepts or
concrete objects, and are essential in involving practical knowledge to solve
problems in low-resource scenarios. However, most existing methods for Natural
Language Understanding (NLU) are mainly focused on textual signals. They do not
simulate human visual imagination ability, which hinders models from inferring
and learning efficiently from limited data samples. Therefore, we introduce an
Imagination-Augmented Cross-modal Encoder (iACE) to solve natural language
understanding tasks from a novel learning perspective -- imagination-augmented
cross-modal understanding. iACE enables visual imagination with external
knowledge transferred from the powerful generative and pre-trained
vision-and-language models. Extensive experiments on GLUE and SWAG show that
iACE achieves consistent improvement over visually-supervised pre-trained
models. More importantly, results in extreme and normal few-shot settings
validate the effectiveness of iACE in low-resource natural language
understanding circumstances.


title:
On the Herdability of Linear Time-Invariant Systems with Special
  Topological Structures

abstract:
  In this paper, we investigate the herdability property, namely the capability
of a system to be driven towards the (interior of the) positive orthant, for
linear time-invariant state-space models. Herdability of certain matrix pairs
(A,B), where A is the adjacency matrix of a multi-agent network, and B is a
selection matrix that singles out a subset of the agents (the "network
leaders"), is explored. The cases when the graph associated with A, G(A), is
directed and clustering balanced (in particular, structurally balanced), or it
has a tree topology and there is a single leader, are investigated.


title:
An improved bound for regular decompositions of $3$-uniform hypergraphs
  of bounded $VC_2$-dimension

abstract:
  A regular partition $\mathcal{P}$ for a $3$-uniform hypergraph $H=(V,E)$
consists of a partition $V=V_1\cup \ldots \cup V_t$ and for each $ij\in
{[t]\choose 2}$, a partition $K_2[V_i,V_j]=P_{ij}^1\cup \ldots \cup
P_{ij}^{\ell}$, such that certain quasirandomness properties hold. The
complexity of $\mathcal{P}$ is the pair $(t,\ell)$. In this paper we show that
if a $3$-uniform hypergraph $H$ has $VC_2$-dimension at most $k$, then there is
a regular partition $\mathcal{P}$ for $H$ of complexity $(t,\ell)$, where
$\ell$ is bounded by a polynomial in the degree of regularity. This is a vast
improvement on the bound arising from the proof of this regularity lemma in
general, in which the bound generated for $\ell$ is of Wowzer type. This can be
seen as a higher arity analogue of the efficient regularity lemmas for graphs
and hypergraphs of bounded VC-dimension due to Alon-Fischer-Newman,
Lov\'{a}sz-Szegedy, and Fox-Pach-Suk.


title: Marginal log-linear models and mediation analysis

abstract:
  We review some not well known results about marginal log-linear models,
derive some new ones and show how they might be relevant in mediation analysis
within logistic regression. In particular, we elaborate on the relation between
interaction parameters defined within different marginal distributions and
describe an algorithm for estimating the sane interaction parameters within
different marginals.


title:
Challenges for variational reduced-density-matrix theory: Total angular
  momentum constraints

abstract:
  The variational two-electron reduced density matrix (v2RDM) method is
generalized for the description of total angular momentum ($J$) and projection
of total angular momentum ($M_{J}$) states in atomic systems described by
non-relativistic Hamiltonians, and it is shown that the approach exhibits
serious deficiencies. Under ensemble $N$-representability constraints, v2RDM
theory fails to retain the appropriate degeneracies among various $J$ states
for fixed spin ($S$) and orbital angular momentum ($L$), and, for fixed $L$,
$S$, and $J$, the manifold of $M_{J}$ states are not necessarily degenerate.
Moreover, a substantial energy error is observed for a system for which the
two-electron reduced density matrix is exactly ensemble $N$-representable; in
this case, the error stems from violations in pure-state $N$-representability
conditions. Unfortunately, such violations do not appear to be good indicators
of the reliability of energies from v2RDM theory in general. Several states are
identified for which energy errors are near zero and yet pure-state conditions
are clearly violated.


title:
The long-term evolution of the atmosphere of Venus: processes and
  feedback mechanisms

abstract:
  In this chapter, we focus on the long-term evolution of the atmosphere of
Venus, and how it has been affected by interior/exterior cycles. The formation
and evolution of Venus's atmosphere, leading to the present-day surface
conditions, remain hotly debated and involve questions that tie into many
disciplines. Here, we explore the mechanisms that shaped the evolution of the
atmosphere, starting with the volatile sources and sinks. Going from the deep
interior to the top of the atmosphere, we describe fundamental processes such
as volcanic outgassing, surface-atmosphere interactions, and atmosphere escape.
Furthermore, we address more complex aspects of the history of Venus, including
the role of meteoritic impacts, how magnetic field generation is tied into
long-term evolution, and the implications of feedback cycles for atmospheric
evolution. Finally, we highlight three plausible end-member evolutionary
pathways that Venus might have followed, from the accretion to its present-day
state, based on current modeling and observations. In a first scenario, the
planet was desiccated early-on, during the magma ocean phase, by atmospheric
escape. In a second scenario, Venus could have harbored surface liquid water
for long periods of time, until its temperate climate was destabilized and it
entered a runaway greenhouse phase. In a third scenario, Venus's inefficient
outgassing could have kept water inside the planet, where hydrogen was trapped
in the core and the mantle was oxidized. We discuss existing evidence and
future observations/missions needed to refine our understanding of the planet's
history and of the complex feedback cycles between the interior, surface, and
atmosphere that operate in the past, present or future of Venus.


title:
Designing an AI-Based Adaptive Controller Augmented with a System
  Identifier for a Micro-Class Robot Equipped with a Vibrating Actuator

abstract:
  In this paper, an adaptive control scheme based on using neural networks is
designed to guarantee the desired behavior of a micro-robot which is equipped
with vibrating actuators and follows the principle of slip-stick movement.
There are two tiny shaking motors which have been utilized to run the
micro-class robotic system. Dynamic modeling equations are expressed by
considering the spring coefficient of the bases. After that, the effect of the
spring on the foundations was investigated. In addition to designing
neural-based controller, an AI-based system identifier has been developed to
help the controller update its parameters and achieve its desired targets.
Using this method, several specific paths for the movement of this micro robot
are simulated. Based on the simulation results, the proposed controlling
strategy guarantees acceptable performance for tracking different paths due to
plotted near-zero errors and handles the nonlinear behavior of the micro-robot
system.


title:
Reconstructing the whole 6D properties of the Sagittarius stream with
  N-body simulation

abstract:
  Many modeling of the Sagittarius (Sgr) stream have been attempted, but they
still have difficulties to reproduce its full 6D space-phase properties. Using
N-body simulation with a Milky Way mass of 5.2$\times10^{11}$ M$_{\odot}$ and a
Sgr mass of 9.3$\times10^{8}$ M$_{\odot}$, we have been able to reproduce well
all 3D spatial features of Sgr stream, including its core, leading and trailing
arms, and their associated bifurcations. Moreover, all reported 3D kinematics
properties of the Sgr stream have been qualitatively reproduced without the
need for a massive LMC, although the latter can not be ruled out from this
work. Moreover, we also find that our model fails in reproducing the exact
behaviors of the stream arms in the energy-angular momentum plane. It let us to
suggest that significant further progress can be only achievable after
introducing a major component in the Sgr progenitor, which is the gas that
dominates all Irregular dwarf galaxies in the Sgr mass range.


title: CR Driven Multi-phase Gas Formed via Thermal Instability

abstract:
  Cosmic rays (CRs) are an important energy source in the circum-galactic
medium (CGM) that impact the multi-phase gas structure and dynamics. We perform
two-dimensional CR-magnetohydrodynamic simulations to investigate the role of
CRs in accelerating multi-phase gas formed via thermal instability. We compare
outflows driven by CRs to those driven by a hot wind with equivalent momentum.
We find that CRs driven outflow produces lower density contrast between cold
and hot gas due to non-thermal pressure support, and yields a more filamentary
cloud morphology. While entrainment in a hot wind can lead to cold gas
increasing due to efficient cooling, CRs tend to suppress cold gas growth. The
mechanism of this suppression depends on magnetic field strength, with CRs
either reducing cooling or shredding the clouds by differential acceleration.
Despite the suppression of cold gas growth, CRs are able to launch the cold
clouds to observed velocities without rapid destruction. The dynamical
interaction between CRs ad multi-phase gas is also sensitive to the magnetic
field strength. In relatively strong fields, the CRs are more important for
direct momentum input to cold gas. In relatively weak fields, the CRs impact
gas primarily by heating, which modifies gas pressure.


title: Evidence of Strong Quantum Turbulence in Bose Einstein Condensates

abstract:
  By combining experiments and numerical simulations which model precisely the
dynamics of shaken atomic Bose-Einstein condensates, we reveal the surprising
nature of quantum turbulence in these systems. Unlike the tangles of vortex
lines described in the superfluid helium literature, we find that a turbulent
atomic condensate contains a mixture of strong fragmented density fluctuations
and small random vortex loops which are not homogeneously distributed. This
unusual form of quantum turbulence, with its own properties and scaling
behaviour, is significantly different from the turbulence which is observed in
either classical or other quantum systems, thus posing a new challenge in
turbulence research.


title: A Novel Region Duplication Detection Algorithm Based on Hybrid Approach

abstract:
  The digital images from various sources are ubiquitous due to easy
availability of high bandwidth Internet. Digital images are easy to tamper with
good or bad intentions. Non-availability of pre-embedded information in digital
images makes the tampering detection process more difficult in case of digital
forensics. Thus, passive image tampering is difficult to detect. There are
various algorithms available for detecting image tampering. However, these
algorithms have some drawbacks, due to which all types of tampering cannot be
detected. In this paper researchers intend to present the types of image
tampering and its detection techniques with example based approach. This paper
also illustrates insights into the various existing algorithms and tries to
find out efficient algorithm out of them.


title:
Changing patterns in electroweak precision with new color-charged
  states: Oblique corrections and the $W$ boson mass

abstract:
  The recent measurement by the CDF Collaboration of the $W$ boson mass is in
significant tension with the Standard Model expectation, showing a discrepancy
of seven standard deviations. A larger value of $m_W$ affects the global
electroweak fit, particularly the best-fit values of the Peskin-Takeuchi
parameters $S$, $T$ (and perhaps $U$) that measure oblique corrections from new
physics. To meet this challenge, we propose some simple models capable of
generating non-negative $S$ and $T$, the latter of which faces the greatest
upward pressure from the CDF measurement in scenarios with $U=0$. Our models
feature weak multiplets of scalars charged under $\mathrm{SU}(3)_{\text{c}}
\times \mathrm{U}(1)_Y$, which cannot attain nonzero vacuum expectation values
but nevertheless produce e.g. $T \neq 0$ given some other mechanism to split
the electrically charged and neutral scalars. We compute the oblique
corrections in these models and identify ample parameter space supporting the
CDF value of $m_W$.


title:
A Physics-Informed Data-Driven Algorithm for Ensemble Forecast of
  Complex Turbulent Systems

abstract:
  A new ensemble forecast algorithm, named as the physics-informed data-driven
algorithm with conditional Gaussian statistics (PIDD-CG), is developed to
predict the time evolution of the probability density functions (PDFs) of
complex turbulent systems with partial observations. The PIDD-CG algorithm
integrates a unique multiscale statistical closure model with an extremely
efficient nonlinear data assimilation scheme to represent the PDF as a mixture
of conditional statistics, which overcomes the curse of dimensionality for
high-dimensional systems. The multiscale features in the time evolution of each
conditional statistics ensemble member effectively captured by an appropriate
combination of physics-informed analytic formulae and recurrent neural
networks. An information metric is adopted as the loss function for the latter
to more accurately calibrate the key turbulent signals with strong
fluctuations. The proposed algorithm succeeds in forecasting both the transient
and statistical equilibrium non-Gaussian PDFs of strongly turbulent systems
with intermittency, regime switching and extreme events.


title:
State-Output Risk-Constrained Quadratic Control of Partially Observed
  Linear Systems

abstract:
  We propose a methodology for performing risk-averse quadratic regulation of
partially observed Linear Time-Invariant (LTI) systems disturbed by process and
output noise. To compensate against the induced variability due to both types
of noises, state regulation is subject to two risk constraints. The latter
renders the resulting controller cautious of stochastic disturbances, by
restricting the statistical variability, namely, a simplified version of the
cumulative expected predictive variance of both the state and the output. Our
proposed formulation results in an optimal risk-averse policy that preserves
favorable characteristics of the classical Linear Quadratic (LQ) control. In
particular, the optimal policy has an affine structure with respect to the
minimum mean square error (mmse) estimates. The linear component of the policy
regulates the state more strictly in riskier directions, where the process and
output noise covariance, cross-covariance, and the corresponding penalties are
simultaneously large. This is achieved by "inflating" the state penalty in a
systematic way. The additional affine terms force the state against pure and
cross third-order statistics of the process and output disturbances. Another
favorable characteristic of our optimal policy is that it can be pre-computed
off-line, thus, avoiding limitations of prior work. Stability analysis shows
that the derived controller is always internally stable regardless of parameter
tuning. The functionality of the proposed risk-averse policy is illustrated
through a working example via extensive numerical simulations.


title: Two-sites quantum island in the quasi-ballistic regime

abstract:
  Quantum Hall edge channels can be combined with metallic regions to
fractionalize electrons and form correlated impurity models. We study a minimal
device, that has been experimentally achieved quite recently, with two floating
islands connected to three edge channels via quantum point contacts in the
integer quantum Hall regime. At high transparency of the quantum point
contacts, we establish a mapping to the boundary sine-Gordon model and thereby
reveal the nature of the quantum critical point. We deduce from this mapping
universal expressions for the conductance and noise, in agreement with the
experimental findings, and discuss the competition between Kondo-like screening
of each individual island and the cooperative transfer of electrons between
them. We further predict that the device operated at finite voltage bias
produces fractional charges $e^* =e/3$ and propose a generalization to $N$
islands with the fractional charge $e^* =e/(N+1)$.


title: Learning to Retrieve Relevant Experiences for Motion Planning

abstract:
  Recent work has demonstrated that motion planners' performance can be
significantly improved by retrieving past experiences from a database.
Typically, the experience database is queried for past similar problems using a
similarity function defined over the motion planning problems. However, to
date, most works rely on simple hand-crafted similarity functions and fail to
generalize outside their corresponding training dataset. To address this
limitation, we propose (FIRE), a framework that extracts local representations
of planning problems and learns a similarity function over them. To generate
the training data we introduce a novel self-supervised method that identifies
similar and dissimilar pairs of local primitives from past solution paths. With
these pairs, a Siamese network is trained with the contrastive loss and the
similarity function is realized in the network's latent space. We evaluate FIRE
on an 8-DOF manipulator in five categories of motion planning problems with
sensed environments. Our experiments show that FIRE retrieves relevant
experiences which can informatively guide sampling-based planners even in
problems outside its training distribution, outperforming other baselines.


title:
Lower bounds on the maximal number of rational points on curves over
  finite fields

abstract:
  For a given genus $g \geq 1$, we give lower bounds for the maximal number of
rational points on a smooth projective absolutely irreducible curve of genus
$g$ over ${\mathbb F}_q$. As a consequence of Katz-Sarnak theory, we first get
for any given $g>0$, any $\varepsilon>0$ and all $q$ large enough, the
existence of a curve of genus $g$ over ${\mathbb F}_q$ with at least $1+q+
(2g-\varepsilon) \sqrt{q}$ rational points. Then using sums of powers of traces
of Frobenius of hyperelliptic curves, we get a lower bound of the form
$1+q+1.71 \sqrt{q}$ valid for $g \geq 3$ and odd $q \geq 11$. Finally, explicit
constructions of towers of curves improve this result, with a bound of the form
$1+q+4 \sqrt{q} -32$ valid for all $g\ge 2$ and for all $q$.


title: LCD subspace codes

abstract:
  A subspace code is a nonempty set of subspaces of vectors space $\mathbb
F^n_q$. Linear codes with complementary duals, or LCD codes, are linear codes
whose intersection with their duals are trivial. In this paper, we introduce a
notion of LCD subspace codes. We show that the nearest-codeword (or
maximum-likelihood) decoding problem for an LCD subspace code reduces to a
problem that is simpler than for a general subspace code. Further, we show that
under some conditions equitable partitions of association schemes yield such
LCD subspace codes and as an illustration of the method give some examples from
distance-regular graphs.


title: The Fundamental Group of Torus Knots

abstract:
  This work is concerned with the calculation of the fundamental group of torus
knots. Torus knots are special types of knots which wind around a torus a
number of times in the longitudinal and meridional directions. We compute and
describe the fundamental group of torus knots by using some concepts in
algebraic topology and group theory. We also calculate the fundamental group of
an arbitrary knot by using an algorithm called the Wirtinger presentation.


title:
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over
  Incomplete Knowledge Bases

abstract:
  Knowledge bases (KBs) are often incomplete and constantly changing in
practice. Yet, in many question answering applications coupled with knowledge
bases, the sparse nature of KBs is often overlooked. To this end, we propose a
case-based reasoning approach, CBR-iKB, for knowledge base question answering
(KBQA) with incomplete-KB as our main focus. Our method ensembles decisions
from multiple reasoning chains with a novel nonparametric reasoning algorithm.
By design, CBR-iKB can seamlessly adapt to changes in KBs without any
task-specific training or fine-tuning. Our method achieves 100% accuracy on
MetaQA and establishes new state-of-the-art on multiple benchmarks. For
instance, CBR-iKB achieves an accuracy of 70% on WebQSP under the incomplete-KB
setting, outperforming the existing state-of-the-art method by 22.3%.


title:
Constraining the equation of state of neutron stars using multimessenger
  observations

abstract:
  Neutron stars are the densest objects known in our visible universe.
Properties of matter inside a neutron star are encoded in its equation of
state, which has wide-ranging uncertainty from a theoretical perspective. With
the current understanding of quantum chromodynamics, it is hard to determine
the interactions of neutron star matter at such high densities. Also performing
many body calculations is computationally intractable. Besides the constitution
of the neutron star core is highly speculative -- it is not ruled out that it
contains exotic matter like strange baryons, meson condensates, quark matter,
etc. Although the matter inside the neutron star is extremely dense, but the
temperature of this object is very cold in most of its life span. We cannot
produce such dense but rather cold material in our laboratory. Since probing
the physics of neutron star matter is inaccessible by our earth based
experiments, we look for astrophysical observations of neutron stars. This
thesis deals with the theoretical and computational techniques required to
translate neutron star observables from astrophysical observations to its
equation of state.


title: Bottomonium-like tetraquarks in a chiral quark model

abstract:
  The low-lying bottomonium-like tetraquarks $b\bar{b}q\bar{q}$ $(q=u,\,d,\,s)$
with spin-parity $J^P=0^+$, $1^+$ and $2^+$, and isospin $I=0,\,1$ or
$\frac{1}{2}$, are systematically investigated within the theoretical framework
of real- and complex-scaling range of a chiral quark model, which has already
been successfully applied in analysis of several multiquark systems. A complete
four-body $S$-wave function, which includes meson-meson, diquark-antidiquark
and K-type arrangements of quarks, along with all possible color configurations
are considered. In the $b\bar{b}q\bar{q}$ $(q=u,\,d)$ tetraquark system, we
found resonance states of $B^{(*)} \bar{B}^{(*)}$, $\Upsilon\omega$,
$\Upsilon\rho$ and $\eta_b \rho$ nature with all possible $I(J^P)$ quantum
numbers. Their masses are generally located in the energy range $11.0-11.3$ GeV
and their widths are less than $10$ MeV. In addition, extremely narrow
resonances, with two-meson strong decay widths less than $1.5$ MeV, are
obtained in both $b\bar{b}u\bar{s}$ and $b\bar{b}s\bar{s}$ tetraquark systems.
Particularly, four radial excitations of $\Upsilon K^*$ and $\eta_b K^*$ are
found at $\sim11.1$ GeV in $J^P=0^+$, $1^+$ and $2^+$ channels of
$b\bar{b}u\bar{s}$ system. One $\Upsilon(1S)\phi(2S)$ resonance state is
obtained at $11.28$ GeV in the $J^P=1^+$ sector.


title:
PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of
  Networked Microgrids Through Full-Field Measurement

abstract:
  A Physics-Informed Dynamic Graph Neural Network (PIDGeuN) is presented to
accurately, efficiently and robustly predict the nonlinear transient dynamics
of microgrids in the presence of disturbances. The graph-based architecture of
PIDGeuN provides a natural representation of the microgrid topology. Using only
the state information that is practically measurable, PIDGeuN employs a time
delay embedding formulation to fully reproduce the system dynamics, avoiding
the dependency of conventional methods on internal dynamic states such as
controllers. Based on a judiciously designed message passing mechanism, the
PIDGeuN incorporates two physics-informed techniques to improve its prediction
performance, including a physics-data-infusion approach to determining the
inter-dependencies between buses, and a loss term to respect the known physical
law of the power system, i.e., the Kirchhoff's law, to ensure the feasibility
of the model prediction. Extensive tests show that PIDGeuN can provide accurate
and robust prediction of transient dynamics for nonlinear microgrids over a
long-term time period. Therefore, the PIDGeuN offers a potent tool for the
modeling of large scale networked microgrids (NMs), with potential applications
to predictive or preventive control in real time applications for the stable
and resilient operations of NMs.


title:
Euclidean-to-Lorentzian wormhole transition and gravitational symmetry
  breaking in the Sachdev-Ye-Kitaev model

abstract:
  We study a two-site Sachdev-Ye-Kitaev (SYK) model with complex couplings and
a weak inter-site interaction. At low temperatures, the system is dual to a
Euclidean wormhole in Jackiw-Teitelboim (JT) gravity plus matter.
Interestingly, the energy spectrum becomes real for sufficiently strong
inter-site coupling despite the Hamiltonian being non-Hermitian. In gravity,
this complex-to-real transition corresponds to a Euclidean-to-Lorentzian
transition: a dynamical restoration of the gravitational SL(2,R) symmetry of
the Lorentzian wormhole, broken to U(1) in the Euclidean wormhole. We show this
by identifying an order parameter for the symmetry breaking and by matching the
oscillating patterns of the Green's functions. Above the transition, the system
can be continued to Lorentzian signature and is dual to an eternal traversable
wormhole. Additionally, we observe a thermal phase transition from the wormhole
to two black holes and provide a detailed matching of the associated physical
quantities. The analysis of level statistics reveals that in a broad range of
parameters the dynamics is quantum chaotic in the universality class of systems
with time reversal invariance.


title:
Correlation between Unconscious Mouse Actions and Human Cognitive
  Workload

abstract:
  Unconscious behaviors are one of the indicators of the human perception
process from a psychological perspective. As a result of perception responses,
hand gestures show behavioral responses from given stimuli. Mouse usages in
Human-Computer Interaction (HCI) show hand gestures that individuals perceive
information processing. This paper presents an investigation of the correlation
between unconscious mouse actions and human cognitive workload. We extracted
mouse behaviors from a Robot Operating System (ROS) file-based dataset that
user responses are reproducible. We analyzed redundant mouse movements to
complete a dual $n$-back game by solely pressing the left and right buttons.
Starting from a hypothesis that unconscious mouse behaviors predict different
levels of cognitive loads, we statistically analyzed mouse movements. We also
validated mouse behaviors with other modalities in the dataset, including
self-questionnaire and eye blinking results. As a result, we found that mouse
behaviors that occur unconsciously and human cognitive workload correlate.


title:
Star formation in disk galaxies and its relation with spiral structure
  in numerical simulations

abstract:
  The spiral structure of a spiral galaxy can be seen through different
observational tracers such as the dust in the interstellar medium, the free
electrons in ionized regions, the molecular gas, or the atomic hydrogen in
H{\alpha} regions. In this work, we use an N-body simulation with
Magnetohydrodynamics (MHD) to investigate the spiral pattern and the star
formation activity in the gas component of a disk galaxy. Some of the questions
that we tackle include: how are galaxies observed through the different
properties of the gas? Does the spiral structure of the galaxy change when we
trace it with the different properties of the gas? Do the spiral arms in the
simulation change its shape and width depending on what property we are
"looking" through? Can we somehow model the shape of the arms to measure their
width consistently? Does this model apply to all the properties? To answer
these questions, we developed a method for the identification and extraction of
the spiral structure in a disk galaxy. Using the results of this procedure, we
further investigate the features of the spiral pattern through the different
properties of the gas, with special attention to the star formation activity
and how it behaves along and across the spiral structure.


title: QuSBT: Search-Based Testing of Quantum Programs

abstract:
  Generating a test suite for a quantum program such that it has the maximum
number of failing tests is an optimization problem. For such optimization,
search-based testing has shown promising results in the context of classical
programs. To this end, we present a test generation tool for quantum programs
based on a genetic algorithm, called QuSBT (Search-based Testing of Quantum
Programs). QuSBT automates the testing of quantum programs, with the aim of
finding a test suite having the maximum number of failing test cases. QuSBT
utilizes IBM's Qiskit as the simulation framework for quantum programs. We
present the tool architecture in addition to the implemented methodology (i.e.,
the encoding of the search individual, the definition of the fitness function
expressing the search problem, and the test assessment w.r.t. two types of
failures). Finally, we report results of the experiments in which we tested a
set of faulty quantum programs with QuSBT to assess its effectiveness.
Repository (code and experimental results):
https://github.com/Simula-COMPLEX/qusbt-tool Video:
https://youtu.be/3apRCtluAn4


title: Dark energy from a geometrical gauge scalar-tensor theory of gravity

abstract:
  In this paper we obtain some cosmological solutions that describe the present
period of accelerating expansion of the universe in the framework of a
geometrical gauge scalar-tensor theory of gravity. The background geometry in
the model is the Weyl integrable and we found a class of power law solutions
for the Weyl scalar field when an invariant metric is employed in a power law
expanding universe. We obtain a deceleration and an equation of state
parameters in agreement with PLANCK 2018 observational data for some specific
parameters of the model. The deceleration parameter tends asymptotically to
$-1/2$.


title:
Cylin-Painting: Seamless 360{\deg} Panoramic Image Outpainting and
  Beyond with Cylinder-Style Convolutions

abstract:
  Image outpainting gains increasing attention since it can generate the
complete scene from a partial view, providing a valuable solution to construct
360{\deg} panoramic images. As image outpainting suffers from the intrinsic
issue of unidirectional completion flow, previous methods convert the original
problem into inpainting, which allows a bidirectional flow. However, we find
that inpainting has its own limitations and is inferior to outpainting in
certain situations. The question of how they may be combined for the best of
both has as yet remained under-explored. In this paper, we provide a deep
analysis of the differences between inpainting and outpainting, which
essentially depends on how the source pixels contribute to the unknown regions
under different spatial arrangements. Motivated by this analysis, we present a
Cylin-Painting framework that involves meaningful collaborations between
inpainting and outpainting and efficiently fuses the different arrangements,
with a view to leveraging their complementary benefits on a consistent and
seamless cylinder. Nevertheless, directly applying the cylinder-style
convolution often generates visually unpleasing results as it could discard
important positional information. To address this issue, we further present a
learnable positional embedding strategy and incorporate the missing component
of positional encoding into the cylinder convolution, which significantly
improves the panoramic results. Note that while developed for image
outpainting, the proposed solution can be effectively extended to other
panoramic vision tasks, such as object detection, depth estimation, and image
super resolution.


title: Safe essential scalar-tensor theories

abstract:
  We discuss the renormalisation group flow of all essential couplings of
quantum gravity coupled to a shift-symmetric scalar field at fourth order in
the derivative expansion. We derive the global structure of the phase diagram,
and identify a bounded region in theory space which is both asymptotically safe
in the ultraviolet, and connects to standard effective field theory in the
infrared. Our system thus satisfies the weak-gravity bound. The allowed
infrared behaviour of the essential four-scalar coupling is restricted by
requiring an ultraviolet completion. This bound can be saturated by a theory
without free parameters, which gives a concrete example for a fully predictive
scalar-tensor theory.


title: Upper bounds for regularity of radicals of ideals and arithmetic degrees

abstract:
  Let $S$ be a polynomial ring in $n$ variables over a field. Let $I$ be a
homogeneous ideal in $S$ generated by forms of degree at most $d$ with
$\text{dim}(S/I)=r$. In the first part of this paper, we show how to derive
from a result of Hoa an upper bound for the regularity of $\sqrt{I}$. More
specifically we show that $\text{reg}(\sqrt{I})\leq d^{(n-1)2^{r-1}}$. In the
second part, we show that the $r$-th arithmetic degree of $I$ is bounded above
by $2\cdot d^{2^{n-r-1}}$. This is done by proving upper bounds for arithmetic
degrees of strongly stable ideals and ideals of Borel type.


title:
Beyond Being Real: A Sensorimotor Control Perspective on Interactions in
  Virtual Reality

abstract:
  We can create Virtual Reality (VR) interactions that have no equivalent in
the real world by remapping spacetime or altering users' body representation,
such as stretching the user's virtual arm for manipulation of distant objects
or scaling up the user's avatar to enable rapid locomotion. Prior research has
leveraged such approaches, what we call beyond-real techniques, to make
interactions in VR more practical, efficient, ergonomic, and accessible. We
present a survey categorizing prior movement-based VR interaction literature as
reality-based, illusory, or beyond-real interactions. We survey relevant
conferences (CHI, IEEE VR, VRST, UIST, and DIS) while focusing on selection,
manipulation, locomotion, and navigation in VR. For beyond-real interactions,
we describe the transformations that have been used by prior works to create
novel remappings. We discuss open research questions through the lens of the
human sensorimotor control system and highlight challenges that need to be
addressed for effective utilization of beyond-real interactions in future VR
applications, including plausibility, control, long-term adaptation, and
individual differences.


title: Automated Audio Captioning using Audio Event Clues

abstract:
  Audio captioning is an important research area that aims to generate
meaningful descriptions for audio clips. Most of the existing research extracts
acoustic features of audio clips as input to encoder-decoder and transformer
architectures to produce the captions in a sequence-to-sequence manner. Due to
data insufficiency and the architecture's inadequate learning capacity,
additional information is needed to generate natural language sentences, as
well as acoustic features. To address these problems, an encoder-decoder
architecture is proposed that learns from both acoustic features and extracted
audio event labels as inputs. The proposed model is based on pre-trained
acoustic features and audio event detection. Various experiments used different
acoustic features, word embedding models, audio event label extraction methods,
and implementation configurations to show which combinations have better
performance on the audio captioning task. Results of the extensive experiments
on multiple datasets show that using audio event labels with the acoustic
features improves the recognition performance and the proposed method either
outperforms or achieves competitive results with the state-of-the-art models.


title: The Triplet Dirac Seesaw in the View of the Recent CDF-II W Mass Anomaly

abstract:
  In the present letter, a Dirac neutrino mass model is presented in the view
of the new result on W boson mass of $m_W^{CDF-II}=80.4335\pm 0.0094$ GeV,
recently reported by the CDF-II experimental collaboration. The newly measured
value of the W mass anomaly shows a 7-$\sigma$ deviation from that predicted by
the standard model. The model explains the CDF-II W boson mass anomaly by
extending the standard model with hypercharge zero vector-like fermion triplet.
Symmetry is amended with a global $U(1)_{B-L}$ which is broken with an
hypercharge zero electroweak triplet. It is demonstrated that the model can
successfully explain CDF-II result, Dirac neutrino mass origin, while
satisfying standard model precision constraints and collider constraints with
scalar and fermion triplet masses in the ranger $1.4$ TeV$-5.2$ TeV and $100$
TeV $-10^{13}$ GeV, respectively.


title: Learning Similarity Preserving Binary Codes for Recommender Systems

abstract:
  Hashing-based Recommender Systems (RSs) are widely studied to provide
scalable services. The existing methods for the systems combine three modules
to achieve efficiency: feature extraction, interaction modeling, and
binarization. In this paper, we study an unexplored module combination for the
hashing-based recommender systems, namely Compact Cross-Similarity Recommender
(CCSR). Inspired by cross-modal retrieval, CCSR utilizes Maximum a Posteriori
similarity instead of matrix factorization and rating reconstruction to model
interactions between users and items. We conducted experiments on MovieLens1M,
Amazon product review, Ichiba purchase dataset and confirmed CCSR outperformed
the existing matrix factorization-based methods. On the Movielens1M dataset,
the absolute performance improvements are up to 15.69% in NDCG and 4.29% in
Recall. In addition, we extensively studied three binarization modules: $sign$,
scaled tanh, and sign-scaled tanh. The result demonstrated that although
differentiable scaled tanh is popular in recent discrete feature learning
literature, a huge performance drop occurs when outputs of scaled $tanh$ are
forced to be binary.


title:
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy,
  Robustness, Fairness, and Explainability

abstract:
  Graph Neural Networks (GNNs) have made rapid developments in the recent
years. Due to their great ability in modeling graph-structured data, GNNs are
vastly used in various applications, including high-stakes scenarios such as
financial analysis, traffic predictions, and drug discovery. Despite their
great potential in benefiting humans in the real world, recent study shows that
GNNs can leak private information, are vulnerable to adversarial attacks, can
inherit and magnify societal bias from training data and lack interpretability,
which have risk of causing unintentional harm to the users and society. For
example, existing works demonstrate that attackers can fool the GNNs to give
the outcome they desire with unnoticeable perturbation on training graph. GNNs
trained on social networks may embed the discrimination in their decision
process, strengthening the undesirable societal bias. Consequently, trustworthy
GNNs in various aspects are emerging to prevent the harm from GNN models and
increase the users' trust in GNNs. In this paper, we give a comprehensive
survey of GNNs in the computational aspects of privacy, robustness, fairness,
and explainability. For each aspect, we give the taxonomy of the related
methods and formulate the general frameworks for the multiple categories of
trustworthy GNNs. We also discuss the future research directions of each aspect
and connections between these aspects to help achieve trustworthiness.


title: Quantum Computation of Hydrogen Bond Dynamics and Vibrational Spectra

abstract:
  Calculating the observable properties of chemical systems is often
classically intractable, and is widely viewed as a promising application of
quantum information processing. This is because a full description of chemical
behavior relies upon the complex interplay of quantum-mechanical electrons and
nuclei, demanding an exponential scaling of computational resources with system
size. While considerable progress has been made in mapping electronic-structure
calculations to quantum hardware, these approaches are unsuitable for
describing the quantum dynamics of nuclei, proton- and hydrogen-transfer
processes, or the vibrational spectra of molecules. Here, we use the QSCOUT
ion-trap quantum computer to determine the quantum dynamics and vibrational
properties of a shared proton within a short-strong hydrogen-bonded system. For
a range of initial states, we experimentally drive the ion-trap system to
emulate the quantum trajectory of the shared proton wavepacket as it evolves
along the potential surface generated by the nuclear frameworks and electronic
structure. We then extract the characteristic vibrational frequencies for the
shared proton motion to spectroscopic accuracy and determine all energy
eigenvalues of the system Hamiltonian to > 99.9% fidelity. Our approach offers
a new paradigm for studying the quantum chemical dynamics and vibrational
spectra of molecules, and when combined with quantum algorithms for electronic
structure, opens the possibility to describe the complete behavior of molecules
using exclusively quantum computation techniques.


title: Expert-Calibrated Learning for Online Optimization with Switching Costs

abstract:
  We study online convex optimization with switching costs, a practically
important but also extremely challenging problem due to the lack of complete
offline information. By tapping into the power of machine learning (ML) based
optimizers, ML-augmented online algorithms (also referred to as expert
calibration in this paper) have been emerging as state of the art, with
provable worst-case performance guarantees. Nonetheless, by using the standard
practice of training an ML model as a standalone optimizer and plugging it into
an ML-augmented algorithm, the average cost performance can be highly
unsatisfactory. In order to address the "how to learn" challenge, we propose
EC-L2O (expert-calibrated learning to optimize), which trains an ML-based
optimizer by explicitly taking into account the downstream expert calibrator.
To accomplish this, we propose a new differentiable expert calibrator that
generalizes regularized online balanced descent and offers a provably better
competitive ratio than pure ML predictions when the prediction error is large.
For training, our loss function is a weighted sum of two different losses --
one minimizing the average ML prediction error for better robustness, and the
other one minimizing the post-calibration average cost. We also provide
theoretical analysis for EC-L2O, highlighting that expert calibration can be
even beneficial for the average cost performance and that the high-percentile
tail ratio of the cost achieved by EC-L2O to that of the offline optimal oracle
(i.e., tail cost ratio) can be bounded. Finally, we test EC-L2O by running
simulations for sustainable datacenter demand response. Our results demonstrate
that EC-L2O can empirically achieve a lower average cost as well as a lower
competitive ratio than the existing baseline algorithms.


title:
Training and Evaluation of Deep Policies using Reinforcement Learning
  and Generative Models

abstract:
  We present a data-efficient framework for solving sequential decision-making
problems which exploits the combination of reinforcement learning (RL) and
latent variable generative models. The framework, called GenRL, trains deep
policies by introducing an action latent variable such that the feed-forward
policy search can be divided into two parts: (i) training a sub-policy that
outputs a distribution over the action latent variable given a state of the
system, and (ii) unsupervised training of a generative model that outputs a
sequence of motor actions conditioned on the latent action variable. GenRL
enables safe exploration and alleviates the data-inefficiency problem as it
exploits prior knowledge about valid sequences of motor actions. Moreover, we
provide a set of measures for evaluation of generative models such that we are
able to predict the performance of the RL policy training prior to the actual
training on a physical robot. We experimentally determine the characteristics
of generative models that have most influence on the performance of the final
policy training on two robotics tasks: shooting a hockey puck and throwing a
basketball. Furthermore, we empirically demonstrate that GenRL is the only
method which can safely and efficiently solve the robotics tasks compared to
two state-of-the-art RL methods.


title:
Adaptive Noisy Data Augmentation for Regularized Estimation and
  Inference in Generalized Linear Models

abstract:
  We propose the AdaPtive Noise Augmentation (PANDA) procedure to regularize
the estimation and inference of generalized linear models (GLMs). PANDA
iteratively optimizes the objective function given noise augmented data until
convergence to obtain the regularized model estimates. The augmented noises are
designed to achieve various regularization effects, including $l_0$, bridge
(lasso and ridge included), elastic net, adaptive lasso, and SCAD, as well as
group lasso and fused ridge. We examine the tail bound of the noise-augmented
loss function and establish the almost sure convergence of the noise-augmented
loss function and its minimizer to the expected penalized loss function and its
minimizer, respectively. We derive the asymptotic distributions for the
regularized parameters, based on which, inferences can be obtained
simultaneously with variable selection. PANDA exhibits ensemble learning
behaviors that help further decrease the generalization error. Computationally,
PANDA is easy to code, leveraging existing software for implementing GLMs,
without resorting to complicated optimization techniques. We demonstrate the
superior or similar performance of PANDA against the existing approaches of the
same type of regularizers in simulated and real-life data. We show that the
inferences through PANDA achieve nominal or near-nominal coverage and are far
more efficient compared to a popular existing post-selection procedure.


title: Collusion-resistant fingerprinting of parallel content channels

abstract:
  The fingerprinting game is analysed when the coalition size $k$ is known to
the tracer, but the colluders can distribute themselves across $L$ TV channels.
The collusion channel is introduced and the extra degrees of freedom for the
coalition are made manifest in our formulation. We introduce a payoff
functional that is analogous to the single TV channel case, and is conjectured
to be closely related to the fingerprinting capacity. For the binary alphabet
case under the marking assumption, and the restriction of access to one TV
channel per person per segment, we derive the asymptotic behavior of the payoff
functional. We find that the value of the maximin game for our payoff is
asymptotically equal to $L^2/k^2 2 \ln 2$, with optimal strategy for the tracer
being the arcsine distribution, and for the coalition being the interleaving
attack across all TV channels, as well as assigning an equal number of
colluders across the $L$ TV channels.


title: On root frames in $\mathbb{R}^d$

abstract:
  \emph{A root frame} for $\mathbb{R}^d$ is a finite frame whose vectors form a
root system. In this note we establish some elementary properties of this class
of frames and prove that root frames constitute a subclass of scalable frames.
In addition, we show that root frames are examples of a larger class of frames
called \emph{eigenframes}.


title: Swarmalators on a ring with distributed couplings

abstract:
  We study a simple model of identical swarmalators, generalizations of phases
oscillators that swarm through space. We confine the movements to a
one-dimensional (1D) ring and consider distributed (non-identical) couplings;
the combination of these two effects captures an aspect of the more realistic
2D swarmalator model \cite{o2017oscillators}. We find new collective states as
well as generalizations of previously reported ones which we describe
analytically. These states imitate the behavior of vinegar eels, catalytic
microswimmers, and other swarmalators which move on quasi-1D rings.


title:
Is the Contralateral Delay Activity (CDA) a robust neural correlate for
  Visual Working Memory (VWM) tasks? A reproducibility study

abstract:
  Visual working memory (VWM) allows us to actively store, update and
manipulate visual information surrounding us. While the underlying neural
mechanisms of VWM remain unclear, contralateral delay activity (CDA), a
sustained negativity over the hemisphere contralateral to the positions of
visual items to be remembered, is often used to study VWM. To investigate if
the CDA is a robust neural correlate for VWM tasks, we reproduced eight
CDA-related studies with a publicly accessible EEG dataset. We used the raw EEG
data from these eight studies and analyzed all of them with the same basic
pipeline to extract CDA. We were able to reproduce the results from all the
studies and show that with a basic automated EEG pipeline we can extract a
clear CDA signal. We share insights from the trends observed across the studies
and raise some questions about the CDA decay and the CDA during the recall
phase, which surprisingly, none of the eight studies did address. Finally, we
also provide reproducibility recommendations based on our experience and
challenges in reproducing these studies.


title:
When can we compute analytically lookback time, age of the universe, and
  luminosity distance?

abstract:
  In Friedmann-Lema\^itre-Robertson-Walker cosmology, it is sometimes possible
to compute analytically lookback time, age of the universe, and luminosity
distance versus redshift, expressing them in terms of a finite number of
elementary functions. We classify these situations using the Chebyshev theorem
of integration and providing examples.


title: Automatic Hardware Trojan Insertion using Machine Learning

abstract:
  Due to the current horizontal business model that promotes increasing
reliance on untrusted third-party Intellectual Properties (IPs), CAD tools, and
design facilities, hardware Trojan attacks have become a serious threat to the
semiconductor industry. Development of effective countermeasures against
hardware Trojan attacks requires: (1) fast and reliable exploration of the
viable Trojan attack space for a given design and (2) a suite of high-quality
Trojan-inserted benchmarks that meet specific standards. The latter has become
essential for the development and evaluation of design/verification solutions
to achieve quantifiable assurance against Trojan attacks. While existing static
benchmarks provide a baseline for comparing different countermeasures, they
only enumerate a limited number of handcrafted Trojans from the complete Trojan
design space. To accomplish these dual objectives, in this paper, we present
MIMIC, a novel AI-guided framework for automatic Trojan insertion, which can
create a large population of valid Trojans for a given design by mimicking the
properties of a small set of known Trojans. While there exist tools to
automatically insert Trojan instances using fixed Trojan templates, they cannot
analyze known Trojan attacks for creating new instances that accurately capture
the threat model. MIMIC works in two major steps: (1) it analyzes structural
and functional features of existing Trojan populations in a multi-dimensional
space to train machine learning models and generate a large number of "virtual
Trojans" of the given design, (2) next, it binds them into the design by
matching their functional/structural properties with suitable nets of the
internal logic structure. We have developed a complete tool flow for MIMIC,
extensively evaluated the framework by exploring several use-cases, and
quantified its effectiveness to demonstrate highly promising results.


title: On Parametric Optimal Execution and Machine Learning Surrogates

abstract:
  We investigate optimal order execution problems in discrete time with
instantaneous price impact and stochastic resilience. First, in the setting of
linear transient price impact we derive a closed-form recursion for the optimal
strategy, extending the deterministic results from Obizhaeva and Wang (J
Financial Markets, 2013). Second, we develop a numerical algorithm based on
dynamic programming and deep learning for the case of nonlinear transient price
impact as proposed by Bouchaud et al. (Quant. Finance, 2004). Specifically, we
utilize an actor-critic framework that constructs two neural-network (NN)
surrogates for the value function and the feedback control. The flexible
scalability of NN functional approximators enables parametric learning, i.e.,
incorporating several model or market parameters as part of the input space.
Precise calibration of price impact, resilience, etc., is known to be extremely
challenging and hence it is critical to understand sensitivity of the execution
policy to these parameters. Our NN learner organically scales across multiple
input dimensions and is shown to accurately approximate optimal strategies
across a wide range of parameter configurations. We provide a fully
reproducible Jupyter Notebook with our NN implementation, which is of
independent pedagogical interest, demonstrating the ease of use of NN
surrogates in (parametric) stochastic control problems.


title:
MASSIVE: A 1M-Example Multilingual Natural Language Understanding
  Dataset with 51 Typologically-Diverse Languages

abstract:
  We present the MASSIVE dataset--Multilingual Amazon Slu resource package
(SLURP) for Slot-filling, Intent classification, and Virtual assistant
Evaluation. MASSIVE contains 1M realistic, parallel, labeled virtual assistant
utterances spanning 51 languages, 18 domains, 60 intents, and 55 slots. MASSIVE
was created by tasking professional translators to localize the English-only
SLURP dataset into 50 typologically diverse languages from 29 genera. We also
present modeling results on XLM-R and mT5, including exact match accuracy,
intent classification accuracy, and slot-filling F1 score. We have released our
dataset, modeling code, and models publicly.


title:
VQGAN-CLIP: Open Domain Image Generation and Editing with Natural
  Language Guidance

abstract:
  Generating and editing images from open domain text prompts is a challenging
task that heretofore has required expensive and specially trained models. We
demonstrate a novel methodology for both tasks which is capable of producing
images of high visual quality from text prompts of significant semantic
complexity without any training by using a multimodal encoder to guide image
generations. We demonstrate on a variety of tasks how using CLIP [37] to guide
VQGAN [11] produces higher visual quality outputs than prior, less flexible
approaches like DALL-E [38], GLIDE [33] and Open-Edit [24], despite not being
trained for the tasks presented. Our code is available in a public repository.


title: A Region-Based Deep Learning Approach to Automated Retail Checkout

abstract:
  Automating the product checkout process at conventional retail stores is a
task poised to have large impacts on society generally speaking. Towards this
end, reliable deep learning models that enable automated product counting for
fast customer checkout can make this goal a reality. In this work, we propose a
novel, region-based deep learning approach to automate product counting using a
customized YOLOv5 object detection pipeline and the DeepSORT algorithm. Our
results on challenging, real-world test videos demonstrate that our method can
generalize its predictions to a sufficient level of accuracy and with a fast
enough runtime to warrant deployment to real-world commercial settings. Our
proposed method won 4th place in the 2022 AI City Challenge, Track 4, with an
F1 score of 0.4400 on experimental validation data.


title: INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL

abstract:
  Model-based reinforcement learning (RL) algorithms designed for handling
complex visual observations typically learn some sort of latent state
representation, either explicitly or implicitly. Standard methods of this sort
do not distinguish between functionally relevant aspects of the state and
irrelevant distractors, instead aiming to represent all available information
equally. We propose a modified objective for model-based RL that, in
combination with mutual information maximization, allows us to learn
representations and dynamics for visual model-based RL without reconstruction
in a way that explicitly prioritizes functionally relevant factors. The key
principle behind our design is to integrate a term inspired by variational
empowerment into a state-space model based on mutual information. This term
prioritizes information that is correlated with action, thus ensuring that
functionally relevant factors are captured first. Furthermore, the same
empowerment term also promotes faster exploration during the RL process,
especially for sparse-reward tasks where the reward signal is insufficient to
drive exploration in the early stages of learning. We evaluate the approach on
a suite of vision-based robot control tasks with natural video backgrounds, and
show that the proposed prioritized information objective outperforms
state-of-the-art model based RL approaches with higher sample efficiency and
episodic returns. https://sites.google.com/view/information-empowerment


title:
Multimodal Proximity and Visuotactile Sensing With a Selectively
  Transmissive Soft Membrane

abstract:
  The most common sensing modalities found in a robot perception system are
vision and touch, which together can provide global and highly localized data
for manipulation. However, these sensing modalities often fail to adequately
capture the behavior of target objects during the critical moments as they
transition out of static, controlled contact with an end-effector to dynamic
and uncontrolled motion. In this work, we present a novel multimodal
visuotactile sensor that provides simultaneous visuotactile and proximity depth
data. The sensor integrates an RGB camera and air pressure sensor to sense
touch with an infrared time-of-flight (ToF) camera to sense proximity by
leveraging a selectively transmissive soft membrane to enable the dual sensing
modalities. We present the mechanical design, fabrication techniques, algorithm
implementations, and evaluation of the sensor's tactile and proximity
modalities. The sensor is demonstrated in three open-loop robotic tasks:
approaching and contacting an object, catching, and throwing. The fusion of
tactile and proximity data could be used to capture key information about a
target object's transition behavior for sensor-based control in dynamic
manipulation.


title:
Spatial-Temporal Hypergraph Self-Supervised Learning for Crime
  Prediction

abstract:
  Crime has become a major concern in many cities, which calls for the rising
demand for timely predicting citywide crime occurrence. Accurate crime
prediction results are vital for the beforehand decision-making of government
to alleviate the increasing concern about the public safety. While many efforts
have been devoted to proposing various spatial-temporal forecasting techniques
to explore dependence across locations and time periods, most of them follow a
supervised learning manner, which limits their spatial-temporal representation
ability on sparse crime data. Inspired by the recent success in self-supervised
learning, this work proposes a Spatial-Temporal Hypergraph Self-Supervised
Learning framework (ST-HSL) to tackle the label scarcity issue in crime
prediction. Specifically, we propose the cross-region hypergraph structure
learning to encode region-wise crime dependency under the entire urban space.
Furthermore, we design the dual-stage self-supervised learning paradigm, to not
only jointly capture local- and global-level spatial-temporal crime patterns,
but also supplement the sparse crime representation by augmenting region
self-discrimination. We perform extensive experiments on two real-life crime
datasets. Evaluation results show that our ST-HSL significantly outperforms
state-of-the-art baselines. Further analysis provides insights into the
superiority of our ST-HSL method in the representation of spatial-temporal
crime patterns. The implementation code is available at
https://github.com/LZH-YS1998/STHSL.


title:
Exploiting sparseness in damage characterization: A close look at the
  regularization techniques

abstract:
  The idea of exploiting sparseness in under-determined damage characterization
problems is not new, and regularizations techniques that tend to promote
sparseness, such as L1-norm minimization, have been investigated in the last
ten years or so. Although various claims of merit have been made, two
interconnected issues put these claims into question, and this paper brings
some attention to the matter. The first is that the relationship between the
structural parameters and the modal features previously considered has been
linear and to ensure that the premise was closely realized, only very small
damage severities have been considered. The second issue, intimately related to
the first, is the fact that the noise, which has been typically taken as small
relative to the "change in the features", is then unrealistically small. In
problems where the damage is sufficiently large, the nonlinear dependence of
the features on the parameters cannot be generally discarded. It is found that
the attainable performance is much less "impressive" that what has been often
claimed. The paper also examines the potential merit of using Lp-norm (0<p<1)
minimization, instead of L1-norm minimization which, to the knowledge of the
writers, has not been previously examined in damage characterization research.
In this case we also find that, contrary to claims made in other areas, this
norm does not lead to any general improvement over the performance attained by
minimizing the L1-norm.


title:
Variable structure in the PDS70 disc and uncertainties in
  radio-interferometric image restoration

abstract:
  The compact mm-wavelength signal in the central cavity of the PDS70 disc,
revealed by deep ALMA observations, is aligned with unresolved Halpha emission,
and is thought to stem from a circum-planetary disc (CPD) around PDS70c. We
revisit the available ALMA data on PDS70c with alternative imaging strategies,
and with special attention to uncertainties and to the impact of the so-called
"JvM correction", which is thought to improve the dynamic range of restored
images. We also propose a procedure for the alignment and joint imaging of
multi-epoch visibility data. We find that the JvM correction exaggerates the
peak signal-to-noise of the data, by up to a factor of 10. In the case of
PDS70, we recover the detection of PDS70c from the July 2019 data, but only at
8sigma. However, its non-detection in Dec. 2017 suggests that PDS70c is
variable by at least 42%+-13% over a 1.75yr time-span, so similar to models of
the Halpha variability. We also pick up fine structure in the inner disc, such
that its peak is offset by ~ 0.04" from the disc centre. The inner disc is
variable too, which we tentatively ascribe to Keplerian rotation as well as
intrinsic morphological changes.


title:
DFRC with Improved Communication-Sensing Trade-off via Private
  Subcarrier Permutations and Pairing with Antennas

abstract:
  Dual function radar communication (DFRC) systems can achieve significant
improvements in spectrum efficiency, system complexity and energy efficiency,
and are attracting a lot of attention for next generation wireless system
design. This paper considers DFRC systems using MIMO radar with a sparse
transmit array, transmitting OFDM waveforms, and assigning shared and private
subcarriers to active transmit antennas. Subcarrier sharing allows antennas to
modulate data symbols onto the same subcarriers and enables high communication
rate, while the use of private subcarriers trades-off communication rate for
sensing performance by enabling the formulation of a virtual array with larger
aperture than the physical receive array. We propose to exploit the permutation
of private subcarriers among the available subcarriers and the pairing between
active antennas and private subcarriers to recover some of the communication
rate loss. Exploiting the $1$-sparse property of private subcarriers, we also
propose a low complexity algorithm to identify private subcarriers and detect
the antenna-subcarrier pairing.


title: A variational characterization of calibrated submanifolds

abstract:
  Let $M$ be a fixed compact oriented embedded submanifold of a manifold
$\overline{M}$. Consider the volume $\mathcal{V} (\overline{g}) = \int_M
\mathsf{vol}_{(M, g)}$ as a functional of the ambient metric $\overline{g}$ on
$\overline{M}$, where $g = \overline{g}|_M$. We show that $\overline{g}$ is a
critical point of $\mathcal{V}$ with respect to a special class of variations
of $\overline{g}$, obtained by varying a calibration $\mu$ on $\overline{M}$ in
a particular way, if and only if $M$ is calibrated by $\mu$. We do not assume
that the calibration is closed. We prove this for almost complex, associative,
coassociative, and Cayley calibrations, generalizing earlier work of Arezzo-Sun
in the almost K\"ahler case. The Cayley case turns out to be particularly
interesting, as it behaves quite differently from the others. We also apply
these results to obtain a variational characterization of Smith maps.


title: Context-Auditor: Context-sensitive Content Injection Mitigation

abstract:
  Cross-site scripting (XSS) is the most common vulnerability class in web
applications over the last decade. Much research attention has focused on
building exploit mitigation defenses for this problem, but no technique
provides adequate protection in the face of advanced attacks. One technique
that bypasses XSS mitigations is the scriptless attack: a content injection
technique that uses (among other options) CSS and HTML injection to infiltrate
data. In studying this technique and others, we realized that the common
property among the exploitation of all content injection vulnerabilities,
including not just XSS and scriptless attacks, but also command injections and
several others, is an unintended context switch in the victim program's parsing
engine that is caused by untrusted user input.
  In this paper, we propose Context-Auditor, a novel technique that leverages
this insight to identify content injection vulnerabilities ranging from XSS to
scriptless attacks and command injections. We implemented Context-Auditor as a
general solution to content injection exploit detection problem in the form of
a flexible, stand-alone detection module. We deployed instances of
Context-Auditor as (1) a browser plugin, (2) a web proxy (3) a web server
plugin, and (4) as a wrapper around potentially-injectable system endpoints.
Because Context-Auditor targets the root cause of content injection
exploitation (and, more specifically for the purpose of our prototype, XSS
exploitation, scriptless exploitation, and command injection), our evaluation
results demonstrate that Context-Auditor can identify and block content
injection exploits that modern defenses cannot while maintaining low throughput
overhead and avoiding false positives.


title:
ITSS: Interactive Web-Based Authoring and Playback Integrated
  Environment for Programming Tutorials

abstract:
  Video-based programming tutorials are a popular form of tutorial used by
authors to guide learners to code. Still, the interactivity of these videos is
limited primarily to control video flow. There are existing works with
increased interactivity that are shown to improve the learning experience.
Still, these solutions require setting up a custom recording environment and
are not well-integrated with the playback environment. This paper describes our
integrated ITSS environment and evaluates the ease of authoring and playback of
our interactive programming tutorials. Our environment is designed to run
within the browser sandbox and is less intrusive to record interactivity
actions. We develop a recording approach that tracks the author's interactivity
actions (e.g., typing code, highlighting words, scrolling panels) on the
browser and stored in text and audio formats. We replay these actions using the
recorded artefacts for learners to have a more interactive, integrated and
realistic playback of the author's actions instead of watching video frames.
Our design goals are 1) efficient recording and playback, 2) extensible
interactivity features to help students learn better, and 3) a scalable
web-based environment. Our first user study of 20 participants who carry out
the author tasks agree that it is efficient and easy to author interactive
videos in our environment with no additional software needed. Our second user
study of 84 students using the environment agrees that the increased
interactivity can help them learn better over a video-based tutorial. Our
performance test shows that the environment can scale to support up to 500
concurrent users. We hope our open-source environment enable more educators to
create interactive programming tutorials.


title:
Multi-UAV Collision Avoidance using Multi-Agent Reinforcement Learning
  with Counterfactual Credit Assignment

abstract:
  Multi-UAV collision avoidance is a challenging task for UAV swarm
applications due to the need of tight cooperation among swarm members for
collision-free path planning. Centralized Training with Decentralized Execution
(CTDE) in Multi-Agent Reinforcement Learning is a promising method for
multi-UAV collision avoidance, in which the key challenge is to effectively
learn decentralized policies that can maximize a global reward cooperatively.
We propose a new multi-agent critic-actor learning scheme called MACA for UAV
swarm collision avoidance. MACA uses a centralized critic to maximize the
discounted global reward that considers both safety and energy efficiency, and
an actor per UAV to find decentralized policies to avoid collisions. To solve
the credit assignment problem in CTDE, we design a counterfactual baseline that
marginalizes both an agent's state and action, enabling to evaluate the
importance of an agent in the joint observation-action space. To train and
evaluate MACA, we design our own simulation environment MACAEnv to closely
mimic the realistic behaviors of a UAV swarm. Simulation results show that MACA
achieves more than 16% higher average reward than two state-of-the-art MARL
algorithms and reduces failure rate by 90% and response time by over 99%
compared to a conventional UAV swarm collision avoidance algorithm in all test
scenarios.


title: Aeolian sediment transport on Io from lava-frost interactions

abstract:
  Surface modification on Jupiter's volcanically active moon, Io, has to date
been attributed almost exclusively to lava emplacement and volcanic plume
deposits. Here we demonstrate that wind-blown transport of sediment may also be
altering the Ionian surface. Specifically, shallow subsurface interactions
between lava and Io's widespread sulfur dioxide (SO$_2$) frost can produce
localized sublimation vapor flows with sufficient gas densities to enable
particle saltation. We calculate anticipated outgassing velocities from
lava-SO$_2$ frost interactions, and compare these to the saltation thresholds
predicted when accounting for the tenuous nature of the sublimated vapor. We
find that saltation may occur if frost temperatures surpass 155 K. Finally we
make the first measurements of the dimensions of linear features in images from
the Galileo probe, previously termed "ridges", which demonstrate certain
similarities to dunes on other planetary bodies. Io joins a growing list of
bodies with tenuous and transient atmospheres where aeolian sediment transport
may be an important control on the landscape.