title: Optical tracking in team sports

abstract:
  Sports analysis has gained paramount importance for coaches, scouts, and
fans. Recently, computer vision researchers have taken on the challenge of
collecting the necessary data by proposing several methods of automatic player
and ball tracking. Building on the gathered tracking data, data miners are able
to perform quantitative analysis on the performance of players and teams. With
this survey, our goal is to provide a basic understanding for quantitative data
analysts about the process of creating the input data and the characteristics
thereof. Thus, we summarize the recent methods of optical tracking by providing
a comprehensive taxonomy of conventional and deep learning methods, separately.
Moreover, we discuss the preprocessing steps of tracking, the most common
challenges in this domain, and the application of tracking data to sports
teams. Finally, we compare the methods by their cost and limitations, and
conclude the work by highlighting potential future research directions.


title:
Beyond the ordinary acoustoelectric effect: superluminal phenomena in
  the acoustic realm and phonon-mediated Bloch gain

abstract:
  It has been shown that coherent phonons can be used as a potent tool for
controlling and enhancing optoelectronic and transport properties of
nanostructured materials. Recent studies revealed that interaction of acoustic
phonons and fast-moving carriers in semiconductor heterostructures can be
accompanied by electron-phonon instabilities that cause ordinary and induced
Cherenkov effects. However, the development of such instabilities is still
poorly understood. Our study shows that other supersonic phenomena, beyond the
Cherenkov instability, are possible for non-equilibrium charge transport in the
miniband semiconductor superlattices (SLs) driven by an acoustic plane wave.
Using semiclassical nonperturbative methods and elements of the bifurcation
theory, we find the conditions for the onset of dynamical instabilities
(bifurcations) which are caused by the emission of specific SL phonons by
supersonic electrons, and their back action on the electrons. Notably, the
underlying radiation mechanism is connected either to normal or anomalous
Doppler effects in full accordance with the Ginzburg-Frank-Tamm theory. The
appearance of induced Doppler effects is also discussed in relation to the
formation of electron bunches propagating through the spatially periodic
structure of the SL. When the amplitude of the acoustic wave exceeds certain
threshold, the dynamical instabilities developed in the system are manifested
as drift velocity reversals, resonances in sound attenuation and absolute
negative mobility. We demonstrate that the discovered superluminal Doppler
phenomena can be utilized for tunable broadband amplification and generation of
GHz-THz electromagnetic waves, which creates a ground for development of novel
phononic devices.


title:
Constrained Bundle Adjustment for Structure From Motion Using
  Uncalibrated Multi-Camera Systems

abstract:
  Structure from motion using uncalibrated multi-camera systems is a
challenging task. This paper proposes a bundle adjustment solution that
implements a baseline constraint respecting that these cameras are static to
each other. We assume these cameras are mounted on a mobile platform,
uncalibrated, and coarsely synchronized. To this end, we propose the baseline
constraint that is formulated for the scenario in which the cameras have
overlapping views. The constraint is incorporated in the bundle adjustment
solution to keep the relative motion of different cameras static. Experiments
were conducted using video frames of two collocated GoPro cameras mounted on a
vehicle with no system calibration. These two cameras were placed capturing
overlapping contents. We performed our bundle adjustment using the proposed
constraint and then produced 3D dense point clouds. Evaluations were performed
by comparing these dense point clouds against LiDAR reference data. We showed
that, as compared to traditional bundle adjustment, our proposed method
achieved an improvement of 29.38%.


title:
Concentration in Lotka-Volterra parabolic equations: an
  asymptotic-preserving scheme

abstract:
  In this paper, we introduce and analyze an asymptotic-preserving scheme for
Lotka-Volterra parabolic equations. It is a class of nonlinear and nonlocal
stiff equations, which describes the evolution of a population structured with
phenotypic trait. In a regime of long time and small mutations, the population
concentrates at a set of dominant traits. The dynamics of this concentration is
described by a constrained Hamilton-Jacobi equation, which is a system coupling
a Hamilton-Jacobi equation with a Lagrange multiplier determined by a
constraint. This coupling makes the equation nonlocal. Moreover, the constraint
does not enjoy much regularity, since it can have jumps.
  The scheme we propose is convergent in all the regimes, and enjoys stability
in the long time and small mutations limit. Moreover, we prove that the
limiting scheme converges towards the viscosity solution of the constrained
Hamilton-Jacobi equation, despite the lack of regularity of the constraint. The
theoretical analysis of the schemes is illustrated and complemented with
numerical simulations.


title: Viscoelastic Cahn--Hilliard models for tumour growth

abstract:
  We introduce a new phase field model for tumour growth where viscoelastic
effects are taken into account. The model is derived from basic thermodynamical
principles and consists of a convected Cahn--Hilliard equation with source
terms for the tumour cells and a convected reaction-diffusion equation with
boundary supply for the nutrient. Chemotactic terms, which are essential for
the invasive behaviour of tumours, are taken into account. The model is
completed by a viscoelastic system constisting of the Navier--Stokes equation
for the hydrodynamic quantities, and a general constitutive equation with
stress relaxation for the left Cauchy--Green tensor associated with the elastic
part of the total mechanical response of the viscoelastic material.
  For a specific choice of the elastic energy density and with an additional
dissipative term accounting for stress diffusion, we prove existence of
global-in-time weak solutions of the viscoelastic model for tumour growth in
two space dimensions $d=2$ by the passage to the limit in a fully-discrete
finite element scheme where a CFL condition, i.e. $\Delta t\leq Ch^2$, is
required.
  Moreover, in arbitrary dimensions $d\in\{2,3\}$, we show stability and
existence of solutions for the fully-discrete finite element scheme, where
positive definiteness of the discrete Cauchy--Green tensor is proved with a
regularization technique that was first introduced by J. W. Barrett, S. Boyaval
("Existence and approximation of a (regularized) Oldroyd-B model". In: M3AS.
21.9 (2011), pp. 1783--1837). After that, we improve the regularity results in
arbitrary dimensions $d\in\{2,3\}$ and in two dimensions $d=2$, where a CFL
condition is required. Then, for $d=2$, we pass to the limit in the
discretization parameters and show that subsequences of discrete solutions
converge to a global-in-time weak solution.
  Finally, we present numerical results.


title: Process Mining on Uncertain Event Data

abstract:
  With the widespread adoption of process mining in organizations, the field of
process science is seeing an increase in the demand for ad-hoc analysis
techniques of non-standard event data. An example of such data are uncertain
event data: events characterized by a described and quantified attribute
imprecision. This paper outlines a research project aimed at developing process
mining techniques able to extract insights from uncertain data. We set the
basis for this research topic, recapitulate the available literature, and
define a future outlook.


title:
A Credible and Robust approach to Ego-Motion Estimation using an
  Automotive Radar

abstract:
  Consistent motion estimation is fundamental for all mobile autonomous
systems. While this sounds like an easy task, often, it is not the case because
of changing environmental conditions affecting odometry obtained from vision,
Lidar, or the wheels themselves. Unsusceptible to challenging lighting and
weather conditions, radar sensors are an obvious alternative. Usually,
automotive radars return a sparse point cloud, representing the surroundings.
Utilizing this information to motion estimation is challenging due to unstable
and phantom measurements, which result in a high rate of outliers. We introduce
a credible and robust probabilistic approach to estimate the ego-motion based
on these challenging radar measurements; intended to be used within a
loosely-coupled sensor fusion framework. Compared to existing solutions,
evaluated on the popular nuScenes dataset and others, we show that our proposed
algorithm is more credible while not depending on explicit correspondence
calculation.


title:
A cryogenic and superconducting inertial sensor for the Lunar
  Gravitational--Wave Antenna, the Einstein Telescope and Selene-physics

abstract:
  The Lunar Gravitational--Wave Antenna is a proposed low-frequency
gravitational-wave detector on the Moon surface. It will be composed of an
array of high-end cryogenic superconducting inertial sensors (CSISs). A
cryogenic environment will be used in combination with superconducting
materials to open up pathways to low-loss actuators and sensor mechanics. CSIS
revolutionizes the (cryogenic) inertial sensor field with a modelled
displacement sensitivity at 0.5 Hz of 3 orders of magnitude better than the
current state-of-the-art. It will allow the Lunar Gravitational-Wave Antenna to
be sensitive below 1 Hz, down to 1 mHz and it will also be employed in the
forthcoming Einstein Telescope --a third-generation gravitational-wave detector
which will make use of cryogenic technologies and that will have an enhanced
sensitivity below 10 Hz. Moreover, CSIS seismic data could also be employed to
obtain new insights about the Moon interior and what we can call the
Selene-physics.


title:
A Video Anomaly Detection Framework based on Appearance-Motion Semantics
  Representation Consistency

abstract:
  Video anomaly detection refers to the identification of events that deviate
from the expected behavior. Due to the lack of anomalous samples in training,
video anomaly detection becomes a very challenging task. Existing methods
almost follow a reconstruction or future frame prediction mode. However, these
methods ignore the consistency between appearance and motion information of
samples, which limits their anomaly detection performance. Anomalies only occur
in the moving foreground of surveillance videos, so the semantics expressed by
video frame sequences and optical flow without background information in
anomaly detection should be highly consistent and significant for anomaly
detection. Based on this idea, we propose Appearance-Motion Semantics
Representation Consistency (AMSRC), a framework that uses normal data's
appearance and motion semantic representation consistency to handle anomaly
detection. Firstly, we design a two-stream encoder to encode the appearance and
motion information representations of normal samples and introduce constraints
to further enhance the consistency of the feature semantics between appearance
and motion information of normal samples so that abnormal samples with low
consistency appearance and motion feature representation can be identified.
Moreover, the lower consistency of appearance and motion features of anomalous
samples can be used to generate predicted frames with larger reconstruction
error, which makes anomalies easier to spot. Experimental results demonstrate
the effectiveness of the proposed method.


title:
Dusty circumbinary discs: inner cavity structures and stopping locations
  of migrating planets

abstract:
  We present the results of two-fluid hydrodynamical simulations of
circumbinary discs consisting of gas and dust, with and without embedded
planets, to examine the influence of the dust on the structure of the tidally
truncated inner cavity and on the parking locations of migrating planets. In
this proof-of-concept study, we consider Kepler-16 and -34 analogues, and
examine dust fluids with Stokes numbers in the range $10^{-4} \le St \le
10^{-1}$ and dust-to-gas ratios of 0.01 and 1. For the canonical dust-to-gas
ratio of 0.01, we find the inclusion of the dust has only a minor effect on the
cavity and stopping locations of embedded planets compared to dust-free
simulations. However, for the enhanced dust-to-gas ratio of unity, assumed to
arise because of significant dust drift and accumulation, we find that the dust
can have a dramatic effect by shrinking and circularising the inner cavity,
which brings the parking locations of planets closer to the central binary.
This work demonstrates the importance of considering both gas and dust in
studies of circumbinary discs and planets, and provides a potential means of
explaining the orbital properties of circumbinary planets such as Kepler-34b,
which have hitherto been difficult to explain using gas-only hydrodynamical
simulations.


title:
Particle Videos Revisited: Tracking Through Occlusions Using Point
  Trajectories

abstract:
  Tracking pixels in videos is typically studied as an optical flow estimation
problem, where every pixel is described with a displacement vector that locates
it in the next frame. Even though wider temporal context is freely available,
prior efforts to take this into account have yielded only small gains over
2-frame methods. In this paper, we revisit Sand and Teller's "particle video"
approach, and study pixel tracking as a long-range motion estimation problem,
where every pixel is described with a trajectory that locates it in multiple
future frames. We re-build this classic approach using components that drive
the current state-of-the-art in flow and object tracking, such as dense cost
maps, iterative optimization, and learned appearance updates. We train our
models using long-range amodal point trajectories mined from existing optical
flow datasets that we synthetically augment with occlusions. We test our
approach in trajectory estimation benchmarks and in keypoint label propagation
tasks, and compare favorably against state-of-the-art optical flow and feature
tracking methods.


title:
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack
  Detector

abstract:
  Due to the importance of Critical Infrastructure (CI) in a nation's economy,
they have been lucrative targets for cyber attackers. These critical
infrastructures are usually Cyber-Physical Systems (CPS) such as power grids,
water, and sewage treatment facilities, oil and gas pipelines, etc. In recent
times, these systems have suffered from cyber attacks numerous times.
Researchers have been developing cyber security solutions for CIs to avoid
lasting damages. According to standard frameworks, cyber security based on
identification, protection, detection, response, and recovery are at the core
of these research. Detection of an ongoing attack that escapes standard
protection such as firewall, anti-virus, and host/network intrusion detection
has gained importance as such attacks eventually affect the physical dynamics
of the system. Therefore, anomaly detection in physical dynamics proves an
effective means to implement defense-in-depth. PASAD is one example of anomaly
detection in the sensor/actuator data, representing such systems' physical
dynamics. We present EPASAD, which improves the detection technique used in
PASAD to detect these micro-stealthy attacks, as our experiments show that
PASAD's spherical boundary-based detection fails to detect. Our method EPASAD
overcomes this by using Ellipsoid boundaries, thereby tightening the boundaries
in various dimensions, whereas a spherical boundary treats all dimensions
equally. We validate EPASAD using the dataset produced by the TE-process
simulator and the C-town datasets. The results show that EPASAD improves
PASAD's average recall by 5.8% and 9.5% for the two datasets, respectively.


title: Design of an Optimal Testbed for Tracking of Tagged Marine Megafauna

abstract:
  Underwater acoustic technologies are a key component for exploring the
behavior of marine megafauna such as sea turtles, sharks, and seals. The
animals are marked with acoustic devices (tags) that periodically emit signals
encoding the device's ID along with sensor data such as depth, temperature, or
the dominant acceleration axis - data that is collected by a network of
deployed receivers. In this work, we aim to optimize the locations of receivers
for best tracking of acoustically tagged marine megafauna. The outcomes of such
tracking allows the evaluation of the animals' motion patterns, their hours of
activity, and their social interactions. In particular, we focus on how to
determine the receivers' deployment positions to maximize the coverage area in
which the tagged animals can be tracked. For example, an overly-condensed
deployment may not allow accurate tracking, whereas a sparse one, may lead to a
small coverage area due to too few detections. We formalize the question of
where to best deploy the receivers as a non-convex constraint optimization
problem that takes into account the local environment and the specifications of
the tags, and offer a sub-optimal, low-complexity solution that can be applied
to large testbeds. Numerical investigation for three stimulated sea
environments shows that our proposed method is able to increase the
localization coverage area by 30%, and results from a test case experiment
demonstrate similar performance in a real sea environment. We share the
implementation of our work to help researchers set up their own acoustic
observatory.


title: Optimal Lane-Free Crossing of CAVs through Intersections

abstract:
  Connected and autonomous vehicles (CAVs), unlike conventional cars, will
utilise the whole space of intersections and cross in a lane-free order. This
paper formulates such a lane-free crossing of intersections as a
multi-objective optimal control problem (OCP) that minimises the overall
crossing time, as well as the energy consumption of CAVs. The proposed OCP is
convexified by applying the dual problem theory to the constraints that avoid
collision of vehicles with each other and with road boundaries. The resulting
OCP is smooth and solvable by gradient-based algorithms. Simulation results
show that the proposed algorithm reduces the crossing time by an average of 40%
and 41% as compared to, respectively, the state-of-the-art reservation-based
and lane-free methods, whilst consuming the same amount of energy. Furthermore,
it is shown that the resulting crossing time of the proposed algorithm is i)
fixed to a constant value regardless of the number of CAVs, and ii) very close
to its theoretical limit.


title:
Custom Sine Waves Are Enough for Imitation Learning of Bipedal Gaits
  with Different Styles

abstract:
  Not until recently, robust bipedal locomotion has been achieved through
reinforcement learning. However, existing implementations rely heavily on
insights and efforts from human experts, which is costly for the iterative
design of robot systems. Also, styles of the learned motion are strictly
limited to that of the reference. In this paper, we propose a new way to learn
bipedal locomotion from a simple sine wave as the reference for foot heights.
With the naive human insight that the two feet should be lifted up
alternatively and periodically, we experimentally demonstrate on the Cassie
robot that, a simple reward function is able to make the robot learn to walk
end-to-end and efficiently without any explicit knowledge of the model. With
custom sine waves, the learned gait pattern can also have customized styles.
Codes will be released at github.com/WooQi57/sin-cassie-rl.


title: Hensel minimality: Geometric criteria for $\ell$-h-minimality

abstract:
  Recently, Cluckers, Halupczok and Rideau-Kikuchi developed a new axiomatic
framework for tame non-Archimedean geometry, called Hensel minimality. It was
extended to mixed characteristic together with the author. Hensel minimality
aims to mimic o-minimality in both strong consequences and wide applicability.
In this article, we continue the study of Hensel minimality, in particular
focusing on $\omega$-h-minimality and $\ell$-h-minimality, for $\ell$ a
positive integer. Our main results include an analytic criterion for
$\ell$-h-minimality, preservation of $\ell$-h-minimality under coarsening of
the valuation and $\ell$-dimensional dimensional geometry.


title: Quantum encoding is suitable for matched filtering

abstract:
  Matched filtering is a powerful signal searching technique used in several
employments from radar and communications applications to gravitational-wave
detection. Here we devise a method for matched filtering with the use of
quantum bits. Our method's asymptotic time complexity does not depend on
template length and, including encoding, is $\mathcal{O}(L(\log_2L)^2)$ for a
data with length $L$ and a template with length $N$, which is classically
$\mathcal{O}(NL)$. Hence our method has superior time complexity over the
classical computation for long templates. We demonstrate our method with real
quantum hardware on 4 qubits and also with simulations.

categories: quant-ph astro-ph.IM cs.CC cs.ET eess.SP


title:
Leverage the Average: Averaged Sampling in Pre-Silicon Side-Channel
  Leakage Assessment

abstract:
  Pre-silicon side-channel leakage assessment is a useful tool to identify
hardware vulnerabilities at design time, but it requires many high-resolution
power traces and increases the power simulation cost of the design. By
downsampling and averaging these high-resolution traces, we show that the power
simulation cost can be considerably reduced without significant loss of
side-channel leakage assessment quality. We introduce a theoretical basis for
our claims. Our results demonstrate up to 6.5-fold power-simulation speed
improvement on a gate-level side-channel leakage assessment of a RISC-V SoC.
Furthermore, we clarify the conditions under which the averaged sampling
technique can be successfully used.


title:
Accelerating Stochastic Sequential Quadratic Programming for Equality
  Constrained Optimization using Predictive Variance Reduction

abstract:
  In this paper, we propose a stochastic method for solving equality
constrained optimization problems that utilizes predictive variance reduction.
Specifically, we develop a method based on the sequential quadratic programming
paradigm that employs variance reduction in the gradient approximations. Under
reasonable assumptions, we prove that a measure of first-order stationarity
evaluated at the iterates generated by our proposed algorithm converges to zero
in expectation from arbitrary starting points, for both constant and adaptive
step size strategies. Finally, we demonstrate the practical performance of our
proposed algorithm on constrained binary classification problems that arise in
machine learning.


title: Stable Matching: Choosing Which Proposals to Make

abstract:
  Why does stable matching work well in practice despite agents only providing
short preference lists? Perhaps the most compelling explanation is due to Lee
(Lee, 2016). He considered a model with preferences based on correlated
cardinal utilities. The utilities were based on common public values for each
agent and individual private values. He showed that for suitable utility
functions, in large markets, for most agents, all stable matchings yield
similar valued utilities.
  By means of a new analysis, we strengthen this result, showing that in large
markets, with high probability, for \emph{all} but the bottommost agents, all
stable matches yield similar valued utilities. We can then deduce that for
\emph{all} but the bottommost agents, relatively short preference lists
suffice.
  Our analysis shows that the key distinction is between models in which the
derivatives of the utility function are bounded, and those in which they can be
unbounded. For the bounded derivative model, we show that for any given
constant $c\ge 1$, with $n$ agents on each side of the market, with probability
$1 - n^{-c}$, for each agent its possible utilities in all stable matches vary
by at most $O((c\ln n/n)^{1/3})$ for all but the bottommost $O((c\ln
n/n)^{1/3})$ fraction of the agents. When the derivatives can be unbounded, we
obtain the following bound on the utility range and the bottommost fraction:
for any constant $\epsilon>0$, for large enough $n$, the bound is $\epsilon$.
Both these bounds are tight. In the bounded derivative model, we also show the
existence of an $\epsilon$-Bayes-Nash equilibrium in which agents make
relatively few proposals. Our results all rely on a new technique for
sidestepping the conditioning between the matching events that occur over the
course of a run of the Deferred Acceptance algorithm. We complement these
theoretical results with a variety of simulation experiments.


title: Contextual Representation Learning beyond Masked Language Modeling

abstract:
  How do masked language models (MLMs) such as BERT learn contextual
representations? In this work, we analyze the learning dynamics of MLMs. We
find that MLMs adopt sampled embeddings as anchors to estimate and inject
contextual semantics to representations, which limits the efficiency and
effectiveness of MLMs. To address these issues, we propose TACO, a simple yet
effective representation learning approach to directly model global semantics.
TACO extracts and aligns contextual semantics hidden in contextualized
representations to encourage models to attend global semantics when generating
contextualized representations. Experiments on the GLUE benchmark show that
TACO achieves up to 5x speedup and up to 1.2 points average improvement over
existing MLMs. The code is available at https://github.com/FUZHIYI/TACO.


title:
Uncertain Case Identifiers in Process Mining: A User Study of the
  Event-Case Correlation Problem on Click Data

abstract:
  Among the many sources of event data available today, a prominent one is user
interaction data. User activity may be recorded during the use of an
application or website, resulting in a type of user interaction data often
called click data. An obstacle to the analysis of click data using process
mining is the lack of a case identifier in the data. In this paper, we show a
case and user study for event-case correlation on click data, in the context of
user interaction events from a mobility sharing company. To reconstruct the
case notion of the process, we apply a novel method to aggregate user
interaction data in separate user sessions-interpreted as cases-based on neural
networks. To validate our findings, we qualitatively discuss the impact of
process mining analyses on the resulting well-formed event log through
interviews with process experts.


title: Cohomological and motivic inclusion-exclusion

abstract:
  We categorify the inclusion-exclusion principle for partially ordered
topological spaces and schemes to a filtration on the derived category of
sheaves. As a consequence, we obtain functorial spectral sequences that
generalize the two spectral sequences of a stratified space and certain
Vassiliev-type spectral sequences; we also obtain Euler characteristic analogs
in the Grothendieck ring of varieties. As an application, we give an
algebro-geometric proof of Vakil and Wood's homological stability conjecture
for the space of smooth hypersurface sections of a smooth projective variety.
In characteristic zero this conjecture was previously established by Aumonier
via topological methods.


title: Self-supervised Speaker Diarization

abstract:
  Over the last few years, deep learning has grown in popularity for speaker
verification, identification, and diarization. Inarguably, a significant part
of this success is due to the demonstrated effectiveness of their speaker
representations. These, however, are heavily dependent on large amounts of
annotated data and can be sensitive to new domains. This study proposes an
entirely unsupervised deep-learning model for speaker diarization.
Specifically, the study focuses on generating high-quality neural speaker
representations without any annotated data, as well as on estimating secondary
hyperparameters of the model without annotations.
  The speaker embeddings are represented by an encoder trained in a
self-supervised fashion using pairs of adjacent segments assumed to be of the
same speaker. The trained encoder model is then used to self-generate
pseudo-labels to subsequently train a similarity score between different
segments of the same call using probabilistic linear discriminant analysis
(PLDA) and further to learn a clustering stopping threshold. We compared our
model to state-of-the-art unsupervised as well as supervised baselines on the
CallHome benchmarks. According to empirical results, our approach outperforms
unsupervised methods when only two speakers are present in the call, and is
only slightly worse than recent supervised models.


title:
An ultra-deep multi-band VLA survey of the faint radio sky (COSMOS-XS):
  New constraints on the cosmic star formation history

abstract:
  We make use of ultra-deep 3 GHz Karl G. Jansky Very Large Array observations
of the COSMOS field from the multi-band COSMOS-XS survey to infer radio
luminosity functions (LFs) of star-forming galaxies (SFGs). Using $\sim$1300
SFGs with redshifts out to $z\sim4.6$, and fixing the faint and bright end
shape of the radio LF to the local values, we find a strong redshift trend that
can be fitted by pure luminosity evolution with the luminosity parameter given
by $\alpha_L \propto (3.40 \pm 0.11) - (0.48 \pm 0.06)z$. We then combine the
ultra-deep COSMOS-XS data-set with the shallower VLA-COSMOS $\mathrm{3\,GHz}$
large project data-set over the wider COSMOS field in order to fit for joint
density+luminosity evolution, finding evidence for significant density
evolution. By comparing the radio LFs to the observed far-infrared (FIR) and
ultraviolet (UV) LFs, we find evidence of a significant underestimation of the
UV LF by $21.6\%\, \pm \, 14.3 \, \%$ at high redshift ($3.3\,<\,z\,<\,4.6$,
integrated down to $0.03\,L^{\star}_{z=3}$). We derive the cosmic star
formation rate density (SFRD) by integrating the fitted radio LFs and find that
the SFRD rises up to $z\,\sim\,1.8$ and then declines more rapidly than
previous radio-based estimates. A direct comparison between the radio SFRD and
a recent UV-based SFRD, where we integrate both LFs down to a consistent limit
($0.038\,L^{\star}_{z=3}$), reveals that the discrepancy between the radio and
UV LFs translates to a significant ($\sim$1 dex) discrepancy in the derived
SFRD at $z>3$, even assuming the latest dust corrections and without accounting
for optically dark sources.


title: Ranking with submodular functions on a budget

abstract:
  Submodular maximization has been the backbone of many important
machine-learning problems, and has applications to viral marketing,
diversification, sensor placement, and more. However, the study of maximizing
submodular functions has mainly been restricted in the context of selecting a
set of items. On the other hand, many real-world applications require a
solution that is a ranking over a set of items. The problem of ranking in the
context of submodular function maximization has been considered before, but to
a much lesser extent than item-selection formulations. In this paper, we
explore a novel formulation for ranking items with submodular valuations and
budget constraints. We refer to this problem as max-submodular ranking (MSR).
In more detail, given a set of items and a set of non-decreasing submodular
functions, where each function is associated with a budget, we aim to find a
ranking of the set of items that maximizes the sum of values achieved by all
functions under the budget constraints. For the MSR problem with cardinality-
and knapsack-type budget constraints we propose practical algorithms with
approximation guarantees. In addition, we perform an empirical evaluation,
which demonstrates the superior performance of the proposed algorithms against
strong baselines.


title:
Plane wave implementation of the magnetic force theorem for magnetic
  exchange constants: Application to bulk Fe, Co and Ni

abstract:
  We present a plane wave implementation of the magnetic force theorem, which
provides a first principles framework for extracting exchange constants
parameterizing a classical Heisenberg model description of magnetic materials.
It is shown that the full microscopic exchange tensor may be expressed in terms
of the static Kohn-Sham susceptibility tensor and the exchange-correlation
magnetic field. This formulation allows one to define arbitrary magnetic sites
localized to predefined spatial regions, hence rendering the problem of finding
Heisenberg parameters independent of any orbital decomposition of the problem.
The susceptibility is calculated in a plane wave basis, which allows for
systematic convergence with respect to unoccupied bands and spatial
representation. We then apply the method to the well-studied problem of
calculating adiabatic spin wave spectra for bulk Fe, Co and Ni, finding good
agreement with previous calculations. In particular, we utilize the freedom of
defining magnetic sites to show that the calculated Heisenberg parameters are
robust towards changes in the definition of magnetic sites. This demonstrates
that the magnetic sites can be regarded as well defined and thus asserts the
relevance of the Heisenberg model description despite the itinerant nature of
the magnetic state.


title:
Automatic Data Augmentation Selection and Parametrization in Contrastive
  Self-Supervised Speech Representation Learning

abstract:
  Contrastive learning enables learning useful audio and speech representations
without ground-truth labels by maximizing the similarity between latent
representations of similar signal segments. In this framework various data
augmentation techniques are usually exploited to help enforce desired
invariances within the learned representations, improving performance on
various audio tasks thanks to more robust embeddings. Now, selecting the most
relevant augmentations has proven crucial for better downstream performances.
Thus, this work introduces a conditional independance-based method which allows
for automatically selecting a suitable distribution on the choice of
augmentations and their parametrization from a set of predefined ones, for
contrastive self-supervised pre-training. This is performed with respect to a
downstream task of interest, hence saving a costly hyper-parameter search.
Experiments performed on two different downstream tasks validate the proposed
approach showing better results than experimenting without augmentation or with
baseline augmentations. We furthermore conduct a qualitative analysis of the
automatically selected augmentations and their variation according to the
considered final downstream dataset.


title: Brittle membranes in finite elasticity

abstract:
  This work is devoted to the variational derivation of a reduced model for
brittle membranes in finite elasticity. The main mathematical tools we develop
for our analysis are: (i) a new density result in $GSBV^{p}$ of functions
satisfying a maximal-rank constraint on the subgradients, which can be
approximated by $C^{1}$-local immersions on regular subdomains of the cracked
set, and (ii) the construction of recovery sequences by means of suitable
$W^{1,\infty}$ diffeomorphisms mapping the regular subdomains onto the
fractured configuration.


title:
Simplified Analysis on Filtering Sensitivity Trade-offs in Continuous-
  and Discrete-Time Systems

abstract:
  A simplified analysis is performed on the Bode-type filtering sensitivity
trade-off integrals, which capture the sensitivity characteristics of the
estimate and estimation error with respect to the process input and estimated
signal in continuous- and discrete-time linear time-invariant filtering
systems. Compared with the previous analyses based on complex analysis and
Cauchy's residue theorem, the analysis results derived from the simplified
method are more explicit, thorough, and require less restrictive assumptions.
For continuous-time filtering systems, our simplified analysis reveals that
apart from the non-minimum phase zero sets reported in the previous literature,
the value and boundedness of filtering sensitivity integrals are also
determined by the leading coefficients, relative degrees, minimum phase zeros,
and poles of plants and filters. By invoking the simplified method, a
comprehensive analysis on the discrete-time filtering sensitivity integrals is
conducted for the first time. Numerical examples are provided to verify the
validity and correctness of the simplified analysis.


title:
Third-harmonic generation of spatially structured light in a
  quasi-periodically poled crystal

abstract:
  Nonlinear optical processes of spatially structured light, including optical
vortex and vector optical fields, have stimulated a lot of interesting physical
effects and found a variety of important applications ranging from optical
imaging to quantum information processing. However, high harmonic generation of
vector optical fields with space-varying polarization states is still a
challenge. Here we demonstrate third harmonic generation of spatially
structured light including vector optical fields, in a nonlinear Sagnac
interferometer containing a carefully designed quasi-periodically poled
potassium titanyl phosphate for the first time. The experimental results are in
good agreement with the theoretical predictions. Our results will enable to
manipulate spatially structured light or photons at new wavelengths and
carrying higher orbital angular momentum. Our approach has the potential
applications for the research of optical skyrmions and may open up new
opportunities to produce spatially structured entangled photons for quantum
communication and computation.


title:
Calculus and fine properties of functions of bounded variation on RCD
  spaces

abstract:
  We generalize the classical calculus rules satisfied by functions of bounded
variation to the framework of RCD spaces. In the infinite dimensional setting
we are able to define an analogue of the distributional differential and on
finite dimensional spaces we prove fine properties and suitable calculus rules,
such as the Vol'pert chain rule for vector valued functions.


title:
Nonlinear manipulation of orbital angular momentum spectra with second-
  and third- harmonic generation in a quasi-periodically poled crystal

abstract:
  Optical orbital angular momentum (OAM), as an important degree of freedom of
light, has been attracted extensive attention, due to its intrinsic feature of
natural discrete infinite dimension. Manipulation of OAM spectra is crucial for
many impressive applications from classical to quantum realms, in particular,
nonlinear manipulation of OAM spectra. Here we realized the nonlinear
manipulation of OAM spectra by using the simultaneous second- and
third-harmonic generation in a single nonlinear crystal of quasi-periodically
poled potassium titanyl phosphate, for fundamental waves with a variety of OAM
spectra, especially for customized OAM spectra of the second and third
harmonics. The experimental results confirmed the theoretical predictions. Our
approach not only provides a novel way to manipulate OAM spectra at new shorter
wavelengths that are hard to be directly generated, but also may find new
applications towards multiplexing in classical optics and high-dimensional
information processing in quantum optics.


title:
Path Defense in Dynamic Defender-Attacker Blotto Games (dDAB) with
  Limited Information

abstract:
  We consider a path guarding problem in dynamic Defender-Attacker Blotto games
(dDAB), where a team of robots must defend a path in a graph against
adversarial agents. Multi-robot systems are particularly well suited to this
application, as recent work has shown the effectiveness of these systems in
related areas such as perimeter defense and surveillance. When designing a
defender policy that guarantees the defense of a path, information about the
adversary and the environment can be helpful and may reduce the number of
resources required by the defender to achieve a sufficient level of security.
In this work, we characterize the necessary and sufficient number of assets
needed to guarantee the defense of a shortest path between two nodes in dDAB
games when the defender can only detect assets within $k$-hops of a shortest
path. By characterizing the relationship between sensing horizon and required
resources, we show that increasing the sensing capability of the defender
greatly reduces the number of defender assets needed to defend the path.


title: Non-inertial quantum clock frames lead to non-Hermitian dynamics

abstract:
  Recently, there have been many attempts to extend the notion of proper time
to quantum mechanics with the use of quantum clocks. Using a similar idea
combined with the relativistic mass-energy equivalence, we consider an
accelerating massive quantum particle with an internal clock system. We show
that the ensuing evolution from the perspective of the particle's internal
clock is non-Hermitian. This result does not rely on specific implementations
of the clock. As a particular consequence, we prove that the effective
Hamiltonian of two gravitationally interacting particles is non-Hermitian from
the perspective of the clock of either particle.


title: Asymptotic behavior for anisotropic fractional energies

abstract:
  In this paper we investigate the asymptotic behavior of anisotropic
fractional energies as the fractional parameter $s\in (0,1)$ approaches both
$s\uparrow 1$ and $s\downarrow 0$ in the spirit of the celebrated papers of
Bourgain-Brezis-Mironescu \cite{BBM} and Maz'ya-Shaposhnikova \cite{MS}.
  Then, focusing con the case $s\uparrow 1$ we analyze the behavior of
solutions to the corresponding minimization problems and finally, we also study
the problem where a homogenization effect is combined with the localization
phenomena that occurs when $s\uparrow 1$.


title:
GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based
  Collaborative Filtering

abstract:
  Content-based collaborative filtering (CCF) provides personalized item
recommendations based on both users' interaction history and items' content
information. Recently, pre-trained language models (PLM) have been used to
extract high-quality item encodings for CCF. However, it is resource-intensive
to finetune PLM in an end-to-end (E2E) manner in CCF due to its multi-modal
nature: optimization involves redundant content encoding for interactions from
users. For this, we propose GRAM (GRadient Accumulation for Multi-modality):
(1) Single-step GRAM which aggregates gradients for each item while maintaining
theoretical equivalence with E2E, and (2) Multi-step GRAM which further
accumulates gradients across multiple training steps, with less than 40\% GPU
memory footprint of E2E. We empirically confirm that GRAM achieves a remarkable
boost in training efficiency based on five datasets from two task domains of
Knowledge Tracing and News Recommendation, where single-step and multi-step
GRAM achieve 4x and 45x training speedup on average, respectively.


title:
Transport coefficients of heavy quarkonia comparing with heavy quark
  coefficients

abstract:
  We revisit the transport coefficients of heavy quarkonia moving in
high-temperature QCD plasmas. The thermal width and mass shift for heavy
quarkonia are closely related to the momentum diffusion coefficient and its
dispersive counterpart for heavy quarks, respectively. For quarkonium at rest
in plasmas the longitudinal gluon part of the color-singlet self-energy diagram
is sufficient to determine the leading-order thermal width, whereas the
momentum dependence is obtained from the transverse gluon channel. Using the
quarkonium-gluon effective vertex based on the dipole interaction of color
charges, we discuss the damping rate, the effective rest and kinetic mass
shifts of slowly moving quarkonia and compare with the corresponding
coefficients of heavy quarks.


title:
Superspace First-order Formalism for Massless Arbitrary Superspin
  Supermultiplets

abstract:
  A new description of free massless superfields of arbitrary superspin $Y$
($Y>1/2$) is proposed. Following the first-order philosophy, we relax some of
the properties (reality, gauge redundancy) of the unconstrained higher spin
prepotentials and we construct first and half order invariants quantities.
These are used to write trivially invariant actions. Additional auxiliary
superfields that play the role of spin connections are used to enforce a new
local symmetry that restores the degrees of freedom.


title: Towards Using Gameplay Videos for Detecting Issues in Video Games

abstract:
  Context. The game industry is increasingly growing in recent years. Every
day, millions of people play video games, not only as a hobby, but also for
professional competitions (e.g., e-sports or speed-running) or for making
business by entertaining others (e.g., streamers). The latter daily produce a
large amount of gameplay videos in which they also comment live what they
experience. Since no software and, thus, no video game is perfect, streamers
may encounter several problems (such as bugs, glitches, or performance issues).
However, it is unlikely that they explicitly report such issues to developers.
The identified problems may negatively impact the user's gaming experience and,
in turn, can harm the reputation of the game and of the producer. Objective. We
aim at proposing and empirically evaluating GELID, an approach for
automatically extracting relevant information from gameplay videos by (i)
identifying video segments in which streamers experienced anomalies; (ii)
categorizing them based on their type and context in which appear (e.g., bugs
or glitches appearing in a specific level or scene of the game); and (iii)
clustering segments that regard the same specific issue. Method. We will build
on top of existing approaches able to identify videos that are relevant for a
specific video game. These represent the input of GELID that processes them to
achieve the defined objectives. We will experiment GELID on several gameplay
videos to understand the extent to which each of its steps is effective.


title:
Hint on new physics from the $W$-boson mass excess$-$axion-like
  particle, dark photon or Chameleon dark energy

abstract:
  The $W$-boson mass ($m_{W}=80.4335 \pm 0.0094 \mathrm{GeV}$) measured by the
CDF collaboration is in excess of the standard model (SM) prediction at a
confidence level of $7\sigma$, which is strongly in favor of the presence of
new particles or fields. In the literature, various new particles and/or fields
have been introduced to explain the astrophysical and experimental data and
their presence in principle may also enhance the $W$-boson mass. Here we
examine the models of axion-like particle (ALP), dark photon as well as
Chameleon dark energy to see whether one of them can provide a solution of the
$W$-boson mass excess. We find that the ALP interpretation is marginally
consistent with the astrophysical constraints except for a mass of $\sim 1$
GeV. For the dark photon, the constraint on the $\xi$ parameter has been
significantly narrowed down but no other astrophysical bounds are found
available to further check this possibility. The possibility of attributing
$W-$boson mass anomaly to the Chameleon dark energy has been ruled out by other
experiments.


title:
Stress accumulation versus shape flattening in frustrated, warped-jigsaw
  particle assemblies

abstract:
  Geometrically frustrated assembly has emerged as an attractive paradigm for
understanding and engineering assemblies with self-limiting, finite equilibrium
dimensions. We propose and study a novel 2D particle based on a so-called
"warped jigsaw" (WJ) shape design: directional bonds in a tapered particle
favor curvature along multi-particle rows that frustrate 2D lattice order. We
investigate how large-scale intra-assembly stress gradients emerge from the
microscopic properties of the particles using a combination of numerical
simulation and continuum elasticity. WJ particles can favor anisotropic ribbon
assemblies, whose lateral width may be self-limiting depending on the relative
strength of cohesive to elastic forces in the assembly, which we show to be
controlled by the range of interactions and degree of shape misfit. The upper
limits of self-limited size are controlled by the crossover between two elastic
modes in assembly: the accumulation of shear with increasing width at small
widths giving way to unbending of preferred row curvature, permitting assembly
to grow to unlimited sizes. We show that the stiffness controlling distinct
elastic modes is governed by combination and placement of repulsive and
attractive binding regions, providing a means to extend the range of
accumulating stress to sizes that are far in excess of the single particle
size, which we corroborate via numerical studies of discrete particles of
variable interactions. Lastly, we relate the ground-state energetics of the
model to lower and upper limits on equilibrium assembly size control set by the
fluctuations of width along the ribbon boundary.


title: Quantum Routing with Teleportation

abstract:
  We study the problem of implementing arbitrary permutations of qubits under
interaction constraints in quantum systems that allow for arbitrarily fast
local operations and classical communication (LOCC). In particular, we show
examples of speedups over swap-based and more general unitary routing methods
by distributing entanglement and using LOCC to perform quantum teleportation.
We further describe an example of an interaction graph for which teleportation
gives a logarithmic speedup in the worst-case routing time over swap-based
routing. We also study limits on the speedup afforded by quantum teleportation
- showing an $O(\sqrt{N \log N})$ upper bound on the separation in routing time
for any interaction graph - and give tighter bounds for some common classes of
graphs.


title: The Complexity of Infinite-Horizon General-Sum Stochastic Games

abstract:
  We study the complexity of computing stationary Nash equilibrium (NE) in
n-player infinite-horizon general-sum stochastic games. We focus on the problem
of computing NE in such stochastic games when each player is restricted to
choosing a stationary policy and rewards are discounted. First, we prove that
computing such NE is in PPAD (in addition to clearly being PPAD-hard). Second,
we consider turn-based specializations of such games where at each state there
is at most a single player that can take actions and show that these
(seemingly-simpler) games remain PPAD-hard. Third, we show that under further
structural assumptions on the rewards computing NE in such turn-based games is
possible in polynomial time. Towards achieving these results we establish
structural facts about stochastic games of broader utility, including
monotonicity of utilities under single-state single-action changes and
reductions to settings where each player controls a single state.


title:
A Low-Cost Robot Science Kit for Education with Symbolic Regression for
  Hypothesis Discovery and Validation

abstract:
  The next generation of physical science involves robot scientists -
autonomous physical science systems capable of experimental design, execution,
and analysis in a closed loop. Such systems have shown real-world success for
scientific exploration and discovery, including the first discovery of a
best-in-class material. To build and use these systems, the next generation
workforce requires expertise in diverse areas including ML, control systems,
measurement science, materials synthesis, decision theory, among others.
However, education is lagging. Educators need a low-cost, easy-to-use platform
to teach the required skills. Industry can also use such a platform for
developing and evaluating autonomous physical science methodologies. We present
the next generation in science education, a kit for building a low-cost
autonomous scientist. The kit was used during two courses at the University of
Maryland to teach undergraduate and graduate students autonomous physical
science. We discuss its use in the course and its greater capability to teach
the dual tasks of autonomous model exploration, optimization, and
determination, with an example of autonomous experimental "discovery" of the
Henderson-Hasselbalch equation.


title: Improving VANET's Performance by Incorporated Fog-Cloud Layer (FCL)

abstract:
  Because of its usefulness in various fields including as safety applications,
traffic control applications, and entertainment applications, VANET is an
essential topic that is now being investigated intensively. VANET confronts
numerous challenges in terms of reaction time, storage capacity, and
reliability, particularly in real-time applications. As a result, merging cloud
computing and cloud computing has recently been researched. The goal of this
study is to develop a system that merges the fog and cloud layers into a single
layer known as the included fog-cloud layer. To lower the time it takes for
real-time applications on VANETs to respond while also improving data flow
management over the Internet and achieving an efficient perception service
while avoiding the high cost of cloud connectivity.


title: Internet of Things Protection and Encryption: A Survey

abstract:
  The Internet of Things (IoT) has enabled a wide range of sectors to interact
effectively with their consumers in order to deliver seamless services and
products. Despite the widespread availability of (IoT) devices and their
Internet connectivity, they have a low level of information security integrity.
A number of security methods were proposed and evaluated in our research, and
comparisons were made in terms of energy and time in the encryption and
decryption processes. A ratification procedure is also performed on the devices
in the main manager, which is regarded as a full firewall for IoT devices. The
suggested algorithm's success has been shown utilizing low-cost Adriano Uno and
Raspberry Pi devices. Arduous Uno has been used to demonstrate the encryption
process in low-energy devices using a variety of algorithms, including Enhanced
Algorithm for Data Integrity and Authentication (EDAI) and raspberry, which
serves as a safety manager in low-energy device molecules. A variety of
enhanced algorithms used in conjunction with Blockchain software have also
assured the security and integrity of the information. These findings and
discussions are presented at the conclusion of the paper.


title: Internet of Robotic Things: Current Technologies and Applications

abstract:
  The Internet of Robotic Things (IoRT) is a new domain that aims to link the
IoT environment with robotic systems and technologies. IoRT connects robotic
systems, connects them to the cloud, and transfers critical information as well
as knowledge exchange to conduct complicated and intricate activities that a
human cannot readily perform. The pertinent notion of IoRT has been discussed
in this paper, along with the issues that this area faces on a daily basis.
Furthermore, technological applications have been examined in order to provide
a better understanding of IoRT and its current development phenomenon. The
study describes three layers of IoRT infrastructure: network and control,
physical, and service and application layer. In the next section, IoRT problems
have been presented, with a focus on data processing and the security and
safety of IoRT technological systems. In addition to discussing the
difficulties, appropriate solutions have been offered and recommended. IoRT is
regarded as an essential technology with the ability to bring about a plethora
of benefits in smart society upon adoption, contributing to the generation and
development of smart cities and industries in the near future.


title:
Interpreting electroweak precision data including the $W$-mass CDF
  anomaly

abstract:
  We perform a global fit of electroweak data, finding that the anomaly in the
$W$ mass claimed by the CDF collaboration can be reproduced as a universal
new-physics correction to the $T$ parameter or $| H^\dagger D_\mu H|^2$
operator. Contributions at tree-level from multi-TeV new physics can fit the
anomaly compatibly with collider bounds: we explore which scalar vacuum
expectation values (such as a triplet with zero hypercharge), $Z'$ vectors
(such as a $Z'$ coupled to the Higgs only), little-Higgs models or
higher-dimensional geometries provide good global fits. On the other hand, new
physics that contributes at loop-level must be around the weak scale to fit the
anomaly. Thereby it generically conflicts with collider bounds, that can be
bypassed assuming special kinematics like quasi-degenerate particles that decay
into Dark Matter (such as an inert Higgs doublet or appropriate supersymmetric
particles).


title:
Discrepancy in Grain Size Estimation of H${}_{2}$O Ice in the Outer
  Solar System and the Interstellar Medium

abstract:
  Widespread detection of amorphous and crystalline water (H${}_{2}$O) ice in
the outer solar system bodies and the interstellar medium has been confirmed
over the past decades. Radiative transfer models (RTMs) are used to estimate
the grain sizes of H${}_{2}$O ice from near-infrared (NIR) wavelengths. Wide
discrepancies in the estimation of H${}_{2}$O ice grain size on the Saturnian
moons (Hansen, 2009), as well as nitrogen (N${}_{2}$) and methane (CH${}_{4}$)
ices on Kuiper belt objects have been reported owing to different scattering
models used (Emran and Chevrier, 2022). We assess the discrepancy in the grain
size estimation of H${}_{2}$O ice at a temperature of 15, 40, 60, and 80 K
(amorphous) and 20, 40, 60, and 80 K (crystalline) - relevant to the outer
solar system and beyond. We compare the single scattering albedos of H${}_{2}$O
ice phases using the Mie theory (Mie, 1908) and Hapke approximation models
(Hapke, 1993) from the optical constant at NIR wavelengths (1 - 5 $\mu$m). This
study reveals that the Hapke approximation models - Hapke slab and internal
scattering model (ISM) - predict grain size of the crystalline phase, overall,
much better compared to the amorphous phase at temperatures of 15 - 80 K.
However, the Hapke slab model estimates much approximate grain sizes, in
general, to that of the Mie model's prediction while ISM exhibits a higher
uncertainty. We recommend using the Mie model for unknown spectra of outer
solar system bodies and beyond in estimating H${}_{2}$O ice grain sizes. While
choosing the approximation model for employing RTMs, we recommend using a Hapke
slab approximation model over the internal scattering model.


title: Rotation rate of high latitude and near polar coronal holes

abstract:
  For the period of 1997-2006, coronal holes detected in the SOHO/EIT 195 $\AA$
full disk calibrated images are used to compute the rotation rates of high
latitude and near polar coronal holes and, their latitudinal variation is
investigated. We find that, for different latitude zones between $80^{o}$ north
and $75^{o}$ south, for all their area, the number of days observed on the
solar disk, and their latitudes, coronal holes rotate rigidly. Estimated
magnitudes of sidereal rotation rate of the coronal holes are: $13.051 \pm
0.206$ deg/day for the equator, $12.993 \pm 0.064$ deg/day in the region of
higher latitudes and, $12.999 \pm 0.329$ deg/day near the polar regions. For
all the latitudes and the area, we have also investigated the annual variation
of rotation rates of these coronal holes. We find that, for all the years,
coronal holes rotate rigidly and their magnitude of equatorial, high latitude
and polar region rotation rates are independent of magnitude of solar activity.


title:
Nonequilibrium effects and transverse spherocity in ultra-relativistic
  proton-nucleus collisions

abstract:
  We investigate the effects of nonequilibrium dynamics in small colliding
systems by comparing a nonequilibrium transport approach, the
Parton-Hadron-String-Dynamics (PHSD), with a (2+1)D viscous hydrodynamic model,
VISHNew, which assumes local equilibrium. Focusing on p+Pb collisions at LHC
energy, we extract the initial conditions for the hydrodynamic model from PHSD,
in order to reduce the impact of the early out-of-equilibrium dynamics and
focus on the traces of nonequilibiurm in the ensuing medium evolution. We find
that quantities like energy density and bulk viscous pressure are highly
inhomogeneous on the transverse plane during the whole evolution, whereas the
hydrodynamic simulations dissolve more efficiently the initial spatial
irregularities, still keeping a high degree of inhomogeneity due to the smaller
size and lifetime of the medium produced in p+Pb collisions with respect to
heavy-ion reactions. As a first step that will help to identify the impact of
these nonequilibrium effects on final observables in proton-nucleus collisions,
we perform an analysis of the transverse spherocity, an event-shape observable
able to distinguish between jetty and isotropic configurations of transverse
momenta. We found that the spherocity distribution in PHSD is slightly shifted
towards the isotropic limit with respect to the hydrodynamic result. Even
though this dissimilarity is partially due to the difference in the final
charged particle production, it mainly comes from the different description
within the two frameworks of the medium produced in small colliding systems.
This finding supports the idea that multi-differential measurements, such as
those based on event categorization according to multiplicity and spherocity,
are useful to study final-state observables in ultrarelativistic proton-nucleus
collisions.


title:
Particle dynamics in non-rotating Konoplya and Zhidenko black hole
  immersed in an external uniform magnetic field

abstract:
  In this paper, we investigate the dynamics of particles in the background of
non-rotating Konoplya and Zhidenko black hole that is immersed in an external
uniform magnetic field. The work involves circular motion of electric and
magnetic particles and particle acceleration. First the motion of electric
charged particles is considered. The effective potential, energy and angular
momentum expressions are obtained along with their graphs. The analysis of ISCO
shows that radii of ISCO decrease with magnetic interaction parameter. Motion
of magnetically charged particles has also been studied.


title: Efficient Partial Credit Grading of Proof Blocks Problems

abstract:
  Proof Blocks is a software tool which allows students to practice writing
mathematical proofs by dragging and dropping lines instead of writing proofs
from scratch. In this paper, we address the problem of assigning partial credit
to students completing Proof Blocks problems. Because of the large solution
space, it is computationally expensive to calculate the difference between an
incorrect student solution and some correct solution, restricting the ability
to automatically assign students partial credit. We propose a novel algorithm
for finding the edit distance from an arbitrary student submission to some
correct solution of a Proof Blocks problem. We benchmark our algorithm on
thousands of student submissions from Fall 2020, showing that our novel
algorithm can perform over 100 times better than the naive algorithm on real
data. Our new algorithm has further applications in grading Parson's Problems,
as well as any other kind of homework or exam problem where the solution space
may be modeled as a directed acyclic graph.


title:
The Dyson-Schwinger equations and the non-perturbative solution of QED:
  exploring the two-photon-two-fermion irreducible vertex

abstract:
  A minimal truncated set of Dyson-Schwinger equations that allow exploring the
non-perturbative regime of QED is derived for a general linear covariant gauge.
This minimal set includes the propagators, the the photon-fermion, and the
two-photon-two-fermion vertices. If the equations for the first three
quantities are exact, to build a closed set of equations, the last one is
truncated ignoring further higher-order Green functions. We also show that the
truncated equation reproduces the lowest order perturbative result for the
two-photon-two-fermion vertex in the limit of the small coupling constant.
Then, the two-photon-two-fermion irreducible vertex is studied by combining the
corresponding Ward-Takahashi identity with the Dyson-Schwinger equation. The
longitudinal part of this irreducible vertex is computed from the
Ward-Takahashi identity, which is solved in the low energy limit when one of
the photons has zero energy (soft photon limit). For this kinematical
configuration, the general expression for the vertex is derived and its form
factors related to the photon-fermion vertex and fermion propagator.


title: Modern applications of machine learning in quantum sciences

abstract:
  In these Lecture Notes, we provide a comprehensive introduction to the most
recent advances in the application of machine learning methods in quantum
sciences. We cover the use of deep learning and kernel methods in supervised,
unsupervised, and reinforcement learning algorithms for phase classification,
representation of many-body quantum states, quantum feedback control, and
quantum circuits optimization. Moreover, we introduce and discuss more
specialized topics such as differentiable programming, generative models,
statistical approach to machine learning, and quantum machine learning.


title: Underwater Image Enhancement Using Pre-trained Transformer

abstract:
  The goal of this work is to apply a denoising image transformer to remove the
distortion from underwater images and compare it with other similar approaches.
Automatic restoration of underwater images plays an important role since it
allows to increase the quality of the images, without the need for more
expensive equipment. This is a critical example of the important role of the
machine learning algorithms to support marine exploration and monitoring,
reducing the need for human intervention like the manual processing of the
images, thus saving time, effort, and cost. This paper is the first application
of the image transformer-based approach called "Pre-Trained Image Processing
Transformer" to underwater images. This approach is tested on the UFO-120
dataset, containing 1500 images with the corresponding clean images.


title: Computational challenges for multi-loop collider phenomenology

abstract:
  Precision measurements at the LHC and future colliders require theory
predictions with uncertainties at the percent level for many observables.
Theory uncertainties due to the perturbative truncation are particularly
relevant and must be reduced to fully exploit the physics potential of collider
experiments. In recent years the theoretical high energy physics community has
made tremendous analytical and numerical advances to address this challenge. In
this white paper, we survey state-of-the-art calculations in perturbative
quantum field theory for collider phenomenology with a particular focus on the
computational requirements at high perturbative orders. We show that these
calculations can have specific high-performance-computing (HPC) profiles that
should to be taken into account in future HPC resource planning.


title:
A Mini-Chemical Scheme with Net Reactions for 3D GCMs I.: Thermochemical
  Kinetics

abstract:
  Growing evidence has indicated that the global composition distribution plays
an indisputable role in interpreting observational data. 3D general circulation
models (GCMs) with a reliable treatment of chemistry and clouds are
particularly crucial in preparing for the upcoming observations. In the effort
of achieving 3D chemistry-climate modeling, the challenge mainly lies in the
expensive computing power required for treating a large number of chemical
species and reactions. Motivated by the need for a robust and computationally
efficient chemical scheme, we devise a mini-chemical network with a minimal
number of species and reactions for H$_2$-dominated atmospheres. We apply a
novel technique to simplify the chemical network from a full kinetics model --
VULCAN by replacing a large number of intermediate reactions with net
reactions. The number of chemical species is cut down from 67 to 12, with the
major species of thermal and observational importance retained, including
H$_2$O, CH$_4$, CO, CO$_2$, C$_2$H$_2$, NH$_3$, and HCN. The size of the total
reactions is greatly reduced from $\sim$ 800 to 20. The mini-chemical scheme is
validated by verifying the temporal evolution and benchmarking the predicted
compositions in four exoplanet atmospheres (GJ 1214b, GJ 436b, HD 189733b, HD
209458b) against the full kinetics of VULCAN. It reproduces the chemical
timescales and composition distributions of the full kinetics well within an
order of magnitude for the major species in the pressure range of 1 bar -- 0.1
mbar across various metallicities and carbon-to-oxygen (C/O) ratios. The small
scale of the mini-chemical scheme permits simple use and fast computation,
which is optimal for implementation in a 3D GCM or a retrieval framework. We
focus on the thermochemical kinetics of net reactions in this paper and address
photochemistry in a follow-up paper.


title:
Low energy SUSY confronted with new measurements of W-boson mass and
  muon g-2

abstract:
  The new CDF II measurement of $W$-boson mass shows a 7$\sigma$ deviation from
the Standard Model (SM) prediction, while the recent FNAL measurement of the
muon $g-2$ shows a 4.2$\sigma$ deviation (combined with the BNL result) from
the SM. Both of them strongly indicate new physics beyond the SM. In this work
we study the implication of both measurements on low energy supersymmetry. With
an extensive exploration of the parameter space of the minimal supersymmetric
standard model (MSSM), we find that in the parameter space allowed by current
experimental constraints from colliders and dark matter detections, the MSSM
can simultaneously explain both measurements at $2\sigma$ level. The favored
parameter space, characterized by a compressed spectrum between bino, wino and
stau, with the stop being around 1 TeV, may be covered in the near future LHC
searches.


title:
Anomalous high-temperature THz nonlinearity in superconductors near the
  metal-insulator transition

abstract:
  The interplay of strong disorder and superconductivity is a topic of
long-term interest in condensed matter physics. Here we explore the nonlinear
THz response of superconducting NbN films close to the 3D metal-insulator
transition. For the least disordered samples, the magnitude of the nonlinear
$\chi^{(3)}$ response follows the temperature dependence of the superfluid
density as expected. In contrast, for high disorder samples near the
metal-insulator transition the $\chi^{(3)}$ nonlinearity persists to
temperatures as high as even 4 times the $T_c$ of the cleanest sample. We
discuss the possible origins of this remarkably large nonlinearity, including
the possibility that it arises in an enhancement of the temperature scales of
superconductivity close to localization. Our work highlights the importance of
finite frequency nonlinear THz experiments in detecting superconducting
correlations even into regions where long-range ordered superconductivity does
not persist.


title:
Impact of the recent measurements of the top-quark and W-boson masses on
  electroweak precision fits

abstract:
  We assess the impact of the very recent measurement of the top-quark mass by
the CMS Collaboration on the fit of electroweak data in the Standard Model and
beyond, with particular emphasis on the prediction for the mass of the W boson.
We then compare this prediction with the average of the corresponding
experimental measurements including the new measurement by the CDF
Collaboration, and discuss its compatibility in the Standard Model, in new
physics models with oblique corrections, and in the dimension-six Standard
Model Effective Field Theory. Finally, we present the updated global fit to
electroweak precision data in these models.


title: Energy spectrum of interacting gas: cluster expansion method

abstract:
  In this paper, we calculate the energy spectrum of interacting gases by
converting the cluster expansion method in statistical mechanics into a method
of solving energy eigenvalues. We obtain an explicit expression of the energy
eigenvalue, by which we can calculate the eigenvalue of an interacting gas from
the interparticle potential directly. As an example, we calculate the energy
spectrum for an interacting gas with soft-sphere potentials.


title: Sensitivity of the MnTe valence band to orientation of magnetic moments

abstract:
  An effective model of the hexagonal (NiAs-structure) manganese telluride
valence band in the vicinity of the A-point of the Brillouin zone is derived.
It is shown that while for the usual antiferromagnetic order (magnetic moments
in the basal plane) band splitting at A is small, their out-of-plane rotation
enhances the splitting dramatically (to about 0.5 eV). We propose extensions of
recent experiments (Moseley et al., Phys. Rev. Materials 6, 014404) where such
inversion of magnetocrystalline anisotropy has been observed in Li-doped MnTe,
to confirm this unusual sensitivity of a semiconductor band structure to
magnetic order.


title: Legitimate against Illegitimate IRSs on MISO Wiretap Channels

abstract:
  The low-cost legitimate intelligent reflecting surfaces (IRSs) are applied to
the wiretap channel in physical layer security to enhance the secrecy rate. In
practice, the eavesdropper can also deploy an IRS, namely illegitimate IRS, to
deteriorate the secrecy rate. This paper studies the interplay between a
transmitter, a legitimate IRS, and an illegitimate IRS in a multiple-input
single-output (MISO) wiretap channel. We formulate a max-min secrecy rate
problem, where all the information is available at the transmitter and the
receivers. We aim to design an efficient transmit beamforming and phase
shifting strategy of the legitimate IRS, under the worst-case secrecy rate
achieved based on optimizing the phase-shifting strategy of the illegitimate
IRS. We propose three solution methods based on the gradient descent ascent
(GDA), the alternate optimization (AO), and the mixed Nash equilibrium (NE) in
zero-sum games in strategic form. Simulation results show that for the
continuous phase-shifting strategies, AO usually does not guarantee
convergence, although it may achieve better performance than GDA in some
iterations. GDA usually converges to a stationary point. Discrete
phase-shifting strategies improve the convergence behavior of AO and GDA, while
there is a single mixed NE with the highest secrecy rate.


title: Metasurface-enhanced Light Detection and Ranging Technology

abstract:
  Deploying advanced imaging solutions to robotic and autonomous systems by
mimicking human vision requires simultaneous acquisition of multiple fields of
views, named the peripheral and fovea regions. Low-resolution peripheral field
provides coarse scene exploration to direct the eye to focus to a highly
resolved fovea region for sharp imaging. Among 3D computer vision techniques,
Light Detection and Ranging (LiDAR) is currently considered at the industrial
level for robotic vision. LiDAR is an imaging technique that monitors pulses of
light at optical frequencies to sense the space and to recover
three-dimensional ranging information. Notwithstanding the efforts on LiDAR
integration and optimization, commercially available devices have slow frame
rate and low image resolution, notably limited by the performance of mechanical
or slow solid-state deflection systems. Metasurfaces (MS) are versatile optical
components that can distribute the optical power in desired regions of space.
Here, we report on an advanced LiDAR technology that uses ultrafast low FoV
deflectors cascaded with large area metasurfaces to achieve large FoV and
simultaneous peripheral and central imaging zones. This technology achieves MHz
frame rate for 2D imaging, and up to KHz for 3D imaging, with extremely large
FoV (up to 150{\deg}deg. on both vertical and horizontal scanning axes). The
use of this disruptive LiDAR technology with advanced learning algorithms
offers perspectives to improve further the perception capabilities and
decision-making process of autonomous vehicles and robotic systems.


title: Learning Polynomial Transformations

abstract:
  We consider the problem of learning high dimensional polynomial
transformations of Gaussians. Given samples of the form $p(x)$, where $x\sim
N(0, \mathrm{Id}_r)$ is hidden and $p: \mathbb{R}^r \to \mathbb{R}^d$ is a
function where every output coordinate is a low-degree polynomial, the goal is
to learn the distribution over $p(x)$. This problem is natural in its own
right, but is also an important special case of learning deep generative
models, namely pushforwards of Gaussians under two-layer neural networks with
polynomial activations. Understanding the learnability of such generative
models is crucial to understanding why they perform so well in practice.
  Our first main result is a polynomial-time algorithm for learning quadratic
transformations of Gaussians in a smoothed setting. Our second main result is a
polynomial-time algorithm for learning constant-degree polynomial
transformations of Gaussian in a smoothed setting, when the rank of the
associated tensors is small. In fact our results extend to any
rotation-invariant input distribution, not just Gaussian. These are the first
end-to-end guarantees for learning a pushforward under a neural network with
more than one layer.
  Along the way, we also give the first polynomial-time algorithms with
provable guarantees for tensor ring decomposition, a popular generalization of
tensor decomposition that is used in practice to implicitly store large
tensors.


title: Dancing under the stars: video denoising in starlight

abstract:
  Imaging in low light is extremely challenging due to low photon counts. Using
sensitive CMOS cameras, it is currently possible to take videos at night under
moonlight (0.05-0.3 lux illumination). In this paper, we demonstrate
photorealistic video under starlight (no moon present, $<$0.001 lux) for the
first time. To enable this, we develop a GAN-tuned physics-based noise model to
more accurately represent camera noise at the lowest light levels. Using this
noise model, we train a video denoiser using a combination of simulated noisy
video clips and real noisy still images. We capture a 5-10 fps video dataset
with significant motion at approximately 0.6-0.7 millilux with no active
illumination. Comparing against alternative methods, we achieve improved video
quality at the lowest light levels, demonstrating photorealistic video
denoising in starlight for the first time.


title: Measuring AI Systems Beyond Accuracy

abstract:
  Current test and evaluation (T&E) methods for assessing machine learning (ML)
system performance often rely on incomplete metrics. Testing is additionally
often siloed from the other phases of the ML system lifecycle. Research
investigating cross-domain approaches to ML T&E is needed to drive the state of
the art forward and to build an Artificial Intelligence (AI) engineering
discipline. This paper advocates for a robust, integrated approach to testing
by outlining six key questions for guiding a holistic T&E strategy.


title: Accessing strongly-coupled systems without compromising them

abstract:
  The last decades have seen a burst of experimental platforms reaching the
so-called strong-coupling regime, where quantum coherent effects dominate over
incoherent processes such as dissipation and thermalization. This has allowed
us to create highly nontrivial quantum states and put counterintuitive
quantum-mechanical effects to test beyond the wildest expectations of the
founding fathers of quantum physics. The strong-coupling regime comes with
certain challenges though: the need for a large isolation makes it difficult to
access the system for control or monitoring purposes. In this work we propose a
way to access such systems through an engineered environment that does not
compromise their strong-coupling effects. As a proof of principle, we apply the
approach to the photon-blockade effect present in nonlinear resonators, but
argue that the mechanism is quite universal. We also propose an architecture
based on superconducting circuits where the required unconventional environment
can be implemented, opening the way to the experimental analysis of our ideas.


title: Structure-aware Protein Self-supervised Learning

abstract:
  Protein representation learning methods have shown great potential to yield
useful representation for many downstream tasks, especially on protein
classification. Moreover, a few recent studies have shown great promise in
addressing insufficient labels of proteins with self-supervised learning
methods. However, existing protein language models are usually pretrained on
protein sequences without considering the important protein structural
information. To this end, we propose a novel structure-aware protein
self-supervised learning method to effectively capture structural information
of proteins. In particular, a well-designed graph neural network (GNN) model is
pretrained to preserve the protein structural information with self-supervised
tasks from a pairwise residue distance perspective and a dihedral angle
perspective, respectively. Furthermore, we propose to leverage the available
protein language model pretrained on protein sequences to enhance the
self-supervised learning. Specifically, we identify the relation between the
sequential information in the protein language model and the structural
information in the specially designed GNN model via a novel pseudo bi-level
optimization scheme. Experiments on several supervised downstream tasks verify
the effectiveness of our proposed method.


title: Intelligent Sight and Sound: A Chronic Cancer Pain Dataset

abstract:
  Cancer patients experience high rates of chronic pain throughout the
treatment process. Assessing pain for this patient population is a vital
component of psychological and functional well-being, as it can cause a rapid
deterioration of quality of life. Existing work in facial pain detection often
have deficiencies in labeling or methodology that prevent them from being
clinically relevant. This paper introduces the first chronic cancer pain
dataset, collected as part of the Intelligent Sight and Sound (ISS) clinical
trial, guided by clinicians to help ensure that model findings yield clinically
relevant results. The data collected to date consists of 29 patients, 509
smartphone videos, 189,999 frames, and self-reported affective and activity
pain scores adopted from the Brief Pain Inventory (BPI). Using static images
and multi-modal data to predict self-reported pain levels, early models show
significant gaps between current methods available to predict pain today, with
room for improvement. Due to the especially sensitive nature of the inherent
Personally Identifiable Information (PII) of facial images, the dataset will be
released under the guidance and control of the National Institutes of Health
(NIH).


title:
Data-Free Quantization with Accurate Activation Clipping and Adaptive
  Batch Normalization

abstract:
  Data-free quantization is a task that compresses the neural network to low
bit-width without access to original training data. Most existing data-free
quantization methods cause severe performance degradation due to inaccurate
activation clipping range and quantization error, especially for low bit-width.
In this paper, we present a simple yet effective data-free quantization method
with accurate activation clipping and adaptive batch normalization. Accurate
activation clipping (AAC) improves the model accuracy by exploiting accurate
activation information from the full-precision model. Adaptive batch
normalization firstly proposes to address the quantization error from
distribution changes by updating the batch normalization layer adaptively.
Extensive experiments demonstrate that the proposed data-free quantization
method can yield surprisingly performance, achieving 64.33% top-1 accuracy of
ResNet18 on ImageNet dataset, with 3.7% absolute improvement outperforming the
existing state-of-the-art methods.


title: Learning Trajectory-Aware Transformer for Video Super-Resolution

abstract:
  Video super-resolution (VSR) aims to restore a sequence of high-resolution
(HR) frames from their low-resolution (LR) counterparts. Although some progress
has been made, there are grand challenges to effectively utilize temporal
dependency in entire video sequences. Existing approaches usually align and
aggregate video frames from limited adjacent frames (e.g., 5 or 7 frames),
which prevents these approaches from satisfactory results. In this paper, we
take one step further to enable effective spatio-temporal learning in videos.
We propose a novel Trajectory-aware Transformer for Video Super-Resolution
(TTVSR). In particular, we formulate video frames into several pre-aligned
trajectories which consist of continuous visual tokens. For a query token,
self-attention is only learned on relevant visual tokens along spatio-temporal
trajectories. Compared with vanilla vision Transformers, such a design
significantly reduces the computational cost and enables Transformers to model
long-range features. We further propose a cross-scale feature tokenization
module to overcome scale-changing problems that often occur in long-range
videos. Experimental results demonstrate the superiority of the proposed TTVSR
over state-of-the-art models, by extensive quantitative and qualitative
evaluations in four widely-used video super-resolution benchmarks. Both code
and pre-trained models can be downloaded at
https://github.com/researchmm/TTVSR.


title:
Feature-enhanced Adversarial Semi-supervised Semantic Segmentation
  Network for Pulmonary Embolism Annotation

abstract:
  This study established a feature-enhanced adversarial semi-supervised
semantic segmentation model to automatically annotate pulmonary embolism lesion
areas in computed tomography pulmonary angiogram (CTPA) images. In current
studies, all of the PE CTPA image segmentation methods are trained by
supervised learning. However, the supervised learning models need to be
retrained and the images need to be relabeled when the CTPA images come from
different hospitals. This study proposed a semi-supervised learning method to
make the model applicable to different datasets by adding a small amount of
unlabeled images. By training the model with both labeled and unlabeled images,
the accuracy of unlabeled images can be improved and the labeling cost can be
reduced. Our semi-supervised segmentation model includes a segmentation network
and a discriminator network. We added feature information generated from the
encoder of segmentation network to the discriminator so that it can learn the
similarity between predicted mask and ground truth mask. This HRNet-based
architecture can maintain a higher resolution for convolutional operations so
the prediction of small PE lesion areas can be improved. We used the labeled
open-source dataset and the unlabeled National Cheng Kung University Hospital
(NCKUH) (IRB number: B-ER-108-380) dataset to train the semi-supervised
learning model, and the resulting mean intersection over union (mIOU), dice
score, and sensitivity achieved 0.3510, 0.4854, and 0.4253, respectively on the
NCKUH dataset. Then, we fine-tuned and tested the model with a small amount of
unlabeled PE CTPA images from China Medical University Hospital (CMUH) (IRB
number: CMUH110-REC3-173) dataset. Comparing the results of our semi-supervised
model with the supervised model, the mIOU, dice score, and sensitivity improved
from 0.2344, 0.3325, and 0.3151 to 0.3721, 0.5113, and 0.4967, respectively.


title:
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes
  for Medical Image Super-Resolution

abstract:
  Super-resolving medical images can help physicians in providing more accurate
diagnostics. In many situations, computed tomography (CT) or magnetic resonance
imaging (MRI) techniques output several scans (modes) during a single
investigation, which can jointly be used (in a multimodal fashion) to further
boost the quality of super-resolution results. To this end, we propose a novel
multimodal multi-head convolutional attention module to super-resolve CT and
MRI scans. Our attention module uses the convolution operation to perform joint
spatial-channel attention on multiple concatenated input tensors, where the
kernel (receptive field) size controls the reduction rate of the spatial
attention and the number of convolutional filters controls the reduction rate
of the channel attention, respectively. We introduce multiple attention heads,
each head having a distinct receptive field size corresponding to a particular
reduction rate for the spatial attention. We integrate our multimodal
multi-head convolutional attention (MMHCA) into two deep neural architectures
for super-resolution and conduct experiments on three data sets. Our empirical
results show the superiority of our attention module over the state-of-the-art
attention mechanisms used in super-resolution. Moreover, we conduct an ablation
study to assess the impact of the components involved in our attention module,
e.g. the number of inputs or the number of heads.


title:
Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule
  Diagnosis

abstract:
  Lung cancer has the highest mortality rate of deadly cancers in the world.
Early detection is essential to treatment of lung cancer. However, detection
and accurate diagnosis of pulmonary nodules depend heavily on the experiences
of radiologists and can be a heavy workload for them. Computer-aided diagnosis
(CAD) systems have been developed to assist radiologists in nodule detection
and diagnosis, greatly easing the workload while increasing diagnosis accuracy.
Recent development of deep learning, greatly improved the performance of CAD
systems. However, lack of model reliability and interpretability remains a
major obstacle for its large-scale clinical application. In this work, we
proposed a multi-task explainable deep-learning model for pulmonary nodule
diagnosis. Our neural model can not only predict lesion malignancy but also
identify relevant manifestations. Further, the location of each manifestation
can also be visualized for visual interpretability. Our proposed neural model
achieved a test AUC of 0.992 on LIDC public dataset and a test AUC of 0.923 on
our in-house dataset. Moreover, our experimental results proved that by
incorporating manifestation identification tasks into the multi-task model, the
accuracy of the malignancy classification can also be improved. This multi-task
explainable model may provide a scheme for better interaction with the
radiologists in a clinical environment.


title:
Characterizing and Understanding the Behavior of Quantized Models for
  Reliable Deployment

abstract:
  Deep Neural Networks (DNNs) have gained considerable attention in the past
decades due to their astounding performance in different applications, such as
natural language modeling, self-driving assistance, and source code
understanding. With rapid exploration, more and more complex DNN architectures
have been proposed along with huge pre-trained model parameters. The common way
to use such DNN models in user-friendly devices (e.g., mobile phones) is to
perform model compression before deployment. However, recent research has
demonstrated that model compression, e.g., model quantization, yields accuracy
degradation as well as outputs disagreements when tested on unseen data. Since
the unseen data always include distribution shifts and often appear in the
wild, the quality and reliability of quantized models are not ensured. In this
paper, we conduct a comprehensive study to characterize and help users
understand the behaviors of quantized models. Our study considers 4 datasets
spanning from image to text, 8 DNN architectures including feed-forward neural
networks and recurrent neural networks, and 42 shifted sets with both synthetic
and natural distribution shifts. The results reveal that 1) data with
distribution shifts happen more disagreements than without. 2)
Quantization-aware training can produce more stable models than standard,
adversarial, and Mixup training. 3) Disagreements often have closer top-1 and
top-2 output probabilities, and $Margin$ is a better indicator than the other
uncertainty metrics to distinguish disagreements. 4) Retraining with
disagreements has limited efficiency in removing disagreements. We opensource
our code and models as a new benchmark for further studying the quantized
models.


title: CookieEnforcer: Automated Cookie Notice Analysis and Enforcement

abstract:
  Online websites use cookie notices to elicit consent from the users, as
required by recent privacy regulations like the GDPR and the CCPA. Prior work
has shown that these notices use dark patterns to manipulate users into making
website-friendly choices which put users' privacy at risk. In this work, we
develop CookieEnforcer, a new system for automatically discovering cookie
notices and deciding on the options that result in disabling all non-essential
cookies. In order to achieve this, we first build an automatic cookie notice
detector that utilizes the rendering pattern of the HTML elements to identify
the cookie notices. Next, CookieEnforcer analyzes the cookie notices and
predicts the set of actions required to disable all unnecessary cookies. This
is done by modeling the problem as a sequence-to-sequence task, where the input
is a machine-readable cookie notice and the output is the set of clicks to
make. We demonstrate the efficacy of CookieEnforcer via an end-to-end accuracy
evaluation, showing that it can generate the required steps in 91% of the
cases. Via a user study, we show that CookieEnforcer can significantly reduce
the user effort. Finally, we use our system to perform several measurements on
the top 5k websites from the Tranco list (as accessed from the US and the UK),
drawing comparisons and observations at scale.


title:
High angular resolution Sunyaev Zel'dovich observations: the case of
  MISTRAL

abstract:
  The MIllimeter Sardinia radio Telescope Receiver based on Array of Lumped
elements kids, MISTRAL, is a millimetric ($\simeq 90GHz$) multipixel camera
being built for the Sardinia Radio Telescope. It is going to be a facility
instrument and will sample the sky with 12 arcsec angular resolution, 4 arcmin
field of view, through 408 Kinetic Inductance Detectors (KIDs). The
construction and the beginning of commissioning is planned to be in 2022.
MISTRAL will allow the scientific community to propose a wide variety of
scientific cases including protoplanetary discs study, star forming regions,
galaxies radial profiles, and high angular resolution measurements of the
Sunyaev Zel'dovich (SZ) effect with the investigation of the morphology of
galaxy cluster and the search for the Cosmic Web.


title:
Non-hydrostatic modelling of the wave-induced response of moored
  floating structures

abstract:
  Predictions of the wave-induced response of floating structures that are
moored in a harbour or coastal waters require an accurate description of the
(nonlinear) evolution of waves over variable bottom topography, the
interactions of the waves with the structure, and the dynamics of the mooring
system. In this paper, we present a new advanced numerical model to simulate
the wave-induced response of a floating structure that is moored in an
arbitrary nearshore region. The model is based on the non-hydrostatic approach,
and implemented in the open-source model SWASH, which provides an efficient
numerical framework to simulate the nonlinear wave evolution over variable
bottom topographies. The model is extended with a solution to the rigid body
that is tightly coupled to the hydrodynamic equations. The model was validated
for two test cases that consider different floating structures of increasing
geometrical complexity: a cylindrical geometry that is representative of a
wave-energy-converter, and a vessel with a more complex shaped hull. A range of
wave conditions were considered, varying from monochromatic to short-crested
sea states. Model predictions of the excitation forces, added mass, radiation
damping, and the wave-induced response agreed well with benchmark solutions to
the potential flow equations. Besides the response to the primary wave
(sea-swell) components, the model was also able to capture the second-order
difference-frequency forcing and response of the moored vessel. Importantly,
the model captured the wave-induced response with a relatively coarse vertical
resolution, allowing for applications at the scale of a realistic harbour or
coastal region. The proposed model thereby provides a new tool to seamlessly
simulate the nonlinear evolution of waves over complex bottom topography and
the wave-induced response of a floating structure that is moored in coastal
waters.


title: Sterile neutrino production at small mixing in the early universe

abstract:
  Sterile neutrinos can be produced in the early universe via interactions with
their active counterparts. For small active-sterile mixing angles, thermal
equilibrium with the standard model plasma is not reached and sterile neutrinos
are only produced via flavor oscillations. We study in detail this regime,
taking into account matter potentials and decoherence effects caused by elastic
scatterings with the plasma. We find that resonant oscillations occurring at
temperatures $T\lesssim 10\,\mathrm{GeV}$ lead to a significant enhancement of
the sterile neutrino production rate. Taking this into account, we improve
constraints on the active-sterile mixing from Big Bang nucleosynthesis and the
cosmic microwave background, excluding mixing angles down to $\theta_s\sim
10^{-10}-10^{-16}$ for sterile neutrino masses in the $10\,\mathrm{MeV}$ to
$10\,\mathrm{GeV}$ range. We observe that if sterile neutrinos predominantly
decay into metastable hidden sector particles, this process provides a novel
dark matter production mechanism, consistent with the sterile neutrino origin
of light neutrino masses via the seesaw mechanism.