title:
Homotopy Invariants of Braided Commutative Algebras and the Deligne
  Conjecture for Finite Tensor Categories

abstract:
  It is easy to find algebras $\mathbb{T}\in\mathcal{C}$ in a finite tensor
category $\mathcal{C}$ that naturally come with a lift to a braided commutative
algebra $\mathsf{T}\in Z(\mathcal{C})$ in the Drinfeld center of $\mathcal{C}$.
In fact, any finite tensor category has at least two such algebras, namely the
monoidal unit $I$ and the canonical end $\int_{X\in\mathcal{C}} X\otimes
X^\vee$. Using the theory of braided operads, we prove that for any such
algebra $\mathbb{T}$ the homotopy invariants, i.e. the derived morphism space
from $I$ to $\mathbb{T}$, naturally come with the structure of a differential
graded $E_2$-algebra. This way, we obtain a rich source of differential graded
$E_2$-algebras in the homological algebra of finite tensor categories. We use
this result to prove that Deligne's $E_2$-structure on the Hochschild cochain
complex of a finite tensor category is induced by the canonical end, its
multiplication and its non-crossing half braiding. With this new and more
explicit description of Deligne's $E_2$-structure, we can lift the
Farinati-Solotar bracket on the Ext algebra of a finite tensor category to an
$E_2$-structure at cochain level. Moreover, we prove that, for a unimodular
pivotal finite tensor category, the inclusion of the Ext algebra into the
Hochschild cochains is a monomorphism of framed $E_2$-algebras, thereby
refining a result of Menichi.


title:
Hybrid Transformer Network for Different Horizons-based Enriched Wind
  Speed Forecasting

abstract:
  Highly accurate different horizon-based wind speed forecasting facilitates a
better modern power system. This paper proposed a novel astute hybrid wind
speed forecasting model and applied it to different horizons. The proposed
hybrid forecasting model decomposes the original wind speed data into IMFs
(Intrinsic Mode Function) using Improved Complete Ensemble Empirical Mode
Decomposition with Adaptive Noise (ICEEMDAN). We fed the obtained subseries
from ICEEMDAN to the transformer network. Each transformer network computes the
forecast subseries and then passes to the fusion phase. Get the primary wind
speed forecasting from the fusion of individual transformer network forecast
subseries. Estimate the residual error values and predict errors using a
multilayer perceptron neural network. The forecast error is added to the
primary forecast wind speed to leverage the high accuracy of wind speed
forecasting. Comparative analysis with real-time Kethanur, India wind farm
dataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model's
superior performance with MAE=1.7096*10^-07, MAPE=2.8416*10^-06,
MRE=2.8416*10^-08, MSE=5.0206*10^-14, and RMSE=2.2407*10^-07 for case study 1
and MAE=6.1565*10^-07, MAPE=9.5005*10^-06, MRE=9.5005*10^-08,
MSE=8.9289*10^-13, and RMSE=9.4493*10^-07 for case study 2 enriched wind speed
forecasting than state-of-the-art methods and reduces the burden on the power
system engineer.

categories: eess.SP cs.LG cs.SY eess.SY physics.ao-ph


title: A Sheaf-Theoretic Construction of Shape Space

abstract:
  We present a sheaf-theoretic construction of shape space -- the space of all
shapes. We do this by describing a homotopy sheaf on the poset category of
constructible sets, where each set is mapped to its Persistent Homology
Transform (PHT). Recent results that build on fundamental work of Schapira have
shown that this transform is injective, thus making the PHT a good summary
object for each shape. Our homotopy sheaf result allows us to "glue" PHTs of
different shapes together to build up the PHT of a larger shape. In the case
where our shape is a polyhedron we prove a generalized nerve lemma for the PHT.
Finally, by re-examining the sampling result of Smale-Niyogi-Weinberger, we
show that we can reliably approximate the PHT of a manifold by a polyhedron up
to arbitrary precision.


title: Preparation for future active learning

abstract:
  It is well documented that students sometimes resist active learning
techniques. A recent study showed how students believed that they learned less
in active learning classrooms than they learned in lectures, even though they
learned more. In this article, we describe a method for introducing active
learning methods to college students that is based on a preparation for future
learning approach. The students who received this introduction to active
learning appeared to be more receptive to group work in the classroom than
students who started the course with an explanation of the reasons and values
of active learning.


title: Interfacial Cavitation

abstract:
  Cavitation has long been recognized as a crucial predictor, or precursor, to
the ultimate failure of various materials, ranging from ductile metals to soft
and biological materials. Traditionally, cavitation in solids is defined as an
unstable expansion of a void or a defect within a material. The critical
applied load needed to trigger this instability - the critical pressure - is a
lengthscale independent material property and has been predicted by numerous
theoretical studies for a breadth of constitutive models. While these studies
usually assume that cavitation initiates from defects in the bulk of an
otherwise homogeneous medium, an alternative and potentially more ubiquitous
scenario can occur if the defects are found at interfaces between two distinct
media within the body. Such interfaces are becoming increasingly common in
modern materials with the use of multi-material composites and layer-by-layer
additive manufacturing methods. However, a criterion to determine the threshold
for interfacial failure, in analogy to the bulk cavitation limit, has yet to be
reported. In this work we fill this gap. Our theoretical model captures a
lengthscale independent limit for interfacial cavitation, and is shown to agree
with our observations at two distinct lengthscales, via two different
experimental systems. To further understand the competition between the two
cavitation modes (bulk versus interface) we expand our investigation beyond the
elastic response to understand the ensuing unstable propagation of delamination
at the interface. A phase diagram summarizes these results, showing regimes in
which interfacial failure becomes the dominant mechanism.


title: Visualizing the turbulent energy cascade: Fourier and physical space

abstract:
  The transfer of kinetic energy from large to small scales is a hallmark of
turbulent fluids. While it is generally well understood in a statistical sense,
a mechanistic understanding of the exact processes is still lacking. In this
manuscript, we filter the velocity field in bands of wavenumbers distributed
logarithmically to investigate the transfer of energy in Fourier space, an
approach which also provides a way of visualizing the energy cascade in real
space. We use this method to study how energy is transferred to small scales in
different flow configurations. In the case of a statistically steady
homogeneous isotropic turbulent flow at moderate Reynolds numbers, we
demonstrate that the transfer between bands of wavenumbers is well described by
a phenomenological model originally proposed by Tennekes & Lumley (1972). We
then apply this method to investigate the formation of small scales during the
interaction between two vortex tubes and show that the transfer of energy in
these problems correlates with the dynamics of vorticity formation at ever
decreasing scales. This enticing correlation between vortices and energy
transfer, however, does not fully capture the transfer of energy in a
structureless homogeneous isotropic turbulent flow, where strain also plays an
important role.


title: CDF W mass anomaly from a dark sector with a Stueckelberg-Higgs portal

abstract:
  We propose an explanation to the new W mass measurement recently reported by
the CDF collaboration, which is larger than the standard model expectation by
about 7 standard deviations. To alleviate the tensions that are imposed on the
electroweak sector by the new W mass measurement, we carry out an analysis in
the Stueckelberg extended standard model where a new neutral gauge boson
appears which mixes with the two neutral gauge bosons in the electroweak sector
both via the Stueckelberg mass terms and via the gauge invariant
Stueckelberg-Higgs portal interaction and spoils the custodial symmetry at the
tree level so that the simple relation between the W boson mass and the Z boson
mass does not hold. We find that such an extension increases the W boson mass
if the new gauge boson mass is larger than the Z boson mass. We further show
that there exists a significant part of the parameter space in the extended
model which includes the CDF mass anomaly and is consistent with the various
observables at the Z pole and consistent with the ATLAS dilepton limits. The
Stueckelberg $Z'_{\rm St}$ boson, which resolves the CDF W mass anomaly, should
be searchable in future LHC experiments.


title:
Non-invertible Condensation, Duality, and Triality Defects in 3+1
  Dimensions

abstract:
  We discuss a variety of codimension-one, non-invertible topological defects
in general 3+1d QFTs with a discrete one-form global symmetry. These include
condensation defects from higher gauging of the one-form symmetries on a
codimension-one manifold, each labeled by a discrete torsion class, and duality
and triality defects from gauging in half of spacetime. The universal fusion
rules between these non-invertible topological defects and the one-form
symmetry surface defects are determined. Interestingly, the fusion coefficients
are generally not numbers, but 2+1d TQFTs, such as invertible SPT phases,
$\mathbb{Z}_N$ gauge theories, and $U(1)_N$ Chern-Simons theories. The
associativity of these algebras over TQFT coefficients relies on nontrivial
facts about 2+1d TQFTs. We further prove that some of these non-invertible
symmetries are intrinsically incompatible with a trivially gapped phase,
leading to nontrivial constraints on renormalization group flows. Duality and
triality defects are realized in many familiar gauge theories, including free
Maxwell theory, non-abelian gauge theories with orthogonal gauge groups, ${\cal
N}=1,$ and ${\cal N}=4$ super Yang-Mills theories.


title: Some remarks on decomposition theorem for proper K\"ahler morphisms

abstract:
  We explain a correct proof of the decomposition theorem for direct images of
constant sheaves by proper K\"ahler morphisms of complex manifolds.


title: Electric field-induced domain wall motion in spin spiral multiferroics

abstract:
  Switching in magnetic materials gives rise to rich physical phenomena and
lies at the heart of their technological applications. Although domain wall
motion in ferro- and antiferromagnets has been studied, in spiral magnets it is
still poorly understood despite 20 years of active research since the discovery
of spiral multiferroics. The problem of the domain wall motion in a spiral
magnet is a compelling one, the more so the magnetic domain walls in cycloidal
spiral phase are also ferroelectric, thus enabling electric control of
magnetism, i.e. domain wall motion under the action of an external electric
field. Phase transition to a spiral phase leads to a formation of chiral
domains with opposite spin rotation senses, that are separated by chiral domain
walls. Spiral order breaks inversion symmetry and induces a ferroelectric
polarization, whose sign is determined by the chirality of the domain. Thus the
spiral order allows for the manipulation of spins via an external electric
field. Here we study domain wall motion in magnets with spiral ground state,
that are the most basic non-collinear magnets. We formulate a simplified
variational model and derive the equation of motion for the domain wall driven
by an external electric field. The results are corroborated with atomistic spin
dynamics simulations. The results suggest a linear dependence of the wall speed
on the external electric field, and a peculiar dependence on the system
geometry and domain structure.


title: On the Locality of Attention in Direct Speech Translation

abstract:
  Transformers have achieved state-of-the-art results across multiple NLP
tasks. However, the self-attention mechanism complexity scales quadratically
with the sequence length, creating an obstacle for tasks involving long
sequences, like in the speech domain. In this paper, we discuss the usefulness
of self-attention for Direct Speech Translation. First, we analyze the
layer-wise token contributions in the self-attention of the encoder, unveiling
local diagonal patterns. To prove that some attention weights are avoidable, we
propose to substitute the standard self-attention with a local efficient one,
setting the amount of context used based on the results of the analysis. With
this approach, our model matches the baseline performance, and improves the
efficiency by skipping the computation of those weights that standard attention
discards.


title: Simulating neutrino echoes induced by secret neutrino interactions

abstract:
  New neutrino interactions beyond the Standard Model (BSM) have been of much
interest in not only particle physics but also cosmology and astroparticle
physics. We numerically investigate the time delay distribution of
astrophysical neutrinos that interact with the cosmic neutrino background.
Using the Monte Carlo method, we develop a framework that enables us to
simulate the time-dependent energy spectra of high-energy neutrinos that
experience even multiple scatterings en route and to handle the sharp increase
in the cross section at the resonance energy. As an example, we focus on the
case of secret neutrino interactions with a scalar mediator. While we find the
excellent agreement between analytical and simulation results for small optical
depths, our simulations enable us to study even optically-thick cases that are
not described by the simplest analytic estimates. Our simulations are used to
understand effects of cosmological redshifts, neutrino spectra and flavors. The
developments will be useful for probing BSM neutrino interactions with not only
current neutrino detectors such as IceCube and Super-Kamiokande but also future
neutrino detectors such as IceCube-Gen2 and Hyper-Kamiokande.


title:
Decentralized Control of Distributed Cloud Networks with Generalized
  Network Flows

abstract:
  Emerging distributed cloud architectures, e.g., fog and mobile edge
computing, are playing an increasingly important role in the efficient delivery
of real-time stream-processing applications such as augmented reality,
multiplayer gaming, and industrial automation. While such applications require
processed streams to be shared and simultaneously consumed by multiple
users/devices, existing technologies lack efficient mechanisms to deal with
their inherent multicast nature, leading to unnecessary traffic redundancy and
network congestion. In this paper, we establish a unified framework for
distributed cloud network control with generalized (mixed-cast) traffic flows
that allows optimizing the distributed execution of the required packet
processing, forwarding, and replication operations. We first characterize the
enlarged multicast network stability region under the new control framework
(with respect to its unicast counterpart). We then design a novel queuing
system that allows scheduling data packets according to their current
destination sets, and leverage Lyapunov drift-plus-penalty theory to develop
the first fully decentralized, throughput- and cost-optimal algorithm for
multicast cloud network flow control. Numerical experiments validate analytical
results and demonstrate the performance gain of the proposed design over
existing cloud network control techniques.


title:
Combined explanation of $W$-mass, muon $g-2$, $R_{K^{(*)}}$ and
  $R_{D^{(*)}}$ anomalies in a singlet-triplet scalar leptoquark model

abstract:
  In addition to the three long-standing anomalies seen in the muon $g-2$,
$R_{K^{(*)}}$, and $R_{D^{(*)}}$ observables by various independent
experiments, the CDF collaboration has found another significant one in the $W$
boson mass. These anomalies might be intertwined at a fundamental level and
have a single new physics explanation. In this paper, we investigate the
possibility of whether these anomalies can be explained in a model of a
weak-singlet $S_1$ and a weak-triplet $S_3$ scalar leptoquarks. We find that
with an appropriate flavour ansatz and mass mixing between the $S_1$ and $S_3$
leptoquarks through a Higgs portal, the model can explain the four anomalies
simultaneously while being consistent with potential spoilsport observables
like $R_{K^{(*)}}^{\nu\nu}$.


title:
On joint properties of vertices with a given degree or label in the
  random recursive tree

abstract:
  In this paper, we study the joint behaviour of the degree, depth and label of
and graph distance between high-degree vertices in the random recursive tree.
We generalise the results obtained by Eslava and extend these to include the
labels of and graph distance between high-degree vertices. The analysis of both
these two properties of high-degree vertices is novel, in particular in
relation to the behaviour of the depth of such vertices. In passing, we also
obtain results for the joint behaviour of the degree and depth of and graph
distance between any fixed number of vertices with a prescribed label. This
combines several isolated results on the degree and depth of and graph distance
between vertices with a prescribed label already present in the literature.
Furthermore, we extend these results to hold jointly for any number of fixed
vertices and improve these results by providing more detailed descriptions of
the distributional limits. Our analysis is based on a correspondence between
the random recursive tree known and a representation of the Kingman
$n$-coalescent.


title: Quartz: Superoptimization of Quantum Circuits (Extended Version)

abstract:
  Existing quantum compilers optimize quantum circuits by applying circuit
transformations designed by experts. This approach requires significant manual
effort to design and implement circuit transformations for different quantum
devices, which use different gate sets, and can miss optimizations that are
hard to find manually. We propose Quartz, a quantum circuit superoptimizer that
automatically generates and verifies circuit transformations for arbitrary
quantum gate sets. For a given gate set, Quartz generates candidate circuit
transformations by systematically exploring small circuits and verifies the
discovered transformations using an automated theorem prover. To optimize a
quantum circuit, Quartz uses a cost-based backtracking search that applies the
verified transformations to the circuit. Our evaluation on three popular gate
sets shows that Quartz can effectively generate and verify transformations for
different gate sets. The generated transformations cover manually designed
transformations used by existing optimizers and also include new
transformations. Quartz is therefore able to optimize a broad range of circuits
for diverse gate sets, outperforming or matching the performance of hand-tuned
circuit optimizers.


title:
Temperature induced modulation of resonant Raman scattering in bilayer
  2H-MoS$_{2}$

abstract:
  The temperature evolution of the resonant Raman scattering from high-quality
bilayer 2H-MoS$_{2}$ encapsulated in hexagonal BN flakes is presented. The
observed resonant Raman scattering spectrum as initiated by the laser energy of
1.96 eV, close to the A excitonic resonance, shows rich and distinct
vibrational features that are otherwise not observed in non-resonant
scattering. The appearance of 1$^{st}$ and 2$^{nd}$ order phonon modes is
unambiguously observed in a broad range of temperatures from 5 K to 320 K. The
spectrum includes the Raman-active modes, $i.e.$ E$_\textrm{1g}^{2}$($\Gamma$)
and A$_\textrm{1g}$($\Gamma$) along with their Davydov-split counterparts,
$i.e.$ E$_\textrm{1u}$($\Gamma$) and B$_\textrm{1u}$($\Gamma$). The temperature
evolution of the Raman scattering spectrum brings forward key observations, as
the integrated intensity profiles of different phonon modes show diverse
trends. The Raman-active A$_{1g}$($\Gamma$) mode, which dominates the Raman
scattering spectrum at $T$=5~K quenches with increasing temperature.
Surprisingly, at room temperature the B$_\textrm{1u}$($\Gamma$) mode, which is
infrared-active in the bilayer, is substantially stronger than its nominally
Raman-active A$_\textrm{1g}$($\Gamma$) counterpart.


title:
Massively Parallel Computation and Sublinear-Time Algorithms for
  Embedded Planar Graphs

abstract:
  While algorithms for planar graphs have received a lot of attention, few
papers have focused on the additional power that one gets from assuming an
embedding of the graph is available. While in the classic sequential setting,
this assumption gives no additional power (as a planar graph can be embedded in
linear time), we show that this is far from being the case in other settings.
We assume that the embedding is straight-line, but our methods also generalize
to non-straight-line embeddings. Specifically, we focus on sublinear-time
computation and massively parallel computation (MPC).
  Our main technical contribution is a sublinear-time algorithm for computing a
relaxed version of an $r$-division. We then show how this can be used to
estimate Lipschitz additive graph parameters. This includes, for example, the
maximum matching, maximum independent set, or the minimum dominating set. We
also show how this can be used to solve some property testing problems with
respect to the vertex edit distance.
  In the second part of our paper, we show an MPC algorithm that computes an
$r$-division of the input graph. We show how this can be used to solve various
classical graph problems with space per machine of $O(n^{2/3+\epsilon})$ for
some $\epsilon>0$, and while performing $O(1)$ rounds. This includes for
example approximate shortest paths or the minimum spanning tree. Our results
also imply an improved MPC algorithm for Euclidean minimum spanning tree.


title:
Write a Line: Tests with Answer Templates and String Completion Hints
  for Self-Learning in a CS1 Course

abstract:
  One of the important scaffolding tasks in programming learning is writing a
line of code performing the necessary action. This allows students to practice
skills in a playground with instant feedback before writing more complex
programs and increases their proficiency when solving programming problems.
However, answers in the form of program code have high variability. Among the
possible approaches to grading and providing feedback, we chose template
matching. This paper reports the results of using regular-expression-based
questions with string completion hints in a CS1 course for 4 years with 497
students. The evaluation results show that Perl-compatible regular expressions
provide good precision and recall (more than 99\%) when used for questions
requiring writing a single line of code while being able to provide
string-completion feedback regardless of how wrong the initial student's answer
is. After introducing formative quizzes with string-completion hints to the
course, the number of questions that teachers and teaching assistants received
about questions in the formative quizzes dropped considerably: most of the
training question attempts resulted in finding the correct answer without help
from the teaching staff. However, some of the students use formative quizzes
just to learn correct answers without actually trying to answer the questions.


title: On the Brumer-Stark Conjecture and Refinements

abstract:
  We state the Brumer-Stark conjecture and motivate it from two perspectives.
Stark's perspective arose in his attempts to generalize the classical Dirichlet
class number formula for the leading term of the Dedekind zeta function at
$s=1$ (equivalently, $s=0$). Brumer's perspective arose by generalizing
Stickelberger's work regarding the factorization of Gauss sums and the
annihilation of class groups of cyclotomic fields. These viewpoints were
synthesized by Tate, who stated the Brumer-Stark conjecture in its current
form.
  The conjecture considers a totally real field $F$ and a finite abelian CM
extension $H/F$. It states the existence of $p$-units in $H$ whose valuations
at places above $p$ are related to the special values of the $L$-functions of
the extension $H/F$ at $s=0$. Essentially equivalently, the conjecture states
that a Stickelberger element associated to $H/F$ annihilates the (appropriately
smoothed) class group of $H$. This conjecture has been refined by many authors
in multiple directions.
  We conclude by stating our results toward these various conjectures and
summarizing the proofs. In particular, we prove the Brumer-Stark conjecture,
Rubin's higher rank version, and Kurihara's conjecture, all "away from 2." We
also prove strong partial results toward Gross's conjecture and the exact
$p$-adic analytic formula for Brumer-Stark units. The key technique involved in
the proofs is Ribet's method. We demonstrate congruences between Hilbert
modular Eisenstein series and cusp forms, and use the associated Galois
representations to construct Galois cohomology classes. These cohomology
classes are interpreted in terms of Ritter-Weiss modules, from which results on
class groups may be deduced.


title:
Constraining spontaneous black hole scalarization in
  scalar-tensor-Gauss-Bonnet theories with current gravitational-wave data

abstract:
  We examine the constraining power of current gravitational-wave data on
scalar-tensor-Gauss-Bonnet theories that allow for the spontaneous
scalarization of black holes. In the fiducial model that we consider, a slowly
rotating black hole must scalarize if its size is comparable to the new length
scale $\lambda$ that the theory introduces, although rapidly rotating black
holes of any mass are effectively indistinguishable from their counterparts in
general relativity. With this in mind, we use the gravitational-wave event
GW190814$\,\unicode{x2014}\,$whose primary black hole has a spin that is
bounded to be small, and whose signal shows no evidence of a scalarized
primary$\,\unicode{x2014}\,$to rule out a narrow region of the parameter space.
In particular, we find that values of ${\lambda \in [56, 96]~M_\odot}$ are
strongly disfavored with a Bayes factor of $0.1$ or less. We also include a
second event, GW151226, in our analysis to illustrate what information can be
extracted when the spins of both components are poorly measured.


title: A stochastic Stein Variational Newton method

abstract:
  Stein variational gradient descent (SVGD) is a general-purpose
optimization-based sampling algorithm that has recently exploded in popularity,
but is limited by two issues: it is known to produce biased samples, and it can
be slow to converge on complicated distributions. A recently proposed
stochastic variant of SVGD (sSVGD) addresses the first issue, producing
unbiased samples by incorporating a special noise into the SVGD dynamics such
that asymptotic convergence is guaranteed. Meanwhile, Stein variational Newton
(SVN), a Newton-like extension of SVGD, dramatically accelerates the
convergence of SVGD by incorporating Hessian information into the dynamics, but
also produces biased samples. In this paper we derive, and provide a practical
implementation of, a stochastic variant of SVN (sSVN) which is both
asymptotically correct and converges rapidly. We demonstrate the effectiveness
of our algorithm on a difficult class of test problems -- the Hybrid Rosenbrock
density -- and show that sSVN converges using three orders of magnitude fewer
gradient evaluations of the log likelihood than its stochastic SVGD
counterpart. Our results show that sSVN is a promising approach to accelerating
high-precision Bayesian inference tasks with modest-dimension,
$d\sim\mathcal{O}(10)$.


title:
Light propagating in a Born-Infeld background as seen by an accelerated
  observer

abstract:
  We study the propagation of light in the Born-Infeld (BI) background as seen
by an accelerated observer. In a Born-Infeld electromagnetic field, light
trajectories are governed by the null geodesics of the effective optical
metric. The accelerated observer is in a Rindler frame, a situation that,
according to the Einstein Equivalence Principle, is equivalent to being in a
uniform gravitational field. The phase velocity of light propagating through a
purely magnetic or electric BI background is determined as measured by the
Rindler observer. The BI field and the acceleration of the frame have opposite
effects on the propagating light: while the intense electric or magnetic BI
background slows down the velocity of light, in the accelerated frame light may
exceed its velocity in vacuum. We consider light propagating parallel or
transversal to the acceleration direction of the Rindler frame. The redshift of
light pulses sent from one Rindler observer to another, in the BI background,
is calculated as well.


title:
Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus)
  using diffusion-based hyperspectral image clustering

abstract:
  Ash dieback (Hymenoscyphus fraxineus) is an introduced fungal disease that is
causing the widespread death of ash trees across Europe. Remote sensing
hyperspectral images encode rich structure that has been exploited for the
detection of dieback disease in ash trees using supervised machine learning
techniques. However, to understand the state of forest health at
landscape-scale, accurate unsupervised approaches are needed. This article
investigates the use of the unsupervised Diffusion and VCA-Assisted Image
Segmentation (D-VIS) clustering algorithm for the detection of ash dieback
disease in a forest site near Cambridge, United Kingdom. The unsupervised
clustering presented in this work has high overlap with the supervised
classification of previous work on this scene (overall accuracy = 71%). Thus,
unsupervised learning may be used for the remote detection of ash dieback
disease without the need for expert labeling.


title:
Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a
  Single, Sequence-Guided Deep Generative Framework

abstract:
  The COVID-19 pandemic has highlighted the urgency for developing more
efficient molecular discovery pathways. As exhaustive exploration of the vast
chemical space is infeasible, discovering novel inhibitor molecules for
emerging drug-target proteins is challenging, particularly for targets with
unknown structure or ligands. We demonstrate the broad utility of a single deep
generative framework toward discovering novel drug-like inhibitor molecules
against two distinct SARS-CoV-2 targets -- the main protease (Mpro) and the
receptor binding domain (RBD) of the spike protein. To perform target-aware
design, the framework employs a target sequence-conditioned sampling of novel
molecules from a generative model. Micromolar-level in vitro inhibition was
observed for two candidates (out of four synthesized) for each target. The most
potent spike RBD inhibitor also emerged as a rare non-covalent antiviral with
broad-spectrum activity against several SARS-CoV-2 variants in live virus
neutralization assays. These results show that a broadly deployable machine
intelligence framework can accelerate hit discovery across different emerging
drug-targets.


title: RNTuple performance: Status and Outlook

abstract:
  Upcoming HEP experiments, e.g. at the HL-LHC, are expected to increase the
volume of generated data by at least one order of magnitude. In order to retain
the ability to analyze the influx of data, full exploitation of modern storage
hardware and systems, such as low-latency high-bandwidth NVMe devices and
distributed object stores, becomes critical. To this end, the ROOT RNTuple I/O
subsystem has been designed to address performance bottlenecks and shortcomings
of ROOT's current state of the art TTree I/O subsystem. RNTuple provides a
backwards-incompatible redesign of the TTree binary format and access API that
evolves the ROOT event data I/O for the challenges of the upcoming decades. It
focuses on a compact data format, on performance engineering for modern storage
hardware, for instance through making parallel and asynchronous I/O calls by
default, and on robust interfaces that are easy to use correctly. In this
contribution, we evaluate the RNTuple performance for typical HEP analysis
tasks. We compare the throughput delivered by RNTuple to popular I/O libraries
outside HEP, such as HDF5 and Apache Parquet. We demonstrate the advantages of
RNTuple for HEP analysis workflows and provide an outlook on the road to its
use in production.

categories: physics.data-an cs.DB physics.acc-ph physics.comp-ph


title:
Bi-coherent states as generalized eigenstates of the position and the
  momentum operators

abstract:
  In this paper we show that the position and the derivative operators, $\hat
q$ and $\hat D$, can be treated as ladder operators connecting the various
vectors of two biorthonormal families, $\mathcal{F}_\varphi$ and
$\mathcal{F}_\psi$. In particular, the vectors in $\mathcal{F}_\varphi$ are
essentially monomials in $x$, $x^k$, while those in $\mathcal{F}_\psi$ are weak
derivatives of the Dirac delta distribution, $\delta^{(m)}(x)$, times some
normalization factor. We also show how bi-coherent states can be constructed
for these $\hat q$ and $\hat D$, both as convergent series of elements of
$\mathcal{F}_\varphi$ and $\mathcal{F}_\psi$, or using two different
displacement-like operators acting on the two vacua of the framework. Our
approach generalizes well known results for ordinary coherent states.


title:
SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for
  Multi-omics Integration

abstract:
  Integration of multi-omics data provides opportunities for revealing
biological mechanisms related to certain phenotypes. We propose a novel method
of multi-omics integration called supervised deep generalized canonical
correlation analysis (SDGCCA) for modeling correlation structures between
nonlinear multi-omics manifolds, aiming for improving classification of
phenotypes and revealing biomarkers related to phenotypes. SDGCCA addresses the
limitations of other canonical correlation analysis (CCA)-based models (e.g.,
deep CCA, deep generalized CCA) by considering complex/nonlinear cross-data
correlations and discriminating phenotype groups. Although there are a few
methods for nonlinear CCA projections for discriminant purposes of phenotypes,
they only consider two views. On the other hand, SDGCCA is the nonlinear
multiview CCA projection method for discrimination. When we applied SDGCCA to
prediction of patients of Alzheimer's disease (AD) and discrimination of early-
and late-stage cancers, it outperformed other CCA-based methods and other
supervised methods. In addition, we demonstrate that SDGCCA can be used for
feature selection to identify important multi-omics biomarkers. In the
application on AD data, SDGCCA identified clusters of genes in multi-omics
data, which are well known to be associated with AD.


title:
Superintegrable and scale invariant quantum mechanical systems with
  position dependent mass

abstract:
  Schroedinger equations with position dependent mass which are scale invariant
and admit second order integrals of motion are classified.


title: Learning Theory of Mind via Dynamic Traits Attribution

abstract:
  Machine learning of Theory of Mind (ToM) is essential to build social agents
that co-live with humans and other agents. This capacity, once acquired, will
help machines infer the mental states of others from observed contextual action
trajectories, enabling future prediction of goals, intention, actions and
successor representations. The underlying mechanism for such a prediction
remains unclear, however. Inspired by the observation that humans often infer
the character traits of others, then use it to explain behaviour, we propose a
new neural ToM architecture that learns to generate a latent trait vector of an
actor from the past trajectories. This trait vector then multiplicatively
modulates the prediction mechanism via a `fast weights' scheme in the
prediction neural network, which reads the current context and predicts the
behaviour. We empirically show that the fast weights provide a good inductive
bias to model the character traits of agents and hence improves mindreading
ability. On the indirect assessment of false-belief understanding, the new ToM
model enables more efficient helping behaviours.


title:
Novel wide spectrum light absorber heterostructures based on
  hBN/In(Ga)Te

abstract:
  Two-dimensional group III monochalcogenides have recently attracted quite
attention for their wide spectrum of optical and electric properties, being
promising candidates for optoelectronic and novel electrical applications,
however in their pristine form they are very sensitive and vulnerable to oxygen
in air. Here we present two newly designed vdW heterostructures based on hBN
(hexagonal boron nitride) and GaTe or InTe monolayer. Using density functional
theory we investigate electronic and optical properties of those structures.
Their moderate band gap and an good absorption coefficient makes them excellent
for absorbers in wide spectrum, covering all from part of IR to far UV
spectrum, with particularly good absorption of UV light. The hBN layer which
can be beneficial for protection of sensitive GaTe and InTe does not only
preserve their optical properties but also enhances it. Moreover, we confirm
that type of stacking does not affect any relevant properties, as all three
stacking types are very similar in total energy and bandstructure is almost the
same for every one. This is especially important for easier experimental
realization.


title:
Generalized Lindblad Master Equation for Measurement-induced Phase
  Transition

abstract:
  The measurement-induced phase transition (MIPT) occurs when the system is
evolving under unitary evolution together with local measurements followed by
post-selection. We propose a generalized version of the Lindblad master
equation as a continuous equation, to describe the dynamics of second R\'enyi
entropy in the MIPT. This generalized Lindblad equation explicitly takes into
account the post-selection in the MIPT, which is realized as the
Einstein-Podolsky-Rosen (EPR) state projection in the equation. Also, this
generalized Lindblad equation preserves the Hermitian, unit trace, and positive
definiteness of the density matrix. We further use the hard-core Bose-Hubbard
model as a concrete example to numerically confirm that our generalized
Lindblad equation is applicable to describing the MIPT.


title: House Price Prediction Based On Deep Learning

abstract:
  Since ancient times, what Chinese people have been pursuing is very simple,
which is nothing more than "to live and work happily, to eat and dress
comfortable". Today, more than 40 years after the reform and opening, people
have basically solved the problem of food and clothing, and the urgent problem
is housing. Nowadays, due to the storm of long-term rental apartment
intermediary platforms such as eggshell, increasing the sense of insecurity of
renters, as well as the urbanization in recent years and the scramble for
people in major cities, this will make the future real estate market
competition more intense. In order to better grasp the real estate price, let
consumers buy a house reasonably, and provide a reference for the government to
formulate policies, this paper summarizes the existing methods of house price
prediction and proposes a house price prediction method based on mixed depth
vision and text features.


title: PR-DAD: Phase Retrieval Using Deep Auto-Decoders

abstract:
  Phase retrieval is a well known ill-posed inverse problem where one tries to
recover images given only the magnitude values of their Fourier transform as
input. In recent years, new algorithms based on deep learning have been
proposed, providing breakthrough results that surpass the results of the
classical methods. In this work we provide a novel deep learning architecture
PR-DAD (Phase Retrieval Using Deep Auto- Decoders), whose components are
carefully designed based on mathematical modeling of the phase retrieval
problem. The architecture provides experimental results that surpass all
current results.


title: Optimizing Tensor Network Contraction Using Reinforcement Learning

abstract:
  Quantum Computing (QC) stands to revolutionize computing, but is currently
still limited. To develop and test quantum algorithms today, quantum circuits
are often simulated on classical computers. Simulating a complex quantum
circuit requires computing the contraction of a large network of tensors. The
order (path) of contraction can have a drastic effect on the computing cost,
but finding an efficient order is a challenging combinatorial optimization
problem.
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural
Networks (GNN) to address the contraction ordering problem. The problem is
extremely challenging due to the huge search space, the heavy-tailed reward
distribution, and the challenging credit assignment. We show how a carefully
implemented RL-agent that uses a GNN as the basic policy construct can address
these challenges and obtain significant improvements over state-of-the-art
techniques in three varieties of circuits, including the largest scale networks
used in contemporary QC.


title: Sampling Strategies for Static Powergrid Models

abstract:
  Machine learning and computational intelligence technologies gain more and
more popularity as possible solution for issues related to the power grid. One
of these issues, the power flow calculation, is an iterative method to compute
the voltage magnitudes of the power grid's buses from power values. Machine
learning and, especially, artificial neural networks were successfully used as
surrogates for the power flow calculation. Artificial neural networks highly
rely on the quality and size of the training data, but this aspect of the
process is apparently often neglected in the works we found. However, since the
availability of high quality historical data for power grids is limited, we
propose the Correlation Sampling algorithm. We show that this approach is able
to cover a larger area of the sampling space compared to different random
sampling algorithms from the literature and a copula-based approach, while at
the same time inter-dependencies of the inputs are taken into account, which,
from the other algorithms, only the copula-based approach does.


title:
An unsupervised approach for semantic place annotation of trajectories
  based on the prior probability

abstract:
  Semantic place annotation can provide individual semantics, which can be of
great help in the field of trajectory data mining. Most existing methods rely
on annotated or external data and require retraining following a change of
region, thus preventing their large-scale applications. Herein, we propose an
unsupervised method denoted as UPAPP for the semantic place annotation of
trajectories using spatiotemporal information. The Bayesian Criterion is
specifically employed to decompose the spatiotemporal probability of the
candidate place into spatial probability, duration probability, and visiting
time probability. Spatial information in ROI and POI data is subsequently
adopted to calculate the spatial probability. In terms of the temporal
probabilities, the Term Frequency Inverse Document Frequency weighting
algorithm is used to count the potential visits to different place types in the
trajectories, and generates the prior probabilities of the visiting time and
duration. The spatiotemporal probability of the candidate place is then
combined with the importance of the place category to annotate the visited
places. Validation with a trajectory dataset collected by 709 volunteers in
Beijing showed that our method achieved an overall and average accuracy of
0.712 and 0.720, respectively, indicating that the visited places can be
annotated accurately without any external data.


title:
Per-clip and per-bitrate adaptation of the Lagrangian multiplier in
  video coding

abstract:
  In the past ten years there have been significant developments in
optimization of transcoding parameters on a per-clip rather than per-genre
basis. In our recent work we have presented per-clip optimization for the
Lagrangian multiplier in Rate controlled compression, which yielded BD-Rate
improvements of approximately 2\% across a corpus of videos using HEVC.
However, in a video streaming application, the focus is on optimizing the
rate/distortion tradeoff at a particular bitrate and not on average across a
range of performance. We observed in previous work that a particular multiplier
might give BD rate improvements over a certain range of bitrates, but not the
entire range. Using different parameters across the range would improve gains
overall. Therefore here we present a framework for choosing the best Lagrangian
multiplier on a per-operating point basis across a range of bitrates. In
effect, we are trying to find the para-optimal gain across bitrate and
distortion for a single clip. In the experiments presented we employ direct
optimization techniques to estimate this Lagrangian parameter path
approximately 2,000 video clips. The clips are primarily from the YouTube-UGC
dataset. We optimize both for bitrate savings as well as distortion metrics
(PSNR, SSIM).


title: Near Optimal Per-Clip Lagrangian Multiplier Prediction in HEVC

abstract:
  The majority of internet traffic is video content. This drives the demand for
video compression to deliver high quality video at low target bitrates.
Optimising the parameters of a video codec for a specific video clip (per-clip
optimisation) has been shown to yield significant bitrate savings. In previous
work we have shown that per-clip optimisation of the Lagrangian multiplier
leads to up to 24% BD-Rate improvement. A key component of these algorithms is
modeling the R-D characteristic across the appropriate bitrate range. This is
computationally heavy as it usually involves repeated video encodes of the high
resolution material at different parameter settings. This work focuses on
reducing this computational load by deploying a NN operating on lower bandwidth
features. Our system achieves BD-Rate improvement in approximately 90% of a
large corpus with comparable results to previous work in direct optimisation.


title:
Live Fast, Die $\alpha$-Enhanced: The Mass-Metallicity-$\alpha$ Relation
  of the Milky Way's Disrupted Dwarf Galaxies

abstract:
  The Milky Way's satellite galaxies ("surviving dwarfs") have been studied for
decades as unique probes of chemical evolution in the low-mass regime. Here we
extend such studies to the "disrupted dwarfs", whose debris constitutes the
stellar halo. We present abundances ([Fe/H], [$\alpha$/Fe]) and stellar masses
for nine disrupted dwarfs with $M_{\star}\approx10^{6}-10^{9}M_{\odot}$ from
the H3 Survey (Sagittarius, $Gaia$-Sausage-Enceladus, Helmi Streams, Sequoia,
Wukong/LMS-1, Cetus, Thamnos, I'itoi, Orphan/Chenab). The surviving and
disrupted dwarfs are chemically distinct: at fixed mass, the disrupted dwarfs
are systematically metal-poor and $\alpha$-enhanced. The disrupted dwarfs
define a mass-metallicity relation (MZR) with a similar slope as the $z=0$ MZR
followed by the surviving dwarfs, but offset to lower metallicities by
$\Delta$[Fe/H]$\approx0.3-0.4$ dex. Dwarfs with larger offsets from the $z=0$
MZR are more $\alpha$-enhanced. In simulations as well as observations,
galaxies with higher $\Delta$[Fe/H] formed at higher redshifts -- exploiting
this, we infer the disrupted dwarfs have typical star-formation truncation
redshifts of $z_{\rm{trunc}}{\sim}1-2$. We compare the chemically inferred
$z_{\rm{trunc}}$ with dynamically inferred accretion redshifts and find almost
all dwarfs are quenched only after accretion. The differences between disrupted
and surviving dwarfs are likely because the disrupted dwarfs assembled their
mass rapidly, at higher redshifts, and within denser dark matter halos that
formed closer to the Galaxy. Our results place novel archaeological constraints
on low-mass galaxies inaccessible to direct high-$z$ studies: (i) the redshift
evolution of the MZR along parallel tracks but offset to lower metallicities
extends to $M_{\star}\approx10^{6}-10^{9}M_{\odot}$; (ii) galaxies at
$z\approx2-3$ are $\alpha$-enhanced with [$\alpha$/Fe]$\approx0.4$.


title:
A new member of the Milky Way's family tree: Characterizing the Pontus
  merger of our Galaxy

abstract:
  We study the $\textit{Pontus}$ structure -- a recently discovered merger that
brought in $\sim7$ globular clusters in the course of the hierarchical build-up
of the Milky Way's halo. Here, we analyse the stellar population of
$\textit{Pontus}$ and examine (1) its phase-space distribution using the
ESA/$\textit{Gaia}$ dataset, (2) its metallicity and chemical abundances (i.e.,
[Fe/H], [$\alpha$/Fe], [Mg/Fe], [Al/Fe]) using the spectroscopic catalogue of
APOGEE DR17, and (3) the colour-magnitude diagram that shows interesting
features, including a possibly double horizontal branch and a small population
of blue stragglers. In sum, the $\textit{Pontus}$ stars show some unique
properties that suggest they likely originated from the merging of an
independent satellite galaxy; future analysis will shed more light on the true
nature of this structure. This chemo-dynamical analysis of $\textit{Pontus}$
stars is another step forward in our bigger quest to characterize
$\textit{all}$ the merging events of our Milky Way.


title:
A deep survey of short GRB host galaxies over $z\sim0-2$: implications
  for offsets, redshifts, and environments

abstract:
  A significant fraction ($\sim$30\%) of well-localized short gamma-ray bursts
(sGRBs) lack a coincident host galaxy. This leads to two main scenarios:
\textit{i}) that the progenitor system merged outside of the visible light of
its host, or \textit{ii}) that the sGRB resided within a faint and distant
galaxy that was not detected by follow-up observations. Discriminating between
these scenarios has important implications for constraining the formation
channels of neutron star mergers, the rate and environments of gravitational
wave sources, and the production of heavy elements in the Universe. In this
work, we present the results of our observing campaign targeted at 31 sGRBs
that lack a putative host galaxy. Our study effectively doubles the sample of
well-studied sGRB host galaxies, now totaling 72 events of which $28\%$ lack a
coincident host galaxy to deep limits ($r$\,$\gtrsim$\,$26$ or
$F110W$\,$\gtrsim$\,$27$ AB mag), and represents the largest homogeneously
selected catalog of sGRB offsets to date. We find that 70\% of sub-arcsecond
localized sGRBs occur within 10 kpc of their host's nucleus, with a median
projected physical offset of $5.6$ kpc. Using this larger population, we
discover a redshift evolution in the locations of sGRBs: bursts at low-$z$
occur at $2\times$ larger offsets compared to those at $z$\,$>$\,$0.5$.
Furthermore, we find evidence for a sample of hostless sGRBs at
$z$\,$\gtrsim$\,$1$ that are indicative of a larger high-$z$ population,
further constraining the sGRB redshift distribution and disfavoring log-normal
delay time models.


title:
New reactor data improves robustness of neutrino mass ordering
  determination

abstract:
  In neutrino oscillation physics numerous exact degeneracies exist under the
name LMA-Dark. These degeneracies make it impossible to determine the sign of
$\Delta m^2_{31}$ known as the atmospheric mass ordering with oscillation
experiments alone in the presence of new neutrino interactions. The combination
of different measurements including multiple oscillation channels and neutrino
scattering experiments lifts some aspects of these degeneracies. In fact,
previous measurements of coherent elastic neutrino nucleus scattering (CEvNS)
by COHERENT already ruled out the LMA-Dark solution for new physics with
mediators heavier than $M_{Z'}\sim50$ MeV while cosmological considerations
disfavor these scenarios for mediators lighters than $M_{Z'}\sim3$ MeV. Here we
leverage new data from the Dresden-II experiment which provides the strongest
bounds on CEvNS with reactor neutrinos to date. We show that this data
completely removes the degeneracies in the $\nu_e$ sector for mediators down to
the MeV scale at which point constraints from the early universe take over.
While the LMA-Dark degeneracy is lifted in the $\nu_e$ sector, it can still be
restored in the $\nu_\mu$ and $\nu_\tau$ sector or with very specific couplings
to up and down quarks, and we speculate on a path forward.


title:
The role of supernova convection for the lower mass gap and the isolated
  binary formation of gravitational wave sources

abstract:
  Understanding astrophysical phenomena involving compact objects requires an
insight about the engine behind core-collapse supernovae (SNe) and the fate of
the stellar collapse of massive stars. In particular, this insight is crucial
in developing an understanding of the origin and formation channels of detected
population of BH-BH, BH-NS and NS-NS mergers. To gain this understanding, we
must tie our current knowledge of pre-SN stars properties and their potential
explosions to the final NS or BH mass distribution. The timescale of convection
growth may have a large effect on the strength of SN explosion and therefore
also on the mass distribution of stellar remnants. In this study we adopt the
new formulas for the relation between the pre-SN star properties and its
remnant from Fryer et al. 2022 in prep. into StarTrack population synthesis
code and check how they impact double compact object (DCO) mergers formed via
isolated binary evolution. The new formulas give one ability to test a wide
spectrum of assumptions on the convection growth time. In particular, different
variants allow for a smooth transition between having a deep lower mass gap and
a remnant mass distribution filled by massive NSs and low mass BHs. In this
paper we present distribution of masses, mass ratios and the local merger rate
densities of DCO mergers for different variants of new remnant mass formulas.
We test them together with different approaches to other highly uncertain
processes. We find that mass distribution of DCO mergers up to m_1+m_2 < 35
Msun is sensitive to adopted assumption on SN convection growth timescale.
Between the two extreme tested variants the probability of compact object
formation within the lower mass gap may differ up to 2 orders of magnitude. The
mass ratio distribution of DCO mergers is significantly influenced by SN model
only for our standard mass transfer stability criteria.


title:
Painlev\'e I and exact WKB: Stokes phenomenon for two-parameter
  transseries

abstract:
  For more than a century, the Painlev\'e I equation has played an important
role in both physics and mathematics. Its two-parameter family of solutions was
studied in many different ways, yet still leads to new surprises and
discoveries. Two popular tools in these studies are the theory of isomonodromic
deformation that uses the exact WKB method, and the asymptotic description of
transcendents in terms of two-parameter transseries. Combining methods from
both schools of thought, and following work by Takei and collaborators, we find
complete, two-parameter connection formulae for solutions when they cross
arbitrary Stokes lines in the complex plane. These formulae allow us to study
Stokes phenomenon for the full two-parameter family of transseries solutions.
In particular, we recover the exact expressions for the Stokes data that were
recently found by Baldino, Schwick, Schiappa and Vega and compare our
connection formulae to theirs. We also explain several ambiguities in relating
transseries parameter choices to actual Painlev\'e transcendents, study the
monodromy of formal solutions, and provide high-precision numerical tests of
our results.


title:
TOI-1696 and TOI-2136: Constraining the Masses of Two Mini-Neptunes with
  HPF

abstract:
  We present the validation of two planets orbiting M dwarfs, TOI-1696b and
TOI-2136b. Both planets are mini-Neptunes orbiting nearby stars, making them
promising prospects for atmospheric characterization with the James Webb Space
Telescope. We validated the planetary nature of both candidates using high
contrast imaging, ground-based photometry, and near-infrared radial velocities.
Adaptive Optics images were taken using the ShARCS camera on the 3 m Shane
Telescope. Speckle images were taken using the NN-Explore Exoplanet Stellar
Speckle Imager on the WIYN 3.5 m telescope. Radii and orbital ephemerides were
refined using a combination of TESS, the diffuser-assisted ARCTIC imager on the
3.5m ARC telescope at Apache Point Observatory, and the 0.6 m telescope at Red
Buttes Observatory. We obtained radial velocities using the Habitable-Zone
Planet Finder on the 10 m Hobby-Eberly Telescope, which enabled us to place
upper limits on the masses of both transiting planets. TOI-1696b (P = 2.5 days;
R$_{p}$ = 3.24 R$_{\oplus}$; M$_{p}$ $<$ 56.6 M$_{\oplus}$) falls into a
sparsely-populated region of parameter space considering its host star's
temperature (T$_{\rm{eff}}$ = 3168 K, M4.5), as planets of its size are quite
rare around mid to late M dwarfs. On the other hand, TOI-2136b (P = 7.85 days;
R$_{p}$ = 2.09 R$_{\oplus}$; M$_{p}$ $<$ 15.0 M$_{\oplus}$) is an excellent
candidate for atmospheric follow-up with JWST.


title:
The galaxy-wide stellar initial mass function in the presence of
  cluster-to-cluster IMF variations

abstract:
  We calculate the integrated galactic initial stellar mass function (IGIMF) in
the presence of IMF variations in clusters. IMF Variations for a population of
clusters are taken into account in the form of Gaussian distributions of the
IMF parameters. For the tapered power law function used here, these are the
slopes at the high and low mass ends, $\Gamma$ and $\gamma$, and the
characteristic mass $M_{ch}$. Variations are modeled by varying the width of
the Gaussian distributions. The reference values are the standard deviations of
the parameters observed for young clusters in the present-day Milky Way
$\sigma_{\Gamma}=0.6$, $\sigma_{\gamma}=0.25$, and $\sigma_{M_{ch}}=0.27$
M$_{\odot}$. Increasing the dispersions of $\gamma$ and $\Gamma$ moderately
flattens the IGIMF at the low and high mass ends. Increasing $\sigma_{M_{ch}}$
shifts the peak of the IGIMF to lower masses, rendering the IGIMF more bottom
heavy. This can explain the bottom heavy stellar mass function of Early-type
galaxies as they are the result of the merger of disk galaxies where the
physical conditions of the star forming gas vary significantly both in time and
space. The effect of IMF variations is compared to that due to other effects
such as variations in the shape of the initial cluster mass function,
metallicity, and galactic SFR. We find that the effect of IMF variations is a
dominant factor that always affects the characteristic mass of the IGIMF. We
compare our results to a sample of ultra-faint dwarf satellite galaxies (UFDs).
Their present-day stellar mass function is an analog to their IGIMF at the time
their stellar populations have formed. We show that the slope of the IGIMF of
the UFDs can only be reproduced when IMF variations of the same order as those
measured in the present-day Milky Way are included. (Abridged)


title:
The probability of galaxy-galaxy strong lensing events in hydrodynamical
  simulations of galaxy clusters

abstract:
  Meneghetti et al. (2020) recently reported an excess of galaxy-galaxy strong
lensing (GGSL) in galaxy clusters compared to expectations from the LCDM
cosmological model. Theoretical estimates of the GGSL probability are based on
the analysis of numerical hydrodynamical simulations in the LCDM cosmology. We
quantify the impact of the numerical resolution and AGN feedback scheme adopted
in cosmological simulations on the predicted GGSL probability and determine if
varying these simulation properties can alleviate the gap with observations. We
repeat the analysis of Meneghetti et al. (2020) on cluster-size halos simulated
with different mass and force resolutions and implementing several independent
AGN feedback schemes. We find that improving the mass resolution by a factor of
ten and twenty-five, while using the same galaxy formation model that includes
AGN feedback, does not affect the GGSL probability. We find similar results
regarding the choice of gravitational softening. On the contrary, adopting an
AGN feedback scheme that is less efficient at suppressing gas cooling and star
formation leads to an increase in the GGSL probability by a factor between
three and six. However, we notice that such simulations form overly massive
subhalos whose contribution to the lensing cross-section would be significant
while their Einstein radii are too large to be consistent with the
observations. The primary contributors to the observed GGSL cross-sections are
subhalos with smaller masses, that are compact enough to become critical for
lensing. The population with these required characteristics appears to be
absent in simulations.


title:
A detection of H$_2$ in a high velocity cloud toward the Large
  Magellanic Cloud

abstract:
  This work presents a new detection of H$_2$ absorption arising in a high
velocity cloud (HVC) associated with either the Milky Way or the Large
Magellanic Cloud (LMC). The absorber was found in an archival Far Ultraviolet
Spectroscopic Explorer spectrum of the LMC star Sk-70$^\circ$32. This is the
fifth well-characterized H$_2$ absorber to be found in the Milky Way's halo and
the second such absorber outside the Magellanic Stream and Bridge. The absorber
has a local standard of rest central velocity of $+$140 km s$^{-1}$ and a H$_2$
column density of $10^{17.5}$ cm$^{-2}$. It is most likely part of a cool and
relatively dense inclusion ($T\approx 75$ K, $n_{\rm H}\sim 100$ cm$^{-3}$) in
a warmer and more diffuse halo cloud. This halo cloud may be part of a
still-rising Milky Way Galactic fountain flow or an outflow from the Large
Magellanic Cloud.


title: Galaxies in the central regions of simulated galaxy clusters

abstract:
  In this paper, we assess the impact of numerical resolution and of the
implementation of energy input from AGN feedback models on the inner structure
of cluster sub-haloes in hydrodynamic simulations. We compare several zoom-in
re-simulations of a sub-sample of the cluster-sized haloes studied in
Meneghetti et al. (2020), obtained by varying mass resolution, softening length
and AGN energy feedback scheme. We study the impact of these different setups
on the subhalo abundances, their radial distribution, their density and mass
profiles and the relation between the maximum circular velocity, which is a
proxy for subhalo compactness. Regardless of the adopted numerical resolution
and feedback model, subhaloes with masses Msub < 1e11Msun/h, the most relevant
mass-range for galaxy-galaxy strong lensing, have maximum circular velocities
~30% smaller than those measured from strong lensing observations of Bergamini
et al. (2019). We also find that simulations with less effective AGN energy
feedback produce massive subhaloes (Msub> 1e11 Msun/h ) with higher maximum
circular velocity and that their Vmax - Msub relation approaches the observed
one. However the stellar-mass number count of these objects exceeds the one
found in observations and we find that the compactness of these simulated
subhaloes is the result of an extremely over-efficient star formation in their
cores, also leading to larger-than-observed subhalo stellar mass. We conclude
that simulations are unable to simultaneously reproduce the observed stellar
masses and compactness (or maximum circular velocities) of cluster galaxies.
Thus, the discrepancy between theory and observations that emerged from the
analysis of Meneghetti et al. (2020) persists. It remains an open question as
to whether such a discrepancy reflects limitations of the current
implementation of galaxy formation models or the LCDM paradigm.


title:
Pegasus V -- a newly discovered ultra-faint dwarf galaxy on the
  outskirts of Andromeda

abstract:
  We report the discovery of an ultra-faint dwarf in the constellation of
Pegasus. Pegasus~V (Peg~V) was initially identified in the public imaging data
release of the DESI Legacy Imaging Surveys and confirmed with deep imaging from
Gemini/GMOS-N. The colour-magnitude diagram shows a sparse red giant branch
(RGB) population and a strong over-density of blue horizontal branch stars. We
measure a distance to Peg~V of $D=692^{+33}_{-31}$~kpc, making it a distant
satellite of Andromeda with $M_V=-6.3\pm0.2$ and a half-light radius of $r_{\rm
half}=89\pm41$~pc. It is located $\sim260$~kpc from Andromeda in the outskirts
of its halo. The RGB is well-fit by a metal-poor isochrone with [Fe/H]$=-3.2$,
suggesting it is very metal poor. This, combined with its blue horizontal
branch could imply that it is a reionisation fossil. This is the first
detection of an ultra-faint dwarf outside the deep Pan-Andromeda Archaeological
Survey area, and points to a rich, faint satellite population in the outskirts
of our nearest neighbour.


title: Embodied Navigation at the Art Gallery

abstract:
  Embodied agents, trained to explore and navigate indoor photorealistic
environments, have achieved impressive results on standard datasets and
benchmarks. So far, experiments and evaluations have involved domestic and
working scenes like offices, flats, and houses. In this paper, we build and
release a new 3D space with unique characteristics: the one of a complete art
museum. We name this environment ArtGallery3D (AG3D). Compared with existing 3D
scenes, the collected space is ampler, richer in visual features, and provides
very sparse occupancy information. This feature is challenging for
occupancy-based agents which are usually trained in crowded domestic
environments with plenty of occupancy information. Additionally, we annotate
the coordinates of the main points of interest inside the museum, such as
paintings, statues, and other items. Thanks to this manual process, we deliver
a new benchmark for PointGoal navigation inside this new space. Trajectories in
this dataset are far more complex and lengthy than existing ground-truth paths
for navigation in Gibson and Matterport3D. We carry on extensive experimental
evaluation using our new space for evaluation and prove that existing methods
hardly adapt to this scenario. As such, we believe that the availability of
this 3D model will foster future research and help improve existing solutions.


title: Localised thermonuclear bursts from accreting magnetic white dwarfs

abstract:
  Nova explosions are caused by global thermonuclear runaways triggered in the
surface layers of accreting white dwarfs. It has been predicted that localised
thermonuclear bursts on white dwarfs can also take place, similar to Type I
X-ray bursts observed in accreting neutron stars. Unexplained rapid bursts from
the binary system TV Columbae, in which mass is accreted onto a
moderately-strong magnetised white dwarf from a low-mass companion, have been
observed on several occasions in the past $\approx40$ years. During these
bursts the optical/UV luminosity increases by a factor of $>3$ in less than an
hour and fades over $\approx10$ hours. Fast outflows have been observed in UV
spectral lines, with velocities $>3500$ km s$^{-1}$, comparable to the escape
velocity from the white dwarf surface. Here we report on optical bursts
observed in TV Columbae as well as in two additional accreting systems, EI
Ursae Majoris and ASASSN-19bh. The bursts have a total energy $\approx~10^{-6}$
those of classical nova explosions ("micronovae"), and bear a strong
resemblance to Type I X-ray bursts. We exclude accretion or stellar magnetic
reconnection events as their origin and suggest thermonuclear runaway events in
magnetically-confined accretion columns as a viable explanation.


title: Isogrowth Cosmology (and How to Map the Universe)

abstract:
  While general relativity ties together the cosmic expansion history and
growth history of large scale structure, beyond the standard model these can
have independent behaviors. We derive expressions for cosmologies with
identical growth histories but different expansion histories, or other
deviations. This provides a relation for isogrowth cosmologies, but also
highlights in general the need for observations to measure each of the growth,
expansion, gravity, and dark matter property histories.


title:
Tree-based solvers for adaptive mesh refinement code FLASH -- III: a
  novel scheme for radiation pressure on dust and gas and radiative transfer
  from diffuse sources

abstract:
  Radiation is an important contributor to the energetics of the interstellar
medium, yet its transport is difficult to solve numerically. We present a novel
approach towards solving radiative transfer of diffuse sources via backwards
ray tracing. Here we focus on the radiative transfer of infrared radiation and
the radiation pressure on dust. The new module, \textsc{TreeRay/RadPressure},
is an extension to the novel radiative transfer method \textsc{TreeRay}
implemented in the grid-based MHD code {\sc Flash}. In
\textsc{TreeRay/RadPressure}, every cell and every star particle is a source of
infrared radiation. We also describe how gas, dust and radiation are coupled
via a chemical network. This allows us to compute the local dust temperature in
thermal equilibrium, leading to a significantly improvement over the classical
grey approximation. In several tests, we demonstrate that the scheme produces
the correct radiative intensities as well as the correct momentum input by
radiation pressure. Subsequently, we apply our new scheme to model massive star
formation from a collapsing, turbulent core of 150 ${\rm M}_\odot$. We trace
the effects of both, ionizing and infrared radiation on the dynamics of the
core. We find that the newborn massive star(s) prevent fragmentation in their
proximity through radiative heating. Over time, dust and radiation temperature
equalize, while the gas temperature can be either warmer due to shock heating
or colder due to insufficient dust-gas coupling. Compared to gravity, the
effects of radiation pressure become significant on the core scale only at an
evolved stage.


title:
Triggering micronovae through magnetically confined accretion flows in
  accreting white dwarfs

abstract:
  Rapid bursts at optical wavelengths have been reported for several accreting
white dwarfs, where the optical luminosity can increase by up to a factor 30 in
less than an hour fading on timescales of several hours, and where the energy
release can reach $\approx10^{39}$ erg ("micronovae"). Several systems have
also shown these bursts to be semi-recurrent on timescales of days to months
and the temporal profiles of these bursts strongly resemble those observed in
Type-I X-ray bursts in accreting neutron stars. It has been suggested that the
observed micronovae may be the result of localised thermonuclear runaways on
the surface layers of accreting white dwarfs. Here we propose a model where
magnetic confinement of the accretion stream on to accreting magnetic white
dwarfs may trigger localised thermonuclear runaways. The proposed model to
trigger micronovae appears to favour magnetic systems with both high white
dwarf masses and high mass-transfer rates.


title:
Substructures in protoplanetary disks imprinted by compact planetary
  systems

abstract:
  The substructures observed in protoplanetary disks may be the signposts of
embedded planets carving gaps or creating vortices. The inferred masses of
these planets often fall in the Jovian regime despite their low abundance
compared to lower-mass planets, partly because previous works often assume that
a single substructure (a gap or vortex) is caused by a single planet. In this
work, we study the possible imprints of compact systems composed of
Neptune-like planets ($\sim10-30\;M_\oplus$) and show that long-standing
vortices are a prevalent outcome when their inter-planetary separation ($\Delta
a$) falls below $\sim8$ times $H_{{\rm p}}$ -- the average disk's scale height
at the planets locations. In simulations where a single planet is unable to
produce long-lived vortices, two-planet systems can preserve them for at least
$5,000$ orbits in two regimes: i) fully-shared density gaps with elongated
vortices around the stable Lagrange points $L_4$ and $L_5$ for the most compact
planet pairs ($\Delta a \lesssim 4.6\; H_{{\rm p}}$); ii) partially-shared gaps
for more widely spaced planets ($\Delta a \sim 4.6 - 8\;H_{{\rm p}}$) forming
vortices in a density ring between the planets through the Rossby wave
instability. The latter case can produce vortices with a wide range of aspect
ratios down to $\sim3$ and can occur for planets captured into the 3:2 (2:1)
mean-motion resonances for disk's aspects ratios of $h\gtrsim 0.033$ ($h\gtrsim
0.057$). We suggest that their long lifetimes are sustained by the interaction
of spiral density waves launched by the neighboring planets. Overall, our
results show that distinguishing imprint of compact systems with Neptune-mass
planets are long-lived vortices inside the density gaps, which in turn are
shallower than single-planet gaps for a fixed gap width.


title:
Detection of Tool based Edited Images from Error Level Analysis and
  Convolutional Neural Network

abstract:
  Image Forgery is a problem of image forensics and its detection can be
leveraged using Deep Learning. In this paper we present an approach for
identification of authentic and tampered images done using image editing tools
with Error Level Analysis and Convolutional Neural Network. The process is
performed on CASIA ITDE v2 dataset and trained for 50 and 100 epochs
respectively. The respective accuracies of the training and validation sets are
represented using graphs.


title: Spatial Search via Memoryless Walk with Selfloop

abstract:
  The defining feature of memoryless quantum walks is that they operate on the
vertex space of a graph, and therefore can be used to produce search algorithms
with minimal memory. We present a memoryless walk that can find a unique marked
vertex on a two-dimensional grid. Our walk is based on the construction
proposed by Falk, which tessellates the grid with squares of size $2 \times 2$.
Our walk uses minimal memory, $O(\sqrt{N \log N})$ applications of the walk
operator, and outputs the marked vertex with vanishing error probability. To
accomplish this, we apply a selfloop to the marked vertex - a technique we
adapt from interpolated walks. We prove that with our explicit choice of
selfloop weight, this forces the action of the walk asymptotically into a
single rotational space. We characterize this space and as a result, show that
our memoryless walk produces the marked vertex with a success probability
asymptotically approaching one.


title:
Rapidly rotating stars and their transiting planets: KELT-17b,
  KELT-19Ab, and KELT-21b in the CHEOPS and TESS era

abstract:
  Rapidly rotating early-type main-sequence stars with transiting planets are
interesting in many aspects. Unfortunately, several astrophysical effects in
such systems are not well understood yet. Therefore, we performed a photometric
mini-survey of three rapidly rotating stars with transiting planets, namely
KELT-17b, KELT-19Ab, and KELT-21b, using the Characterising Exoplanets
Satellite (CHEOPS), complemented with Transiting Exoplanet Survey Satellite
(TESS) data, and spectroscopic data. We aimed at investigating the spin-orbit
misalignment and its photometrical signs, therefore the high-quality light
curves of the selected objects were tested for transit asymmetry, transit
duration variations, and orbital precession. In addition, we performed transit
time variation analyses, obtained new stellar parameters, and refined the
system parameters. For KELT-17b and KELT-19Ab we obtained significantly smaller
planet radius as found before. The gravity-darkening effect is very small
compared to the precision of CHEOPS data. We can report only on a tentative
detection of the stellar inclination of KELT-21, which is about 60 deg. In
KELT-17b and KELT-19Ab we were able to exclude long-term transit duration
variations causing orbital precession. The shorter transit duration of
KELT-19Ab compared to the discovery paper is probably a consequence of a
smaller planet radius. KELT-21b is promising from this viewpoint, but further
precise observations are needed. We did not find any convincing evidence for
additional objects in the systems.


title: AutoField: Automating Feature Selection in Deep Recommender Systems

abstract:
  Feature quality has an impactful effect on recommendation performance.
Thereby, feature selection is a critical process in developing deep
learning-based recommender systems. Most existing deep recommender systems,
however, focus on designing sophisticated neural networks, while neglecting the
feature selection process. Typically, they just feed all possible features into
their proposed deep architectures, or select important features manually by
human experts. The former leads to non-trivial embedding parameters and extra
inference time, while the latter requires plenty of expert knowledge and human
labor effort. In this work, we propose an AutoML framework that can adaptively
select the essential feature fields in an automatic manner. Specifically, we
first design a differentiable controller network, which is capable of
automatically adjusting the probability of selecting a particular feature
field; then, only selected feature fields are utilized to retrain the deep
recommendation model. Extensive experiments on three benchmark datasets
demonstrate the effectiveness of our framework. We conduct further experiments
to investigate its properties, including the transferability, key components,
and parameter sensitivity.


title: Music Source Separation with Generative Flow

abstract:
  Full supervision models for source separation are trained on mixture-source
parallel data and have achieved superior performance in recent years. However,
large-scale and naturally mixed parallel training data are difficult to obtain
for music, and such models are difficult to adapt to mixtures with new sources.
Source-only supervision models, in contrast, only require clean sources for
training; They learn source models and then apply these models to separate the
mixture.


title: USNO Bright Star Catalog, version 1

abstract:
  USNO Bright Star Catalog (UBSC) is a new astrometric catalog of 1423
brightest stars covering the entire sky, which is published online. It is
nearly complete to $V=3$ mag except for three stellar systems. A combined
astrometric solution of the Hipparcos Intermediate Astrometry Data and two
dedicated ground-based campaigns in 2013 -- 2020 is the basis for this catalog.
The astrometric parameters for each star include position coordinates,
parallax, proper motion components, and their covariances on the Hipparcos mean
epoch 1991.25. 64 percent of the catalog are flagged as known or suspected
double or binary stars. UBSC lists 68 stars missing in Gaia EDR3 and another
114 stars without Gaia parallaxes or proper motions. The formal precision
achieved for proper motions is comparable to that of Gaia.


title: Named Entity Recognition for Partially Annotated Datasets

abstract:
  The most common Named Entity Recognizers are usually sequence taggers trained
on fully annotated corpora, i.e. the class of all words for all entities is
known. Partially annotated corpora, i.e. some but not all entities of some
types are annotated, are too noisy for training sequence taggers since the same
entity may be annotated one time with its true type but not another time,
misleading the tagger. Therefore, we are comparing three training strategies
for partially annotated datasets and an approach to derive new datasets for new
classes of entities from Wikipedia without time-consuming manual data
annotation. In order to properly verify that our data acquisition and training
approaches are plausible, we manually annotated test datasets for two new
classes, namely food and drugs.


title:
Factors that influence the adoption of human-AI collaboration in
  clinical decision-making

abstract:
  Recent developments in Artificial Intelligence (AI) have fueled the emergence
of human-AI collaboration, a setting where AI is a coequal partner. Especially
in clinical decision-making, it has the potential to improve treatment quality
by assisting overworked medical professionals. Even though research has started
to investigate the utilization of AI for clinical decision-making, its
potential benefits do not imply its adoption by medical professionals. While
several studies have started to analyze adoption criteria from a technical
perspective, research providing a human-centered perspective with a focus on
AI's potential for becoming a coequal team member in the decision-making
process remains limited. Therefore, in this work, we identify factors for the
adoption of human-AI collaboration by conducting a series of semi-structured
interviews with experts in the healthcare domain. We identify six relevant
adoption factors and highlight existing tensions between them and effective
human-AI collaboration.


title: Photometric single-view dense 3D reconstruction in endoscopy

abstract:
  Visual SLAM inside the human body will open the way to computer-assisted
navigation in endoscopy. However, due to space limitations, medical endoscopes
only provide monocular images, leading to systems lacking true scale. In this
paper, we exploit the controlled lighting in colonoscopy to achieve the first
in-vivo 3D reconstruction of the human colon using photometric stereo on a
calibrated monocular endoscope. Our method works in a real medical environment,
providing both a suitable in-place calibration procedure and a depth estimation
technique adapted to the colon's tubular geometry. We validate our method on
simulated colonoscopies, obtaining a mean error of 7% on depth estimation,
which is below 3 mm on average. Our qualitative results on the EndoMapper
dataset show that the method is able to correctly estimate the colon shape in
real human colonoscopies, paving the ground for true-scale monocular SLAM in
endoscopy.


title:
A homogenization result in finite plasticity and its application to
  high-contrast media

abstract:
  We carry out a variational study for integral functionals that model the
stored energy of a heterogeneous material governed by finite-strain
elastoplasticity with hardening. Assuming that the composite has a periodic
microscopic structure, we firstly establish the {\Gamma}-convergence of the
energies in the limiting of vanishing periodicity. Then, in the second part of
the paper, we use the result to derive a macroscopic description for an
elastoplastic medium with high-contrast microstructure. Specifically, we
consider a composite obtained by filling the voids of a periodically perforated
stiff matrix by soft inclusions. Again, we study the {\Gamma}-convergence of
the related energy functionals as the periodicity tends to zero. The main
challenge is posed by the lack of coercivity brought about by the degeneracy of
the material properties in the soft part. We prove that the {\Gamma}-limit,
which we compute with respect to a suitable notion of convergence, is the sum
of the contributions resulting from each of the two components separately.


title: Photoinduced metallization of excitonic insulators

abstract:
  Utilizing the time-dependent density-matrix renormalization group technique,
we numerically prove photoinduced pairing states in the extended
Falicov-Kimball model (EFKM) at half filling, both with and without internal
SU(2) symmetry. In the time-dependent photoemission spectra an extra band
appears above the Fermi energy after pulse irradiation, indicating an
insulator-to-metal transition. Even in the absence of the SU(2) structure, the
pair correlations are enhanced during the pump, and afterwards decrease over
time. This implies the possible metallization of Ta$_2$NiSe$_5$, a strong
candidate for an excitonic insulator material, for which the EFKM is considered
to be the minimal theoretical model. Simulating the photoemission with
optimized pulse parameters, we demonstrate a photoinduced quantum phase
transition, in accord with recent findings in time- and angle-resolved
photoemission spectroscopy experiments on Ta$_2$NiSe$_5$.


title:
Choosing the number of factors in factor analysis with incomplete data
  via a hierarchical Bayesian information criterion

abstract:
  The Bayesian information criterion (BIC), defined as the observed data log
likelihood minus a penalty term based on the sample size $N$, is a popular
model selection criterion for factor analysis with complete data. This
definition has also been suggested for incomplete data. However, the penalty
term based on the `complete' sample size $N$ is the same no matter whether in a
complete or incomplete data case. For incomplete data, there are often only
$N_i<N$ observations for variable $i$, which means that using the `complete'
sample size $N$ implausibly ignores the amounts of missing information inherent
in incomplete data. Given this observation, a novel criterion called
hierarchical BIC (HBIC) for factor analysis with incomplete data is proposed.
The novelty is that it only uses the actual amounts of observed information,
namely $N_i$'s, in the penalty term. Theoretically, it is shown that HBIC is a
large sample approximation of variational Bayesian (VB) lower bound, and BIC is
a further approximation of HBIC, which means that HBIC shares the theoretical
consistency of BIC. Experiments on synthetic and real data sets are conducted
to access the finite sample performance of HBIC, BIC, and related criteria with
various missing rates. The results show that HBIC and BIC perform similarly
when the missing rate is small, but HBIC is more accurate when the missing rate
is not small.


title: Optimize_Prime@DravidianLangTech-ACL2022: Emotion Analysis in Tamil

abstract:
  This paper aims to perform an emotion analysis of social media comments in
Tamil. Emotion analysis is the process of identifying the emotional context of
the text. In this paper, we present the findings obtained by Team
Optimize_Prime in the ACL 2022 shared task "Emotion Analysis in Tamil." The
task aimed to classify social media comments into categories of emotion like
Joy, Anger, Trust, Disgust, etc. The task was further divided into two
subtasks, one with 11 broad categories of emotions and the other with 31
specific categories of emotion. We implemented three different approaches to
tackle this problem: transformer-based models, Recurrent Neural Networks
(RNNs), and Ensemble models. XLM-RoBERTa performed the best on the first task
with a macro-averaged f1 score of 0.27, while MuRIL provided the best results
on the second task with a macro-averaged f1 score of 0.13.


title:
Exploration of Machine Learning Classification Models Used for
  Behavioral Biometrics Authentication

abstract:
  Mobile devices have been manufactured and enhanced at growing rates in the
past decades. While this growth has significantly evolved the capability of
these devices, their security has been falling behind. This contrast in
development between capability and security of mobile devices is a significant
problem with the sensitive information of the public at risk. Continuing the
previous work in this field, this study identifies key Machine Learning
algorithms currently being used for behavioral biometric mobile authentication
schemes and aims to provide a comprehensive review of these algorithms when
used with touch dynamics and phone movement. Throughout this paper the
benefits, limitations, and recommendations for future work will be discussed.


title:
4D-MultispectralNet: Multispectral Stereoscopic Disparity Estimation
  using Human Masks

abstract:
  Multispectral stereoscopy is an emerging field. A lot of work has been done
in classical stereoscopy, but multispectral stereoscopy is not studied as
frequently. This type of stereoscopy can be used in autonomous vehicles to
complete the information given by RGB cameras. It helps to identify objects in
the surroundings when the conditions are more difficult, such as in night
scenes. This paper focuses on the RGB-LWIR spectrum. RGB-LWIR stereoscopy has
the same challenges as classical stereoscopy, that is occlusions, textureless
surfaces and repetitive patterns, plus specific ones related to the different
modalities. Finding matches between two spectrums adds another layer of
complexity. Color, texture and shapes are more likely to vary from a spectrum
to another. To address this additional challenge, this paper focuses on
estimating the disparity of people present in a scene. Given the fact that
people's shape is captured in both RGB and LWIR, we propose a novel method that
uses segmentation masks of the human in both spectrum and than concatenate them
to the original images before the first layer of a Siamese Network. This method
helps to improve the accuracy, particularly within the one pixel error range.


title: Non-detection of CHIME/FRB sources with the Arecibo Observatory

abstract:
  In this work, we present follow-up observations of two known repeating fast
radio bursts (FRBs) and seven non-repeating FRBs with complex morphology
discovered with CHIME/FRB. These observations were conducted with the Arecibo
Observatory 327 MHz receiver. We detected no additional bursts from these
sources, nor did CHIME/FRB detect any additional bursts from these sources
during our follow-up program. Based on these non-detections, we provide
constraints on the repetition rate, for all nine sources. We calculate
repetition rates using both a Poisson distribution of repetition and the
Weibull distribution of repetition presented by Oppermann et al. (2018). For
both distributions, we find repetition upper limits of the order $\lambda =
10^{-2} - 10^{-1} \text{hr}^{-1}$ for all sources. These rates are much lower
than those recently published for notable repeating FRBs like FRB 20121102A and
FRB 20201124A, suggesting the possibility of a low-repetition sub-population.


title: Light-cone distribution amplitudes of heavy mesons with QED effects

abstract:
  We discuss the QED-generalized leading-twist light-cone distribution
amplitudes of heavy mesons, that appear in QCD$\times$QED factorization
theorems for exclusive two-body $B$ decays. In the presence of electrically
charged particles, these functions should be more appropriately regarded as
soft functions for heavy-meson decays into two back-to-back particles. In this
paper, we derive the one-loop anomalous dimension of these soft functions and
study their behaviour under renormalization-scale evolution, obtaining an exact
solution in Laplace space. In addition, we provide numerical solutions for the
soft functions and analytical solutions to all orders in the strong and to
first order in the electromagnetic coupling. For the inverse (and
inverse-logarithmic) moments, we obtain an all-order solution in both
couplings. We further provide numerical estimates for QED corrections to the
inverse moments.


title: Indiscriminate Data Poisoning Attacks on Neural Networks

abstract:
  Data poisoning attacks, in which a malicious adversary aims to influence a
model by injecting "poisoned" data into the training process, have attracted
significant recent attention. In this work, we take a closer look at existing
poisoning attacks and connect them with old and new algorithms for solving
sequential Stackelberg games. By choosing an appropriate loss function for the
attacker and optimizing with algorithms that exploit second-order information,
we design poisoning attacks that are effective on neural networks. We present
efficient implementations that exploit modern auto-differentiation packages and
allow simultaneous and coordinated generation of tens of thousands of poisoned
points, in contrast to existing methods that generate poisoned points one by
one. We further perform extensive experiments that empirically explore the
effect of data poisoning attacks on deep neural networks.


title:
Behind the Machine's Gaze: Biologically Constrained Neural Networks
  Exhibit Human-like Visual Attention

abstract:
  By and large, existing computational models of visual attention tacitly
assume perfect vision and full access to the stimulus and thereby deviate from
foveated biological vision. Moreover, modelling top-down attention is generally
reduced to the integration of semantic features without incorporating the
signal of a high-level visual tasks that have shown to partially guide human
attention. We propose the Neural Visual Attention (NeVA) algorithm to generate
visual scanpaths in a top-down manner. With our method, we explore the ability
of neural networks on which we impose the biological constraints of foveated
vision to generate human-like scanpaths. Thereby, the scanpaths are generated
to maximize the performance with respect to the underlying visual task (i.e.,
classification or reconstruction). Extensive experiments show that the proposed
method outperforms state-of-the-art unsupervised human attention models in
terms of similarity to human scanpaths. Additionally, the flexibility of the
framework allows to quantitatively investigate the role of different tasks in
the generated visual behaviours. Finally, we demonstrate the superiority of the
approach in a novel experiment that investigates the utility of scanpaths in
real-world applications, where imperfect viewing conditions are given.


title: Superfluid stiffness of a KTaO3-based two-dimensional electron gas

abstract:
  After almost twenty years of intense work on the celebrated LaAlO3/SrTiO3
system, the recent discovery of a superconducting two-dimensional electron
gases (2-DEG) in (111)-oriented KTaO3-based heterostructures injects new
momentum to the field of oxides interfaces. However, while both interfaces
share common properties, experiments also suggest important differences between
the two systems. Here, we report gate tunable superconductivity in 2-DEGs
generated at the surface of a (111)-oriented KTaO3 crystal by the simple
sputtering of a thin Al layer. We use microwave transport to show that
(111)-KTaO3 2-DEGs exhibit a node-less superconducting order parameter with a
gap value significantly larger than expected within a simple BCS weak-coupling
limit model. Consistent with the two-dimensional nature of superconductivity,
we evidence a well-defined Berezinsky-Kosterlitz-Thouless type of transition,
which was not reported on SrTiO3-based interfaces. Our finding offers
innovative perspectives for fundamental science but also for device
applications in a variety of fields such as spin-orbitronics and topological
electronics.


title: Gravitational wave modes in matter

abstract:
  A general linear gauge-invariant equation for dispersive gravitational waves
(GWs) propagating in matter is derived. This equation describes, on the same
footing, both the usual tensor modes and the gravitational modes strongly
coupled with matter. It is shown that the effect of matter on the former is
comparable to diffraction and therefore negligible within the
geometrical-optics approximation. However, this approximation is applicable to
modes strongly coupled with matter due to their large refractive index. GWs in
ideal gas are studied using the kinetic average-Lagrangian approach and the
gravitational polarizability of matter that we have introduced earlier. In
particular, we show that this formulation subsumes the kinetic Jeans
instability as a collective GW mode with a peculiar polarization, which is
derived from the dispersion matrix rather than assumed a priori. This forms a
foundation for systematically extending GW theory to GW interactions with
plasmas, where symmetry considerations alone are insufficient to predict the
wave polarization.


title:
Conditional Value at Risk-Sensitive Solar Hosting Capacity Analysis in
  Distribution Networks

abstract:
  Solar hosting capacity analysis (HCA) assesses the ability of a distribution
network to host distributed solar generation without seriously violating
distribution network constraints. In this paper, we consider risk-sensitive HCA
that limits the risk of network constraint violations with a collection of
scenarios of solar irradiance and nodal power demands, where risk is modeled
via the conditional value at risk (CVaR) measure. First, we consider the
question of maximizing aggregate installed solar capacities, subject to risk
constraints and solve it as a second-order cone program (SOCP) with a standard
conic relaxation of the feasible set with power flow equations. Second, we
design an incremental algorithm to decide whether a configuration of solar
installations has acceptable risk of constraint violations, modeled via CVaR.
The algorithm circumvents explicit risk computation by incrementally
constructing inner and outer polyhedral approximations of the set of acceptable
solar installation configurations from prior such tests conducted. Our
numerical examples study the impact of risk parameters, the number of scenarios
and the scalability of our framework.


title:
Information-theoretic Limits for Testing Community Structures in
  Weighted Networks

abstract:
  Community detection refers to the problem of clustering the nodes of a
network into groups. Existing inferential methods for community structure
mainly focus on unweighted (binary) networks. Many real-world networks are
nonetheless weighted and a common practice is to dichotomize a weighted network
to an unweighted one which is known to result in information loss. Literature
on hypothesis testing in the latter situation is still missing. In this paper,
we study the problem of testing the existence of community structure in
weighted networks. Our contributions are threefold: (a). We use the (possibly
infinite-dimensional) exponential family to model the weights and derive the
sharp information-theoretic limit for the existence of consistent test. Within
the limit, any test is inconsistent; and beyond the limit, we propose a useful
consistent test. (b). Based on the information-theoretic limits, we provide the
first formal way to quantify the loss of information incurred by dichotomizing
weighted graphs into unweighted graphs in the context of hypothesis testing.
(c). We propose several new and practically useful test statistics. Simulation
study show that the proposed tests have good performance. Finally, we apply the
proposed tests to an animal social network.