title:
Galactic angular momentum in the IllustrisTNG simulation -- I.
  Connection to morphology, halo spin, and black hole mass

abstract:
  We use the TNG100 simulation of the IllustrisTNG project to investigate the
stellar specific angular momenta ($j_{\ast}$) of $\sim$12,000 central galaxies
at $z=0$ in a full cosmological context, with stellar masses ($M_{\ast}$)
ranging from $10^{9}$ to $10^{12} \, {\rm M}_{\odot}$. We find that the
$j_{\ast}$-$M_{\ast}$ relations for early-type and late-type galaxies in
IllustrisTNG are in good overall agreement with observations, and that these
galaxy types typically `retain' $\sim$10-20 and $\sim$50-60 per cent of their
host haloes' specific angular momenta, respectively, with some dependence on
the methodology used to measure galaxy morphology. We present results for
kinematic as well as visual-like morphological measurements of the simulated
galaxies. Next, we explore the scatter in the $j_{\ast}$-$M_{\ast}$ relation
with respect to the spin of the dark matter halo and the mass of the
supermassive black hole (BH) at the galactic centre. We find that galaxies
residing in faster spinning haloes, as well as those hosting less massive BHs,
tend to have a higher specific angular momentum. We also find that, at fixed
galaxy or halo mass, halo spin and BH mass are anticorrelated with each other,
probably as a consequence of more efficient gas flow toward the galactic centre
in slowly rotating systems. Finally, we show that halo spin plays an important
role in determining galaxy sizes - larger discs form at the centres of
faster-rotating haloes - although the trend breaks down for massive galaxies
with $M_{\ast} \gtrsim 10^{11} \, {\rm M}_{\odot}$, roughly the mass scale at
which a galaxy's stellar mass becomes dominated by accreted stars.


title:
Standard Model Predictions for Rare $K$ and $B$ Decays without
  $|V_{cb}|$ and $|V_{ub}|$ Uncertainties

abstract:
  The persistent tensions between inclusive and exclusive determinations of
$|V_{cb}|$ and $|V_{ub}|$ weaken the power of theoretically clean rare $K$ and
$B$ decays in the search for new physics (NP). We demonstrate how this
uncertainty can be practically removed by considering within the SM suitable
ratios of various branching ratios. This includes the branching ratios for
$K^+\to\pi^+\nu\bar\nu$, $K_{L}\to\pi^0\nu\bar\nu$, $K_S\to\mu^+\mu^-$,
$B_{s,d}\to\mu^+\mu^-$ and $B\to K(K^*)\nu\bar\nu$. Also $\epsilon_K$, $\Delta
M_d$, $\Delta M_s$ and the mixing induced CP-asymmetry $S_{\psi K_S}$, all
measured already very precisely, play an important role in this analysis. The
highlights of our analysis are 16 $|V_{cb}|$ and $|V_{ub}|$ independent ratios
that often are independent of the CKM arameters or depend only on the angles
$\beta$ and $\gamma$ in the Unitarity Triangle with $\beta$ already precisely
known and $\gamma$ to be measured precisely in the coming years by the LHCb and
Belle II collaborations. Once $\gamma$ Once $\gamma$ is measured precisely
these 16 ratios taken together are expected to be a powerful tool in the search
for new physics. Assuming no NP in $|\epsilon_K|$ and $S_{\psi K_S}$ we
determine independently of $|V_{cb}|$:
$\mathcal{B}(K^+\to\pi^+\nu\bar\nu)_\text{SM}= (8.60\pm0.42)\times 10^{-11}$
and $\mathcal{B}(K_L\to\pi^0\nu\bar\nu)_\text{SM}=(2.94\pm 0.15)\times
10^{-11}$. This are the most precise determinations to date. Assuming no NP in
$\Delta M_{s,d}$ allows to obtain analogous results for all $B$ decay branching
ratios considered in our paper without any CKM uncertainties.


title: Dark Photon Stars: Formation and Role as Dark Matter Substructure

abstract:
  Any new vector boson with non-zero mass (a `dark photon' or `Proca boson')
that is present during inflation is automatically produced at this time from
vacuum fluctuations and can comprise all or a substantial fraction of the
observed dark matter density, as shown by Graham, Mardon, and Rajendran. We
demonstrate, utilising both analytic and numerical studies, that such a
scenario implies an extremely rich dark matter substructure arising purely from
the interplay of gravitational interactions and quantum effects. Due to a
remarkable parametric coincidence between the size of the primordial density
perturbations and the scale at which quantum pressure is relevant, a
substantial fraction of the dark matter inevitably collapses into
gravitationally bound solitons, which are fully quantum coherent objects. The
central densities of these `dark photon star', or `Proca star', solitons are
typically a factor $10^6$ larger than the local background dark matter density,
and they have characteristic masses of $10^{-16} M_\odot (10^{-5}{\rm
eV}/m)^{3/2}$, where $m$ is the mass of the vector. During and post soliton
production a comparable fraction of the energy density is initially stored in,
and subsequently radiated from, long-lived quasi-normal modes. Furthermore, the
solitons are surrounded by characteristic `fuzzy' dark matter halos in which
quantum wave-like properties are also enhanced relative to the usual virialized
dark matter expectations. Lower density compact halos, with masses a factor of
$\sim 10^5$ greater than the solitons, form at much larger scales. We argue
that, at minimum, the solitons are likely to survive to the present day without
being tidally disrupted. This rich substructure, which we anticipate also
arises from other dark photon dark matter production mechanisms, opens up a
wide range of new direct and indirect detection possibilities, as we discuss in
a companion paper.


title:
Quantum Dropout for Efficient Quantum Approximate Optimization Algorithm
  on Combinatorial Optimization Problems

abstract:
  A combinatorial optimization problem becomes very difficult in situations
where the energy landscape is rugged and the global minimum locates in a narrow
region of the configuration space. When using the quantum approximate
optimization algorithm (QAOA) to tackle these hard cases, we find that
difficulty mainly originates from the QAOA quantum circuit instead of the cost
function. To alleviate the issue, we selectively drop out the clauses defining
the quantum circuit while keeping the cost function intact. Due to the
combinatorial nature of the optimization problems, the dropout of clauses in
the circuit does not affect the solution. Our numerical results confirm QAOA's
performance improvements with various types of quantum-dropout implementation.


title:
0-Form, 1-Form and 2-Group Symmetries via Cutting and Gluing of
  Orbifolds

abstract:
  Orbifold singularities of M-theory constitute the building blocks of a broad
class of supersymmetric quantum field theories (SQFTs). In this paper we show
how the local data of these geometries determines global data on the resulting
higher symmetries of these systems. In particular, via a process of cutting and
gluing, we show how local orbifold singularities encode the 0-form, 1-form and
2-group symmetries of the resulting SQFTs. Geometrically, this is obtained from
the possible singularities which extend to the boundary of the non-compact
geometry. The resulting category of boundary conditions then captures these
symmetries, and is equivalently specified by the orbifold homology of the
boundary geometry. We illustrate these general points in the context of a
number of examples, including 5D superconformal field theories engineered via
orbifold singularities, 5D gauge theories engineered via singular elliptically
fibered Calabi-Yau threefolds, as well as 4D SQCD-like theories engineered via
M-theory on non-compact $G_2$ spaces.


title: Radiation, entanglement and islands from a boundary local quench

abstract:
  We study the entanglement and the energy density of the radiation emitted
after a local quench in a boundary conformal field theory. We use the operator
product expansion (OPE) to predict the early- and late-time behavior of the
entanglement entropy and we find, under mild assumptions, a universal form for
the leading term, which we test on some treatable two-dimensional examples. We
also derive a general upper bound on the entanglement, valid along the full
time evolution. In two dimensions, the bound is computed analytically, while in
higher dimensions it is evaluated at early and late time via the OPE. These CFT
predictions are then compared with a doubly-holographic setup where the CFT is
interpreted as a reservoir for the radiation produced on an end-of-the-world
brane. After finding the gravitational dual of a boundary local quench, we
compute the time evolution of the holographic entanglement entropy, whose
late-time behavior is in perfect agreement with the CFT predictions. In the
brane+bath picture, unitarity of the time evolution is preserved thanks to the
formation of an island. The holographic results can be recovered explicitly
from the island formula, in the limit where the tension of the brane is close
to the maximal value.


title:
Motivations for a Large Self-Interacting Dark Matter Cross Section from
  Milky Way Satellites

abstract:
  We explore the properties of Milky Way subhalos in self-interacting dark
matter models for moderate cross sections of 1 to 5 cm$^2$g$^{-1}$ using
high-resolution zoom-in N-body simulations. We include the gravitational
potential of a baryonic disk and bulge matched to the Milky Way, which is
critical for getting accurate predictions. The predicted number and
distribution of subhalos within the host halo are similar for 1 and 5
cm$^2$g$^{-1}$ models, and they agree with observations of Milky Way satellite
galaxies only if subhalos with peak circular velocity over all time > 4.5 km/s
are able to form galaxies. We do not find distinctive signatures in the
pericenter distribution of the subhalos that could help distinguish the models.
Using an analytic model to extend the simulation results, we are able to show
that subhalos in models with cross sections between 1 and 5 cm$^2$g$^{-1}$ are
not dense enough to match the densest ultra-faint and classical dwarf
spheroidal galaxies in the Milky Way. This motivates velocity-dependent cross
sections with values larger than 5 cm$^2$g$^{-1}$ at the velocities relevant
for the satellites such that core collapse would occur in some of the
ultra-faint and classical dwarf spheroidals.


title: The Turn-Down of the Baryonic Tully-Fisher Relation at Low Galaxy Masses

abstract:
  The ratio of baryonic to dark matter in present-day galaxies constrains
galaxy formation theories and can be determined empirically via the baryonic
Tully-Fisher relation (BTFR), which compares a galaxy's baryonic mass
(M$_{bary}$) to its maximum rotation velocity (V$_{max}$). The BTFR is
well-determined at M$_{bary}>10^8$ M$_{\odot}$, but poorly constrained at lower
masses due to small samples and the challenges of measuring rotation velocities
in this regime. For 25 galaxies with high-quality data and M$_{bary}<\sim10^8$
M$_{\odot}$, we estimate M$_{bary}$ from infrared, optical, and HI observations
and Vmax from the HI gas rotation. Many of the V$_{max}$ values are lower
limits because the velocities are still rising at the edge of the detected HI
disks; consequently, most of our sample has lower velocities than expected from
extrapolations of the BTFR at higher masses. To estimate V$_{max}$, we map each
galaxy to a dark matter halo assuming density profiles with and without cores,
and find that the cored profiles match the data better. When we compare the
V$_{max}$ values derived from the cored density profiles to our M$_{bary}$
measurements, we find a turndown of the BTFR at low masses that is consistent
with CDM predictions and implying baryon fractions of 1-10% of the cosmic
value. Although we are limited by the sample size and assumptions inherent in
mapping measured rotational velocities to theoretical rotation curves, our
results suggest that the galaxy formation efficiency drops at masses below
M$_{bary}\sim10^8$ M$_{\odot}$, corresponding to M$_{200}\sim10^{10}$
M$_{\odot}$.


title: Non-local computation of quantum circuits with small light cones

abstract:
  The task of non-local quantum computation requires implementation of a
unitary on $n$ qubits between two parties with only one round of communication,
ideally with minimal pre-shared entanglement. We introduce a new protocol that
makes use of the fact that port-based teleportation costs much less
entanglement when done only on a small number of qubits at a time. Whereas
previous protocols have entanglement cost independent of the unitary or scaling
with its complexity, the cost of the new protocol scales with the
\emph{non-locality} of the unitary. Specifically, it takes the form $\sim
n^{4V}$ with $V$ the maximum volume of a past light cone in a circuit
implementing the unitary. Thus we can implement unitary circuits with $V\sim
O(1)$ using polynomial entanglement, and those with $V\sim \mathrm{polylog}(n)$
using quasi-polynomial entanglement. For a general unitary circuit with $d$
layers of $k$-qubit gates $V$ is at most $k^d$, but if geometric locality is
imposed it is at most polynomial in $d$. We give an explicit class of unitaries
for which our protocol's entanglement cost scales better than any known
protocol. We also show that several extensions can be made without
significantly affecting the entanglement cost - arbitrary local pre- and
post-processing; global Clifford pre- and post-processing; and the addition of
a polynomial number of auxiliary systems.


title: SiMCa: Sinkhorn Matrix Factorization with Capacity Constraints

abstract:
  For a very broad range of problems, recommendation algorithms have been
increasingly used over the past decade. In most of these algorithms, the
predictions are built upon user-item affinity scores which are obtained from
high-dimensional embeddings of items and users. In more complex scenarios, with
geometrical or capacity constraints, prediction based on embeddings may not be
sufficient and some additional features should be considered in the design of
the algorithm. In this work, we study the recommendation problem in the setting
where affinities between users and items are based both on their embeddings in
a latent space and on their geographical distance in their underlying euclidean
space (e.g., $\mathbb{R}^2$), together with item capacity constraints. This
framework is motivated by some real-world applications, for instance in
healthcare: the task is to recommend hospitals to patients based on their
location, pathology, and hospital capacities. In these applications, there is
somewhat of an asymmetry between users and items: items are viewed as static
points, their embeddings, capacities and locations constraining the allocation.
Upon the observation of an optimal allocation, user embeddings, items
capacities, and their positions in their underlying euclidean space, our aim is
to recover item embeddings in the latent space; doing so, we are then able to
use this estimate e.g. in order to predict future allocations. We propose an
algorithm (SiMCa) based on matrix factorization enhanced with optimal transport
steps to model user-item affinities and learn item embeddings from observed
data. We then illustrate and discuss the results of such an approach for
hospital recommendation on synthetic data.


title:
Structure of $^{83}$As, $^{85}$As and $^{87}$As: from semi-magicity to
  $\gamma$-softness

abstract:
  The structure of $^{83}$As, $^{85}$As and $^{87}$As have been studied in
fusion-fission reaction $^{238}$U+$^9$Be. Fission fragments were identified in
mass and atomic number using the VAMOS++ spectrometer and the coincident
$\gamma$-rays were detected in the $\gamma$-ray tracking array AGATA. New
transitions in $^{83}$As and $^{85}$As are reported and placed in the level
schemes. A level scheme of the excited states in $^{87}$As is proposed for the
first time. The data are interpreted in frame of Large-Scale Shell-Model
calculations, SU3 symmetries and Beyond Mean-Field frameworks. A spherical
regime at magic number $N$=50 is predicted and the location of the proton
$g_{9/2}$ orbital is proposed for the first time. Development of collectivity
in a prolate deformed, $\gamma$-soft regime in the open shell cases $^{85}$As
and $^{87}$As, most neutron-rich isotopes beyond $N$=50, is concluded. Data and
theoretical calculations give confidence to a relatively high extrapolated
excitation energy about 4 MeV of the $9/2^+$ state in $^{79}$Cu, one proton
above $^{78}$Ni.


title:
Targeted large mass ratio numerical relativity surrogate waveform model
  for GW190814

abstract:
  Gravitational wave observations of large mass ratio compact binary mergers
like GW190814 highlight the need for reliable, high-accuracy waveform templates
for such systems. We present NRHybSur2dq15, a new surrogate model trained on
hybridized numerical relativity (NR) waveforms with mass ratios $q\leq15$, and
aligned spins $|\chi_{1z}|\leq0.5$ and $\chi_{2z}=0$. We target the parameter
space of GW190814-like events as large mass ratio NR simulations are very
expensive. The model includes the (2,2), (2,1), (3,3), (4,4), and (5,5)
spin-weighted spherical harmonic modes, and spans the entire LIGO bandwidth
(with $f_{\mathrm{low}}=20$ Hz) for total masses $M \gtrsim 9.5 \, M_{\odot}$.
NRHybSur2dq15 accurately reproduces the hybrid waveforms, with mismatches below
$\sim 2 \times 10^{-3}$ for total masses $10 \, M_{\odot} \leq M \leq 300 \,
M_{\odot}$. This is at least an order of magnitude improvement over existing
semi-analytical models for GW190814-like systems. Finally, we reanalyze
GW190814 with the new model and obtain source parameter constraints consistent
with previous work.


title:
Snowmass White Paper: Effective Field Theories for Condensed Matter
  Systems

abstract:
  We review recent progress and a number of future directions for applications
of effective field theory methods to condensed matter systems broadly defined.
Our emphasis is on areas that have allowed a fertile exchange of ideas between
high energy physics and many-body theory. We discuss developments in the
effective field theory of spontaneous symmetry breaking, of hydrodynamics and
non-equilibrium dynamics more generally, fracton phases of matter, and
dualities between 2+1 dimensional field theories. We furthermore discuss the
application of effective field theory to non-Fermi liquids, the dynamics of
entanglement entropy and to condensed matter aspects of cosmology.


title:
Explaining the hints for lepton flavour universality violation with
  three $S_2$ leptoquark generations

abstract:
  Leptoquarks are prime candidates for explaining the intriguing hints for
lepton flavour universality violation. In particular, the $SU(2)_L$ doublet of
scalar leptoquarks $S_2$ is capable of providing an explanation for the
tensions between the measurements and the Standard Model predictions in
$(g-2)_\mu$, $b\to s\ell^+ \ell^-$ and $b\to c\tau\nu$ processes, as well as in
non-resonant di-electron production. However, in the minimal setup with a
single leptoquark generation, a common explanation for all these issues is not
possible as this would lead to unacceptably large charged lepton flavour
violation. We therefore propose a model with three generations of $S_2$, each
coupling exclusively to a single lepton flavour, \textit{i.e.}~a model
extending the Standard Model particle content by an electroquark, a muoquark
and a tauquark. We show that after taking into account other constraints, such
as those originating from electroweak precision observables and $\Delta F=2$
processes, it is possible to provide a combined explanation for all these hints
of lepton flavour universality violation. Moreover, we find that the presence
of the tauquark can generate a dimension-six ${\cal O}_9^U$ operator via
off-shell photon penguin diagrams, which, together with the muoquark
contribution, further improves the global fit to $b \to s \ell^+ \ell^-$ data.


title: Hamilton Cycles on Dense Regular Digraphs and Oriented Graphs

abstract:
  We prove that for every $\varepsilon > 0$ there exists $n_0=n_0(\varepsilon)$
such that every regular oriented graph on $n > n_0$ vertices and degree at
least $(1/4 + \varepsilon)n$ has a Hamilton cycle. This establishes an
approximate version of a conjecture of Jackson from 1981. We also establish a
result related to a conjecture of K\"uhn and Osthus about the Hamiltonicity of
regular directed graphs with suitable degree and connectivity conditions.


title:
Online Next-Best-View Planner for 3D-Exploration and Inspection With a
  Mobile Manipulator Robot

abstract:
  Robotic systems performing end-user oriented autonomous exploration can be
deployed in different scenarios which not only require mapping but also
simultaneous inspection of regions of interest for the end-user. In this work,
we propose a novel Next-Best-View (NBV) planner which can perform full
exploration and user-oriented exploration with inspection of the regions of
interest using a mobile manipulator robot. We address the
exploration-inspection problem as an instance of Multi-Objective Optimization
(MOO) and propose a weighted-sum-based information gain function for computing
NBVs for the RGB-D camera mounted on the arm. For both types of exploration
tasks, we compare our approach with an existing state-of-the-art exploration
method as the baseline and demonstrate our improvements in terms of total
volume mapped and lower computational requirements. The real experiments with a
mobile manipulator robot demonstrate the practicability and effectiveness of
our approach outdoors.


title: Seamless lightning nowcasting with recurrent-convolutional deep learning

abstract:
  A deep learning model is presented to nowcast the occurrence of lightning at
a five-minute time resolution 60 minutes into the future. The model is based on
a recurrent-convolutional architecture that allows it to recognize and predict
the spatiotemporal development of convection, including the motion, growth and
decay of thunderstorm cells. The predictions are performed on a stationary
grid, without the use of storm object detection and tracking. The input data,
collected from an area in and surrounding Switzerland, comprise ground-based
radar data, visible/infrared satellite data and derived cloud products,
lightning detection, numerical weather prediction and digital elevation model
data. We analyze different alternative loss functions, class weighting
strategies and model features, providing guidelines for future studies to
select loss functions optimally and to properly calibrate the probabilistic
predictions of their model. Based on these analyses, we use focal loss in this
study, but conclude that it only provides a small benefit over cross entropy,
which is a viable option if recalibration of the model is not practical.


title:
Introducing causal inference in the energy-efficient building design
  process

abstract:
  "What-if" questions are intuitively generated during the design process.
Engineers and architects need to inherently conduct design decisions,
progressing from one phase to another. They either use empirical domain
experience, simulations, or data-driven methods to conduct consequential
feedback. We take an example in an interdisciplinary domain, energy-efficient
building design scenario, to argue that the current methods for decision
support have four limitations: 1. Less carefully inspected parametric
independence raise risks of biased results and spurious relationships. 2. The
integration gap between data-driven methods and knowledge-based approaches. 3.
Less explicit model interpretability for informed decision-making. 4. Ambiguous
boundaries for machine assistance during the design process. In this paper, we
first clarify the nature of dynamic personal experience and constant principal
knowledge in design. Sequentially, we introduce the causal inference into the
energy-efficient design domain by proposing a two-step process to reveal and
analyze the parametric dependencies within the design space by identifying the
design causal diagram with interventions. The causal diagram provides a nexus
for integrating domain knowledge with data-driven methods and allows for
interpretability and testability against domain experience. The extraction of
causal structures from the data is close to the common design reasoning
process. As an illustration, we applied the properties of the proposed
estimators through simulations. The paper is concluded with a feasibility study
that demonstrates the realization of the proposed framework.


title: SETI, evolution and human history merged into a mathematical model

abstract:
  In this paper we propose a new mathematical model capable of merging
Darwinian Evolution, Human History and SETI into a single mathematical scheme:
1) Darwinian Evolution over the last 3.5 billion years is defined as one
particular realization of a certain stochastic process called Geometric
Brownian Motion (GBM). This GBM yields the fluctuations in time of the number
of species living on Earth. Its mean value curve is an increasing exponential
curve, i.e. the exponential growth of Evolution. 2) In 2008 this author
provided the statistical generalization of the Drake equation yielding the
number N of communicating ET civilizations in the Galaxy. N was shown to follow
the lognormal probability distribution. 3) We call "b-lognormals" those
lognormals starting at any positive time b ("birth") larger than zero. Then the
exponential growth curve becomes the geometric locus of the peaks of a
one-parameter family of b-lognormals: this is our way to re-define Cladistics.
4) b-lognormals may be also be interpreted as the lifespan of any living being
(a cell, or an animal, a plant, a human, or even the historic lifetime of any
civilization). Applying this new mathematical apparatus to Human History, leads
to the discovery of the exponential progress between Ancient Greece and the
current USA as the envelope of all b-lognormals of Western Civilizations over a
period of 2500 years. 5) We then invoke Shannon's Information Theory. The
b-lognormals' entropy turns out to be the index of "development level" reached
by each historic civilization. We thus get a numerical estimate of the entropy
difference between any two civilizations, like the Aztec-Spaniard difference in
1519. 6) In conclusion, we have derived a mathematical scheme capable of
estimating how much more advanced than Humans an Alien Civilization will be
when the SETI scientists will detect the first hints about ETs.

categories: q-bio.PE astro-ph.EP math.PR physics.soc-ph


title: Towards a Perceptual Model for Estimating the Quality of Visual Speech

abstract:
  Generating realistic lip motions to simulate speech production is key for
driving natural character animations from audio. Previous research has shown
that traditional metrics used to optimize and assess models for generating lip
motions from speech are not a good indicator of subjective opinion of animation
quality. Yet, running repetitive subjective studies for assessing the quality
of animations can be time-consuming and difficult to replicate. In this work,
we seek to understand the relationship between perturbed lip motion and
subjective opinion of lip motion quality. Specifically, we adjust the degree of
articulation for lip motion sequences and run a user-study to examine how this
adjustment impacts the perceived quality of lip motion. We then train a model
using the scores collected from our user-study to automatically predict the
subjective quality of an animated sequence. Our results show that (1) users
score lip motions with slight over-articulation the highest in terms of
perceptual quality; (2) under-articulation had a more detrimental effect on
perceived quality of lip motion compared to the effect of over-articulation;
and (3) we can automatically estimate the subjective perceptual score for a
given lip motion sequences with low error rates.


title:
Bayesian structural learning of microbiota systems from count
  metagenomic data

abstract:
  Metagenomics combined with high-resolution sequencing techniques have enabled
researchers to study the genomes of entire microbial communities. Unraveling
interactions between these communities is of vital importance to understand how
microbes influence human health and disease. However, learning these
interactions from microbiome data is challenging, due to the high
dimensionality, discreteness, broad dispersion levels, compositionality and
excess of zero counts that characterize these data. In this paper, we develop a
copula graphical model for structure learning in these settings. In particular,
we advocate the use of discrete Weibull regression for linking the marginal
distributions to external covariates, which are often available in genomic
studies but rarely used for network inference, coupled with a Gaussian copula
to model the joint distribution of the counts. An efficient Bayesian procedure
for structural learning is implemented in the R package BDgraph and returns
inference of the marginals and of the dependency structure, providing
simultaneous differential analysis and graph uncertainty estimates. A
simulation study and a real data analysis of microbiome data show the
usefulness of the proposed approach at inferring networks from high-dimensional
count data in general, and its relevance in the context of microbiota data
analyses in particular.


title: Granular gas of inelastic and rough Maxwell particles

abstract:
  The most widely used model for granular gases is perhaps the inelastic
hard-sphere model (IHSM), where the grains are assumed to be perfectly smooth
spheres colliding with a constant coefficient of normal restitution. A much
more tractable model is the inelastic Maxwell model (IMM), in which the
velocity-dependent collision rate is replaced by an effective mean-field
constant. This simplification has been taken advantage of by many researchers
to find a number of exact results within the IMM. On the other hand, both the
IHSM and IMM neglect the impact of roughness -- generally present in real
grains -- on the dynamic properties of a granular gas. This is remedied by the
inelastic rough hard-sphere model (IRHSM), where, apart from the coefficient of
normal restitution, a constant coefficient of tangential restitution is
introduced. In parallel to the simplification carried out when going from the
IHSM to the IMM, we propose in this paper an inelastic rough Maxwell model
(IRMM) as a simplification of the IRHSM. The tractability of the proposed model
is illustrated by the exact evaluation of the collisional moments of first and
second degree, and the most relevant ones of third and fourth degree. The
results are applied to the evaluation of the rotational-to-translational
temperature ratio and the velocity cumulants in the homogeneous cooling state.


title: Zipper Codes

abstract:
  Zipper codes are a framework for describing spatially-coupled product-like
codes. Many well-known codes, such as staircase codes and braided block codes,
are subsumed into this framework. New types of codes such as tiled diagonal and
delayed diagonal zipper codes are introduced along with their software
simulation results. Stall patterns that can arise in iterative decoding are
analyzed, giving a means of error floor estimation.


title: CP violation and flavor invariants in the seesaw effective field theory

abstract:
  In this paper, we systematically study the algebraic structure of the ring of
the flavor invariants and the sources of CP violation in the seesaw effective
field theory (SEFT), which includes the dimension-five Weinberg operator and
one dimension-six operator at the tree-level matching. For the first time, we
calculate the Hilbert series and explicitly construct all the primary flavor
invariants in the SEFT. We show that all the physical parameters can be
extracted using the primary invariants and any CP-violating observable can be
expressed as the linear combination of CP-odd flavor invariants. The
calculation of the Hilbert series shows that there is an equal number of
primary flavor invariants in the SEFT and in the full seesaw model, which
reveals the intimate connection between the flavor space of the SEFT and that
of its ultraviolet theory. A proper matching procedure of the flavor invariants
is accomplished between the SEFT and the full seesaw model, through which one
can establish a direct link between the CP asymmetries in leptogenesis and
those in low-energy neutrino oscillations.


title: Spinning-enabled Wireless Amphibious Origami Millirobot

abstract:
  Wireless millimeter-scale origami robots that can locomote in narrow spaces
and morph their shapes have recently been explored with great potential for
biomedical applications. Existing millimeter-scale origami devices usually
require separate geometrical components for locomotion and functions, which
increases the complexity of the robotic systems and their operation upon
limited locomotion modes. Additionally, none of them can achieve both on-ground
and in-water locomotion. Here we report a magnetically actuated amphibious
origami millirobot that integrates capabilities of spinning-enabled multimodal
locomotion, controlled delivery of liquid medicine, and cargo transportation
with wireless operation. This millirobot takes full advantage of the
geometrical features and folding/unfolding capability of Kresling origami, a
triangulated hollow cylinder, to fulfill multifunction: its geometrical
features are exploited for generating omnidirectional locomotion in various
working environments, including on unstructured ground, in liquids, and at
air-liquid interfaces through rolling, flipping, and spinning-induced
propulsion; the folding/unfolding is utilized as a pumping mechanism for
integrated multifunctionality such as controlled delivery of liquid medicine;
furthermore, the spinning motion provides a sucking mechanism for targeted
solid cargo transportation. This origami millirobot breaks the conventional way
of utilizing origami folding only for shape reconfiguration and integrates
multiple functions in one simple body. We anticipate the reported magnetic
amphibious origami millirobots have the potential to serve as minimally
invasive devices for biomedical diagnoses and treatments.


title: Event Coreference Resolution for Contentious Politics Events

abstract:
  We propose a dataset for event coreference resolution, which is based on
random samples drawn from multiple sources, languages, and countries. Early
scholarship on event information collection has not quantified the contribution
of event coreference resolution. We prepared and analyzed a representative
multilingual corpus and measured the performance and contribution of the
state-of-the-art event coreference resolution approaches. We found that almost
half of the event mentions in documents co-occur with other event mentions and
this makes it inevitable to obtain erroneous or partial event information. We
showed that event coreference resolution could help improving this situation.
Our contribution sheds light on a challenge that has been overlooked or hard to
study to date. Future event information collection studies can be designed
based on the results we present in this report. The repository for this study
is on https://github.com/emerging-welfare/ECR4-Contentious-Politics.


title: Approximate Function Evaluation via Multi-Armed Bandits

abstract:
  We study the problem of estimating the value of a known smooth function $f$
at an unknown point $\boldsymbol{\mu} \in \mathbb{R}^n$, where each component
$\mu_i$ can be sampled via a noisy oracle. Sampling more frequently components
of $\boldsymbol{\mu}$ corresponding to directions of the function with larger
directional derivatives is more sample-efficient. However, as
$\boldsymbol{\mu}$ is unknown, the optimal sampling frequencies are also
unknown. We design an instance-adaptive algorithm that learns to sample
according to the importance of each coordinate, and with probability at least
$1-\delta$ returns an $\epsilon$ accurate estimate of $f(\boldsymbol{\mu})$. We
generalize our algorithm to adapt to heteroskedastic noise, and prove
asymptotic optimality when $f$ is linear. We corroborate our theoretical
results with numerical experiments, showing the dramatic gains afforded by
adaptivity.


title: Flux Compactifications, dS Vacua and the Swampland

abstract:
  One of the main goals of the field of string phenomenology is to describe the
properties of our spacetime in low-energy effective models related to and
consistent with string theory. It is known that our universe undergoes
accelerated expansion, which can be described in Einstein's theory of general
relativity as a positive cosmological constant. Within string theory
descriptions, this is realized by a scalar field with a positive value of its
potential. Oftentimes, this is modeled by having the scalar at a stable,
positive minimum. One then obtains a de Sitter spacetime. Constructions of de
Sitter spaces from string theory are not straightforward and face many
criticisms. In this thesis, we address several aspects related to this topic.
We investigate the description of general branes in supergravity and in
particular describe the uplifting anti-D3-brane of the KKLT model, including
all world-volume fields. Additionally, we translate the type IIB based KKLT
model into type IIA and introduce a mechanism that allows for the rapid
construction of many de Sitter solutions. Related to this, we investigate
models based on twisted 7-tori from M-theory and their relation to type II
supergravity. These setups can also yield de Sitter solutions using the same
mass production mechanism. One of the more recent and serious claims against
the construction of de Sitter spaces in string theory, the de Sitter swampland
conjecture, is also addressed here. We highlight the difficulty of obtaining a
stable, positive vacuum energy in classical type IIA flux compactifications and
their reliance on O-plane sources. Then, we turn our attention to the
conjecture itself. Contrary to the initial claim, we find many unstable de
Sitter points in numerical searches and go on to present our own refined
conjecture that differs in several key aspects from the refined version of the
original conjecture.


title:
Optical-force-mediated coupling between levitated nanospheres can go
  ultrastrong

abstract:
  We theoretically investigate the effect of optical-force-mediated
interactions onto the quantum dynamics of a pair of dielectric nanospheres
optically trapped in two neighboring optical tweezers. Thanks to the
interference between the tweezer beams and the elastically scattered light by
the other nanosphere, the effective inter nanosphere coupling can reach the
ultrastrong coupling regime. Experimentally accessible signatures of the
entangled nature of the quantum ground state are highlighted, including
entanglement witnesses based on position/momentum correlation functions and a
sizable dynamical Casimir excitation upon a fast modulation of the tweezer's
properties.


title:
The Contribution of M-Dwarf Flares to the Thermal Escape of Potentially
  Habitable Planet Atmospheres

abstract:
  The habitability of planets around M dwarfs ($\lesssim 0.5 M_\odot$) can be
affected by the XUV (X rays + extreme UV) emission of these stars, with flares
occasionally increasing the XUV flux by more than 2 orders of magnitude above
quiescent levels. This wavelength range can warm and ionize terrestrial
planets' upper atmospheres, which expands the planetary radius and promotes
atmospheric loss. In this work, we study the contribution of the XUV flux due
to flares on the atmospheric escape of Earth-like planets orbiting M dwarfs
through numerical simulations. We considered the first Gyr of planets with
initial surface water abundances between 1 and 10 terrestrial oceans (TO), a
small primordial hydrogen envelope ($\le$ $10^{-3}$~$M_{\oplus}$), and with
host star masses between 0.2 and 0.6 $M_{\odot}$. In this parameter range, we
find that flares can remove up to two TO more than nonflaring stars, which, in
some cases, translates to a doubling of the total water loss. We also find that
flaring can increase atmospheric oxygen partial pressures by hundreds of bars
in some cases. These results were obtained by adding a new module for flares to
the \vplanet software package and upgrading its atmospheric escape module to
account for Roche lobe overflow and radiation/recombination-limited escape.


title:
A matching design for augmenting a randomized clinical trial with
  external control

abstract:
  The use of information from real world to assess the effectiveness of medical
products is becoming increasingly popular and more acceptable by regulatory
agencies. According to a strategic real-world evidence framework published by
U.S. Food and Drug Administration, a hybrid randomized controlled trial that
augments internal control arm with real-world data is a pragmatic approach
worth more attention. In this paper, we aim to improve on existing matching
designs for such a hybrid randomized controlled trial. In particular, we
propose to match the entire concurrent randomized clinical trial (RCT) such
that (1) the matched external control subjects used to augment the internal
control arm are as comparable as possible to the RCT population, (2) every
active treatment arm in an RCT with multiple treatments is compared with the
same control group, and (3) matching can be conducted and the matched set
locked before treatment unblinding to better maintain the data integrity.
Besides a weighted estimator, we also introduce a bootstrap method to obtain
its variance estimation. The finite sample performance of the proposed method
is evaluated by simulations based on data from a real clinical trial.


title: Smectite phase separation is driven by emergent interfacial dynamics

abstract:
  Smectite clay minerals have an outsize impact on the response of clay-rich
media to common stimuli, such as water imbibition and ion exchange, motivating
extensive effort to understand microscopic behaviors resulting from these
processes such as swelling and exfoliation. Nonetheless, there is no general
consensus about the microscopic forces that govern smectite properties, which
are model systems for understanding colloidal and interfacial phenomena more
generally. We find that the complex free energy surface arising from the
interplay of at least four intermolecular forces and their nonlinear couplings
that control local particle-particle interactions leads to dynamic, unstable
equilibria between distinct phases. Mechanical disequilibrium arising from
osmotic gradients between curved or interacting interfaces drive the dynamic
exchange of layers and ions between dense and dilute phases via avalanche
transitions that are sustained by thermal fluctuations. We suggest that the
surprising interfacial dynamics displayed by smectite minerals, arising from
the vastly different mobilities of water, ions and mineral, makes them
fundamentally distinct from non-clay minerals because their structures are
easily perturbed away from simultaneous chemical and mechanical equilibrium.


title:
EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments
  with Both Quantitative and Qualitative Factors

abstract:
  Computer experiments with both quantitative and qualitative (QQ) inputs are
commonly used in science and engineering applications. Constructing desirable
emulators for such computer experiments remains a challenging problem. In this
article, we propose an easy-to-interpret Gaussian process (EzGP) model for
computer experiments to reflect the change of the computer model under the
different level combinations of qualitative factors. The proposed modeling
strategy, based on an additive Gaussian process, is flexible to address the
heterogeneity of computer models involving multiple qualitative factors. We
also develop two useful variants of the EzGP model to achieve computational
efficiency for data with high dimensionality and large sizes. The merits of
these models are illustrated by several numerical examples and a real data
application.


title: Half-Inverse Gradients for Physical Deep Learning

abstract:
  Recent works in deep learning have shown that integrating differentiable
physics simulators into the training process can greatly improve the quality of
results. Although this combination represents a more complex optimization task
than supervised neural network training, the same gradient-based optimizers are
typically employed to minimize the loss function. However, the integrated
physics solvers have a profound effect on the gradient flow as manipulating
scales in magnitude and direction is an inherent property of many physical
processes. Consequently, the gradient flow is often highly unbalanced and
creates an environment in which existing gradient-based optimizers perform
poorly. In this work, we analyze the characteristics of both physical and
neural network optimizations to derive a new method that does not suffer from
this phenomenon. Our method is based on a half-inversion of the Jacobian and
combines principles of both classical network and physics optimizers to solve
the combined optimization task. Compared to state-of-the-art neural network
optimizers, our method converges more quickly and yields better solutions,
which we demonstrate on three complex learning problems involving nonlinear
oscillators, the Schroedinger equation and the Poisson problem.


title: The $\Sigma$-invariants of $S$-arithmetic subgroups of Borel groups

abstract:
  Given a Chevalley group $\mathcal{G}$ of classical type and a Borel subgroup
$\mathcal{B} \subseteq \mathcal{G}$, we compute the $\Sigma$-invariants of the
$S$-arithmetic groups $\mathcal{B}(\mathbb{Z}[1/N])$, where $N$ is a product of
large enough primes. To this end, we let $\mathcal{B}(\mathbb{Z}[1/N])$ act on
a Euclidean building $X$ that is given by the product of Bruhat--Tits buildings
$X_p$ associated to $\mathcal{G}$, where $p$ runs over the primes dividing $N$.
In the course of the proof we introduce necessary and sufficient conditions for
convex functions on $\mbox{CAT(0)}$-spaces to be continuous. We apply these
conditions to associate to each simplex at infinity $\tau \subset
\partial_{\infty} X$ its so-called parabolic building $X^{\tau}$, which we
study from a geometric point of view. Moreover, we introduce new techniques in
combinatorial Morse theory, which enable us to take advantage of the concept of
essential $n$-connectivity rather than actual $n$-connectivity. Most of our
building theoretic results are proven in the general framework of spherical and
Euclidean buildings. For example, we prove that the complex opposite each
chamber in a spherical building $\Delta$ contains an apartment, provided
$\Delta$ is thick enough and $\mbox{Aut}(\Delta)$ acts chamber transitively on
$\Delta$.


title: Probing Factually Grounded Content Transfer with Factual Ablation

abstract:
  Despite recent success, large neural models often generate factually
incorrect text. Compounding this is the lack of a standard automatic evaluation
for factuality--it cannot be meaningfully improved if it cannot be measured.
Grounded generation promises a path to solving both of these problems: models
draw on a reliable external document (grounding) for factual information,
simplifying the challenge of factuality. Measuring factuality is also
simplified--to factual consistency, testing whether the generation agrees with
the grounding, rather than all facts. Yet, without a standard automatic metric
for factual consistency, factually grounded generation remains an open problem.
  We study this problem for content transfer, in which generations extend a
prompt, using information from factual grounding. Particularly, this domain
allows us to introduce the notion of factual ablation for automatically
measuring factual consistency: this captures the intuition that the model
should be less likely to produce an output given a less relevant grounding
document. In practice, we measure this by presenting a model with two grounding
documents, and the model should prefer to use the more factually relevant one.
We contribute two evaluation sets to measure this. Applying our new evaluation,
we propose multiple novel methods improving over strong baselines.


title: Analysis of quantum decay law: Is quantum tunneling really exponential?

abstract:
  The exponential decay law is well established since its first derivation in
1928, however it is not exact but only an approximate description. In recent
years some experimental and theoretical indications for non-exponential decay
have been documented. First we solve analytically the time-dependent
Schr\"odinger equation in one dimension for a potential consisting of an
infinite wall plus a rectangular barrier with finite width and also a cut
harmonic oscillator potential by considering it as a sequence of square
potentials. Then using the staggered Leap-Frog method, we solve the
time-dependent Schr\"odinger equation for the cut harmonic oscillator
potential. In both methods, time dependence of the survival probability of the
particle and the decay parameter {\lambda} are analyzed. The results exhibit
non-exponential behavior for survival probability at short and intermediate
times.


title: Learning Compressed Embeddings for On-Device Inference

abstract:
  In deep learning, embeddings are widely used to represent categorical
entities such as words, apps, and movies. An embedding layer maps each entity
to a unique vector, causing the layer's memory requirement to be proportional
to the number of entities. In the recommendation domain, a given category can
have hundreds of thousands of entities, and its embedding layer can take
gigabytes of memory. The scale of these networks makes them difficult to deploy
in resource constrained environments. In this paper, we propose a novel
approach for reducing the size of an embedding table while still mapping each
entity to its own unique embedding. Rather than maintaining the full embedding
table, we construct each entity's embedding "on the fly" using two separate
embedding tables. The first table employs hashing to force multiple entities to
share an embedding. The second table contains one trainable weight per entity,
allowing the model to distinguish between entities sharing the same embedding.
Since these two tables are trained jointly, the network is able to learn a
unique embedding per entity, helping it maintain a discriminative capability
similar to a model with an uncompressed embedding table. We call this approach
MEmCom (Multi-Embedding Compression). We compare with state-of-the-art model
compression techniques for multiple problem classes including classification
and ranking. On four popular recommender system datasets, MEmCom had a 4%
relative loss in nDCG while compressing the input embedding sizes of our
recommendation models by 16x, 4x, 12x, and 40x. MEmCom outperforms the
state-of-the-art techniques, which achieved 16%, 6%, 10%, and 8% relative loss
in nDCG at the respective compression ratios. Additionally, MEmCom is able to
compress the RankNet ranking model by 32x on a dataset with millions of users'
interactions with games while incurring only a 1% relative loss in nDCG.


title:
An Ultraluminous Supersoft Source in a Dwarf Galaxy of Abell 85: An
  Intermediate-Mass Black Hole Candidate

abstract:
  We study a large sample of dwarf galaxies using archival Chandra X-ray
observations, with the aim of detecting accreting intermediate-mass black holes
(IMBHs). IMBHs are expected to inhabit dwarf galaxies and to produce specific
signatures in terms of luminosity and X-ray spectra. We report the discovery of
an X-ray source associated with an Abell 85 dwarf galaxy that fits the IMBH
description. The stellar mass of the host galaxy is estimated to be 2 $\times$
10$^8$ $M_\odot$, which makes it one of the least massive galaxies to
potentially host an accreting black hole. The source is detected in the soft
band, under 1 keV, while undetected at higher energies. The X-ray luminosity is
$\approx$ 10$^{41}$ erg s$^{-1}$, making it almost three orders of magnitude
more luminous than the most luminous stellar-mass supersoft emitters. From the
galaxy stellar mass vs. black hole mass relation, we estimate the mass to be
within the intermediate regime. Another method that resulted in an intermediate
mass relies on the fact that supersoft emission is expected to be associated
with high accretion rates, approaching the Eddington limit. We suggest that the
observed offset of the X-ray source from the galactic center ($\approx$ 1.8
kpc) is due to galaxy interactions, and we present evidence from the literature
that supports the relation between black hole activity and galaxy interactions.


title: Optimal Dose-Limited Phase Estimation without Entanglement

abstract:
  Phase estimation is one of the most important facets of quantum metrology,
with applications in sensing, microscopy, and quantum computation. When
estimating a phase shift in a lossy medium, there is an upper bound on the
attainable information per particle sent through the phase shift. Previously,
only entanglement-enhanced measurements have been shown to saturate this bound.
We introduce a measurement scheme which can saturate the bound without relying
on entanglement.


title: Stability of the Non-Symmetric Space $E_7/\mathrm{PSO}(8)$

abstract:
  We prove that the normal metric on the homogeneous space
$E_7/\mathrm{PSO}(8)$ is stable with respect to the Einstein-Hilbert action,
thereby exhibiting the first known example of a non-symmetric metric of
positive scalar curvature with this property.


title: AI system for fetal ultrasound in low-resource settings

abstract:
  Despite considerable progress in maternal healthcare, maternal and perinatal
deaths remain high in low-to-middle income countries. Fetal ultrasound is an
important component of antenatal care, but shortage of adequately trained
healthcare workers has limited its adoption. We developed and validated an
artificial intelligence (AI) system that uses novice-acquired "blind sweep"
ultrasound videos to estimate gestational age (GA) and fetal malpresentation.
We further addressed obstacles that may be encountered in low-resourced
settings. Using a simplified sweep protocol with real-time AI feedback on sweep
quality, we have demonstrated the generalization of model performance to
minimally trained novice ultrasound operators using low cost ultrasound devices
with on-device AI integration. The GA model was non-inferior to standard fetal
biometry estimates with as few as two sweeps, and the fetal malpresentation
model had high AUC-ROCs across operators and devices. Our AI models have the
potential to assist in upleveling the capabilities of lightly trained
ultrasound operators in low resource settings.


title:
Fundamentals in Peaceman Model for Well-Block radius For Non-Linear
  Flows Near Well

abstract:
  We consider sewing machinery between finite difference and analytical
solutions defined at different scale: far away and near source of the
perturbation of the flow. One of the essences of the approach is that coarse
problem and boundary value problem in the proxy of the source model two
different flows. We are proposing method to glue solution via total fluxes,
which is predefined on coarse grid. It is important to mention that the coarse
solution "does not see" boundary.
  From industrial point of view our report provide mathematical tool for
analytical interpretation of simulated data for fluid flow around a well in a
porous medium. It can be considered as a mathematical "shirt" on famous
Peaceman well-block radius formula for linear (Darcy) radial flow but can be
applied in much more general scenario.
  As an important case, we consider nonlinear Forchheimer flow. In the article
we rigorously obtained well-block radius, explicitly depending on
$\beta-$Forchheimer factor and total rate of the flow on the well, and provide
generalization of the Dake Formula and evaluation of the $D-$factor.


title:
Mapping the $N = 40$ Island of Inversion: Precision Mass Measurements of
  Neutron-rich Fe Isotopes

abstract:
  Nuclear properties across the chart of nuclides are key to improving and
validating our understanding of the strong interaction in nuclear physics. We
present high-precision mass measurements of neutron-rich Fe isotopes performed
at the TITAN facility. The multiple-reflection time-of-flight mass spectrometer
(MR-ToF-MS), achieving a resolving power greater than $600\,000$ for the first
time, enabled the measurement of $^{63-70}$Fe, including first-time
high-precision direct measurements ($\delta m/m \sim 10^{-7}$) of $^{68-70}$Fe,
as well as the discovery of a long-lived isomeric state in $^{69}$Fe. These
measurements are accompanied by both mean-field and ab initio calculations
using the most recent realizations which enable theoretical assignment of the
spin-parities of the $^{69}$Fe ground and isomeric states. Together with
mean-field calculations of quadrupole deformation parameters for the Fe isotope
chain, these results benchmark a maximum of deformation in the $N = 40$ island
of inversion in Fe, and shed light on trends in level densities indicated in
the newly-refined mass surface.


title:
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement
  Learning

abstract:
  In this paper, we consider the infinite-horizon reach-avoid zero-sum game
problem, where the goal is to find a set in the state space, referred to as the
reach-avoid set, such that the system starting at a state therein could be
controlled to reach a given target set without violating constraints under the
worst-case disturbance. We address this problem by designing a new value
function with a contracting Bellman backup, where the super-zero level set,
i.e., the set of states where the value function is evaluated to be
non-negative, recovers the reach-avoid set. Building upon this, we prove that
the proposed method can be adapted to compute the viability kernel, or the set
of states which could be controlled to satisfy given constraints, and the
backward reachable set, or the set of states that could be driven towards a
given target set. Finally, we propose to alleviate the curse of dimensionality
issue in high-dimensional problems by extending Conservative Q-Learning, a deep
reinforcement learning technique, to learn a value function such that the
super-zero level set of the learned value function serves as a (conservative)
approximation to the reach-avoid set. Our theoretical and empirical results
suggest that the proposed method could learn reliably the reach-avoid set and
the optimal control policy even with neural network approximation.


title: Characterizing Alternative Monetization Strategies on YouTube

abstract:
  One of the key emerging roles of the YouTube platform is providing creators
the ability to generate revenue from their content and interactions. Alongside
tools provided directly by the platform, such as revenue-sharing from
advertising, creators co-opt the platform to use a variety of off-platform
monetization opportunities. In this work, we focus on studying and
characterizing these alternative monetization strategies. Leveraging a large
longitudinal YouTube dataset of popular creators, we develop a taxonomy of
alternative monetization strategies and a simple methodology to automatically
detect their usage. We then proceed to characterize the adoption of these
strategies. First, we find that the use of external monetization is expansive
and increasingly prevalent, used in 18% of all videos, with 61% of channels
using one such strategy at least once. Second, we show that the adoption of
these strategies varies substantially among channels of different kinds and
popularity, and that channels that establish these alternative revenue streams
often become more productive on the platform. Lastly, we investigate how
potentially problematic channels -- those that produce Alt-lite, Alt-right, and
Manosphere content -- leverage alternative monetization strategies, finding
that they employ a more diverse set of such strategies significantly more often
than a carefully chosen comparison set of channels. This finding complicates
YouTube's role as a gatekeeper, since the practice of excluding
policy-violating content from its native on-platform monetization may not be
effective. Overall, this work provides an important step towards broadening the
understanding of the monetary incentives behind content creation on YouTube.


title:
Closing the Generalization Gap of Cross-silo Federated Medical Image
  Segmentation

abstract:
  Cross-silo federated learning (FL) has attracted much attention in medical
imaging analysis with deep learning in recent years as it can resolve the
critical issues of insufficient data, data privacy, and training efficiency.
However, there can be a generalization gap between the model trained from FL
and the one from centralized training. This important issue comes from the
non-iid data distribution of the local data in the participating clients and is
well-known as client drift. In this work, we propose a novel training framework
FedSM to avoid the client drift issue and successfully close the generalization
gap compared with the centralized training for medical image segmentation tasks
for the first time. We also propose a novel personalized FL objective
formulation and a new method SoftPull to solve it in our proposed framework
FedSM. We conduct rigorous theoretical analysis to guarantee its convergence
for optimizing the non-convex smooth objective function. Real-world medical
image segmentation experiments using deep FL validate the motivations and
effectiveness of our proposed method.


title:
Improving Heuristic-based Process Discovery Methods by Detecting Optimal
  Dependency Graphs

abstract:
  Heuristic-based methods are among the most popular methods in the process
discovery area. This category of methods is composed of two main steps: 1)
discovering a dependency graph 2) determining the split/join patterns of the
dependency graph. The current dependency graph discovery techniques of
heuristic-based methods select the initial set of graph arcs according to
dependency measures and then modify the set regarding some criteria. This can
lead to selecting the non-optimal set of arcs. Also, the modifications can
result in modeling rare behaviors and, consequently, low precision and
non-simple process models. Thus, constructing dependency graphs through
selecting the optimal set of arcs has a high potential for improving graphs
quality. Hence, this paper proposes a new integer linear programming model that
determines the optimal set of graph arcs regarding dependency measures.
Simultaneously, the proposed method can eliminate some other issues that the
existing methods cannot handle completely; i.e., even in the presence of loops,
it guarantees that all tasks are on a path from the initial to the final tasks.
This approach also allows utilizing domain knowledge by introducing appropriate
constraints, which can be a practical advantage in real-world problems. To
assess the results, we modified two existing methods of evaluating process
models to make them capable of measuring the quality of dependency graphs.
According to assessments, the outputs of the proposed method are superior to
the outputs of the most prominent dependency graph discovery methods in terms
of fitness, precision, and especially simplicity.


title:
Numerical Solution for a Class of Evolution Differential Equations with
  $p$-Laplacian and Memory

abstract:
  In this paper we make a study of a partial integral differential equation
with $p$-Laplacian using a mixed finite element method. Two stable and
convergent fixed point schemes are proposed to solve the nonlinear algebraic
system. Using the implementation of the method in Matlab environment, we
numerically analyse the convergence with an example. Some other examples are
presented in order to illustrate several asymptotic behaviours and some
localization effects of the solutions.


title:
Observation of Radon Mitigation in MicroBooNE by a Liquid Argon
  Filtration System

abstract:
  The MicroBooNE liquid argon time projection chamber (LArTPC) maintains a high
level of liquid argon purity through the use of a filtration system that
removes electronegative contaminants in continuously-circulated liquid,
recondensed boil off, and externally supplied argon gas. We use the MicroBooNE
LArTPC to reconstruct MeV-scale radiological decays. Using this technique we
measure the liquid argon filtration system's efficacy at removing radon. This
is studied by placing a 500 kBq $^{222}$Rn source upstream of the filters and
searching for a time-dependent increase in the number of radiological decays in
the LArTPC. The liquid argon filtration system was able to remove greater than
99.999% of the radon injected into the system. Furthermore, a radiological
survey of the filters found that the copper-based filter material was the
primary medium that removed the $^{222}$Rn. This is the first observation of
radon mitigation in liquid argon with a large-scale copper-based filter and
could offer a radon mitigation solution for future large LArTPCs.


title: PiTSBiCG: Parallel in Time Stable Bi-Conjugate Gradient Algorithm

abstract:
  This paper presents a new algorithm for the parallel in time (PiT) numerical
simulation of time dependent partial/ordinary differential equations. We
propose a reliable alternative to the well know parareal in time algorithm, by
formulating the parallel in time problem algebraically and solve it using an
adapted Bi-Conjugate gradient stabilized method. The proposed Parallel in time
Stable Bi-Conjugate algorithm (PiTSBiCG) has a great potential in stabilizing
the parallel resolution for a variety of problems. In this work, we describe
the mathematical approach to the new algorithm and provide numerical evidences
that show its superiority to the standard parareal method.


title:
Thickness-dependent spin bistable transitions in single-crystalline
  molecular 2D material

abstract:
  The advent of two-dimensional (2D) crystals has led to numerous scientific
breakthroughs. Conventional 2D systems have in-plane covalent bonds and a weak
out-of-plane van-der-Waals bond. Here we report a new type of 2D material
composed of discrete magnetic molecules, where anisotropic van-der-Waals
interactions bond the molecules into a 2D packing. Through mechanical
exfoliation, we can obtain single-crystalline molecular monolayers, which can
be readily integrated into other 2D systems. Optical spectroscopy suggests the
few-layered molecules preserve the temperature-induced spin-crossover switching
observed in the bulk form but show a drastic increase in thermal hysteresis
unique to these thin 2D molecule assemblies. The trapping of spin bistability
with decreasing layer number can arise from domain wall dynamics in reduced
dimensions. Our results establish molecular solids with strong anisotropy of
intermolecular interactions as precursors to a novel class of 2D materials,
affording new possibilities to control functionalities through substrate and
interlayer interactions.


title:
A Comprehensive Study of Energy Dependence of Particle Ratios in $pp$
  Collisions from SPS to LHC Energies

abstract:
  A comprehensive study has been preformed to estimate the kaon to pion
($K^+$/$\pi^+$, $K^-$/$\pi^-$) ratio and total kaon to total pion ($K$/$\pi$)
ratio as a function of energy in $pp$ collisions at different energies i.e.,
\sqrts~= 6.3, 17.3, 62.4, 200, 900 GeV, 2.76 TeV, 7 TeV, 13 TeV and 14 TeV
using EPOS1.99, EPOS LHC, HIJING, QGSJET II and Sibyll model simulations.
Significant presence of horn in $K^+$/$\pi^+$ and $K^-$/$\pi^-$ ratio is
suggested by experimental data at lower energies, which is confirmed by HIJING
and EPOS LHC model. A smooth increase in $K/\pi$ ratio is also seen at higher
energies. The current model simulations predict the similar increase in the
ratio with increasing energies. Almost all the models suggest a saturation in
$K/\pi$ ratio at the LHC energies. On the basis of previous available
measurements, we also give predictions of these ratios at \sqrts~= 13 and 14
TeV. All the model predictions at \sqrts~= 13 and 14 TeV suggest a saturation
in the ratio at higher energies. However, QGSJTE II predictions are higher as
compared to other models.


title: Orbital physics: glorious past, bright future

abstract:
  Transition metal (TM) compounds present a very big class of materials with
quite diverse properties. There are among them insulators, metals, systems with
insulator-metal transitions; most magnetic systems are TM compounds; there are
among them also (high-Tc) superconductors. Their very rich properties are
largely determined by the strong interplay of different degrees of freedom:
charge; spin; orbital; lattice. Orbital effects play a very important role in
these systems -- and not only in them! The study of this field, initiated by
Goodenough almost 70 years ago, turned out to be very fruitful and produced a
lot of important results. In this short review I first discuss the basics of
orbital physics, summarize the main achievements in this big field, in which
Goodenough played a pivotal role and which are nowadays widely used to explain
many properties of TM compounds. In the main part of the text I discuss novel
developments and perspectives in orbital physics, which is still a very active
field of research, constantly producing new surprises.


title: Automated Materials Spectroscopy Analysis using Genetic Algorithms

abstract:
  We introduce a Genetic Algorithm (GA) based, open-source project to solve
multi-objective optimization problems of materials characterization data
analysis including EXAFS, XPS and nanoindentation. The modular design and
multiple crossover and mutation options make the software extensible for
additional materials characterization applications too. This automation of the
analysis is crucial in the era when instrumentation acquires data orders of
magnitude more rapidly than it can be analyzed by hand. Our results
demonstrated good fitness scores with minimal human intervention.


title: Simulating the Study of Exoplanets Using Photonic Spectrographs

abstract:
  Photonic spectrographs offer a highly miniaturized, flexible, and stable
on-chip solution for astronomical spectroscopy and can be used for various
science cases such as determining the atmospheric composition of exoplanets to
understand their habitability, formation, and evolution. Arrayed Waveguide
Gratings (AWGs) have shown the best promise to be used as an astrophotonic
spectrograph. We developed a publically-available tool to conduct a preliminary
examination of the capability of the AWGs in spectrally resolving exoplanet
atmospheres. We derived the Line-Spread-Function (LSF) as a function of
wavelength and the Full-Width-at-Half-Maximum (FWHM) of the LSF as a function
of spectral line width to evaluate the response of a discretely- and
continuously-sampled low-resolution AWG (R $\sim$ 1000). We observed that the
LSF has minimal wavelength dependence ($\sim$5\%), irrespective of the offset
with respect to the center-wavelengths of the AWG channels, contrary to the
previous assumptions. We further confirmed that the observed FWHM scales
linearly with the emission line width. Finally, we present simulated extraction
of a sample molecular absorption spectrum with the discretely- and
continuously-sampled low-resolution AWGs. From this, we show that while the
discrete AWG matches its expected resolving power, the continuous AWG
spectrograph can, in principle, achieve an effective resolution significantly
greater ($\sim$ 2x) than the discrete AWG. This detailed examination of the
AWGs will be foundational for future deployment of AWG spectrographs for
astronomical science cases such as exoplanet atmospheres.

categories: astro-ph.IM astro-ph.EP physics.ins-det physics.optics


title:
Quarkonium polarization in Pb+Pb collisions in the improved color
  evaporation model

abstract:
  We apply our calculation of direct $J/\psi$ production in the improved color
evaporation mode at ${\mathcal O}(\alpha_s^3)$ in collinear factorization to
Pb+Pb collisions. The cold nuclear matter effects of nuclear modifications of
the parton densities and intrinsic transverse momentum broadening are included.
We find that the polarization is independent of these effects. Our calculations
are compared to the Pb+Pb polarization parameters extracted by the ALICE
Collaboration at $\sqrt{s_{NN}} = 5.02$ TeV.


title: Subreddit Links Drive Community Creation and User Engagement on Reddit

abstract:
  On Reddit, individual subreddits are used to organize content and connect
users. One mode of interaction is the subreddit link, which occurs when a user
makes a direct reference to a subreddit in another community. Based on the
ubiquity of these references, we have undertaken a study on subreddit links on
Reddit, with the goal of understanding their impact on both the referenced
subreddit, and on the subreddit landscape as a whole. By way of an extensive
observational study along with several natural experiments using the entire
history of Reddit, we were able to determine that (1) subreddit links are a
significant driver of new suberddit creation; (2) subreddit links (2a)
substantially drive activity in the referenced subreddit, and (2b) are
frequently created in response to high levels of activity in the referenced
subreddit; and (3) the graph of subreddit links has become less dense and more
treelike over time. We conclude with a discussion of how these results confirm,
add to, and in some cases conflict with existing theories on
information-seeking behavior and self-organizing behavior in online social
systems.


title: Planar Phylogenetic Networks

abstract:
  A phylogenetic network is a rooted, directed acyclic graph, with a single
root, whose sinks correspond to a set of species. Such networks are commonly
used to represent the evolution of species that have undergone reticulate
evolution. Recently, there has been great interest in developing the theory
behind and algorithms for constructing such networks. One issue with
phylogenetic networks is that, unlike rooted evolutionary trees, they can be
highly non-planar (i.e. it is not possible to draw them in the plane without
some arcs crossing). This can make them difficult to visualise and interpret.
In this paper we investigate properties of planar phylogenetic networks and
algorithms for deciding whether or not phylogenetic networks have certain
special planarity properties. In particular, as well as defining some natural
subclasses of planar phylogenetic networks and working out their
interrelationships, we characterize when the well-known level-$k$ networks are
planar as well as showing that planar regular networks are closely related to
pyramids. Our results make use of the highly developed field of planar
digraphs, and we believe that the link between phylogenetic networks and this
field should prove useful in future for developing new approaches to both
construct and visualise phylogenetic networks.


title:
ViewFormer: NeRF-free Neural Rendering from Few Images Using
  Transformers

abstract:
  Novel view synthesis is a long-standing problem. In this work, we consider a
variant of the problem where we are given only a few context views sparsely
covering a scene or an object. The goal is to predict novel viewpoints in the
scene, which requires learning priors. The current state of the art is based on
Neural Radiance Fields (NeRFs), and while achieving impressive results, the
methods suffer from long training times as they require evaluating thousands of
3D point samples via a deep neural network for each image. We propose a 2D-only
method that maps multiple context views and a query pose to a new image in a
single pass of a neural network. Our model uses a two-stage architecture
consisting of a codebook and a transformer model. The codebook is used to embed
individual images into a smaller latent space, and the transformer solves the
view synthesis task in this more compact space. To train our model efficiently,
we introduce a novel branching attention mechanism that allows us to use the
same model not only for neural rendering but also for camera pose estimation.
Experimental results on real-world scenes show that our approach is competitive
compared to NeRF-based methods while not reasoning in 3D, and it is faster to
train.


title:
Botnets Breaking Transformers: Localization of Power Botnet Attacks
  Against the Distribution Grid

abstract:
  Traditional botnet attacks leverage large and distributed numbers of
compromised internet-connected devices to target and overwhelm other devices
with internet packets. With increasing consumer adoption of high-wattage
internet-facing "smart devices", a new "power botnet" attack emerges, where
such devices are used to target and overwhelm power grid devices with unusual
load demand. We introduce a variant of this attack, the power-botnet
weardown-attack, which does not intend to cause blackouts or short-term acute
instability, but instead forces expensive mechanical components to activate
more frequently, necessitating costly replacements / repairs. Specifically, we
target the on-load tap-changer (OLTC) transformer, which uses a mechanical
switch that responds to change in load demand. In our analysis and simulations,
these attacks can halve the lifespan of an OLTC, or in the most extreme cases,
reduce it to $2.5\%$ of its original lifespan. Notably, these power botnets are
composed of devices not connected to the internal SCADA systems used to control
power grids. This represents a new internet-based cyberattack that targets the
power grid from the outside. To help the power system to mitigate these types
of botnet attacks, we develop attack-localization strategies. We formulate the
problem as a supervised machine learning task to locate the source of power
botnet attacks. Within a simulated environment, we generate the training and
testing dataset to evaluate several machine learning algorithm based
localization methods, including SVM, neural network and decision tree. We show
that decision-tree based classification successfully identifies power botnet
attacks and locates compromised devices with at least $94\%$ improvement of
accuracy over a baseline "most-frequent" classifier.


title: Discovering Objects that Can Move

abstract:
  This paper studies the problem of object discovery -- separating objects from
the background without manual labels. Existing approaches utilize appearance
cues, such as color, texture, and location, to group pixels into object-like
regions. However, by relying on appearance alone, these methods fail to
separate objects from the background in cluttered scenes. This is a fundamental
limitation since the definition of an object is inherently ambiguous and
context-dependent. To resolve this ambiguity, we choose to focus on dynamic
objects -- entities that can move independently in the world. We then scale the
recent auto-encoder based frameworks for unsupervised object discovery from toy
synthetic images to complex real-world scenes. To this end, we simplify their
architecture, and augment the resulting model with a weak learning signal from
general motion segmentation algorithms. Our experiments demonstrate that,
despite only capturing a small subset of the objects that move, this signal is
enough to generalize to segment both moving and static instances of dynamic
objects. We show that our model scales to a newly collected, photo-realistic
synthetic dataset with street driving scenarios. Additionally, we leverage
ground truth segmentation and flow annotations in this dataset for thorough
ablation and evaluation. Finally, our experiments on the real-world KITTI
benchmark demonstrate that the proposed approach outperforms both heuristic-
and learning-based methods by capitalizing on motion cues.


title: A Geometric Interpretation of Ranicki Duality

abstract:
  Our main theorem provides an $(R,K)$ chain isomorphism: $ T\Delta^*X\cong
C(X_K) $. Here $T$ is the Ranicki Duality functor; $\Delta^*X$ is the
simplicial cochain complex of the simplicial complex $X$, with control map
$\pi:X \to K$ and $C(X_K) $ is the cellular chain complex of a CW complex
$X_K$.


title:
Collaborative Computing Support for Analysis Facilities Exploiting
  Software as Infrastructure Techniques

abstract:
  Prior to the public release of Kubernetes it was difficult to conduct joint
development of elaborate analysis facilities due to the highly non-homogeneous
nature of hardware and network topology across compute facilities. However,
since the advent of systems like Kubernetes and OpenShift, which provide
declarative interfaces for building fault-tolerant and self-healing deployments
of networked software, it is possible for multiple institutes to collaborate
more effectively since resource details are abstracted away through various
forms of hardware and software virtualization. In this whitepaper we will
outline the development of two analysis facilities: "Coffea-casa" at University
of Nebraska Lincoln and the "Elastic Analysis Facility" at Fermilab, and how
utilizing platform abstraction has improved the development of common software
for each of these facilities, and future development plans made possible by
this methodology.


title:
Contacts, motion and chain-breaking in a two-dimensional granular system
  displaced by an intruder

abstract:
  We investigate numerically how the motion of an intruder within a
two-dimensional granular system affects its structure and produces drag on the
intruder. We made use of discrete numerical simulations in which a larger disk
(intruder) is driven at constant speed amid smaller disks confined in a
rectangular cell. By varying the intruder's velocity and the basal friction, we
obtained the resultant force on the intruder and the instantaneous network of
contact forces, which we analyze at both the cell and grain scales. We found
that there is a bearing network that percolates forces from the intruder toward
the walls, being responsible for jammed regions and high values of the drag
force, and a dissipative network that percolates small forces within the
grains, in agreement with previous experiments on compressed granular systems.
In addition, we found the anisotropy levels of the contact network for
different force magnitudes and regions, that the force network can reach
regions far downstream of the intruder by the end of the intruder's motion,
that the extent of the force network decreases with decreasing the basal
friction, and that the void region (cavity) that appears downstream the
intruder tends to disappear for lower values of the basal friction.
Interestingly, our results show that grains within the bearing chains creep
while the chains break, revealing the mechanism by which bearing chains
collapse.


title:
A Closer Look at Knowledge Distillation with Features, Logits, and
  Gradients

abstract:
  Knowledge distillation (KD) is a substantial strategy for transferring
learned knowledge from one neural network model to another. A vast number of
methods have been developed for this strategy. While most method designs a more
efficient way to facilitate knowledge transfer, less attention has been put on
comparing the effect of knowledge sources such as features, logits, and
gradients. This work provides a new perspective to motivate a set of knowledge
distillation strategies by approximating the classical KL-divergence criteria
with different knowledge sources, making a systematic comparison possible in
model compression and incremental learning. Our analysis indicates that logits
are generally a more efficient knowledge source and suggests that having
sufficient feature dimensions is crucial for the model design, providing a
practical guideline for effective KD-based transfer learning.


title:
A-posteriori optimization for increasing manufacturing tolerances in
  stellarator coil design

abstract:
  It was recently shown in [Wechsung et. al., Proc. Natl. Acad. Sci. USA, 2022,
to appear] that there exist electromagnetic coils that generate magnetic fields
which are excellent approximations to quasi-symmetric fields and have very good
particle confinement properties. Using a Gaussian process based model for coil
perturbations, we investigate the impact of manufacturing errors on the
performance of these coils. We show that even fairly small errors result in
noticeable performance degradation and that stochastic optimization is not able
to mitigate these errors significantly. We then formulate a new optimization
problem for computing optimal adjustments of the coil positions and currents
without changing the shapes of the coil. These a-posteriori adjustments are
able to reduce the impact of coil errors by an order of magnitude, providing a
new perspective for dealing with manufacturing uncertainties in stellarator
design.


title: Privacy-Preserving Reinforcement Learning Beyond Expectation

abstract:
  Cyber and cyber-physical systems equipped with machine learning algorithms
such as autonomous cars share environments with humans. In such a setting, it
is important to align system (or agent) behaviors with the preferences of one
or more human users. We consider the case when an agent has to learn behaviors
in an unknown environment. Our goal is to capture two defining characteristics
of humans: i) a tendency to assess and quantify risk, and ii) a desire to keep
decision making hidden from external parties. We incorporate cumulative
prospect theory (CPT) into the objective of a reinforcement learning (RL)
problem for the former. For the latter, we use differential privacy. We design
an algorithm to enable an RL agent to learn policies to maximize a CPT-based
objective in a privacy-preserving manner and establish guarantees on the
privacy of value functions learned by the algorithm when rewards are
sufficiently close. This is accomplished through adding a calibrated noise
using a Gaussian process mechanism at each step. Through empirical evaluations,
we highlight a privacy-utility tradeoff and demonstrate that the RL agent is
able to learn behaviors that are aligned with that of a human user in the same
environment in a privacy-preserving manner


title: Concept-based Adversarial Attacks: Tricking Humans and Classifiers Alike

abstract:
  We propose to generate adversarial samples by modifying activations of upper
layers encoding semantically meaningful concepts. The original sample is
shifted towards a target sample, yielding an adversarial sample, by using the
modified activations to reconstruct the original sample. A human might (and
possibly should) notice differences between the original and the adversarial
sample. Depending on the attacker-provided constraints, an adversarial sample
can exhibit subtle differences or appear like a "forged" sample from another
class. Our approach and goal are in stark contrast to common attacks involving
perturbations of single pixels that are not recognizable by humans. Our
approach is relevant in, e.g., multi-stage processing of inputs, where both
humans and machines are involved in decision-making because invisible
perturbations will not fool a human. Our evaluation focuses on deep neural
networks. We also show the transferability of our adversarial examples among
networks.


title: The geometry of gauged (super)conformal mechanics

abstract:
  Motivated by recently studied examples, we undertake a systematic study of
conformal invariance in one-dimensional sigma models where an isometry group
has been gauged. Perhaps surprisingly, we uncover classes of sigma models which
are only scale invariant in their ungauged form and become fully conformally
invariant only after gauging. In these cases the target space satisfies a
deformation of the well-known conformal geometry constraints. We consider
bosonic models as well as their $\mathcal{N} = 1,2,4$ supersymmetric
extensions. We solve the quantum ordering ambiguities in implementing (super-)
conformal symmetry on the physical Hilbert space. Examples of our general
results are furnished by the $D(2,1;0)$-invariant Coulomb branch quiver models
relevant for black hole physics.


title: Boreas: A Multi-Season Autonomous Driving Dataset

abstract:
  The Boreas dataset was collected by driving a repeated route over the course
of one year, resulting in stark seasonal variations and adverse weather
conditions such as rain and falling snow. In total, the Boreas dataset contains
over 350km of driving data featuring a 128-channel Velodyne Alpha-Prime lidar,
a 360 degree Navtech CIR304-H scanning radar, a 5MP FLIR Blackfly S camera, and
centimetre-accurate post-processed ground truth poses. At launch, our dataset
will support live leaderboards for odometry, metric localization, and 3D object
detection. The dataset and development kit are available at:
https://www.boreas.utias.utoronto.ca


title: On the basis set selection for molecular core-level $GW$ calculations

abstract:
  The $GW$ approximation has been recently gaining popularity among the method
for simulating molecular core-level X-ray photoemission spectra. Traditionally,
$GW$ core-level binding energies have been computed using either the cc-pV$n$Z
or def2-$n$ZVP basis set families, extrapolating the obtained results to the
complete basis set limit, followed by a an element-specific relativistic
correction. Despite of achieving good accuracy, these binding energies are
chronically underestimated. By using first-row elements and standard techniques
known to offer good cost-accuracy ratio in other theories, we show that the
cc-pV$n$Z and def2-$n$ZVP families show large contraction errors and lead to
unreliable complete basis set extrapolations. On the other hand, we demonstrate
that uncontracted versions of these basis sets offer vastly improved
convergence. Even faster convergence can be obtained using core-rich,
property-optimized, basis sets families like pcSseg-$n$, pcJ-$n$ and ccX-$n$Z.
Finally, we also show that the improvement over the core properties does not
degrade the calculation of the valence excitations, and thus offer a balanced
description of both core and valence regions.


title:
Equitable Ability Estimation in Neurodivergent Student Populations with
  Zero-Inflated Learner Models

abstract:
  At present, the educational data mining community lacks many tools needed for
ensuring equitable ability estimation for Neurodivergent (ND) learners. On one
hand, most learner models are susceptible to under-estimating ND ability since
confounding contexts cannot be held accountable (e.g. consider dyslexia and
text-heavy assessments), and on the other, few (if any) existing datasets are
suited for appraising model and data bias in ND contexts. In this paper we
attempt to model the relationships between context (delivery and response
types) and performance of ND students with zero-inflated learner models. This
approach facilitates simulation of several expected ND behavioural traits,
provides equitable ability estimates across all student groups from generated
datasets, increases interpretability confidence, and can double the number of
learning opportunities for ND students in some cases. Our approach consistently
out-performs baselines in our experiments and can also be applied to many other
learner modelling frameworks


title:
Neuromorphic metamaterials for mechanosensing and perceptual associative
  learning

abstract:
  Physical systems exhibiting neuromechanical functions promise to enable
structures with directly encoded autonomy and intelligence. We report on a
class of neuromorphic metamaterials embodying bioinspired mechanosensing,
memory, and learning functionalities obtained by leveraging mechanical
instabilities and flexible memristive materials. Our prototype system comprises
a multistable metamaterial whose bistable units filter, amplify, and transduce
external mechanical inputs over large areas into simple electrical signals
using piezoresistivity. We record these mechanically transduced signals using
non-volatile flexible memristors that remember sequences of mechanical inputs,
providing a means to store spatially distributed mechanical signals in
measurable material states. The accumulated memristance changes resulting from
the sequential mechanical inputs allow us to physically encode a Hopfield
network into our neuromorphic metamaterials. This physical network learns a
series of external spatially distributed input patterns. Crucially, the learned
patterns input into our neuromorphic metamaterials can be retrieved from the
final accumulated state of our memristors. Therefore, our system exhibits the
ability to learn without supervised training and retain spatially distributed
inputs with minimal external overhead. Our system's embodied mechanosensing,
memory, and learning capabilities establish an avenue for synthetic
neuromorphic metamaterials enabling the learning of touch-like sensations
covering large areas for robotics, autonomous systems, wearables, and morphing
structures.


title: Importance Sampling Placement in Off-Policy Temporal-Difference Methods

abstract:
  A central challenge to applying many off-policy reinforcement learning
algorithms to real world problems is the variance introduced by importance
sampling. In off-policy learning, the agent learns about a different policy
than the one being executed. To account for the difference importance sampling
ratios are often used, but can increase variance in the algorithms and reduce
the rate of learning. Several variations of importance sampling have been
proposed to reduce variance, with per-decision importance sampling being the
most popular. However, the update rules for most off-policy algorithms in the
literature depart from per-decision importance sampling in a subtle way; they
correct the entire TD error instead of just the TD target. In this work, we
show how this slight change can be interpreted as a control variate for the TD
target, reducing variance and improving performance. Experiments over a wide
range of algorithms show this subtle modification results in improved
performance.


title: On the Hilbert-Samuel coefficients of Frobenius powers of an ideal

abstract:
  We provide suitable conditions under which the asymptotic limit of the
Hilbert-Samuel coefficients of the Frobenius powers of an
$\mathfrak{m}$-primary ideal exists in a Noetherian local ring
$(R,\mathfrak{m})$ with prime characteristic $p>0.$ This, in turn, gives an
expression of the Hilbert-Kunz multiplicity of powers of the ideal. We also
prove that for a face ring $R$ of a simplicial complex and an ideal $J$
generated by pure powers of the variables, the generalized Hilbert-Kunz
function $\ell(R/(J^{[q]})^k)$ is a polynomial for all $q,k$ and also give an
expression of the generalized Hilbert-Kunz multiplicity of powers of $J$ in
terms of Hilbert-Samuel multiplicity of $J.$ We conclude by giving a
counter-example to a conjecture proposed by I. Smirnov which connects the
stability of an ideal with the asymptotic limit of the first Hilbert
coefficient of the Frobenius power of the ideal.


title: Should Radar Replace Lidar in All-Weather Mapping and Localization?

abstract:
  Radar is a rich sensing modality that is a compelling alternative to lidar
for its inherent robustness to precipitation, dust, and fog. In this paper, we
investigate radar-based mapping and localization as an alternative to lidar by
demonstrating the performance of our own radar-based pipeline. We present
extensive comparisons against a lidar-based pipeline across varying seasonal
and weather conditions using our own publicly available dataset collected on a
repeated route over the course of one year. Our experiments show that while
lidar achieves better overall localization accuracy, our radar-based pipeline
achieves comparable SE(2) results using a fraction of the computational and
memory resources required by the lidar-based pipeline. We additionally
demonstrate our radar-based pipeline localizing against lidar maps with better
accuracy than the current state of the art for radar-to-lidar localization.
Code for this project can be found at: https://github.com/utiasASRL/vtr3


title:
Electrochemical stability of ZnMn2O4: Understanding Zn-ion rechargeable
  battery capacity and degradation

abstract:
  We present a refined Mn-Zn-H$_2$O Pourbaix diagram with the emphasis on
parameters relevant for the Zn/MnO$_2$ rechargeable cells. It maps out
boundaries of electrochemical stability for MnO$_2$, ZnMn$_2$O$_4$,
ZnMn$_3$O$_7$, and MnOOH. The diagram helps to rationalize experimental
observation on processes and phases occurring during charge/discharge,
including the position of charge/discharge redox peaks and capacity fade
observed in rechargeable aqueous Zn-ion batteries for stationary storage. The
proposed Pourbaix diagram is validated by observing the pH-dependent
transformation of electrolytic manganese dioxide to hetaerolite and
chalcophanite during discharge and charge, respectively. Our results can guide
the selection of operating conditions (the potential range and pH) for existing
aqueous Zn/MnO$_2$ rechargeable cells to maximise their longevity. In addition,
the relation between electrochemical stability boundaries and operating
conditions can be used as an additional design criterion in exploration of
future cathode materials for aqueous rechargeable batteries.