title: Polynomial orbits in totally minimal systems

abstract:
  Inspired by the recent work of Glasner, Huang, Shao, Weiss and Ye, we prove
that the maximal $\infty$-step pro-nilfactor $X_\infty$ of a minimal system
$(X,T)$ is the topological characteristic factor along polynomials in a certain
sense. Namely, we show that by an almost one to one modification of $\pi:X\to
X_\infty$, the induced open extension $\pi^*:X^*\to X_\infty^*$ has the
following property: for any $d\in \mathbb{N}$, any open subsets
$V_0,V_1,\ldots,V_d$ of $X^*$ with $\bigcap_{i=0}^d \pi^*(V_i)\neq \emptyset$
and any distinct non-constant integer polynomials $p_i$ with $p_i(0)=0$ for
$i=1,\ldots,d$, there exists some $n\in \mathbb{Z}$ such that $V_0\cap
T^{-p_1(n)}V_1\cap \ldots \cap T^{-p_d(n)}V_d \neq \emptyset$. where an integer
polynomial is the polynomial with rational coefficients taking integer values
on the integers.
  As an application, the following result is obtained: for a totally minimal
system $(X,T)$ and integer polynomials $p_1,\ldots,p_d$, if every non-trivial
integer combination of $p_1,\ldots,p_d$ is not constant, then there is a dense
$G_\delta$ subset $\Omega$ of $ X$ such that the set \[ \{(T^{p_1(n)}x,\ldots,
T^{p_d(n)}x):n\in \mathbb{Z}\} \] is dense in $X^d$ for every $x\in \Omega$.


title: On the Northcott property of zeta functions over function fields

abstract:
  Pazuki and Pengo defined a Northcott property for special values of zeta
functions of number fields and certain motivic $L$-functions. We determine the
values for which the Northcott property holds over function fields with
constant field $\mathbb{F}_q$ outside the critical strip. We then use a case by
case approach for some values inside the critical strip, notably $Re (s) <
\frac{1}{2} - \frac{\log 2}{\log q}$ and for $s$ real such that $1/2 \leq s
\leq 1$, and we obtain a partial result for complex $s$ in the case $1/2<
Re(s)\leq 1$ using recent advances on the Shifted Moments Conjecture over
function fields.


title:
The role of slow magnetostrophic waves in the formation of the axial
  dipole in planetary dynamos

abstract:
  The preference for the axial dipole in planetary dynamos is investigated
through the analysis of wave motions in spherical dynamo models. Our study
focuses on the role of slow magnetostrophic waves, which are generated from
localized balances between the Lorentz, Coriolis and buoyancy (MAC) forces.
Since the slow waves are known to intensify with increasing field strength,
simulations in which the field grows from a small seed towards saturation are
useful in understanding the role of these waves in dynamo action. Axial group
velocity measurements in the energy-containing scales show that fast inertial
waves slightly modified by the magnetic field and buoyancy are dominant under
weak fields. However, the dominance of the slow waves is evident for strong
fields satisfying $|\omega_M/\omega_C| \sim $ 0.1, where $\omega_M$ and
$\omega_C$ are the frequencies of the Alfv\'en and inertial waves respectively.
A MAC wave window of azimuthal wavenumbers is identified wherein helicity
generation by the slow waves strongly correlates with dipole generation.
Analysis of the magnetic induction equation suggests a poloidal--poloidal field
conversion in the formation of the dipole. Finally, the attenuation of slow
waves may result in polarity reversals in a strongly driven Earth's core.


title: Hunting extreme BL Lacertae blazars with Fermi-LAT

abstract:
  The emission of very-high-energy photons (VHE, E>100 GeV$) in blazars is
closely connected to the production of ultra-relativistic particles and the
role of these gamma-ray sources as cosmic particle accelerators. This work
focuses on a selection of 22 gamma-ray objects from the 2BIGB catalog of
high-synchrotron-peaked sources, which are classified as blazar candidates of
uncertain type in the 4FGL-DR2 catalog. We study these sources by means of a
re-analysis of the first 10 years of gamma-ray data taken with the Fermi Large
Area Telescope, including the attenuation by the extragalactic background
light. Their broadband spectral energy distributions are also evaluated, using
multi-wavelength archival data in the radio, optical, and X-ray bands, in terms
of one-zone synchrotron-self-Compton models, adding an external Compton
component when needed. Out of this analysis, we identify 17 new extreme
high-synchrotron-peaked (EHSP) candidates and compare their physical parameters
with those of prototypical EHSP blazars. Finally, the resulting models are used
to assess their detectability by the present and future generation of
ground-based imaging atmospheric Cherenkov telescopes. We find two VHE
candidates within the reach of the current and next generation of Cherenkov
telescopes: J0847.0-2336 and J1714.0-2029.


title:
Refined Convergence Rates for Maximum Likelihood Estimation under Finite
  Mixture Models

abstract:
  We revisit convergence rates for maximum likelihood estimation (MLE) under
finite mixture models. The Wasserstein distance has become a standard loss
function for the analysis of parameter estimation in these models, due in part
to its ability to circumvent label switching and to accurately characterize the
behaviour of fitted mixture components with vanishing weights. However, the
Wasserstein metric is only able to capture the worst-case convergence rate
among the remaining fitted mixture components. We demonstrate that when the
log-likelihood function is penalized to discourage vanishing mixing weights,
stronger loss functions can be derived to resolve this shortcoming of the
Wasserstein distance. These new loss functions accurately capture the
heterogeneity in convergence rates of fitted mixture components, and we use
them to sharpen existing pointwise and uniform convergence rates in various
classes of mixture models. In particular, these results imply that a subset of
the components of the penalized MLE typically converge significantly faster
than could have been anticipated from past work. We further show that some of
these conclusions extend to the traditional MLE. Our theoretical findings are
supported by a simulation study to illustrate these improved convergence rates.


title:
Newton-like components in the Chebyshev-Halley family of degree $n$
  polynomials

abstract:
  We study the Cebyshev-Halley methods applied to the family of polynomials
$f_{n,c}(z)=z^n+c$, for $n\ge 2$ and $c\in \mathbb{C}^{*}$. We prove the
existence of parameters such that the immediate basins of attraction
corresponding to the roots of unity are infinitely connected. We also prove
that, for $n \ge 2$, the corresponding dynamical plane contains a connected
component of the Julia set, which is a quasiconformal deformation of the Julia
set of the map obtained by applying Newton's method to $f_{n,-1}$.


title:
Global Convergence of Sub-gradient Method for Robust Matrix Recovery:
  Small Initialization, Noisy Measurements, and Over-parameterization

abstract:
  In this work, we study the performance of sub-gradient method (SubGM) on a
natural nonconvex and nonsmooth formulation of low-rank matrix recovery with
$\ell_1$-loss, where the goal is to recover a low-rank matrix from a limited
number of measurements, a subset of which may be grossly corrupted with noise.
We study a scenario where the rank of the true solution is unknown and
over-estimated instead. The over-estimation of the rank gives rise to an
over-parameterized model in which there are more degrees of freedom than
needed. Such over-parameterization may lead to overfitting, or adversely affect
the performance of the algorithm. We prove that a simple SubGM with small
initialization is agnostic to both over-parameterization and noise in the
measurements. In particular, we show that small initialization nullifies the
effect of over-parameterization on the performance of SubGM, leading to an
exponential improvement in its convergence rate. Moreover, we provide the first
unifying framework for analyzing the behavior of SubGM under both outlier and
Gaussian noise models, showing that SubGM converges to the true solution, even
under arbitrarily large and arbitrarily dense noise values, and--perhaps
surprisingly--even if the globally optimal solutions do not correspond to the
ground truth. At the core of our results is a robust variant of restricted
isometry property, called Sign-RIP, which controls the deviation of the
sub-differential of the $\ell_1$-loss from that of an ideal, expected loss. As
a byproduct of our results, we consider a subclass of robust low-rank matrix
recovery with Gaussian measurements, and show that the number of required
samples to guarantee the global convergence of SubGM is independent of the
over-parameterized rank.


title:
Hamilton-Jacobi equations on graphs with applications to semi-supervised
  learning and data depth

abstract:
  Shortest path graph distances are widely used in data science and machine
learning, since they can approximate the underlying geodesic distance on the
data manifold. However, the shortest path distance is highly sensitive to the
addition of corrupted edges in the graph, either through noise or an
adversarial perturbation. In this paper we study a family of Hamilton-Jacobi
equations on graphs that we call the $p$-eikonal equation. We show that the
$p$-eikonal equation with $p=1$ is a provably robust distance-type function on
a graph, and the $p\to \infty$ limit recovers shortest path distances. While
the $p$-eikonal equation does not correspond to a shortest-path graph distance,
we nonetheless show that the continuum limit of the $p$-eikonal equation on a
random geometric graph recovers a geodesic density weighted distance in the
continuum. We consider applications of the $p$-eikonal equation to data depth
and semi-supervised learning, and use the continuum limit to prove asymptotic
consistency results for both applications. Finally, we show the results of
experiments with data depth and semi-supervised learning on real image
datasets, including MNIST, FashionMNIST and CIFAR-10, which show that the
$p$-eikonal equation offers significantly better results compared to shortest
path distances.


title: Twisted multilayer nodal superconductors

abstract:
  Twisted bilayers of nodal superconductors were recently proposed as a
promising platform to host superconducting phases that spontaneously break
time-reversal symmetry. Here we extend this analysis to twisted multilayers,
focusing on two high-symmetry stackings with alternating ($\pm \theta$) and
constant ($\theta$) twist angles. In analogy to alternating-twist multilayer
graphene, the former can be mapped to twisted bilayers with renormalized
interlayer couplings, along with a remnant gapless monolayer when the number of
layers $L$ is odd. In contrast, the latter exhibits physics beyond twisted
bilayers, including the occurrence of `magic angles' characterized by cubic
band crossings when $L \mod 4 = 3$. Owing to their power-law divergent density
of states, such multilayers are highly susceptible to secondary instabilities.
Within a BCS mean-field theory, defined in the continuum and on a lattice, we
find that both stackings host chiral topological superconductivity in extended
regions of their phase diagrams.


title: cosFormer: Rethinking Softmax in Attention

abstract:
  Transformer has shown great successes in natural language processing,
computer vision, and audio processing. As one of its core components, the
softmax attention helps to capture long-range dependencies yet prohibits its
scale-up due to the quadratic space and time complexity to the sequence length.
Kernel methods are often adopted to reduce the complexity by approximating the
softmax operator. Nevertheless, due to the approximation errors, their
performances vary in different tasks/corpus and suffer crucial performance
drops when compared with the vanilla softmax attention. In this paper, we
propose a linear transformer called cosFormer that can achieve comparable or
better accuracy to the vanilla transformer in both casual and cross attentions.
cosFormer is based on two key properties of softmax attention: i).
non-negativeness of the attention matrix; ii). a non-linear re-weighting scheme
that can concentrate the distribution of the attention matrix. As its linear
substitute, cosFormer fulfills these properties with a linear operator and a
cosine-based distance re-weighting mechanism. Extensive experiments on language
modeling and text understanding tasks demonstrate the effectiveness of our
method. We further examine our method on long sequences and achieve
state-of-the-art performance on the Long-Range Arena benchmark. The source code
is available at https://github.com/OpenNLPLab/cosFormer.


title: Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics

abstract:
  Numerous toolkits have been developed to support ethical AI development.
However, toolkits, like all tools, encode assumptions in their design about
what work should be done and how. In this paper, we conduct a qualitative
analysis of 27 AI ethics toolkits to critically examine how the work of ethics
is imagined and how it is supported by these toolkits. Specifically, we examine
the discourses toolkits rely on when talking about ethical issues, who they
imagine should do the work of ethics, and how they envision the work practices
involved in addressing ethics. We find that AI ethics toolkits largely frame
the work of AI ethics to be technical work for individual technical
practitioners, despite calls for engaging broader sets of stakeholders in
grappling with social aspects of AI ethics, and without contending with the
organizational and political implications of AI ethics work in practice. Among
all toolkits, we identify a mismatch between the imagined work of ethics and
the support the toolkits provide for doing that work. We identify a lack of
guidance around how to navigate organizational power dynamics as they relate to
performing ethical work. We use these omissions to chart future work for
researchers and designers of AI ethics toolkits.


title: Multi-Channel Speech Denoising for Machine Ears

abstract:
  This work describes a speech denoising system for machine ears that aims to
improve speech intelligibility and the overall listening experience in noisy
environments. We recorded approximately 100 hours of audio data with
reverberation and moderate environmental noise using a pair of microphone
arrays placed around each of the two ears and then mixed sound recordings to
simulate adverse acoustic scenes. Then, we trained a multi-channel speech
denoising network (MCSDN) on the mixture of recordings. To improve the
training, we employ an unsupervised method, complex angular central Gaussian
mixture model (cACGMM), to acquire cleaner speech from noisy recordings to
serve as the learning target. We propose a MCSDN-Beamforming-MCSDN framework in
the inference stage. The results of the subjective evaluation show that the
cACGMM improves the training data, resulting in better noise reduction and user
preference, and the entire system improves the intelligibility and listening
experience in noisy situations.


title:
Social network analysis of Staphylococcus aureus carriage in a general
  youth population

abstract:
  Staphylococcus aureus nasal carriage increases risk of infection and has been
associated with lifestyle behavior and biological host characteristics. We used
social network analysis to evaluate whether contacts have the same S. aureus
genotype, or whether contagiousness is an indirect effect of contacts sharing
the same lifestyle or characteristics.
  The Fit Futures 1 study collected data on social contact among 1038 first
level students in the same high school district in Norway. S. aureus persistent
carriage was determined from two nasal swab cultures and genotype from
spa-typing of a positive throat swab culture. Bootstrap, t-tests, logistic
regression, and autocorrelation were used to evaluate social network influence
on host risk factors and S. aureus carriage.
  Both persistent carriage and spa-type were transmitted in the social network
(p<0.001). The probability of carriage increased by 3.7% and 5.0% for each
additional S. aureus positive friend, in univariable regression and
multivariable autocorrelation analysis respectively. Male sex was associated
with a 15% lower risk of transmission compared to women, although the
prevalence of carriage was higher for men (36% versus 24%). Medium physical
activity, medium and high alcohol-use, and normal-weight students had higher
number of contacts, and increased risk of transmission (p<0.002).
  We demonstrate direct social transmission of S. aureus in a general youth
population. Lifestyle factors are associated with risk of transmission
suggesting indirect social group effects from having more similar environmental
exposures. The predominance in carriage is determined by sex-specific
predisposing host characteristics as social transmission is less frequent than
in females. Better understanding of how social interactions influence S. aureus
carriage dynamics in the population is important for developing new preventive
measures.


title:
Simulating rare kaon decays $K^{+}\to\pi^{+}\ell^{+}\ell^{-}$ using
  domain wall lattice QCD with physical light quark masses

abstract:
  We report the first calculation using physical light-quark masses of the
electromagnetic form factor $V(z)$ describing the long-distance contributions
to the $K^+\to\pi^+\ell^+\ell^-$ decay amplitude. The calculation is performed
on a 2+1 flavor domain wall fermion ensemble with inverse lattice spacing
$a^{-1}=1.730(4)$GeV. We implement a Glashow-Iliopoulos-Maiani cancellation by
extrapolating to the physical charm-quark mass from three below-charm masses.
We obtain $V(z=0.013(2))=-0.87(4.44)$, achieving a bound for the value. The
large statistical error arises from stochastically estimated quark loops.


title: Computation of Miura surfaces for general Dirichlet boundary conditions

abstract:
  A nonlinear partial differential equation (PDE) that models the possible
shapes that a periodic Miura tessellation can take in the homogenization limit
has been established recently and solved only in specific cases. In this paper,
the existence and uniqueness of a solution to the PDE is proved for general
Dirichlet boundary conditions. Then a H^2-conforming discretization is
introduced to approximate the solution of the PDE and a fixed point algorithm
is proposed to solve the associated discrete problem. A convergence proof for
the method is given as well as a convergence rate. Finally, numerical
experiments show the robustness of the method and that non trivial shapes can
be achieved using periodic Miura tessellations.


title: Mixed Hodge modules and real groups

abstract:
  Let $G$ be a complex reductive group, $\theta \colon G \to G$ an involution,
and $K = G^\theta$. In arXiv:1206.5547, W. Schmid and the second named author
proposed a program to study unitary representations of the corresponding real
form $G_\mathbb{R}$ using $K$-equivariant twisted mixed Hodge modules on the
flag variety of $G$ and their polarizations. In this paper, we make the first
significant steps towards implementing this program. Our first main result
gives an explicit combinatorial formula for the Hodge numbers appearing in the
composition series of a standard module in terms of the Lusztig-Vogan
polynomials. Our second main result is a polarized version of the Jantzen
conjecture, stating that the Jantzen forms on the composition factors are
polarizations of the underlying Hodge modules. Our third main result states
that, for regular Beilinson-Bernstein data, the minimal $K$-type of an
irreducible Harish-Chandra module lies in the lowest piece of the Hodge
filtration of the corresponding Hodge module. An immediate consequence of our
results is a Hodge-theoretic proof of the signature multiplicity formula of
arXiv:1212.2192, which was the inspiration for this work.


title:
A lateral resolution metric for static single molecule localization
  microscopy images from time-resolved pair correlation functions

abstract:
  Single molecule localization microscopy (SMLM) permits the visualization of
cellular structures an order of magnitude smaller than the diffraction limit of
visible light, and an accurate, objective evaluation of the resolution of an
SMLM dataset is an essential aspect of the image processing and analysis
pipeline. Here we present a simple method that uses the pair autocorrelation
function evaluated both in space and time to measure the time-interval
dependent point-spread function of SMLM images of static samples. Using this
approach, we demonstrate that experimentally obtained images typically have
effective point spread functions that are broader than expected from the
localization precision alone, due to additional uncertainty arising from drift
and drift correction algorithms. This resolution metric reports on how
precisely one can measure pairwise distances between labeled objects and is
complementary to the commonly used Fourier Ring Correlation metric that also
considers spatial sampling. The method is demonstrated on simulated
localizations, DNA origami rulers, and cellular structures labelled by
dye-conjugated antibodies or fluorescent fusion proteins.


title: The homology of a Temperley-Lieb algebra on an odd number of strands

abstract:
  We show that the homology of any Temperley-Lieb algebra $\mathcal{TL}_n(a)$
on an odd number of strands vanishes in positive degrees. This improves a
result obtained by Boyd-Hepworth. In addition we present alternative arguments
for the following two vanishing results of Boyd-Hepworth. (1) The stable
homology of Temperley-Lieb algebras is trivial. (2) If the parameter $a \in R$
is a unit, then the homology of any Temperley-Lieb algebra is concentrated in
degree zero.


title: On graphs with distance Laplacian eigenvalues of multiplicity $n-4$

abstract:
  Let $G$ be a connected simple graph with $n$ vertices. The distance Laplacian
matrix $D^{L}(G)$ is defined as $D^L(G)=Diag(Tr)-D(G)$, where $Diag(Tr)$ is the
diagonal matrix of vertex transmissions and $D(G)$ is the distance matrix of
$G$. The eigenvalues of $D^{L}(G)$ are the distance Laplacian eigenvalues of
$G$ and are denoted by $\partial_{1}^{L}(G)\geq \partial_{2}^{L}(G)\geq \dots
\geq \partial_{n}^{L}(G)$. The largest eigenvalue $\partial_{1}^{L}(G)$ is
called the distance Laplacian spectral radius. Lu et al. (2017), Fernandes et
al. (2018) and Ma et al. (2018) completely characterized the graphs having some
distance Laplacian eigenvalue of multiplicity $n-3$. In this paper, we
characterize the graphs having distance Laplacian spectral radius of
multiplicity $n-4$ together with one of the distance Laplacian eigenvalue as
$n$ of multiplicity either 3 or 2. Further, we completely determine the graphs
for which the distance Laplacian eigenvalue $n$ is of multiplicity $n-4$.


title:
Internal stabilization of an underactuated linear parabolic system via
  modal decomposition

abstract:
  This work concerns the internal stabilization of underactuated linear systems
of $m$ heat equations in cascade, where the control is placed internally in the
first equation only and the diffusion coefficients are distinct. Combining the
modal decomposition method with a recently introduced state-transformation
approach for observation problems, a proportional-type stabilizing control is
given explicitly. It is based on a transformation for the ODE system
corresponding to the comparatively unstable modes into a target one, where
calculation of the stabilization law is independent of the arbitrarily large
number of them. This provides a finite-dimensional counterpart of a recently
appeared infinite-dimensional one, which led to Lyapunov stabilization. The
present approach answers to the problem of stabilization with actuators not
appearing in all the states and when boundary control results do not apply.


title: Efficiency of photonic state tomography affected by fiber attenuation

abstract:
  In this article, we investigate the efficiency of photonic state tomography
in the presence of fiber attenuation. The theoretical formalism of the photon
loss is provided by implementing methods of open quantum systems. The quantum
state is reconstructed from photon counts obtained for symmetric
informationally complete POVMs. The number of photons that reach the detectors
is numerically modeled by the binomial distribution, which describes the loss
of light caused by the medium. This approach allows us to study the quality of
state tomography versus the length of the fiber. In particular, we focus on
entangled qubits and qutrits, which are sent through fibers of different
lengths. The amount of entanglement detected by the measurement scheme is
quantified and presented on graphs. The results demonstrate how the quality of
photonic tomography depends on the distance between the source and the
receiver.


title:
Stochastic Dynamic Programming Heuristic for the (R, s, S) Policy
  Parameters Computation

abstract:
  The (R, s, S) is a stochastic inventory control policy widely used by
practitioners. In an inventory system managed according to this policy, the
inventory is reviewed at instant R; if the observed inventory position is lower
than the reorder level s an order is placed. The order's quantity is set to
raise the inventory position to the order-up-to-level S. This paper introduces
a new stochastic dynamic program (SDP) based heuristic to compute the (R, s, S)
policy parameters for the non-stationary stochastic lot-sizing problem with
backlogging of the excessive demand, fixed order and review costs, and linear
holding and penalty costs. In a recent work, Visentin et al. (2021) present an
approach to compute optimal policy parameters under these assumptions. Our
model combines a greedy relaxation of the problem with a modified version of
Scarf's (s, S) SDP. A simple implementation of the model requires a prohibitive
computational effort to compute the parameters. However, we can speed up the
computations by using K-convexity property and memorisation techniques. The
resulting algorithm is considerably faster than the state-of-the-art, extending
its adoptability by practitioners. An extensive computational study compares
our approach with the algorithms available in the literature.


title:
Hybridizing Physical and Data-driven Prediction Methods for
  Physicochemical Properties

abstract:
  We present a generic way to hybridize physical and data-driven methods for
predicting physicochemical properties. The approach `distills' the physical
method's predictions into a prior model and combines it with sparse
experimental data using Bayesian inference. We apply the new approach to
predict activity coefficients at infinite dilution and obtain significant
improvements compared to the data-driven and physical baselines and established
ensemble methods from the machine learning literature.


title:
Making a Radical Misogynist: How online social engagement with the
  Manosphere influences traits of radicalization

abstract:
  The algorithms and the interactions facilitated by online platforms have been
used by radical groups to recruit vulnerable individuals to their cause. This
has resulted in the sharp growth of violent events and deteriorating online
discourse. The Manosphere, a collection of radical anti-feminist communities,
is one such group which has attracted attention due to their rapid growth and
increasingly violent real world outbursts. In this paper, we examine the social
engagements between Reddit users who have participated in feminist discourse
and the Manosphere communities on Reddit to understand the process of
development of traits associated with the adoption of extremist ideologies. By
using existing research on the psychology of radicalization we track how
specific types of social engagement with the Manosphere influence the
development of traits associated with radicalization. Our findings show that:
(1) participation, even by the simple act of joining the Manosphere, has a
significant influence on the language and outlook traits of a user, (2)
Manosphere elites are extremely effective propagators of radical traits and
cause their increase even outside the Manosphere, and (3) community perception
can heavily influence a user's behavior. Finally, we examine how our findings
can help draft community and platform moderation policies to help mitigate the
problem of online radicalization.


title: Grammar-Based Grounded Lexicon Learning

abstract:
  We present Grammar-Based Grounded Lexicon Learning (G2L2), a lexicalist
approach toward learning a compositional and grounded meaning representation of
language from grounded data, such as paired images and texts. At the core of
G2L2 is a collection of lexicon entries, which map each word to a tuple of a
syntactic type and a neuro-symbolic semantic program. For example, the word
shiny has a syntactic type of adjective; its neuro-symbolic semantic program
has the symbolic form {\lambda}x. filter(x, SHINY), where the concept SHINY is
associated with a neural network embedding, which will be used to classify
shiny objects. Given an input sentence, G2L2 first looks up the lexicon entries
associated with each token. It then derives the meaning of the sentence as an
executable neuro-symbolic program by composing lexical meanings based on
syntax. The recovered meaning programs can be executed on grounded inputs. To
facilitate learning in an exponentially-growing compositional space, we
introduce a joint parsing and expected execution algorithm, which does local
marginalization over derivations to reduce the training time. We evaluate G2L2
on two domains: visual reasoning and language-driven navigation. Results show
that G2L2 can generalize from small amounts of data to novel compositions of
words.


title:
The Flavourful Present and Future of 2HDMs at the Collider Energy
  Frontier

abstract:
  We study the intersection of flavour and collider physics for
Two-Higgs-Doublet models of Type I and II. Drawing from the flavour
precision-LHC exotics search complementarity, we also provide a projection of
the future sensitivity that can be achieved in light of currently available
analyses. On the one hand, we find that the parameter space of the 2HDM can be
explored significantly further with more data from the LHC with some
complementarity with flavour physics. On the other hand, flavour physics
results alongside their projections remain powerful tools to constrain the
model space in regions where direct sensitivity to new states via exotics
searches is lost. Our results further highlight the recently observed flavour
physics anomalies as important drivers of new physics searches in the future;
we also touch on implications for a strong first order electroweak phase
transition.


title: Fast Dynamic Updates and Dynamic SpGEMM on MPI-Distributed Graphs

abstract:
  Sparse matrix multiplication (SpGEMM) is a fundamental kernel used in many
diverse application areas, both numerical and discrete. Recently, SpGEMM has
received growing attention regarding implementations for specific (parallel)
architectures. Yet, this concerns only the static problem, where both input
matrices do not change. In many applications, however, matrices (or their
corresponding graphs) change over time. Although recomputing from scratch is
very expensive, we are not aware of any dynamic SpGEMM algorithms in the
literature.
  In this paper, we thus propose a batch-dynamic algorithm for MPI-based
parallel computing. Building on top of a distributed graph/matrix data
structure that allows for fast updates, our dynamic SpGEMM reduces the
communication volume significantly. It does so by exploiting that updates
change far fewer matrix entries than there are non-zeros in the input operands.
Our experiments with popular benchmark graphs show that our approach pays off.
For batches of insertions or removals of matrix entries, our dynamic SpGEMM is
substantially faster than the static state-of-the-art algorithm in CombBLAS 2.0
and in CTF.


title:
Comment on "Scrutinizing pion-pion scattering in light of recent lattice
  phase shifts"

abstract:
  We comment on the description [arXiv:2202.03124] of the lattice phase shifts
for the $f_0$(500) resonance in S-wave pion-pion scattering obtained for a pion
mass of 391 MeV by a bound-state pole and a virtual-state pole.


title: Geometric structures as variational objects, II

abstract:
  This note is the sequel of "Geometric structures as variational objects, I."
It generalizes the main result and perspectives of that work to a class of
geometric structures that includes integrable almost-complex structures.


title: Some Reality Properties of Finite Simple Orthogonal Groups

abstract:
  We prove several reality properties for finite simple orthogonal groups. For
any prime power $q$ and $m\geq 1$, we show that all real conjugacy classes are
strongly real in the simple groups $\mathrm{P}\Omega^{\pm}(4m+2,q), m \geq 1$,
except in the case $\mathrm{P}\Omega^{-}(4m+2,q)$ with $q \equiv 3(\mathrm{mod}
\; 4)$, and we construct weakly real classes in this exceptional case for any
$m$. We also show that no irreducible complex character of
$\mathrm{P}\Omega^{\pm}(n,q)$ can have Frobenius-Schur indicator $-1$, except
possibly in the case $\mathrm{P}\Omega^{-}(4m+2,q)$ with $q \equiv
3(\mathrm{mod} \; 4)$.


title:
Should I send this notification? Optimizing push notifications decision
  making by modeling the future

abstract:
  Most recommender systems are myopic, that is they optimize based on the
immediate response of the user. This may be misaligned with the true objective,
such as creating long term user satisfaction. In this work we focus on mobile
push notifications, where the long term effects of recommender system decisions
can be particularly strong. For example, sending too many or irrelevant
notifications may annoy a user and cause them to disable notifications.
However, a myopic system will always choose to send a notification since
negative effects occur in the future. This is typically mitigated using
heuristics. However, heuristics can be hard to reason about or improve, require
retuning each time the system is changed, and may be suboptimal. To counter
these drawbacks, there is significant interest in recommender systems that
optimize directly for long-term value (LTV). Here, we describe a method for
maximising LTV by using model-based reinforcement learning (RL) to make
decisions about whether to send push notifications. We model the effects of
sending a notification on the user's future behavior. Much of the prior work
applying RL to maximise LTV in recommender systems has focused on session-based
optimization, while the time horizon for notification decision making in this
work extends over several days. We test this approach in an A/B test on a major
social network. We show that by optimizing decisions about push notifications
we are able to send less notifications and obtain a higher open rate than the
baseline system, while generating the same level of user engagement on the
platform as the existing, heuristic-based, system.


title:
Slip competition and rotation suppression in tantalum and copper during
  dynamic uniaxial compression

abstract:
  When compressed, a metallic specimen will generally experience changes to its
crystallographic texture due to plasticity-induced rotation. Ultrafast x-ray
diffraction techniques make it possible to measure rotation of this kind in
targets dynamically compressed over nanosecond timescales to the kind of
pressures ordinarily encountered in planetary interiors. The axis and the
extent of the local rotation can provide hints as to the combination of
plasticity mechanisms activated by the rapid uniaxial compression, thus
providing valuable information about the underlying dislocation kinetics
operative during extreme loading conditions. We present large-scale molecular
dynamics simulations of shock-induced lattice rotation in three model crystals
whose behavior has previously been characterized in dynamic-compression
experiments: tantalum shocked along its [101] direction, and copper shocked
along either [001] or [111]. We find that, in all three cases, the texture
changes predicted by the simulations are consistent with those measured
experimentally using in situ x-ray diffraction. We show that while tantalum
loaded along [101] and copper loaded along [001] both show pronounced rotation
due to asymmetric multiple slip, the orientation of copper shocked along [111]
is predicted to be stabilized by opposing rotations arising from competing,
symmetrically equivalent slip systems.


title:
MATCHA: A Fast and Energy-Efficient Accelerator for Fully Homomorphic
  Encryption over the Torus

abstract:
  Fully Homomorphic Encryption over the Torus (TFHE) allows arbitrary
computations to happen directly on ciphertexts using homomorphic logic gates.
However, each TFHE gate on state-of-the-art hardware platforms such as GPUs and
FPGAs is extremely slow ($>0.2ms$). Moreover, even the latest FPGA-based TFHE
accelerator cannot achieve high energy efficiency, since it frequently invokes
expensive double-precision floating point FFT and IFFT kernels. In this paper,
we propose a fast and energy-efficient accelerator, MATCHA, to process TFHE
gates. MATCHA supports aggressive bootstrapping key unrolling to accelerate
TFHE gates without decryption errors by approximate multiplication-less integer
FFTs and IFFTs, and a pipelined datapath. Compared to prior accelerators,
MATCHA improves the TFHE gate processing throughput by $2.3\times$, and the
throughput per Watt by $6.3\times$.


title: GRAPHSHAP: Motif-based Explanations for Black-box Graph Classifiers

abstract:
  Most methods for explaining black-box classifiers (e.g., on tabular data,
images, or time series) rely on measuring the impact that the
removal/perturbation of features has on the model output. This forces the
explanation language to match the classifier features space. However, when
dealing with graph data, in which the basic features correspond essentially to
the adjacency information describing the graph structure (i.e., the edges),
this matching between features space and explanation language might not be
appropriate. In this regard, we argue that (i) a good explanation method for
graph classification should be fully agnostic with respect to the internal
representation used by the black-box; and (ii) a good explanation language for
graph classification tasks should be represented by higher-order structures,
such as motifs. The need to decouple the feature space (edges) from the
explanation space (motifs) is thus a major challenge towards developing
actionable explanations for graph classification tasks. In this paper we
introduce GRAPHSHAP, a Shapley-based approach able to provide motif-based
explanations for black-box graph classifiers, assuming no knowledge whatsoever
about the model or its training data: the only requirement is that the
black-box can be queried at will. Furthermore, we introduce additional
auxiliary components such as a synthetic graph dataset generator, algorithms
for subgraph mining and ranking, a custom graph convolutional layer, and a
kernel to approximate the explanation scores while maintaining linear time
complexity. Finally, we test GRAPHSHAP on a real-world brain-network dataset
consisting of patients affected by Autism Spectrum Disorder and a control
group. Our experiments highlight how the classification provided by a black-box
model can be effectively explained by few connectomics patterns.


title:
Learning and Evaluating Graph Neural Network Explanations based on
  Counterfactual and Factual Reasoning

abstract:
  Structural data well exists in Web applications, such as social networks in
social media, citation networks in academic websites, and threads data in
online forums. Due to the complex topology, it is difficult to process and make
use of the rich information within such data. Graph Neural Networks (GNNs) have
shown great advantages on learning representations for structural data.
However, the non-transparency of the deep learning models makes it non-trivial
to explain and interpret the predictions made by GNNs. Meanwhile, it is also a
big challenge to evaluate the GNN explanations, since in many cases, the
ground-truth explanations are unavailable.
  In this paper, we take insights of Counterfactual and Factual (CF^2)
reasoning from causal inference theory, to solve both the learning and
evaluation problems in explainable GNNs. For generating explanations, we
propose a model-agnostic framework by formulating an optimization problem based
on both of the two casual perspectives. This distinguishes CF^2 from previous
explainable GNNs that only consider one of them. Another contribution of the
work is the evaluation of GNN explanations. For quantitatively evaluating the
generated explanations without the requirement of ground-truth, we design
metrics based on Counterfactual and Factual reasoning to evaluate the necessity
and sufficiency of the explanations. Experiments show that no matter
ground-truth explanations are available or not, CF^2 generates better
explanations than previous state-of-the-art methods on real-world datasets.
Moreover, the statistic analysis justifies the correlation between the
performance on ground-truth evaluation and our proposed metrics.


title:
Solving Hamiltonian Cycle Problem using Quantum $\mathbb{Z}_2$ Lattice
  Gauge Theory

abstract:
  The Hamiltonian cycle (HC) problem in graph theory is a well-known
NP-complete problem. We present an approach in terms of $\mathbb{Z}_2$ lattice
gauge theory (LGT) defined on the lattice with the graph as its dual. When the
coupling parameter $g$ is less than the critical value $g_c$, the ground state
is a superposition of all configurations with closed strings of spins in a same
single-spin state, which can be obtained by using an adiabatic quantum
algorithm with time complexity $O(\frac{1}{g_c^2} \sqrt{ \frac{1}{\varepsilon}
N_e^{3/2}(N_v^3 + \frac{N_e}{g_c}}))$, where $N_v$ and $N_e$ are the numbers of
vertices and edges of the graph respectively. A subsequent search for a HC
among those closed-strings solves the HC problem. For some random samples of
small graphs, we demonstrate that the dependence of the average value of $g_c$
on $\sqrt{N_{hc}}$, $N_{hc}$ being the number of HCs, and that of the average
value of $\frac{1}{g_c}$ on $N_e$ are both linear. It is thus suggested that
for some graphs, the HC problem may be solved in polynomial time. A possible
quantum algorithm using $g_c$ to infer $N_{hc}$ is also discussed.


title: Designing Word Filter Tools for Creator-led Comment Moderation

abstract:
  Online social platforms centered around content creators often allow comments
on content, where creators moderate the comments they receive. As creators can
face overwhelming numbers of comments, with some of them harassing or hateful,
platforms typically provide tools such as word filters for creators to automate
aspects of moderation. From needfinding interviews with 19 creators about how
they use existing tools, we found that they struggled with writing good filters
as well as organizing and revisiting their filters, due to the difficulty of
determining what the filters actually catch. To address these issues, we
present FilterBuddy, a system that supports creators in authoring new filters
or building from existing filter lists, as well as organizing their filters and
visualizing what comments are captured over time. We conducted an early-stage
evaluation of FilterBuddy with YouTube creators, finding that participants see
FilterBuddy not just as a moderation tool, but also a means to organize their
comments to better understand their audiences.


title:
New constraints on primordial features from the galaxy two-point
  correlation function

abstract:
  Features in the primordial power spectrum represent the imprinted signal in
the density perturbations of the physics and evolution of the early Universe. A
measurement of such signals will represents the need to go beyond the minimal
assumption made for the initial conditions of the cosmological perturbations.
For the first time, we study different templates with undamped oscillations or
a bump from the two-point correlation function measured from BOSS DR12 galaxies
constraining the amplitude of the features to be at most a few percent.
Constraints are competitive to the ones obtained with {\em Planck} DR3.


title:
Resonance-enhanced vibrational spectroscopy of molecules on a
  superconductor

abstract:
  Molecular vibrational spectroscopy with the scanning tunneling microscope is
feasible but usually detects few vibrational modes. We harness sharp
Yu-Shiba-Rusinov (YSR) states observed from molecules on a superconductor to
significantly enhance the vibrational signal. From a lead phthalocyanin
molecule 46 vibrational peaks are resolved enabling a comparison with
calculated modes. The energy resolution is improved beyond the thermal
broadening limit and shifts induced by neighbor molecules or the position of
the microscope tip are determined. Vice versa, spectra of vibrational modes are
used to measure the effect of an electrical field on the energy of YSR states.
The method may help to further probe the interaction of molecules with their
environment and to better understand selection rules for vibrational
excitations.


title:
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding
  Unfairness

abstract:
  Much of machine learning research focuses on predictive accuracy: given a
task, create a machine learning model (or algorithm) that maximizes accuracy.
In many settings, however, the final prediction or decision of a system is
under the control of a human, who uses an algorithm's output along with their
own personal expertise in order to produce a combined prediction. One ultimate
goal of such collaborative systems is "complementarity": that is, to produce
lower loss (equivalently, greater payoff or utility) than either the human or
algorithm alone. However, experimental results have shown that even in
carefully-designed systems, complementary performance can be elusive. Our work
provides three key contributions. First, we provide a theoretical framework for
modeling simple human-algorithm systems and demonstrate that multiple prior
analyses can be expressed within it. Next, we use this model to prove
conditions where complementarity is impossible, and give constructive examples
of where complementarity is achievable. Finally, we discuss the implications of
our findings, especially with respect to the fairness of a classifier. In sum,
these results deepen our understanding of key factors influencing the combined
performance of human-algorithm systems, giving insight into how algorithmic
tools can best be designed for collaborative environments.


title:
Objectives of platform research: A co-citation and systematic literature
  review analysis

abstract:
  Business economics research on digital platforms often overlooks existing
knowledge from other fields of research leading to conceptual ambiguity and
inconsistent findings. To reduce these restrictions and foster the utilization
of the extensive body of literature, we apply a mixed methods design to
summarize the key findings of scientific platform research. Our bibliometric
analysis identifies 14 platform-related research fields. Conducting a
systematic qualitative content analysis, we identify three primary research
objectives related to platform ecosystems: (1) general literature defining and
unifying research on platforms; (2) exploitation of platform and ecosystem
strategies; (3) improvement of platforms and ecosystems. Finally, we discuss
the identified insights from a business economics perspective and present
promising future research directions that could enhance business economics and
management research on digital platforms and platform ecosystems.


title: On Acceleration in Three Dimensions

abstract:
  We go "back to basics", studying accelerating systems in $2+1$ AdS gravity
\textit{ab initio}. We find three classes of geometry, which we interpret by
studying holographically their physical parameters. From these, we construct
stationary, accelerating point particles; one-parameter extensions of the BTZ
family resembling an accelerating black hole; and find new solutions including
a novel accelerating "BTZ geometry" not continuously connected to the BTZ black
hole as well as some black funnel solutions.


title: Multi-stage Ensemble Model for Cross-market Recommendation

abstract:
  This paper describes the solution of our team PolimiRank for the WSDM Cup
2022 on cross-market recommendation. The goal of the competition is to
effectively exploit the information extracted from different markets to improve
the ranking accuracy of recommendations on two target markets. Our model
consists in a multi-stage approach based on the combination of data belonging
to different markets. In the first stage, state-of-the-art recommenders are
used to predict scores for user-item couples, which are ensembled in the
following 2 stages, employing a simple linear combination and more powerful
Gradient Boosting Decision Tree techniques. Our team ranked 4th in the final
leaderboard.


title: On the ideas of the origin of eukaryotes: a critical review

abstract:
  The origin and early evolution of eukaryotes are one of the major transitions
in the evolution of life on earth. One of its most interesting aspects is the
emergence of cellular organelles, their dynamics, their functions, and their
divergence. Cell compartmentalization and architecture in prokaryotes is a less
understood complex property. In eukaryotes it is related to cell size, specific
genomic architecture, evolution of cell cycles, biogenesis of membranes and
endosymbiotic processes. Explaining cell evolution through form and function
demands an interdisciplinary approach focused on microbial diversity,
phylogenetic and functional cell biology. Two centuries of views on eukaryotic
origin have completed the disciplinary tools necessarily to answer these
questions. We have moved from Haeckel SCALA NATURAE to the un-rooted tree of
life. However, the major relations among cell domains are still elusive and
keep the nature of eukaryotic ancestor enigmatic. Here we present a review on
state of art views of eukaryogenesis; the background and perspectives of
different disciplines involved in this topic


title: Gravitational quasinormal modes for Lifshitz black branes

abstract:
  We study the scalar and vector channels of gravitational quasinormal modes
for Lifshitz black branes emerging in Einstein-Maxwell-Dilaton and
Einstein-Proca theories in four and five dimensions, finding significant
differences between the two models. In particular, rather surprisingly, in the
Einstein-Maxwell-Dilaton model the dispersion relations for the shear and sound
modes are given by $\omega_{shear} \sim -i\,k^4$ and $\omega_{sound}\sim-i
\,k^2$, while in the Einstein-Proca model they take the more conventional form
$\omega_{shear} \sim -i\,k^2$ and $\omega_{sound}\sim k$, the proportionality
constants depend on the dynamical exponent and the appropriate factors of
temperature. Through the holographic duality, this calculation provides
information about the relaxation of the momentum and energy flux operators in a
putative dual Lifshitz field theory. Comparing with the dispersion relations
obtained directly by considering Lifshitz hydrodynamics suggest that the mass
density of the equilibrium state in the Einstein-Maxwell-Dilaton model is
infinite.


title: LAMP: Extracting Text from Gradients with Language Model Priors

abstract:
  Recent work shows that sensitive user data can be reconstructed from gradient
updates, breaking the key privacy promise of federated learning. While success
was demonstrated primarily on image data, these methods do not directly
transfer to other domains such as text. In this work, we propose LAMP, a novel
attack tailored to textual data, that successfully reconstructs original text
from gradients. Our key insight is to model the prior probability of the text
with an auxiliary language model, utilizing it to guide the search towards more
natural text. Concretely, LAMP introduces a discrete text transformation
procedure that minimizes both the reconstruction loss and the prior text
probability, as provided by the auxiliary language model. The procedure is
alternated with a continuous optimization of the reconstruction loss, which
also regularizes the length of the reconstructed embeddings. Our experiments
demonstrate that LAMP reconstructs the original text significantly more
precisely than prior work: we recover 5x more bigrams and $23\%$ longer
subsequences on average. Moreover, we are first to recover inputs from batch
sizes larger than 1 for textual models. These findings indicate that gradient
updates of models operating on textual data leak more information than
previously thought.


title: Equivariant log-concavity of graph matchings

abstract:
  We show that the graded vector space spanned by matchings in any graph is
strongly equivariantly log-concave, viewed as a graded representation of the
graph automorphism group. Our proof provides an explicit construction of a
family of equivariant injections of representations.


title: Cycle structure of random parking functions

abstract:
  We initiate the study of the cycle structure of uniformly random parking
functions. Using the combinatorics of parking completions, we compute the
asymptotic expected value of the number of cycles of any fixed length. We
obtain an upper bound on the total variation distance between the joint
distribution of cycle counts and independent Poisson random variables using a
multivariate version of Stein's method via exchangeable pairs. Under a mild
condition, the process of cycle counts converges in distribution to a process
of independent Poisson random variables.


title: Optimal polynomial smoothers for multigrid V-cycles

abstract:
  The idea of using polynomial methods to improve simple smoother iterations
within a multigrid method for a symmetric positive definite (SPD) system is
revisited. When the single-step smoother itself corresponds to an SPD operator,
there is in particular a very simple iteration, a close cousin of the Chebyshev
semi-iterative method, based on the Chebyshev polynomials of the fourth instead
of first kind, that optimizes a two-level bound going back to Hackbusch. A full
V-cycle bound for general polynomial smoothers is derived using the V-cycle
theory of McCormick. The fourth-kind Chebyshev iteration is quasi-optimal for
the V-cycle bound. The optimal polynomials for the V-cycle bound can be found
numerically, achieving an about 18% lower error contraction factor bound than
the fourth-kind Chebyshev iteration, asymptotically as the number of smoothing
steps goes to infinity. Implementation of the optimized iteration is discussed,
and the performance of the polynomial smoothers are illustrated with a simple
numerical example.


title:
Observational properties of puffy disks: radiative GRMHD spectra of
  mildly sub-Eddington accretion

abstract:
  Numerical general relativistic radiative magnetohydrodynamic simulations of
accretion disks around a stellar mass black hole with a luminosity above 0.5 of
the Eddington value reveal their stratified, elevated vertical structure. We
refer to these thermally stable numerical solutions as puffy disks. Above a
dense and geometrically thin core of dimensionless thickness $h/r \sim 0.1$,
crudely resembling a classic thin accretion disk, a puffed-up, geometrically
thick layer of lower density and $h/r \sim 1.0$ is formed. We discuss the
observational properties of puffy disks, in particular the geometrical
obscuration of the inner disk by the elevated puffy region at higher observing
inclinations, and collimation of the radiation along the accretion disk spin
axis, which may explain the apparent super-Eddington luminosity of some X-ray
objects. We also present synthetic spectra of puffy disks, and show that they
are qualitatively similar to those of a Comptonized thin disk. We demonstrate
that the existing xspec spectral fitting models provide good fits to synthetic
observations of puffy disks, but cannot correctly recover the input black hole
spin. The puffy region remains optically thick to scattering; in its spectral
properties the puffy disk roughly resembles that of a warm corona sandwiching
the disk core. We suggest that puffy disks may correspond to X-ray binary
systems of luminosities above 0.3 of the Eddington luminosity in the
intermediate spectral states.


title: Universality of empirical risk minimization

abstract:
  Consider supervised learning from i.i.d. samples $\{{\boldsymbol
x}_i,y_i\}_{i\le n}$ where ${\boldsymbol x}_i \in\mathbb{R}^p$ are feature
vectors and ${y} \in \mathbb{R}$ are labels. We study empirical risk
minimization over a class of functions that are parameterized by $\mathsf{k} =
O(1)$ vectors ${\boldsymbol \theta}_1, . . . , {\boldsymbol \theta}_{\mathsf k}
\in \mathbb{R}^p$ , and prove universality results both for the training and
test error. Namely, under the proportional asymptotics $n,p\to\infty$, with
$n/p = \Theta(1)$, we prove that the training error depends on the random
features distribution only through its covariance structure. Further, we prove
that the minimum test error over near-empirical risk minimizers enjoys similar
universality properties. In particular, the asymptotics of these quantities can
be computed $-$to leading order$-$ under a simpler model in which the feature
vectors ${\boldsymbol x}_i$ are replaced by Gaussian vectors ${\boldsymbol
g}_i$ with the same covariance. Earlier universality results were limited to
strongly convex learning procedures, or to feature vectors ${\boldsymbol x}_i$
with independent entries. Our results do not make any of these assumptions. Our
assumptions are general enough to include feature vectors ${\boldsymbol x}_i$
that are produced by randomized featurization maps. In particular we explicitly
check the assumptions for certain random features models (computing the output
of a one-layer neural network with random weights) and neural tangent models
(first-order Taylor approximation of two-layer networks).


title: The Exact Class of Graph Functions Generated by Graph Neural Networks

abstract:
  Given a graph function, defined on an arbitrary set of edge weights and node
features, does there exist a Graph Neural Network (GNN) whose output is
identical to the graph function? In this paper, we fully answer this question
and characterize the class of graph problems that can be represented by GNNs.
We identify an algebraic condition, in terms of the permutation of edge weights
and node features, which proves to be necessary and sufficient for a graph
problem to lie within the reach of GNNs. Moreover, we show that this condition
can be efficiently verified by checking quadratically many constraints. Note
that our refined characterization on the expressive power of GNNs are
orthogonal to those theoretical results showing equivalence between GNNs and
Weisfeiler-Lehman graph isomorphism heuristic. For instance, our
characterization implies that many natural graph problems, such as min-cut
value, max-flow value, and max-clique size, can be represented by a GNN. In
contrast, and rather surprisingly, there exist very simple graphs for which no
GNN can correctly find the length of the shortest paths between all nodes. Note
that finding shortest paths is one of the most classical problems in Dynamic
Programming (DP). Thus, the aforementioned negative example highlights the
misalignment between DP and GNN, even though (conceptually) they follow very
similar iterative procedures. Finally, we support our theoretical results by
experimental simulations.


title:
Plasticity and evolvability under environmental variability: the joint
  role of fitness-based selection and niche-limited competition

abstract:
  The diversity and quality of natural systems have been a puzzle and
inspiration for communities studying artificial life. It is now widely admitted
that the adaptation mechanisms enabling these properties are largely influenced
by the environments they inhabit. Organisms facing environmental variability
have two alternative adaptation mechanisms operating at different timescales:
\textit{plasticity}, the ability of a phenotype to survive in diverse
environments and \textit{evolvability}, the ability to adapt through mutations.
Although vital under environmental variability, both mechanisms are associated
with fitness costs hypothesized to render them unnecessary in stable
environments. In this work, we study the interplay between environmental
dynamics and adaptation in a minimal model of the evolution of plasticity and
evolvability. We experiment with different types of environments characterized
by the presence of niches and a climate function that determines the fitness
landscape. We empirically show that environmental dynamics affect plasticity
and evolvability differently and that the presence of diverse ecological niches
favors adaptability even in stable environments. We perform ablation studies of
the selection mechanisms to separate the role of fitness-based selection and
niche-limited competition. Results obtained from our minimal model allow us to
propose promising research directions in the study of open-endedness in
biological and artificial systems.


title: General Cyclical Training of Neural Networks

abstract:
  This paper describes the principle of "General Cyclical Training" in machine
learning, where training starts and ends with "easy training" and the "hard
training" happens during the middle epochs. We propose several manifestations
for training neural networks, including algorithmic examples (via
hyper-parameters and loss functions), data-based examples, and model-based
examples. Specifically, we introduce several novel techniques: cyclical weight
decay, cyclical batch size, cyclical focal loss, cyclical softmax temperature,
cyclical data augmentation, cyclical gradient clipping, and cyclical
semi-supervised learning. In addition, we demonstrate that cyclical weight
decay, cyclical softmax temperature, and cyclical gradient clipping (as three
examples of this principle) are beneficial in the test accuracy performance of
a trained model. Furthermore, we discuss model-based examples (such as
pretraining and knowledge distillation) from the perspective of general
cyclical training and recommend some changes to the typical training
methodology. In summary, this paper defines the general cyclical training
concept and discusses several specific ways in which this concept can be
applied to training neural networks. In the spirit of reproducibility, the code
used in our experiments is available at \url{https://github.com/lnsmith54/CFL}.


title:
Data-SUITE: Data-centric identification of in-distribution incongruous
  examples

abstract:
  Systematic quantification of data quality is critical for consistent model
performance. Prior works have focused on out-of-distribution data. Instead, we
tackle an understudied yet equally important problem of characterizing
incongruous regions of in-distribution (ID) data, which may arise from feature
space heterogeneity. To this end, we propose a paradigm shift with Data-SUITE:
a data-centric framework to identify these regions, independent of a
task-specific model. DATA-SUITE leverages copula modeling, representation
learning, and conformal prediction to build feature-wise confidence interval
estimators based on a set of training instances. These estimators can be used
to evaluate the congruence of test instances with respect to the training set,
to answer two practically useful questions: (1) which test instances will be
reliably predicted by a model trained with the training instances? and (2) can
we identify incongruous regions of the feature space so that data owners
understand the data's limitations or guide future data collection? We
empirically validate Data-SUITE's performance and coverage guarantees and
demonstrate on cross-site medical data, biased data, and data with concept
drift, that Data-SUITE best identifies ID regions where a downstream model may
be reliable (independent of said model). We also illustrate how these
identified regions can provide insights into datasets and highlight their
limitations.


title: Adiabatic Quantum Computing for Multi Object Tracking

abstract:
  Multi-Object Tracking (MOT) is most often approached in the
tracking-by-detection paradigm, where object detections are associated through
time. The association step naturally leads to discrete optimization problems.
As these optimization problems are often NP-hard, they can only be solved
exactly for small instances on current hardware. Adiabatic quantum computing
(AQC) offers a solution for this, as it has the potential to provide a
considerable speedup on a range of NP-hard optimization problems in the near
future. However, current MOT formulations are unsuitable for quantum computing
due to their scaling properties. In this work, we therefore propose the first
MOT formulation designed to be solved with AQC. We employ an Ising model that
represents the quantum mechanical system implemented on the AQC. We show that
our approach is competitive compared with state-of-the-art optimization-based
approaches, even when using of-the-shelf integer programming solvers. Finally,
we demonstrate that our MOT problem is already solvable on the current
generation of real quantum computers for small examples, and analyze the
properties of the measured solutions.


title:
Helium absorption in exoplanet atmospheres is connected to stellar
  coronal abundances

abstract:
  Transit observations in the helium triplet around 10830 Angstrom are a
successful tool to study exoplanetary atmospheres and their mass loss. Forming
those lines requires ionisation and recombination of helium in the exoplanetary
atmosphere. This ionisation is caused by stellar photons at extreme
ultra-violet (EUV) wavelengths; however, no currently active telescopes can
observe this part of the stellar spectrum. The relevant part of the stellar EUV
spectrum consists of individual emission lines, many of them being formed by
iron at coronal temperatures. The stellar iron abundance in the corona is often
observed to be depleted for high-activity low-mass stars due to the first
ionisation potential (FIP) effect. I show that stars with high versus low
coronal iron abundances follow different scaling laws that tie together their
X-ray emission and the narrow-band EUV flux that causes helium ionisation. I
also show that the stellar iron to oxygen abundance ratio in the corona can be
measured reasonably well from X-ray CCD spectra, yielding similar results to
high-resolution X-ray observations. Taking coronal iron abundance into account,
the currently observed large scatter in the relationship of EUV irradiation
with exoplanetary helium transit depths can be reduced, improving the target
selection criteria for exoplanet transmission spectroscopy. In particular,
previously puzzling non-detections of helium for Neptunic exoplanets are now in
line with expectations from the revised scaling laws.


title:
Entropy-limited higher-order central scheme for neutron star merger
  simulations

abstract:
  Numerical relativity simulations are the only way to calculate exact
gravitational waveforms from binary neutron star mergers and to design
templates for gravitational-wave astronomy. The accuracy of these numerical
calculations is critical in quantifying tidal effects near merger that are
currently one of the main sources of uncertainty in merger waveforms. In this
work, we explore the use of an entropy-based flux-limiting scheme for
high-order, convergent simulations of neutron star spacetimes. The scheme
effectively tracks the stellar surface and physical shocks using the residual
of the entropy equation thus allowing the use of unlimited central flux schemes
in regions of smooth flow. We perform the first neutron star merger simulations
with such a method and demonstrate up to fourth-order convergence in the
gravitational waveform phase. The scheme reduces the phase error up to a factor
five when compared to state-of-the-art high-order characteristic schemes and
can be employed for producing faithful tidal waveforms for gravitational-wave
modelling.


title: Accelerating Earth-Bound Dark Matter

abstract:
  A fraction of the dark matter may consist of a particle species that
interacts much more strongly with the Standard Model than a typical weakly
interacting massive particle (WIMP) of similar mass. Such a strongly
interacting dark matter component could have avoided detection in searches for
WIMP-like dark matter through its interactions with the material in the
atmosphere and the Earth that slow it down significantly before reaching
detectors underground. These same interactions can also enhance the density of
a strongly interacting dark matter species near the Earth's surface to well
above the local galactic dark matter density. In this work we propose two new
methods of detecting strongly interacting dark matter based on accelerating the
enhanced population expected in the Earth through scattering. The first
approach is to use underground nuclear accelerator beams to upscatter the
ambient dark matter population into a WIMP-style detector located downstream.
In the second technique, dark matter is upscattered with an intense thermal
source and detected with a low-threshold dark matter detector. We also discuss
potential candidates for strongly interacting dark matter and we show that the
scenario can be naturally realized with a hidden fermion coupled to a sub-GeV
dark photon.


title: $T\bar{T}$ Deformations in Curved Space from 4D Chern-Simons Theory

abstract:
  In this paper, we shed new light onto $T\bar{T}$-deformations by engineering
them on 2D surface defects, supporting chiral and antichiral CFT's, in a 4D
Chern-Simons bulk. This approach is motivated by various connections between
$T\bar{T}$-deformations, gravity and Chern-Simons theory and suggests that the
formalism developed in this paper and the gravity picture of
$T\bar{T}$-deformations should be closely related.


title:
TOI-2119: A transiting brown dwarf orbiting an active M-dwarf from
  NASA's TESS mission

abstract:
  We report the discovery of TOI-2119b, a transiting brown dwarf (BD) that
orbits and is completely eclipsed by an active M-dwarf star. Using light curve
data from the Transiting Exoplanet Survey Satellite mission and follow up
high-resolution Doppler spectroscopic observations, we find the BD has a radius
of $R_b = 1.08 \pm 0.03{\rm R_J}$, a mass of $M_b = 64.4 \pm 2.3{\rm M_J}$, an
orbital period of $P = 7.200865 \pm 0.00002$ days, and an eccentricity of
$e=0.337\pm 0.002$. The host star has a mass of $M_\star = 0.53 \pm 0.02{\rm
M_\odot}$, a radius of $R_\star= 0.50 \pm 0.01{\rm R_\odot}$, an effective
temperature of $T_{\rm eff} = 3621 \pm 48{\rm K}$, and a metallicity of $\rm
[Fe/H]=+0.06\pm 0.08$. TOI-2119b joins an emerging population of transiting BDs
around M-dwarf host stars, with TOI-2119 being the ninth such system. These
M-dwarf--brown dwarf systems occupy mass ratios near $q = M_b/M_\star \approx
0.1$, which separates them from the typical mass ratios for systems with
transiting substellar objects and giant exoplanets that orbit more massive
stars. TOI-2119 is also one of the most active M-dwarf stars known to host a
transiting brown dwarf. The nature of the secondary eclipse of the BD by the
star enables us to estimate the effective temperature of the substellar object
to be $2030\pm 84 {\rm K}$, which is consistent with predictions by substellar
evolutionary models.


title: Dark Photons and Displaced Vertices at the MUonE Experiment

abstract:
  MUonE is a proposed experiment designed to measure the hadronic vacuum
polarization contribution to muon $g-2$ through elastic $\mu-e$ scattering. As
such it employs an extremely high-resolution tracking apparatus. We point out
that this makes MUonE also a very promising experiment to search for displaced
vertices from light, weakly-interacting new particles. We demonstrate its
potential by showing how it has excellent sensitivity to dark photons in the
mass range $10~\mathrm{MeV} \le m_{A'} \le 100~\mathrm{MeV}$ and kinetic mixing
parameter $10^{-5} \le \epsilon e \le 10^{-3}$, through the process $\mu^-\,
e^- \to \mu^-\, e^-\, A'$ followed by $A'\to e^+e^-$.


title: Resolving Black-Hole Microstructure with New Momentum Carriers

abstract:
  All known horizonless black-hole microstate geometries correspond to brane
sources that acquire a finite size, and hence break the spherical symmetry of
the black hole. We construct, for the first time, solutions with zero horizon
area that have the same charges as a three-charge F1-NS5-P Type-IIA black hole
and preserve this spherical symmetry. The momentum of these solutions is
carried by longitudinal D0-D4 density fluctuations inside the NS5-branes. We
argue that these solutions should be interpreted as the long-throat limit of a
family of smooth, horizonless microstate geometries, called superstrata, where
such geometries degenerate. The existence of these geometries indicates that a
finite-size horizon does not appear even in the singular corners of the moduli
space of three-charge microstate geometries.


title:
Fluctuating intertwined stripes in the strange metal regime of the
  Hubbard model

abstract:
  Strongly correlated electron systems host a variety of poorly understood
correlations in their high temperature normal state. Unlike ordered phases
defined by order parameters, these normal state phases are often defined
through unconventional properties such as strange metallic transport or
spectroscopic pseudogaps. Characterizing the microscopic correlations in the
normal state is necessary to elucidate mechanisms that lead to these properties
and their connection to ground state orders. Here we establish the presence of
intertwined charge and spin stripes in the strange metal normal state of the
Hubbard model using determinant quantum Monte Carlo calculations. The charge
and spin density waves constituting the stripes are fluctuating and
short-ranged, yet they obey a mutual commensurability relation and remain
microscopically interlocked, as evidenced through measurements of three-point
spin-spin-hole correlation functions. Our findings demonstrate the ability of
many-body numerical simulations to unravel the microscopic correlations that
define quantum states of matter.


title:
Quantifying radial migration in the Milky Way: Inefficient over short
  timescales but essential to the very outer disc beyond ~15 kpc

abstract:
  Stellar radial migration plays an important role in reshaping a galaxy's
structure and the radial distribution of stellar population properties. In this
work, we revisit reported observational evidence for radial migration and
quantify its strength using the age--[Fe/H] distribution of stars across the
Milky Way with APOGEE data. We find a broken age--[Fe/H] relation in the
Galactic disc at $r>6$ kpc, with a more pronounced break at larger radii. To
quantify the strength of radial migration, we assume stars born at each radius
have a unique age and metallicity, and then decompose the metallicity
distribution function (MDF) of mono-age young populations into different
Gaussian components that originated from various birth radii at $r_{\rm
birth}<13$ kpc. We find that, at ages of 2 and 3 Gyr, roughly half the stars
were formed within 1 kpc of their present radius, and very few stars ($<5$%)
were formed more than 4 kpc away from their present radius. These results
suggest limited short distance radial migration and inefficient long distance
migration in the Milky Way during the last 3 Gyr. In the very outer disc beyond
15~kpc, the observed age--[Fe/H] distribution is consistent with the prediction
of pure radial migration from smaller radii, suggesting a migration origin of
the very outer disc. We also estimate intrinsic metallicity gradients at ages
of 2 and 3 Gyr of $-0.061$ dex kpc$^{-1}$ and $-0.063$ dex kpc$^{-1}$,
respectively.


title: Jet broadening in dense inhomogeneous matter

abstract:
  In this work, we study the jet momentum broadening in an inhomogeneous dense
QCD medium. The transverse profile of this nuclear matter is described within a
gradient expansion, and we focus on the leading gradient contributions. The
leading parton is allowed to interact multiple times with the background
through the soft gluon exchanges. We derive the associated final particle
distribution using both the GLV opacity series and the BDMPS-Z formalism. We
further discuss the modified factorization of the initial and final state
effects and its consequences for phenomenological applications in the context
of heavy-ion collisions and deep inelastic scattering. Finally, we present the
broadening probability (describing the final state effects) in several limiting
regimes, and give its numerical estimates for phenomenologically motivated sets
of parameters.


title: The irreducible mass of LIGO's black holes

abstract:
  The mass of a Kerr black hole can be separated into irreducible and
rotational components -the former is a lower limit to the energy that cannot be
possibly extracted from the event horizon and is related to its area. Here we
compute the irreducible masses of the stellar-mass black holes observed by
gravitational-wave interferometers LIGO and Virgo. Using single-event data, we
present a re-parametrization of the posterior distribution that explicitly
highlights the irreducible and rotational contributions to the total energy. We
exploit the area law to rank the black-hole mergers observed to date according
to their irreversibility. Using population fits, we compute the rate by which
the total area of black-hole horizons increases due to the observable mergers.


title: Intrinsic alignments of bulges and discs

abstract:
  Galaxies exhibit coherent alignments with local structure in the Universe.
This effect, called Intrinsic Alignments (IA), is an important contributor to
the systematic uncertainties for wide-field weak lensing surveys. On
cosmological distance scales, intrinsic shape alignments have been observed in
red galaxies, which are usually bulge-dominated; while blue galaxies, which are
mostly disc-dominated, exhibit shape alignments consistent with a null
detection. However, disc-dominated galaxies typically consist of two prominent
structures: disc and bulge. Since the bulge component has similar properties as
elliptical galaxies and is thought to have formed in a similar fashion,
naturally one could ask whether the bulge components exhibit similar alignments
as ellipticals? In this paper, we investigate how different components of
galaxies exhibit IA in the TNG100-1 cosmological hydrodynamical simulation, as
well as the dependence of IA on the fraction of stars in rotation-dominated
structures at $z=0$. The measurements were controlled for mass differences
between the samples. We find that the bulges exhibit significantly higher IA
signals, with a nonlinear alignment model amplitude of $A_I =
2.98^{+0.36}_{-0.37}$ compared to the amplitude for the galaxies as a whole
(both components), $A_I = 1.13^{+0.37}_{-0.35}$. The results for bulges are
statistically consistent with those for elliptical galaxies, which have $A_I =
3.47^{+0.57}_{-0.57}$. These results highlight the importance of studying
galaxy dynamics in order to understand galaxy alignments and their cosmological
implications.


title: The fate of observers in circular motion

abstract:
  In Newtonian physics or in general relativity, energy dissipation causes
observers moving along circular orbits to slowly spiral towards the source of
the gravitational field. We show that the loss of energy has the same effect in
any theory of gravity respecting the weak equivalence principle, by exhibiting
an intimate relation between the energy of a massive test particle and the
stability of its orbit. Ultimately, massive particles either plunge, or are
driven towards minima of the generalized Newtonian potential, where they become
static. In addition, we construct a toy metric which displays an unbound
innermost stable circular orbit, allowing particles that reach this orbit to be
expelled away.


title:
A statistical study of the optical spectral variability in gamma-ray
  blazars

abstract:
  Blazars optical emission is generally dominated by relativistic jets,
although the host galaxy, accretion disk and broad line region (BLR) may also
contribute significantly. Disentangling their contributions has been
challenging for years due to the dominance of the jet. To quantify the
contributions to the spectral variability, we use the statistical technique for
dimensionality reduction Non-Negative Matrix Factorization on a spectroscopic
data set of 26 $\gamma$-ray blazars. This technique allows to model large
numbers of spectra in terms of a reduced number of components.We use a priori
knowledge to obtain components associated to meaningful physical processes. The
sources are classified according to their optical spectrum as host-galaxy
dominated BL Lac objects (BL Lacs), BL Lacs, or Flat Spectrum Radio Quasars
(FSRQs). Host-galaxy sources show less variability, as expected, and
bluer-when-brighter trends, as the other BL Lacs. For FSRQs, more complicated
colour-flux behaviours are observed: redder-when-brighter for low states
saturating above a certain level and, in some cases, turning to
bluer-when-brighter. We are able to reproduce the variability observed during
10 years using only 2 to 4 components, depending on the type. The simplest
scenario corresponds to host-galaxy blazars, whose spectra are reconstructed
using the stellar population and a power law for the jet. BL Lac spectra are
reproduced using from 2 to 4 power laws. Different components can be associated
to acceleration/cooling processes taking place in the jet. The reconstruction
of FSRQs also incorporates a QSO-like component to account for the BLR, plus a
very steep power law, associated to the accretion disk.


title: Modeling overcontact binaries, I. The effect of tidal deformation

abstract:
  In the realm of massive stars, strong binary interaction is commonplace. One
extreme case are overcontact systems, which is expected to be part of the
evolution of all stars evolving towards a merger, and is hypothesized to play a
role in the formation of binary black holes. However, important simplifications
are made to model the evolution of overcontact binaries, namely that tidal or
rotational deformation is frequently ignored. Yet, both observation and theory
show that overcontact stars are heavily tidally deformed, leaving a potentially
important effect on the outer layers unaccounted for in models. In this work we
develop the methodology to represented tidally deformed stars. Using numerical
methods, we compute the structure correction factors to the 1D spherical
stellar structure equations due to the binary Roche potential, and compare them
to existing results and the structure corrections of single rotating stars. We
implement the new structure correction factors into the stellar evolution code
MESA and explore several case studies. We compare the differences between our
simulations when no rotation is included, when we treat rotation using single
star corrections (i.e. only accounting for centrifugal deformation) or when we
use tidal deformation. We find that ignoring rotation in deformed detached
eclipsing binaries can produce a radius discrepancy of up to 5%. The difference
between tidal and single star centrifugal distortion models is more benign at
1%, showing that single rotating star models are a suitable approximation of
tidally deformed stars in a binary system. In overcontact configurations, we
find a similar 5% variation in surface properties as a result of tidal
distortion with respect to non-rotating models, showing that it is
inappropriate to model binary stars that fill their Roche lobe significantly,
as non-rotating.


title: Scalable approach to many-body localization via quantum data

abstract:
  We are interested in how quantum data can allow for practical solutions to
otherwise difficult computational problems. A notoriously difficult phenomenon
from quantum many-body physics is the emergence of many-body localization
(MBL). So far, is has evaded a comprehensive analysis. In particular, numerical
studies are challenged by the exponential growth of the Hilbert space
dimension. As many of these studies rely on exact diagonalization of the
system's Hamiltonian, only small system sizes are accessible. In this work, we
propose a highly flexible neural network based learning approach that, once
given training data, circumvents any computationally expensive step. In this
way, we can efficiently estimate common indicators of MBL such as the adjacent
gap ratio or entropic quantities. Our estimator can be trained on data from
various system sizes at once which grants the ability to extrapolate from
smaller to larger ones. Moreover, using transfer learning we show that already
a two-dimensional feature vector is sufficient to obtain several different
indicators at various energy densities at once. We hope that our approach can
be applied to large-scale quantum experiments to provide new insights into
quantum many-body physics.


title:
Explaining lepton-flavor non-universality and self-interacting dark
  matter with $L_\mu-L_\tau$

abstract:
  Experimental hints for lepton-flavor universality violation in the muon's
magnetic moment as well as neutral- and charged-current $B$-meson decays
require Standard-Model extensions by particles such as leptoquarks that
generically lead to unacceptably fast rates of charged lepton flavor violation
and proton decay. We propose a model based on a gauged $U(1)_{L_\mu-L_\tau}$
that eliminates all these unwanted decays by symmetry rather than finetuning
and efficiently explains $(g-2)_\mu$, $R_{K^{(*)}}$, $R_{D^{(*)}}$, and
neutrino masses. The $U(1)_{L_\mu-L_\tau}$ furthermore acts as a stabilizing
symmetry for dark matter and the light $Z'$ gauge boson mediates
velocity-dependent dark-matter self-interactions that resolve the small-scale
structure problems. Lastly, even the Hubble tension can be ameliorated via the
light $Z'$ contribution to the relativistic degrees of freedom.


title: Tricritical point in the quantum Hamiltonian mean-field model

abstract:
  Engineering long-range interactions in experimental platforms has been
achieved with great success in a large variety of quantum systems in recent
years. Inspired by this progress, we propose a generalization of the classical
Hamiltonian mean-field model to fermionic particles. We study the phase diagram
and thermodynamic properties of the model in the canonical ensemble for
ferromagnetic interactions as a function of temperature and hopping. At zero
temperature, small charge fluctuations drive the many-body system through a
first order quantum phase transition from an ordered to a disordered phase at
zero temperature. At higher temperatures, the fluctuation-induced phase
transition remains first order initially and switches to second order only at a
tricritical point. Our results offer an intriguing example of tricriticality in
a quantum system with long-range couplings, which bears direct experimental
relevance. The analysis is performed by exact diagonalization and mean-field
theory.


title: Iterated Belief Change, Computationally

abstract:
  Iterated Belief Change is the research area that investigates principles for
the dynamics of beliefs over (possibly unlimited) many subsequent belief
changes. In this paper, we demonstrate how iterated belief change is connected
to computation. In particular, we show that iterative belief revision is Turing
complete, even under the condition that broadly accepted principles like the
Darwiche-Pearl postulates for iterated revision hold.


title: Mixing Particle Production for Relaxion Mechanism

abstract:
  We consider the production of two heavy gauge bosons as a relaxation stopping
mechanism. In this work, we analyse the conditions for a tachyonic mode for a
linear combination of gauge bosons and show that the criteria are significantly
different than the single gauge boson case. Moreover, the implementation of the
mechanism on the $U(1)'$ model is demonstrated. We discuss various constraints
for the relaxion mechanism. The phenomenology of the heavy gauge boson is also
explored. We finally show a benchmark point of parameter space considering all
constraints from relaxion and the $U(1)'$ mixing sector.


title:
Do direct detection experiments constrain axionlike particles coupled to
  electrons?

abstract:
  Several laboratory experiments have published limits on axionlike particles
(ALPs) with feeble couplings to electrons and masses in the keV-MeV range,
under the assumption that such ALPs comprise the dark matter. We note that ALPs
decay radiatively into photons, and show that for a large subset of the
parameter space ostensibly probed by these experiments, the lifetime of the
ALPs is shorter than the age of the universe. Such ALPs cannot consistently
make up the dark matter, which significantly affects the interpretation of
published limits from GERDA, Edelweiss-III, SuperCDMS and Majorana. Moreover,
constraints from gamma-ray and X-ray astronomy exclude an even wider range of
the ALP-electron coupling, and supersede all current experimental limits on
dark matter ALPs in the 6 keV to 1 MeV mass range. These conclusions are rather
model-independent, and can only be avoided at the expense of significant
fine-tuning in theories where the ALP has additional couplings to other
particles.


title: Supercuspidal representations in non-defining characteristics

abstract:
  We show that a mod-$\ell$-representation of a p-adic group arising from the
analogue of Yu's construction is supercuspidal if and only if it arises from a
supercuspidal representation of a finite reductive group. This has been
previously shown by Henniart and Vigneras under the assumption that the second
adjointness holds, a statement that is not yet available in the literature.


title: Pancosmic Relativity and Nature's Hierarchies

abstract:
  We define `third derivative' General Relativity, by promoting the integration
measure in Einstein-Hilbert action to be an arbitrary $4$-form field strength.
We project out its local fluctuations by coupling it to another $4$-form field
strength. This ensures that the gravitational sector contains only the usual
massless helicity-2 propagating modes. Adding the charges to these $4$-forms
allows for discrete variations of the coupling parameters of conventional
General Relativity: $G_N, \Lambda, H_0$, and even $\langle {\tt Higgs }\rangle$
are all variables which can change by jumps. Hence de Sitter is unstable to
membrane nucleation. Using this instability we explain how the cosmological
constant problem can be solved. The scenario utilizes the idea behind the
irrational axion, but instead of an axion it requires one more $4$-form field
strength and corresponding charged membranes. When the membrane charges satisfy
the constraint $\frac{2\kappa_{\tt eff}^2 \kappa^2 |{\cal Q}_i|}{3{\cal T}^2_i}
< 1$, the theory which ensues exponentially favors a huge hierarchy
$\Lambda/\mpl^4 \ll 1$ instead of $\Lambda/\mpl^4 \simeq 1$. The discharges
produce the distribution of the values of $\Lambda$ described by the saddle
point approximation of the Euclidean path integral.


title: Bounded t-structures on the category of perfect complexes

abstract:
  Let $X$ be a finite-dimensional, noetherian scheme. Antieau, Gepner and
Heller conjectured that its derived category of perfect complexes has a bounded
t-structure if and only if $X$ is regular. We prove the conjecture for
separated $X$.


title:
RemixIT: Continual self-training of speech enhancement models via
  bootstrapped remixing

abstract:
  We present RemixIT, a simple yet effective self-supervised method for
training speech enhancement without the need of a single isolated in-domain
speech nor a noise waveform. Our approach overcomes limitations of previous
methods which make them dependent on clean in-domain target signals and thus,
sensitive to any domain mismatch between train and test samples. RemixIT is
based on a continuous self-training scheme in which a pre-trained teacher model
on out-of-domain data infers estimated pseudo-target signals for in-domain
mixtures. Then, by permuting the estimated clean and noise signals and remixing
them together, we generate a new set of bootstrapped mixtures and corresponding
pseudo-targets which are used to train the student network. Vice-versa, the
teacher periodically refines its estimates using the updated parameters of the
latest student models. Experimental results on multiple speech enhancement
datasets and tasks not only show the superiority of our method over prior
approaches but also showcase that RemixIT can be combined with any separation
model as well as be applied towards any semi-supervised and unsupervised domain
adaptation task. Our analysis, paired with empirical evidence, sheds light on
the inside functioning of our self-training scheme wherein the student model
keeps obtaining better performance while observing severely degraded
pseudo-targets.


title: Simultaneous Stoquasticity

abstract:
  Stoquastic Hamiltonians play a role in the computational complexity of the
local Hamiltonian problem as well as the study of classical simulability. In
particular, stoquastic Hamiltonians can be straightforwardly simulated using
Monte Carlo techniques. We address the question of whether two or more
Hamiltonians may be made simultaneously stoquastic via a unitary
transformation. This question has important implications for the complexity of
simulating quantum annealing where quantum advantage is related to the
stoquasticity of the Hamiltonians involved in the anneal. We find that for
almost all problems no such unitary exists and show that the problem of
determining the existence of such a unitary is equivalent to identifying if
there is a solution to a system of polynomial (in)equalities in the matrix
elements of the initial and transformed Hamiltonians. Solving such a system of
equations is NP-hard. We highlight a geometric understanding of this problem in
terms of a collection of generalized Bloch vectors.


title: Characterizing cubic hypersurfaces via projective geometry

abstract:
  We use the cut and paste relation $[Y^{[2]}] = [Y][\mathbb{P}^m] +
\mathbb{L}^2 [F(Y)]$ in $K_0(\text{Var}_k)$ of Galkin--Shinder for cubic
hypersurfaces arising from projective geometry to characterize cubic
hypersurfaces of sufficiently high dimension under certain numerical or
genericity conditions. Removing the conditions involving the middle Betti
number from the numerical conditions used extends the possible $Y$ to cubic
hypersurfaces, complete intersections of two quadric hypersurfaces, or complete
intersections of two quartic hypersurfaces. The same method also gives a family
of other cut and paste relations that can only possibly be satisfied by cubic
hypersurfaces.


title:
The Farthest Quasar Mini-BAL Outflow from its Central Source: VLT/UVES
  Observation of SDSSJ0242+0049

abstract:
  We analyze VLT/UVES observations of the quasar SDSS J024221.87+004912.6. We
identify four absorption outflow systems: a CIV BAL at $v\approx -18,000 \text{
km s}^{-1}$, and three narrower low-ionization systems with centroid velocities
ranging from -1200 to -3500 km s$^{-1}$. These outflows show similar physical
attributes to the [OIII] outflows studied by arXiv:1305.6922. We find that two
of the systems are energetic enough to contribute to AGN feedback, with one
system reaching above 5% of the quasar's Eddington luminosity. We also find
that this system is at a distance of 67 kpc away from the quasar, the farthest
detected mini-BAL absorption outflow from its central source to date. In
addition, we examine the time variability of the BAL, and find that its
velocity monotonically increases, while the trough itself becomes shallower
over time.


title: Good filtrations for generalized Schur algebras

abstract:
  Given a quasi-hereditary superalgebra $A$, the first author and R. Muth have
defined generalized Schur bi-superalgebras $T^A(n)$ and proved that these
algebras are again quasi-hereditary. In particular, $T^A(n)$ comes with a
family of standard modules. Developing the work of Donkin and Mathieu on good
filtrations, we prove that tensor product of standard modules over $T^A(n)$ has
a standard filtration.


title:
Fast online inference for nonlinear contextual bandit based on
  Generative Adversarial Network

abstract:
  This work addresses the efficiency concern on inferring a nonlinear
contextual bandit when the number of arms $n$ is very large. We propose a
neural bandit model with an end-to-end training process to efficiently perform
bandit algorithms such as Thompson Sampling and UCB during inference. We
advance state-of-the-art time complexity to $O(\log n)$ with approximate
Bayesian inference, neural random feature mapping, approximate global maxima
and approximate nearest neighbor search. We further propose a generative
adversarial network to shift the bottleneck of maximizing the objective for
selecting optimal arms from inference time to training time, enjoying
significant speedup with additional advantage of enabling batch and parallel
processing. %The generative model can inference an approximate argmax of the
posterior sampling in logarithmic time complexity with the help of approximate
nearest neighbor search. Extensive experiments on classification and
recommendation tasks demonstrate order-of-magnitude improvement in inference
time no significant degradation on the performance.