title: Maximum-utility popular matchings with bounded instability

abstract:
  In a graph where vertices have preferences over their neighbors, a matching
is called popular if it does not lose a head-to-head election against any other
matching when the vertices vote between the matchings. Popular matchings can be
seen as an intermediate category between stable matchings and maximum-size
matchings. In this paper, we aim to maximize the utility of a matching that is
popular but admits only a few blocking edges.
  For general graphs already finding a popular matching with at most one
blocking edge is NP-complete. For bipartite instances, we study the problem of
finding a maximum-utility popular matching with a bound on the number (or more
generally, the cost) of blocking edges applying a multivariate approach. We
show classical and parameterized hardness results for severely restricted
instances. By contrast, we design an algorithm for instances where preferences
on one side admit a master list, and show that this algorithm is optimal.


title: Ontology-Mediated Querying on Databases of Bounded Cliquewidth

abstract:
  We study the evaluation of ontology-mediated queries (OMQs) on databases of
bounded cliquewidth from the viewpoint of parameterized complexity theory. As
the ontology language, we consider the description logics $\mathcal{ALC}$ and
$\mathcal{ALCI}$ as well as the guarded two-variable fragment GF$_2$ of
first-order logic. Queries are atomic queries (AQs), conjunctive queries (CQs),
and unions of CQs. All studied OMQ problems are fixed-parameter linear (FPL)
when the parameter is the size of the OMQ plus the cliquewidth. Our main
contribution is a detailed analysis of the dependence of the running time on
the parameter, exhibiting several interesting effects.


title:
Wavelet neural operator: a neural operator for parametric partial
  differential equations

abstract:
  With massive advancements in sensor technologies and Internet-of-things, we
now have access to terabytes of historical data; however, there is a lack of
clarity in how to best exploit the data to predict future events. One possible
alternative in this context is to utilize operator learning algorithm that
directly learn nonlinear mapping between two functional spaces; this
facilitates real-time prediction of naturally arising complex evolutionary
dynamics. In this work, we introduce a novel operator learning algorithm
referred to as the Wavelet Neural Operator (WNO) that blends integral kernel
with wavelet transformation. WNO harnesses the superiority of the wavelets in
time-frequency localization of the functions and enables accurate tracking of
patterns in spatial domain and effective learning of the functional mappings.
Since the wavelets are localized in both time/space and frequency, WNO can
provide high spatial and frequency resolution. This offers learning of the
finer details of the parametric dependencies in the solution for complex
problems. The efficacy and robustness of the proposed WNO are illustrated on a
wide array of problems involving Burger's equation, Darcy flow, Navier-Stokes
equation, Allen-Cahn equation, and Wave advection equation. Comparative study
with respect to existing operator learning frameworks are presented. Finally,
the proposed approach is used to build a digital twin capable of predicting
Earth's air temperature based on available historical data.


title: Sectorial Paley-Wiener Theorem

abstract:
  By correcting, simplifying and extending a result of Morimoto, we prove a
Paley-Wiener type theorem for functions of exponential type in a sector. It
serves as a sectorial analogue of Polya's theorem on the indicator of entire
functions and improves a result of Dzhrbashyan and Avetisyan by finding the
maximal convex set of analytic continuation inside a sector.


title:
Systematically Measuring Ultra-Diffuse Galaxies (SMUDGes). III. The
  Southern SMUDGes Catalog

abstract:
  We present a catalog of 5598 ultra-diffuse galaxy (UDG) candidates with
effective radius $r_e > 5.3$ arcsec distributed throughout the southern portion
of the DESI Legacy Imaging Survey covering $\sim$ 15000 deg$^2$. The catalog is
most complete for physically large ($r_e > 2.5$ kpc) UDGs lying in the redshift
range $1800 \lesssim cz/{\rm km\ s}^{-1} \lesssim 7000$, where the lower bound
is defined by where incompleteness becomes significant for large objects on the
sky and the upper bound by our minimum angular size selection criterion.
Because physical size is integral to the definition of a UDG, we develop a
method {of distance estimation} using existing redshift surveys. With three
different galaxy samples, two of which contain UDGs with spectroscopic
redshifts, we estimate that the method has a redshift accuracy of $\sim$ 75%
when the method converges, although larger, more representative spectroscopic
UDG samples are needed to fully understand the behavior of the method. We are
able to estimate distances for 1079 of our UDG candidates (19%). Finally, to
illustrate uses of the catalog, we present distance independent and dependent
results. In the latter category we establish that the red sequence of UDGs lies
on the extrapolation of the red sequence relation for bright ellipticals and
that the environment-color relation is at least qualitatively similar to that
of high surface brightness galaxies. Both of these results challenge some of
the models proposed for UDG evolution.


title:
Intelligent Reflecting Surface Aided Mobile Edge Computing With Binary
  Offloading: Energy Minimization for IoT Devices

abstract:
  Mobile edge computing (MEC) is envisioned as a promising technique to support
computation-intensive and timecritical applications in future Internet of
Things (IoT) era. However, the uplink transmission performance will be highly
impacted by the hostile wireless channel, the low bandwidth, and the low
transmission power of IoT devices. Recently, intelligent reflecting surface
(IRS) has drawn much attention because of its capability to control the
wireless environments so as to enhance the spectrum and energy efficiencies of
wireless communications. In this paper, we consider an IRS-aided multidevice
MEC system where each IoT device follows the binary offloading policy, i.e., a
task has to be computed as a whole either locally or remotely at the edge
server. We aim to minimize the total energy consumption of devices by jointly
optimizing the binary offloading modes, the CPU frequencies, the offloading
powers, the offloading times and the IRS phase shifts for all devices. Two
algorithms, which are greedy-based and penalty-based, are proposed to solve the
challenging nonconvex and discontinuous problem. It is found that the
penalty-based method has only linear complexity with respect to the number of
devices, but it performs close to the greedy-based method with cubic complexity
with respect to number of devices. Furthermore, binary offloading via IRS
indeed saves more energy compared to the case without IRS.


title:
Neutrino mass and mass ordering: No conclusive evidence for normal
  ordering

abstract:
  The extraction of the neutrino mass ordering is one of the major challenges
in particle physics and cosmology, not only for its implications for a
fundamental theory of mass generation in nature, but also for its decisive role
in the scale of future neutrinoless double beta decay experimental searches. It
has been recently claimed that current oscillation, beta decay and cosmological
limits on the different observables describing the neutrino mass parameter
space provide robust decisive Bayesian evidence in favor of the normal ordering
of the neutrino mass spectrum [arXiv:2203.14247]. We further investigate these
strong claims using a rich and wide phenomenology, with different sampling
techniques of the neutrino parameter space. Contrary to the findings of Jimenez
et al [arXiv:2203.14247], no decisive evidence for the normal mass ordering is
found. Neutrino mass ordering analyses must rely on priors and
parameterizations that are ordering-agnostic: robust results should be regarded
as those in which the preference for the normal neutrino mass ordering is
driven exclusively by the data, while we find a difference of up to a factor of
33 in the Bayes factors among the different priors and parameterizations
exploited here. An ordering-agnostic prior would be represented by the case of
parameterizations sampling over the two mass splittings and a mass scale, or
those sampling over the individual neutrino masses via normal prior
distributions only. In this regard, we show that the current significance in
favor of the normal mass ordering should be taken as $2.7\sigma$ (i.e. moderate
evidence), mostly driven by neutrino oscillation data.


title: On the monoid of partial isometries of a cycle graph

abstract:
  In this paper we consider the monoid $DPC_n$ of all partial isometries of a
$n$-cycle graph $C_n$. We show that $DPC_n$ is the submonoid of the monoid
$PORI_n$ of all oriented partial permutations on a $n$-chain whose elements are
precisely all restrictions of the dihedral group of order $2n$. For this
purpose, a useful characterization of an oriented transformation is established
first. Our main aims are to determine the rank and to exhibit a presentation of
$DPC_n$. We also describe Green's relations of $DPC_n$ and calculate its
cardinal.


title: Validating Approximate Slope Homogeneity in Large Panels

abstract:
  Statistical inference for large data panels is omnipresent in modern economic
applications. An important benefit of panel analysis is the possibility to
reduce noise and thus to guarantee stable inference by intersectional pooling.
However, it is wellknown that pooling can lead to a biased analysis if
individual heterogeneity is too strong. In classical linear panel models, this
trade-off concerns the homogeneity of slope parameters, and a large body of
tests has been developed to validate this assumption. Yet, such tests can
detect inconsiderable deviations from slope homogeneity, discouraging pooling,
even when practically beneficial. In order to permit a more pragmatic analysis,
which allows pooling when individual heterogeneity is sufficiently small, we
present in this paper the concept of approximate slope homogeneity. We develop
an asymptotic level $\alpha$ test for this hypothesis, that is uniformly
consistent against classes of local alternatives. In contrast to existing
methods, which focus on exact slope homogeneity and are usually sensitive to
dependence in the data, the proposed test statistic is (asymptotically) pivotal
and applicable under simultaneous intersectional and temporal dependence.
Moreover, it can accommodate the realistic case of panels with large
intersections. A simulation study and a data example underline the usefulness
of our approach.


title: Thermal Transport in 2D Nematic Superconductors

abstract:
  We study the thermal transport in a two-dimensional system with coexisting
superconducting (SC) and nematic orders. We analyze the nature of the
coexistence phase in a tight-binding square lattice where the nematic state is
modelled as a $d$-wave Pomeranchuk type instability and the feedback of the
symmetry breaking nematic state on the SC order is accounted for by mixing of
the $s$, $d$ paring interaction. The electronic thermal conductivity is
computed within the framework of Boltzmann kinetic theory where the impurity
scattering collision is treated in the both the Born and Unitary limits. We
present qualitative, analytical, and numerical results that show that the heat
transport properties of SC states emerging from a nematic background are quite
distinct and depend on the degree of anisotropy of the SC gap induced by
nematicity. We describe the influence of the Fermi surface topology, the van
Hove singularities, and the presence or absence of zero energy excitations in
the coexistence phase on the the low temperature behaviour of the thermal
conductivity. Our main conclusion is that the interplay of nematic and SC
orders has visible signatures in the thermal transport which can be used to
infer SC gap structure in the coexistence phase.


title:
Discrete-Time System of an Intracellular Delayed HIV Model with CTL
  Immune Response

abstract:
  In [Math. Comput. Sci. 12 (2018), no. 2, 111--127], a delayed model
describing the dynamics of the Human Immunodeficiency Virus (HIV) with
Cytotoxic T Lymphocytes (CTL) immune response is investigated by Allali,
Harroudi and Torres. Here, we propose a discrete-time version of that model,
which includes four nonlinear difference equations describing the evolution of
uninfected, infected, free HIV viruses, and CTL immune response cells and
includes intracellular delay. Using suitable Lyapunov functions, we prove the
global stability of the disease free equilibrium point and of the two endemic
equilibrium points. We finalize by making some simulations and showing,
numerically, the consistence of the obtained theoretical results.


title: Quotients of the Highwater algebra and its cover

abstract:
  Axial algebras are a class of non-associative algebra with a strong link to
finite (especially simple) groups which have recently received much attention.
Of primary interest are the axial algebras of Monster type $(\alpha, \beta)$,
of which the Griess algebra (with the Monster as its automorphism group) is an
important motivating example. In this paper, we complete the classification of
the symmetric $2$-generated primitive axial algebras of Monster type $(\alpha,
\beta)$.
  By previous work of Yabe, and Franchi and Mainardis, any such algebra is
either explicitly known, or is a quotient of the infinite-dimensional Highwater
algebra $\mathcal{H}$, or its characteristic $5$ cover $\hat{\mathcal{H}}$. In
this paper, we classify the ideals of $\mathcal{H}$ and $\hat{\mathcal{H}}$ and
thus their quotients. Moreover, we give explicit bases for the ideals. In fact,
we proceed in a unified way, by defining a cover $\hat{\mathcal{H}}$ of
$\mathcal{H}$ in all characteristics and classifying its ideals. Our new
algebra $\hat{\mathcal{H}}$ has a previously unseen fusion law and provides an
insight into why the Highwater algebra has a cover which is of Monster type
only in characteristic $5$.


title:
We the Droplets: A Constitutional Approach to Active and Self-Propelled
  Emulsions

abstract:
  The field of active matter, and particularly active emulsions, is growing
rapidly, with significant progress made recently on both theoretical and
experimental fronts. Here, we summarize experimental research progress related
to active droplets. The constitution of active droplets, in particular the
chemical compositions and structure of interfaces, is critical. We discuss how
emulsion properties such as mechanism of motion, speed, trajectory, interaction
strength, and lifetime are related to the droplet composition. We consider not
only traditional single emulsions but also more complex variants, such as Janus
droplets, Pickering emulsions, and multiple emulsions. Active behavior of
isolated droplets as well as pairwise and multibody interactions between
droplets is described. The influence of physical barriers that shape the local
chemical gradients and fluid flow is also highlighted. This review provides
perspective on the past, current, promising future experimental research
directions in active droplet research.


title:
Two-step Planning of Dynamic UAV Trajectories using Iterative
  $\delta$-Spaces

abstract:
  UAV trajectory planning is often done in a two-step approach, where a
low-dimensional path is refined to a dynamic trajectory. The resulting
trajectories are only locally optimal, however. On the other hand, direct
planning in higher-dimensional state spaces generates globally optimal
solutions but is time-consuming and thus infeasible for time-constrained
applications. To address this issue, we propose $\delta$-Spaces, a pruned
high-dimensional state space representation for trajectory refinement. It does
not only contain the area around a single lower-dimensional path but consists
of the union of multiple near-optimal paths. Thus, it is less prone to local
minima. Furthermore, we propose an anytime algorithm using $\delta$-Spaces of
increasing sizes. We compare our method against state-of-the-art search-based
trajectory planning methods and evaluate it in 2D and 3D environments to
generate second-order and third-order UAV trajectories.


title:
Graphical Games for UAV Swarm Control Under Time-Varying Communication
  Networks

abstract:
  We propose a unified framework for coordinating Unmanned Aerial Vehicle (UAV)
swarms operating under time-varying communication networks. Our framework
builds on the concept of graphical games, which we argue provides a compelling
paradigm to subsume the interaction structures found in networked UAV swarms
thanks to the shared local neighborhood properties. We present a general-sum,
factorizable payoff function for cooperative UAV swarms based on the aggregated
local states and yield a Nash equilibrium for the stage games. Further, we
propose a decomposition-based approach to solve stage-graphical games in a
scalable and decentralized fashion by approximating virtual, mean
neighborhoods. Finally, we discuss extending the proposed framework toward
general-sum stochastic games by leveraging deep Q-learning and model-predictive
control.


title: Dynamical mean-field theory: from ecosystems to reaction networks

abstract:
  Both natural ecosystems and biochemical reaction networks involve populations
of heterogeneous agents whose cooperative and competitive interactions lead to
a rich dynamics of species' abundances, albeit at vastly different scales. The
maintenance of diversity in large ecosystems is a longstanding puzzle, towards
which recent progress has been made by the derivation of dynamical mean-field
theories of random models. In particular, it has recently been shown that these
random models have a chaotic phase in which abundances display wild
fluctuations. When modest spatial structure is included, these fluctuations are
stabilized and diversity is maintained. If and how these phenomena have
parallels in biochemical reaction networks is currently unknown. Making this
connection is of interest since life requires cooperation among a large number
of molecular species, and the origin of life is hotly debated. In this work, we
find a reaction network whose large-scale behavior recovers the random
Lotka-Volterra model recently considered in theoretical ecology. We clarify the
assumptions necessary to derive its large-scale description, and reveal the
underlying assumptions made on the noise to recover previous dynamical
mean-field theories. Then, we show how local detailed balance and the
positivity of reaction rates, which are key physical requirements of chemical
reaction networks, provide obstructions towards the construction of an
associated dynamical mean-field theory of biochemical reaction networks. We
outline prospects and challenges for the future, and argue for a synthetic
approach to a physical theory of the origin of life.

categories: cond-mat.stat-mech cond-mat.dis-nn nlin.AO q-bio.MN q-bio.PE


title:
Correlating the CDF $W$-boson mass shift with the $b \to s \ell^+
  \ell^-$ anomalies

abstract:
  The recently updated measurement of the $W$-boson mass by the CDF
collaboration exhibits a $7\sigma$ deviation from the SM expectation, which may
imply a sign of new physics beyond the SM. The observed discrepancy could be
explained by new fermions that carry the electroweak gauge charges and affect
the vacuum polarization of gauge bosons. Notably, if the new fermions also have
the same quantum numbers as of the SM quarks, they can mix with the latter and
modify the penguin diagrams governing the $b \to s \ell^+ \ell^-$ transitions.
Therefore, the $W$-boson mass shift could be related to the $b \to s \ell^+
\ell^-$ anomalies observed by the LHCb collaboration during the past few years.
To investigate this possibility, we consider in this paper a model containing a
vector-like top partner gauged under a new $U(1)^\prime$ symmetry. It is found
that the latest CDF $m_W$ measurement and the $b \to s \ell^+ \ell^-$ anomalies
can be simultaneously accommodated at $2\sigma$ level.


title:
Numerical analysis of non-local calculus on finite weighted graphs, with
  application to reduced-order modelling of dynamical systems

abstract:
  We present an approach to reduced-order modelling that builds off recent
graph-theoretic work for representation, exploration, and analysis of computed
states of physical systems (Banerjee et al., Comp. Meth. App. Mech. Eng., 351,
501-530, 2019). We extend a non-local calculus on finite weighted graphs to
build such models by exploiting polynomial expansions and Taylor series. In the
general framework for non-local calculus on graphs, the graph edge weights are
intricately linked to the embedding of the graph, and consequently to the
definition of the derivatives. In a previous communication (Duschenes and
Garikipati, arXiv:2105.01740), we have shown that radially symmetric,
continuous edge weights derived from, for example Gaussian functions, yield
inconsistent results in the resulting non-local derivatives when compared
against the corresponding local, differential derivative definitions. Taking
inspiration from finite difference methods, we algorithmically compute edge
weights, considering the embedding of the local neighborhood of each graph
vertex. Given this procedure, we ensure the consistency of the non-local
derivatives in this setting, a crucial requirement for numerical applications.
We show that we can achieve any desired orders of accuracy of derivatives, in a
chosen number of dimensions without symmetry assumptions in the underlying
data. Finally, we present two example applications of extracting reduced-order
models using this non-local calculus, in the form of ordinary differential
equations from parabolic partial differential equations of progressively
greater complexity.


title:
The extreme scarcity of dust-enshrouded red supergiants: consequences
  for producing stripped stars via winds

abstract:
  Quiescent mass-loss during the red supergiant (RSG) phase has been shown to
be far lower than prescriptions typically employed in single-star evolutionary
models. Importantly, RSG winds are too weak to drive the production of
Wolf-Rayets (WRs) and stripped-envelope supernovae (SE-SNe) at initial masses
of roughly 20--40$M_{\odot}$. If single-stars are to make WRs and SE-SNe, this
shifts the burden of mass-loss to rare dust-enshrouded RSGs (DE-RSGs), objects
claimed to represent a short-lived high mass-loss phase. Here, we take a fresh
look at the purported DE-RSGs. By modeling the mid-IR excesses of the full
sample of RSGs in the LMC, we find that only one RSG has both a high mass-loss
rate (\mdot $\ge$ 10$^{-4}$ $M_{\odot}$ yr$^{-1}$) and a high optical
circumstellar dust extinction (7.92 mag). This one RSG is WOH G64, and it is
the only one of the 14 originally proposed DE-RSGs that is actually dust
enshrouded. The rest appear to be either normal RSGs without strong
infrared-excess, or lower-mass asymptotic giant branch (AGB) stars. Only one
additional object in the full catalog of RSGs (not previously identified as a
DE-RSG) shows strong mid-IR excess. We conclude that if DE-RSGs do represent a
pre-SN phase of enhanced \mdot\ in single-stars, it is extremely short-lived,
only capable of removing $\leq$2\msun\ of material. This rules out the
single-star post-RSG pathway for the production of WRs, LBVs, and SE-SN.
Single-star models should not employ \mdot-prescriptions based on these extreme
objects for any significant fraction of the RSG phase.


title: WeakSATD: Detecting Weak Self-admitted Technical Debt

abstract:
  Speeding up development may produce technical debt, i.e., not-quite-right
code for which the effort to make it right increases with time as a sort of
interest. Developers may be aware of the debt as they admit it in their code
comments. Literature reports that such a self-admitted technical debt survives
for a long time in a program, but it is not yet clear its impact on the quality
of the code in the long term. We argue that self-admitted technical debt
contains a number of different weaknesses that may affect the security of a
program. Therefore, the longer a debt is not paid back the higher is the risk
that the weaknesses can be exploited. To discuss our claim and rise the
developers' awareness of the vulnerability of the self-admitted technical debt
that is not paid back, we explore the self-admitted technical debt in the
Chromium C-code to detect any known weaknesses. In this preliminary study, we
first mine the Common Weakness Enumeration repository to define heuristics for
the automatic detection and fix of weak code. Then, we parse the C-code to find
self-admitted technical debt and the code block it refers to. Finally, we use
the heuristics to find weak code snippets associated to self-admitted technical
debt and recommend their potential mitigation to developers. Such knowledge can
be used to prioritize self-admitted technical debt for repair. A prototype has
been developed and applied to the Chromium code. Initial findings report that
55\% of self-admitted technical debt code contains weak code of 14 different
types.


title:
Semi-Supervised Cascaded Clustering for Classification of Noisy Label
  Data

abstract:
  The performance of supervised classification techniques often deteriorates
when the data has noisy labels. Even the semi-supervised classification
approaches have largely focused only on the problem of handling missing labels.
Most of the approaches addressing the noisy label data rely on deep neural
networks (DNN) that require huge datasets for classification tasks. This poses
a serious challenge especially in process and manufacturing industries, where
the data is limited and labels are noisy. We propose a semi-supervised cascaded
clustering (SSCC) algorithm to extract patterns and generate a cascaded tree of
classes in such datasets. A novel cluster evaluation matrix (CEM) with
configurable hyperparameters is introduced to localize and eliminate the noisy
labels and invoke a pruning criterion on cascaded clustering. The algorithm
reduces the dependency on expensive human expertise for assessing the accuracy
of labels. A classifier generated based on SSCC is found to be accurate and
consistent even when trained on noisy label datasets. It performed better in
comparison with the support vector machines (SVM) when tested on multiple
noisy-label datasets, including an industrial dataset. The proposed approach
can be effectively used for deriving actionable insights in industrial settings
with minimal human expertise.


title: Symmetric Layer-Rainbow Colorations of Cubes

abstract:
  Can we color the $n^3$ cells of an $n\times n\times n$ cube $L$ with $n^2$
colors in such a way that each layer parallel to each face contains each color
exactly once and that the coloring is symmetric so that $L_{ij\ell}=L_{j\ell
i}=L_{\ell ij}$ for distinct $i,j,\ell \in \{1,\dots,n\}$, and $L_{iij}=L_{jj
i}, L_{iji}=L_{jij}, L_{ij j}=L_{jii}$ for $i,j\in \{1,\dots,n\}$?
  Using transportation networks, we show that such a coloring is possible if
and only if $n\equiv 0,2 \mod 3$ (with two exceptions, $n=1$ and $n\neq 3$).
Motivated by the designs of experiments, the study of these objects (without
symmetry) was initiated by Kishen and Fisher in the 1940's. These objects are
also closely related to orthogonal arrays whose existence has been extensively
investigated, and they are natural three-dimensional analogues of symmetric
latin squares.


title: User-Centric Gender Rewriting

abstract:
  In this paper, we define the task of gender rewriting in contexts involving
two users (I and/or You) - first and second grammatical persons with
independent grammatical gender preferences. We focus on Arabic, a
gender-marking morphologically rich language. We develop a multi-step system
that combines the positive aspects of both rule-based and neural rewriting
models. Our results successfully demonstrate the viability of this approach on
a recently created corpus for Arabic gender rewriting, achieving 88.42 M2 F0.5
on a blind test set. Our proposed system improves over previous work on the
first-person-only version of this task, by 3.05 absolute increase in M2 F0.5.
We demonstrate a use case of our gender rewriting system by using it to
post-edit the output of a commercial MT system to provide personalized outputs
based on the users' grammatical gender preferences. We make our code, data, and
models publicly available.


title: Quantum Computing Approaches for Mission Covering Optimization

abstract:
  We study quantum computing algorithms for solving certain constrained
resource allocation problems we coin as Mission Covering Optimization (MCO). We
compare formulations of constrained optimization problems using Quantum
Annealing techniques and the Quantum Alternating Operator Ansatz (Hadfield et
al. arXiv:1709.03489v2, a generalized algorithm of the Quantum Approximate
Optimization Algorithm, Farhi et al. arXiv:1411.4028v1) on D-Wave and IBM
machines respectively using the following metrics: cost, timing, constraints
held, and qubits used. We provide results from two different MCO scenarios and
analyze results.


title:
Results from The COPAINS Pilot Survey: four new brown dwarfs and a high
  companion detection rate for accelerating stars

abstract:
  The last decade of direct imaging (DI) searches for sub-stellar companions
has uncovered a widely diverse sample that challenges the current formation
models, while highlighting the intrinsically low occurrence rate of wide
companions, especially at the lower end of the mass distribution. These results
clearly show how blind surveys, crucial to constrain the underlying planet and
sub-stellar companion population, are not an efficient way to increase the
sample of DI companions. It is therefore becoming clear that efficient target
selection methods are essential to ensure a larger number of detections. We
present the results of the COPAINS Survey conducted with SPHERE/VLT, searching
for sub-stellar companions to stars showing significant proper motion
differences (Delta mu) between different astrometric catalogues. We observed
twenty-five stars and detected ten companions, including four new brown dwarfs:
HIP 21152 B, HIP 29724 B, HD 60584 B and HIP 63734 B. Our results clearly
demonstrates how astrometric signatures, in the past only giving access to
stellar companions, can now thanks to Gaia reveal companions well in the
sub-stellar regime. We also introduce FORECAST (Finley Optimised REtrieval of
Companions of Accelerating STars), a tool which allows to check the agreement
between position and mass of the detected companions with the measured Delta
mu. Given the agreement between the values of the masses of the new sub-stellar
companions from the photometry with the model-independent ones obtained with
FORECAST, the results of COPAINS represent a significant increase of the number
of potential benchmarks for brown dwarf and planet formation and evolution
theories.


title:
Universal subdiffusive behavior at band edges from transfer matrix
  exceptional points

abstract:
  Understanding symmetries and transitions in non-Hermitian matrices and their
physical consequences is of tremendous interest in the context of open quantum
systems. The spectrum of one dimensional tight-binding chain with periodic
on-site potential can be obtained by casting the problem in terms of $2 \times
2$ transfer matrices. We find that these non-Hermitian matrices have a symmetry
akin to parity-time symmetry, and hence show transitions across exceptional
points. The exceptional points of the transfer matrix of a unit cell correspond
to the band edges of the spectrum. When connected to two zero temperature baths
at two ends, this consequently leads to subdiffusive scaling of conductance
with system size, with an exponent $2$, if the chemical potential of the baths
are equal to the band edges. We further demonstrate the existence of a
dissipative quantum phase transition as the chemical potential is tuned across
any band edge. Remarkably, this feature is analogous to transition across a
mobility edge in quasi-periodic systems. This behavior is universal,
irrespective of the details of the periodic potential and the number of bands
of the underlying lattice. It, however, has no analog in absence of the baths.

categories: quant-ph cond-mat.dis-nn cond-mat.mes-hall cond-mat.stat-mech


title: FEDNEST: Federated Bilevel, Minimax, and Compositional Optimization

abstract:
  Standard federated optimization methods successfully apply to stochastic
problems with \textit{single-level} structure. However, many contemporary ML
problems -- including adversarial robustness, hyperparameter tuning, and
actor-critic -- fall under nested bilevel programming that subsumes minimax and
compositional optimization. In this work, we propose FedNest: A federated
alternating stochastic gradient method to address general nested problems. We
establish provable convergence rates for FedNest in the presence of
heterogeneous data and introduce variations for bilevel, minimax, and
compositional optimization. FedNest introduces multiple innovations including
federated hypergradient computation and variance reduction to address
inner-level heterogeneity. We complement our theory with experiments on
hyperparameter \& hyper-representation learning and minimax optimization that
demonstrate the benefits of our method in practice. Code is available at
https://github.com/mc-nya/FedNest.


title:
The Extremal GDoF Gain of Optimal versus Binary Power Control in $K$
  User Interference Networks Is $\Theta(\sqrt{K})$

abstract:
  Using ideas from Generalized Degrees of Freedom (GDoF) analyses and extremal
network theory, this work studies the extremal gain of optimal power control
over binary power control, especially in large interference networks, in search
of new theoretical insights. Whereas numerical studies have established that in
most practical settings binary power control is close to optimal, the extremal
analysis shows not only there exist settings where the gain from optimal power
control can be quite significant, but also bounds the extremal values of such
gains from a GDoF perspective. As its main contribution, we explicitly
characterizes the extremal GDoF gain of optimal over binary power control as
$\Theta(\sqrt{K})$ for all $K$. In particular, the extremal gain is bounded
between $\lfloor \sqrt{K}\rfloor$ and $2.5\sqrt{K}$ for every $K$. For
$K=2,3,4,5,6$ users, the precise extremal gain is $1, 3/2, 2, 9/4$ and $41/16$,
respectively. Networks shown to achieve the extremal gain may be interpreted as
multi-tier heterogeneous networks. It is worthwhile to note that because of
their focus on asymptotic analysis, the sharp characterizations of extremal
gains are valuable primarily from a theoretical perspective, and not as
contradictions to the conventional wisdom that binary power control is
generally close to optimal in practical, non-asymptotic settings.


title:
$W$ boson mass, dark matter and $(g-2)_\ell$ in ScotoZee neutrino mass
  model

abstract:
  We present a model of radiative neutrino masses, Scotogenic model with a
singly charged scalar (ScotoZee model), which resolves recently reported
deviations in $W$ boson mass as well as lepton $g-2$, and naturally admits a
scalar or a fermionc dark matter. We find that the mass splitting, of $\sim
100$ GeV among the inert doublets fields, required by the shift in $W$ boson
mass can be evaded by introducing a singlet scalar, which also is a key to
resolving $(g-2)_\mu$ (also, $(g-2)_e$) anomaly with in $1\sigma$. We show
consistency of this framework with dark matter relic abundance, while
satisfying constraints from charged lepton flavor violation, direct detection
as well as collider constraints. We show that the model gives predictions for
the lepton flavor violating processes testable in upcoming experiments.


title: Thinned Germanium Substrates for III-V Multijunction Solar Cells

abstract:
  Multijunction solar cells are usually grown on Ge substrates. This implies
several disadvantages that hinder the performance of the whole multijunction
and limit their possible applications. The drawbacks caused by the substrate
are: heavier devices, higher operation temperatures, lower performance and lack
of photon confinement. In this work we propose thinning the substrate as a
valid solution to the aforementioned challenges. The influence of the substrate
thickness on the Ge subcell performance inside a multijunction is simulated
using 2D TCAD tools. Simulation results point to the back surface recombination
as the key parameter to enhance the development of thinned Ge subcells. Ge
substrates have been thinned down, achieving 115{\mu}m thick samples. Finally,
solar cells have been manufactured out of the thinned substrates proving a
limited degradation and showing the feasibility of this process to manufacture
Ge subcells thinned down up to 115{\mu}m.


title: When does a particle arrive?

abstract:
  We compare the different proposals that have appeared in the literature to
describe a measurement of the time of arrival of a quantum particle at a
detector. We show that there are multiple regimes where different proposals
give inequivalent, experimentally discriminable, predictions. This analysis
paves the way for future experimental tests.


title: Chasing Streams with Existential Rules

abstract:
  We study reasoning with existential rules to perform query answering over
streams of data. On static databases, this problem has been widely studied, but
its extension to rapidly changing data has not yet been considered. To bridge
this gap, we extend LARS, a well-known framework for rule-based stream
reasoning, to support existential rules. For that, we show how to translate
LARS with existentials into a semantics-preserving set of existential rules. As
query answering with such rules is undecidable in general, we describe how to
leverage the temporal nature of streams and present suitable notions of
acyclicity that ensure decidability.


title:
Nonequilibrium symmetry-protected topological order: emergence of
  semilocal Gibbs ensembles

abstract:
  We consider nonequilibrium time evolution in quantum spin chains after a
global quench. We show that global symmetries can invalidate the standard
picture of local relaxation to a (generalised) Gibbs ensemble and provide a
solution to the problem. We introduce in particular a family of statistical
ensembles, which we dub "semilocal (generalised) Gibbs ensembles". The issue
arises when the Hamiltonian possesses conservation laws that are not
(pseudo)local but act as such in the symmetry-restricted space where time
evolution occurs. Because of them, the stationary state emerging at infinite
time can exhibit exceptional features. We focus on a specific example with a
spin-flip symmetry, which is the commonest global symmetry encountered in
spin-$1/2$ chains. Among the exceptional properties, we find that, at late
times, the excess of entropy of a spin block triggered by a local perturbation
in the initial state grows logarithmically with the subsystem's length. We
establish a connection with symmetry-protected topological order in equilibrium
at zero temperature and study the melting of the order induced either by a
(symmetry-breaking) rotation of the initial state or by an increase of the
temperature.

categories: cond-mat.stat-mech math-ph math.MP quant-ph


title:
COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked
  Vehicles

abstract:
  Optical sensors and learning algorithms for autonomous vehicles have
dramatically advanced in the past few years. Nonetheless, the reliability of
today's autonomous vehicles is hindered by the limited line-of-sight sensing
capability and the brittleness of data-driven methods in handling extreme
situations. With recent developments of telecommunication technologies,
cooperative perception with vehicle-to-vehicle communications has become a
promising paradigm to enhance autonomous driving in dangerous or emergency
situations. We introduce COOPERNAUT, an end-to-end learning model that uses
cross-vehicle perception for vision-based cooperative driving. Our model
encodes LiDAR information into compact point-based representations that can be
transmitted as messages between vehicles via realistic wireless channels. To
evaluate our model, we develop AutoCastSim, a network-augmented driving
simulation framework with example accident-prone scenarios. Our experiments on
AutoCastSim suggest that our cooperative perception driving models lead to a
40% improvement in average success rate over egocentric driving models in these
challenging driving situations and a 5 times smaller bandwidth requirement than
prior work V2VNet. COOPERNAUT and AUTOCASTSIM are available at
https://ut-austin-rpl.github.io/Coopernaut/.


title:
Semi-supervised learning approaches for predicting South African
  political sentiment for local government elections

abstract:
  This study aims to understand the South African political context by
analysing the sentiments shared on Twitter during the local government
elections. An emphasis on the analysis was placed on understanding the
discussions led around four predominant political parties ANC, DA, EFF and
ActionSA. A semi-supervised approach by means of a graph-based technique to
label the vast accessible Twitter data for the classification of tweets into
negative and positive sentiment was used. The tweets expressing negative
sentiment were further analysed through latent topic extraction to uncover
hidden topics of concern associated with each of the political parties. Our
findings demonstrated that the general sentiment across South African Twitter
users is negative towards all four predominant parties with the worst negative
sentiment among users projected towards the current ruling party, ANC, relating
to concerns cantered around corruption, incompetence and loadshedding.


title:
Matched Design for Marginal Causal Effect on Restricted Mean Survival
  Time in Observational Studies

abstract:
  Investigating the causal relationship between exposure and the time-to-event
outcome is an important topic in biomedical research. Previous literature has
discussed the potential issues of using the hazard ratio as a marginal causal
effect measure due to its noncollapsibility property. In this paper, we
advocate using the restricted mean survival time (RMST) difference as the
marginal causal effect measure, which is collapsible and has a simple
interpretation as the difference of area under survival curves over a certain
time horizon. To address both measured and unmeasured confounding, a matched
design with sensitivity analysis is proposed. Matching is used to pair similar
treated and untreated subjects together, which is more robust to outcome model
misspecification. Our propensity score matched RMST difference estimator is
shown to be asymptotically unbiased and the corresponding variance estimator is
calculated by accounting for the correlation due to matching. The simulation
study also demonstrates that our method has adequate empirical performance and
outperforms many competing methods used in practice. To assess the impact of
unmeasured confounding, we develop a sensitivity analysis strategy by adapting
the E-value approach to matched data. We apply the proposed method to the
Atherosclerosis Risk in Communities Study (ARIC) to examine the causal effect
of smoking on stroke-free survival.


title:
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised
  Relation Extraction

abstract:
  Unsupervised relation extraction aims to extract the relationship between
entities from natural language sentences without prior information on
relational scope or distribution. Existing works either utilize self-supervised
schemes to refine relational feature signals by iteratively leveraging adaptive
clustering and classification that provoke gradual drift problems, or adopt
instance-wise contrastive learning which unreasonably pushes apart those
sentence pairs that are semantically similar. To overcome these defects, we
propose a novel contrastive learning framework named HiURE, which has the
capability to derive hierarchical signals from relational feature space using
cross hierarchy attention and effectively optimize relation representation of
sentences under exemplar-wise contrastive learning. Experimental results on two
public datasets demonstrate the advanced effectiveness and robustness of HiURE
on unsupervised relation extraction when compared with state-of-the-art models.


title: Density functions of periodic sequences

abstract:
  Periodic point sets model all solid crystalline materials whose structures
are determined in a rigid form and should be studied up to rigid motion or
isometry preserving inter-point distances. In 2021 H.Edelsbrunner et al.
introduced an infinite sequence of density functions that are continuous
isometry invariants of periodic point sets. These density functions turned out
to be highly non-trivial even in dimension 1 for periodic sequences of points
in the line. This paper fully describes the density functions of any periodic
sequence and their symmetry properties. The explicit description theoretically
confirms coincidences of density functions that were previously computed only
through finite samples.


title:
Virtual and Augmented Reality-Based Assistive Interfaces for Upper-limb
  Prosthesis Control and Rehabilitation

abstract:
  Functional upper-limb prosthetic training can improve users performance in
controlling prostheses and has been incorporated into occupational therapy for
individuals in need. In recent years, virtual reality (VR) and augmented
reality (AR) technologies have been shown to be promising avenues to improve
the convenience of rehabilitative prosthesis training systems. However, it is
uncertain if the comprehensive efficacy and effectiveness of VR or AR assistive
tools are adequate compared to conventional prosthetic tools and if not,
whether enhancements can be made through incorporation of other technical
paradigms.
  This work first presents a mixed reality system we developed for prosthesis
control and training. Five able-bodied subjects are involved to perform
three-dimensional object manipulation tasks in analogous AR and VR
environments. Multiple evaluation metrics are applied to assess subjects
performances within the two paradigms. Based on the comparative analysis, we
find that VR-based environment promotes more efficient motion along with higher
task completion rate and path efficiency while AR paradigm allows subjects to
perform motor tasks with shorter time consumed. Another study is conducted to
evaluate the efficiency and feasibility of AR-facilitated prosthesis control
system compared to that in real-world and if any technical additions can be
applied to improve the AR-based system. Three able-bodied subjects were engaged
in the experiment to perform object manipulation tasks in a) physical
environment, b) AR-without-bypass environment, and c) AR-with-bypass
environment. Based on the results obtained from the assessment, we conclude
that while our AR-based system modestly lags behind the effectiveness of
physical systems, the study conducted using a bypass prosthesis suggests that
AR system has the potential to improve the efficacy of prosthesis control.


title:
Comment on: High Mixing Entropy Enhanced Energy States in Metallic
  Glasses

abstract:
  A recent paper by Juntao Huo et al [Chin. Phys. Lett. 39 (2022) 0464011]
reported a correlation between the entropy of mixing (and the corresponding
energy state) and the thermal stability and mechanical parameters, for three
Zr-Ti-Cu-Ni-Be metallic glasses (MG) including a high-entropy one (HEMG). The
authors dismissed a possible compositional contribution to the variations of
the thermal stability and mechanical parameters studied.


title:
Weak Deflection Angle and Greybody Bound of Magnetized Regular Black
  Hole

abstract:
  In this paper, we examine the weak deflection angle and greybody bound for
magnetized regular black hole.$~$ For this purpose, we apply the Gauss-Bonnet
theorem on the black hole and obtain the deflection angle in plasma and
non-plasma mediums.$~$ Moreover, we investigate graphically the effect of
impact parameter on the deflection angle for regular black hole in both
mediums. We examine that deflection angle goes to infinity when the impact
parameter approaches to zero. We also observe that deflection angle shows
negative behaviour at $q=0.6$ and $q=2.09$ but at $0.6<q<2.09$, angle shows the
positive behaviour. Furthermore, we study the rigorous bound phenomenon of the
greybody factor in the background for magnetized regular black hole. Later, we
analyze the graphical behaviour of greybody bound with respect to different
values of $\omega$ and observe that at small values of $\omega$, bound is
increasing but for large values, bound is decreasing. Afterthat we examine that
when we put $G=1$, $l=0$ and $q=0$, then all results for magnetized regular
black hole solution reduces into results of the Schwarzschild black hole
solution.


title:
A Hybrid Classical-Quantum framework for solving Free Boundary Value
  Problems and Applications in Modeling Electric Contact Phenomena

abstract:
  In this paper we elaborate a hybrid classical-quantum framework which allows
one to model and solve heat and mass transfer problems occurring in electric
contacts. We utilize special functions and Harrow-Hassidim-Lloyd (HHL) quantum
algorithm for finding temperature and arc flux functions exactly and
approximately for the Stefan type problems. The Stefan type problems we are
considering are based on the Generalized Heat Equation with free boundaries. As
examples we consider exact and approximate solutions of inverse one-phase and
two-phase Stefan problems. An Inverse Generalized One-Phase Stefan Problem is
considered as a model problem. Computational experiments were conducted and
demonstrated on IBM Quantum Machine.


title: A novel way to metric of higher dimensional rotating black holes

abstract:
  We wish to carry forward to higher dimensions the insightful and novel method
of obtaining the Kerr metric proposed by one of us in \cite{Dadhich} for
deriving Myers-Perry rotating black hole metric. We begin with a flat spacetime
metric written in oblate spheroidal coordinates (ellipsoidal geometry)
appropriate for inclusion of rotation, and then introduce arbitrary functions
to bring in gravitational potential due to mass which are then determined by
requiring that massless particle experiences no acceleration while massive
particle feels the Newtonian acceleration at large $r$. We have further
generalized the method to include the cosmological constant $\Lambda$ to obtain
the Myers-Perry-dS/AdS black hole metric.


title: Minimum Cost Intervention Design for Causal Effect Identification

abstract:
  Pearl's do calculus is a complete axiomatic approach to learn the
identifiable causal effects from observational data. When such an effect is not
identifiable, it is necessary to perform a collection of often costly
interventions in the system to learn the causal effect. In this work, we
consider the problem of designing the collection of interventions with the
minimum cost to identify the desired effect. First, we prove that this problem
is NP-hard, and subsequently propose an algorithm that can either find the
optimal solution or a logarithmic-factor approximation of it. This is done by
establishing a connection between our problem and the minimum hitting set
problem. Additionally, we propose several polynomial-time heuristic algorithms
to tackle the computational complexity of the problem. Although these
algorithms could potentially stumble on sub-optimal solutions, our simulations
show that they achieve small regrets on random graphs.


title:
Near-Horizon Quantum Dynamics of 4-d Einstein Gravity from 2-d JT
  Gravity

abstract:
  We study quantum fluctuations in the lightcone metric of the 4-d
Einstein-Hilbert action via dimensional reduction to Jackiw-Teitelboim (JT)
gravity. In particular, we show that, in Einstein gravity, the causal
development of a region in flat Minkowski spacetime, near a horizon defined by
light sheets, can be described by an effective two-dimensional dilaton theory.
This enables us to make use of known solutions of the JT action, where the
spacetime position of a horizon has quantum uncertainty due to metric
fluctuations. This quantum uncertainty can be then directly related to the
original 4-d light cone coordinates, allowing us to compute the uncertainty in
the time of a photon to travel from tip-to-tip of a causal diamond in flat 4-d
Minkowski space. We find that both Planck and infrared scales (with the latter
set by the size of the causal diamond) enter the uncertainty in photon travel
time, such that the quantum fluctuation in the arrival time may be observably
large.


title: Neutrino Portals, Terrestrial Upscattering, and Atmospheric Neutrinos

abstract:
  We consider the upscattering of atmospheric neutrinos in the interior of the
Earth producing heavy neutral leptons (HNLs) which subsequently decay inside
large volume detectors (e.g. Super-Kamiokande or DUNE). We compute the flux of
upscattered HNLs arriving at a detector, and the resultant event rate of
visible decay products. Using Super-Kamiokande's atmospheric neutrino dataset
we find new leading constraints for dipole couplings to any flavor with HNL
masses between roughly 10 MeV and 100 MeV. For mass mixing with tau neutrinos,
we probe new parameter space near HNL masses of $\sim 20$ MeV with prospects
for substantial future improvements. We also discuss prospects at future
experiments such as DUNE, JUNO, and Hyper-Kamiokande.


title: Two-Higgs-Doublet Model and Quark-Lepton Unification

abstract:
  We study the Two-Higgs-Doublet Model predicted in the minimal theory for
quark-lepton unification that can describe physics at the low scale. We discuss
the relations among the different decay widths of the new Higgs bosons and
study their phenomenology at the Large Hadron Collider. As a result of matter
unification, this theory predicts a correlation between the decay widths of the
heavy Higgs bosons into tau leptons and bottom quarks. We point out how to
probe this theory using these relations and discuss the relevant flavor
constraints.


title: Constraining the Progenitor System of the Type Ia Supernova 2021aefx

abstract:
  We present high-cadence optical and ultraviolet light curves of the normal
Type Ia supernova (SN) 2021aefx, which shows an early bump during the first two
days of observation. This bump may be a signature of interaction between the
exploding white dwarf and a nondegenerate binary companion, or it may be
intrinsic to the white dwarf explosion mechanism. In the case of the former,
the short duration of the bump implies a relatively compact main-sequence
companion star, although this conclusion is viewing-angle dependent. Our
best-fit companion-shocking and double-detonation models both overpredict the
UV luminosity during the bump, and existing nickel-shell models do not match
the strength and timescale of the bump. We also present nebular spectra of SN
2021aefx, which do not show the hydrogen or helium emission expected from a
nondegenerate companion, as well as a radio nondetection that rules out all
symbiotic progenitor systems and most accretion disk winds. Our analysis places
strong but conflicting constraints on the progenitor of SN 2021aefx; no current
model can explain all of our observations.


title:
PanScales parton showers for hadron collisions: formulation and
  fixed-order studies

abstract:
  We formulate PanScales parton showers for hadron collisions so as to achieve
next-to-leading logarithmic (NLL) accuracy across a broad set of observables.
We do so specifically for colour singlet production. Relative to the existing
PanScales final-state showers, the main new question is that of how to
redistribute momentum imbalances from initial-state branching across the
remainder of the event. We presents tests of the showers at fixed order,
including the treatment of full colour for soft-collinear emissions and of spin
correlations in both the soft and collinear domains. We also include
comparisons to a formulation of a standard dipole shower, the current
leading-logarithmic state of the art. A forthcoming companion paper will
explore all-order tests of the new showers.


title:
The THESAN project: ionizing escape fractions of reionization-era
  galaxies

abstract:
  A fundamental requirement for reionizing the Universe is that a sufficient
fraction of the ionizing photons emitted by galaxies successfully escapes into
the intergalactic medium. However, due to the scarcity of high-redshift
observational data, the sources driving reionization remain uncertain. In this
work we calculate the ionizing escape fractions ($f_{\rm esc}$) of
reionization-era galaxies from the state-of-the-art THESAN simulations, which
combine an accurate radiation-hydrodynamic solver AREPO-RT with the well-tested
IllustrisTNG galaxy formation model to self-consistently simulate both
small-scale galaxy physics and large-scale reionization throughout a large
patch of the universe ($L_{\rm box} = 95.5\,\rm cMpc$). This allows the
formation of numerous massive haloes ($M_{\rm halo} \gtrsim 10^{10}\,{\rm
M_{\odot}}$), which are often statistically underrepresented in previous
studies but are believed to be important to achieve rapid reionization. We find
that low-mass galaxies ($M_{\rm stars} \lesssim 10^7\,{\rm M_{\odot}}$) are the
main drivers of reionization above $z \gtrsim 7$, while high-mass galaxies
($M_{\rm stars} \gtrsim 10^8\,{\rm M_{\odot}}$) dominate the escaped ionizing
photon budget at lower redshifts. The variation in halo escape fractions
decreases for higher-mass haloes, which can be understood from the more settled
galactic structure, SFR stability, and fraction of sightlines within each halo
significantly contributing to the escaped flux. We show that dust is capable of
reducing the escape fractions of massive galaxies, but the impact on the global
$f_{\rm esc}$ depends on the dust model. Finally, AGN are unimportant for
reionization in THESAN and their escape fractions are lower than stellar ones
due to being located near the centres of galaxy gravitational potential wells.


title:
The ASAS-SN Catalog of Variable Stars X: Discovery of 116,000 New
  Variable Stars Using g-band Photometry

abstract:
  The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical
survey to monitor the entire sky, currently with a cadence of $\lesssim 24$
hours down to $g \lesssim 18.5$ mag. ASAS-SN has routinely operated since 2013,
collecting $\sim$ 2,000 to over 7,500 epochs of $V$ and $g-$band observations
per field to date. This work illustrates the first analysis of ASAS-SN's newer,
deeper, higher cadence $g-$band data. From an input source list of ${\sim}55$
million isolated sources with $g<18$~mag, we identified $1.5\times10^6$
variable star candidates using a random forest classifier trained on features
derived from $\textit{Gaia}$, 2MASS, and AllWISE. Using ASAS-SN $g-$band light
curves, and an updated random forest classifier augmented with data from
Citizen ASAS-SN, we classified the candidate variables into 8 broad variability
types. We present a catalog of ${\sim}116,000$ new variable stars with high
classification probabilities, including ${\sim}111,000$ periodic variables and
${\sim}5,000$ irregular variables. We also recovered ${\sim}263,000$ known
variable stars.


title: A Celestial Matrix Model

abstract:
  We construct a Hermitian random matrix model that provides a stable
non-perturbative completion of Cangemi-Jackiw (CJ) gravity, a two-dimensional
theory of flat spacetimes. The matrix model reproduces, to all orders in the
topological expansion, the Euclidean partition function of CJ gravity with an
arbitrary number of boundaries. The non-perturbative completion enables the
exact computation of observables in flat space quantum gravity which we use to
explicitly characterize the Bondi Hamiltonian spectrum. We discuss the
implications of our results for the flat space S-matrix and black holes.


title:
On-sky reconstruction of Keck Primary Mirror Piston Offsets using a
  Zernike Wavefront Sensor

abstract:
  The next generation of large ground- and space-based optical telescopes will
have segmented primary mirrors. Co-phasing the segments requires a sensitive
wavefront sensor capable of measuring phase discontinuities. The Zernike
wavefront sensor (ZWFS) is a passive wavefront sensor that has been
demonstrated to sense segmented-mirror piston, tip, and tilt with picometer
precision in laboratory settings. We present the first on-sky results of an
adaptive optics fed ZWFS on a segmented aperture telescope, W.M. Keck
Observatory's Keck II. Within the Keck Planet Imager and Characterizer (KPIC)
light path, the ZWFS mask operates in the H-band using an InGaAs detector
(CRED2). We piston segments of the primary mirror by a known amount and measure
the mirror's shape using both the ZWFS and a phase retrieval method on data
acquired with the facility infrared imager, NIRC2. In the latter case, we
employ slightly defocused NIRC2 images and a modified Gerchberg-Saxton phase
retrieval algorithm to estimate the applied wavefront error. We find good
agreement when comparing the phase retrieval and ZWFS reconstructions, with
average measurements of 408 +/- 23 nm and 394 +/- 46 nm, respectively, for
three segments pistoned by 400 nm of optical path difference (OPD). Applying
various OPDs, we are limited to 100 nm OPD of applied piston due to our
observations' insufficient averaging of adaptive optics residuals. We also
present simulations of the ZWFS that help explain the systematic offset
observed in the ZWFS reconstructed data.


title:
The Four-Loop Rapidity Anomalous Dimension and Event Shapes to Fourth
  Logarithmic Order

abstract:
  We obtain the quark and gluon rapidity anomalous dimension to fourth order in
QCD. We calculate the N$^3$LO rapidity anomalous dimensions to higher order in
the dimensional regulator and make use of the soft/rapidity anomalous dimension
correspondence in conjunction with the recent determination of the N$^4$LO
threshold anomalous dimensions to achieve our result. We show that the results
for the quark and gluon rapidity anomalous dimensions at four loops are related
by generalized Casimir scaling. Using the N$^4$LO rapidity anomalous dimension,
we perform the resummation of the Energy-Energy Correlation in the back-to-back
limit at N$^4$LL, achieving for the first time the resummation of an event
shape at this logarithmic order. We present numerical results and observe a
reduction of perturbative uncertainties on the resummed cross section to below
1%.


title:
Cosmological simulations with rare and frequent dark matter
  self-interactions

abstract:
  Dark matter (DM) with self-interactions is a promising solution for the
small-scale problems of the standard cosmological model. Here we perform the
first cosmological simulation of frequent DM self-interactions, corresponding
to small-angle DM scatterings. The focus of our analysis lies in finding and
understanding differences to the traditionally assumed rare DM (large-angle)
self scatterings. For this purpose, we compute the distribution of DM
densities, the matter power spectrum, the two-point correlation function and
the halo and subhalo mass functions. Furthermore, we investigate the density
profiles of the DM haloes and their shapes. We find that overall large-angle
and small-angle scatterings behave fairly similarly with a few exceptions. In
particular, the number of satellites is considerably suppressed for frequent
compared to rare self-interactions with the same cross-section. Overall we
observe that while differences between the two cases may be difficult to
establish using a single measure, the degeneracy may be broken through a
combination of multiple ones. For instance, the combination of satellite counts
with halo density or shape profiles could allow discriminating between rare and
frequent self-interactions. As a by-product of our analysis, we provide - for
the first time - upper limits on the cross-section for frequent
self-interactions.


title:
Deep Potential: Recovering the gravitational potential from a snapshot
  of phase space

abstract:
  One of the major goals of the field of Milky Way dynamics is to recover the
gravitational potential field. Mapping the potential would allow us to
determine the spatial distribution of matter - both baryonic and dark -
throughout the Galaxy. We present a novel method for determining the
gravitational field from a snapshot of the phase-space positions of stars,
based only on minimal physical assumptions, which makes use of recently
developed tools from the field of deep learning. We first train a normalizing
flow on a sample of observed six-dimensional phase-space coordinates of stars,
obtaining a smooth, differentiable approximation of the distribution function.
Using the Collisionless Boltzmann Equation, we then find the gravitational
potential - represented by a feed-forward neural network - that renders this
distribution function stationary. This method, which we term "Deep Potential,"
is more flexible than previous parametric methods, which fit restricted classes
of analytic models of the distribution function and potential to the data. We
demonstrate Deep Potential on mock datasets, and demonstrate its robustness
under various non-ideal conditions. Deep Potential is a promising approach to
mapping the density of the Milky Way and other stellar systems, using rich
datasets of stellar positions and kinematics now being provided by Gaia and
ground-based spectroscopic surveys.


title: The halo finding problem revisited: a deep revision of the ASOHF code

abstract:
  Context. New-generation cosmological simulations are providing huge amounts
of data, whose analysis becomes itself a cutting-edge computational problem. In
particular, the identification of gravitationally bound structures, known as
halo finding, is one of the main analyses. A handful of codes developed to
tackle this task have been presented during the last years.
  Aims. We present a deep revision of the already existing code ASOHF. The
algorithm has been throughfully redesigned in order to improve its capabilities
to find bound structures and substructures, both using dark matter particles
and stars, its parallel performance, and its abilities to handle simulation
outputs with vast amounts of particles. This upgraded version of ASOHF is
conceived to be a publicly available tool.
  Methods. A battery of idealised and realistic tests are presented in order to
assess the performance of the new version of the halo finder.
  Results. In the idealised tests, ASOHF produces excellent results, being able
to find virtually all the structures and substructures placed within the
computational domain. When applied to realistic data from simulations, the
performance of our finder is fully consistent with the results from other
commonly used halo finders, with remarkable performance in substructure
detection. Besides, ASOHF turns out to be extremely efficient in terms of
computational cost.
  Conclusions. We present a public, deeply revised version of the ASOHF halo
finder. The new version of the code produces remarkable results finding haloes
and subhaloes in cosmological simulations, with an excellent parallel
performance and with a contained computational cost.


title:
Tuning between continuous time crystals and many-body scars in
  long-range XYZ spin chains

abstract:
  Persistent oscillatory dynamics in non-equilibrium many-body systems is a
tantalizing manifestation of ergodicity breakdown that continues to attract
much attention. Recent works have focused on two classes of such systems:
discrete time crystals and quantum many-body scars (QMBS). While both systems
host oscillatory dynamics, its origin is expected to be fundamentally
different: discrete time crystal is a phase of matter which spontaneously
breaks the $\mathbb{Z}_2$ symmetry of the external periodic drive, while QMBS
span a subspace of non-thermalizing eigenstates forming an su(2) algebra
representation. Here we ask a basic question: is there a physical system that
allows to tune between these two dynamical phenomena? In contrast to much
previous work, we investigate the possibility of a \emph{continuous} time
crystal (CTC) in undriven, energy-conserving systems exhibiting
prethermalization. We introduce a long-range XYZ spin model and show that it
encompasses both a CTC phase as well as QMBS. We map out the dynamical phase
diagram using numerical simulations based on exact diagonalization and
time-dependent variational principle in the thermodynamic limit. We identify a
regime where QMBS and CTC order co-exist, and we discuss experimental protocols
that reveal their similarities as well as key differences.

categories: cond-mat.str-el cond-mat.quant-gas cond-mat.stat-mech quant-ph


title: Magic of the ground state of the transverse field Ising model

abstract:
  Ground states of quantum many-body systems are both entangled and possess a
kind of quantum complexity as their preparation requires universal resources
that go beyond the Clifford group and stabilizer states. These resources -
sometimes dubbed as magic - are also the crucial ingredient for quantum
advantage. We study the behavior of the stabilizer R\'enyi entropy in the
integrable transverse field Ising spin chain. We show that the locality of
interactions results in a localized stabilizer R\'enyi entropy in the gapped
phase thus making this quantity computable in terms of local quantities in the
gapped phase, while measurements involving $L$ spins are necessary at the
critical point to obtain an error scaling with $O(L^{-1})$.


title: Anomalies from an effective field theory perspective

abstract:
  The path-integral measure of a gauge-invariant fermion theory is transformed
under the chiral transformation and leads to an elegant derivation of the
anomalous chiral Ward-Takahashi identities, as we know from the seminal work of
Fujikawa. We present in this work an alternative and illuminating way to
calculate the Jacobian in the path-integral measure from the Covariant
Derivative Expansion technique used in Effective Field Theory. We present
several ways to customize the crucial regularization such that the anomaly is
located in the desired current, which is unprecedented within the path integral
approach. We are then able to derive, in a transparent and unified way the
covariant, consistent, gravitational and scale anomalies.


title: The Four Loop QCD Rapidity Anomalous Dimension

abstract:
  The rapidity anomalous dimension controls the scaling of transverse momentum
dependent observables in the Sudakov region. In a conformal theory it is
equivalent to the soft anomalous dimension, but in QCD this relation is broken
by anomalous terms proportional to the $\beta$-function. In this paper we first
give a simple proof of this relation using two different representations of the
energy-energy correlator observable. We then calculate the anomalous terms to
three loops by computing the three-loop fully differential soft function to
$\mathcal{O}(\epsilon)$. Combined with recent perturbative data from the study
of on-shell form factors and splitting functions, this allows us to derive the
four loop rapidity anomalous dimension in QCD.


title:
Dark matter direct detection from the single phonon to the nuclear
  recoil regime

abstract:
  In most direct detection experiments, the free nuclear recoil description of
dark matter scattering breaks down for masses $\lesssim$ 100 MeV, or when the
recoil energy is comparable to a few times the typical phonon energy. For dark
matter lighter than 1 MeV, scattering via excitation of a single phonon
dominates and has been computed previously, but for the intermediate mass range
or higher detector thresholds, multiphonon processes dominate. We perform the
first calculation of the scattering rate via multiphonon production for the
entire keV-GeV dark matter mass range, assuming a harmonic crystal target. We
provide an analytic description that connects the single phonon, multiphonon,
and the nuclear recoil regimes. Our results are implemented in the public
package $\texttt{DarkELF}$.


title: Magnetized oscillatory double-diffusive convection

abstract:
  We study the properties of oscillatory double-diffusive convection (ODDC) in
the presence of a uniform vertical background magnetic field. ODDC takes place
in stellar regions that are unstable according to the Schwarzschild criterion
and stable according to the Ledoux criterion (sometimes called semiconvective
regions), which are often predicted to reside just outside the core of
intermediate-mass main sequence stars. Previous hydrodynamic studies of ODDC
have shown that the basic instability saturates into a state of weak wave-like
convection, but that a secondary instability can sometimes transform it into a
state of layered convection, where layers then rapidly merge and grow until the
entire region is fully convective. We find that magnetized ODDC has very
similar properties overall, with some important quantitative differences. A
linear stability analysis reveals that the fastest-growing modes are unaffected
by the field, but that other modes are. Numerically, the magnetic field is seen
to influence the saturation of the basic instability, overall reducing the
turbulent fluxes of temperature and composition. This in turn affects layer
formation, usually delaying it, and occasionally suppressing it entirely for
sufficiently strong fields. Further work will be needed, however, to determine
the field strength above which layer formation is actually suppressed in stars.
Potential observational implications are briefly discussed.

categories: astro-ph.SR astro-ph.EP physics.flu-dyn physics.plasm-ph


title: Parton physics of the large-$N_c$ mesons

abstract:
  We initiate the studies on the structural physics of the tower of stable
large-$N_c$ mesons through a first computation of the collinear quark-structure
of a large-$N_c$ pion using lattice Monte-Carlo methods. We adapt the
large-$N_c$ continuum reduction for the determination of meson correlation
functions involving the spatially-extended quasi-PDF operators as a perfect
strategy to concentrate only on the short perturbative length scales. We find
the internal structures of pion in the large-$N_c$ and $N_c=3$ theories to be
quite similar. Interestingly, we find hints that even the observed differences
could arise to a large extent via the different perturbative QCD evolution in
the two theories from similar initial conditions at low factorization scales.


title:
The SEDIGISM survey: Molecular cloud morphology. II. Integrated source
  properties

abstract:
  The Structure, Excitation, and Dynamics of the Inner Galactic InterStellar
Medium (SEDIGISM) survey has produced high (spatial and spectral) resolution
$^{13}$CO (2-1) maps of the Milky Way. It has allowed us to investigate the
molecular interstellar medium in the inner Galaxy at an unprecedented level of
detail and characterise it into molecular clouds. In a previous paper, we have
classified the SEDIGISM clouds into four morphologies. However, how the
properties of the clouds vary for these four morphologies is not well
understood. Here, we use the morphological classification of SEDIGISM clouds to
find connections between the cloud morphologies, their integrated properties,
and their location on scaling relation diagrams. We observe that ring-like
clouds show the most peculiar properties, having, on average, higher masses,
sizes, aspect ratios and velocity dispersions compared to other morphologies.
We speculate that this is related to the physical mechanisms that regulate
their formation and evolution, for example, turbulence from stellar feedback
can often results in the creation of bubble-like structures. We also see a
trend of morphology with virial parameter whereby ring-like, elongated, clumpy
and concentrated clouds have virial parameters in a decreasing order. Our
findings provide a foundation for a better understanding of the molecular cloud
behaviour based on their measurable properties.


title: A viable $L_e-L_\mu$ model with $\mu\to e$ violation

abstract:
  We extend the Standard Model gauge group by $U(1)_{L_e-L_\mu}$ and introduce
two scalars, a doublet and a singlet, that are charged under this new group and
have lepton flavour violating couplings. Since in this model $\mu \to e$
processes can only be mediated by $\mu\to \tau\times \tau\to e$ interactions,
bounds from $\mu\to e$ transitions can be avoided while allowing for accessible
new physics. We consider a $Z'$ boson with a mass of $M_{Z'}\simeq10~$GeV and a
gauge coupling $g'\simeq 10^{-4}$, which is in reach of Belle-II. Neutrino
masses and mixing angles can also be accounted for if sterile neutrinos are
added to the spectrum.


title: PBH assisted search for QCD axion dark matter

abstract:
  The entropy production prior to BBN era is one of ways to prevent QCD axion
with the decay constant $F_{a}\in[10^{12}{\rm GeV},10^{16}{\rm GeV}]$ from
overclosing the universe when the misalignment angle is $\theta_{\rm
i}=\mathcal{O}(1)$. As such, it is necessarily accompanied by an early
matter-dominated era (EMD) provided the entropy production is achieved via the
decay of a heavy particle. In this work, we consider the possibility of
formation of primordial black holes during the EMD era with the assumption of
the enhanced primordial scalar perturbation on small scales ($k>10^{4}{\rm
Mpc}^{-1}$). In such a scenario, it is expected that PBHs with axion halo
accretion develop to ultracompact minihalos (UCMHs). We study how UCMHs so
obtained could be of great use in the experimental search for QCD axion dark
matter with $F_{a}\in[10^{12}{\rm GeV},10^{16}{\rm GeV}]$.


title: Aspects of Holography in Conical $AdS_3$

abstract:
  We study the Feynman propagator of free scalar fields in $AdS_3$ with a
conical defect. The propagator is built by solving the bulk equation of motion,
summing over the modes of the field, and taking the boundary limit. We then
perform several consistency checks. In the dual CFT, the operator responsible
for the defect creates a highly excited state. We consider the exchange of the
Virasoro identity block in the heavy-light limit to obtain an expression for
the propagator sensitive to the mass of the defect. In $AdS_3/\mathbb{Z}_n$, we
treat the propagator by the method of images and in the geodesic approximation.
More generally, we argue that long-range correlations of the scalar are
suppressed as the defect becomes more massive: we find a continuous phase
transition in the correlator at the BTZ threshold and examine its critical
behavior. Finally, we apply our results to holographic entanglement entropy
using an analogy between our scalars and replica twist fields.


title: Reducing the Sign Problem with Line Integrals

abstract:
  We present a novel strategy to strongly reduce the severity of the sign
problem, using line integrals along paths of changing imaginary action. Highly
oscillating regions along these paths cancel out, decreasing their
contributions. As a result, sampling with standard Monte-Carlo techniques
becomes possible in cases which otherwise requires methods taking advantage of
complex analysis, such as Lefschetz-thimbles or Complex Langevin. We lay out
how to write down an ordinary differential equation for the line integrals. As
an example of its usage, we apply the results to a 1d quantum mechanical
anharmonic oscillator with a $x^4$ potential in real time, finite temperature.


title:
Framework and Tools for the Simulation and Analysis of the Radio
  Emission from Air Showers at IceCube

abstract:
  The Surface Enhancement of the IceTop air-shower array will include the
addition of radio antennas and scintillator panels, co-located with the
existing ice-Cherenkov tanks and covering an area of about 1 km$^2$. Together,
these will increase the sensitivity of the IceCube Neutrino Observatory to the
electromagnetic and muonic components of cosmic-ray-induced air showers at the
South Pole. The inclusion of the radio technique necessitates an expanded set
of simulation and analysis tools to explore the radio-frequency emission from
air showers in the 70 MHz to 350 MHz band. In this paper we describe the
software modules that have been developed to work with time- and
frequency-domain information within IceCube's existing software framework,
IceTray, which is used by the entire IceCube collaboration. The software
includes a method by which air-shower simulation, generated using CoREAS, can
be reused via waveform interpolation, thus overcoming a significant
computational hurdle in the field.


title:
Kinetic Simulations of Instabilities and Particle Acceleration in
  Cylindrical Magnetized Relativistic Jets

abstract:
  Relativistic magnetized jets, such as those from AGN, GRBs and XRBs, are
susceptible to current- and pressure-driven MHD instabilities that can lead to
particle acceleration and non-thermal radiation. Here we investigate the
development of these instabilities through 3D kinetic simulations of
cylindrically symmetric equilibria involving toroidal magnetic fields with
electron-positron pair plasma. Generalizing recent treatments by valves et al.
(2018) and Davelaar et al. (2020), we~consider a~range of~initial structures
in~which the~force due~to~toroidal magnetic field is~balanced by~a~combination
of~forces due~to~axial magnetic field and~gas pressure. We~argue that
the~particle energy limit identified by Alves et al. (2018) is~due
to~the~finite duration of~the~fast magnetic dissipation phase. We~find a~rather
minor role of~electric fields parallel to~the~local magnetic fields in~particle
acceleration. In~all investigated cases a~kink mode arises in~the~central core
region with a~growth timescale consistent with the~predictions of~linearized
MHD models. In the~case of~a~gas-pressure-balanced (Z-pinch) profile, we
identify a~weak local pinch mode well outside the~jet core.
  We argue that pressure-driven modes are important for relativistic jets, in
regions where sufficient gas pressure is produced by other dissipation
mechanisms.


title: Multivariate Prediction Intervals for Random Forests

abstract:
  Accurate uncertainty estimates can significantly improve the performance of
iterative design of experiments, as in Sequential and Reinforcement learning.
For many such problems in engineering and the physical sciences, the design
task depends on multiple correlated model outputs as objectives and/or
constraints. To better solve these problems, we propose a recalibrated
bootstrap method to generate multivariate prediction intervals for bagged
models and show that it is well-calibrated. We apply the recalibrated bootstrap
to a simulated sequential learning problem with multiple objectives and show
that it leads to a marked decrease in the number of iterations required to find
a satisfactory candidate. This indicates that the recalibrated bootstrap could
be a valuable tool for practitioners using machine learning to optimize systems
with multiple competing targets.


title: Group-Invariant Quantum Machine Learning

abstract:
  Quantum Machine Learning (QML) models are aimed at learning from data encoded
in quantum states. Recently, it has been shown that models with little to no
inductive biases (i.e., with no assumptions about the problem embedded in the
model) are likely to have trainability and generalization issues, especially
for large problem sizes. As such, it is fundamental to develop schemes that
encode as much information as available about the problem at hand. In this work
we present a simple, yet powerful, framework where the underlying invariances
in the data are used to build QML models that, by construction, respect those
symmetries. These so-called group-invariant models produce outputs that remain
invariant under the action of any element of the symmetry group $\mathfrak{G}$
associated to the dataset. We present theoretical results underpinning the
design of $\mathfrak{G}$-invariant models, and exemplify their application
through several paradigmatic QML classification tasks including cases when
$\mathfrak{G}$ is a continuous Lie group and also when it is a discrete
symmetry group. Notably, our framework allows us to recover, in an elegant way,
several well known algorithms for the literature, as well as to discover new
ones. Taken together, we expect that our results will help pave the way towards
a more geometric and group-theoretic approach to QML model design.


title:
Thermal and thermoelectric properties of an antiferromagnetic
  topological insulator MnBi$_2$Te$_4$

abstract:
  The discovery of an intrinsic antiferromagnetic topological insulator
(AFM-TI) in MnBi$_2$Te$_4$ has attracted intense attention, most of which lies
on its electrical properties. In this paper, we report electronic, thermal, and
thermoelectric transport studies of this newly found AFM-TI. The temperature
and magnetic field dependence of its resistivity, thermal conductivity, and
Seebeck coefficient indicate strong coupling between charge, lattice, and spin
degrees of freedom in this system. Furthermore, MnBi$_2$Te$_4$ exhibits a large
anomalous Nernst signal, which is associated with non-zero Berry curvature of
the field-induced canted antiferromagnetic state.


title: Slow-fast normal forms arising from piecewise smooth vector fields

abstract:
  We studied piecewise smooth differential systems of the form $$\dot{z} = Z(z)
= \dfrac{1 + \operatorname{sgn}(F)}{2}X(z) + \dfrac{1 -
\operatorname{sgn}(F)}{2}Y(z),$$ where $F: \mathbb{R}^{n}\rightarrow
\mathbb{R}$ is a smooth map having 0 as a regular value. We consider linear
regularizations of the vector field $Z$ given by $$\dot{z}= Z_{\varepsilon}(z)
= \dfrac{1 + \varphi(F/\varepsilon)}{2}X(z) +\dfrac{1 - \varphi(F
/\varepsilon)}{2}Y(z),$$where $\varphi$ is a transition function (not
necessarily monotonic) and nonlinear regularizations of the vector field $Z$
whose transition function is monotonic. It is a well-known fact that the
regularized system is a slow-fast system. The main contribution of this paper
is the study of typical singularities of slow-fast systems that arise from
(linear or nonlinear) regularizations. We developed an algorithm to construct
suitable transition functions, and we apply these ideas in order to create
slow-fast singularities from normal forms of piecewise smooth vector fields. We
present examples of transition functions that, after regularization of a PSVF
normal form, generate normally hyperbolic, fold, transcritical, and pitchfork
singularities.


title:
DeepBayes -- an estimator for parameter estimation in stochastic
  nonlinear dynamical models

abstract:
  Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world
applications. Yet, estimating the unknown parameters of stochastic, nonlinear
dynamical models remains a challenging problem. The majority of existing
methods employ maximum likelihood or Bayesian estimation. However, these
methods suffer from some limitations, most notably the substantial
computational time for inference coupled with limited flexibility in
application. In this work, we propose DeepBayes estimators that leverage the
power of deep recurrent neural networks in learning an estimator. The method
consists of first training a recurrent neural network to minimize the
mean-squared estimation error over a set of synthetically generated data using
models drawn from the model set of interest. The a priori trained estimator can
then be used directly for inference by evaluating the network with the
estimation data. The deep recurrent neural network architectures can be trained
offline and ensure significant time savings during inference. We experiment
with two popular recurrent neural networks -- long short term memory network
(LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our
proposed method on different example models and perform detailed comparisons
with state-of-the-art approaches. We also provide a study on a real-world
nonlinear benchmark problem. The experimental evaluations show that the
proposed approach is asymptotically as good as the Bayes estimator.


title: Anomalous Fluctuations of Extremes in Many-Particle Diffusion

abstract:
  In many-particle diffusions, particles that move the furthest and fastest can
play an outsized role in physical phenomena. A theoretical understanding of the
behavior of such extreme particles is nascent. A classical model, in the spirit
of Einstein's treatment of single-particle diffusion, has each particle taking
independent homogeneous random walks. This, however, neglects the fact that all
particles diffuse in a common and often inhomogeneous environment that can
affect their motion. A more sophisticated model treats this common environment
as a space-time random biasing field which influences each particle's
independent motion. While the bulk (or typical particle) behavior of these two
models has been found to match to high degree, recent theoretical work of
Barraquand, Corwin and Le Doussal on a one-dimensional exactly solvable version
of this random environment model suggests that the extreme behavior is quite
different between the two models. We transform these asymptotic (in system size
and time) results into physically applicable predictions. Using high precision
numerical simulations we reconcile different asymptotic phases in a manner that
matches numerics down to realistic system sizes, amenable to experimental
confirmation. We characterize the behavior of extreme diffusion in the random
environment model by the presence of a new phase with anomalous fluctuations
related to the Kardar-Parisi-Zhang universality class and equation.


title:
Morpho-kinematic modeling of the expanding ejecta of the extremely slow
  nova V1280 Scorpii

abstract:
  Morphology of nova ejecta is essential for fully understanding the physical
processes involved in nova eruptions. We studied the 3D morphology of the
expanding ejecta of the extremely slow nova V1280 Sco with a unique light
curve. Synthetic line profile spectra were compared to the observed [O III]
4959, 5007 and [N II] 5755 emission line profiles in order to find the best-fit
morphology, inclination angle, and maximum expansion velocity of the ejected
shell. We derive the best fitting expansion velocity, inclination, and squeeze
as $V_{\rm exp} = 2100^{+100}_{-100}$ \,km\,s$^{-1}$, $i = 80^{+1}_{-3}$ deg,
and $squ = 1.0^{+0.0}_{-0.1}$ using [O III] line profiles, and $V_{\rm exp} =
1600^{+100}_{-100}$ \,km\,s$^{-1}$, $i = 81^{+2}_{-4}$ deg, and $squ =
1.0^{+0.0}_{-0.1}$ using [N II] 5755 line profile. A high inclination angle is
consistent with the observational results showing multiple absorption lines
originating from clumpy gases which are produced in dense and slow equatorially
focused outflows. Based on additional observational features such as optical
flares near the maximum light and dust formation on V1280 Sco, a model of
internal shock interaction between slow ejecta and fast wind proposed for the
$\gamma$-ray emission detected in other novae seems to be applicable to this
extremely slow and peculiar nova. Increasing the sample size of novae whose
morphology is studied will be helpful in addressing long-standing mysteries in
novae such as the dominant energy source to power the optical light at the
maximum, optical flares near the maximum, clumpiness of the ejecta, and dust
formation.


title: Time Shifts to Reduce the Size of Reservoir Computers

abstract:
  A reservoir computer is a type of dynamical system arranged to do
computation. Typically, a reservoir computer is constructed by connecting a
large number of nonlinear nodes in a network that includes recurrent
connections. In order to achieve accurate results, the reservoir usually
contains hundreds to thousands of nodes. This high dimensionality makes it
difficult to analyze the reservoir computer using tools from dynamical systems
theory. Additionally, the need to create and connect large numbers of nonlinear
nodes makes it difficult to design and build analog reservoir computers that
can be faster and consume less power than digital reservoir computers. We
demonstrate here that a reservoir computer may be divided into two parts; a
small set of nonlinear nodes (the reservoir), and a separate set of
time-shifted reservoir output signals. The time-shifted output signals serve to
increase the rank and memory of the reservoir computer, and the set of
nonlinear nodes may create an embedding of the input dynamical system. We use
this time-shifting technique to obtain excellent performance from an
opto-electronic delay-based reservoir computer with only a small number of
virtual nodes. Because only a few nonlinear nodes are required, construction of
a reservoir computer becomes much easier, and delay-based reservoir computers
can operate at much higher speeds.


title:
Visualization of Model Parameter Sensitivity along Trajectories in
  Numerical Weather Predictions

abstract:
  Numerical weather prediction models rely on parameterizations for
subgrid-scale processes, e.g., for cloud microphysics. These parameterizations
are a well-known source of uncertainty in weather forecasts that can be
quantified via algorithmic differentiation, which computes the sensitivities of
atmospheric variables to changes in model parameters. It is particularly
interesting to use sensitivities to analyze the validity of physical
assumptions on which microphysical parameterizations in the numerical model
source code are based. In this article, we consider the use case of strongly
ascending trajectories, so-called warm conveyor belt trajectories, known to
have a significant impact on intense surface precipitation rates in
extratropical cyclones. We present visual analytics solutions to analyze the
sensitivities of rain mass density to large numbers of model parameters along
such trajectories. We propose an interactive visual interface that enables a) a
comparative analysis of the temporal development of parameter sensitivities on
a set of trajectories, b) an effective comparison of the distributions of
selected sensitivities at a single location on each trajectory, and c) an
assessment of the spatio-temporal relationships between parameter sensitivities
and the shape of trajectories. We demonstrate how our approach enables
atmospheric scientists to interactively analyze the uncertainty in the
microphysical parameterizations, and along the trajectories, with respect to
selected state variables. We apply our approach to the analysis of convective
trajectories within the extratropical cyclone "Vladiana", which occurred
between 22-25 September 2016 over the North Atlantic.


title:
Fine-Grained Address Segmentation for Attention-Based Variable-Degree
  Prefetching

abstract:
  Machine learning algorithms have shown potential to improve prefetching
performance by accurately predicting future memory accesses. Existing
approaches are based on the modeling of text prediction, considering
prefetching as a classification problem for sequence prediction. However, the
vast and sparse memory address space leads to large vocabulary, which makes
this modeling impractical. The number and order of outputs for multiple cache
line prefetching are also fundamentally different from text prediction. We
propose TransFetch, a novel way to model prefetching. To reduce vocabulary
size, we use fine-grained address segmentation as input. To predict unordered
sets of future addresses, we use delta bitmaps for multiple outputs. We apply
an attention-based network to learn the mapping between input and output.
Prediction experiments demonstrate that address segmentation achieves 26% - 36%
higher F1-score than delta inputs and 15% - 24% higher F1-score than page &
offset inputs for SPEC 2006, SPEC 2017, and GAP benchmarks. Simulation results
show that TransFetch achieves 38.75% IPC improvement compared with no
prefetching, outperforming the best-performing rule-based prefetcher BOP by
10.44%, and ML-based prefetcher Voyager by 6.64%.


title:
VWA: Hardware Efficient Vectorwise Accelerator for Convolutional Neural
  Network

abstract:
  Hardware accelerators for convolution neural networks (CNNs) enable real-time
applications of artificial intelligence technology. However, most of the
existing designs suffer from low hardware utilization or high area cost due to
complex dataflow. This paper proposes a hardware efficient vectorwise CNN
accelerator that adopts a 3$\times$3 filter optimized systolic array using 1-D
broadcast dataflow to generate partial sum. This enables easy reconfiguration
for different kinds of kernels with interleaved input or elementwise input
dataflow. This simple and regular data flow results in low area cost while
attains high hardware utilization. The presented design achieves 99\%, 97\%,
93.7\%, 94\% hardware utilization for VGG-16, ResNet-34, GoogLeNet, and
Mobilenet, respectively. Hardware implementation with TSMC 40nm technology
takes 266.9K NAND gate count and 191KB SRAM to support 168GOPS throughput and
consumes only 154.98mW when running at 500MHz operating frequency, which has
superior area and power efficiency than other designs.


title: VSCNN: Convolution Neural Network Accelerator With Vector Sparsity

abstract:
  Hardware accelerator for convolution neural network (CNNs) enables real time
applications of artificial intelligence technology. However, most of the
accelerators only support dense CNN computations or suffers complex control to
support fine grained sparse networks. To solve above problem, this paper
presents an efficient CNN accelerator with 1-D vector broadcasted input to
support both dense network as well as vector sparse network with the same
hardware and low overhead. The presented design achieves 1.93X speedup over the
dense CNN computations.