title:
The PUMA project. III. Incidence and properties of ionised gas disks in
  ULIRGs, associated velocity dispersion and its dependence on starburstiness

abstract:
  A classical scenario suggests that ULIRGs transform colliding spiral galaxies
into a spheroid dominated early-type galaxy. Recent high-resolution simulations
have instead shown that, under some circumstances, rotation disks can be
preserved during the merging process or rapidly regrown after coalescence. Our
goal is to analyze in detail the ionised gas kinematics in a sample of ULIRGs
to infer the incidence of gas rotational dynamics in late-stage interacting
galaxies and merger remnants. We analysed MUSE data of a sample of 20 nearby
(z<0.165) ULIRGs, as part of the "Physics of ULIRGs with MUSE and ALMA" (PUMA)
project. We found that 27% individual nuclei are associated with kpc-scale
disk-like gas motions. The rest of the sample displays a plethora of gas
kinematics, dominated by winds and merger-induced flows, which make the
detection of rotation signatures difficult. On the other hand, the incidence of
stellar disk-like motions is ~2 times larger than gaseous disks, as the former
are probably less affected by winds and streams. The eight galaxies with a
gaseous disk present relatively high intrinsic gas velocity dispersion (sigma =
30-85 km/s), rotationally-supported motions (with gas rotation velocity over
velocity dispersion vrot/sigma > 1-8), and dynamical masses in the range
(2-7)x1e10 Msun. By combining our results with those of local and high-z disk
galaxies from the literature, we found a significant correlation between sigma
and the offset from the main sequence (MS), after correcting for their
evolutionary trends. Our results confirm the presence of kpc-scale rotating
disks in interacting galaxies and merger remnants, with an incidence going from
27% (gas) to ~50% (stars). The ULIRGs gas velocity dispersion is up to a factor
of ~4 higher than in local normal MS galaxies, similar to high-z starbursts as
presented in the literature.


title:
Astraeus VI: Hierarchical assembly of AGN and their large-scale effect
  during the Epoch of Reionization

abstract:
  In this work, the sixth of a series, we use the ASTRAEUS (semi-numerical
rAdiative tranSfer coupling of galaxy formaTion and Reionization in N-body dark
matter simUlationS) framework to investigate the nature of the sources that
reionized the Universe. We extend ASTRAEUS, which already couples a galaxy
formation semi-analytical model with a detailed semi-numerical reionization
scheme, to include a model for black hole formation, growth, and the production
of ionizing radiation from associated AGN (active galactic nuclei). We
calibrate our fiducial AGN model to reproduce the bolometric luminosity
function at z ~ 5, and explore the role of the resulting AGN population in
reionizing the Universe. We find that in all the models yielding a reasonable
AGN luminosity function, galaxies dominate overwhelmingly the ionizing budget
during the Epoch of Reionization, with AGN accounting for 1-10% of the ionizing
budget at z=6 and starting to play a role only below z < 5.


title: EKO: Evolution Kernel Operators

abstract:
  We present a new QCD evolution library for unpolarized parton distribution
functions: EKO. The program solves DGLAP equations up to
next-to-next-to-leading order. The unique feature of EKO is the computation of
solution operators, which are independent of the boundary condition, can be
stored and quickly applied to evolve several initial PDFs. The EKO approach
combines the power of $N$-space solutions with the flexibility of a $x$-space
delivery, that allows for an easy interface with existing codes. The code is
fully open source and written in Python, with a modular structure in order to
facilitate usage, readability and possible extensions. We provide a set of
benchmarks with similar available tools, finding good agreement.


title: Discovering Distribution Shifts using Latent Space Representations

abstract:
  Rapid progress in representation learning has led to a proliferation of
embedding models, and to associated challenges of model selection and practical
application. It is non-trivial to assess a model's generalizability to new,
candidate datasets and failure to generalize may lead to poor performance on
downstream tasks. Distribution shifts are one cause of reduced
generalizability, and are often difficult to detect in practice. In this paper,
we use the embedding space geometry to propose a non-parametric framework for
detecting distribution shifts, and specify two tests. The first test detects
shifts by establishing a robustness boundary, determined by an intelligible
performance criterion, for comparing reference and candidate datasets. The
second test detects shifts by featurizing and classifying multiple subsamples
of two datasets as in-distribution and out-of-distribution. In evaluation, both
tests detect model-impacting distribution shifts, in various shift scenarios,
for both synthetic and real-world datasets.


title: Selective Network Linearization for Efficient Private Inference

abstract:
  Private inference (PI) enables inference directly on cryptographically secure
data. While promising to address many privacy issues, it has seen limited use
due to extreme runtimes. Unlike plaintext inference, where latency is dominated
by FLOPs, in PI non-linear functions (namely ReLU) are the bottleneck. Thus,
practical PI demands novel ReLU-aware optimizations. To reduce PI latency we
propose a gradient-based algorithm that selectively linearizes ReLUs while
maintaining prediction accuracy. We evaluate our algorithm on several standard
PI benchmarks. The results demonstrate up to $4.25\%$ more accuracy (iso-ReLU
count at 50K) or $2.2\times$ less latency (iso-accuracy at 70\%) than the
current state of the art and advance the Pareto frontier across the
latency-accuracy space. To complement empirical results, we present a "no free
lunch" theorem that sheds light on how and when network linearization is
possible while maintaining prediction accuracy.


title: Bayesian inference of three-dimensional gas maps: II. Galactic HI

abstract:
  The 21-cm emission from atomic hydrogen (HI) is one of the most important
tracers of the structure and dynamics of the interstellar medium. Thanks to
Galactic rotation, the line is Doppler shifted and, assuming a model for the
velocity field, data from gas line surveys can be deprojected along the line of
sight. However, given our vantage point in the Galaxy, such a reconstruction
suffers from a number of ambiguities. Here, we argue that those can be cured by
exploiting the spatial coherence of the gas density that is implied by the
physical processes shaping it. We have adopted a Bayesian inference framework
that allows reconstructing the three-dimensional map of HI and quantifying its
uncertainty. We employ data from the HI4PI compilation to produce
three-dimensional maps of Galactic HI. The reconstructed density shows
structure on a variety of scales. In particular, some spurs and spiral arms can
be identified with ease. We discuss the morphology of the surface mass density
and the radial and vertical profiles. The reconstructed three-dimensional HI
densities are available at https://doi.org/10.5281/zenodo.5956696.


title: Individually Addressable Artificial Atoms in Silicon Photonics

abstract:
  Artificial atoms in solids have emerged as leading systems for quantum
information processing tasks such as quantum networking, sensing, and
computing. A central goal is to develop platforms for precise and scalable
control of individually addressable artificial atoms that feature efficient
optical interfaces. Color centers in silicon, such as the recently-isolated
carbon-related 'G-center', exhibit emission directly into the
telecommunications O-band and can leverage the maturity of silicon-on-insulator
(SOI) photonics. Here, we demonstrate generation and individual addressing of
G-center artificial atoms in a SOI photonic integrated circuit (PIC) platform.
Focusing on the neutral charge state emission at 1278nm, we observe
waveguide-coupled single photon emission with an exceptionally narrow
inhomogeneous distribution with standard deviation of 1.1nm, an excited state
lifetime of 8.3$\pm$0.7ns, and no degradation after months of operation. This
demonstration opens the path to quantum information processing based on
implantable artificial atoms in very large scale integrated (VLSI) photonics.


title:
UCLCHEMCMC: A MCMC Inference tool for Physical Parameters of Molecular
  Clouds

abstract:
  We present the publicly available, open source code UCLCHEMCMC, designed to
estimate physical parameters of an observed cloud of gas by combining Monte
Carlo Markov Chain (MCMC) sampling with chemical and radiative transfer
modeling. When given the observed values of different emission lines,
UCLCHEMCMC runs a Bayesian parameter inference, using a MCMC algorithm to
sample the likelihood and produce an estimate of the posterior probability
distribution of the parameters. UCLCHEMCMC takes a full forward modeling
approach, generating model observables from the physical parameters via
chemical and radiative transfer modeling. While running UCLCHEMCMC, the created
chemical models and radiative transfer code results are stored in an SQL
database, preventing redundant model calculations in future inferences. This
means that the more UCLCHEMCMC is used, the more efficient it becomes. Using
UCLCHEM and RADEX, the increase of efficiency is nearly two orders of
magnitude, going from 5185.33 \pm 1041.96 s for ten walkers to take one
thousand steps when the database is empty, to 68.89 \pm 45.39 s when nearly all
models requested are in the database. In order to demonstrate its usefulness we
provide an example inference of UCLCHEMCMC to estimate the physical parameters
of mock data, and perform two inferences on the well studied prestellar core,
L1544, one of which show that it is important to consider the substructures of
an object when determining which emission lines to use.


title: Differentiable Simulation of Inertial Musculotendons

abstract:
  We propose a simple and practical approach for incorporating the effects of
muscle inertia, which has been ignored by previous musculoskeletal simulators
in both graphics and biomechanics. In our approach, we express the motion of
the musculotendons in terms of the motion of the skeletal joints using a chain
of Jacobians, so that at the top level, only the reduced degrees of freedom of
the skeleton are used to completely drive both bones and musculotendons. Our
approach can handle all commonly used musculotendon path types, including those
with multiple path points and wrapping surfaces. For muscle paths involving
wrapping surfaces, we use neural networks to model the Jacobians, trained using
existing wrapping surface libraries, which allows us to effectively handle the
discontinuities that occur when musculotendon paths collide with wrapping
surfaces. We demonstrate support for higher-order time integrators, complex
joints, inverse dynamics, Hill-type muscle models, and differentiability. We
also show that in the limit, as the muscle mass is reduced to zero, our
approach gracefully degrades to existing simulators in graphics and
biomechanics without support for muscle inertia.


title: Scrambling and quantum feedback in a nanomechanical system

abstract:
  The question of how swiftly entanglement spreads over a system has attracted
vital interest. In this regard, the out-of-time ordered correlator (OTOC) is a
quantitative measure of the entanglement spreading process. Particular interest
concerns the propagation of quantum correlations in the lattice systems, {\it
e.g.}, spin chains. In a seminal paper D. A. Roberts, D. Stanford and L.
Susskind, J. High Energy Phys. 03, 051, (2015) the concept of the OTOC's radius
was introduced. The radius of the OTOC defines the front line reached by the
spread of entanglement. Beyond this radius operators commute. In the present
work, we propose a model of two nanomechanical systems coupled with two
Nitrogen-vacancy (NV) center spins. Oscillators are coupled to each other
directly while NV spins are not. Therefore, the correlation between the NV
spins may arise only through the quantum feedback exerted from the first NV
spin to the first oscillator and transferred from the first oscillator to the
second oscillator via the direct coupling. Thus nonzero OTOC between NV spins
quantifies the strength of the quantum feedback. We show that NV spins cannot
exert quantum feedback on classical nonlinear oscillators. We also discuss the
inherently quantum case with a linear quantum harmonic oscillator indirectly
coupling the two spins and verify that in the classical limit of the
oscillator, the OTOC vanishes.


title: On the T-equivariant cohomology of Hessenberg varieties

abstract:
  For an endomorphism $s:V\rightarrow V$ of a finite dimensional complex vector
space and an action of a torus $T$ on the full flag variety
$\text{GL}_n({\mathbb C})/B$, we give a description of its fixed point set when
$s$ is semisimple or regular nilpotent. We also compute the one dimensional
orbits of this action on the Hessenberg subvariety $\text{Hes}(s,h)\subseteq
\text{GL}_n({\mathbb C})/B$ for any Hessenberg function $h$. For the action of
the one dimensional torus $S$ and a regular nilpotent endomorphism
$N:V\rightarrow V$, we give a new computation of the equivariant cohomology of
the Hessenberg variety $\text{Hes}(N,h)$ for any Hessenberg function using
determinantal conditions.


title: Inferring potential landscapes from noisy trajectories

abstract:
  While particle trajectories encode information on their governing potentials,
potentials can be challenging to robustly extract from trajectories.
Measurement errors may corrupt a particle's position, and sparse sampling of
the potential limits data in higher-energy regions such as barriers. We develop
a Bayesian method to infer potentials of arbitrary shape alongside measurement
noise. As an alternative to Gaussian process priors over potentials, we
introduce structured kernel interpolation to the Natural Sciences which allows
us to extend our analysis to large data sets. Our method is validated on 1D and
2D experimental trajectories for particles in a feedback trap.


title: Explicit Reciprocity Laws for Formal Drinfeld Modules

abstract:
  In this paper, we prove explicit reciprocity laws for a class of formal
Drinfeld modules having stable reduction of height one, in the spirit of those
existing in characteristic zero (cf. the work of Wiles). We begin by defining
the Kummer pairing in the language of formal Drinfeld modules defined over
local fields of positive characteristic. We then prove explicit formulas for
this pairing in terms of the logarithm of the formal Drinfeld module, a certain
Coleman power series, torsion points and the trace. Our results extend the
explicit formulas already proved by Angl\`es for Carlitz modules, and by Bars
and Longhi for sign-normalized rank one Drinfeld modules. The approach followed
is similar to the ones followed in the previously mentioned papers, taking into
account the subtleties derived from the fact that the formal Drinfeld modules
considered are formal power series, and are no longer polynomials.


title:
Analysis of Independent Learning in Network Agents: A Packet Forwarding
  Use Case

abstract:
  Multi-Agent Reinforcement Learning (MARL) is nowadays widely used to solve
real-world and complex decisions in various domains. While MARL can be
categorized into independent and cooperative approaches, we consider the
independent approach as a simple, more scalable, and less costly method for
large-scale distributed systems, such as network packet forwarding. In this
paper, we quantitatively and qualitatively assess the benefits of leveraging
such independent agents learning approach, in particular IQL-based algorithm,
for packet forwarding in computer networking, using the Named Data Networking
(NDN) architecture as a driving example. We put multiple IQL-based forwarding
strategies (IDQF) to the test and compare their performances against very basic
forwarding schemes and simple topologies/traffic models to highlight major
challenges and issues. We discuss the main issues related to the poor
performance of IDQF and quantify the impact of these issues on isolation when
training and testing the IDQF models under different model tuning parameters
and network topologies/characteristics.


title:
Elliptic Harnack's inequality for a singular nonlinear parabolic
  equation in non-divergence form

abstract:
  We prove an elliptic Harnack's inequality for a general form of a parabolic
equation that generalizes both the standard parabolic $p$-Laplace equation and
the normalized version that has been proposed in stochastic game theory. This
version of the inequality doesn't require the intrinsic waiting time and we get
the estimate with the same time level on both sides of the inequality.


title:
An explicit dual control approach for constrained reference tracking of
  uncertain linear systems

abstract:
  A finite horizon optimal tracking problem is considered for linear dynamical
systems subject to parametric uncertainties in the state-space matrices and
exogenous disturbances. A suboptimal solution is proposed using a model
predictive control (MPC) based explicit dual control approach which enables
active uncertainty learning. A novel algorithm for the design of robustly
invariant online terminal sets and terminal controllers is presented. Set
membership identification is used to update the parameter uncertainty online. A
predicted worst-case cost is used in the MPC optimization problem to model the
dual effect of the control input. The cost-to-go is estimated using
contractivity of the proposed terminal set and the remaining time horizon, so
that the optimizer can estimate future benefits of exploration. The proposed
dual control algorithm ensures robust constraint satisfaction and recursive
feasibility, and navigates the exploration-exploitation trade-off using a
robust performance metric.


title:
Learning Interpretable, High-Performing Policies for Continuous Control
  Problems

abstract:
  Gradient-based approaches in reinforcement learning (RL) have achieved
tremendous success in learning policies for continuous control problems. While
the performance of these approaches warrants real-world adoption in domains,
such as in autonomous driving and robotics, these policies lack
interpretability, limiting deployability in safety-critical and
legally-regulated domains. Such domains require interpretable and verifiable
control policies that maintain high performance. We propose Interpretable
Continuous Control Trees (ICCTs), a tree-based model that can be optimized via
modern, gradient-based, RL approaches to produce high-performing, interpretable
policies. The key to our approach is a procedure for allowing direct
optimization in a sparse decision-tree-like representation. We validate ICCTs
against baselines across six domains, showing that ICCTs are capable of
learning interpretable policy representations that parity or outperform
baselines by up to 33$\%$ in autonomous driving scenarios while achieving a
$300$x-$600$x reduction in the number of policy parameters against deep
learning baselines.


title:
Euler Obstructed Cooper Pairing in Twisted Bilayer Graphene: Nematic
  Nodal Superconductivity and Bounded Superfluid Weight

abstract:
  Magic-angle twisted bilayer graphene (MATBG) hosts normal-state nearly-flat
bands with nonzero Euler numbers and shows superconductivity. In this work, we
study the effects of the nontrivial normal-state band topology on the
intervalley $C_{2z}\mathcal{T}$-invariant mean-field Cooper pairing order
parameter in MATBG. We show that the pairing order parameter can always be
split into a trivial channel and an Euler obstructed channel in all gauges for
the normal-state basis, generalizing the previously-studied channel splitting
in the Chern gauge. The nonzero normal-state Euler numbers require the pairing
gap function of the Euler obstructed channel to have zeros, while the trivial
channel can have a nonvanishing pairing gap function. When the pairing is
spontaneously nematic, we find that a sufficiently-dominant Euler obstructed
channel with two zeros typically leads to nodal superconductivity. Under the
approximation of exactly-flat bands, we find that the mean-field
zero-temperature superfluid weight is generally bounded from below, no matter
whether the Euler obstructed channel is dominant or not, generalizing the
previously-derived bound for the uniform s-wave pairing. We numerically verify
these statements for pairings derived from a local attractive interaction. Our
work suggests that Euler obstructed Cooper pairing may play an essential role
in the superconducting MATBG.


title: A note on the complex and bicomplex valued neural networks

abstract:
  In this paper we first write a proof of the perceptron convergence algorithm
for the complex multivalued neural networks (CMVNNs). Our primary goal is to
formulate and prove the perceptron convergence algorithm for the bicomplex
multivalued neural networks (BMVNNs) and other important results in the theory
of neural networks based on a bicomplex algebra.


title: Testing Gravity with Black Hole Shadow Subrings

abstract:
  The black hole shadow, first observed by the Event Horizon Telescope in 2017,
is the newest method for studying black holes and understanding gravity. Much
work has gone into understanding the shadow of a Kerr black hole, including all
of the complex astrophysics of the accretion disk, and there are numerous
studies of the ideal shadow in non-Kerr black holes and exotic compact objects.
This paper presents one of the first studies of the black hole shadow of
non-Kerr black holes when the illumination source is an accretion disk. In
particular, the ability of current and future very long baseline interformeters
to estimate the physical parameters of the black hole spacetime and accretion
disk is investigated using two different parametrized black hole metrics that
encode a number of possible deviations from Kerr. Both the full shadow image
and the individual subrings of the shadow are analyzed as the higher order
subrings are weakly dependent on the disk physics and may be a more viable
observable for studying the spacetime. The results suggest that with current
telescope capabilities and any future earth-based telescopes it will be quite
difficult to place strong constraints on departures from the Kerr spacetime,
primarily due to the low resolution and strong degeneracies between the
spacetime parameters. More optimistically, space-based interferometers may be
capable of testing the Kerr nature of black holes and general relativity to
comparable or better precision than is currently possible with other
observations.


title: On some notions of rank for matrices over tracts

abstract:
  Given a tract $F$ in the sense of Baker and Bowler and a matrix $A$ with
entries in $F$, we define several notions of rank for $A$. In this way, we are
able to unify and find conceptually satisfying proofs for various results about
ranks of matrices that one finds scattered throughout the literature.


title:
Strong convergence of an fractional exponential integrator scheme for
  the finite element discretization of time-fractional SPDE driven by standard
  and fractional Brownian motions

abstract:
  The aim of this work is to provide the first strong convergence result of
numerical approximation of a general time-fractional second order stochastic
partial differential equation involving a Caputo derivative in time of order
$\alpha\in(\frac 12; 1)$ and driven simultaneously by a multiplicative standard
Brownian motion and additive fBm with Hurst parameter $H\in(\frac 12, 1)$, more
realistic to model the random effects on transport of particles in medium with
thermal memory. We prove the existence and uniqueness results and perform the
spatial discretization using the finite element and the temporal discretization
using a fractional exponential integrator scheme. We provide the temporal and
spatial convergence proofs for our fully discrete scheme and the result shows
that the convergence orders depend on the regularity of the initial data, the
power of the fractional derivative, and the Hurst parameter $H$.


title:
Simple approach to current-induced effects -- bond weakening in metal
  chains

abstract:
  We present a simple, first principles scheme for calculating mechanical
properties of nonequilibrium bulk systems assuming an ideal ballistic
distribution function for the electronic states described by the external
voltage bias. This allows for fast calculations of estimates of the
current-induced stresses inside bulk systems carrying a ballistic current. The
stress is calculated using the Hellmann-Feynman theorem, and is in agreement
with the derivative of the nonequilibrium free energy. We illustrate the theory
and present results for one-dimensional (1D) metal chains. We find that the
susceptibility of the yield stress to the applied voltage agrees with the
ordering of break voltages among the metals found in experiments. In
particular, gold is seen to be the most stable under strong current, while
aluminum is the least stable.


title:
Performance Evaluation of Structured and Semi-Structured Bioinformatics
  Tools: A Comparative Study

abstract:
  There is a wide range of available biological databases developed by
bioinformatics experts, employing different methods to extract biological data.
In this paper, we investigate and evaluate the performance of some of these
methods in terms of their ability to efficiently access bioinformatics
databases using web-based interfaces. These methods retrieve bioinformatics
information using structured and semi-structured data tools, which are able to
retrieve data from remote database servers. This study distinguishes each of
these approaches and contrasts these tools. We used Sequence Retrieval System
(SRS) and Entrez search tools for structured data, while Perl and BioPerl
search programs were used for semi-structured data to retrieve complex queries
including a combination of text and numeric information. The study concludes
that the use of semi-structured data tools for accessing bioinformatics
databases is a viable alternative to the structured tools, though each method
is shown to have certain inherent advantages and disadvantages.


title:
Towards optimal sampling for learning sparse approximation in high
  dimensions

abstract:
  In this chapter, we discuss recent work on learning sparse approximations to
high-dimensional functions on data, where the target functions may be scalar-,
vector- or even Hilbert space-valued. Our main objective is to study how the
sampling strategy affects the sample complexity -- that is, the number of
samples that suffice for accurate and stable recovery -- and to use this
insight to obtain optimal or near-optimal sampling procedures. We consider two
settings. First, when a target sparse representation is known, in which case we
present a near-complete answer based on drawing independent random samples from
carefully-designed probability measures. Second, we consider the more
challenging scenario when such representation is unknown. In this case, while
not giving a full answer, we describe a general construction of sampling
measures that improves over standard Monte Carlo sampling. We present examples
using algebraic and trigonometric polynomials, and for the former, we also
introduce a new procedure for function approximation on irregular (i.e.,
nontensorial) domains. The effectiveness of this procedure is shown through
numerical examples. Finally, we discuss a number of structured sparsity models,
and how they may lead to better approximations.


title:
A Fast Network Exploration Strategy to Profile Low Energy Consumption
  for Keyword Spotting

abstract:
  Keyword Spotting nowadays is an integral part of speech-oriented user
interaction targeted for smart devices. To this extent, neural networks are
extensively used for their flexibility and high accuracy. However, coming up
with a suitable configuration for both accuracy requirements and hardware
deployment is a challenge. We propose a regression-based network exploration
technique that considers the scaling of the network filters ($s$) and
quantization ($q$) of the network layers, leading to a friendly and
energy-efficient configuration for FPGA hardware implementation. We experiment
with different combinations of $\mathcal{NN}\scriptstyle\langle q,\,s\rangle
\displaystyle$ on the FPGA to profile the energy consumption of the deployed
network so that the user can choose the most energy-efficient network
configuration promptly. Our accelerator design is deployed on the Xilinx AC 701
platform and has at least 2.1$\times$ and 4$\times$ improvements on energy and
energy efficiency results, respectively, compared to recent hardware
implementations for keyword spotting.


title:
Study of a viscous $\Lambda$WDM model: Near equilibrium condition,
  entropy production, and cosmological constraints

abstract:
  In this paper, we investigated the cosmological evolution of an exact
solution recently found in [N. Cruz, E. Gonz\'alez, and J. Jovel, Phys. Rev. D
\textbf{105}, 024047 (2022)], which describe a viscous warm $\Lambda$DM model
($\Lambda$WDM) at late times, where the DM component obeys a polytropic
equation of state (EoS), which experience dissipative effects with a bulk
viscosity proportional to its the energy density. This solution has the
particularity of having a very similar behavior to the $\Lambda$CDM model for
small values of dissipation, evolving also to a de Sitter type expansion in the
very far future. We explore firstly the thermodynamic consistences of this
solution in the framework of Eckart's theory of non-perfect fluids, focusing on
the fulfillment of the two following conditions: the near equilibrium and the
positiveness of the entropy production. We explore the range of parameters of
the model that allow fulfilling the two conditions at the same time, founding
that a viscous WDM component is compatible with both conditions, being in this
sense, a viable model from the thermodynamic point of view. Besides, we use
thermodynamics analysis to define the best prior definition for the
cosmological parameters related to warmness and dissipation. We also show that
this viscous $\Lambda$WDM model can describe the combined SNe Ia + OHD data in
the same way as the $\Lambda$CDM. The cosmological data constraint at $3\sigma$
CL allows an upper limit on the bulk viscosity, to be of the order of
$\xi_{0}\sim10^{6}Pa\times s$ in agreement with some previous investigations.
Our results support that the inclusion of a dissipative WDM, as an extension of
the standard cosmological model, leads to a both thermodynamically consistent
and properly fitted cosmological evolution.


title:
A Discourse on MetODS: Meta-Optimized Dynamical Synapses for
  Meta-Reinforcement Learning

abstract:
  Recent meta-reinforcement learning work has emphasized the importance of
mnemonic control for agents to quickly assimilate relevant experience in new
contexts and suitably adapt their policy. However, what computational
mechanisms support flexible behavioral adaptation from past experience remains
an open question. Inspired by neuroscience, we propose MetODS (for
Meta-Optimized Dynamical Synapses), a broadly applicable model of
meta-reinforcement learning which leverages fast synaptic dynamics influenced
by action-reward feedback. We develop a theoretical interpretation of MetODS as
a model learning powerful control rules in the policy space and demonstrate
empirically that robust reinforcement learning programs emerge spontaneously
from them. We further propose a formalism which efficiently optimizes the
meta-parameters governing MetODS synaptic processes. In multiple experiments
and domains, MetODS outperforms or compares favorably with previous
meta-reinforcement learning approaches. Our agents can perform one-shot
learning, approaches optimal exploration/exploitation strategies, generalize
navigation principles to unseen environments and demonstrate a strong ability
to learn adaptive motor policies.


title:
Observation of wave-packet branching through an engineered conical
  intersection

abstract:
  In chemical reactions, the interplay between coherent evolution and
dissipation is central to determining key properties such as the rate and
yield. Of particular interest are cases where two potential energy surfaces
cross at features known as conical intersections (CIs), resulting in
nonadiabatic dynamics that may promote ultrafast and highly efficient reactions
when rovibrational damping is present. A prominent chemical reaction that
involves a CI is the cis-trans isomerization reaction in rhodopsin, which is
crucial to vision. CIs in real molecular systems are typically investigated via
optical pump-probe spectroscopy, which has demanding spectral bandwidth and
temporal resolution requirements, and where precise control of the environment
is challenging. A complementary approach for understanding chemical reactions
is to use quantum simulators that can provide access to a wider range of
observables, though thus far combining strongly interacting linear
(rovibrational) and nonlinear (electronic) degrees of freedom with engineered
dissipation has yet to be demonstrated. Here, we create a tunable CI in a
hybrid qubit-oscillator circuit QED processor and simultaneously track both a
reactive wave-packet and electronic qubit in the time-domain. We identify
dephasing of the electronic qubit as the mechanism that drives wave-packet
branching along the reactive coordinate in our model. Furthermore, we directly
observe enhanced branching when the wave-packet passes through the CI. Thus,
the forces that influence a chemical reaction can be viewed as an effective
measurement induced dephasing rate that depends on the position of the
wave-packet relative to the CI. Our results set the groundwork for more complex
simulations of chemical dynamics, offering deeper insight into the role of
dissipation in determining macroscopic quantities of interest such as the
quantum yield of a chemical reaction.


title:
Marius++: Large-Scale Training of Graph Neural Networks on a Single
  Machine

abstract:
  Graph Neural Networks (GNNs) have emerged as a powerful model for ML over
graph-structured data. Yet, scalability remains a major challenge for using
GNNs over billion-edge inputs. The creation of mini-batches used for training
incurs computational and data movement costs that grow exponentially with the
number of GNN layers as state-of-the-art models aggregate information from the
multi-hop neighborhood of each input node. In this paper, we focus on scalable
training of GNNs with emphasis on resource efficiency. We show that out-of-core
pipelined mini-batch training in a single machine outperforms resource-hungry
multi-GPU solutions. We introduce Marius++, a system for training GNNs over
billion-scale graphs. Marius++ provides disk-optimized training for GNNs and
introduces a series of data organization and algorithmic contributions that 1)
minimize the memory-footprint and end-to-end time required for training and 2)
ensure that models learned with disk-based training exhibit accuracy similar to
those fully trained in mixed CPU/GPU settings. We evaluate Marius++ against
PyTorch Geometric and Deep Graph Library using seven benchmark (model, data
set) settings and find that Marius++ with one GPU can achieve the same level of
model accuracy up to 8$\times$ faster than these systems when they are using up
to eight GPUs. For these experiments, disk-based training allows Marius++
deployments to be up to 64$\times$ cheaper in monetary cost than those of the
competing systems.


title: Conjectures on Symmetric Queues in Heavy Traffic

abstract:
  This note describes several open questions concerning scaling limits of
queue-length processes of symmetric queues in heavy traffic, distinguishing
between service-time distributions with finite and infinite variance.


title:
The end of 'set it and forget it' pricing? Opportunities for
  market-based freight contracts

abstract:
  In the for-hire truckload market, firms often experience unexpected
transportation cost increases due to contracted transportation service provider
(carrier) load rejections. The dominant procurement strategy results in
long-term, fixed-price contracts that become obsolete as transportation
providers' networks change and freight markets fluctuate between times of over
and under supply. We build behavioral models of the contracted carrier's load
acceptance decision under two distinct freight market conditions based on
empirical load transaction data. With the results, we quantify carriers'
likelihood of sticking to the contract as their best known alternative priced
load options increase and become more attractive; in other words, carriers'
contract price stickiness. Finally, we explore carriers' contract price
stickiness for different lane, freight, and carrier segments and offer insights
for shippers to identify where they can expect to see substantial improvement
in contracted carrier load acceptance as they consider alternative,
market-based pricing strategies.


title:
Convergence Analysis of Virtual Element Method for Nonlinear Nonlocal
  Dynamic Plate Equation

abstract:
  In this article, we have considered a nonlinear nonlocal time dependent
fourth order equation demonstrating the deformation of a thin and narrow
rectangular plate. We propose $C^1$ conforming virtual element method (VEM) of
arbitrary order, $k\ge2$, to approximate the model problem numerically. We
employ VEM to discretize the space variable and fully implicit scheme for
temporal variable. Well-posedness of the fully discrete scheme is proved under
certain conditions on the physical parameters, and we derive optimal order of
convergence in both space and time variable. Finally, numerical experiments are
presented to illustrate the behaviour of the proposed numerical scheme.


title:
Statistical Methods for Accommodating Immortal Time: A Selective Review
  and Comparison

abstract:
  Epidemiologic studies and clinical trials with a survival outcome are often
challenged by immortal time (IMT), a period of follow-up during which the
survival outcome cannot occur because of the observed later treatment
initiation. It has been well recognized that failing to properly accommodate
IMT leads to biased estimation and misleading inference. Accordingly, a series
of statistical methods have been developed, from the simplest by including or
excluding IMT to various weightings and the more recent sequential methods. Our
literature review suggests that the existing developments are often
"scattered", and there is a lack of comprehensive review and direct comparison.
To fill this knowledge gap and better introduce this important topic especially
to biomedical researchers, we provide this review to comprehensively describe
the available methods, discuss their advantages and disadvantages, and equally
important, directly compare their performance via simulation and the analysis
of the Stanford heart transplant data. The key observation is that the
time-varying treatment modeling and sequential trial methods tend to provide
unbiased estimation, while the other methods may result in substantial bias. We
also provide an in-depth discussion on the interconnections with causal
inference.


title: A Search for Alternative Solid Rocket Propellant Oxidizers

abstract:
  Carbon-based caged and heterocyclic compounds tend to have strained molecular
structures leading to high heats of formation and energetic behavior. In the
current paper, molecular modelling calculations for 10 caged compounds of this
type along with 2 strained aliphatic compounds and 4 simple cyclic chains are
presented in view of their possible use as oxidizers in propulsion
applications. Density functional theory (B3LYP) was employed for the geometry
optimization of the proposed molecular structure using the 6-311++G(d,p) basis
set. Heats of formation of the compounds were calculated using the molecular
modeling results and their specific impulses were computed using the NASA CEA
[1] software package to evaluate their potentials as propellant oxidizers.


title:
Boundary-aware Information Maximization for Self-supervised Medical
  Image Segmentation

abstract:
  Unsupervised pre-training has been proven as an effective approach to boost
various downstream tasks given limited labeled data. Among various methods,
contrastive learning learns a discriminative representation by constructing
positive and negative pairs. However, it is not trivial to build reasonable
pairs for a segmentation task in an unsupervised way. In this work, we propose
a novel unsupervised pre-training framework that avoids the drawback of
contrastive learning. Our framework consists of two principles: unsupervised
over-segmentation as a pre-train task using mutual information maximization and
boundary-aware preserving learning. Experimental results on two benchmark
medical segmentation datasets reveal our method's effectiveness in improving
segmentation performance when few annotated images are available.


title:
Direct observation of a dynamical glass transition in a nanomagnetic
  artificial Hopfield network

abstract:
  Spin glasses, generally defined as disordered systems with randomized
competing interactions, are a widely investigated complex system. Theoretical
models describing spin glasses are broadly used in other complex systems, such
as those describing brain function, error-correcting codes, or stock-market
dynamics. This wide interest in spin glasses provides strong motivation to
generate an artificial spin glass within the framework of artificial spin ice
systems. Here, we present the experimental realization of an artificial spin
glass consisting of dipolar coupled single-domain Ising-type nanomagnets
arranged onto an interaction network that replicates the aspects of a Hopfield
neural network. Using cryogenic x-ray photoemission electron microscopy
(XPEEM), we performed temperature-dependent imaging of thermally driven moment
fluctuations within these networks and observed characteristic features of a
two-dimensional Ising spin glass. Specifically, the temperature dependence of
the spin glass correlation function follows a power law trend predicted from
theoretical models on two-dimensional spin glasses. Furthermore, we observe
clear signatures of the hard to observe rugged spin glass free energy in the
form of sub-aging, out of equilibrium autocorrelations and a transition from
stable to unstable dynamics.

categories: cond-mat.dis-nn cond-mat.mes-hall cond-mat.stat-mech cs.NE nlin.CD


title:
99 oscillating red-giant stars in binary systems with NASA TESS and NASA
  Kepler identified from the SB9-Catalogue

abstract:
  Oscillating red-giant stars in binary systems are an ideal testbed for
investigating the structure and evolution of stars in the advanced phases of
evolution. With 83 known red giants in binary systems, of which only ~40 have
determined global seismic parameters and orbital parameters, the sample is
small compared to the numerous known oscillating stars. The detection of
red-giant binary systems is typically obtained from the signature of stellar
binarity in space photometry. The time base of such data biases the detection
towards systems with shorter periods and orbits of insufficient size to allow a
red giant to fully extend as it evolves up the red-giant branch. Consequently,
the sample shows an excess of H-shell burning giants while containing very few
stars in the He-core burning phase. From the ninth catalogue of spectroscopic
binary orbits (SB9), we identified candidate systems hosting a red-giant
primary component. Searching space photometry from the NASA missions Kepler,
K2, and TESS (Transiting Exoplanet Survey Satellite) as well as the BRITE
(BRIght Target Explorer) constellation mission, we find 99 systems, which were
previously unknown to host an oscillating giant component. The revised search
strategy allowed us to extend the range of orbital periods of systems hosting
oscillating giants up to 26000days. Such wide orbits allow a rich population of
He-core burning primaries, which are required for a complete view of stellar
evolution from binary studies. Tripling the size of the sample of known
oscillating red-giant stars in binary systems is an important step towards an
ensemble approach for seismology and tidal studies. While for non-eclipsing
binaries the inclination is unknown, such a seismically well-characterized
sample will be a treasure trove in combination with Gaia astrometric orbits for
binary~systems.


title:
Modeling long-term photometric data of trans-Neptunian objects and
  Centaurs

abstract:
  Trans-Neptunian objects and Centaurs are small solar system bodies that
reside in the outer parts of the solar system. These objects present
photometric behaviours that are influenced due to a change in their aspect
angle. Using absolute photometric measurements and rotational light-curves at
different location on their orbits allow to model their photometric behaviour
and obtain physical properties such as pole orientation, shape, density, and
even detecting different peculiarities as departure from hydrostatic
equilibrium or rings. In this work, I present how these models are performed in
order to extract different physical properties. A review of the objects for
which their long-term photometric behaviour has been modeled is also given.


title:
Preserving the Stoichiometry of Triple-Cation Perovskites by
  Carrier-Gas-Free Antisolvent Spraying

abstract:
  The use of antisolvents during the fabrication of solution-processed lead
halide perovskite layers is increasingly common. Usually, the antisolvent is
applied by pipetting during the spin-coating process, which often irreversibly
alters the composition of the perovskite layer, resulting in the formation of
PbI2 at the surface and bulk of the perovskite layer. Here, we demonstrate that
by applying the antisolvent via carrier-gas free spraying, the stoichiometry of
the perovskite layer is far better preserved. Consequently, the photovoltaic
performance of triple cation photovoltaic devices fabricated in an inverted
architecture is enhanced, mainly due to an increase in the open-circuit
voltage. By exploring different volumes of antisolvent, we show that spraying
as little as 60 uL results in devices with power conversion efficiencies as
high as 21%. Moreover, solar cells with sprayed antisolvent are more stable
than those fabricated by pipetting the antisolvent.


title:
Strong europium luminescence in lanthan-euripium-erbium-calcium-vanadate
  nanocrystals, the result of codoping optimization

abstract:
  Co-doped with calcium, europium and erbium ions lanthanum vanadate
crystalline nanoparticles are synthesized and investigated with a goal to
clarify the mechanisms of calcium and erbium impurities effects on europium
ions luminescence and to find compositions with enhanced luminescence
intensity. The XRD analysis reveals dependence of crystal structure on dopants
concentration: monoclinic crystal phase is observed for low dopant
concentrations and content of tetragonal crystal phase increases with dopant
concentrations increase. The SEM investigation reveals formation of
nanoparticles with two types of shapes. Photoluminescence spectra consist of
lines caused by inner transitions in the erbium and europium ions. It has been
shown that dependence of the europium ions luminescence intensity on the
calcium and erbium concentrations is caused by cumulative effects of dopants on
crystal lattice structure, on defects in the first coordination sphere of the
europium ions and on efficiency of excitation energy transfer.


title: Deep Learning-based Assessment of Hepatic Steatosis on chest CT

abstract:
  Purpose: Automatic methods are required for the early detection of hepatic
steatosis to avoid progression to cirrhosis and cancer. Here, we developed a
fully automated deep learning pipeline to quantify hepatic steatosis on
non-contrast enhanced chest computed tomography (CT) scans. Materials and
Methods: We developed and evaluated our pipeline on chest CT images of 1,431
randomly selected National Lung Screening Trial (NLST) participants. A dataset
of 451 CT scans with volumetric liver segmentations of expert readers was used
for training a deep learning model. For testing, in an independent dataset of
980 CT scans hepatic attenuation was manually measured by an expert reader on
three cross-sectional images at different hepatic levels by selecting three
circular regions of interest. Additionally, 100 randomly selected cases of the
test set were volumetrically segmented by expert readers. Hepatic steatosis on
the test set was defined as mean hepatic attenuation of < 40 Hounsfield unit.
Spearman correlation was conducted to analyze liver fat quantification accuracy
and the Cohen's Kappa coefficient was calculated for hepatic steatosis
prediction reliability. Results: Our pipeline demonstrated strong performance
and achieved a mean dice score of 0.970 for the volumetric liver segmentation.
The spearman correlation of the liver fat quantification was 0.954 (P <0.0001)
between the automated and expert reader measurements. The cohen's kappa
coefficient was 0.875 for automatic assessment of hepatic steatosis.
Conclusion: We developed a fully automatic deep learning-based pipeline for the
assessment of hepatic steatosis in chest CT images. With the fast and cheap
screening of hepatic steatosis, our pipeline has the potential to help initiate
preventive measures to avoid progression to cirrhosis and cancer.


title:
Best of both worlds: Synergistically derived material properties via
  additive manufacturing of nanocomposites

abstract:
  With an exponential rise in the popularity and availability of additive
manufacturing (AM), a large focus has been directed toward research in this
topic's movement, while trying to distinguish themselves from similar works by
simply adding nanomaterials to their process. Though nanomaterials can add
impressive properties to nanocomposites (NCs), there are expansive amounts of
opportunities that are left unexplored by simply combining AM with NCs without
discovering synergistic effects and novel emerging material properties that are
not possible by each of these alone. Cooperative, evolving properties of NCs in
AM can be investigated at the processing, morphological, and architectural
levels. Each of these categories are studied as a function of the amplifying
relationship between nanomaterials and AM, with each showing the systematically
selected material and method to advance the material performance, explore
emergent properties, as well as improve the AM process itself. Innovative,
advanced materials are key to faster development cycles in disruptive
technologies for bioengineering, defense, and transportation sectors. This is
only possible by focusing on synergism and amplification within additive
manufacturing of nanocomposites.


title:
Multivariate error modeling and uncertainty quantification using
  importance (re-)weighting for Monte Carlo simulations in particle transport

abstract:
  Fast and accurate predictions of uncertainties in the computed dose are
crucial for the determination of robust treatment plans in radiation therapy.
This requires the solution of particle transport problems with uncertain
parameters or initial conditions. Monte Carlo methods are often used to solve
transport problems especially for applications which require high accuracy. In
these cases, common non-intrusive solution strategies that involve repeated
simulations of the problem at different points in the parameter space quickly
become infeasible due to their long run-times. Intrusive methods however limit
the usability in combination with proprietary simulation engines. In our
previous paper [51], we demonstrated the application of a new non-intrusive
uncertainty quantification approach for Monte Carlo simulations in proton dose
calculations with normally distributed errors on realistic patient data. In
this paper, we introduce a generalized formulation and focus on a more in-depth
theoretical analysis of this method concerning bias, error and convergence of
the estimates. The multivariate input model of the proposed approach further
supports almost arbitrary error correlation models. We demonstrate how this
framework can be used to model and efficiently quantify complex auto-correlated
and time-dependent errors.


title:
Data-Driven Materials Discovery and Synthesis using Machine Learning
  Methods

abstract:
  Experimentally [1-38] and computationally [39-50] validated machine learning
(ML) articles are sorted based on the size of the training data: 1-100,
101-10000, and 10000+ in a comprehensive set summarizing legacy and recent
advances in the field. The review emphasizes the interrelated fields of
synthesis, characterization, and prediction. Size range 1-100 consists mostly
of Bayesian optimization (BO) articles, whereas 101-10000 consists mostly of
support vector machine (SVM) articles. The articles often use combinations of
ML, feature selection (FS), adaptive design (AD), high-throughput (HiTp)
techniques, and domain knowledge to enhance predictive performance and/or model
interpretability. Grouping cross-validation (G-CV) techniques curb overly
optimistic extrapolative predictive performance. Smaller datasets relying on AD
are typically able to identify new materials with desired properties but do so
in a constrained design space. In larger datasets, the low-hanging fruit of
materials optimization is typically already discovered, and the models are
generally less successful at extrapolating to new materials, especially when
the model training data favors a particular type of material. The large
increase of ML materials science articles that perform experimental or
computational validation on the predicted results demonstrates the
interpenetration of materials informatics with the materials science discipline
and an accelerating materials discovery for real-world applications.


title: Fagan's Construction, Strange Roots, and Tchoukaillon Solitaire

abstract:
  In this paper we examine a procedure that, on starting with an integer $n$,
results in a pair of equal integers that are no greater than $n$. We call the
resulting value the \textit{strange root} of $n$ and we show how this
strange-root-finding procedure is intimately linked to the game of Tchoukaillon
solitaire. We analyze the strange-root-finding procedure in reverse to
determine when a prescribed value is the strange root of at most two integers.
We present a conjecture about strange roots and translate this conjecture into
one involving Tchoukaillon solitaire.


title: Fully Automated Tree Topology Estimation and Artery-Vein Classification

abstract:
  We present a fully automatic technique for extracting the retinal vascular
topology, i.e., how the different vessels are connected to each other, given a
single color fundus image. Determining this connectivity is very challenging
because vessels cross each other in a 2D image, obscuring their true paths. We
validated the usefulness of our extraction method by using it to achieve
state-of-the-art results in retinal artery-vein classification.
  Our proposed approach works as follows. We first segment the retinal vessels
using our previously developed state-of-the-art segmentation method. Then, we
estimate an initial graph from the extracted vessels and assign the most likely
blood flow to each edge. We then use a handful of high-level operations (HLOs)
to fix errors in the graph. These HLOs include detaching neighboring nodes,
shifting the endpoints of an edge, and reversing the estimated blood flow
direction for a branch. We use a novel cost function to find the optimal set of
HLO operations for a given graph. Finally, we show that our extracted vascular
structure is correct by propagating artery/vein labels along the branches. As
our experiments show, our topology-based artery-vein labeling achieved
state-of-the-art results on multiple datasets. We also performed several
ablation studies to verify the importance of the different components of our
proposed method.


title: Dynamics of a Charged Thomas Oscillator in an External Magnetic Field

abstract:
  In this letter, we provide a detailed numerical examination of the dynamics
of a charged Thomas oscillator in an external magnetic field. We do so by
adopting and then modifying the cyclically symmetric Thomas oscillator to study
the dynamics of a charged particle in an external magnetic field. These
dynamical behaviours for weak and strong field strength parameters fall under
two categories; conservative and dissipative. The system shows a complex
quasi-periodic attractor whose topology depends on initial conditions for high
field strengths in the conservative regime. There is a transition from
adiabatic motion to chaos on decreasing the field strength parameter. In the
dissipative regime, the system is chaotic for weak field strength and weak
damping but shows a limit cycle for high field strengths. Such behaviour is due
to an additional negative feedback loop that comes into action at high field
strengths and forces the system dynamics to be stable in periodic oscillations.
For weak damping and weak field strength, the system dynamics mimic Brownian
motion via chaotic walks.


title: A proof of the Erd\H{o}s primitive set conjecture

abstract:
  A set of integers greater than 1 is primitive if no member in the set divides
another. Erd\H{o}s proved in 1935 that the series $f(A) = \sum_{a\in A}1/(a
\log a)$ is uniformly bounded over all choices of primitive sets $A$. In 1988
he asked if this bound is attained for the set of prime numbers. In this
article we answer in the affirmative. As further applications of the method, we
make progress towards a question of Erd\H{o}s, S\'ark\"ozy, and Szemer\'edi
from 1968. We also refine the classical Davenport-Erd\H{o}s theorem on infinite
divisibility chains, and extend a result of Erd\H{o}s, S\'ark\"ozy, and
Szemer\'edi from 1966.


title:
Using Large-scale Heterogeneous Graph Representation Learning for Code
  Review Recommendations

abstract:
  Code review is an integral part of any mature software development process,
and identifying the best reviewer for a code change is a well accepted problem
within the software engineering community. Selecting a reviewer who lacks
expertise and understanding can slow development or result in more defects. To
date, most reviewer recommendation systems rely primarily on historical file
change and review information; those who changed or reviewed a file in the past
are the best positioned to review in the future. We posit that while these
approaches are able to identify and suggest qualified reviewers, they may be
blind to reviewers who have the needed expertise and have simply never
interacted with the changed files before. To address this, we present CORAL, a
novel approach to reviewer recommendation that leverages a socio-technical
graph built from the rich set of entities (developers, repositories, files,
pull requests, work-items, etc.) and their relationships in modern source code
management systems. We employ a graph convolutional neural network on this
graph and train it on two and a half years of history on 332 repositories. We
show that CORAL is able to model the manual history of reviewer selection
remarkably well. Further, based on an extensive user study, we demonstrate that
this approach identifies relevant and qualified reviewers who traditional
reviewer recommenders miss, and that these developers desire to be included in
the review process. Finally, we find that "classical" reviewer recommendation
systems perform better on smaller (in terms of developers) software projects
while CORAL excels on larger projects, suggesting that there is "no one model
to rule them all."


title:
Three-axis torque investigation of interfacial exchange coupling in a
  NiFe/CoO bilayer micromagnetic disk

abstract:
  Micrometer diameter bilayers of NiFe (permalloy, Py) and cobalt oxide (CoO)
deposited on nanomechanical resonators were used to investigate exchange bias
effects. The mechanical compliances of two resonator axes were enhanced by
severing one torsion arm, resulting in a unique three-axis resonator that
responds resonantly to torques generated by a three-axis RF field. Our
technique permits simultaneous measurement of three orthogonal torque
components. Measurements of the anisotropies associated with interfacial
exchange coupling effects have been made. At cryogenic temperatures,
observations of shifted linear hysteresis loops confirmed the presence of
exchange bias from the Py/CoO interface. An in-plane rotating DC bias field was
used to probe in-plane anisotropies through the out-of-plane torque. Training
effects in the rotational hysteresis data were observed and showed that
features due to interfacial coupling did not diminish irrespective of
substantial training of the unidirectional anisotropy. The data from the
rotational hysteresis loops were fit with parameters from a macrospin solution
to the Landau-Lifshitz-Gilbert equation. Each parameter of the exchange bias
model accounts for specific features of the rotational loop.


title:
Automatic Identification of Self-Admitted Technical Debt from Different
  Sources

abstract:
  Technical debt refers to taking shortcuts to achieve short-term goals while
sacrificing the maintainability and evolvability of software systems. Nowadays,
there is a trend that researchers focus on technical debt that is explicitly
admitted by developers, namely Self-Admitted Technical Debt or SATD. However,
there are no approaches available for automatically identifying SATD from
multiple sources. Therefore, we propose and evaluate an approach MT-Text-CNN
for SATD identification in multiple sources. Our findings show that our
approach outperforms baseline approaches which achieves an average F1- score of
0.611 when detecting four types of SATD (i.e., code/design debt, requirement
debt, documentation debt, and test debt) from source code comments, commit
messages, pull requests, and issue tracking systems.


title: Bregman Plug-and-Play Priors

abstract:
  The past few years have seen a surge of activity around integration of deep
learning networks and optimization algorithms for solving inverse problems.
Recent work on plug-and-play priors (PnP), regularization by denoising (RED),
and deep unfolding has shown the state-of-the-art performance of such
integration in a variety of applications. However, the current paradigm for
designing such algorithms is inherently Euclidean, due to the usage of the
quadratic norm within the projection and proximal operators. We propose to
broaden this perspective by considering a non-Euclidean setting based on the
more general Bregman distance. Our new Bregman Proximal Gradient Method variant
of PnP (PnP-BPGM) and Bregman Steepest Descent variant of RED (RED-BSD) replace
the traditional updates in PnP and RED from the quadratic norms to more general
Bregman distance. We present a theoretical convergence result for PnP-BPGM and
demonstrate the effectiveness of our algorithms on Poisson linear inverse
problems.


title:
The impact of feature importance methods on the interpretation of defect
  classifiers

abstract:
  Classifier specific (CS) and classifier agnostic (CA) feature importance
methods are widely used (often interchangeably) by prior studies to derive
feature importance ranks from a defect classifier. However, different feature
importance methods are likely to compute different feature importance ranks
even for the same dataset and classifier. Hence such interchangeable use of
feature importance methods can lead to conclusion instabilities unless there is
a strong agreement among different methods. Therefore, in this paper, we
evaluate the agreement between the feature importance ranks associated with the
studied classifiers through a case study of 18 software projects and six
commonly used classifiers. We find that: 1) The computed feature importance
ranks by CA and CS methods do not always strongly agree with each other. 2) The
computed feature importance ranks by the studied CA methods exhibit a strong
agreement including the features reported at top-1 and top-3 ranks for a given
dataset and classifier, while even the commonly used CS methods yield vastly
different feature importance ranks. Such findings raise concerns about the
stability of conclusions across replicated studies. We further observe that the
commonly used defect datasets are rife with feature interactions and these
feature interactions impact the computed feature importance ranks of the CS
methods (not the CA methods). We demonstrate that removing these feature
interactions, even with simple methods like CFS improves agreement between the
computed feature importance ranks of CA and CS methods. In light of our
findings, we provide guidelines for stakeholders and practitioners when
performing model interpretation and directions for future research, e.g.,
future research is needed to investigate the impact of advanced feature
interaction removal methods on computed feature importance ranks of different
CS methods.


title: Condensation Jacobian with Adaptivity

abstract:
  We present a new approach that allows large time steps in dynamic
simulations. Our approach, ConJac, is based on condensation, a technique for
eliminating many degrees of freedom (DOFs) by expressing them in terms of the
remaining degrees of freedom. In this work, we choose a subset of nodes to be
dynamic nodes, and apply condensation at the velocity level by defining a
linear mapping from the velocities of these chosen dynamic DOFs to the
velocities of the remaining quasistatic DOFs. We then use this mapping to
derive reduced equations of motion involving only the dynamic DOFs. We also
derive a novel stabilization term that enables us to use complex nonlinear
material models. ConJac remains stable at large time steps, exhibits highly
dynamic motion, and displays minimal numerical damping. In marked contrast to
subspace approaches, ConJac gives exactly the same configuration as the full
space approach once the static state is reached. Furthermore, ConJac can
automatically choose which parts of the object are to be simulated dynamically
or quasistatically. Finally, ConJac works with a wide range of moderate to
stiff materials, supports anisotropy and heterogeneity, handles topology
changes, and can be combined with existing solvers including rigid body
dynamics.


title:
Reconstruction of the initial function from the solution of the
  fractional wave equation measured in two geometric settings

abstract:
  Photoacoustic tomography (PAT) is a novel and rapidly promising technique in
the field of medical imaging, based on the generation of acoustic waves inside
an object of interest by stimulating non-ionizing laser pulses. This acoustic
wave is measured using the detector on the outside of the object and converted
into an image of the human body by several inversions. Thus, one of
mathematical problems in PAT is how to recover the initial function from the
solution of the wave equation on the outside of the object. In this study we
consider the fractional wave equation and assume that the point-like detectors
are located on the sphere and hyperplane. We provide how to recover the initial
function from the data, the solution of the fractional wave equation, measured
on the sphere and hyperplane.


title:
Fractional-Order Shell Theory: Formulation and Application to the
  Analysis of Nonlocal Cylindrical Panels

abstract:
  We present a theoretical and computational framework based on fractional
calculus for the analysis of the nonlocal static response of cylindrical shell
panels. The differ-integral nature of fractional derivatives allows an
efficient and accurate methodology to account for the effect of long-range
(nonlocal) interactions in curved structures. More specifically, the use of
frame-invariant fractional-order kinematic relations enables a physically,
mathematically, and thermodynamically consistent formulation to model the
nonlocal elastic interactions. In order to evaluate the response of these
nonlocal shells under practical scenarios involving generalized loads and
boundary conditions, the fractional-Finite Element Method (f-FEM) is extended
to incorporate shell elements based on the first-order shear-deformable
displacement theory. Finally, numerical studies are performed exploring both
the linear and the geometrically nonlinear static response of nonlocal
cylindrical shell panels. This study is intended to provide a general
foundation to investigate the nonlocal behavior of curved structures by means
of fractional order models.


title:
Deep Dynamic Effective Connectivity Estimation from Multivariate Time
  Series

abstract:
  Recently, methods that represent data as a graph, such as graph neural
networks (GNNs) have been successfully used to learn data representations and
structures to solve classification and link prediction problems. The
applications of such methods are vast and diverse, but most of the current work
relies on the assumption of a static graph. This assumption does not hold for
many highly dynamic systems, where the underlying connectivity structure is
non-stationary and is mostly unobserved. Using a static model in these
situations may result in sub-optimal performance. In contrast, modeling changes
in graph structure with time can provide information about the system whose
applications go beyond classification. Most work of this type does not learn
effective connectivity and focuses on cross-correlation between nodes to
generate undirected graphs. An undirected graph is unable to capture direction
of an interaction which is vital in many fields, including neuroscience. To
bridge this gap, we developed dynamic effective connectivity estimation via
neural network training (DECENNT), a novel model to learn an interpretable
directed and dynamic graph induced by the downstream classification/prediction
task. DECENNT outperforms state-of-the-art (SOTA) methods on five different
tasks and infers interpretable task-specific dynamic graphs. The dynamic graphs
inferred from functional neuroimaging data align well with the existing
literature and provide additional information. Additionally, the temporal
attention module of DECENNT identifies time-intervals crucial for predictive
downstream task from multivariate time series data.


title:
JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity
  Detection using Zero and One Shot Learning

abstract:
  Large Language Models have been successful in a wide variety of Natural
Language Processing tasks by capturing the compositionality of the text
representations. In spite of their great success, these vector representations
fail to capture meaning of idiomatic multi-word expressions (MWEs). In this
paper, we focus on the detection of idiomatic expressions by using binary
classification. We use a dataset consisting of the literal and idiomatic usage
of MWEs in English and Portuguese. Thereafter, we perform the classification in
two different settings: zero shot and one shot, to determine if a given
sentence contains an idiom or not. N shot classification for this task is
defined by N number of common idioms between the training and testing sets. In
this paper, we train multiple Large Language Models in both the settings and
achieve an F1 score (macro) of 0.73 for the zero shot setting and an F1 score
(macro) of 0.85 for the one shot setting. An implementation of our work can be
found at
https://github.com/ashwinpathak20/Idiomaticity_Detection_Using_Few_Shot_Learning.


title: Malleable Agents for Re-Configurable Robotic Manipulators

abstract:
  Re-configurable robots potentially have more utility and flexibility for many
real-world tasks. Designing a learning agent to operate such robots requires
adapting to different configurations. While deep reinforcement learning has had
immense success in robotic manipulation, domain adaptation is a significant
problem that limits its applicability to real-world robotics. We focus on
robotic arms with multiple rigid links connected by joints. Recent attempts
have performed domain adaptation and Sim2Real transfer to provide robustness to
robotic arm dynamics and sensor/camera variations. However, there have been no
previous attempts to adapt to robotic arms with a varying number of links. We
propose an RL agent with sequence neural networks embedded in the deep neural
network to adapt to robotic arms that have a varying number of links. Further,
with the additional tool of domain randomization, this agent adapts to
different configurations with varying number/length of links and dynamics
noise. We perform simulations on a 2D N-link arm to show the ability of our
network to transfer and generalize efficiently.


title: A Temporal-Difference Approach to Policy Gradient Estimation

abstract:
  The policy gradient theorem (Sutton et al., 2000) prescribes the usage of a
cumulative discounted state distribution under the target policy to approximate
the gradient. Most algorithms based on this theorem, in practice, break this
assumption, introducing a distribution shift that can cause the convergence to
poor solutions. In this paper, we propose a new approach of reconstructing the
policy gradient from the start state without requiring a particular sampling
strategy. The policy gradient calculation in this form can be simplified in
terms of a gradient critic, which can be recursively estimated due to a new
Bellman equation of gradients. By using temporal-difference updates of the
gradient critic from an off-policy data stream, we develop the first estimator
that sidesteps the distribution shift issue in a model-free way. We prove that,
under certain realizability conditions, our estimator is unbiased regardless of
the sampling strategy. We empirically show that our technique achieves a
superior bias-variance trade-off and performance in presence of off-policy
samples.


title:
Textured Mesh Quality Assessment: Large-Scale Dataset and Deep
  Learning-based Quality Metric

abstract:
  Over the past decade, 3D graphics have become highly detailed to mimic the
real world, exploding their size and complexity. Certain applications and
device constraints necessitate their simplification and/or lossy compression,
which can degrade their visual quality. Thus, to ensure the best Quality of
Experience (QoE), it is important to evaluate the visual quality to accurately
drive the compression and find the right compromise between visual quality and
data size. In this work, we focus on subjective and objective quality
assessment of textured 3D meshes. We first establish a large-scale dataset,
which includes 55 source models quantitatively characterized in terms of
geometric, color, and semantic complexity, and corrupted by combinations of 5
types of compression-based distortions applied on the geometry, texture mapping
and texture image of the meshes. This dataset contains over 343k distorted
stimuli. We propose an approach to select a challenging subset of 3000 stimuli
for which we collected 148929 quality judgments from over 4500 participants in
a large-scale crowdsourced subjective experiment. Leveraging our subject-rated
dataset, a learning-based quality metric for 3D graphics was proposed. Our
metric demonstrates state-of-the-art results on our dataset of textured meshes
and on a dataset of distorted meshes with vertex colors. Finally, we present an
application of our metric and dataset to explore the influence of distortion
interactions and content characteristics on the perceived quality of compressed
textured meshes.


title:
Pir\'a: A Bilingual Portuguese-English Dataset for Question-Answering
  about the Ocean

abstract:
  Current research in natural language processing is highly dependent on
carefully produced corpora. Most existing resources focus on English; some
resources focus on languages such as Chinese and French; few resources deal
with more than one language. This paper presents the Pir\'a dataset, a large
set of questions and answers about the ocean and the Brazilian coast both in
Portuguese and English. Pir\'a is, to the best of our knowledge, the first QA
dataset with supporting texts in Portuguese, and, perhaps more importantly, the
first bilingual QA dataset that includes this language. The Pir\'a dataset
consists of 2261 properly curated question/answer (QA) sets in both languages.
The QA sets were manually created based on two corpora: abstracts related to
the Brazilian coast and excerpts of United Nation reports about the ocean. The
QA sets were validated in a peer-review process with the dataset contributors.
We discuss some of the advantages as well as limitations of Pir\'a, as this new
resource can support a set of tasks in NLP such as question-answering,
information retrieval, and machine translation.


title:
Reproducibility of the Standard Model of diffusion in white matter on
  clinical MRI systems

abstract:
  Estimating intra- and extra-axonal microstructure parameters, such as volume
fractions and diffusivities, has been one of the major efforts in brain
microstructure imaging with MRI. The Standard Model (SM) of diffusion in white
matter has unified various modeling approaches based on impermeable narrow
cylinders embedded in locally anisotropic extra-axonal space. However,
estimating the SM parameters from a set of conventional diffusion MRI (dMRI)
measurements is ill-conditioned. Multidimensional dMRI helps resolve the
estimation degeneracies, but there remains a need for clinically feasible
acquisitions that yield robust parameter maps. Here we find optimal
multidimensional protocols by minimizing the mean-squared error of machine
learning-based SM parameter estimates for two 3T scanners with corresponding
gradient strengths of $40$ and $80\,\unit{mT/m}$. We assess intra-scanner and
inter-scanner repeatability for 15-minute optimal protocols by scanning 20
healthy volunteers twice on both scanners. The coefficients of variation all SM
parameters except free water fraction are $\lesssim 10\%$ voxelwise and $1-4
\%$ for their region-averaged values. As the achieved SM reproducibility
outcomes are similar to those of conventional diffusion tensor imaging, our
results enable robust in vivo mapping of white matter microstructure in
neuroscience research and in the clinic.


title:
Arithmetic differential geometry in the arithmetic PDE setting, I:
  connections

abstract:
  This is the first in a series on papers developing an arithmetic PDE analogue
of Riemannian geometry. The role of partial derivatives is played by Fermat
quotient operations with respect to several Frobenius elements in the absolute
Galois group of a $p$-adic field. Existence and uniqueness of geodesics and of
Levi-Civita and Chern connections are proved in this context. In a sequel to
this paper a theory of arithmetic Riemannian curvature and characteristic
classes will be developed.


title:
Inflation with two-form field: the production of primordial black holes
  and gravitational waves

abstract:
  Antisymmetric tensor field (two-form field) is a ubiquitous component in
string theory and generally couples to the scalar sector through its kinetic
term. In this paper, we propose a cosmological scenario that the particle
production of two-form field, which is triggered by the background motion of
the coupled inflaton field, occurs at the intermediate stage of inflation and
generates the sizable amount of primordial black holes as dark matter after
inflation. We also compute the secondary gravitational waves sourced by the
curvature perturbation and show that the resultant power spectra are testable
with the future space-based laser interferometers.


title:
The relationship of the Gaussian curvature with the curvature of a
  Cowen-Douglas operator

abstract:
  It has been recently shown that if $K$ is a sesqui-analytic scalar valued
non-negative definite kernel on a domain $\Omega$ in $\mathbb C^m$, then the
function $\big(K^2\partial_i\bar{\partial}_j\log K\big )_{i,j=1}^ m,$ is also a
non-negative definite kernel on $\Omega$. In this paper, we discuss two
consequences of this result. The first one strengthens the curvature inequality
for operators in the Cowen-Douglas class $B_1(\Omega)$ while the second one
gives a relationship of the reproducing kernel of a submodule of certain
Hilbert modules with the curvature of the associated quotient module.


title:
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary
  Time-Series

abstract:
  Real-world time-series datasets often violate the assumptions of standard
supervised learning for forecasting -- their distributions evolve over time,
rendering the conventional training and model selection procedures suboptimal.
In this paper, we propose a novel method, Self-Adaptive Forecasting (SAF), to
modify the training of time-series forecasting models to improve their
performance on forecasting tasks with such non-stationary time-series data. SAF
integrates a self-adaptation stage prior to forecasting based on `backcasting',
i.e. predicting masked inputs backward in time. This is a form of test-time
training that creates a self-supervised learning problem on test samples before
performing the prediction task. In this way, our method enables efficient
adaptation of encoded representations to evolving distributions, leading to
superior generalization. SAF can be integrated with any canonical
encoder-decoder based time-series architecture such as recurrent neural
networks or attention-based architectures. On synthetic and real-world datasets
in domains where time-series data are known to be notoriously non-stationary,
such as healthcare and finance, we demonstrate a significant benefit of SAF in
improving forecasting accuracy.


title:
Model-Free Reinforcement Learning for Symbolic Automata-encoded
  Objectives

abstract:
  Reinforcement learning (RL) is a popular approach for robotic path planning
in uncertain environments. However, the control policies trained for an RL
agent crucially depend on user-defined, state-based reward functions. Poorly
designed rewards can lead to policies that do get maximal rewards but fail to
satisfy desired task objectives or are unsafe. There are several examples of
the use of formal languages such as temporal logics and automata to specify
high-level task specifications for robots (in lieu of Markovian rewards).
Recent efforts have focused on inferring state-based rewards from formal
specifications; here, the goal is to provide (probabilistic) guarantees that
the policy learned using RL (with the inferred rewards) satisfies the
high-level formal specification. A key drawback of several of these techniques
is that the rewards that they infer are sparse: the agent receives positive
rewards only upon completion of the task and no rewards otherwise. This
naturally leads to poor convergence properties and high variance during RL. In
this work, we propose using formal specifications in the form of symbolic
automata: these serve as a generalization of both bounded-time temporal
logic-based specifications as well as automata. Furthermore, our use of
symbolic automata allows us to define non-sparse potential-based rewards which
empirically shape the reward surface, leading to better convergence during RL.
We also show that our potential-based rewarding strategy still allows us to
obtain the policy that maximizes the satisfaction of the given specification.


title: BAM: Bayes with Adaptive Memory

abstract:
  Online learning via Bayes' theorem allows new data to be continuously
integrated into an agent's current beliefs. However, a naive application of
Bayesian methods in non stationary environments leads to slow adaptation and
results in state estimates that may converge confidently to the wrong parameter
value. A common solution when learning in changing environments is to
discard/downweight past data; however, this simple mechanism of "forgetting"
fails to account for the fact that many real-world environments involve
revisiting similar states. We propose a new framework, Bayes with Adaptive
Memory (BAM), that takes advantage of past experience by allowing the agent to
choose which past observations to remember and which to forget. We demonstrate
that BAM generalizes many popular Bayesian update rules for non-stationary
environments. Through a variety of experiments, we demonstrate the ability of
BAM to continuously adapt in an ever-changing world.


title:
Parameter-free Online Linear Optimization with Side Information via
  Universal Coin Betting

abstract:
  A class of parameter-free online linear optimization algorithms is proposed
that harnesses the structure of an adversarial sequence by adapting to some
side information. These algorithms combine the reduction technique of Orabona
and P{\'a}l (2016) for adapting coin betting algorithms for online linear
optimization with universal compression techniques in information theory for
incorporating sequential side information to coin betting. Concrete examples
are studied in which the side information has a tree structure and consists of
quantized values of the previous symbols of the adversarial sequence, including
fixed-order and variable-order Markov cases. By modifying the context-tree
weighting technique of Willems, Shtarkov, and Tjalkens (1995), the proposed
algorithm is further refined to achieve the best performance over all adaptive
algorithms with tree-structured side information of a given maximum order in a
computationally efficient manner.


title:
An Experimental Design Approach for Regret Minimization in Logistic
  Bandits

abstract:
  In this work we consider the problem of regret minimization for logistic
bandits. The main challenge of logistic bandits is reducing the dependence on a
potentially large problem dependent constant $\kappa$ that can at worst scale
exponentially with the norm of the unknown parameter $\theta_{\ast}$. Abeille
et al. (2021) have applied self-concordance of the logistic function to remove
this worst-case dependence providing regret guarantees like
$O(d\log^2(\kappa)\sqrt{\dot\mu T}\log(|\mathcal{X}|))$ where $d$ is the
dimensionality, $T$ is the time horizon, and $\dot\mu$ is the variance of the
best-arm. This work improves upon this bound in the fixed arm setting by
employing an experimental design procedure that achieves a minimax regret of
$O(\sqrt{d \dot\mu T\log(|\mathcal{X}|)})$. Our regret bound in fact takes a
tighter instance (i.e., gap) dependent regret bound for the first time in
logistic bandits. We also propose a new warmup sampling algorithm that can
dramatically reduce the lower order term in the regret in general and prove
that it can replace the lower order term dependency on $\kappa$ to
$\log^2(\kappa)$ for some instances. Finally, we discuss the impact of the bias
of the MLE on the logistic bandit problem, providing an example where $d^2$
lower order regret (cf., it is $d$ for linear bandits) may not be improved as
long as the MLE is used and how bias-corrected estimators may be used to make
it closer to $d$.


title:
Probing Plasma Physics with Spectral Index Maps of Accreting Black Holes
  on Event Horizon Scales

abstract:
  The Event Horizon Telescope (EHT) collaboration has produced the first
resolved images of M87*, the supermassive black hole at the centre of the
elliptical galaxy M87. As both technology and analysis pipelines improve, it
will soon become possible to produce spectral index maps of black hole
accretion flows on event horizon scales. In this work, we predict spectral
index maps of both M87* and Sgr A* by applying the general relativistic
radiative transfer (GRRT) code {\sc ipole} to a suite of general relativistic
magnetohydrodynamic (GRMHD) simulations. We analytically explore how the
spectral index increases with increasing magnetic field strength, electron
temperature, and optical depth. Consequently, spectral index maps grow more
negative with increasing radius in almost all models, since all of these
quantities tend to be maximized near the event horizon. Additionally, photon
ring geodesics exhibit more positive spectral indices, since they sample the
innermost regions of the accretion flow with the most extreme plasma
conditions. Spectral index maps are sensitive to highly uncertain plasma
heating prescriptions (the electron temperature and distribution function).
However, if our understanding of these aspects of plasma physics can be
tightened, even the spatially unresolved spectral index around 230 GHz can be
used to discriminate between models. In particular, Standard and Normal
Evolution (SANE) flows tend to exhibit more negative spectral indices than
Magnetically Arrested Disk (MAD) flows due to differences in the characteristic
magnetic field strength and temperature of emitting plasma.


title:
Pressure-Strain Interaction as the Energy Dissipation Estimate in
  Collisionless Plasma

abstract:
  The dissipative mechanism in weakly collisional plasma is a topic that
pervades decades of studies without a consensus solution. We compare several
energy dissipation estimates based on energy transfer processes in plasma
turbulence and provide justification for the pressure-strain interaction as a
direct estimate of the energy dissipation rate. The global and scale-by-scale
energy balances are examined in 2.5D and 3D kinetic simulations. We show that
the global internal energy increase and the temperature enhancement of each
species are directly tracked by the pressure-strain interaction. The
incompressive part of the pressure-strain interaction dominates over its
compressive part in all simulations considered. The scale-by-scale energy
balance is quantified by scale filtered Vlasov-Maxwell equations, a kinetic
plasma approach, and the lag dependent von K\'arm\'an-Howarth equation, an
approach based on fluid models. We find that the energy balance is exactly
satisfied across all scales, but the lack of a well-defined inertial range
influences the distribution of the energy budget among different terms in the
inertial range. Therefore, the widespread use of the Yaglom relation to
estimating dissipation rate is questionable in some cases, especially when the
scale separation in the system is not clearly defined. In contrast, the
pressure-strain interaction balances exactly the dissipation rate at kinetic
scales regardless of the scale separation.


title:
Equilateral Triangulations and The Postcritical Dynamics of Meromorphic
  Functions

abstract:
  We show that any dynamics on any planar set $S$ discrete in some domain $D$
can be realized by the postcritical dynamics of a function holomorphic in $D$,
up to a small perturbation. A key step in the proof, and a result of
independent interest, is that any planar domain $D$ can be equilaterally
triangulated with triangles whose diameters $\rightarrow0$ (at any prescribed
rate) near $\partial D$.


title:
A Novel Service Deployment Policy in Fog Computing Considering The
  Degree of Availability and Fog Landscape Utilization Using Multiobjective
  Evolutionary Algorithms

abstract:
  Fog computing is a promising paradigm for real-time and mission-critical
Internet of Things (IoT) applications. Regarding the high distribution,
heterogeneity, and limitation of fog resources, applications should be placed
in a distributed manner to fully utilize these resources. In this paper, we
propose a linear formulation for assuring the different availability
requirements of application services while maximizing the utilization of fog
resources. We also compare three multiobjective evolutionary algorithms, namely
MOPSO, NSGA-II, and MOEA/D for a trade-off between the mentioned optimization
goals. The evaluation results in the iFogSim simulator demonstrate the
efficiency of all three algorithms and a generally better behavior of MOPSO
algorithm in terms of obtained objective values, application deadline
satisfaction, and execution time.


title:
Observation of Gigahertz Topological Valley Hall Effect in
  Nanoelectromechanical Phononic Crystals

abstract:
  Topological phononics offers numerous opportunities in manipulating elastic
waves that can propagate in solids without being backscattered. Due to the lack
of nanoscale imaging tools that aid the system design, however, acoustic
topological metamaterials have been mostly demonstrated in macroscale systems
operating at low (kilohertz to megahertz) frequencies. Here, we report the
realization of gigahertz topological valley Hall effect in
nanoelectromechanical AlN membranes. Propagation of elastic wave through
phononic crystals is directly visualized by microwave microscopy with
unprecedented sensitivity and spatial resolution. The valley Hall edge states,
protected by band topology, are vividly seen in both real- and momentum-space.
The robust valley-polarized transport is evident from the wave transmission
across local disorder and around sharp corners, as well as the power
distribution into multiple edge channels. Our work paves the way to exploit
topological physics in integrated acousto-electronic systems for classical and
quantum information processing in the microwave regime.


title:
Introduction to the dynamics of disordered systems: equilibrium and
  gradient descent

abstract:
  This manuscript contains the lecture notes of the short courses given by one
of us (F.Z.) at the summer school "Fundamental Problems in Statistical Physics
XV", held in Brunico, Italy, in July 2021, and, just before that, at the summer
school "Glassy Systems and Inter-Disciplinary Applications", held in Cargese,
France, in June 2021. The course was a short introductory overview of the
dynamics of disordered systems, focused in particular on the equilibrium
dynamics (with the associated glass transition), and on the simplest case of
off-equilibrium dynamics, namely the gradient descent dynamics. A few selected
topics (and references) are chosen, based on the authors' own taste and
competences, and on pedagogical reasons, without aiming at a complete review of
the subject.


title: OMLT: Optimization & Machine Learning Toolkit

abstract:
  The optimization and machine learning toolkit (OMLT) is an open-source
software package incorporating neural network and gradient-boosted tree
surrogate models, which have been trained using machine learning, into larger
optimization problems. We discuss the advances in optimization technology that
made OMLT possible and show how OMLT seamlessly integrates with the algebraic
modeling language Pyomo. We demonstrate how to use OMLT for solving
decision-making problems in both computer science and engineering.


title:
On the Efficiency and Quality of Protection of Preprovisioning in
  Elastic Optical Networks

abstract:
  The study of protection techniques, such as pre-provisioning (off-line) and
provisioning (on-line), has been explored in several ways in the optical
network literature. In the new Elastic Optical Network (EON) paradigm, the
pre-provisioning techniques were still little explored. Preprovisioning implies
the prior allocation of resources in the network for the transport and
protection of future connection demands, while the provisioning implies the
allocation of resources when the demand arrives in the network. Applying
preprovisioning reduces the downtime experienced by a connection after a
failure, which will reduce unavailability and potentially avoid penalties for
violation of Service Level Agreements (SLA) established with client networks.
This work aims to explore the main protection techniques and evaluate their
efficient in the EON scenario. The performance evaluation show that the use of
preprovisioning techniques are more efficient, significantly reducing the
network unavailability and bandwidth usage in EON networks. Our solution has an
unavailability 40 times lower than shared solutions being only 4% above the
optimum.


title: Generalized Causal Tree for Uplift Modeling

abstract:
  Uplift modeling is crucial in various applications ranging from marketing and
policy-making to personalized recommendations. The main objective is to learn
optimal treatment allocations for a heterogeneous population. A primary line of
existing work modifies the loss function of the decision tree algorithm to
identify cohorts with heterogeneous treatment effects. Another line of work
estimates the individual treatment effects separately for the treatment group
and the control group using off-the-shelf supervised learning algorithms. The
former approach that directly models the heterogeneous treatment effect is
known to outperform the latter in practice. However, the existing tree-based
methods are mostly limited to a single treatment and a single control use case,
except for a handful of extensions to multiple discrete treatments. In this
paper, we fill this gap in the literature by proposing a generalization to the
tree-based approaches to tackle multiple discrete and continuous-valued
treatments. We focus on a generalization of the well-known causal tree
algorithm due to its desirable statistical properties, but our generalization
technique can be applied to other tree-based approaches as well. We perform
extensive experiments to showcase the efficacy of our method when compared to
other methods.


title: Parallel Quantum Chemistry on Noisy Intermediate-Scale Quantum Computers

abstract:
  A novel parallel hybrid quantum-classical algorithm for the solution of the
quantum-chemical ground-state energy problem on gate-based quantum computers is
presented. This approach is based on the reduced density-matrix functional
theory (RDMFT) formulation of the electronic structure problem. For that
purpose, the density-matrix functional of the full system is decomposed into an
indirectly coupled sum of density-matrix functionals for all its subsystems
using the adaptive cluster approximation to RDMFT. The approximations involved
in the decomposition and the adaptive cluster approximation itself can be
systematically converged to the exact result. The solutions for the
density-matrix functionals of the effective subsystems involves a constrained
minimization over many-particle states that are approximated by parametrized
trial states on the quantum computer similarly to the variational quantum
eigensolver. The independence of the density-matrix functionals of the
effective subsystems introduces a new level of parallelization and allows for
the computational treatment of much larger molecules on a quantum computer with
a given qubit count. In addition, for the proposed algorithm techniques are
presented to reduce the qubit count, the number of quantum programs, as well as
its depth. The new approach is demonstrated for Hubbard-like systems on IBM
quantum computers based on superconducting transmon qubits.