title:
DIS physics at the EIC and LHeC and connections to the future LHC and
  $\nu$A programs

abstract:
  Deeply-inelastic scattering (DIS) stands to enter a golden age with the
prospect of precision programs at the Electron-Ion Collider (EIC) and Large
Hadron-electron Collider (LHeC). While these programs will be of considerable
importance to resolving longstanding issues in (non)perturbative QCD as well as
hadronic and nuclear structure, they will also have valuable implications for a
wider range of physics at the Energy and Intensity Frontiers, including at the
High-Luminosity LHC (HL-LHC) and future $\nu A$ facilities. In this plenary
contribution, we highlight a number of salient examples of the potential HEP
impact from the complementary EIC and LHeC programs drawn from their respective
Yellow Report and Whitepaper. Interested readers are encouraged to consult the
extensive studies and literature from which these examples are taken for more
detail.


title: Triple-leptoquark interactions for tree- and loop-level proton decays

abstract:
  We study the impact of triple-leptoquark interactions on matter stability for
two specific proton decay topologies that arise at the tree- and one-loop level
if and when they coexist. We demonstrate that the one-loop level topology is
much more relevant than the tree-level one when it comes to the proton decay
signatures despite the usual loop-suppression factor. We subsequently present
detailed analysis of the triple-leptoquark interaction effects on the proton
stability within one representative scenario to support our claim, where the
scenario in question simultaneously features a tree-level topology that yields
three-body proton decay $p\to e^+ e^+ e^-$ and a one-loop level topology that
induces two-body proton decays $p\to \pi^0 e^+$ and $p\to \pi^+ \bar{\nu}$. We
also provide a comprehensive list of the leading-order proton decay channels
for all non-trivial cubic and quartic contractions involving three scalar
leptoquark multiplets that generate triple-leptoquark interactions of our
interest, where in the latter case one of the scalar multiplets is the Standard
Model Higgs doublet.


title: Elements of Celestial Conformal Field Theory

abstract:
  In celestial holography, four-dimensional scattering amplitudes are
considered as two-dimensional conformal correlators of a putative
two-dimensional celestial conformal field theory (CCFT). The simplest way of
converting momentum space amplitudes into CCFT correlators is by taking their
Mellin transforms with respect to light-cone energies. For massless particles,
like gluons, however, such a construction leads to three-point and four-point
correlators that vanish everywhere except for a measure zero hypersurface of
celestial coordinates. This is due to the four-dimensional momentum
conservation law that constrains the insertion points of the operators
associated with massless particles. These correlators are reminiscent of
Coulomb gas correlators that, in the absence of background charges, vanish due
to charge conservation. We supply the background momentum by coupling
Yang-Mills theory to a background dilaton field, with the (complex) dilaton
source localized on the celestial sphere. This picture emerges from the
physical interpretation of the solutions of the system of differential
equations discovered by Banerjee and Ghosh. We show that the solutions can be
written as Mellin transforms of the amplitudes evaluated in such a dilaton
background. The resultant three-gluon and four-gluon amplitudes are
single-valued functions of celestial coordinates enjoying crossing symmetry and
all other properties expected from standard CFT correlators. We use them to
extract OPEs and compare them with the OPEs extracted from multi-gluon
celestial amplitudes without a dilaton background. We perform the conformal
block decomposition of the four-gluon single-valued correlator and determine
the dimensions, spin and group representations of the entire primary field
spectrum of the Yang-Mills sector of CCFT.


title:
The intracluster light on Frontier Fields clusters Abell 370 and Abell
  S1063

abstract:
  We analyzed the contribution of the intracluster light (ICL) to the total
luminosity of two massive galaxy clusters observed by the Hubble Space
Telescope within the Frontier Fields program, Abell 370 (z ~ 0.375) and Abell
S1063 (z ~ 0.348), in order to correlate it with the dynamical stage of these
systems.We applied an algorithm based on the Chebyshev-Fourier functions called
CICLE, specially developed to disentangle the ICL from the light of galaxies
and measure the ICL fraction. We measured the ICL fraction in three broadband
optical filters, F435W, F606W, and F814W, without assuming any prior hypothesis
about the ICL physical properties or morphology. The results obtained from the
ICL fraction vary between ~7% - 25%, and ~3% - 22% for both A370 and AS1063,
respectively, which are consistent with theoretical predictions for the total
amount of ICL obtained by ICL formation and evolution simulations.We found
enhanced ICL fractions in the intermediate filter F606W for both clusters and
we suggest that this is due to the presence of an excess of
younger/lower-metallicity stars in the ICL compared to the cluster galaxies. We
conclude that both Abell 370 and Abell S1063 are merging systems since they
exhibit a similar feature as merging CLASH and Frontier Fields clusters
sub-sample previously analyzed. We compare these results to the dynamical
indicators obtained through different methods and we reinforce the use of ICL
as a new and independent method to determine the dynamical state of clusters of
galaxies.


title:
Improved Wald formalism and First Law of Dyonic Black Strings with Mixed
  Chern-Simons Terms

abstract:
  We study the first law of thermodynamics of dyonic black strings carrying a
linear momentum in type IIA string theory compactified on K3 with leading order
$\alpha'$ corrections. The low energy effective action contains mixed
Chern-Simons terms of the form $-2B_{(2)}\wedge {\rm tr}(R(\Gamma_\pm)\wedge
R(\Gamma_\pm))$ which is equivalent to $2H_{(3)}\wedge
\mathrm{CS}_{(3)}(\Gamma_\pm)$ up to a total derivative. We find that the naive
application of Wald entropy formula leads to two different answers associated
with the two formulations of the mixed Chern-Simons terms. Surprisingly,
neither of them satisfies the first law of thermodynamics for other conserved
charges computed unambiguously using the standard methods. We resolve this
problem by carefully evaluating the full infinitesimal Hamiltonian at both
infinity and horizon, including contributions from terms proportional to the
Killing vector which turn out to be nonvanishing on the horizon and
indispensable to establish the first law. We find that the infinitesimal
Hamiltionian associated with $-2B_{(2)}\wedge {\rm tr}(R(\Gamma_\pm)\wedge
R(\Gamma_\pm))$ requires an improvement via adding a closed but non-exact term,
which vanishes when the string does not carry either the magnetic charge or
linear momentum. Consequently, both formulations of the mixed Chern-Simons
terms yield the same result of the entropy that however does not agree with the
Wald entropy formula.


title: The Microphysics of Early Dark Energy

abstract:
  Early Dark Energy (EDE) relies on scalar field dynamics to resolve the Hubble
tension, by boosting the pre-recombination length scales and thereby raising
the CMB-inferred value of the Hubble constant into agreement with late universe
probes. However, the collateral effect of scalar field microphysics on the
linear perturbation spectra appears to preclude a fully satisfactory solution.
$H_0$ is not raised without the inclusion of a late universe prior, and the
"$S_8$-tension", a discrepancy between early- and late-universe measurements of
the structure growth parameter, is exacerbated. What if EDE is not a scalar
field? Here, we investigate whether different microphysics, encoded in the
constitutive relationships between pressure and energy density fluctuations,
can relieve these tensions. We show that EDE with an anisotropic sound speed
can soften both the $H_0$ and $S_8$ tensions while still providing a quality
fit to CMB data. Future observations from the CMB-S4 experiment may be able to
distinguish the underlying microphysics at the $4\sigma$ level, and thereby
test whether a scalar field or some richer physics is at work.


title: The deepest problem: some perspectives on quantum gravity

abstract:
  Quantum gravity is likely the deepest problem facing current physics. While
traditionally associated with short distance nonrenormalizability, it is
evident that the long distance problem of unitarity, arising at high energies
with black hole formation, is more profound. This reveals a conflict between
foundational principles of quantum field theory: those of quantum mechanics,
relativity, and locality. Difficulties modifying quantum mechanics suggest a
"quantum-first" approach, with other principles as mathematical properties of a
quantum space of states. A challenge is how to describe locality, in terms of
Hilbert space structure. Perturbative gravity gives clues, with structure
apparently different than in field theory. The mathematical structure of
subsystems plausibly supplants conventional locality and plays a foundational
role in the theory. This view differs from one of spacetime "emerging" from
another quantum system. If a black hole behaves as a subsystem, a "black hole
theorem" says that unitarity requires interactions with its environment
depending on its state, or more drastic phenomena. Minimal interactions can be
parameterized, in an effective approach; they could arise from wormholes or
other fundamental dynamics. These or other near-horizon modifications
potentially alter electromagnetic or gravitational signatures of this strong
gravity region, now being probed in a new era of observation; it is important
to seek observational clues for or constraints on such scenarios. One may also
investigate quantum gravity via its S-matrix. New perturbative structure has
been discovered there, but the harder question again goes beyond to the
nonperturbative regime. Long-distance behavior of amplitudes indicates novel
analytic behavior; further exploration may provide important clues. Other key
questions regard quantum description of cosmologies, and of associated
observables.


title:
A Parallelized Bayesian Approach To Accelerated Gravitational-Wave
  Background Characterization

abstract:
  The characterization of nanohertz-frequency gravitational waves (GWs) with
pulsar-timing arrays requires a continual expansion of datasets and monitored
pulsars. Whereas detection of the stochastic GW background is predicated on
measuring a distinctive pattern of inter-pulsar correlations, characterizing
the background's spectrum is driven by information encoded in the power spectra
of the individual pulsars' time series. We propose a new technique for rapid
Bayesian characterization of the stochastic GW background that is fully
parallelized over pulsar datasets. This Factorized Likelihood (FL) technique
empowers a modular approach to parameter estimation of the GW background,
multi-stage model selection of a spectrally-common stochastic process and
quadrupolar inter-pulsar correlations, and statistical cross-validation of
measured signals between independent pulsar sub-arrays. We demonstrate the
equivalence of this technique's efficacy with the full pulsar-timing array
likelihood, yet at a fraction of the required time. Our technique is fast,
easily implemented, and trivially allows for new data and pulsars to be
combined with legacy datasets without re-analysis of the latter.


title:
Excitations in the Higher Lattice Gauge Theory Model for Topological
  Phases I: Overview

abstract:
  In this series of papers, we study a Hamiltonian model for 3+1d topological
phases, based on a generalisation of lattice gauge theory known as "higher
lattice gauge theory". Higher lattice gauge theory has so called "2-gauge
fields" describing the parallel transport of lines, just as ordinary gauge
fields describe the parallel transport of points. In the Hamiltonian model this
is represented by having labels on the plaquettes of the lattice, as well as
the edges. In this paper we summarize our findings in an accessible manner,
with more detailed results and proofs to be presented in the other papers in
the series. The Hamiltonian model supports both point-like and loop-like
excitations, with non-trivial braiding between these excitations. We explicitly
construct operators to produce and move these excitations, and use these to
find the loop-loop and point-loop braiding relations. These creation operators
also reveal that some of the excitations are confined, costing energy to
separate. This is discussed in the context of condensation/confinement
transitions between different cases of this model. We also discuss the
topological charges of the model and use explicit measurement operators to
re-derive a relationship between the number of charges measured by a 2-torus
and the ground-state degeneracy of the model on the 3-torus. From these
measurement operators, we can see that the ground state degeneracy on the
3-torus is related to the number of types of linked loop-like excitations.


title:
Gravitational waves from patterns of electroweak symmetry breaking: an
  effective perspective

abstract:
  The future space-borne gravitational wave (GW) detectors would provide a
promising probe for the new physics beyond the standard model that admits the
first-order phase transitions. The predictions for the GW background vary
sensitively among different concrete particle physics models but also share a
large degeneracy in the model buildings, which motivates an effective model
description on the phase transition based on different patterns of the
electroweak symmetry breaking (EWSB). In this paper, using the scalar $N$-plet
model as a demonstration, we propose an effective classification for three
different patterns of EWSB: (1) radiative symmetry breaking with classical
scale invariance, (2) Higgs mechanism in generic scalar extension, and (3)
higher dimensional operators. We conclude that a strong first-order phase
transition could be realized for (1) and (2) with a small quartic coupling and
a small isospin of an additional $N$-plet field for the light scalar field
model with and without the classical scale invariance, and (3) with a large
mixing coupling between scalar fields and a large isospin of the $N$-plet field
for the heavy scalar field model.


title:
Controlling Epidemic Spread using Probabilistic Diffusion Models on
  Networks

abstract:
  The spread of an epidemic is often modeled by an SIR random process on a
social network graph. The MinINF problem for optimal social distancing involves
minimizing the expected number of infections, when we are allowed to break at
most $B$ edges; similarly the MinINFNode problem involves removing at most $B$
vertices. These are fundamental problems in epidemiology and network science.
While a number of heuristics have been considered, the complexity of these
problems remains generally open. In this paper, we present two bicriteria
approximation algorithms for MinINF, which give the first non-trivial
approximations for this problem. The first is based on the cut sparsification
result of Karger \cite{karger:mathor99}, and works when the transmission
probabilities are not too small. The second is a Sample Average Approximation
(SAA) based algorithm, which we analyze for the Chung-Lu random graph model. We
also extend some of our results to tackle the MinINFNode problem.


title:
Unified treatment of exact and approximate scalar electromagnetic wave
  scattering

abstract:
  Under conditions of strong scattering, a dilemma often arises regarding the
best numerical method to use. Main competitors are the Born series, the Beam
Propagation Method, and direct solution of the Lippmann-Schwinger equation.
However, analytical relationships between the three methods have not yet, to
our knowledge, been explicitly stated. Here, we bridge this gap in the
literature. In addition to overall insight about aspects of optical scattering
that are best numerically captured by each method, our approach allows us to
derive approximate error bounds to be expected under various scattering
conditions.


title: Analysis of Unitarity in Conformal Quantum Gravity

abstract:
  We perform a canonical quantization of Weyl's conformal gravity by means of
the covariant operator formalism and investigate the unitarity of the resulting
quantum theory. After reducing the originally fourth order theory to second
order in time derivatives via the introduction of an auxiliary tensor field, we
identify the full Fock space of quantum states under a BRST construction that
includes Faddeev-Popov ghost fields corresponding to Weyl transformations.
Using the Kugo-Ojima quartet mechanism, we identify the physical subspace of
quantum states and find that the subspace containing the transverse spin-2
states comes equipped with an indefinite inner product metric and a
one-particle Hamiltonian that possesses only a single eigenstate. We construct
the LSZ reduction formula for the S-matrix in this spin-2 subspace and find
that unitarity is violated in scattering events. The explicit way in which this
violation occurs represents a new view on the ghost-problem in quadratic
theories of quantum gravity.


title: The learning phases in NN: From Fitting the Majority to Fitting a Few

abstract:
  The learning dynamics of deep neural networks are subject to controversy.
Using the information bottleneck (IB) theory separate fitting and compression
phases have been put forward but have since been heavily debated. We approach
learning dynamics by analyzing a layer's reconstruction ability of the input
and prediction performance based on the evolution of parameters during
training. We show that a prototyping phase decreasing reconstruction loss
initially, followed by reducing classification loss of a few samples, which
increases reconstruction loss, exists under mild assumptions on the data. Aside
from providing a mathematical analysis of single layer classification networks,
we also assess the behavior using common datasets and architectures from
computer vision such as ResNet and VGG.


title:
A hybrid level-set / embedded boundary method applied to
  solidification-melt problems

abstract:
  In this paper, we introduce a novel way to represent the interface for
two-phase flows with phase change. We combine a level-set method with a
Cartesian embedded boundary method and take advantage of both. This is part of
an effort to obtain a numerical strategy relying on Cartesian grids allowing
the simulation of complex boundaries with possible change of topology while
retaining a high-order representation of the gradients on the interface and the
capability of properly applying boundary conditions on the interface. This
leads to a two-fluid conservative second-order numerical method. The ability of
the method to correctly solve Stefan problems, onset dendrite growth with and
without anisotropy is demonstrated through a variety of test cases. Finally, we
take advantage of the two-fluid representation to model a Rayleigh--B\'enard
instability with a melting boundary.


title: A Similarity Approach to Cities and Features

abstract:
  Characterizing the structure of cities constitutes an important task since
the identification of similar cities can promote sharing of respective
experiences. In the present work, we consider 20 European cities from 5
respective countries and with comparable populations, each of which
characterized in terms of four topological as well as one geometrical feature.
These cities are then mapped into respective networks by considering their
pairwise similarity as gauged by the coincidence methodology, which consists of
combining the Jaccard and interiority indices. The methodology incorporates a
parameter alpha that can control the relative contribution of features with the
same or opposite signs to the overall similarity. Interestingly, the maximum
modularity cities network is obtained for a non-standard parameter
configuration, showing that it could not be obtained were not for the adoption
of the parameter alpha. The network with maximum modularity presents four
communities that can be directly related to four of the five considered
countries, corroborating not only the effectiveness of the adopted features and
similarity methodology, but also indicating a surprising tendency of the cities
from a same country of being similar, while differing from cities from other
countries. The coincidence methodology was then applied in order to investigate
the effect of several features combinations on the respectively obtained
networks, leading to a highly modular features network containing four main
communities that can be understood as the main possible models for the
considered cities.


title:
Efficient Distributed Machine Learning via Combinatorial Multi-Armed
  Bandits

abstract:
  We consider the distributed stochastic gradient descent problem, where a main
node distributes gradient calculations among $n$ workers from which at most $b
\leq n$ can be utilized in parallel. By assigning tasks to all the workers and
waiting only for the $k$ fastest ones, the main node can trade-off the error of
the algorithm with its runtime by gradually increasing $k$ as the algorithm
evolves. However, this strategy, referred to as adaptive k sync, can incur
additional costs since it ignores the computational efforts of slow workers. We
propose a cost-efficient scheme that assigns tasks only to $k$ workers and
gradually increases $k$. As the response times of the available workers are
unknown to the main node a priori, we utilize a combinatorial multi-armed
bandit model to learn which workers are the fastest while assigning gradient
calculations, and to minimize the effect of slow workers. Assuming that the
mean response times of the workers are independent and exponentially
distributed with different means, we give empirical and theoretical guarantees
on the regret of our strategy, i.e., the extra time spent to learn the mean
response times of the workers. Compared to adaptive k sync, our scheme achieves
significantly lower errors with the same computational efforts while being
inferior in terms of speed.


title:
OpenKBP-Opt: An international and reproducible evaluation of 76
  knowledge-based planning pipelines

abstract:
  We establish an open framework for developing plan optimization models for
knowledge-based planning (KBP) in radiotherapy. Our framework includes
reference plans for 100 patients with head-and-neck cancer and high-quality
dose predictions from 19 KBP models that were developed by different research
groups during the OpenKBP Grand Challenge. The dose predictions were input to
four optimization models to form 76 unique KBP pipelines that generated 7600
plans. The predictions and plans were compared to the reference plans via: dose
score, which is the average mean absolute voxel-by-voxel difference in dose a
model achieved; the deviation in dose-volume histogram (DVH) criterion; and the
frequency of clinical planning criteria satisfaction. We also performed a
theoretical investigation to justify our dose mimicking models. The range in
rank order correlation of the dose score between predictions and their KBP
pipelines was 0.50 to 0.62, which indicates that the quality of the predictions
is generally positively correlated with the quality of the plans. Additionally,
compared to the input predictions, the KBP-generated plans performed
significantly better (P<0.05; one-sided Wilcoxon test) on 18 of 23 DVH
criteria. Similarly, each optimization model generated plans that satisfied a
higher percentage of criteria than the reference plans. Lastly, our theoretical
investigation demonstrated that the dose mimicking models generated plans that
are also optimal for a conventional planning model. This was the largest
international effort to date for evaluating the combination of KBP prediction
and optimization models. In the interest of reproducibility, our data and code
is freely available at https://github.com/ababier/open-kbp-opt.


title:
Ionization potentials and electron affinities of Rg, Cn, Nh, and Fl
  superheavy elements

abstract:
  The successive ionization potentials (IPs) and electron affinities (EAs) for
superheavy elements with $111 \leq Z \leq 114$, namely, Rg, Cn, Nh, and Fl are
reexamined using the relativistic Fock-space coupled-cluster method with
nonperturbative single (S), double (D), and triple (T) cluster amplitudes
(FS-CCSDT). For the most of considered quantities, the triple-amplitude
contributions turn out to be important. The Breit and frequency-dependent Breit
corrections are evaluated by means of the configuration-interaction method. The
quantum-electrodynamics corrections to the IPs and EAs are taken into account
within the model-QED-operator approach. The obtained results are within 0.10 eV
uncertainty.


title: Understanding viscoelastic flow instabilities: Oldroyd-B and beyond

abstract:
  The Oldroyd-B model has been used extensively to predict a host of
instabilities in shearing flows of viscoelastic fluids, often realized
experimentally using polymer solutions. The present review, written on the
occasion of the birth centenary of James Oldroyd, provides an overview of
instabilities found across major classes of shearing flows. These comprise (i)
the canonical rectilinear shearing flows including plane Couette, plane and
pipe Poiseuille flows; (ii) viscometric shearing flows with curved streamlines
such as those in the Taylor-Couette, cone-and-plate and parallel-plate
geometries; (iii) non-viscometric shearing flows with an underlying extensional
flow topology such as the flow in a cross-slot device; and (iv) multilayer
shearing flows. While the underlying focus in all these cases is on results
obtained using the Oldroyd-B model, we also discuss their relation to the
actual instability, and as to how the shortcomings of the Oldroyd-B model may
be overcome by the use of more realistic constitutive models. All the three
commonly used tools of stability analysis, viz., modal linear stability,
nonmodal stability, and weakly nonlinear stability analyses are discussed, with
supporting evidence from experiments and numerical simulations as appropriate.
Despite only accounting for a shear-rate-independent viscosity and first normal
stress coefficient, the Oldroyd-B model is able to qualitatively predict the
majority of instabilities in the aforementioned shearing flows. The review also
highlights, where appropriate, open questions in the area of viscoelastic
stability.


title: Quantum Neuron with Separable-State Encoding

abstract:
  The use of advanced quantum neuron models for pattern recognition
applications requires fault tolerance. Therefore, it is not yet possible to
test such models on a large scale in currently available quantum processors. As
an alternative, we propose a quantum perceptron (QP) model that uses a reduced
number of multi-qubit gates and is therefore less susceptible to quantum errors
in current actual quantum computers with limited tolerance. The proposed
quantum algorithm is superior to its classical counterpart, although since it
does not take full advantage of quantum entanglement, it provides a lower
encoding power than other quantum algorithms using multiple qubit entanglement.
However, the use of separable-sate encoding allows for testing the algorithm
and different training schemes at a large scale in currently available
non-fault tolerant quantum computers. We demonstrate the performance of the
proposed model by implementing a few qubits version of the QP in a simulated
quantum computer. The proposed QP uses an N-ary encoding of the binary input
data characterizing the patterns. We develop a hybrid (quantum-classical)
training procedure for simulating the learning process of the QP and test their
efficiency.


title: Porous cylinder arrays for optimal wake and drag characteristics

abstract:
  The root systems of mangroves, a tree species found in intertidal tropical
and sub-tropical coastal zones, provide a natural barrier that dissipates wave
energy effectively and reduces sediment erosion. In this work, we use a
combination of experiments and numerical simulations to examine the wake and
drag characteristics of porous arrays of cylinders, which serve as simplified
models of mangrove root networks. Optimal arrangements of the arrays are
obtained by coupling Navier-Stokes simulations with a multi-objective
optimization algorithm, which seeks configurations that minimize wake enstrophy
and maximize drag acting on the porous structure. These optimal configurations
are investigated using Particle Image Velocimetry, and the internal and
external flow around the porous arrays are analyzed using a combination of
Proper Orthogonal Decomposition and Lagrangian particle tracking. Large
variations in drag and enstrophy are observed by varying the relative positions
of the cylinders, which indicates that the geometrical arrangement of porous
arrays plays a prominent role in determining wake and drag characteristics. A
sensitivity analysis suggests that enstrophy is more sensitive than drag to
specific cylinder placement, and depends on distinctive flow patterns that
develop in the interior due to interactions among individual cylinders. Arrays
with higher drag are found to have higher average flow speeds in the wake and
give rise to time-varying Lagrangian Coherent Structures, making them
unfavorable for particle deposition and erosion. Overall, the results indicate
that high-level metrics such as array porosity may not be sufficient on their
own for predicting wake and drag characteristics.


title:
A note on feebly compact semitopological symmetric inverse semigroups of
  a bounded finite rank

abstract:
  We study feebly compact shift-continuous $T_1$-topologies on the symmetric
inverse semigroup $\mathscr{I}_\lambda^n$ of finite transformations of the rank
$\leqslant n$. It is proved that such $T_1$-topology is sequentially pracompact
if and only if it is feebly compact. Also, we show that every shift-continuous
feebly $\omega$-bounded $T_1$-topology on $\mathscr{I}_\lambda^n$ is compact.


title:
Contextualize differential privacy in image database: a lightweight
  image differential privacy approach based on principle component analysis
  inverse

abstract:
  Differential privacy (DP) has been the de-facto standard to preserve
privacy-sensitive information in database. Nevertheless, there lacks a clear
and convincing contextualization of DP in image database, where individual
images' indistinguishable contribution to a certain analysis can be achieved
and observed when DP is exerted. As a result, the privacy-accuracy trade-off
due to integrating DP is insufficiently demonstrated in the context of
differentially-private image database. This work aims at contextualizing DP in
image database by an explicit and intuitive demonstration of integrating
conceptional differential privacy with images. To this end, we design a
lightweight approach dedicating to privatizing image database as a whole and
preserving the statistical semantics of the image database to an adjustable
level, while making individual images' contribution to such statistics
indistinguishable. The designed approach leverages principle component analysis
(PCA) to reduce the raw image with large amount of attributes to a lower
dimensional space whereby DP is performed, so as to decrease the DP load of
calculating sensitivity attribute-by-attribute. The DP-exerted image data,
which is not visible in its privatized format, is visualized through PCA
inverse such that both a human and machine inspector can evaluate the
privatization and quantify the privacy-accuracy trade-off in an analysis on the
privatized image database. Using the devised approach, we demonstrate the
contextualization of DP in images by two use cases based on deep learning
models, where we show the indistinguishability of individual images induced by
DP and the privatized images' retention of statistical semantics in deep
learning tasks, which is elaborated by quantitative analyses on the
privacy-accuracy trade-off under different privatization settings.


title: Towards Verifiable Federated Learning

abstract:
  Federated learning (FL) is an emerging paradigm of collaborative machine
learning that preserves user privacy while building powerful models.
Nevertheless, due to the nature of open participation by self-interested
entities, it needs to guard against potential misbehaviours by legitimate FL
participants. FL verification techniques are promising solutions for this
problem. They have been shown to effectively enhance the reliability of FL
networks and help build trust among participants. Verifiable federated learning
has become an emerging topic of research that has attracted significant
interest from the academia and the industry alike. Currently, there is no
comprehensive survey on the field of verifiable federated learning, which is
interdisciplinary in nature and can be challenging for researchers to enter
into. In this paper, we bridge this gap by reviewing works focusing on
verifiable FL. We propose a novel taxonomy for verifiable FL covering both
centralised and decentralised FL settings, summarise the commonly adopted
performance evaluation approaches, and discuss promising directions towards a
versatile verifiable FL framework.


title: Single Trajectory Nonparametric Learning of Nonlinear Dynamics

abstract:
  Given a single trajectory of a dynamical system, we analyze the performance
of the nonparametric least squares estimator (LSE). More precisely, we give
nonasymptotic expected $l^2$-distance bounds between the LSE and the true
regression function, where expectation is evaluated on a fresh, counterfactual,
trajectory. We leverage recently developed information-theoretic methods to
establish the optimality of the LSE for nonparametric hypotheses classes in
terms of supremum norm metric entropy and a subgaussian parameter. Next, we
relate this subgaussian parameter to the stability of the underlying process
using notions from dynamical systems theory. When combined, these developments
lead to rate-optimal error bounds that scale as $T^{-1/(2+q)}$ for suitably
stable processes and hypothesis classes with metric entropy growth of order
$\delta^{-q}$. Here, $T$ is the length of the observed trajectory, $\delta \in
\mathbb{R}_+$ is the packing granularity and $q\in (0,2)$ is a complexity term.
Finally, we specialize our results to a number of scenarios of practical
interest, such as Lipschitz dynamics, generalized linear models, and dynamics
described by functions in certain classes of Reproducing Kernel Hilbert Spaces
(RKHS).


title: Private Online Prefix Sums via Optimal Matrix Factorizations

abstract:
  Motivated by differentially-private (DP) training of machine learning models
and other applications, we investigate the problem of computing prefix sums in
the online (streaming) setting with DP. This problem has previously been
addressed by special-purpose tree aggregation schemes with hand-crafted
estimators. We show that these previous schemes can all be viewed as specific
instances of a broad class of matrix-factorization-based DP mechanisms, and
that in fact much better mechanisms exist in this class.
  In particular, we characterize optimal factorizations of linear queries under
online constraints, deriving existence, uniqueness, and explicit expressions
that allow us to efficiently compute optimal mechanisms, including for online
prefix sums. These solutions improve over the existing state-of-the-art by a
significant constant factor, and avoid some of the artifacts introduced by the
use of the tree data structure.


title: The vertical structure of debris disks and the impact of gas

abstract:
  The vertical structure of debris disks provides clues about their dynamical
evolution and the collision rate of the unseen planetesimals. Thanks to the
ever-increasing angular resolution of contemporary instruments and facilities,
we are beginning to constrain the scale height of a handful of debris disks,
either at near-infrared or millimeter wavelengths. Nonetheless, this is often
done for individual targets only. We present here the geometric modeling of
eight disks close to edge-on, all observed with the same instrument (SPHERE)
and using the same mode (dual-beam polarimetric imaging). Motivated by the
presence of CO gas in two out of the eight disks, we then investigate the
impact that gas can have on the scale height by performing N-body simulations
including gas drag and collisions. We show that gas can quickly alter the
dynamics of particles (both in the radial and vertical directions), otherwise
governed by gravity and radiation pressure. We find that, in the presence of
gas, particles smaller than a few tens of microns can efficiently settle toward
the midplane at the same time as they migrate outward beyond the birth ring.
For second generation gas ($M_\mathrm{gas} \leq 0.1$ $M_\oplus$), the vertical
settling should be best observed in scattered light images compared to
observations at millimeter wavelengths. But if the gas has a primordial origin
($M_\mathrm{gas} \geq 1$ $M_\oplus$), the disk will appear very flat both at
near-infrared and sub-mm wavelengths. Finally, far beyond the birth ring, our
results suggest that the surface brightness profile can be as shallow as $\sim
-2.25$.


title: Causal Process Mining from Relational Databases with Domain Knowledge

abstract:
  The plethora of algorithms in the research field of process mining builds on
directly-follows relations. Even though various improvements have been made in
the last decade, there are serious weaknesses of these relationships. Once
events associated with different objects that relate with a cardinality of 1:N
and N:M to each other, techniques based on directly-follows relations produce
spurious relations, self-loops, and back-jumps. This is due to the fact that
event sequence as described in classical event logs differs from event
causation. In this paper, we address the research problem of representing the
causal structure of process-related event data. To this end, we develop a new
approach called Causal Process Mining. This approach renounces the use of flat
event logs and considers relational databases of event data as an input. More
specifically, we transform the relational data structures based on the Causal
Process Template into what we call Causal Event Graph. We evaluate our approach
and compare its outputs with techniques based on directly-follows relations in
a case study with an European food production company. Our results demonstrate
that directly-follows miners produce a large number of spurious relationships,
which our approach captures correctly.


title:
Tensor-based Channel Tracking for RIS-Empowered Multi-User MIMO Wireless
  Systems

abstract:
  The accurate estimation of Channel State Information (CSI) is of crucial
importance for the successful operation of Multiple-Input Multiple-Output
(MIMO) communication systems, especially in a Multi-User (MU) time-varying
environment and when employing the emerging technology of Reconfigurable
Intelligent Surfaces (RISs). Their predominantly passive nature renders the
estimation of the channels involved in the user-RIS-base station link a quite
challenging problem. Moreover, the time-varying nature of most of the realistic
wireless channels drives up the cost of real-time channel tracking
significantly, especially when RISs of massive size are deployed. In this
paper, we develop a channel tracking scheme for the uplink of RIS-enabled MU
MIMO systems in the presence of channel fading. The starting point is a tensor
representation of the received signal and we rely on its PARAllel FACtor
(PARAFAC) analysis to both get the initial estimate and track the channel time
variation. Simulation results for various system settings are reported, which
validate the feasibility and effectiveness of the proposed channel tracking
approach.


title:
FAMIE: A Fast Active Learning Framework for Multilingual Information
  Extraction

abstract:
  This paper presents FAMIE, a comprehensive and efficient active learning (AL)
toolkit for multilingual information extraction. FAMIE is designed to address a
fundamental problem in existing AL frameworks where annotators need to wait for
a long time between annotation batches due to the time-consuming nature of
model training and data selection at each AL iteration. This hinders the
engagement, productivity, and efficiency of annotators. Based on the idea of
using a small proxy network for fast data selection, we introduce a novel
knowledge distillation mechanism to synchronize the proxy network with the main
large model (i.e., BERT-based) to ensure the appropriateness of the selected
annotation examples for the main model. Our AL framework can support multiple
languages. The experiments demonstrate the advantages of FAMIE in terms of
competitive performance and time efficiency for sequence labeling with AL. We
publicly release our code (\url{https://github.com/nlp-uoregon/famie}) and demo
website (\url{http://nlp.uoregon.edu:9000/}). A demo video for FAMIE is
provided at: \url{https://youtu.be/I2i8n_jAyrY}.


title: Locating high-frequency line disturbances with the frequency mismatch

abstract:
  In an attempt to provide an efficient method for line disturbance
identification in complex networks of diffusively coupled agents, we recently
proposed to leverage the frequency mismatch. The frequency mismatch filters out
the intricate combination of interactions induced by the network structure and
quantifies to what extent the trajectory of each agent is affected by the
disturbance. In this previous work, we provided some analytical evidence of its
efficiency when the perturbation is assumed to be slow. In the present work, we
claim that the frequency mismatch performs actually well for most disturbance
regimes. This is shown through a series of simulations and is backed up by an
analytical argument. Therefore, we argue that the frequency mismatch is an
efficient and elegant tool for line disturbance location in complex networks of
diffusively coupled agents.


title:
A flexible approach for causal inference with multiple treatments and
  clustered survival outcomes

abstract:
  When drawing causal inferences about the effects of multiple treatments on
clustered survival outcomes using observational data, we need to address
implications of the multilevel data structure, multiple treatments, censoring
and unmeasured confounding for causal analyses. Few off-the-shelf causal
inference tools are available to simultaneously tackle these issues. We develop
a flexible random-intercept accelerated failure time model, in which we use
Bayesian additive regression trees to capture arbitrarily complex relationships
between censored survival times and pre-treatment covariates and use the random
intercepts to capture cluster-specific main effects. We develop an efficient
Markov chain Monte Carlo algorithm to draw posterior inferences about the
population survival effects of multiple treatments and examine the variability
in cluster-level effects. We further propose an interpretable sensitivity
analysis approach to evaluate the sensitivity of drawn causal inferences about
treatment effect to the potential magnitude of departure from the causal
assumption of no unmeasured confounding. Expansive simulations empirically
validate and demonstrate good practical operating characteristics of our
proposed methods. Applying the proposed methods to a dataset on older high-risk
localized prostate cancer patients drawn from the National Cancer Database, we
evaluate the comparative effects of three treatment approaches on patient
survival, and assess the ramifications of potential unmeasured confounding. The
methods developed in this work are readily available in the $\textsf{R}$
package $\textsf{riAFTBART}$.


title: The dichotomy property of ${\rm SL}_2(R)$-A short note

abstract:
  A recent paper by Polterovich, Shalom and Shem-Tov has shown that
non-discrete, conjugation invariant norms on arithmetic Chevalley groups of
higher rank give rise to very restricted topologies. Namely, such topologies
always have profinite norm-completions. In this note, we sketch an argument
showing that this also holds for ${\rm SL}_2(R)$ for $R$ a ring of algebraic
integers with infinitely many units.


title:
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug
  Discovery

abstract:
  Machine learning has huge potential to revolutionize the field of drug
discovery and is attracting increasing attention in recent years. However,
lacking domain knowledge (e.g., which tasks to work on), standard benchmarks
and data preprocessing pipelines are the main obstacles for machine learning
researchers to work in this domain. To facilitate the progress of machine
learning for drug discovery, we develop TorchDrug, a powerful and flexible
machine learning platform for drug discovery built on top of PyTorch. TorchDrug
benchmarks a variety of important tasks in drug discovery, including molecular
property prediction, pretrained molecular representations, de novo molecular
design and optimization, retrosynthsis prediction, and biomedical knowledge
graph reasoning. State-of-the-art techniques based on geometric deep learning
(or graph machine learning), deep generative models, reinforcement learning and
knowledge graph reasoning are implemented for these tasks. TorchDrug features a
hierarchical interface that facilitates customization from both novices and
experts in this domain. Tutorials, benchmark results and documentation are
available at https://torchdrug.ai. Code is released under Apache License 2.0.


title:
Fredholm backstepping for critical operators and application to rapid
  stabilization for the linearized water waves

abstract:
  Fredholm-type backstepping transformation, introduced by Coron and L\"u, has
become a powerful tool for rapid stabilization with fast development over the
last decade. Its strength lies in its systematic approach, allowing to deduce
rapid stabilization from approximate controllability. But limitations with the
current approach exist for operators of the form $|D_x|^\alpha$ for $\alpha \in
(1,3/2]$. We present here a new compactness/duality method which hinges on
Fredholm's alternative to overcome the $\alpha=3/2$ threshold. More precisely,
the compactness/duality method allows to prove the existence of a Riesz basis
for the backstepping transformation for skew-adjoint operator verifying
$\alpha>1$, a key step in the construction of the Fredholm backstepping
transformation, where the usual methods only work for $\alpha>3/2$. The
illustration of this new method is shown on the rapid stabilization of the
linearized capillary-gravity water wave equation exhibiting an operator of
critical order $\alpha=3/2$.


title: Instability of a dusty vortex

abstract:
  We investigate the effect of inertial particles dispersed in a circular patch
of finite radius on the stability of a two-dimensional Rankine vortex in
semi-dilute dusty flows. Unlike the particle-free case where no unstable modes
exist, we show that the feedback force from the particles triggers a novel
instability. The mechanisms driving the instability are characterized using
linear stability analysis for weakly inertial particles and further validated
against Eulerian-Lagrangian simulations. We show that the particle-laden vortex
is always unstable if the mass loading $M>0$. Surprisingly, even non-inertial
particles destabilize the vortex by a mechanism analogous to the centrifugal
Rayleigh-Taylor instability in radially stratified vortex with density jump. We
identify a critical mass loading above which an eigenmode m becomes unstable.
This critical mass loading drops to zero as m increases. When particles are
inertial, modes that fall below the critical mass loading become unstable,
whereas, modes above it remain unstable but with lower growth rates compared to
the non-inertial case. Comparison with Eulerian-Lagrangian simulations shows
that growth rates computed from simulations match well the theoretical
predictions. Past the linear stage, we observe the emergence of high-wavenumber
modes that turn into spiraling arms of concentrated particles emanating out of
the core, while regions of particle-free flow are sucked inward. The vorticity
field displays similar pattern which lead to the breakdown of the initial
Rankine structure. This novel instability for a dusty vortex highlights how the
feedback force from the disperse phase can induce the breakdown of an otherwise
resilient vortical structure.


title: Equidistribution and counting of periodic flat tori

abstract:
  Let $G$ be a semisimple Lie group without compact factor and $\Gamma < G$ a
torsion-free, cocompact, irreducible lattice. According to Selberg, periodic
orbits of regular Weyl chamber flows live on maximal flat periodic tori of the
space of Weyl chambers. We prove that these flat periodic tori equidistribute
exponentially fast towards the quotient of the Haar measure. From the
equidistribution formula, we deduce a higher rank prime geodesic theorem. These
counting and equidistribution results also hold in the non cocompact, finite
covolume case for $G=\mathrm{SL}(d,\mathbb{R})$ and
$\Gamma<\mathrm{SL}(d,\mathbb{Z})$ a finite index subgroup.


title: Turbulent dynamo in the two-phase interstellar medium

abstract:
  Magnetic fields are a dynamically important component of the turbulent
interstellar medium (ISM) of star-forming galaxies. These magnetic fields are
due to a dynamo action, which is a process of converting turbulent kinetic
energy to magnetic energy. A dynamo that acts at scales less than the turbulent
driving scale is known as the turbulent dynamo. The ISM is a multiphase medium
and observations suggests that the properties of magnetic fields differ with
the phase. Here, we aim to study how the properties of the turbulent dynamo
depend on the phase. We simulate the non-isothermal turbulent dynamo in a
two-phase medium (most previous work assumes an isothermal gas). We find that
the growth rate of magnetic fields in the exponentially growing stage is
similar in both the phases, and this is because of a roughly equal amount of
vorticity being generated in each phase. We further compute each term
responsible for amplification and destruction of vorticity and show that the
amplification of vorticity by turbulent motions is a dominant term (similar in
both the phases) followed by the baroclinic term (only present in
non-isothermal gases, higher in the warm phase) and the term for viscous
interactions in the presence of logarithmic density gradients (higher in the
cold phase). We find that the final ratio of magnetic to turbulent kinetic
energy is lower due to a stronger Lorentz force. We find that the
non-isothermal turbulent dynamo is less efficient than its isothermal
counterpart.


title:
A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification
  From Analytical Augmented Sample Moments

abstract:
  Data-Augmentation (DA) is known to improve performance across tasks and
datasets. We propose a method to theoretically analyze the effect of DA and
study questions such as: how many augmented samples are needed to correctly
estimate the information encoded by that DA? How does the augmentation policy
impact the final parameters of a model? We derive several quantities in
close-form, such as the expectation and variance of an image, loss, and model's
output under a given DA distribution. Those derivations open new avenues to
quantify the benefits and limitations of DA. For example, we show that common
DAs require tens of thousands of samples for the loss at hand to be correctly
estimated and for the model training to converge. We show that for a training
loss to be stable under DA sampling, the model's saliency map (gradient of the
loss with respect to the model's input) must align with the smallest
eigenvector of the sample variance under the considered DA augmentation,
hinting at a possible explanation on why models tend to shift their focus from
edges to textures.


title: SAT Backdoors: Depth Beats Size

abstract:
  For several decades, much effort has been put into identifying classes of CNF
formulas whose satisfiability can be decided in polynomial time. Classic
results are the linear-time tractability of Horn formulas (Aspvall, Plass, and
Tarjan, 1979) and Krom (i.e., 2CNF) formulas (Dowling and Gallier, 1984).
Backdoors, introduced by Williams Gomes and Selman (2003), gradually extend
such a tractable class to all formulas of bounded distance to the class.
Backdoor size provides a natural but rather crude distance measure between a
formula and a tractable class. Backdoor depth, introduced by M\"{a}hlmann,
Siebertz, and Vigny (2021), is a more refined distance measure, which admits
the utilization of different backdoor variables in parallel. Bounded backdoor
size implies bounded backdoor depth, but there are formulas of constant
backdoor depth and arbitrarily large backdoor size.
  We propose FPT approximation algorithms to compute backdoor depth into the
classes Horn and Krom. This leads to a linear-time algorithm for deciding the
satisfiability of formulas of bounded backdoor depth into these classes. We
base our FPT approximation algorithm on a sophisticated notion of obstructions,
extending M\"{a}hlmann et al.'s obstruction trees in various ways, including
the addition of separator obstructions. We develop the algorithm through a new
game-theoretic framework that simplifies the reasoning about backdoors.
  Finally, we show that bounded backdoor depth captures tractable classes of
CNF formulas not captured by any known method.


title: $\mathbb{N}$-Graph $C^*$-Algebras

abstract:
  In this paper we generalize the notion of a $k$-graph into (countable)
infinite rank. We then define our $C^*$-algebra in a similar way as in
$k$-graph $C^*$-algebras. With this construction we are able to find analogues
to the Gauge Invariant Uniqueness and Cuntz-Krieger Uniqueness Theorems. We
also show that the $\mathbb{N}$-graph $C^*$-algebras can be viewed as the
inductive limit of $k$-graph $C^*$-algebras. This gives a nice way to describe
the gauge-invariant ideal structure. Additionally, we describe the vertex-set
for regular gauge-invariant ideals of our $N$-graph $C^*$-algebras. We then
take our construction of the $\mathbb{N}$-graph into the algebraic setting and
receive many similarities to the $C^*$-algebra construction.


title: Exterior algebras in matroid theory

abstract:
  Ordered blueprints are algebraic objects that generalize monoids and ordered
semirings, and $\mathbb{F}_1^{\pm}$-algebras are ordered blueprints that have
an element $\epsilon$ that acts as $-1$. In this work we introduce an analogue
of the exterior algebra for $\mathbb{F}_1^{\pm}$-algebras that provides a new
cryptomorphism for matroids. We also show how to recover the usual exterior
algebra if the $\mathbb{F}_1^{\pm}$-algebra comes from a ring, and the
Giansiracusa Grassmann algebra if the $\mathbb{F}_1^{\pm}$-algebra comes from
an idempotent semifield.


title: CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs

abstract:
  We present CortexODE, a deep learning framework for cortical surface
reconstruction. CortexODE leverages neural ordinary different equations (ODEs)
to deform an input surface into a target shape by learning a diffeomorphic
flow. The trajectories of the points on the surface are modeled as ODEs, where
the derivatives of their coordinates are parameterized via a learnable
Lipschitz-continuous deformation network. This provides theoretical guarantees
for the prevention of self-intersections. CortexODE can be integrated to an
automatic learning-based pipeline, which reconstructs cortical surfaces
efficiently in less than 6 seconds. The pipeline utilizes a 3D U-Net to predict
a white matter segmentation from brain Magnetic Resonance Imaging (MRI) scans,
and further generates a signed distance function that represents an initial
surface. Fast topology correction is introduced to guarantee homeomorphism to a
sphere. Following the isosurface extraction step, two CortexODE models are
trained to deform the initial surface to white matter and pial surfaces
respectively. The proposed pipeline is evaluated on large-scale neuroimage
datasets in various age groups including neonates (25-45 weeks), young adults
(22-36 years) and elderly subjects (55-90 years). Our experiments demonstrate
that the CortexODE-based pipeline can achieve less than 0.2mm average geometric
error while being orders of magnitude faster compared to conventional
processing pipelines.


title:
Large deviations for subcomplex counts and Betti numbers in
  multi-parameter simplicial complexes

abstract:
  We consider the multi-parameter random simplicial complex as a higher
dimensional extension of the classical Erd\"os-R\'enyi graph. We investigate
appearance of "unusual" topological structures in the complex from the point of
view of large deviations. We first study upper tail large deviation
probabilities for subcomplex counts, deriving the order of magnitude of such
probabilities at the logarithmic scale precision. The obtained results are then
applied to analyze large deviations for the number of simplices at the critical
dimension and below. Finally, these results are also used to deduce large
deviation estimates for Betti numbers of the complex in the critical dimension.


title:
Energy Efficiency Tradeoffs for Sub-THz Multi-User MIMO Base Station
  Receivers

abstract:
  Sub-terahertz (sub-THz) antenna array architectures significantly impact
power usage and communications capacity in multi-user multiple-input
multiple-output (MU-MIMO) systems. In this work, we compare the energy
efficiency and spectral efficiency of three MU-MIMO capable array architectures
for base station receivers. We provide a sub-THz circuits power analysis, based
on our review of state-of-the-art D-band and G-band components, and compare
communications capabilities through wideband simulations. Our analysis reveals
that digital arrays can provide the highest spectral efficiency and energy
efficiency, due to the high power consumption of sub-THz active phase shifters
or when SNR and system spectral efficiency requirements are high.


title:
Coupling of acoustic and drift modes, harmonic modons in astrophysical
  dusty plasma and a new mode in the Comet Halley

abstract:
  This manuscript, presents a theoretical study of the linear and nonlinear
characteristics of acoustic and drift waves in a bounded inhomogenious dusty
plasma which has potential applications in space and lab environments. In this
analysis, flow of all plasma particles is assumed to be along the axial
direction whereas gradients in velocity are considered to be along the radial
direction. First we study coupling of dust modified ion acoustic (DMA) and
drift (DMD) waves (linear analysis) at fast time when dynamics of dust remain
inactive, later formation of nonlinear vortex like structures are examined. In
the second case at slow time when dust participates in dynamics, coupling
between ultra low frequency dust acoustic (UDA) and dust drift (ULD) waves is
studied and modons like solutions are obtained in the nonlinear analysis.
Existence of the vortex solution with azimuthal harmonics higher than the
dipole vortex has been studied both analytically and numerically. Later we
extend our analysis to consider applications of dust particle's presence in the
comet Halley and tropical mesospheric dusty plasma where in earlier case we
report that that unexpectedly comet Halley plasma admits a new mode where
presence of dust in background does not have any affect, this mode is similar
to the convective cell mode where electrostatic drift (ExB) cancels out,
density gradients also vanish due to the presence of negative ions and only
polarization drift contributes to the mode. For the linear analysis of this
mode, there is no coupling observed but this the convective cell mode evolves
whereas nonlinear dynamics of this mode are found to obey the vortex-like
solution.


title: Self-Supervised Representation Learning via Latent Graph Prediction

abstract:
  Self-supervised learning (SSL) of graph neural networks is emerging as a
promising way of leveraging unlabeled data. Currently, most methods are based
on contrastive learning adapted from the image domain, which requires view
generation and a sufficient number of negative samples. In contrast, existing
predictive models do not require negative sampling, but lack theoretical
guidance on the design of pretext training tasks. In this work, we propose the
LaGraph, a theoretically grounded predictive SSL framework based on latent
graph prediction. Learning objectives of LaGraph are derived as self-supervised
upper bounds to objectives for predicting unobserved latent graphs. In addition
to its improved performance, LaGraph provides explanations for recent successes
of predictive models that include invariance-based objectives. We provide
theoretical analysis comparing LaGraph to related methods in different domains.
Our experimental results demonstrate the superiority of LaGraph in performance
and the robustness to decreasing of training sample size on both graph-level
and node-level tasks.


title: Rings of Bounded Continuous Functions

abstract:
  We examine several classical concepts from topology and functional analysis,
using methods of commutative algebra. We show that these various concepts are
all controlled by BC R-rings and their maximal spectra.
  A BC R-ring is a ring A that is isomorphic to the ring of bounded continuous
R-valued functions on some compact topological space X. These rings are not
topologized.
  We prove that the category of BC R-rings is dual to the category of compact
topological spaces. Next we prove that for every topological space X the ring
of bounded continuous functions on it is a BC R-ring. These theorems combined
yield an algebraic construction of the Stone-Cech Compactification of an
arbitrary topological space.
  There is a similar notion of BC C-ring. Every BC C-ring A has a canonical
involution. The canonical hermitian subring of A is a BC R-ring, and this is an
equivalence of categories from BC C-rings to BC R-rings.
  Let K be either R or C. We prove that a BC K-ring A has a canonical norm on
it, making it into a Banach K-ring. We then prove that the forgetful functor is
an equivalence from Banach^* K-rings (better known as commutative unital C^*
K-algebras) to BC K-rings. The quasi-inverse of the forgetful functor endows a
BC K-ring with its canonical norm, and the canonical involution when K = C.
  Stone topological spaces, also known as profinite topological spaces, are
traditionally related to boolean rings - this is Stone Duality. We give a BC
ring characterization of Stone spaces. From that we obtain a very easy proof of
the fact that the Stone-Cech Compactification of a discrete space is a Stone
space.
  Most of the results in this paper are not new. However, most of our proofs
seem to be new - and our methods could potentially lead to genuine progress
related to these classical topics.


title: Task-Agnostic Graph Explanations

abstract:
  Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph
structured data. Due to their broad applications, there is an increasing need
to develop tools to explain how GNNs make decisions given graph structured
data. Existing learning-based GNN explanation approaches are task-specific in
training and hence suffer from crucial drawbacks. Specifically, they are
incapable of producing explanations for a multitask prediction model with a
single explainer. They are also unable to provide explanations in cases where
the GNN is trained in a self-supervised manner, and the resulting
representations are used in future downstream tasks. To address these
limitations, we propose a Task-Agnostic GNN Explainer (TAGE) trained under
self-supervision with no knowledge of downstream tasks. TAGE enables the
explanation of GNN embedding models without downstream tasks and allows
efficient explanation of multitask models. Our extensive experiments show that
TAGE can significantly speed up the explanation efficiency by using the same
model to explain predictions for multiple downstream tasks while achieving
explanation quality as good as or even better than current state-of-the-art GNN
explanation approaches.


title:
On the precise deviations of the characteristic polynomial of a random
  matrix

abstract:
  In this paper, using techniques developed in our earlier works on the theory
of mod-Gaussian convergence, we prove precise moderate and large deviation
results for the logarithm of the characteristic polynomial of a random unitary
matrix. In the case where the unitary matrix is chosen according to the Haar
measure, the logarithms of the probabilities of fluctuations of order $A=O(N)$
of the logarithm of the characteristic polynomial have been estimated by
Hughes, Keating and O'Connell. In this work we give an equivalent of the
probabilities themselves (without the logarithms), and we do so for the more
general case of a matrix from the circular $\beta$ ensemble for any parameter
$\beta > 0$. In comparison to previous results from F\'eray-M\'eliot-Nikeghbali
(2016) and Dal Borgo-Hovhannisyan-Rouault (2019), we considerably extend the
range of fluctuations for which precise estimates can be written.


title:
Mass Transfer as an Explanation for the Lifetime-Travel Time Discrepancy
  in IT Librae

abstract:
  The eclipsing binary IT Librae is an unusual system of two B-type stars that
is situated about 1 kpc above the galactic plane. The binary was probably
ejected from its birthplace in the disk, but the implied time-of-flight to its
current location exceeds the evolutionary lifetime of the primary star. Here we
present a study of new high dispersion spectroscopy and an exquisite light
curve from the Kepler K2 mission in order to determine the system properties
and resolve the timescale discrepancy. We derive a revised spectroscopic orbit
from radial velocity measurements and determine the component effective
temperatures through comparison of reconstructed and model spectra ($T_1 = 23.8
\pm 1.8$ kK, $T_2 = 13.7 \pm 2.5$ kK). We use the Eclipsing Light Curve (ELC)
code to model the K2 light curve, and from the inclination of the fit, we
derive the component masses ($M_1 = 9.6 \pm 0.6 M_\odot$, $M_2 = 4.2 \pm 0.2
M_\odot$) and mean radii ($R_1 = 6.06 \pm 0.16 R_\odot$, $R_2 = 5.38 \pm 0.14
R_\odot$). The secondary star is overluminous for its mass and appears to fill
its Roche lobe. This indicates that IT~Librae is a post-mass transfer system in
which the current secondary was the mass donor star. The current primary star
was rejuvenated by mass accretion, and its evolutionary age corresponds to the
time since the mass transfer stage. Consequently, the true age of the binary is
larger than the ejection time-of-flight, thus resolving the timescale
discrepancy.


title:
Single-shot Hyper-parameter Optimization for Federated Learning: A
  General Algorithm & Analysis

abstract:
  We address the relatively unexplored problem of hyper-parameter optimization
(HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface
Aggregation (FLoRA), a general FL-HPO solution framework that can address use
cases of tabular data and any Machine Learning (ML) model including gradient
boosting training algorithms and therefore further expands the scope of FL-HPO.
FLoRA enables single-shot FL-HPO: identifying a single set of good
hyper-parameters that are subsequently used in a single FL training. Thus, it
enables FL-HPO solutions with minimal additional communication overhead
compared to FL training without HPO. We theoretically characterize the
optimality gap of FL-HPO, which explicitly accounts for the heterogeneous
non-IID nature of the parties' local data distributions, a dominant
characteristic of FL systems. Our empirical evaluation of FLoRA for multiple ML
algorithms on seven OpenML datasets demonstrates significant model accuracy
improvements over the considered baseline, and robustness to increasing number
of parties involved in FL-HPO training.


title:
Dimensions on Lattice Ordered Abelian Groups and Model Theory of Modules
  over Pr\"ufer Domains

abstract:
  We prove a transfer theorem which, when combined with the
Jaffard-Kaplansky-Ohm Theorem, allows results in model theory of modules over
B\'ezout domains to be translated into results over Pr\"ufer domains via their
value groups. Extending work of Puninski and Toffalori, we show that the
extended positive cone of the value group of a Pr\"ufer domain has m-dimension
if and only if its lattice of pp-$1$-formulae has breadth (equivalently width)
and that these dimensions are equal. Further, we show that the existence of
these dimensions is equivalent to the lattice of pp-$1$-formulae having
m-dimension (and hence to its Ziegler spectrum having Cantor-Bendixson rank)
and the non-existence of superdecomposable pure-injective modules. Finally, we
give a best possible upper bound for the m-dimension of the pp-$1$-lattice of a
Pr\"ufer domain in terms of the m-dimension of the extended positive cone of
its value group and show that all ordinals which are not of the form
$\lambda+1$ for $\lambda$ a limit ordinal occur as the m-dimension of the
pp-$1$-lattice of a B\'ezout domain.


title:
A Developmentally-Inspired Examination of Shape versus Texture Bias in
  Machines

abstract:
  Early in development, children learn to extend novel category labels to
objects with the same shape, a phenomenon known as the shape bias. Inspired by
these findings, Geirhos et al. (2019) examined whether deep neural networks
show a shape or texture bias by constructing images with conflicting shape and
texture cues. They found that convolutional neural networks strongly preferred
to classify familiar objects based on texture as opposed to shape, suggesting a
texture bias. However, there are a number of differences between how the
networks were tested in this study versus how children are typically tested. In
this work, we re-examine the inductive biases of neural networks by adapting
the stimuli and procedure from Geirhos et al. (2019) to more closely follow the
developmental paradigm and test on a wide range of pre-trained neural networks.
Across three experiments, we find that deep neural networks exhibit a
preference for shape rather than texture when tested under conditions that more
closely replicate the developmental procedure.


title: Anomalib: A Deep Learning Library for Anomaly Detection

abstract:
  This paper introduces anomalib, a novel library for unsupervised anomaly
detection and localization. With reproducibility and modularity in mind, this
open-source library provides algorithms from the literature and a set of tools
to design custom anomaly detection algorithms via a plug-and-play approach.
Anomalib comprises state-of-the-art anomaly detection algorithms that achieve
top performance on the benchmarks and that can be used off-the-shelf. In
addition, the library provides components to design custom algorithms that
could be tailored towards specific needs. Additional tools, including
experiment trackers, visualizers, and hyper-parameter optimizers, make it
simple to design and implement anomaly detection models. The library also
supports OpenVINO model optimization and quantization for real-time deployment.
Overall, anomalib is an extensive library for the design, implementation, and
deployment of unsupervised anomaly detection models from data to the edge.


title:
A PDE-free, neural network-based eddy viscosity model coupled with RANS
  equations

abstract:
  Most turbulence models used in Reynolds-averaged Navier-Stokes (RANS)
simulations are partial differential equations (PDE) that describe the
transport of turbulent quantities. Such quantities include turbulent kinetic
energy for eddy viscosity models and the Reynolds stress tensor (or its
anisotropy) in differential stress models. However, such models all have
limitations in their robustness and accuracy. Inspired by the successes of
machine learning in other scientific fields, researchers have developed
data-driven turbulence models. Recently, a nonlocal vector-cloud neural network
with embedded invariance was proposed, with its capability demonstrated in
emulating passive tracer transport in laminar flows. Building upon this
success, we use nonlocal neural network mapping to model the transport physics
in the k-epsilon model and couple it to RANS solvers, leading to a PDE-free
eddy-viscosity model. We demonstrate the robustness and stability of the RANS
solver with a neural network-based turbulence model on flows over periodic
hills of parameterized geometries. Our work serves as a proof of concept for
using a vector-cloud neural network as an alternative to traditional turbulence
models in coupled RANS simulations. The success of the coupling paves the way
for neural network-based emulation of Reynolds stress transport models.


title: First exit time for a discrete time parallel queue

abstract:
  We consider a discrete time parallel queue, which is two-queue network, where
at each time-slot there is a the same batch arrival to both queues and at each
queue there is a random service available. The service law at each time-slot
for each queue is different. Let $(Q_n^1, Q_n^2)$ be the queue length at $n$th
time-slot. We present several open questions related to the steady-state of
this queue.


title:
Simplified algorithms for adaptive experiment design in parameter
  estimation

abstract:
  In experiments to estimate parameters of a parametric model, Bayesian
experiment design allows measurement settings to be chosen based on utility,
which is the predicted improvement of parameter distributions due to modeled
measurement results. In this paper we compare information-theory-based utility
with three alternative utility algorithms. Tests of these utility alternatives
in simulated adaptive measurements demonstrate large improvements in
computational speed with slight impacts on measurement efficiency.


title: Learning Smooth Neural Functions via Lipschitz Regularization

abstract:
  Neural implicit fields have recently emerged as a useful representation for
3D shapes. These fields are commonly represented as neural networks which map
latent descriptors and 3D coordinates to implicit function values. The latent
descriptor of a neural field acts as a deformation handle for the 3D shape it
represents. Thus, smoothness with respect to this descriptor is paramount for
performing shape-editing operations. In this work, we introduce a novel
regularization designed to encourage smooth latent spaces in neural fields by
penalizing the upper bound on the field's Lipschitz constant. Compared with
prior Lipschitz regularized networks, ours is computationally fast, can be
implemented in four lines of code, and requires minimal hyperparameter tuning
for geometric applications. We demonstrate the effectiveness of our approach on
shape interpolation and extrapolation as well as partial shape reconstruction
from 3D point clouds, showing both qualitative and quantitative improvements
over existing state-of-the-art and non-regularized baselines.


title: Total consensus under high reproductive-variance conditions

abstract:
  Star-shaped branching patterns of genealogies are common in marine species.
High-fecundity marine populations are characterized by low ratios of effective
to actual population size, which reflect high variance in reproductive success
among parents in mass spawns. When extreme reproduction events occur, offspring
from very few parents dominate the population (whereby multiple mergers, or
subsets of lineages with star-like trees, arise) and thus, the loss of genetic
diversity is significant. Under high reproductive-variance conditions (assuming
that reproduction occurs by sampling from the Pareto distribution), this paper
explores the distribution of heterozygosity across generations. The result
shows that zero heterozygosity is not achieved, implying that the populations
may decline without evident loss of genetic variation. It is also found that
there are singularities in the heterozygosity distributions. However, in the
case of high reproductive variance, the locations of the singular points subtly
deviate from those of the case where reproduction occurs by Wright-Fisher
sampling.


title: Observation and origin of non-thermal hard X-rays from Jupiter

abstract:
  Electrons accelerated on Earth by a rich variety of wave scattering or
stochastic processes generate hard non-thermal X-ray bremsstrahlung up to >~ 1
MeV and power Earth's various types of aurorae. Although Jupiter's magnetic
field is an order of magnitude larger than Earth's, space-based telescopes have
previously detected X-rays only up to ~7 keV. On the basis of theoretical
models of the Jovian auroral X-ray production, X-ray emission in the ~2-7 keV
band has been interpreted as thermal (arising from electrons characterized by a
Maxwell-Boltzmann distribution) bremsstrahlung. Here we report the observation
of hard X-rays in the 8-20 keV band from the Jovian aurorae, obtained with the
NuSTAR X-ray observatory. The X-rays fit to a flat power-law model with slope
0.60+/-0.22 - a spectral signature of non-thermal, hard X-ray bremsstrahlung.
We determine the electron flux and spectral shape in the keV to MeV energy
range using coeval in situ measurements by the Juno spacecraft's JADE and JEDI
instruments. Jovian electron spectra of the form we observe have previously
been interpreted to arise in stochastic acceleration, rather than coherent
acceleration by electric fields. We reproduce the X-ray spectral shape and
approximate flux observed by NuSTAR, and explain the non-detection of hard
X-rays by Ulysses, by simulating the non-thermal population of electrons
undergoing precipitating electron energy loss, secondary electron generation
and bremsstrahlung emission in a model Jovian atmosphere. The results highlight
the similarities between the processes generating hard X-ray auroras on Earth
and Jupiter, which may be occurring on Saturn, too.


title:
Investigating the Future Potential of an Upgraded ALMA to Image Planet
  Forming Disks at Sub-au Scales

abstract:
  In recent years, ALMA has been able to observe large-scale substructures
within protoplanetary disks. Comparison with the predictions from models of
planet-disk interaction has indicated that most of these disk substructures can
be explained by the presence of planets with the mass of Neptune or larger at
orbital radii of $\approx 5 - 100$ au. Better resolution is needed to observe
structures closer to the star, where terrestrial planets are expected to form,
as well as structures opened by planets with masses lower than Neptune. We
investigate the capabilities of a possible extension to ALMA that would double
the longest baseline lengths in the array to detect and resolve disk
substructures opened by Earth-mass and Super Earth planets at orbital radii of
$1-5$ au. By simulating observations of a family of disk models using this
extended configuration in ALMA Bands 6 and 7, we show that an upgraded ALMA
would detect gaps in disks formed by super-Earths as close as 1 au, as well as
Earth-mass planets down to $2-3$ au from the young host stars in nearby star
forming regions.


title:
Approximating Output Probabilities of Shallow Quantum Circuits which are
  Geometrically-local in any Fixed Dimension

abstract:
  We present a classical algorithm that, for any $D$-dimensional
geometrically-local, quantum circuit $C$ of polylogarithmic-depth, and any bit
string $x \in {0,1}^n$, can compute the quantity $|<x|C|0^{\otimes n}>|^2$ to
within any inverse-polynomial additive error in quasi-polynomial time, for any
fixed dimension $D$. This is an extension of the result [CC21], which
originally proved this result for $D = 3$. To see why this is interesting, note
that, while the $D = 1$ case of this result follows from standard use of Matrix
Product States, known for decades, the $D = 2$ case required novel and
interesting techniques introduced in [BGM19]. Extending to the case $D = 3$ was
even more laborious and required further new techniques introduced in [CC21].
Our work here shows that, while handling each new dimension has historically
required a new insight, and fixed algorithmic primitive, based on known
techniques for $D \leq 3$, we can now handle any fixed dimension $D > 3$.
  Our algorithm uses the Divide-and-Conquer framework of [CC21] to approximate
the desired quantity via several instantiations of the same problem type, each
involving $D$-dimensional circuits on about half the number of qubits as the
original. This division step is then applied recursively, until the width of
the recursively decomposed circuits in the $D^{th}$ dimension is so small that
they can effectively be regarded as $(D-1)$-dimensional problems by absorbing
the small width in the $D^{th}$ dimension into the qudit structure at the cost
of a moderate increase in runtime. The main technical challenge lies in
ensuring that the more involved portions of the recursive circuit decomposition
and error analysis from [CC21] still hold in higher dimensions, which requires
small modifications to the analysis in some places.


title: Constructing Generic Effective Field Theory for All Masses and Spins

abstract:
  We fully solve the long-standing problem of operator basis construction for
fields with any masses and spins. Based on the on-shell method, we propose a
novel method to systematically construct a complete set of lowest dimensional
amplitude bases at any given dimension through semi-standard Young tableaus of
Lorentz subgroup $SU(2)_r$ and global symmetry $U(N)$ ($N$ is the number of
external legs), which can be directly mapped into physical operator bases. We
first construct a complete set of monomial bases whose dimension is not the
lowest and a redundant set of bases that always contains a complete set of
amplitude bases with the lowest dimension. Then we decompose the bases of the
redundant set into the monomial bases from low to high dimension and eliminate
the linear correlation bases. Finally, the bases with the lowest dimension can
be picked up. We also propose a matrix projection method to construct the
massive amplitude bases involving identical particles. The operator bases of a
generic massive effective field theory can be efficiently constructed by the
computer programs. A complete set of four-vector operators at dimensions up to
six is presented.


title: Flat tori with large Laplacian eigenvalues in dimensions up to eight

abstract:
  We consider the optimization problem of maximizing the $k$-th Laplacian
eigenvalue, $\lambda_{k}$, over flat $d$-dimensional tori of fixed volume. For
$k=1$, this problem is equivalent to the densest lattice sphere packing
problem. For larger $k$, this is equivalent to the NP-hard problem of finding
the $d$-dimensional (dual) lattice with longest $k$-th shortest lattice vector.
As a result of extensive computations, for $d \leq 8$, we obtain a sequence of
flat tori, $T_{k,d}$, each of volume one, such that the $k$-th Laplacian
eigenvalue of $T_{k,d}$ is very large; for each (finite) $k$ the $k$-th
eigenvalue exceeds the value in (the $k\to \infty$ asymptotic) Weyl's law by a
factor between 1.54 and 2.01, depending on the dimension. Stationarity
conditions are derived and numerically verified for $T_{k,d}$ and we describe
the degeneration of the tori as $k \to \infty$.


title:
Spatial decay of discretely self-similar solutions to the Navier-Stokes
  equations

abstract:
  Forward self-similar and discretely self-similar weak solutions of the
Navier-Stokes equations are known to exist globally in time for large
self-similar and discretely self-similar initial data and are known to be
regular outside of a space-time paraboloid. In this paper, we establish spatial
decay rates for such solutions which hold in the region of regularity provided
the initial data has locally sub-critical regularity away from the origin. In
particular, we (1) lower the H\"older regularity of the data required to obtain
an optimal decay rate for the nonlinear part of the flow compared to the
existing literature, (2) establish new decay rates without logarithmic
corrections for some smooth data, (3) provide new decay rates for solutions
with rough data, and, as an application of our decay rates, (4) provide new
upper bounds on how rapidly potentially {non-unique}, scaling invariant local
energy solutions can separate away from the origin.


title: Remarks on the use of objective probabilities in Bell-CHSH inequalities

abstract:
  The violation of Bell inequalities is often interpreted as showing that, if
hidden variables exist, they must be contextual and non local. But they can
also be explained questioning the probability space employed, or the validity
of the Kolmogorov axioms. In this article we explore the additional constrains
which can be deduced from two widely used objetive probability theories:
frequentism and propensities.
  One of the strongest objections in the deduction of one version of Bell
inequalities goes about the probability space, which assumes the existence of
values for the output of the experiment in each run, while only two of the four
values can be measured each time, making them counterfactual. It is shown that
frequentism rejects the possibility of using counterfactual situations, while
long-run propensities allow their use. In this case the introduction of
locality and contextuality does not help to explain the violation, and an
alternative explanation could point to a failure of the probability.
  Single case propensities were designed to associate probabilities to single
events, but they need to be conditional to the whole universe, and do not have
a clear link with the observed relative frequencies. It heavily limits their
use.


title: Effect of minimal length uncertainty on neutrino oscillation

abstract:
  In this paper, we studied the effect of the minimal length on neutrino
oscillation in a static magnetic field. In the framework of the generalized
uncertainty principle, we reformulate the Hamiltonian for a relativistic
neutrino moving in a magnetic field oriented along the z-direction of Cartesian
coordinates. Using the modified energy spectrum, we obtained the oscillation
probability for different neutrino flavors. In addition, we obtained the energy
differences for the neutrino-mass eigenstates. We found that the energy and
energy difference depend on the minimal length parameter, and the energy
difference becomes independent of the minimal length when the magnetic field is
not present. In addition, we found that the modified probability of oscillation
differs from the usual probability of oscillation if a magnetic field is
present. Using the current experimental data, we estimated the upper bound on
the deformation parameter and the minimal length, and found that the upper
bound on the minimal length was less than the electroweak scale.


title: Skewness as a test of dark energy perturbations

abstract:
  We investigate the role played by dark energy perturbations in the skewness
$S_3$ of large-scale matter distribution. We consider a two-fluid universe
composed by matter and dark energy, with perturbations in both components, and
we estimate numerically the skewness of the matter density field as a function
of the dark energy parameters. We characterize today's $S_3$ value for
quintessence and phantom dark energy cosmologies as well as its dependence on
the matter density parameter $\Omega_{m0}$ and the dark energy sound speed
$c^2_s$ with accurate numerical fitting. These fits can be used to test
cosmology against future high quality data on large scale structure.


title:
Genuinely entangled subspaces of maximal dimensions (and many smaller
  ones) cannot be constructed from orthogonal unextendible product bases

abstract:
  We consider the open problem of whether there exist genuinely entangled
subspaces complementary to orthogonal unextendible product bases. We solve this
problem in the negative for many cardinalities of the latter in any
multipartite scenario, including the minimal ones corresponding to genuinely
entangled subspaces of the maximal dimensions in all cases. The proof is
elementary and only uses the necessary and sufficient condition of [Bennett et
al., PRL 82, 5383 (1999)] for a set of product states to be unextendible.


title: On the Complexity of Some Variations of Sorting by Transpositions

abstract:
  One of the main challenges in Computational Biology is to find the
evolutionary distance between two organisms. In the field of comparative
genomics, one way to estimate such distance is to find a minimum cost sequence
of rearrangements (large scale mutations) needed to transform one genome into
another, which is called the rearrangement distance. In the past decades, these
problems were studied considering many types of rearrangements (such as
reversals, transpositions, transreversals, and revrevs) and considering the
same weight for all rearrangements, or different weights depending on the types
of rearrangements. The complexity of the problems involving reversals,
transpositions, and both rearrangements is known, even though the hardness
proof for the problem combining reversals and transpositions was recently
given. In this paper, we enhance the knowledge for these problems by proving
that models involving transpositions alongside reversals, transreversals, and
revrevs are NP-hard, considering weights w1 for reversals and w2 for the other
rearrangements such that w2/w1 is less than or equal to 1.5. In addition, we
address a cost function related to the number of fragmentations caused by a
rearrangement, proving that the problem of finding a minimum cost sorting
sequence, considering the fragmentation cost function with some restrictions,
is NP-hard for transpositions and the combination of reversals and
transpositions.


title:
Programmable Interface for Statistical & Simulation Models (PRISM):
  Towards Greater Accessibility of Clinical and Healthcare Decision Models

abstract:
  Background: Increasingly, decision-making in healthcare relies on computer
models, be it clinical prediction models at point of care or decision-analytic
models at the policymaking level. Given the important role models play in both
contexts, their structure and implementation be rigorously scrutinized. The
ability to interrogate input/output associations without facing barriers can
improve quality assurance mechanisms while satisfying privacy/confidentiality
concerns and facilitating the integration of models into decision-making. This
paper reports on the development of Programmable Interface for Statistical &
Simulation Models (PRISM), a cloud-based platform for model accessibility.
Methods: PRISM emphasizes two main principles: 1) minimal specifications on the
side of model developer to make the model fit for cloud hosting, and 2) making
client access completely independent of the resource requirement and software
dependencies of the model. The server architecture integrates a RESTful
Application Programming Interface (API) infrastructure, JSON for data transfer,
a routing layer for access management, container technology for management of
computer resources and package dependencies, and the capacity for synchronous
or asynchronous model calls. Results: We discuss the architecture, the minimal
API standards that enable a universal language for access to such models, the
underlying server infrastructure, and the standards used for data transfer. An
instance of PRISM is available as a service via the Peer Models Network
http://peermodelsnetwork.com. Through a series of case studies, we demonstrate
how interrogating models becomes possible in standardized fashion, in a way
that is irrespective of the specifics of any model. Conclusions: We have
developed a publicly accessible platform and minimalist standards that
facilitate model accessibility for both clinical and policy models.


title:
Virtual Maps for Autonomous Exploration of Cluttered Underwater
  Environments

abstract:
  We consider the problem of autonomous mobile robot exploration in an unknown
environment, taking into account a robot's coverage rate, map uncertainty, and
state estimation uncertainty. This paper presents a novel exploration framework
for underwater robots operating in cluttered environments, built upon
simultaneous localization and mapping (SLAM) with imaging sonar. The proposed
system comprises path generation, place recognition forecasting, belief
propagation and utility evaluation using a virtual map, which estimates the
uncertainty associated with map cells throughout a robot's workspace. We
evaluate the performance of this framework in simulated experiments, showing
that our algorithm maintains a high coverage rate during exploration while also
maintaining low mapping and localization error. The real-world applicability of
our framework is also demonstrated on an underwater remotely operated vehicle
(ROV) exploring a harbor environment.


title:
Vision Models Are More Robust And Fair When Pretrained On Uncurated
  Images Without Supervision

abstract:
  Discriminative self-supervised learning allows training models on any random
group of internet images, and possibly recover salient information that helps
differentiate between the images. Applied to ImageNet, this leads to object
centric features that perform on par with supervised features on most
object-centric downstream tasks. In this work, we question if using this
ability, we can learn any salient and more representative information present
in diverse unbounded set of images from across the globe. To do so, we train
models on billions of random images without any data pre-processing or prior
assumptions about what we want the model to learn. We scale our model size to
dense 10 billion parameters to avoid underfitting on a large data size. We
extensively study and validate our model performance on over 50 benchmarks
including fairness, robustness to distribution shift, geographical diversity,
fine grained recognition, image copy detection and many image classification
datasets. The resulting model, not only captures well semantic information, it
also captures information about artistic style and learns salient information
such as geolocations and multilingual word embeddings based on visual content
only. More importantly, we discover that such model is more robust, more fair,
less harmful and less biased than supervised models or models trained on object
centric datasets such as ImageNet.


title: Vectorization of the Jacobi-type singular value decomposition method

abstract:
  The eigenvalue decomposition (EVD) of (a batch of) Hermitian matrices of
order two has a role in many numerical algorithms, of which the one-sided
Jacobi method for the singular value decomposition (SVD) is the prime example.
In this paper the batched EVD is vectorized, with a vector-friendly data layout
and the AVX-512 SIMD instructions of Intel CPUs, alongside other key components
of a real and a complex OpenMP-parallel Jacobi-type SVD method, inspired by the
sequential xGESVJ routines from LAPACK. These vectorized building blocks should
be portable to other platforms, with unconditional reproducibility guaranteed
for the batched EVD and several others. No avoidable overflow of the results
can occur with the proposed EVD or SVD. The measured accuracy of the proposed
EVD often surpasses that of the xLAEV2 routines from LAPACK. While the batched
EVD outperforms the matching sequence of xLAEV2 calls, speedup of the parallel
SVD is modest but can be improved and is already beneficial with enough
threads. Regardless of their number, the proposed SVD method gives identical
results, but of somewhat lower accuracy than xGESVJ.


title:
MMZDA: Enabling Social Welfare Maximization in Cross-Silo Federated
  Learning

abstract:
  As one of the typical settings of Federated Learning (FL), cross-silo FL
allows organizations to jointly train an optimal Machine Learning (ML) model.
In this case, some organizations may try to obtain the global model without
contributing their local training, lowering the social welfare. In this paper,
we model the interactions among organizations in cross-silo FL as a public
goods game for the first time and theoretically prove that there exists a
social dilemma where the maximum social welfare is not achieved in Nash
equilibrium. To overcome this social dilemma, we employ the Multi-player
Multi-action Zero-Determinant (MMZD) strategy to maximize the social welfare.
With the help of the MMZD, an individual organization can unilaterally control
the social welfare without extra cost. Since the MMZD strategy can be adopted
by all organizations, we further study the scenario where multiple
organizations jointly adopt the MMZD strategy and form an MMZD Alliance
(MMZDA). We prove theoretically that the MMZDA strategy strengthens the control
of the maximum social welfare. Experimental results validate that the MMZD
strategy is effective in maximizing the social welfare and the MMZDA can
achieve a larger maximum value.


title: Learning-Enabled Robust Control with Noisy Measurements

abstract:
  We present a constructive approach to bounded $\ell_2$-gain adaptive control
with noisy measurements for linear time-invariant scalar systems with uncertain
parameters belonging to a finite set. The gain bound refers to the closed-loop
system, including the learning procedure. The approach is based on forward
dynamic programming to construct a finite-dimensional information state
consisting of $\mathcal H_\infty$-observers paired with a recursively computed
performance metric. We do not assume prior knowledge of a stabilizing
controller.