title:
Herschel-ATLAS Data Release III: Near-infrared counterparts in the South
  Galactic Pole field -- Another 100,000 submillimetre galaxies

abstract:
  In this paper we present the third data release (DR3) of the Herschel
Astrophysical Terahertz Large Area Survey (H-ATLAS). We identify likely
near-infrared counterparts to submillimetre sources in the South Galactic Pole
(SGP) field using the VISTA VIKING survey. We search for the most probable
counterparts within 15 arcsec of each Herschel source using a probability
measure based on the ratio between the likelihood the true counterpart is found
close to the submillimetre source and the likelihood that an unrelated object
is found in the same location. For 110 374 (57.0$\%$) sources we find galaxies
on the near-infrared images where the probability that the galaxy is associated
to the source is greater than 0.8. We estimate the false identification rate to
be 4.8$\%$, with a probability that the source has an associated counterpart on
the VIKING images of 0.835$\pm$0.009. We investigate the effects of
gravitational lensing and present 41 (0.14 deg$^{-2}$) candidate lensed systems
with observed flux densities > 100 mJy at 500 $\mu$m. We include in the data
release a probability that each source is gravitationally lensed and discover
an additional 5 923 sources below 100 mJy that have a probability greater than
0.94 of being gravitationally lensed. We estimate that $\sim$ 400 - 1 000
sources have multiple true identifications in VIKING based on the similarity of
redshift estimates for multiple counterparts close to a Herschel source. The
data described in this paper can be found at the H-ATLAS website.


title: Skew Brownian motion with dry friction: joint density approach

abstract:
  This note concerns distributions of Skew Brownian motion with dry friction
and its occupation time. These distributions were obtained in [2] by using the
Laplace transform and joint characteristic functions. We provide an alternative
approach to deriving these distributions. Our approach is based on using the
results for Skew Brownian motion obtained in [3].


title: Transparency principle for carbon emissions drives sustainable finance

abstract:
  Alignment of financial market incentives and carbon emissions disincentives
is key to limiting global warming. Regulators and standards bodies have made a
start by requiring some carbon-related disclosures and proposing others. Here
we go further and propose a Carbon Equivalence Principle: all financial
products shall contain a description of the equivalent carbon flows from
greenhouse gases that the products enable, as well as their existing
description in terms of cash flows. This description of the carbon flows
enabled by the project shall be compatible with existing bank systems that
track cashflows so that carbon flows have equal standing to cash flows. We
demonstrate that this transparency alone can align incentives by applying it to
project finance examples for power generation and by following through the
financial analysis. The financial requirements to offset costs of carbon flows
enabled in the future radically change project costs, and risk that assets
become stranded, thus further increasing costs. This observation holds
whichever partner in the project bears the enabled-carbon costs. Mitigating
these risks requires project re-structuring to include negative emissions
technologies. We also consider that sequestered carbon needs to remain
sequestered permanently, e.g., for at least one hundred years. We introduce
mixed financial-physical solutions to minimise this permanence cost, and price
to them. This complements previous insurance-based proposals with lesser scope.
For financial viability we introduce project designs that are financially
net-zero, and as a consequence are carbon negative. Thus we see that adoption
of the Carbon Equivalence Principle for financial products aligns incentives,
requires product redesign, and is simply good financial management driving
sustainability.


title:
Theoretical and numerical investigation of internal conical refraction
  of structured light beams

abstract:
  We present an ab-initio numerical investigation of the internal conical
refraction of structured light beams in a biaxial crystal. Starting from the
solutions of the Fresnel equation, a theoretical analysis is developed without
assuming any analytical approximation, thus obtaining a set of exact equations
that can be solved by standard methods of integration for any impinging light
beam. As examples of applications, we consider the particular cases of linearly
and circularly polarized Gaussian and Bessel beams inside a KTP crystal. The
numerical calculations follow the evolution of the refracted beam inside the
crystal. It is seen that for realistic boundary conditions, a refraction cone
appears in a certain range of distances within the crystal and its shape is
rather sensitive to the initial conditions.


title: The Design and Analysis of a Mobility Game

abstract:
  In this paper, we study a routing and travel-mode choice problem for mobility
systems with a multimodal transportation network as a mobility game with
coupled hybrid action sets. The mobility resources (modes of transportation)
may experience delays that grow with the aggregate utilization of the resource.
We develop a theoretical framework based on repeated non-cooperative game
theory for the travelers' routing and travel-mode choice within a general
mobility system. This framework aims to study the behavioral impact of the
travelers' decision-making on efficiency. We consider the traffic congestion
and the waiting times at different transport hubs and introduce mobility
monetary incentives as part of a pricing scheme. We show that the travelers'
selfish behavior results in a Nash equilibrium, and then we perform a Price of
Anarchy analysis to establish that the mobility system's inefficiencies remain
relatively low as the number of travelers increases. We deviate from the
standard game-theoretic analysis of decision-making by extending our modeling
framework to capture the subjective behavior of travelers using prospect
theory. Finally, we provide a simple example to showcase the effectiveness of
our mobility game and incentives.


title: Range dependent Hamiltonian Algorithm for numerical QUBO formulation

abstract:
  With the advent and development of quantum computers, various quantum
algorithms that can solve linear equations and eigenvalues faster than
classical computers have been developed. The Harrow-Hassidim-Lloyd algorithm is
an algorithm that can solve linear equations in a gate model quantum computer.
Still, it is constrained by the use of quantum RAM and the size limit of the
matrix according to the total number of qubits in the quantum computer.
Recently, Jun and Lee developed a QUBO model for solving linear systems and
eigenvalue problems in the quantum computer. However, even though their model
uses 2048 qubits, the number of qubits for variables that can be used for the
problem is only 64. To solve this problem, we introduce an algorithm that can
be used by dividing the size of the entire domain according to the number of
qubits. We also form a QUBO model related to each subregion.


title:
On Single Server Private Information Retrieval with Private Coded Side
  Information

abstract:
  Motivated by an open problem and a conjecture, this work studies the problem
of single server private information retrieval with private coded side
information (PIR-PCSI) that was recently introduced by Heidarzadeh et al. The
goal of PIR-PCSI is to allow a user to efficiently retrieve a desired message
$\bm{W}_{\bm{\theta}}$, which is one of $K$ independent messages that are
stored at a server, while utilizing private side information of a linear
combination of a uniformly chosen size-$M$ subset
($\bm{\mathcal{S}}\subset[K]$) of messages. The settings PIR-PCSI-I and
PIR-PCSI-II correspond to the constraints that $\bm{\theta}$ is generated
uniformly from $[K]\setminus\bm{\mathcal{S}}$, and $\bm{\mathcal{S}}$,
respectively. In each case, $(\bm{\theta},\bm{\mathcal{S}})$ must be kept
private from the server. The capacity is defined as the supremum over message
and field sizes, of achievable rates (number of bits of desired message
retrieved per bit of download) and is characterized by Heidarzadeh et al. for
PIR-PCSI-I in general, and for PIR-PCSI-II for $M>(K+1)/2$ as $(K-M+1)^{-1}$.
For $2\leq M\leq (K+1)/2$ the capacity of PIR-PCSI-II remains open, and it is
conjectured that even in this case the capacity is $(K-M+1)^{-1}$. We show the
capacity of PIR-PCSI-II is equal to $2/K$ for $2 \leq M \leq \frac{K+1}{2}$,
which is strictly larger than the conjectured value, and does not depend on $M$
within this parameter regime. Remarkably, half the side-information is found to
be redundant. We also characterize the infimum capacity (infimum over fields
instead of supremum), and the capacity with private coefficients. The results
are generalized to PIR-PCSI-I ($\theta\in[K]\setminus\mathcal{S}$) and PIR-PCSI
($\theta\in[K]$) settings.


title: A short note on a theorem by Eliahou and Fromentin

abstract:
  In this note, we give an alternative proof for a theorem by Eliahou and
Fromentin, which exhibit a remarkable property of the sequence $(n'_g)$, where
$n'_g$ denotes the number of gapsets with genus $g$ and depth at most $3$.


title: Domain Walls in the Heisenberg-Ising Spin-1/2 Chain

abstract:
  In this paper we obtain formulas for the distribution of the left-most
up-spin in the Heisenberg-Ising spin-1/2 chain with anisotropy parameter
$\Delta$, also known as the XXZ spin-1/2 chain, on the one-dimensional lattice
$\mathbb{Z}$ with domain wall initial conditions. We use the Bethe Ansatz to
solve the Schr$\"o$dinger equation and a recent antisymmetrization identity of
Cantini, Colomo, and Pronko (arXiv:1906.07636) to simplify the marginal
distribution of the left-most up-spin. In the $\Delta=0$ case, the distribution
$F_2$ arises. In the $\Delta \neq 0$ case, we propose a conjectural series
expansion type formula based on a saddle point analysis. The conjectural
formula turns out to be a Fredholm series expansion in the $\Delta \rightarrow
0$ limit and recovers the result for $\Delta = 0$.


title: Regularity of primes associated with polynomial parametrisations

abstract:
  We prove a doubly exponential bound for the Castelnuovo-Mumford regularity of
prime ideals defining varieties with polynomial parametrisation.


title: A new discrete theory of pseudoconvexity

abstract:
  Recently geometric hypergraphs that can be defined by intersections of
pseudohalfplanes with a finite point set were defined in a purely combinatorial
way. This led to extensions of earlier results about points and halfplanes to
pseudohalfplanes, including polychromatic colorings and discrete Helly-type
theorems about pseudohalfplanes.
  Here we continue this line of research and introduce the notion of convex
sets of such pseudohalfplane hypergraphs. In this context we prove several
results corresponding to classical results about convexity, namely Helly
Theorem, Carath\'eodory's Theorem, Kirchberger's Theorem, Separation Theorem,
Radon's Theorem and the Cup-Cap Theorem. These results imply the respective
results about pseudoconvex sets in the plane defined using pseudohalfplanes.
  It turns out that most of our results can be also proved using oriented
matroids and topological affine planes (TAPs) but our approach is different
from both of them. Compared to oriented matroids, our theory is based on a
linear ordering of the vertex set which makes our definitions and proofs quite
different and perhaps more elementary. Compared to TAPs, which are continuous
objects, our proofs are purely combinatorial and again quite different in
flavor. Altogether, we believe that our new approach can further our
understanding of these fundamental convexity results.


title:
High Power Superconducting Electron Linear Accelerators For Industrial
  Application

abstract:
  Fermilab is executing a technology development program to develop a compact
yet powerful electron accelerator. We are leveraging R&D breakthroughs in SRF
cavities, cost-effective radio-frequency sources, modern cryo-coolers, and high
average current electron guns. We show that a single accelerator module can
deliver average beam power as high as 1 MW through detailed thermal, RF, and
particle simulations.


title:
Numerical simulation of thermal conductivity of stainless steel and
  Al-12Si powders for additive manufacturing

abstract:
  A three-dimensional model of a partially melted powder bed with particles
stochastically distributed in size and space coordinates has been developed.
Numerical simulation of temperature distributions in stainless steel AISI 316L
and Al-12Si powders in vacuum, air and argon has been performed to analyze
unsteady heat transfer in a porous medium. The numerical model demonstrates a
large effect of heat transfer through the gas phase in case of powders with low
thermal conductivities like stainless steels. At the porosity level of 65\% and
above, the mechanism of heat transfer drastically changes and a linear
dependence of thermal conductivity on porosity frequently used in literature
becomes incorrect. The effects of the consolidation coefficient and size
distribution on effective heat transfer in powders are discussed. The obtained
dependencies of the effective thermal conductivity on porosity and the
consolidation coefficient could be used in additive manufacturing applications.


title: GKM actions on cohomogeneity one manifolds

abstract:
  We consider compact manifolds $M$ with a cohomogeneity one action of a
compact Lie group $G$ such that the orbit space $M/G$ is a closed interval. For
$T$ a maximal torus of $G$, we find necessary and sufficient conditions on the
group diagram of $M$ such that the $T$-action on $M$ is of GKM type, and
describe its GKM graph. The general results are illustrated on explicit
examples.


title: Exact factorizations and extensions of finite tensor categories

abstract:
  We extend \cite{G} to the nonsemisimple case. We define and study exact
factorizations $\B=\A\bullet \C$ of a finite tensor category $\B$ into a
product of two tensor subcategories $\A,\C\subset \B$, and relate exact
factorizations of finite tensor categories to exact sequences of finite tensor
categories with respect to exact module categories \cite{EG}. We apply our
results to study exact factorizations of quasi-Hopf algebras, and extensions of
a finite group scheme theoretical tensor category \cite{G2} by another one. We
also provide several examples to illustrate our results.


title: Superconducting Accelerators for High-Power X-ray production

abstract:
  To date, linear accelerators (linacs) as electron sources used to produce
ionizing radiation for industrial purposes have been limited to less than 100
kW. When the electron beam is used directly, this is sufficient for most
potential applications. However, when the electron beam is used for the
production of photons (x-rays) which are then to be used in an application,
this is not sufficient to compete with other sources of photons (gamma rays
from cobalt-60). This paper will discuss a compact superconducting RF (CSRF)
accelerator system that relies on emerging technologies that will be able to
produce electron beam powers into the 100s of kW with efficiencies much better
than present linacs. The focus is to produce x-ray beams for medical device
sterilization to provide alternatives for the present use of cobalt-60 for this
purpose.


title:
Disk precession to explain the super-orbital modulation of LMC X-4:
  results from the Swift monitoring campaign

abstract:
  We studied the spectral changes of the high-mass X-ray binary system LMC X-4
to understand the origin and mechanisms beyond its super-orbital modulation
(30.4 days). To this aim, we obtained a monitoring campaign with Swift/XRT
(0.3-10 keV) and complemented these data with the years-long Swift/BAT survey
data (15-60 keV). We found a self-consistent, physically motivated, description
of the broadband X-ray spectrum using a Swift/XRT and a NuSTAR observation at
the epoch of maximum flux. We decomposed the spectrum into the sum of a
bulk+thermal Comptonization, a disk-reflection component and a soft
contribution from a standard Shakura-Sunyaev accretion disk. We applied this
model to 20 phase-selected Swift spectra along the super-orbital period. We
found a phase-dependent flux ratio of the different components, whereas the
absorption column does not significantly vary. The disk emission is decoupled
with respect to the hard flux. We interpret this as a geometrical effect in
which the inner parts of the disk are tilted with respect to the obscuring
outer regions.


title:
Simulating Malicious Attacks on VANETs for Connected and Autonomous
  Vehicle Cybersecurity: A Machine Learning Dataset

abstract:
  Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networks
with wireless communication between vehicles and roadside infrastructure to
support safe operation. However, cybersecurity attacks pose a threat to VANETs
and the safe operation of CAVs. This study proposes the use of simulation for
modelling typical communication scenarios which may be subject to malicious
attacks. The Eclipse MOSAIC simulation framework is used to model two typical
road scenarios, including messaging between the vehicles and infrastructure -
and both replay and bogus information cybersecurity attacks are introduced. The
model demonstrates the impact of these attacks, and provides an open dataset to
inform the development of machine learning algorithms to provide anomaly
detection and mitigation solutions for enhancing secure communications and safe
deployment of CAVs on the road.


title:
SIRS sequence as frequency filter: A simulation and phantom study of
  spin-lock preparations among composite signals

abstract:
  Extracting quantitative information of neuronal signals by non-invasive
imaging is an outstanding challenge for understanding brain function and
pathology. However, state-of-the-art techniques offer low sensitivity to deep
electrical sources. Stimulus induced rotary saturation (SIRS) is a recently
proposed magnetic resonance imaging (MRI) sequence that detects oscillatory
magnetic fields using a spin-lock preparation. Phantom experiments and
simulations proved its efficiency and sensitivity, but the susceptibility of
the method to field inhomogeneities is still not well understood. In this
study, we present a simulation model for the double resonance effect that
enables the simulation of any oscillating neuronal field. We analyzed the
performance of three spin-lock preparations and their response to field
inhomogeneities in the presence of a resonant oscillating field. We show that
the composite spin-lock preparation is more robust against field variations
within the double resonance effect at the expense of losing contrast amplitude.
We studied the SIRS contrast as a filter of spectral frequencies and its
capability to recover information about the spectral components of a composite
signal. This study sets the bases to move one step further towards the clinical
application of MR-based neuronal current imaging.


title: Misinformation Detection in Social Media Video Posts

abstract:
  With the growing adoption of short-form video by social media platforms,
reducing the spread of misinformation through video posts has become a critical
challenge for social media providers. In this paper, we develop methods to
detect misinformation in social media posts, exploiting modalities such as
video and text. Due to the lack of large-scale public data for misinformation
detection in multi-modal datasets, we collect 160,000 video posts from Twitter,
and leverage self-supervised learning to learn expressive representations of
joint visual and textual data. In this work, we propose two new methods for
detecting semantic inconsistencies within short-form social media video posts,
based on contrastive learning and masked language modeling. We demonstrate that
our new approaches outperform current state-of-the-art methods on both
artificial data generated by random-swapping of positive samples and in the
wild on a new manually-labeled test set for semantic misinformation.


title: On the Role of Channel Capacity in Learning Gaussian Mixture Models

abstract:
  This paper studies the sample complexity of learning the $k$ unknown centers
of a balanced Gaussian mixture model (GMM) in $\mathbb{R}^d$ with spherical
covariance matrix $\sigma^2\mathbf{I}$. In particular, we are interested in the
following question: what is the maximal noise level $\sigma^2$, for which the
sample complexity is essentially the same as when estimating the centers from
labeled measurements? To that end, we restrict attention to a Bayesian
formulation of the problem, where the centers are uniformly distributed on the
sphere $\sqrt{d}\mathcal{S}^{d-1}$. Our main results characterize the exact
noise threshold $\sigma^2$ below which the GMM learning problem, in the large
system limit $d,k\to\infty$, is as easy as learning from labeled observations,
and above which it is substantially harder. The threshold occurs at $\frac{\log
k}{d} = \frac12\log\left( 1+\frac{1}{\sigma^2} \right)$, which is the capacity
of the additive white Gaussian noise (AWGN) channel. Thinking of the set of $k$
centers as a code, this noise threshold can be interpreted as the largest noise
level for which the error probability of the code over the AWGN channel is
small. Previous works on the GMM learning problem have identified the minimum
distance between the centers as a key parameter in determining the statistical
difficulty of learning the corresponding GMM. While our results are only proved
for GMMs whose centers are uniformly distributed over the sphere, they hint
that perhaps it is the decoding error probability associated with the center
constellation as a channel code that determines the statistical difficulty of
learning the corresponding GMM, rather than just the minimum distance.

categories: cs.IT cs.LG math.IT math.ST stat.ML stat.TH


title: Complete stable minimal hypersurfaces in positively curved 4-manifolds

abstract:
  We show that the combination of non-negative sectional curvature (or
$2$-intermediate Ricci curvature) and strict positivity of scalar curvature
forces rigidity of complete (non-compact) two-sided stable minimal
hypersurfaces in a $4$-manifold with bounded curvature. In particular, this
implies the nonexistence of complete two-sided stable minimal hypersurface in a
closed $4$-manifold with positive sectional curvature.
  Our work leads to new comparison results. We also construct various examples
showing rigidity of stable minimal hypersurfaces can fail under other curvature
conditions.


title: PIC Simulations of One-side Multipactor on Dielectric

abstract:
  Breakdown of dielectric RF windows is an important issue for particle
accelerators and high-power RF sources. One of the generally considered reasons
for the RF windows failure is the multipactor on dielectric surface. The
multipactor may be responsible for excessive heating of dielectric and
discharge of charges that accumulated in ceramic due to secondary emission. In
this study the comprehensive self-consistent PIC simulations with space charge
effect were performed in order to better understand the dynamic of one-side
multipactor development and floating potential on dielectric induced by the
emission. The important correlations between the multipactor parameters at
saturation and the secondary emission properties of dielectric and the applied
RF field parameters were found and are reported in the paper.


title: Parallel Virtual Machines Placement with Provable Guarantees

abstract:
  Network Function Virtualization (NFV) carries the potential for on-demand
deployment of network algorithms in virtual machines (VMs). In large clouds,
however, VM resource allocation incurs delays that hinder the dynamic scaling
of such NFV deployment. Parallel resource management is a promising direction
for boosting performance, but it may significantly increase the communication
overhead and the decline ratio of deployment attempts. Our work analyzes the
performance of various placement algorithms and provides empirical evidence
that state-of-the-art parallel resource management dramatically increases the
decline ratio of deterministic algorithms but hardly affects randomized
algorithms. We, therefore, introduce APSR -- an efficient parallel random
resource management algorithm that requires information only from a small
number of hosts and dynamically adjusts the degree of parallelism to provide
provable decline ratio guarantees. We formally analyze APSR, evaluate it on
real workloads, and integrate it into the popular OpenStack cloud management
platform. Our evaluation shows that APSR matches the throughput provided by
other parallel schedulers, while achieving up to 13x lower decline ratio and a
reduction of over 85% in communication overheads.


title: Certification of Gaussian Boson Sampling via graph theory

abstract:
  Gaussian Boson Sampling is a non-universal model for quantum computing
inspired by the original formulation of the Boson Sampling problem. Nowadays,
it represents a paradigmatic quantum platform to reach the quantum advantage
regime in a specific computational model. Indeed, thanks to the implementation
in photonics-based processors, the latest Gaussian Boson Sampling experiments
have reached a level of complexity where the quantum apparatus has solved the
task faster than currently up-to-date classical strategies. In addition, recent
studies have identified possible applications beyond the inherent sampling
task. In particular, a direct connection between photon counting of a genuine
Gaussian Boson Sampling device and the number of perfect matchings in a graph
has been established. In this work, we propose to exploit such a connection to
benchmark Gaussian Boson Sampling experiments. We interpret the properties of
the feature vectors of the graph encoded in the device as a signature of
correct sampling from the true input state. Within this framework, two
approaches that exploit the distributions of graph feature vectors and graph
kernels are presented. Our results provide a novel approach to the actual need
for tailored algorithms to benchmark large-scale Gaussian Boson Samplers.


title: Privacy Preserving Visual Question Answering

abstract:
  We introduce a novel privacy-preserving methodology for performing Visual
Question Answering on the edge. Our method constructs a symbolic representation
of the visual scene, using a low-complexity computer vision model that jointly
predicts classes, attributes and predicates. This symbolic representation is
non-differentiable, which means it cannot be used to recover the original
image, thereby keeping the original image private. Our proposed hybrid solution
uses a vision model which is more than 25 times smaller than the current
state-of-the-art (SOTA) vision models, and 100 times smaller than end-to-end
SOTA VQA models. We report detailed error analysis and discuss the trade-offs
of using a distilled vision model and a symbolic representation of the visual
scene.


title: Tunable nodal kagome superconductivity in charge ordered RbV3Sb5

abstract:
  Unconventional superconductors often feature competing orders, small
superfluid density, and nodal electronic pairing. While unusual
superconductivity has been proposed in the kagome metals AV3Sb5, key
spectroscopic evidence has remained elusive. Here we utilize pressure-tuned (up
to 1.85 GPa) and ultra-low temperature (down to 18 mK) muon spin spectroscopy
to uncover the unconventional nature of superconductivity in RbV3Sb5. At
ambient pressure, we detect an enhancement of the width of the internal
magnetic field distribution sensed by the muon ensemble, indicative of
time-reversal symmetry breaking charge order. Remarkably, the superconducting
state displays nodal energy gap and a reduced superfluid density, which can be
attributed to the competition with the novel charge order. Upon applying
pressure, the charge-order transitions are suppressed, the superfluid density
increases, and the superconducting state progressively evolves from nodal to
nodeless. Once charge order is eliminated, we find a superconducting pairing
state that is not only fully gapped, but also spontaneously breaks
time-reversal symmetry. Our results point to unprecedented tunable nodal kagome
superconductivity competing with time-reversal symmetry-breaking charge order
and offer unique insights into the nature of the pairing state.


title:
Varification of Electron's Mass Deriving from Interactive Dark Sector
  and Cosmological Observation

abstract:
  We investigate the model where electrons and dark matter interact with dark
energy through the rolling of a scalar field which comes from extra dimensional
theories such as the braneworld theory and Brans-Dicke theory. In this model,
dark energy couples to dark matter and electrons, which leads to larger values
of the mass energies of dark matter and electrons in the early universe. We
also fit our model to the cosmological data. By analyzing the data from Planck,
baryon acoustic oscillation (BAO), light curves (Pantheon), and type-Ia
supernovae (SH0ES), it can be seen that the Hubble tension is relieved in our
model and the coupling parameter prefers a non-zero value with a significance
of over 2{\sigma}.


title: Combinatorial Exploration: An algorithmic framework for enumeration

abstract:
  Combinatorial Exploration is a new domain-agnostic algorithmic framework to
automatically and rigorously study the structure of combinatorial objects and
derive their counting sequences and generating functions. We describe how it
works and provide an open-source Python implementation. As a prerequisite, we
build up a new theoretical foundation for combinatorial decomposition
strategies and combinatorial specifications.
  We then apply Combinatorial Exploration to the domain of permutation
patterns, to great effect. We rederive hundreds of results in the literature in
a uniform manner and prove many new ones. These results can be found in a new
public database, the Permutation Pattern Avoidance Library (PermPAL) at
https://permpal.com. Finally, we give three additional proofs-of-concept,
showing examples of how Combinatorial Exploration can prove results in the
domains of alternating sign matrices, polyominoes, and set partitions.


title: Learning Model Predictive Control for Quadrotors

abstract:
  Aerial robots can enhance their safe and agile navigation in complex and
cluttered environments by efficiently exploiting the information collected
during a given task. In this paper, we address the learning model predictive
control problem for quadrotors. We design a learning receding--horizon
nonlinear control strategy directly formulated on the system nonlinear manifold
configuration space SO(3)xR^3. The proposed approach exploits past successful
task iterations to improve the system performance over time while respecting
system dynamics and actuator constraints. We further relax its computational
complexity making it compatible with real-time quadrotor control requirements.
We show the effectiveness of the proposed approach in learning a minimum time
control task, respecting dynamics, actuators, and environment constraints.
Several experiments in simulation and real-world set-up validate the proposed
approach.


title:
Finite- and Fixed-Time Nonovershooting Stabilizers and Safety Filters by
  Homogeneous Feedback

abstract:
  Non-overshooting stabilization is a form of safe control where the setpoint
chosen by the user is at the boundary of the safe set. Exponential
non-overshooting stabilization, including suitable extensions to systems with
deterministic and stochastic disturbances, has been solved by the second author
and his coauthors. In this paper we develop homogeneous feedback laws for
fixed-time nonovershooting stabilization for nonlinear systems that are
input-output linearizable with a full relative degree, i.e., for systems that
are diffeomorphically equivalent to the chain of integrators. These homogeneous
feedback laws can also assume the secondary role of `fixed-time safety filters'
(FxTSf filters) which keep the system within the closed safe set for all time
but, in the case where the user's nominal control commands approach to the
unsafe set, allow the system to reach the boundary of the safe set no later
than a desired time that is independent of nominal control and independent of
the value of the state at the time the nominal control begins to be overridden.


title: Students' perspectives on computational challenges in physics class

abstract:
  High school science classrooms across the United States are answering calls
to make computation a part of science learning. The problem is that there is
little known about the barriers to learning that computation might bring to a
science classroom or about how to help students overcome these challenges. This
case study explores these challenges from the perspectives of students in a
high school physics classroom with a newly revamped, computation-integrated
curriculum. Focusing mainly on interviews to center the perspectives of
students, we found that computation is a double-edged sword: It can make
science learning more authentic for students who are familiar with it, but it
can also generate frustration and an aversion towards physics for students who
are not.


title: Matchings in matroids over abelian groups

abstract:
  We formulate and prove matroid analogues of results concerning matchings in
groups. A matching in an abelian group $(G,+)$ is a bijection $f:A\to B$
between two finite subsets $A,B$ of $G$ satisfying $a+f(a)\notin A$ for all
$a\in A$. A group $G$ has the matching property if for every two finite subsets
$A,B \subset G$ of the same size with $0 \notin B$, there exists a matching
from $A$ to $B$. In [19] it was proved that an abelian group has the matching
property if and only if it is torsion-free or cyclic of prime order. Here we
consider a similar question in a matroid setting. We introduce an analogous
notion of matching between matroids whose ground sets are subsets of an abelian
group $G$, and we obtain criteria for the existence of such matchings. Our
tools are classical theorems in matroid theory, group theory and additive
number theory.


title:
Active Uncertainty Learning for Human-Robot Interaction: An Implicit
  Dual Control Approach

abstract:
  Predictive models are effective in reasoning about human motion, a crucial
part that affects safety and efficiency in human-robot interaction. However,
robots often lack access to certain key parameters of such models, for example,
human's objectives, their level of distraction, and willingness to cooperate.
Dual control theory addresses this challenge by treating unknown parameters as
stochastic hidden states and identifying their values using information
gathered during control of the robot. Despite its ability to optimally and
automatically trade off exploration and exploitation, dual control is
computationally intractable for general human-in-the-loop motion planning,
mainly due to nested trajectory optimization and human intent prediction. In
this paper, we present a novel algorithmic approach to enable active
uncertainty learning for human-in-the-loop motion planning based on the
implicit dual control paradigm. Our approach relies on sampling-based
approximation of stochastic dynamic programming, leading to a model predictive
control problem that can be readily solved by real-time gradient-based
optimization methods. The resulting policy is shown to preserve the dual
control effect for generic human predictive models with both continuous and
categorical uncertainty. The efficacy of our approach is demonstrated with
simulated driving examples.


title:
End-to-end Automatic Logic Optimization Exploration via Domain-specific
  Multi-armed Bandit

abstract:
  Recent years have seen increasing employment of decision intelligence in
electronic design automation (EDA), which aims to reduce the manual efforts and
boost the design closure process in modern toolflows. However, existing
approaches either require a large number of labeled data and expensive training
efforts, or are limited in practical EDA toolflow integration due to
computation overhead. This paper presents a generic end-to-end sequential
decision making framework FlowTune for synthesis tooflow optimization, with a
novel high-performance domain-specific, multi-stage multi-armed bandit (MAB)
approach. This framework addresses optimization problems on Boolean
optimization problems such as a) And-Inv-Graphs (# nodes), b) Conjunction
Normal Form (CNF) minimization (# clauses) for Boolean Satisfiability; logic
synthesis and technology mapping problems such as c) post static timing
analysis (STA) delay and area optimization for standard-cell technology
mapping, and d) FPGA technology mapping for 6-in LUT architectures. Moreover,
we demonstrate the high extnsibility and generalizability of the proposed
domain-specific MAB approach with end-to-end FPGA design flow, evaluated at
post-routing stage, with two different FPGA backend tools (OpenFPGA and VPR)
and two different logic synthesis representations (AIGs and MIGs). FlowTune is
fully integrated with ABC [1], Yosys [2], VTR [3], LSOracle [4], OpenFPGA [5],
and industrial tools, and is released publicly. The experimental results
conducted on various design stages in the flow all demonstrate that our
framework outperforms both hand-crafted flows [1] and ML explored flows [6],
[7] in quality of results, and is orders of magnitude faster compared to
ML-based approaches.


title:
Control Co-design of Actively Controlled Lightweight Structures for
  High-acceleration Precision Motion Systems

abstract:
  Precision motion stages are an essential part of a wide range of
manufacturing equipment, and their motion performance are critical to the
quality and throughput of the systems. The drastically increasing demand for
higher manufacturing throughput in various processes necessities the
development of next-generation motion systems with reduced moving weight and
high control bandwidth. However, the reduction of moving stage's weight can
lower the stage's structural resonance frequencies, making the hardware
dynamics and controller design problem strongly coupled. Aiming at this
challenge, this paper proposes a new formulation of nested hardware and control
co-design framework for precision motion stages. The proposed framework
explicitly optimizes the closed-loop control bandwidth with guaranteed
robustness, and explicitly considers the limits in the physical system. Two
case studies, including a motivating example using lumped-parameter mechanical
system and a finite-element-simulated lightweight motion stage, are being used
to evaluate the effectiveness of the proposed nested CCD framework. Simulation
results show that the proposed nested CCD framework has 42\% of weight
reduction and 28\% bandwidth improvement compared with a sequential design
baseline, which demonstrates the efficacy of the proposed approach.


title:
Revisiting the Full Sets of Orbital Parameters for the XO-3 System: No
  evidence for Temporal Variation of the Spin-Orbit Angle

abstract:
  We present 12 new transit light curves and 16 new out-of-transit radial
velocity measurements for the XO-3 system. By modelling our newly collected
measurements together with archival photometric and Doppler velocimetric data,
we confirmed the unusual configuration of the XO-3 system, which contains a
massive planet ($M_P=11.92^{+0.59}_{-0.63} M_J$) on a relatively eccentric
($e=0.2853^{+0.0027}_{-0.0026}$) and short-period ($3.19152 \pm 0.00145\,$day)
orbit around a massive star ($M_*=1.219^{+0.090}_{-0.095} M_{\odot}$).
Furthermore, we find no strong evidence for a temporal change of either $V\sin
i_{*}$ (and by extension, the stellar spin vector of XO-3), or the transit
profile (and thus orbital angular momentum vector of XO-3b). We conclude that
the discrepancy in previous Rossiter-McLaughlin measurements ($70.0^{\circ} \pm
15.0^{\circ}$ (Hebrard et al. 2008); $37.3^{\circ} \pm 3.7^{\circ}$ (Winn et
al. 2009); $37.3^{\circ} \pm 3.0^{\circ}$ (Hirano et al. 2011)) may have
stemmed from systematic noise sources.


title: Can nuclear matter consist of $\alpha$-particles?

abstract:
  A sufficient condition for the spatial collapse in an infinite system of
interacting Bose particles is obtained on the basis of the variational
principle with the use of trial functions with the Jastrow pair correlation
factors. The instability of a hypothetical infinite system of
$\alpha$-particles with respect to the spatial collapse is shown under the
assumption of the Ali - Bodmer interaction potentials between such Bose
particles. Thus, it becomes clear why the hypothetical nuclear matter is
naturally treated with the use of at least the nucleon degrees of freedom.


title: Superconductivity in hydrated Lix(H2O)yTaS2

abstract:
  We have systematically studied the structural and physical properties of the
superconducting hydrated Lix(H2O)yTaS2. The powder X-ray diffraction patterns
suggest that all the samples are single-phase compounds, and the crystal
structure is similar to that of 2H-TaS2. The transition temperature to
superconductivity shows a dome-shape dependence on the lithium content x with a
maximum Tc of 4.6 K for x=0.42, which is larger than in corresponding optimally
doped 2H-TaS2 superconductors without water or organic intercalants (Tc ~ 4.2
K). There are no signs for a charge-density-wave formation in hydrated
Lix(H2O)yTaS2. While our magnetic data indicate a rather strongly type-II
behavior, heat-capacity measurements reveal, like in other 2H-TaS2-type
compounds. From the corresponding Sommerfeld constants and Debye temperatures,
we can derive the parameter describing the electron-phonon coupling and the
electron density of states DOS(EF) at the Fermi level as functions of x. While
the variation of the DOS(EF) is consistent with that of Tc, indicating that the
lithium intercalation is tuning Tc via changing the DOS(EF) in
2H-Lix(H2O)yTaS2, the simultaneous changes of electron-phonon coupling and
Debye temperatures may also play a certain role.


title:
Two Numerical Approaches for Nonlinear Weakly Singular Integral
  Equations

abstract:
  Singularity subtraction for linear weakly singular Fredholm integral
equations of the second kind is generalized to nonlinear integral equations.
Two approaches are presented: The Classical Approach discretizes the nonlinear
problem, and uses some finite dimensional linearization process to solve
numerically the discrete problem. Its convergence is proved under mild
hypotheses on the nonlinearity and the quadrature rule of the singularity
subtraction scheme. The New Approach is based on linearization of the problem
in its infinite dimensional setting, and discretization of the sequence of
linear problems by singularity subtraction. It is more efficient than the
former, as two numerical experiments confirm.


title: A Subjective Quality Study for Video Frame Interpolation

abstract:
  Video frame interpolation (VFI) is one of the fundamental research areas in
video processing and there has been extensive research on novel and enhanced
interpolation algorithms. The same is not true for quality assessment of the
interpolated content. In this paper, we describe a subjective quality study for
VFI based on a newly developed video database, BVI-VFI. BVI-VFI contains 36
reference sequences at three different frame rates and 180 distorted videos
generated using five conventional and learning based VFI algorithms. Subjective
opinion scores have been collected from 60 human participants, and then
employed to evaluate eight popular quality metrics, including PSNR, SSIM and
LPIPS which are all commonly used for assessing VFI methods. The results
indicate that none of these metrics provide acceptable correlation with the
perceived quality on interpolated content, with the best-performing metric,
LPIPS, offering a SROCC value below 0.6. Our findings show that there is an
urgent need to develop a bespoke perceptual quality metric for VFI. The BVI-VFI
dataset is publicly available and can be accessed at
https://danielism97.github.io/BVI-VFI/.


title:
Don't Lie to Me! Robust and Efficient Explainability with Verified
  Perturbation Analysis

abstract:
  A variety of methods have been proposed to try to explain how deep neural
networks make their decisions. Key to those approaches is the need to sample
the pixel space efficiently in order to derive importance maps. However, it has
been shown that the sampling methods used to date introduce biases and other
artifacts, leading to inaccurate estimates of the importance of individual
pixels and severely limit the reliability of current explainability methods.
Unfortunately, the alternative -- to exhaustively sample the image space is
computationally prohibitive. In this paper, we introduce EVA (Explaining using
Verified perturbation Analysis) -- the first explainability method guarantee to
have an exhaustive exploration of a perturbation space. Specifically, we
leverage the beneficial properties of verified perturbation analysis -- time
efficiency, tractability and guaranteed complete coverage of a manifold -- to
efficiently characterize the input variables that are most likely to drive the
model decision. We evaluate the approach systematically and demonstrate
state-of-the-art results on multiple benchmarks.


title: Sharp H\"older Regularity for Nirenberg's Complex Frobenius Theorem

abstract:
  Nirenberg's famous complex Frobenius theorem gives necessary and sufficient
conditions on a locally integrable structure for when the manifold is locally
diffeomorphic to $\mathbb R^r\times\mathbb C^m\times \mathbb R^{N-r-2m}$
through a coordinate chart $F$ in such a way that the structure is locally
spanned by $F^*\frac\partial{\partial t^1},\dots,F^*\frac\partial{\partial
t^r},F^*\frac\partial{\partial z^1},\dots,F^*\frac\partial{\partial z^m}$,
where we have given $\mathbb R^r\times\mathbb C^m \times\mathbb R^{N-r-2m}$
coordinates $(t,z,s)$. In this paper, we give the optimal H\"older-Zygmund
regularity for the coordinate charts which achieve this realization. Namely, if
the structure has H\"older-Zygmund regularity of order $\alpha>1$, then the
coordinate chart $F$ that maps to $\mathbb R^r\times\mathbb C^m \times\mathbb
R^{N-r-2m}$ may be taken to have H\"older-Zygmund regularity of order $\alpha$,
and this is sharp. Furthermore, we can choose this $F$ in such a way that the
vector fields $F^*\frac\partial{\partial t^1},\dots,F^*\frac\partial{\partial
t^r},F^*\frac\partial{\partial z^1},\dots,F^*\frac\partial{\partial z^m}$ on
the original manifold have H\"older-Zygmund regularity of order
$\alpha-\varepsilon$ for every $\varepsilon>0$, and we give an example to show
that the regularity for $F^*\frac\partial{\partial z}$ is optimal.


title: Rankings in the Zerkani network by a game theoretical approach

abstract:
  This paper introduces the Banzhaf and Banzhaf-Owen values as novel centrality
measures for ranking terrorists in a network. This new approach let integrate
the complete topology (i.e. nodes and edges) of the network and a coalitional
structure on the nodes of the network. More precisely, the characteristics of
the nodes (e.g., terrorists) of the network and their possible relationships
(e.g., types of communication links), as well as coalitional information (e.g.
level of hierarchies) independent of the network. First, for both centrality
measures, we provide approximation algorithms and the corresponding R-codes.
Second, as illustration, we rank the members of the Zerkani network,
responsible for the attacks in Paris (2015) and Brussels (2016). Finally, we
give a comparison between the rankings established by Banzhaf and Banzhaf-Owen
and the rankings obtained when using the Shapley value (cf. Hamers et al.,
2019) and the Owen value as centrality measures


title:
Enhancing Deformable Convolution based Video Frame Interpolation with
  Coarse-to-fine 3D CNN

abstract:
  This paper presents a new deformable convolution-based video frame
interpolation (VFI) method, using a coarse to fine 3D CNN to enhance the
multi-flow prediction. This model first extracts spatio-temporal features at
multiple scales using a 3D CNN, and estimates multi-flows using these features
in a coarse-to-fine manner. The estimated multi-flows are then used to warp the
original input frames as well as context maps, and the warped results are fused
by a synthesis network to produce the final output. This VFI approach has been
fully evaluated against 12 state-of-the-art VFI methods on three commonly used
test databases. The results evidently show the effectiveness of the proposed
method, which offers superior interpolation performance over other state of the
art algorithms, with PSNR gains up to 0.19dB.


title:
Multi-Modal Data Fusion in Enhancing Human-Machine Interaction for
  Robotic Applications: A Survey

abstract:
  Human-machine interaction has been around for several decades now, with new
applications emerging every day. One of the major goals that remain to be
achieved is designing an interaction similar to how a human interacts with
another human. Therefore, there is a need to develop interactive systems that
could replicate a more realistic and easier human-machine interaction. On the
other hand, developers and researchers need to be aware of state-of-the-art
methodologies being used to achieve this goal. We present this survey to
provide researchers with state-of-the-art data fusion technologies implemented
using multiple inputs to accomplish a task in the robotic application domain.
Moreover, the input data modalities are broadly classified into uni-modal and
multi-modal systems and their application in myriad industries, including the
health care industry, which contributes to the medical industry's future
development. It will help the professionals to examine patients using different
modalities. The multi-modal systems are differentiated by a combination of
inputs used as a single input, e.g., gestures, voice, sensor, and haptic
feedback. All these inputs may or may not be fused, which provides another
classification of multi-modal systems. The survey concludes with a summary of
technologies in use for multi-modal systems.


title:
The Buratti-Horak-Rosa Conjecture Holds for Some Underlying Sets of Size
  Three

abstract:
  The Buratti-Horak-Rosa Conjecture concerns the possible multisets of
edge-labels of a Hamiltonian path in the complete graph with vertex labels $0,
1, \ldots, {v-1}$ under a particular induced edge-labeling. The conjecture has
been shown to hold when the underlying set of the multiset has size at most~2,
is a subset of $\{1,2,3,4\}$ or $\{1,2,3,5\}$, or is $\{1,2,6\}$, $\{1,2,8\}$
or $\{1,4,5\}$, as well as partial results for many other underlying sets. We
use the method of growable realizations to show that the conjecture holds for
each underlying set $U = \{ x,y,z \}$ when $\max(U) \leq 7$ or when $xyz \leq
24$, with the possible exception of $U = \{1,2,11\}$. We also show that for any
even $x$ the validity of the conjecture for the underlying set $\{ 1,2,x \}$
follows from the validity of the conjecture for finitely many multisets with
this underlying set.


title:
Solving Multi-Period Financial Planning Models: Combining Monte Carlo
  Tree Search and Neural Networks

abstract:
  This paper introduces the MCTS algorithm to the financial world and focuses
on solving significant multi-period financial planning models by combining a
Monte Carlo Tree Search algorithm with a deep neural network. The MCTS provides
an advanced start for the neural network so that the combined method
outperforms either approach alone, yielding competitive results. Several
innovations improve the computations, including a variant of the upper
confidence bound applied to trees (UTC) and a special lookup search. We compare
the two-step algorithm with employing dynamic programs/neural networks. Both
approaches solve regime switching models with 50-time steps and transaction
costs with twelve asset categories. Heretofore, these problems have been
outside the range of solvable optimization models via traditional algorithms.


title: A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer

abstract:
  We describe a light-weight yet performant system for hyper-parameter
optimization that approximately minimizes an overall scalar cost function that
is obtained by combining multiple performance objectives using a
target-priority-limit scalarizer. It also supports a trade-off mode, where the
goal is to find an appropriate trade-off among objectives by interacting with
the user. We focus on the common scenario where there are on the order of tens
of hyper-parameters, each with various attributes such as a range of continuous
values, or a finite list of values, and whether it should be treated on a
linear or logarithmic scale. The system supports multiple asynchronous
simulations and is robust to simulation stragglers and failures.


title:
Hardness of the (Approximate) Shortest Vector Problem: A Simple Proof
  via Reed-Solomon Codes

abstract:
  $\newcommand{\NP}{\mathsf{NP}}\newcommand{\GapSVP}{\textrm{GapSVP}}$We give a
simple proof that the (approximate, decisional) Shortest Vector Problem is
$\NP$-hard under a randomized reduction. Specifically, we show that for any $p
\geq 1$ and any constant $\gamma < 2^{1/p}$, the $\gamma$-approximate problem
in the $\ell_p$ norm ($\gamma$-$\GapSVP_p$) is not in $\mathsf{RP}$ unless $\NP
\subseteq \mathsf{RP}$. Our proof follows an approach pioneered by Ajtai (STOC
1998), and strengthened by Micciancio (FOCS 1998 and SICOMP 2000), for showing
hardness of $\gamma$-$\GapSVP_p$ using locally dense lattices. We construct
such lattices simply by applying "Construction A" to Reed-Solomon codes with
suitable parameters, and prove their local density via an elementary argument
originally used in the context of Craig lattices.
  As in all known $\NP$-hardness results for $\GapSVP_p$ with $p < \infty$, our
reduction uses randomness. Indeed, it is a notorious open problem to prove
$\NP$-hardness via a deterministic reduction. To this end, we additionally
discuss potential directions and associated challenges for derandomizing our
reduction. In particular, we show that a close deterministic analogue of our
local density construction would improve on the state-of-the-art explicit
Reed-Solomon list-decoding lower bounds of Guruswami and Rudra (STOC 2005 and
IEEE Trans. Inf. Theory 2006).
  As a related contribution of independent interest, we also give a
polynomial-time algorithm for decoding $n$-dimensional "Construction A
Reed-Solomon lattices" (with different parameters than those used in our
hardness proof) to a distance within an $O(\sqrt{\log n})$ factor of
Minkowski's bound. This asymptotically matches the best known distance for
decoding near Minkowski's bound, due to Mook and Peikert (IEEE Trans. Inf.
Theory 2022), whose work we build on with a somewhat simpler construction and
analysis.


title: Ab-initio Contrast Estimation and Denoising of Cryo-EM Images

abstract:
  Background and Objective: The contrast of cryo-EM images vary from one to
another, primarily due to the uneven thickness of ice layers. The variation of
contrast can affect the quality of 2-D class averaging, 3-D ab-initio modeling,
and 3-D heterogeneity analysis. Contrast estimation is currently performed
during 3-D iterative refinement. As a result, the estimates are not available
for class averaging and ab-initio modeling. However, these methods require good
initial estimates of 3-D volumes and 3-D rotations of molecules. This paper
aims to solve the contrast estimation problem in the ab-initio stage, without
estimating the 3-D volume.
  Methods: The key observation underlying our analysis is that the 2-D
covariance matrix of the raw images is related to the covariance of the
underlying clean images, the noise variance, and the contrast variability
between images. We show that the contrast variability can be derived from the
2-D covariance matrix and use the existing Covariance Wiener Filtering (CWF)
framework to estimate it. We also demonstrate a modification of CWF to estimate
the contrast of individual images.
  Results: Our method improves the contrast estimation by a large margin,
compared to the previous CWF method. Its estimation accuracy is often
comparable to that of an oracle that knows the ground truth covariance of the
clean images. The more accurate contrast estimation also improves the quality
of image denoising as demonstrated in both synthetic and experimental datasets.
  Conclusions: This paper proposes an effective method for contrast estimation
directly from noisy images without using any 3-D volume information. It enables
contrast correction in the earlier stage of single particle analysis, and may
improve the accuracy of downstream processing.


title: Non-local slicing approaches for NNLO QCD in MCFM

abstract:
  We present the implementation of several processes at Next-to-Next-to Leading
Order (NNLO) accuracy in QCD in the parton-level Monte Carlo program MCFM. The
processes treated are $pp\to H$, $W^\pm$, $Z$, $W^\pm H$, $ZH$, $W^\pm\gamma$,
$Z\gamma$ and $\gamma\gamma$ and, for the first time in the code, $W^+W^-$,
$W^\pm Z$ and $ZZ$. Decays of the unstable bosons are fully included, resulting
in a flexible fully differential Monte Carlo code. The NNLO corrections have
been calculated using two non-local slicing approaches, isolating the doubly
unresolved region by cutting on the zero-jettiness, ${\cal T}_0$, or on $q_T$,
the transverse momentum of the colour singlet final-state particles. We find
that for most, but not all processes the $q_T$ slicing method leads to smaller
power corrections for equal computational burden.


title:
Uniting Nesterov and Heavy Ball Methods for Uniform Global Asymptotic
  Stability of the Set of Minimizers

abstract:
  We propose a hybrid control algorithm that guarantees fast convergence and
uniform global asymptotic stability of the unique minimizer of a continuously
differentiable, (nonstrongly) convex objective function. The algorithm,
developed using hybrid system tools, employs a uniting control strategy, in
which Nesterov's accelerated gradient descent is used "globally" and the heavy
ball method is used "locally," relative to the minimizer. Without knowledge of
its location, the proposed hybrid control strategy switches between these
accelerated methods to ensure convergence to the minimizer without
oscillations, with a (hybrid) convergence rate that preserves the convergence
rates of the individual optimization algorithms. We analyze key properties of
the resulting closed-loop system including existence of solutions, uniform
global asymptotic stability, and convergence rate. Additionally, attractivity
properties of Nesterov's method are analyzed, and extensions on convergence
rate results in the existing literature are presented. Numerical results
validate the findings and demonstrate the robustness of the uniting algorithm.


title: Attracting and Retaining OSS Contributors with a Maintainer Dashboard

abstract:
  Tools and artifacts produced by open source software (OSS) have been woven
into the foundation of the technology industry. To keep this foundation intact,
the open source community needs to actively invest in sustainable approaches to
bring in new contributors and nurture existing ones. We take a first step at
this by collaboratively designing a maintainer dashboard that provides
recommendations on how to attract and retain open source contributors. For
example, by highlighting project goals (e.g., a social good cause) to attract
diverse contributors and mechanisms to acknowledge (e.g., a "rising
contributor" badge) existing contributors. Next, we conduct a project-specific
evaluation with maintainers to better understand use cases in which this tool
will be most helpful at supporting their plans for growth. From analyzing
feedback, we find recommendations to be useful at signaling projects as
welcoming and providing gentle nudges for maintainers to proactively recognize
emerging contributors. However, there are complexities to consider when
designing recommendations such as the project current development state (e.g.,
deadlines, milestones, refactoring) and governance model. Finally, we distill
our findings to share what the future of recommendations in open source looks
like and how to make these recommendations most meaningful over time.


title:
Disentangling Successor Features for Coordination in Multi-agent
  Reinforcement Learning

abstract:
  Multi-agent reinforcement learning (MARL) is a promising framework for
solving complex tasks with many agents. However, a key challenge in MARL is
defining private utility functions that ensure coordination when training
decentralized agents. This challenge is especially prevalent in unstructured
tasks with sparse rewards and many agents. We show that successor features can
help address this challenge by disentangling an individual agent's impact on
the global value function from that of all other agents. We use this
disentanglement to compactly represent private utilities that support stable
training of decentralized agents in unstructured tasks. We implement our
approach using a centralized training, decentralized execution architecture and
test it in a variety of multi-agent environments. Our results show improved
performance and training time relative to existing methods and suggest that
disentanglement of successor features offers a promising approach to
coordination in MARL.


title:
A Short Survey on Business Models of Decentralized Finance (DeFi)
  Protocols

abstract:
  Decentralized Finance (DeFi) services are moving traditional financial
operations to the Internet of Value (IOV) by exploiting smart contracts,
distributed ledgers, and clever heterogeneous transactions among different
protocols. The exponential increase of the Total Value Locked (TVL) in DeFi
foreshadows a bright future for automated money transfers in a plethora of
services. In this short survey paper, we describe the business model for
different DeFi domains - namely, Protocols for Loanable Funds (PLFs),
Decentralized Exchanges (DEXs), and Yield Aggregators. We claim that the
current state of the literature is still unclear how to value thousands of
different competitors (tokens) in DeFi. With this work, we abstract the general
business model for different DeFi domains and compare them. Finally, we provide
open research challenges that will involve heterogeneous domains such as
economics, finance, and computer science.


title: Virtual Linearity for KPP Reaction-Diffusion Equations

abstract:
  We show that long time solution dynamic for general
reaction-advection-diffusion equations with KPP reactions is virtually linear
in the following sense. Its leading order depends on the non-linear reaction
only through its linearization at $u=0$, and it can also be recovered for
general initial data by instead solving the PDE for restrictions of the initial
condition to unit cubes on $\bbR^d$ (the latter means that non-linear
interaction of these restricted solutions has only lower order effects on the
overall solution dynamic). The result holds under a uniform bound on the
advection coefficient, which we show to be sharp. We also extend it to models
with non-local diffusion and KPP reactions.


title: Arithmetical structures on dominated polynomials

abstract:
  In~\cite{algorithmic} was given an algorithm that computes arithmetical
structures on matrices. We use some of the ideas contained there to get an
algorithm that computes arithmetical structures over dominated polynomials. A
dominated polynomial is an integer multivariate polynomial such that contains a
monomial which is divided by all its monomials.


title:
High-dimensional dynamic factor models: a selective survey and lines of
  future research

abstract:
  High-Dimensional Dynamic Factor Models are presented in detail: The main
assumptions and their motivation, main results, illustrations by means of
elementary examples. In particular, the role of singular ARMA models in the
theory and applications of High-Dimensional Dynamic Factor Models is
discussed.The emphasis of the paper is on model classes and their structure
theory, rather than on estimation in the narrow sense. Our aim is not a
comprehensive survey. Rather we try to point out promising lines of research
and applications that have not yet been sufficiently developed.


title:
Expected number of faces in a random embedding of any graph is at most
  linear

abstract:
  A random 2-cell embedding of a given graph $G$ is obtained by choosing a
random local rotation around every vertex. We analyze the expected number of
faces of such an embedding, which is equivalent to studying its average genus.
In 1991, Stahl proved that the expected number of faces in a random embedding
of an arbitrary graph of order $n$ is at most $n\log(n)$. While there are many
families of graphs whose expected number of faces is $\Theta(n)$, none are
known where the expected number would be super-linear. This lead to the
conjecture that there is a linear upper bound. In this note we confirm the
conjecture by proving that for any $n$-vertex multigraph, the expected number
of faces in a random 2-cell embedding is at most $n(1+H_m)$, where $m$ is the
maximum edge-multiplicity and $H_m$ denotes the $m$th harmonic number. This
bound is best possible.


title:
Differentiable Preisach Modeling for Characterization and Optimization
  of Accelerator Systems with Hysteresis

abstract:
  Future improvements in particle accelerator performance is predicated on
increasingly accurate online modeling of accelerators. Hysteresis effects in
magnetic, mechanical, and material components of accelerators are often
neglected in online accelerator models used to inform control algorithms, even
though reproducibility errors from systems exhibiting hysteresis are not
negligible in high precision accelerators. In this work, we combine the
classical Preisach model of hysteresis with machine learning techniques to
efficiently create non-parametric, high-fidelity models of arbitrary systems
exhibiting hysteresis. We demonstrate that our technique accurately predicts
hysteresis effects in physical accelerator magnets. We also experimentally
demonstrate how these methods can be used in-situ, where the hysteresis model
is combined with a Bayesian statistical model of the beam response, allowing
characterization of hysteresis in accelerator magnets solely from measurements
of the beam. Furthermore, we explore how using these joint hysteresis-beam
models allows us to overcome optimization performance limitations when
hysteresis effects are ignored.


title: Travelling waves in a PDE-ODE coupled system with nonlinear diffusion

abstract:
  We analyze travelling wave (TW) solutions for nonlinear systems consisting of
an ODE coupled to a degenerate PDE with a diffusion coefficient that vanishes
as the solution tends to zero and blows up as it approaches its maximum value.
Stable TW solutions for such systems have previously been observed numerically
as well as in biological experiments on the growth of cellulolytic biofilms. In
this work, we provide an analytical justification for these observations and
prove existence and stability results for TW solutions of such models.
  Using the TW ansatz and a first integral, the system is reduced to an
autonomous dynamical system with two unknowns. Analysing the system in the
corresponding phase-plane, the existence of a unique TW is shown, which
possesses a sharp front and a diffusive tail, and is moving with a constant
speed. The linear stability of the TW in two space dimensions is proven under
suitable assumptions on the initial data. Finally, numerical simulations are
presented that affirm the theoretical predictions on the existence, stability,
and parametric dependence of the TWs.


title:
Deep optical study of the mixed-morphology supernova remnant G 132.7+1.3
  (HB3)

abstract:
  We present optical ccd images of the large supernova remnant (SNR)
G132.7$+$1.3 (HB3) covering its full extent for the first time, in the emission
lines of H$\alpha+$[N II], [S II] and [O III], where new and known filamentary
and diffuse structures are detected. These observations are supplemented by new
low-resolution long-slit spectra and higher-resolution images in the same
emission lines. Both the flux-calibrated images and spectra confirm that the
optical emission originates from shock-heated gas since the [S II]/H$\alpha$
$>$ 0.4. Our findings are also consistent with the recently developed emission
line ratio diagnostics for distinguishing SNRs from H II regions. A
multi-wavelength comparison among our optical data and relevant observations in
radio, X-rays, $\gamma$-rays and CO bands, provided additional evidence on the
interaction of HB3 with the surrounding clouds and clarified the borders of the
SNR and the adjacent cloud. We discuss the supernova (SN) properties and
evolution that led to the current observables of HB3 and we show that the
remnant has most likely passed at the pressure driven snowplow phase. The
estimated SN energy was found to be $\left(3.7 \pm 1.5\right) \times 10^{51}$
erg and the current SNR age $\left(5.1 \pm 2.1\right) \times 10^4$ yrs. We
present an alternative scenario according to which the SNR evolved in the wind
bubble cavity excavated by the progenitor star and currently is interacting
with its density walls. We show that the overall mixed morphology properties of
HB3 can be explained if the SN resulted by a Wolf-Rayet progenitor star with
mass $\sim 34 \rm~M_{\rm\odot}$.


title: Nonverbal Sound Detection for Disordered Speech

abstract:
  Voice assistants have become an essential tool for people with various
disabilities because they enable complex phone- or tablet-based interactions
without the need for fine-grained motor control, such as with touchscreens.
However, these systems are not tuned for the unique characteristics of
individuals with speech disorders, including many of those who have a
motor-speech disorder, are deaf or hard of hearing, have a severe stutter, or
are minimally verbal. We introduce an alternative voice-based input system
which relies on sound event detection using fifteen nonverbal mouth sounds like
"pop," "click," or "eh." This system was designed to work regardless of ones'
speech abilities and allows full access to existing technology. In this paper,
we describe the design of a dataset, model considerations for real-world
deployment, and efforts towards model personalization. Our fully-supervised
model achieves segment-level precision and recall of 88.6% and 88.4% on an
internal dataset of 710 adults, while achieving 0.31 false positives per hour
on aggressors such as speech. Five-shot personalization enables satisfactory
performance in 84.5% of cases where the generic model fails.


title: Dynamics of stochastic integrate-and-fire networks

abstract:
  The neural dynamics generating sensory, motor, and cognitive functions are
commonly understood through field theories for neural population activity.
Classic neural field theories are derived from highly simplified, discrete
state models of individual neurons. This calls into question their ability to
describe the population dynamics of biophysical neural models. Here, we develop
a general framework for studying mean field dynamics and fluctuation effects in
integrate-and-fire networks. We construct the joint density functional for
membrane potentials and spike trains in networks of integrate-and-fire neurons
with stochastic spike emission. This reveals exact and approximate mean field
theories for the activity in these networks. These mean field theories are
simple neural activity equations with a new nonlinearity: a rate-dependent
leak, which approximates the spike-driven resets of neurons' membrane
potentials. We study the impact of spike resets on population dynamics, finding
bistability between quiescent and active states in homogenous and
excitatory-inhibitory pulse-coupled networks. In both cases, we compute the
phase diagram for quiescent, active and bistable regimes in the coupling
strengths and external input strength. We also find that fluctuations suppress
activity in the active states. We then examine the relative roles of spike
resets and recurrent inhibition in stabilizing network activity. A paradoxical
reduction of inhibitory firing rates after stimulation is commonly understood
to be a signature of an inhibitory-stabilized regime. Such paradoxical
responses occur in wide regions of parameter space. Spike resets dynamically
stabilize even excitatory-only networks, however, so that recurrent inhibition
is not necessary to stabilize activity. Finally, we discuss perturbative and
exact methods for examining fluctuations in these networks.


title: The treewidth and pathwidth of graph unions

abstract:
  For two graphs $G_1$ and $G_2$ on the same vertex set $[n]:=\{1,2, \ldots,
n\}$, and a permutation $\varphi$ of $[n]$, the union of $G_1$ and $G_2$ along
$\varphi$ is the graph which is the union of $G_2$ and the graph obtained from
$G_1$ by renaming its vertices according to $\varphi$. We examine the behaviour
of the treewidth and pathwidth of graphs under this "gluing" operation. We show
that under certain conditions on $G_1$ and $G_2$, we may bound those parameters
for such unions in terms of their values for the original graphs, regardless of
what permutation $\varphi$ we choose. In some cases, however, this is only
achievable if $\varphi$ is chosen carefully, while yet in others, it is always
impossible to achieve boundedness. More specifically, among other results, we
prove that if $G_1$ has treewidth $k$ and $G_2$ has pathwidth $\ell$, then they
can be united into a graph of treewidth at most $k + 3 \ell + 1$. On the other
hand, we show that for any natural number $c$ there exists a pair of trees
$G_1$ and $G_2$ whose every union has treewidth more than $c$.


title: Rate of homogenization for fully-coupled McKean-Vlasov SDEs

abstract:
  We consider a fully-coupled slow-fast system of McKean-Vlasov SDEs with full
dependence on the slow and fast component and on the law of the slow component
and derive convergence rates to its homogenized limit. We do not make
periodicity assumptions, but we impose conditions on the fast motion to
guarantee ergodicity. In the course of the proof we obtain related ergodic
theorems and we gain results on the regularity of Poisson type of equations and
of the associated Cauchy-Problem on the Wasserstein space that are of
independent interest.


title: K-Means for Noise-Insensitive Multi-Dimensional Feature Learning

abstract:
  Many measurement modalities which perform imaging by probing an object
pixel-by-pixel, such as via Photoacoustic Microscopy, produce a
multi-dimensional feature (typically a time-domain signal) at each pixel. In
principle, the many degrees of freedom in the time-domain signal would admit
the possibility of significant multi-modal information being implicitly
present, much more than a single scalar "brightness", regarding the underlying
targets being observed. However, the measured signal is neither a weighted-sum
of basis functions (such as principal components) nor one of a set of
prototypes (K-means), which has motivated the novel clustering method proposed
here. Signals are clustered based on their shape, but not amplitude, via
angular distance and centroids are calculated as the direction of maximal
intra-cluster variance, resulting in a clustering algorithm capable of learning
centroids (signal shapes) that are related to the underlying, albeit unknown,
target characteristics in a scalable and noise-robust manner.


title:
Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence
  Lifetime Microscopic Images with H&E-Stained Histology Images

abstract:
  Autofluorescence lifetime images reveal unique characteristics of endogenous
fluorescence in biological samples. Comprehensive understanding and clinical
diagnosis rely on co-registration with the gold standard, histology images,
which is extremely challenging due to the difference of both images. Here, we
show an unsupervised image-to-image translation network that significantly
improves the success of the co-registration using a conventional
optimisation-based regression network, applicable to autofluorescence lifetime
images at different emission wavelengths. A preliminary blind comparison by
experienced researchers shows the superiority of our method on co-registration.
The results also indicate that the approach is applicable to various image
formats, like fluorescence intensity images. With the registration, stitching
outcomes illustrate the distinct differences of the spectral lifetime across an
unstained tissue, enabling macro-level rapid visual identification of lung
cancer and cellular-level characterisation of cell variants and common types.
The approach could be effortlessly extended to lifetime images beyond this
range and other staining technologies.


title:
Quantum Games and Interactive Tools for Quantum Technologies Outreach
  and Education

abstract:
  In this article, we provide an extensive overview of a wide range of quantum
games and interactive tools that have been employed by the community in recent
years. The paper presents selected tools, as described by their developers. The
list includes Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta,
QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, ScienceAtHome, and
The Virtual Quantum Optics Laboratory. Additionally, we present events for
quantum game development: hackathons, game jams, and semester projects.
Furthermore, we discuss the Quantum Technologies Education for Everyone
(QUTE4E) pilot project, which illustrates an effective integration of these
interactive tools with quantum outreach and education activities. Finally, we
aim at providing guidelines for incorporating quantum games and interactive
tools in pedagogic materials to make quantum technologies more accessible for a
wider population.


title: Architecture Agnostic Federated Learning for Neural Networks

abstract:
  With growing concerns regarding data privacy and rapid increase in data
volume, Federated Learning(FL) has become an important learning paradigm.
However, jointly learning a deep neural network model in a FL setting proves to
be a non-trivial task because of the complexities associated with the neural
networks, such as varied architectures across clients, permutation invariance
of the neurons, and presence of non-linear transformations in each layer. This
work introduces a novel Federated Heterogeneous Neural Networks (FedHeNN)
framework that allows each client to build a personalised model without
enforcing a common architecture across clients. This allows each client to
optimize with respect to local data and compute constraints, while still
benefiting from the learnings of other (potentially more powerful) clients. The
key idea of FedHeNN is to use the instance-level representations obtained from
peer clients to guide the simultaneous training on each client. The extensive
experimental results demonstrate that the FedHeNN framework is capable of
learning better performing models on clients in both the settings of
homogeneous and heterogeneous architectures across clients.


title:
Achieving Reliable and Repeatable Electrochemical Impedance Spectroscopy
  of Rechargeable Batteries at Extra-Low Frequencies

abstract:
  There is a need for techniques for efficient and accurate measurement of the
impedance of rechargeable batteries at extra-low frequencies (ELFs, typically
below 10uHz), as these reflect real usage and cycling patterns, and their
importance in fractional battery circuit modeling is becoming increasingly
apparent. Major impediments include the time required to perform such
measurements, and `drift' in impedance values when measurements are taken from
the same battery at different times. Moreover, commercial impedance analyzers
are generally unable to measure at frequencies of the order of microhertz. We
describe here our use of programmable two-quadrant power supplies to deliver
multiple small signal measurement tones in the presence of large signal
`working' currents, and our use of these data to generate impedance
measurements with good precision and in reasonable time.


title:
Evaluation of a Positron-Emission-Tomography-based SiPM readout for
  Compact Segmented Neutron Imagers

abstract:
  Gamma-ray emission from special nuclear material (SNM) is relatively easy to
shield from detection using modest amounts of high-Z material. In contrast,
fast-neutrons are much more penetrating and can escape relatively thick high-Z
shielding without losing significant energy. Furthermore, fast neutrons provide
a clear and unambiguous signature of the presence of SNM with few competing
natural background sources. The challenge of detecting fast neutrons is
twofold. First, the neutron flux from SNM are only a fraction of the
corresponding gamma-ray flux. Second, fast neutrons can be difficult to
differentiate from gamma rays. The ability to discriminate gamma rays from
neutrons combined with neutron imaging can yield large benefit to isolate the
localized SNM neutron source from background. With the recent developments of
pulse-shape-sensitive plastic scintillators that offer excellent
gamma-ray/neutron discrimination, and arrays of silicon photomultipliers
combined with highly scalable and fast positron-emission-tomography (PET)
multi-channel readout systems, field-deployable neutron imagers suitable for
SNM detection might now be within reach. In this paper, we present a
characterization of the performance of a recently available commercial
PET-scanner readout, including its sensitivity to pulse-shape differences
between fast neutrons and gamma rays, energy and timing resolution, as well as
linearity and dynamic range. We find that, while the pulse-shape discrimination
is achievable with stilbene, further improvement of the readout is required to
achieve it with the best available plastic scintillators. The time and energy
resolution appear to be adequate for neutron imaging in some circumstances.

categories: physics.ins-det hep-ex nucl-ex physics.med-ph


title: Explainable Predictive Process Monitoring: A User Evaluation

abstract:
  Explainability is motivated by the lack of transparency of black-box Machine
Learning approaches, which do not foster trust and acceptance of Machine
Learning algorithms. This also happens in the Predictive Process Monitoring
field, where predictions, obtained by applying Machine Learning techniques,
need to be explained to users, so as to gain their trust and acceptance. In
this work, we carry on a user evaluation on explanation approaches for
Predictive Process Monitoring aiming at investigating whether and how the
explanations provided (i) are understandable; (ii) are useful in decision
making tasks;(iii) can be further improved for process analysts, with different
Machine Learning expertise levels. The results of the user evaluation show
that, although explanation plots are overall understandable and useful for
decision making tasks for Business Process Management users -- with and without
experience in Machine Learning -- differences exist in the comprehension and
usage of different plots, as well as in the way users with different Machine
Learning expertise understand and use them.


title: Further Collapses in TFNP

abstract:
  We show $\textsf{EOPL}=\textsf{PLS}\cap\textsf{PPAD}$. Here the class
$\textsf{EOPL}$ consists of all total search problems that reduce to the
End-of-Potential-Line problem, which was introduced in the works by Hubacek and
Yogev (SICOMP 2020) and Fearnley et al. (JCSS 2020). In particular, our result
yields a new simpler proof of the breakthrough collapse
$\textsf{CLS}=\textsf{PLS}\cap\textsf{PPAD}$ by Fearnley et al. (STOC 2021). We
also prove a companion result $\textsf{SOPL}=\textsf{PLS}\cap\textsf{PPADS}$,
where $\textsf{SOPL}$ is the class associated with the Sink-of-Potential-Line
problem.


title:
Design of a Littrow-type diode laser with independent control of cavity
  length and grating rotation

abstract:
  We present a novel extended-cavity diode laser (ECDL) based on a modified
Littrow configuration. The coarse wavelength adjustment via the rotation of a
diffraction grating is decoupled from the fine tuning of the external cavity
modes by positioning a piezo transducer behind the diode laser, making the
laser robust against misalignment and hysteresis even with long external
cavities. Two laser prototypes with external cavities of different lengths were
tested with a 780 nm laser diode, and locked to an atomic reference. We observe
a mode-hop-free frequency tunability broader than the free spectral range of
the external cavity upon changes of its length. The design is well suited to
atomic and molecular experiments demanding a high level of stability over time.

categories: physics.ins-det cond-mat.other physics.atom-ph physics.optics


title: Combinatorial formula for arithmetic density

abstract:
  Let $d_S$ denote the arithmetic density of a subset $S \subseteq \mathbb N$.
We derive a power series in $q\in \mathbb C$, $|q|<1$, with co\"efficients
related to integer partitions and integer compositions, that yields $1/d_S$ in
the limit as $q\to 1$ radially.


title:
Paving the Way towards 800 Gbps Quantum-Secured Optical Channel
  Deployment in Mission-Critical Environments

abstract:
  This article describes experimental research studies conducted towards
understanding the implementation aspects of high-capacity quantum-secured
optical channels in mission-critical metro-scale operational environments based
on Quantum Key Distribution (QKD) technology. The test bed for this research
study was carefully designed to mimic such environments. To the best of our
knowledge, this is the first time that an 800 Gbps quantum-secured optical
channel--along with several other Dense Wavelength Division Multiplexed (DWDM)
channels on the C-band and multiplexed with the QKD channel on the O-band--was
established at distances up to 100 km, with secure-key rates relevant for
practical industry use cases. In addition, during the course of these trials,
transporting a blockchain application over this established channel was
utilized as a demonstration of securing a financial transaction in transit over
a quantum-secured optical channel. In a real-world operational environment,
deployment of such high-capacity quantum-secured optical channels multiplexed
with the quantum channel will inevitably introduce challenges due to their
strict requirements, such as high launch powers and polarization fluctuations.
Therefore, in the course of this research, experimental studies were conducted
on the impact on the system performance--and specifically on the quantum
channel--of several degradation factors present in real-world operational
environments, including inter-channel interference (due to Raman scattering and
nonlinear effects), attenuation, polarization fluctuations and distance
dependency. The findings of this research pave the way towards the deployment
of QKD-secured optical channels in high-capacity, metro-scale, mission-critical
operational environments, such as Inter-Data Center Interconnects.


title: General-purpose, long-context autoregressive modeling with Perceiver AR

abstract:
  Real-world data is high-dimensional: a book, image, or musical performance
can easily contain hundreds of thousands of elements even after compression.
However, the most commonly used autoregressive models, Transformers, are
prohibitively expensive to scale to the number of inputs and layers needed to
capture this long-range structure. We develop Perceiver AR, an autoregressive,
modality-agnostic architecture which uses cross-attention to map long-range
inputs to a small number of latents while also maintaining end-to-end causal
masking. Perceiver AR can directly attend to over a hundred thousand tokens,
enabling practical long-context density estimation without the need for
hand-crafted sparsity patterns or memory mechanisms. When trained on images or
music, Perceiver AR generates outputs with clear long-term coherence and
structure. Our architecture also obtains state-of-the-art likelihood on
long-sequence benchmarks, including 64 x 64 ImageNet images and PG-19 books.


title:
LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for
  Forecasting, with an Application to Electricity Smart Meter Data

abstract:
  Accurate electricity demand forecasts play a crucial role in sustainable
power systems. To enable better decision-making especially for demand
flexibility of the end-user, it is necessary to provide not only accurate but
also understandable and actionable forecasts. To provide accurate forecasts
Global Forecasting Models (GFM) trained across time series have shown superior
results in many demand forecasting competitions and real-world applications
recently, compared with univariate forecasting approaches. We aim to fill the
gap between the accuracy and the interpretability in global forecasting
approaches. In order to explain the global model forecasts, we propose Local
Interpretable Model-agnostic Rule-based Explanations for Forecasting (LIMREF),
a local explainer framework that produces k-optimal impact rules for a
particular forecast, considering the global forecasting model as a black-box
model, in a model-agnostic way. It provides different types of rules that
explain the forecast of the global model and the counterfactual rules, which
provide actionable insights for potential changes to obtain different outputs
for given instances. We conduct experiments using a large-scale electricity
demand dataset with exogenous features such as temperature and calendar
effects. Here, we evaluate the quality of the explanations produced by the
LIMREF framework in terms of both qualitative and quantitative aspects such as
accuracy, fidelity, and comprehensibility and benchmark those against other
local explainers.


title: From Feynman graphs to Witten diagrams

abstract:
  We investigate the possibility of generalizing Gopakumar's microscopic
derivation [1] of Witten diagrams in large N free quantum field theory to
interacting theories. For simplicity we consider a massless, matrix valued real
scalar field with $\Phi^h$ interaction in d-dimensions. Using Schwinger's
proper time formulation and organizing the sum over Feynman graphs by the
number of loops $\ell$, we show that the two-point function can be expressed as
a sum over boundary-to-boundary propagators of bulk scalars in $AdS_{d+1}$ with
mass determined by $\ell$.


title: Strong Lyman continuum emitting galaxies show intense CIV 1550 emission

abstract:
  Using the Space Telescope Imaging Spectrograph, we have obtained ultraviolet
(UV) spectra from $\sim 1200$ to 2000 \AA\ of known Lyman continuum (LyC)
emitting galaxies at low redshift ($z \sim 0.3-0.4$) with varying absolute LyC
escape fractions (fesc $\sim 0.01 - 0.72$). Our observations include in
particular the galaxy J1243+4646, which has the highest known LyC escape
fraction at low redshift. While all galaxies are known Lyman alpha emitters, we
consistently detect an inventory of additional emission lines, including CIV
1550, HeII 1640, OIII] 1666, and CIII] 1909, whose origin is presumably
essentially nebular. CIV 1550 emission is detected above 4 $\sigma$ in six out
of eight galaxies, with equivalent widths of EW(CIV)$=12-15$ Ang for two
galaxies, which exceeds the previously reported maximum emission in low-$z$
star-forming galaxies. We detect CIV 1550 emission in all LyC emitters with
escape fractions fesc $> 0.1$ and find a tentative increase in the flux ratio
CIV 1550/ CIII] 1909 with fesc. Based on the data, we propose a new criterion
to select and classify strong leakers (galaxies with fesc $> 0.1$): CIV 1550/
CIII] 1909 $> 0.75$. Finally, we also find HeII 1640 emission in all the strong
leakers with equivalent widths from 3 to 8 Ang rest frame. These are among the
highest values observed in star-forming galaxies and are primarily due to a
high rate of ionizing photon production. The nebular HeII 1640 emission of the
strong LyC emitters does not require harder ionizing spectra at $>54$ eV
compared to those of typical star-forming galaxies at similarly low
metallicity.


title: Bohemian Matrix Geometry

abstract:
  A Bohemian matrix family is a set of matrices all of whose entries are drawn
from a fixed, usually discrete and hence bounded, subset of a field of
characteristic zero. Originally these were integers -- hence the name, from the
acronym BOunded HEight Matrix of Integers (BOHEMI) -- but other kinds of
entries are also interesting. Some kinds of questions about Bohemian matrices
can be answered by numerical computation, but sometimes exact computation is
better. In this paper we explore some Bohemian families (symmetric, upper
Hessenberg, or Toeplitz) computationally, and answer some open questions posed
about the distributions of eigenvalue densities.


title:
Homeotopy groups of leaf spaces of one-dimensional foliations on
  non-compact surfaces with non-compact leaves

abstract:
  Let $Z$ be a non-compact two-dimensional manifold obtained from a family of
open strips $\mathbb{R}\times(0,1)$ with boundary intervals by gluing those
strips along some pairs of their boundary intervals. Every such strip has a
natural foliation into parallel lines $\mathbb{R}\times t$, $t\in(0,1)$, and
boundary intervals which gives a foliation $\Delta$ on all of $Z$. Denote by
$\mathcal{H}(Z,\Delta)$ the group of all homeomorphisms of $Z$ that maps leaves
of $\Delta$ onto leaves and by $\mathcal{H}(Z/\Delta)$ the group of
homeomorphisms of the space of leaves endowed with the corresponding compact
open topologies. Recently, the authors identified the homeotopy group
$\pi_0\mathcal{H}(Z,\Delta)$ with a group of automorphisms of a certain graph
$G$ with the additional structure which encodes the combinatorics of gluing $Z$
from strips. That graph is in a certain sense dual to the space of leaves
$Z/\Delta$.
  On the other hand, for every $h\in\mathcal{H}(Z,\Delta)$ the induced
permutation $k$ of leaves of $\Delta$ is in fact a homeomorphism of $Z/\Delta$
and the correspondence $h\mapsto k$ is a homomorphism
$\psi:\mathcal{H}(\Delta)\to\mathcal{H}(Z/\Delta)$. The aim of the present
paper is to show that $\psi$ induces a homomorphism of the corresponding
homeotopy groups
$\psi_0:\pi_0\mathcal{H}(Z,\Delta)\to\pi_0\mathcal{H}(Z/\Delta)$ which turns
out to be either injective or having a kernel $\mathbb{Z}_2$. This gives a dual
description of $\pi_0\mathcal{H}(Z,\Delta)$ in terms of the space of leaves.


title:
An SMP-Based Algorithm for Solving the Constrained Utility Maximization
  Problem via Deep Learning

abstract:
  We consider the utility maximization problem under convex constraints with
regard to theoretical results which allow the formulation of algorithmic
solvers which make use of deep learning techniques. In particular for the case
of random coefficients, we prove a stochastic maximum principle (SMP), which
also holds for utility functions $U$ with $\mathrm{id}_{\mathbb{R}^{+}} \cdot
U'$ being not necessarily nonincreasing, like the power utility functions,
thereby generalizing the SMP proved by Li and Zheng (2018). We use this SMP
together with the strong duality property for defining a new algorithm, which
we call deep primal SMP algorithm. Numerical examples illustrate the
effectiveness of the proposed algorithm - in particular for higher-dimensional
problems and problems with random coefficients, which are either path dependent
or satisfy their own SDEs. Moreover, our numerical experiments for constrained
problems show that the novel deep primal SMP algorithm overcomes the deep SMP
algorithm's (see Davey and Zheng (2021)) weakness of erroneously producing the
value of the corresponding unconstrained problem. Furthermore, in contrast to
the deep controlled 2BSDE algorithm from Davey and Zheng (2021), this algorithm
is also applicable to problems with path dependent coefficients. As the deep
primal SMP algorithm even yields the most accurate results in many of our
studied problems, we can highly recommend its usage. Moreover, we propose a
learning procedure based on epochs which improved the results of our algorithm
even further. Implementing a semi-recurrent network architecture for the
control process turned out to be also a valuable advancement.


title:
Universal properties of the isotropic Laplace operator on homogeneous
  trees

abstract:
  Let $P$ be the isotropic nearest neighbor transition operator on a
homogeneous tree. We consider the $\lambda$-eigenfunctions of $P$ for $\lambda$
outside its $\ell^2$ spectrum, i.e., the eigenfunctions with eigenvalue
$\gamma=\lambda - 1$ of the Laplace operator $Delta=P- \mathbb I$, and also the
$\lambda-$polyharmonic functions, that is, the union of the kernels of
$(Delta-\gamma \mathbb I)^n$ for $n\geqslant 0$. We prove that, on a suitable
Banach space generated by the $\lambda-$polyharmonic functions, the operator
$e^{Delta-\gamma \mathbb I}$ is hypercyclic, although $Delta-\gamma \mathbb I$
is not.


title:
The efficacy and generalizability of conditional GANs for posterior
  inference in physics-based inverse problems

abstract:
  In this work, we train conditional Wasserstein generative adversarial
networks to effectively sample from the posterior of physics-based Bayesian
inference problems. The generator is constructed using a U-Net architecture,
with the latent information injected using conditional instance normalization.
The former facilitates a multiscale inverse map, while the latter enables the
decoupling of the latent space dimension from the dimension of the measurement,
and introduces stochasticity at all scales of the U-Net. We solve PDE-based
inverse problems to demonstrate the performance of our approach in quantifying
the uncertainty in the inferred field. Further, we show the generator can learn
inverse maps which are local in nature, which in turn promotes generalizability
when testing with out-of-distribution samples.