title:
Fair Coordination of Distributed Energy Resources with Volt-Var Control
  and PV Curtailment

abstract:
  This paper presents a novel distributed optimal power flow (DOPF) method for
fair distributed energy resource (DER) coordination in the context of mandated
rooftop PV inverter control modes. In practice, inverter reactive power control
is increasingly required by grid connection codes, which often unfairly curtail
PV generation of prosumers towards the end of low-voltage feeders. Similarly,
optimization-based DER coordination methods that aim solely for
technically-efficient DER coordination do not consider the distribution of PV
curtailment across customers. To address these concerns, we develop a tractable
multi-objective DOPF method for optimal DER coordination that (i) curtails PV
generation fairly across prosumers, and (ii) incorporates a standard
piecewise-linear volt-var control reactive power control function without using
integer variables. Three equity principles representing different
interpretations of fairness are implemented in our coordination method; namely,
egalitarian, proportional and uniform dynamic PV curtailment redistribution.
The performance of our approach is demonstrated on low-voltage distribution
feeders of different sizes (5, 10, 25, 50 and 100 prosumers) using two network
topologies: line topology without lateral spurs and tree topology with lateral
spurs. Each network considers three levels of PV penetration, giving 30 test
systems in total. The results demonstrate the effectiveness of the proposed
DOPF method for fair DER coordination: PV curtailment is equitably distributed
among prosumers with a computational burden on par with conventional DOPF
approaches. Moreover, different fairness methods result in different patterns
of curtailment, which a regulator may choose between.


title: Structure of the Bogoliubov-Valatin canonical basis set

abstract:
  We discuss the mathematical properties of the Bogoliubov-Valatin basis set of
quasiparticle wave functions for a fermion system, with particular emphasis on
the properties of the canonical basis set. The properties of the canonical
basis set, apart from their definition, are largely unknown. In particular,
what is the physically required size of the canonical wave function set in
order to correctly describe superfluid systems. While the cardinality of the
set of quasiparticle wave functions for an isolated system in vacuum is
$\mathfrak{c} = |\mathbb{R}^3|$, the basis set for a finite system in a finite
volume is countable, with cardinality $\aleph_0 = |\mathbb{Z}| = |\mathbb{N}|$.
However, the size of the canonical basis set for an isolated system in a finite
volume or for a periodic system is typically much smaller than the size of the
entire basis set, and it is determined by the level of the spatial resolution.
We show how one can get insight into the character of the canonical wave
functions and we justify the minimum number of canonical wave functions needed
for a given system.


title: Bilinear spaces over a fixed field are simple unstable

abstract:
  We study the model theory of vector spaces with a bilinear form over a fixed
field. For finite fields this can be, and has been, done in the classical
framework of full first-order logic. For infinite fields we need different
logical frameworks. First we take a category-theoretic approach, which requires
very little set-up. We show that linear independence forms a simple unstable
independence relation. With some more work we then show that we can also work
in the framework of positive logic, which is much more powerful than the
category-theoretic approach and much closer to the classical framework of full
first-order logic. We fully characterise the existentially closed models of the
arising positive theory. Using the independence relation from before we
conclude that the theory is simple unstable, in the sense that dividing has
local character but there are many distinct types. We also provide positive
version of what is commonly known as the Ryll-Nardzewski theorem for
$\omega$-categorical theories in full first-order logic, from which we conclude
that bilinear spaces over a countable field are $\omega$-categorical.


title: Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction

abstract:
  Many algorithms have been developed to solve the inverse problem of coded
aperture snapshot spectral imaging (CASSI), i.e., recovering the 3D
hyperspectral images (HSIs) from a 2D compressive measurement. In recent years,
learning-based methods have demonstrated promising performance and dominated
the mainstream research direction. However, existing CNN-based methods show
limitations in capturing long-range dependencies and non-local self-similarity.
Previous Transformer-based methods densely sample tokens, some of which are
uninformative, and calculate the multi-head self-attention (MSA) between some
tokens that are unrelated in content. This does not fit the spatially sparse
nature of HSI signals and limits the model scalability. In this paper, we
propose a novel Transformer-based method, coarse-to-fine sparse Transformer
(CST), firstly embedding HSI sparsity into deep learning for HSI
reconstruction. In particular, CST uses our proposed spectra-aware screening
mechanism (SASM) for coarse patch selecting. Then the selected patches are fed
into our customized spectra-aggregation hashing multi-head self-attention
(SAH-MSA) for fine pixel clustering and self-similarity capturing.
Comprehensive experiments show that our CST significantly outperforms
state-of-the-art methods while requiring cheaper computational costs. The code
and models will be made public.


title: Jamming on deformable surfaces

abstract:
  Jamming is a fundamental transition that governs the mechanical behavior of
particulate media, including sand, foam and dense suspensions but also
biological tissues: Upon compression, particulate media can change from freely
flowing to a disordered solid. Jamming has previously been conceived as a bulk
phenomenon involving particle motions in fixed geometries. In a diverse class
of soft materials, however, solidification can take place in a deformable
geometry, such as on the surface of a fluid droplet or in the formation of a
bijel. In these systems the nature and dynamics of jamming remains unknown.
Here we propose and study a scenario we call metric jamming that aims to
capture the complex interactions between shape and particles. Unlike classical
jamming processes that exhibit discrete mechanical transitions, surprisingly we
find that metric jammed states possess mechanical properties that are
continuously tunable between those of classical jammed and conventional elastic
media. New types of vibrational mode that couple particulate and surface
degrees of freedom are also observed. Our work lays the groundwork for a
unified understanding of jamming in deformable geometries, to exploit jamming
for the control and stabilization of shape in self-assembly processes, and
provides new tools to interrogate solidification processes in deformable media
more generally.


title:
STOPGAP -- a Time-of-Flight Extension for the Belle II TOP Barrel PID
  System as a Demonstrator for CMOS Fast Timing Sensors

abstract:
  The Belle II barrel region is instrumented with the Time of Propagation (TOP)
particle identification system based on sixteen fused-silica bars arranged
around the interaction point acting as Cherenkov radiator. Due to the
mechanical design of the TOP system these quartz bars do not overlap, but leave
a gap of around 2cm between them. This leads to around 6% of all tracks in the
nominal TOP acceptance region to escape without traversing any of the quartz
bars and thus not giving any usable particle identification information from
the TOP system and an additional 3% of tracks being degraded due to edge
effects. We propose a possible solution to remedy these gaps in the TOP
acceptance in the form of a Supplemental TOP GAP instrumentation (STOPGAP) that
covers the dead area between adjacent quartz bars with fast silicon detectors
to directly measure the time-of-flight of traversing particles for particle
identification purposes. Modern, fast timing silicon sensors and readouts can
offer sufficient time resolution for the task at hand, so that STOPGAP modules
could be built compact enough to fit into the limited space available in the
area of interest between the Belle II central drift chamber (CDC) and the TOP
system.
  In this article, we present a simulation study demonstrating the feasibility
of a silicon time-of-flight system based on its reconstruction performance in
$\Upsilon(4S)\rightarrow B\bar{B}$ events simulated using the Belle II
simulation and reconstruction software. We discuss the performance requirements
for possible sensor technologies and demonstrate that such a project could be
realised with novel, fast monolithic CMOS sensors that are expected to reach
MIP timing resolutions of down to 50ps. Additionally, we discuss the use of
fast timing layers at lower radii for track triggering as well as particle
identification at low momenta <1GeV/c.


title: Collective Variable for Metadynamics Derived from AlphaFold Output

abstract:
  AlphaFold is a neural-network-based tool for the prediction of 3D structures
of protein. In CASP14, a blind structure prediction challenge, it performed
significantly better than other competitors, which makes it the best available
structure prediction tool. One of the outputs of AlphaFold is the probability
profile of residue-residue distances. This makes it possible to score any
conformation of the studied protein to express its compliance with the
AlphaFold model. Here we show how this score can be used to drive protein
folding simulation by metadynamics and parallel tempering metadynamics. By
parallel tempering metadynamics, we simulated folding of a mini-protein
Trp-cage beta hairpin and predicted their folding equilibria. We see the
potential of AlphaFold-based collective variable in applications beyond
structure prediction, such as in structure refinement or prediction of the
outcome of a mutation.


title:
Formal languages, quadratic Diophantine equations and the Heisenberg
  group

abstract:
  We show that EDT0L languages can be used to describe the solutions to
one-variable equations in the Heisenberg group. To do this, we first express
the solutions to quadratic equations with two variables in the ring of integers
using EDT0L languages, and then we reduce the question of solving a
one-variable equation in the Heisenberg group to solving an equation in the
ring of integers.


title:
Federated Minimax Optimization: Improved Convergence Analyses and
  Algorithms

abstract:
  In this paper, we consider nonconvex minimax optimization, which is gaining
prominence in many modern machine learning applications such as GANs.
Large-scale edge-based collection of training data in these applications calls
for communication-efficient distributed optimization algorithms, such as those
used in federated learning, to process the data. In this paper, we analyze
Local stochastic gradient descent ascent (SGDA), the local-update version of
the SGDA algorithm. SGDA is the core algorithm used in minimax optimization,
but it is not well-understood in a distributed setting. We prove that Local
SGDA has \textit{order-optimal} sample complexity for several classes of
nonconvex-concave and nonconvex-nonconcave minimax problems, and also enjoys
\textit{linear speedup} with respect to the number of clients. We provide a
novel and tighter analysis, which improves the convergence and communication
guarantees in the existing literature. For nonconvex-PL and
nonconvex-one-point-concave functions, we improve the existing complexity
results for centralized minimax problems. Furthermore, we propose a
momentum-based local-update algorithm, which has the same convergence
guarantees, but outperforms Local SGDA as demonstrated in our experiments.


title: Quasi $\alpha$-Firmly Nonexpansive Mappings in Wasserstein Spaces

abstract:
  This paper aims to introduce the concept of quasi $\alpha$-firmly
nonexpansive mappings in Wasserstein-2 spaces over $\R^d$ and to analyze
properties of these mappings. We prove that for quasi $\alpha$-firmly
nonexpansive mappings satisfying a certain quadratic growth condition, the
fixed point iterations converge in the narrow topology. As a byproduct, we will
get the known convergence of the counterpart of the proximal point algorithm in
Wasserstein spaces. Finally, we apply our results to show that cyclic proximal
point algorithms for minimizing the sum of certain functionals on Wasserstein
spaces converge under appropriate assumptions.


title:
Transition frequency measurement of highly excited Rydberg states of
  87Rb for a wide range of principal quantum numbers

abstract:
  We report our measurements of the absolute transition frequencies of 5P_3/2,F
= 3 to nS and nD Rydberg states of 87Rb with high principal quantum numbers in
a wide range of values (n = 45-124). The measurements were performed using
Rydberg Electromagnetically Induced Transparency (EIT) in ladder-type
three-level systems. We measure the transition frequencies with an accuracy of
less than 2 MHz. We determine the values of the Rydberg-Ritz parameter for 87Rb
from our experimental measurements of the transition frequencies. Our
measurements of the absolute transition frequencies of the highly excited
Rydberg states would be useful for diverse applications in quantum information
processing, quantum simulation and quantum sensing with Rydberg atoms.


title: An explicit lifting construction of CAP forms on O(1,5)

abstract:
  We explicitly construct non-tempered cusp forms on the orthogonal group
O(1,5) of signature (1+,5-). Given a definite quaternion algebra B over
$\mathbb{Q}$, the orthogonal group is attached to the indefinite quadratic
space of rank 6 with the anisotropic part defined by the reduced norm of B. Our
construction can be viewed as a generalization of [22] to the case of any
definite quaternion algebras, for which we note that [22] takes up the case
where the discriminant of B is two. Unlike [22] the method of the construction
is to consider the theta lifting from Maass cusp forms to O(1,5), following the
formulation by Borcherds. The cuspidal representations generated by our cusp
forms are studied in detail. We determine all local components of the cuspidal
representations and show that our cusp forms are CAP forms.


title: High order corrected trapezoidal rules for a class of singular integrals

abstract:
  We present a family of high order trapezoidal rule-based quadratures for a
class of singular integrals, where the integrand has a point singularity. The
singular part of the integrand is expanded in a Taylor series involving terms
of increasing smoothness. The quadratures are based on the trapezoidal rule,
with the quadrature weights for Cartesian nodes close to the singularity
judiciously corrected based on the expansion. High order accuracy can be
achieved by utilizing a sufficient number of correction nodes around the
singularity to approximate the terms in the series expansion. The derived
quadratures are applied to the Implicit Boundary Integral formulation of
surface integrals involving the Laplace layer kernels.


title:
Binary Classification Under $\ell_0$ Attacks for General Noise
  Distribution

abstract:
  Adversarial examples have recently drawn considerable attention in the field
of machine learning due to the fact that small perturbations in the data can
result in major performance degradation. This phenomenon is usually modeled by
a malicious adversary that can apply perturbations to the data in a constrained
fashion, such as being bounded in a certain norm. In this paper, we study this
problem when the adversary is constrained by the $\ell_0$ norm; i.e., it can
perturb a certain number of coordinates in the input, but has no limit on how
much it can perturb those coordinates. Due to the combinatorial nature of this
setting, we need to go beyond the standard techniques in robust machine
learning to address this problem. We consider a binary classification scenario
where $d$ noisy data samples of the true label are provided to us after
adversarial perturbations. We introduce a classification method which employs a
nonlinear component called truncation, and show in an asymptotic scenario, as
long as the adversary is restricted to perturb no more than $\sqrt{d}$ data
samples, we can almost achieve the optimal classification error in the absence
of the adversary, i.e. we can completely neutralize adversary's effect.
Surprisingly, we observe a phase transition in the sense that using a converse
argument, we show that if the adversary can perturb more than $\sqrt{d}$
coordinates, no classifier can do better than a random guess.


title:
Regularizing effects of the entropy functional in optimal transport and
  planning problems

abstract:
  We analyze optimal transport problems with additional entropic cost evaluated
along curves in the Wasserstein space which join two probability measures
$m_0,m_1$. The effect of the additional entropy functional results into an
elliptic regularization for the (so-called) Kantorovich potentials of the dual
problem. Assuming the initial and terminal measures to be positive and smooth,
we prove that the optimal curve remains smooth for all time. We focus on the
case that the transport problem is set on a convex bounded domain in the
$d$-dimensional Euclidean space (with no-flux condition on the boundary), but
we also mention the case of Gaussian-like measures in the whole space. The
approach follows ideas introduced by P.-L. Lions in the theory of mean-field
games \cite{L-college}. The result provides with a smooth approximation of
minimizers in optimization problems with penalizing congestion terms, which
appear in mean-field control or mean-field planning problems. This allows us to
exploit new estimates for this kind of problems by using displacement convexity
properties in the Eulerian approach.


title:
Neuro-symbolic Natural Logic with Introspective Revision for Natural
  Language Inference

abstract:
  We introduce a neuro-symbolic natural logic framework based on reinforcement
learning with introspective revision. The model samples and rewards specific
reasoning paths through policy gradient, in which the introspective revision
algorithm modifies intermediate symbolic reasoning steps to discover
reward-earning operations as well as leverages external knowledge to alleviate
spurious reasoning and training inefficiency. The framework is supported by
properly designed local relation models to avoid input entangling, which helps
ensure the interpretability of the proof paths. The proposed model has built-in
interpretability and shows superior capability in monotonicity inference,
systematic generalization, and interpretability, compared to previous models on
the existing datasets.


title:
Re-ordering of Hadamard matrix using Fourier transform and gray-level
  co-occurrence matrix for compressive single-pixel imaging

abstract:
  One of the most active research fields in single-pixel imaging is the
influence of the sampling basis and its order in the quality of the
reconstructed images. This paper presents two new orders, ascending scale (AS)
and ascending inertia (AI), of the Hadamard basis and test their performance,
using simulation and experimental methods, for low sampling ratios (0.5 to
0.01). These orders were compared with two state-of-the-art orders,
cake-cutting (CC) and total gradient (TG), using TVAL3 as the reconstruction
algorithm and three noise levels. These newly proposed orders have better
reconstructed image quality on the simulation data set (110 images) and
achieved structure similarity index values higher than CC order. The
experimental data set (2 images) showed that the AS and AI orders performed
better with a sampling ratio of 0.5, while for lower sampling ratio the
performance of AS, AI and CC was similar. The TG order performed worst in the
majority of the cases. Finally, the simulation results present clear evidence
that peak signal-to-noise ratio (PSNR) is not a reliable image quality
assessment (IQA) metric to assess image reconstruction quality in the context
of single pixel imaging.


title: Fourier characterizations of Pilipovic spaces

abstract:
  Let $f$ be a function or distribution on $\rr d$. We show that $f$ belongs to
a certain Pilipovi{\'c} space, if and only if $f$ and suitable partial
fractional Fourier transforms of $f$ satisfy certain types of estimates.


title: PET: A new Dataset for Process Extraction from Natural Language Text

abstract:
  Although there is a long tradition of work in NLP on extracting entities and
relations from text, to date there exists little work on the acquisition of
business processes from unstructured data such as textual corpora of process
descriptions. With this work we aim at filling this gap and establishing the
first steps towards bridging data-driven information extraction methodologies
from Natural Language Processing and the model-based formalization that is
aimed from Business Process Management. For this, we develop the first corpus
of business process descriptions annotated with activities, gateways, actors
and flow information. We present our new resource, including a detailed
overview of the annotation schema and guidelines, as well as a variety of
baselines to benchmark the difficulty and challenges of business process
extraction from text.


title: Diffuse polarized foregrounds from component separation with QUIJOTE-MFI

abstract:
  Polarized component maps in the Northern Sky are derived from the QUIJOTE-MFI
wide survey data at 11 and 13 GHz, the WMAP K and Ka bands and all Planck
polarized channels using the parametric component separation method B-SeCRET.
The addition of QUIJOTE-MFI data significantly improves the uncertainty in the
parameter estimation of the low frequency dominant foreground, in particular
the estimation of the synchrotron spectral index. We find statistically
significant spatial variability across the sky. A power law model of the
synchrotron emission provides a good fit of the data outside the galactic plane
but fails to track the complexity of this region. Moreover, when we assume a
synchrotron model with uniform curvature we find, in the 95% confidence region,
a non-zero $c_s$ value. However, there is not sufficient statistical
significance to determine which model is favoured.


title: Information recoverability of noisy quantum states

abstract:
  Classical information, also known as shadow information, carried by quantum
states could be corrupted as they undergo undesirable noises modelled by
quantum channels. Due to quantum channels' destructive nature, a fundamental
problem in quantum information theory is how well we can retrieve information
from noisy quantum states. In this work, we introduce a systematic framework to
quantitatively study this problem. First, we prove that classical information
can be retrieved if and only if the querying observable is in the image of the
whole observable space under the channel's adjoint. Second, we further define
the dimension of the evolved observable space as the quantum channel's
effective shadow dimension that quantifies the information recoverability of
the channel. This quantity can be computed as the rank of a matrix derived from
the channel's Kraus operators, and it induces the shadow destructivity as a
measure with meaningful properties. Third, we resolve the minimum cost of
retrieving a piece of shadow information from the required number of copies of
a noisy state. We in particular show that the optimal cost and the
corresponding protocol are efficiently computable by semidefinite programming.
As applications, we derive the ultimate limits on shadow retrieving costs for
generalized amplitude damping channels and mixed Pauli channels and employ the
corresponding protocol to mitigate errors in tasks of ground state energy
estimation.


title: Unsupervised Alignment of Distributional Word Embeddings

abstract:
  Cross-domain alignment play a key roles in tasks ranging from machine
translation to transfer learning. Recently, purely unsupervised methods
operating on monolingual embeddings have successfully been used to infer a
bilingual lexicon without relying on supervision. However, current state-of-the
art methods only focus on point vectors although distributional embeddings have
proven to embed richer semantic information when representing words. In this
paper, we propose stochastic optimization approach for aligning probabilistic
embeddings. Finally, we evaluate our method on the problem of unsupervised word
translation, by aligning word embeddings trained on monolingual data. We show
that the proposed approach achieves good performance on the bilingual lexicon
induction task across several language pairs and performs better than the
point-vector based approach.


title:
Compositeness of weakly S-wave bound states from next-to-leading
  Weinberg's relations

abstract:
  We discuss a model-independent estimator of the likelihood of the
compositeness of a shallow S-wave bound or virtual state. The approach is based
on an extension of Weinberg's relations in Phys. Rev. 137, B672 (1965) and it
relies only on the proximity of the energy of the state to the two-hadron
threshold to which it significantly couples. As explicit applications, we
analyse the case of the deuteron, the $^1{\rm S}_0$ nucleon-nucleon virtual
state and the exotic ${D_{s0}^\ast(2317)}^\pm$, and find strong support to the
molecular interpretation in all cases. Results are less conclusive for the
${D_{s0}^\ast(2317)}^\pm$, since this narrow resonance is not a loosely bound
state, and the approach employed here is in at the limit of its applicability.


title:
A Unified Network Equilibrium for E-Hailing Platform Operation and
  Customer Mode Choice

abstract:
  This paper aims to combine both economic and network user equilibrium for
ride-sourcing and ride-pooling services, while endogenously optimizing the
pooling sequence of two origin-destination (OD) pairs. With the growing
popularity of ride-sourcing and ride-pooling services provided by
transportation network companies (TNC), there lacks a theoretical network
equilibrium model that accounts for the emerging ride-pooling service, due to
the challenge in enumerating all possible combinations of OD pairs pooling and
sequencing. This paper proposes a unified network equilibrium framework that
integrates three modules, including travelers' modal choice between e-pooling
and e-solo services, e-platforms' decision on vehicle dispatching and
driver-passenger matching, and network congestion. To facilitate the
representation of vehicle and passenger OD flows and pooling options, a layered
OD hypergraph is created encompassing ride-sourcing and ride-pooling services
over origins and destination nodes. Numerical examples are performed on both
small and large road networks to demonstrate the efficiency of our model. The
proposed equilibrium framework can efficiently assist policymakers and urban
planners to evaluate the impact of TNCs on traffic congestion, and also help
TNCs with pricing and fleet sizing optimization.


title:
Joint-optimization of Node placement and UAV's Trajectory for
  Self-sustaining Air-Ground IoT system

abstract:
  Due to the sustainable power supply and environment-friendly features,
self-powered IoT devices have been increasingly employed in various fields such
as providing observation data in remote areas, especially in rural areas or
post-disaster scenarios. Generally, through multi-hop relay, the sensed data of
those self-powered IoT devices are collected by the sink node which connects to
the IoT backbones. However, due to the remoteness, the sink needs to be located
at the border of the monitoring area where both the backbone of IoT and
electrical infrastructures are accessible. Under such deployment, significant
data flow and relay overhead will incur considering the large scale of the
monitoring area. Motivated by this issue, this paper aims to design a
UAV-assisted self-powered heterogeneous system to provide comprehensive
monitoring data. In this system, because of the superiority of the unmanned
aerial vehicle (UAV) in the easy deployment, We dispatch UAV collects data from
self-powered IoT devices, periodically so as to alleviate the data overflow.
Moreover, based on that the self-powered IoT devices are expected to have a
more considerable capability in the heavy data flow area, we also developed a
placement upgrade strategy to upgrade the general homogeneous self-powered IoT
system to the heterogeneous self-powered IoT system. Simulation results
indicated the developed UAV-assisted self-powered heterogeneous system can
achieve around $1.28\times$ the amount of data delivery to sink compared with
the homogeneous self-powered IoT system.


title: Compartmentalizing the cuprate strange metal

abstract:
  It has long been recognized that the key to unlocking the mystery of cuprate
high-Tc superconductivity lies in understanding the anomalous normal state from
which pairs form and condense. While many of its defining properties have been
identified, they are often considered either at a singular doping level or as
an isolated phenomenon as a function of doping. As a result, their relation to
each other and to the pseudogap (PG), strange metal (SM) and
non-superconducting (non-SC) regimes that define the cuprate phase diagram has
yet to be elucidated. Here, we report a high-field in-plane MR study on several
cuprate families spanning all 3 regimes that reveal a complex yet nonetheless
systematic evolution of the form of the MR, with each regime possessing its own
distinct scaling behavior. In the PG regime, the MR exhibits pure H/T^2 scaling
at low fields and H-linearity at the highest field strengths. While the
H-linearity persists inside the SM regime, the scaling changes abruptly to H/T.
The size of the H-linear slope, meanwhile, is found to be correlated with both
the T-linear resistivity coefficient and Tc, strengthening the characterization
of the SM regime as a quantum critical phase. We interpret the omnipresence of
H-linear MR across both regimes as a signature of highly anisotropic, possibly
discontinuous features on the Fermi surface. Finally, within the non-SC,
Fermi-liquid regime, we observe a recovery of conventional Kohler scaling. This
comprehensive study establishes the distinct nature of the magnetotransport
within each regime and identifies power-law scaling of the normal state MR as a
defining feature of SC hole-doped cuprates. The incompatibility of such
power-law scaling with any known variant of Boltzmann transport theory
motivates the quest for an altogether new theoretical framework, one in which
the MR is entirely decoupled from elastic impurity scattering.


title: Threshold surface reactions and local time resetting

abstract:
  In this paper we consider a threshold surface absorption mechanism for a
particle diffusing in a domain containing a single target $\calU $. The target
boundary $\partial \calU$ is taken to be a reactive surface that modifies an
internal state $Z_t$ of the particle when in contact with the surface at time
$t$, with $Z_0=h$. The state $Z_t$ is taken to be a monotonically decreasing
function of the so-called boundary local time, and absorption occurs as soon as
$Z_t$ reaches zero. (The boundary local time is a Brownian functional that
determines the amount of time that the particle spends in a neighborhood of
$\partial \calU$.) We first show how to analyze threshold surface absorption in
terms of the joint probability density or generalized propagator
$P_0(\x,\ell,t|\x_0)$ for the pair $(\X_t,\ell_t)$ in the case of a perfectly
reflecting surface, where $\X_t$ and $\ell_t$ denote the particle position and
local time at time $t$, respectively, and $\x_0$ is the initial position. We
then introduce a generalized stochastic resetting protocol in which both the
position $\X_t$ and the internal state $Z_t$ are reset to their initial values,
$\X_t\rightarrow \x_0$ and $Z_t\rightarrow h$, at a Poisson rate $r$. The
latter is mathematically equivalent to resetting the boundary local time,
$\ell_t\rightarrow 0$. Since resetting is governed by a renewal process, the
survival probability with resetting can be expressed in terms of the survival
probability without resetting, which means that the statistics of absorption
can be determined by calculating the Laplace transform of $P_0(\x,\ell,t|\x_0)$
with respect to $t$. We contrast this with the case where only particle
position is reset, which is not governed by a renewal process. We illustrate
the theory using the simple examples of diffusion on the half-line and a
spherical target in a spherical domain.


title:
The Connection Between Nesterov's Accelerated Methods and Halpern
  Fixed-Point Iterations

abstract:
  We derive a direct connection between Nesterov's accelerated first-order
algorithm and the Halpern fixed-point iteration scheme for approximating a
solution of a co-coercive equation. We show that one method is equivalent to
another via a simple transformation, leading to a simple convergence proof for
Nesterov's accelerated scheme. Alternatively, we directly establish convergence
rates of Nesterov's accelerated variant, and as a consequence, we obtain a new
convergence rate of the Halpern fixed-point iteration. Next, we apply our
results to different methods such as proximal-point, forwarb-backward
splitting, and three-operator splitting methods for solving monotone
inclusions, where our convergence guarantees are applicable. Since the forward
scheme requires the co-coerciveness of the underlying operator, we derive new
Nesterov's accelerated variants for both recent extra-anchored gradient and
past-extra anchored gradient methods which can avoid the co-coerciveness
assumption. For the extra-anchored gradient method, we consider two variants
studied in [43] for the monotone case and in [21] for the co-monotone case. The
latter covers the former as a special case and has a tighter convergence rate.
For the past-extra anchored gradient method (an accelerated variant of the
optimistic gradient method) [41], we obtain a new Nesterov's accelerated
variant with two past-iterate correction terms that may help to develop new
Nesterov's accelerated methods for minimax problems and their continuous views.
We test our theoretical results on two numerical examples, where the actual
convergence rates match well the theoretical rates up to a constant factor.


title: Quantifying fermionic interactions from the violation of Wick's theorem

abstract:
  In contrast to interacting systems the ground state of free systems has a
highly ordered pattern of quantum correlations, as witnessed by Wick's
decomposition. Here, we quantify the effect of interactions by measuring the
violation they cause on Wick's decomposition. In particular, we express this
violation in terms of the low entanglement spectrum of fermionic systems.
Moreover, we establish a relation between the Wick's theorem violation and the
interaction distance, the smallest distance between the reduced density matrix
of the system and that of the optimal free model closest to the interacting
one. Our work provides the means to quantify the effect of interactions in
physical systems though measurable quantum correlations.


title:
A possible interpretation of $\Lambda$ baryon spectrum with pentaquark
  components

abstract:
  The $\Lambda$ baryon spectrum is studied within the SU(3) flavor symmetry in
a constituent quark model. We found that it is rather difficult to accommodate
some negative-parity $\Lambda$ resonances as single $q^2s$ ($q = u,\,d$ quarks)
states in the conventional three-quark picture. The ground $q^3s\bar q$
pentaquark mass spectrum is evaluated and a possible interpretation is proposed
in the work: the observed $\Lambda(1405)1/2^{-}$, $\Lambda(1670)1/2^{-}$ and
$\Lambda(1800)1/2^{-}$ are three-state mixtures of two $p$-wave $q^2s$ states
and one ground $q^3s\bar q$ pentaquark state, so are the
$\Lambda(1520)3/2^{-}$, $\Lambda(1690)3/2^{-}$ and $\Lambda(2050)3/2^{-}$
resonances.


title: Mitigation of Electron Cloud Effects in the FCC-ee Collider

abstract:
  Electron clouds forming inside the beam vacuum chamber due to photoemission
and secondary emission may limit the accelerator performance. Specifically, the
electron clouds can blow up the vertical emittance of a positron beam, through
a head-tail-type single-bunch instability, if the central electron density
exceeds a certain threshold value, that can be estimated analytically. Using
the codes PyECLOUD and VSim, we carried out detailed simulations of the
electron-cloud build up for the main arcs and the damping ring of the FCC-ee
collider, in order to identify the effective photoemission rate and secondary
emission yield required for achieving and maintaining the design emittance. To
this end, we present the simulated electron density at the centre of the beam
pipe for various bunch spacings, secondary emission yields, and photoemission
parameters, in the damping ring and in the arcs of the collider positron ring.
To gain further insight into the underlying dynamics, the obtained spatial and
energy distributions of the cloud electrons are illustrated as a function of
time. In addition, we compare results obtained for two different secondary
emission models ("Furman-Pivi" and "ECLOUD"), thereby indicating the
uncertainty inherent in this type of study, without any prototype vacuum
chambers yet available. We also point out a few situations where the two
secondary-emission models yield similar density values. Finally, based on our
simulation results for two different design variants, we conclude that the new
parameter baseline of the FCC-ee will facilitate electron-cloud mitigation.


title:
CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using
  Clustering

abstract:
  Most semantic segmentation approaches of Hyperspectral images (HSIs) use and
require preprocessing steps in the form of patching to accurately classify
diversified land cover in remotely sensed images. These approaches use patching
to incorporate the rich neighborhood information in images and exploit the
simplicity and segmentability of the most common HSI datasets. In contrast,
most landmasses in the world consist of overlapping and diffused classes,
making neighborhood information weaker than what is seen in common HSI
datasets. To combat this issue and generalize the segmentation models to more
complex and diverse HSI datasets, in this work, we propose our novel flagship
model: Clustering Ensemble U-Net (CEU-Net). CEU-Net uses the ensemble method to
combine spectral information extracted from convolutional neural network (CNN)
training on a cluster of landscape pixels. Our CEU-Net model outperforms
existing state-of-the-art HSI semantic segmentation methods and gets
competitive performance with and without patching when compared to baseline
models. We highlight CEU-Net's high performance across Botswana, KSC, and
Salinas datasets compared to HybridSN and AeroRIT methods.


title:
VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality
  Prediction

abstract:
  We present the Versatile Grasp Quality Convolutional Neural Network
(VGQ-CNN), a grasp quality prediction network for 6-DOF grasps. VGQ-CNN can be
used when evaluating grasps for objects seen from a wide range of camera poses
or mobile robots without the need to retrain the network. By defining the grasp
orientation explicitly as an input to the network, VGQ-CNN can evaluate 6-DOF
grasp poses, moving beyond the 4-DOF grasps used in most image-based grasp
evaluation methods like GQ-CNN. We train VGQ-CNN on our new Versatile Grasp
dataset (VG-dset), containing 6-DOF grasps observed from a wide range of camera
poses. VGQ-CNN achieves a balanced accuracy of 82.1% on our test-split while
generalising to a variety of camera poses. Meanwhile, it achieves competitive
performance for overhead cameras and top-grasps with a balanced accuracy of
74.2% compared to GQ-CNN's 76.6%. We also propose a modified network
architecture, FAST-VGQ-CNN, that speeds up inference using a shared encoder
architecture and can make 128 grasp quality predictions in 12ms on a CPU. Code
and data are available at https://figshare.com/s/b12b37b14b747b10524e.


title: Minimal subdynamics and minimal flows without characteristic measures

abstract:
  Given a countable group $G$ and a $G$-flow $X$, a measure $\mu\in P(X)$ is
called characteristic if it is $\mathrm{Aut}(X, G)$-invariant. Frisch and Tamuz
asked about the existence of a minimal $G$-flow, for any group $G$, which does
not admit a characteristic measure. We construct for every countable group $G$
such a minimal flow. Along the way, we are motivated to consider a family of
questions we refer to as minimal subdynamics: Given a countable group $G$ and a
collection of infinite subgroups $\{\Delta_i: i\in I\}$, when is there a
faithful $G$-flow for which every $\Delta_i$ acts minimally?


title: Structural & Granger CAUSALITY for IoT Digital Twin

abstract:
  In this foundational expository article on the application of Causality
Analysis in IoT, we establish the basic theory and algorithms for estimating
Structural and Granger causality factors from measured multichannel sensor data
(vector timeseries). Vector timeseries is modeled as a Structural Vector
Autoregressive (SVAR) model; utilizing Kalman Filter and Independent Component
Analysis (ICA) methods, Structural and generalized Granger causality factors
are estimated. The estimated causal factors are presented as a Fence graph
which we call Causal Digital Twin. Practical applications of Causal Digital
Twin are demonstrated on NASA Prognostic Data Repository Bearing data
collection. Use of Causal Digital Twin for counterfactual experiments are
indicated.
  Causal Digital Twin is a horizontal solution that applies to diverse use
cases in multiple industries such as Industrial, Manufacturing, Automotive,
Consumer, Building and Smart City.


title:
Stochastic factors and string stability of traffic flow: Analytical
  investigation and numerical study based on car-following models

abstract:
  The emergence dynamics of traffic flow instabilities has always attracted
particular attention. Considerable efforts have been devoted to studying the
stability of traffic flow by using different traffic models. However, the
previous works have overwhelmingly dealt with the deterministic models with
less emphasis on the presence of stochastic factors. Recent empirical and
theoretical findings have demonstrated that stochastic factors contribute
significantly to traffic flow instability. Moreover, it has been established
that the presence of stochastic factors is responsible for the observed concave
growth pattern of traffic oscillations. In this paper, we study analytically
the string stability of a general class of continuous stochastic car following
model by the mean of generalized Lyapunov equation. We have found, indeed, that
the presence of stochasticity destabilizes traffic performance. Under a
stochastic environment, the impact of stochasticity depends on both the
sensitivity to the gap and to the velocity difference. Our theoretical analysis
has been validated by numerical simulations for three typical car-following
models. Finally, we have calibrated and validated the stochastic car following
models against empirical data. It is found that considering the presence of
stochasticity reproduces the emergence of traffic instabilities and captures
the concave growth pattern of traffic oscillations. Our results further
highlight theoretically and numerically that the presence of stochasticity
should be explicitly considered when modeling traffic flow for a more accurate
prediction of traffic instabilities.


title:
The management of scientific and technological infrastructures: the case
  of the Mexican National Laboratories

abstract:
  The effectiveness of research units is assessed on the basis of their
performance in relation to scientific, technological and innovation production,
the quality of their results and their contribution to the solution of
scientific and social problems. This paper examines the management practices
employed in some Mexican National Laboratories in order to identify those that
could explain their effectiveness in meeting their objectives. The results of
other works that propose common elements among laboratories with outstanding
performance are used and verified directly in the field. Considering the
inherent complexity of each field of knowledge and the socio-spatial
characteristics in which the laboratories operate, we report which management
practices are relevant for their effectiveness, how they contribute to their
consolidation as fundamental scientific and technological infrastructures, how
these can be translated into indicators that support the evaluation of their
performance, and still pending.


title: Axioms for typefree subjective probability

abstract:
  We formulate and explore two basic axiomatic systems of typefree subjective
probability. One of them explicates a notion of finitely additive probability.
The other explicates a concept of infinitely additive probability. It is argued
that the first of these systems is a suitable background theory for formally
investigating controversial principles about typefree subjective probability.


title:
An Environmental Feature Representation in I-vector Space for Room
  Verification and Metadata Estimation

abstract:
  This paper investigates the application of environmental feature
representations for room verification tasks and acoustic meta-data estimation.
Audio recordings contain both speaker and non-speaker information. We refer to
the non-speaker-related information, including channel and other environmental
factors, as e-vectors. I-vectors, commonly used in speaker identification, are
extracted in the total variability space and capture both speaker and
channel-environment information without discrimination. Accordingly, e-vectors
can be extracted from i-vectors using methods such as linear discriminant
analysis. In this paper, we first demonstrate that e-vectors can be
successfully applied to room verification tasks with a low equal error rate.
Second, we propose two methods for estimating metadata information --
signal-to-noise (SNR) and reverberation (T60) -- from these e-vectors. When
comparing our system to contemporary global SNR estimation methods, in terms of
accuracy, we perform favorably even with low dimensional i-vectors. Lastly, we
show that room verification tasks can be improved if e-vectors are augmented
with the extracted metadata information.


title: On Fourier multipliers with rapidly oscillating symbols

abstract:
  We provide asymptotically sharp bounds for the $L_p$ norms of the Fourier
multipliers with the symbols $e^{i\lambda \varphi(\xi/|\xi|)}$, where
$\lambda\in \mathbb{R}$ is a large parameter.


title: Time-dependent Interactions in Tunnelling Dynamics

abstract:
  In this paper, the tunnelling of a particle through a potential barrier is
investigated in the presence of a time-dependent perturbation. The latter is
attributed to the process of the energy measurement of the scattered particle.
The time-dependent Schrodinger equation of the model is exactly solved. The
calculation of the probability density inside the barrier proves that the
tunnelling dynamics is determined not only by the transmitted and reflected
waves but also by their interference. Furthermore, the interference term is
time-dependent and contribute to the scattering process duration. The
tunnelling time is calculated as the time to stop the flow of probability
density inside the barrier. This is the minimum duration of the measurement
process before detecting the particle beyond the barrier. Based on this, a new
method of estimating the tunnelling time by energy experimental measuring is
proposed.


title:
A-Optimal Split Questionnaire Designs for Multivariate Continuous
  Variables

abstract:
  A split questionnaire design (SQD), an alternative to full questionnaires,
can reduce the response burden and improve survey quality. One can design a
split questionnaire to reduce the information loss from missing data induced by
the split questionnaire. This study develops a methodology for finding optimal
SQD (OSQD) for multivariate continuous variables, applying a probabilistic
design and optimality criterion approach. Our method employs previous survey
data to compute the Fisher information matrix and A-optimality criterion to
find OSQD for the current survey study. We derive theoretical findings on the
relationship between the correlation structure and OSQD and the robustness of
local OSQD. We conduct simulation studies to compare local and two global
OSQDs; mini-max OSQD and Bayes OSQD) to baselines. We also apply our method to
the 2016 Pet Demographic Survey (PDS) data. In both simulation studies and the
real data application, local and global OSQDs outperform the baselines.


title:
Low-profile Button Sensor Antenna Design for Wireless Medical Body Area
  Networks

abstract:
  A button sensor antenna for wireless medical body area networks (WMBAN) is
presented, which works through the IEEE 802.11b/g/n standard. Due to strong
interaction between the sensor antenna and the body, an innovative robust
system is designed with a small footprint that can serve on- and off-body
healthcare applications. The measured and simulated results are in good
agreement. The design offers a wide range of omnidirectional radiation patterns
in free space, with a reflection coefficient (S11) of -29.30 (-30.97) dB in the
lower (upper) bands. S11 reaches up to -23.07 (-27.07) dB and -30.76 (-31.12)
dB, respectively, on the human body chest and arm. The Specific Absorption Rate
(SAR) values are below the regulatory limitations for both 1-gram (1.6 W/Kg)
and 10-gram tissues (2.0 W/Kg). Experimental tests of the read range validate
the results of a maximum coverage range of 40 meters.


title:
Group index matched frequency conversion in lithium niobate on insulator
  waveguides

abstract:
  Sources of spectrally engineered photonic states are a key resource in
several quantum technologies. Of particular importance are the so-called
factorizable biphoton states which possess no spectral entanglement and hence,
are ideal for heralded generation of high-purity single photons. An essential
prerequisite for generating these states through nonlinear frequency conversion
is the control over the group indices of the photonic modes of the source.
Here, we show that thin-film lithium niobate on insulator (LNOI) is an
excellent platform for this purpose. We design and fabricate periodically poled
ridge waveguides in LNOI to demonstrate group index engineering of its guided
photonic modes and harness this control to experimentally realize on-chip group
index matched type-II sum-frequency generation (SFG) and photon-pair creation
through spontaneous parametric down-conversion (SPDC). Also, we numerically
study the role of the top cladding layer in tuning the dispersion properties of
the ridge waveguide structures and reveal a distinctive difference between the
air and silica-clad designs which are currently among the two most common
device cladding configurations in LNOI. We expect that these results will be
relevant for various classical and quantum applications where dispersion
control is crucial in tailoring the nonlinear response of the LNOI-based
devices.


title:
Reverse Engineering $\ell_p$ attacks: A block-sparse optimization
  approach with recovery guarantees

abstract:
  Deep neural network-based classifiers have been shown to be vulnerable to
imperceptible perturbations to their input, such as $\ell_p$-bounded norm
adversarial attacks. This has motivated the development of many defense
methods, which are then broken by new attacks, and so on. This paper focuses on
a different but related problem of reverse engineering adversarial attacks.
Specifically, given an attacked signal, we study conditions under which one can
determine the type of attack ($\ell_1$, $\ell_2$ or $\ell_\infty$) and recover
the clean signal. We pose this problem as a block-sparse recovery problem,
where both the signal and the attack are assumed to lie in a union of subspaces
that includes one subspace per class and one subspace per attack type. We
derive geometric conditions on the subspaces under which any attacked signal
can be decomposed as the sum of a clean signal plus an attack. In addition, by
determining the subspaces that contain the signal and the attack, we can also
classify the signal and determine the attack type. Experiments on digit and
face classification demonstrate the effectiveness of the proposed approach.


title:
Getting Critical: Making Sense of the EU Cybersecurity Framework for
  Cloud Providers

abstract:
  In this chapter, we review how the EU cybersecurity regulatory framework
impacts providers of cloud computing services. We examine the evolving
regulatory treatment of cloud services as an enabler of the EU's digital
economy and question whether all cloud services should be treated as critical
infrastructure. Further, we look at how the safeguarding and incident
notification obligations under the General Data Protection Regulation ('GDPR')
and the Network and Information Systems Directive ('NISD') apply to cloud
providers. We also consider the proposed revision of the NISD and look at newly
developed voluntary assurance mechanisms for cloud providers, including codes
of conduct and certification schemes. We conclude that, since cloud providers
are typically subject to both NISD and GDPR and to the jurisdiction of multiple
regulators, they face divergent regulatory approaches, which can lead to
unintended outcomes and high compliance costs.


title: Efficient Image Representation Learning with Federated Sampled Softmax

abstract:
  Learning image representations on decentralized data can bring many benefits
in cases where data cannot be aggregated across data silos. Softmax cross
entropy loss is highly effective and commonly used for learning image
representations. Using a large number of classes has proven to be particularly
beneficial for the descriptive power of such representations in centralized
learning. However, doing so on decentralized data with Federated Learning is
not straightforward as the demand on FL clients' computation and communication
increases proportionally to the number of classes. In this work we introduce
federated sampled softmax (FedSS), a resource-efficient approach for learning
image representation with Federated Learning. Specifically, the FL clients
sample a set of classes and optimize only the corresponding model parameters
with respect to a sampled softmax objective that approximates the global full
softmax objective. We examine the loss formulation and empirically show that
our method significantly reduces the number of parameters transferred to and
optimized by the client devices, while performing on par with the standard full
softmax method. This work creates a possibility for efficiently learning image
representations on decentralized data with a large number of classes under the
federated setting.


title:
Low-light Image and Video Enhancement via Selective Manipulation of
  Chromaticity

abstract:
  Image acquisition in low-light conditions suffers from poor quality and
significant degradation in visual aesthetics. This affects the visual
perception of the acquired image and the performance of various computer vision
and image processing algorithms applied after acquisition. Especially for
videos, the additional temporal domain makes it more challenging, wherein we
need to preserve quality in a temporally coherent manner. We present a simple
yet effective approach for low-light image and video enhancement. To this end,
we introduce "Adaptive Chromaticity", which refers to an adaptive computation
of image chromaticity. The above adaptivity allows us to avoid the costly step
of low-light image decomposition into illumination and reflectance, employed by
many existing techniques. All stages in our method consist of only point-based
operations and high-pass or low-pass filtering, thereby ensuring that the
amount of temporal incoherence is negligible when applied on a per-frame basis
for videos. Our results on standard lowlight image datasets show the efficacy
of our algorithm and its qualitative and quantitative superiority over several
state-of-the-art techniques. For videos captured in the wild, we perform a user
study to demonstrate the preference for our method in comparison to
state-of-the-art approaches.


title: The Low Frequency Perspective on Fast Radio Bursts

abstract:
  Fast radio bursts (FRBs) represent one of the most exciting astrophysical
discoveries of the recent past. The study of their low-frequency emission,
which was only effectively picked up about ten years after their discovery, has
helped shape the field thanks to some of the most important detections to date.
Observations between 400 and 800 MHz, carried out by the CHIME/FRB telescope,
in particular, have led to the detection of ~500 FRBs in little more than 1
year and, among them, ~20 repeating sources. Detections at low frequencies have
uncovered a nearby population that we can study in detail via continuous
monitoring and targeted campaigns. The latest, most important discoveries
include: periodicity, both at the days level in repeaters and at the
millisecond level in apparently non-repeating sources; the detection of an
FRB-like burst from a galactic magnetar; and the localisation of an FRB inside
a globular cluster in a nearby galaxy. The systematic study of the population
at low frequencies is important for the characterisation of the environment
surrounding the FRBs and, at a global level, to understand the environment of
the local universe. This review is intended to give an overview of the efforts
leading to the current rich variety of low-frequency studies and to put into a
common context the results achieved in order to trace a possible roadmap for
future progress in the field.


title:
Nonaffinity controls critical slowing down and rheology near the onset
  of rigidity

abstract:
  Fluid-immersed networks and dense suspensions typically reside near a
boundary between soft (or fluid-like) and rigid (or solid-like) mechanical
regimes. This boundary can be crossed either by varying the concentration or by
deformation. Near the onset or loss of rigidity, dissipation limiting nonaffine
rearrangements dominate the macroscopic viscoelastic response, giving rise to
diverging relaxation times and power-law rheology. Here, we derive a simple
relationship between nonaffinity and excess viscosity in fluid-immersed
amorphous materials. We then demonstrate this relationship and its rheological
consequences in simulations of stress relaxation in strained filament networks
and dense suspensions.


title: Mapping single electron spins with magnetic tomography

abstract:
  Mapping the positions of single electron spins is a highly desired goal for
applications such as nanoscale magnetic resonance imaging and quantum network
characterization. Here, we demonstrate a method based on rotating an external
magnetic field to identify the precise location of single electron spins in the
vicinity of a quantum spin sensor. We use a nitrogen-vacancy center in diamond
as a quantum sensor and modulate the dipolar coupling to a proximate electron
spin in the crystal by varying the magnetic field vector. The modulation of the
dipolar coupling contains information on the coordinates of the spin, from
which we extract its position with an uncertainty of 1.2 {\AA}. We show that
the method can be used to detect electron spins with subnanometer precision up
to 10 nm away from the sensor. The magnetic tomography method can be applied to
distance measurements for studying the structure of individual molecules.


title:
Redshift factor and the small mass-ratio limit in binary black hole
  simulations

abstract:
  We present a calculation of the Detweiler redshift factor in binary black
hole simulations based on its relation to the surface gravity. The redshift
factor has far-reaching applications in analytic approximations, gravitational
self-force calculations, and conservative two-body dynamics. By specializing to
non-spinning, quasi-circular binaries with mass ratios ranging from $m_A/m_B =
1$ to $m_A/m_B = 9.5$ we are able to recover the leading small-mass-ratio (SMR)
prediction with relative differences of order $10^{-5}$ from simulations alone.
The next-to-leading order term that we extract agrees with the SMR prediction
arising from self-force calculations, with differences of a few percent. These
deviations from the first-order conservative prediction are consistent with
non-adiabatic effects that can be accommodated in an SMR expansion. This fact
is also supported by a comparison to the conservative post-Newtonian prediction
of the redshifts. For the individual redshifts, a re-expansion in terms of the
symmetric mass ratio $\nu$ does not improve the convergence of the series.
However we find that when looking at the sum of the redshift factors of both
back holes, $z_A + z_B$, which is symmetric under the exchange of the masses, a
re-expansion in $\nu$ accelerates its convergence. Our work provides further
evidence of the surprising effectiveness of SMR approximations in modeling even
comparable mass binary black holes.


title:
A Brain-Inspired Low-Dimensional Computing Classifier for Inference on
  Tiny Devices

abstract:
  By mimicking brain-like cognition and exploiting parallelism,
hyperdimensional computing (HDC) classifiers have been emerging as a
lightweight framework to achieve efficient on-device inference. Nonetheless,
they have two fundamental drawbacks, heuristic training process and ultra-high
dimension, which result in sub-optimal inference accuracy and large model sizes
beyond the capability of tiny devices with stringent resource constraints. In
this paper, we address these fundamental drawbacks and propose a
low-dimensional computing (LDC) alternative. Specifically, by mapping our LDC
classifier into an equivalent neural network, we optimize our model using a
principled training approach. Most importantly, we can improve the inference
accuracy while successfully reducing the ultra-high dimension of existing HDC
models by orders of magnitude (e.g., 8000 vs. 4/64). We run experiments to
evaluate our LDC classifier by considering different datasets for inference on
tiny devices, and also implement different models on an FPGA platform for
acceleration. The results highlight that our LDC classifier offers an
overwhelming advantage over the existing brain-inspired HDC models and is
particularly suitable for inference on tiny devices.


title:
Joint Learning of Salient Object Detection, Depth Estimation and Contour
  Extraction

abstract:
  Benefiting from color independence, illumination invariance and location
discrimination attributed by the depth map, it can provide important
supplemental information for extracting salient objects in complex
environments. However, high-quality depth sensors are expensive and can not be
widely applied. While general depth sensors produce the noisy and sparse depth
information, which brings the depth-based networks with irreversible
interference. In this paper, we propose a novel multi-task and multi-modal
filtered transformer (MMFT) network for RGB-D salient object detection (SOD).
Specifically, we unify three complementary tasks: depth estimation, salient
object detection and contour estimation. The multi-task mechanism promotes the
model to learn the task-aware features from the auxiliary tasks. In this way,
the depth information can be completed and purified. Moreover, we introduce a
multi-modal filtered transformer (MFT) module, which equips with three
modality-specific filters to generate the transformer-enhanced feature for each
modality. The proposed model works in a depth-free style during the testing
phase. Experiments show that it not only significantly surpasses the
depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts
a high-quality depth map and salient contour at the same time. And, the
resulted depth map can help existing RGB-D SOD methods obtain significant
performance gain.


title: A backing detector for order-keV neutrons

abstract:
  We have designed and tested a large-area (0.15~m$^2$) neutron detector based
on neutron capture on \ce{^{6}Li}. The neutron detector design has been
optimized for the purpose of tagging the scattering angle of keV-scale
neutrons. These neutron detectors would be employed to calibrate the low-energy
($<$100 eV) nuclear recoil in detectors for dark matter and coherent elastic
neutrino nucleus scattering (CE$\nu$NS). We describe the design, construction,
and characterization of a prototype. The prototype is designed to have a
tagging efficiency of $\sim$25\% at the relevant $\mathcal{O}$(keV) neutron
energies, and with a mean capture time of $\sim$17$~\mu$s. The prototype was
characterized using a \ce{^{252}Cf} neutron source and agreement with the
simulation was observed within a few percent level.


title: CTRW approximations for fractional equations with variable order

abstract:
  The standard diffusion processes are known to be obtained as the limits of
appropriate random walks. These prelimiting random walks can be quite different
however. The diffusion coefficient can be made responsible for the size of
jumps or for the intensity of jumps. The "rough" diffusion limit does not feel
the difference. The situation changes, if we model jump-type approximations via
CTRW with non-exponential waiting times. If we make the diffusion coefficient
responsible for the size of jumps and take waiting times from the domain of
attraction of an $\alpha$-stable law with a constant intensity $\al$, then the
standard scaling would lead in the limit of small jumps and large intensities
to the most standard fractional diffusion equation. However, if we choose the
CTRW approximations with fixed jump sizes and use the diffusion coefficient to
distinguish intensities at different points, then we obtain in the limit the
equations with variable position-dependent fractional derivatives. In this
paper we build rigorously these approximations and prove their convergence to
the corresponding fractional equations for the cases of multidimensional
diffusions and more general Feller processes.


title:
On the regularity of Dirichlet problem for fully non-linear elliptic
  equations on Hermitian manifolds

abstract:
  We derive the solvability and regularity of the Dirichlet problem for fully
non-linear elliptic equations possibly with degenerate right-hand side on
Hermitian manifolds, through establishing a quantitative version of boundary
estimate under a subsolution assumption. In addition, we construct the
subsolution when the background manifold is a product of a closed Hermitian
manifold with a compact Riemann surface with boundary.


title:
Convergence Rate Analysis of Galerkin Approximation of Inverse Potential
  Problem

abstract:
  In this work we analyze the inverse problem of recovering the space-dependent
potential coefficient in an elliptic / parabolic problem from distributed
observation. We establish novel (weighted) conditional stability estimates
under very mild conditions on the problem data. Then we provide an error
analysis of a standard reconstruction scheme based on the standard output
least-squares formulation with Tikhonov regularization (by an $H^1$-seminorm
penalty), which is then discretized by the Galerkin finite element method with
continuous piecewise linear finite elements in space (and also backward Euler
method in time for parabolic problems). We present a detailed analysis of the
discrete scheme, and provide convergence rates in a weighted $L^2(\Omega)$ for
discrete approximations with respect to the exact potential. The error bounds
are explicitly dependent on the noise level, regularization parameter and
discretization parameter(s). Under suitable conditions, we also derive error
estimates in the standard $L^2(\Omega)$ and interior $L^2$ norms. The analysis
employs sharp a priori error estimates and nonstandard test functions. Several
numerical experiments are given to complement the theoretical analysis.


title:
Calibration and localization of optically pumped magnetometers using
  electromagnetic coils

abstract:
  In this paper, we propose a method to estimate the position, orientation and
gain of a magnetic field sensor using a set of (large) electromagnetic coils.
We apply the method for calibrating an array of optically pumped magnetometers
(OPMs) for magnetoencephalography (MEG). We first measure the magnetic fields
of the coils at multiple known positions using a well-calibrated triaxial
magnetometer and model these discreetly sampled fields using vector-spherical
harmonics (VSH) functions. We then localize and calibrate a sensor by
minimizing the sum of squared errors between the model signals and the sensor
responses to the coil fields. We show that by using homogeneous and first-order
gradient fields, the sensor parameters (gain, position, and orientation) can be
obtained from a set of linear equations with pseudo-inverses of two matrices.
By determining the coil currents based on the VSH models, the coil fields can
approximate these low-order field components, yielding computationally simple
initial estimates of the sensor parameters. As a first test of the method, we
placed a fluxgate magnetometer at multiple positions and estimated the RMS
position, orientation, and gain errors of the method to be 1.0 mm, 0.2{\deg}
and 0.8%, respectively. Last, we calibrated a 48-channel OPM array. The
accuracy of the OPM calibration was tested by using the OPM array to localize
magnetic dipoles in a phantom, which resulted in an average position error of
3.3 mm. The results demonstrate the feasibility of using electromagnetic coils
to calibrate and localize OPMs for MEG.


title:
Algebraic deformations and Fourier--Mukai transforms for Calabi--Yau
  manifolds

abstract:
  Given a pair of derived-equivalent Calabi--Yau manifolds of dimension more
than two, we prove that the derived equivalence can be extended to general
fibers of versal deformations. As an application, we give a new proof of the
Pfaffian--Grassmannian derived equivalence.


title: Tensor networks for High Energy Physics: contribution to Snowmass 2021

abstract:
  Tensor network methods are becoming increasingly important for high-energy
physics, condensed matter physics and quantum information science (QIS). We
discuss the impact of tensor network methods on lattice field theory, quantum
gravity and QIS in the context of High Energy Physics (HEP). These tools will
target calculations for strongly interacting systems that are made difficult by
sign problems when conventional Monte Carlo and other importance sampling
methods are used. Further development of methods and software will be needed to
make a significant impact in HEP. We discuss the roadmap to perform quantum
chromodynamics (QCD) related calculations in the coming years. The research is
labor intensive and requires state of the art computational science and
computer science input for its development and validation. We briefly discuss
the overlap with other science domains and industry.


title:
A new implementation of the geometric method for solving the Eady slice
  equations

abstract:
  We present a new implementation of the geometric method of Cullen & Purser
(1984) for solving the semi-geostrophic Eady slice equations which model large
scale atmospheric flows and frontogenesis. The geometric method is a Lagrangian
discretisation, where the PDE is approximated by a particle system. An
important property of the discretisation is that it is energy conserving. We
restate the geometric method in the language of semi-discrete optimal transport
theory and exploit this to develop a fast implementation that combines the
latest results from numerical optimal transport theory with a novel adaptive
time-stepping scheme. Our results enable a controlled comparison between the
Eady-Boussinesq vertical slice equations and their semi-geostrophic
approximation. We provide further evidence that weak solutions of the
Eady-Boussinesq vertical slice equations converge to weak solutions of the
semi-geostrophic Eady slice equations as the Rossby number tends to zero.


title:
Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language
  Transfer Learning

abstract:
  Despite achieving state-of-the-art zero-shot performance, existing
vision-language models, e.g., CLIP, still fall short of domain-specific
classification tasks, e.g., Fungi Classification. In the context of few-shot
transfer learning, traditional fine-tuning fails to prevent highly expressive
model from exploiting spurious correlations in the training data. On the other
hand, although model-agnostic meta-learning (MAML) presents as a natural
alternative for transfer learning, the expensive computation due to implicit
second-order optimization limits its use in large-scale models and datasets. In
this work we aim to further improve the generalization of existing
vision-language models on unseen tasks via a simple yet efficient fine-tuning
strategy based on uniform task sampling. We term our method as Model-Agnostic
Multitask Fine-tuning (MAMF). Compared with MAML, MAMF discards the bi-level
optimization and uses only first-order gradients, which makes it easily
scalable and computationally efficient. Due to the uniform task sampling
procedure, MAMF consistently outperforms the classical fine-tuning method for
few-shot transfer learning on five benchmark datasets. Empirically, we further
discover that the effectiveness of first-order MAML is highly dependent on the
zero-shot performance of the pretrained model, and our simple algorithm can
outperform first-order MAML on more challenging datasets with low zero-shot
performance.


title: What Matters For Meta-Learning Vision Regression Tasks?

abstract:
  Meta-learning is widely used in few-shot classification and function
regression due to its ability to quickly adapt to unseen tasks. However, it has
not yet been well explored on regression tasks with high dimensional inputs
such as images. This paper makes two main contributions that help understand
this barely explored area. \emph{First}, we design two new types of
cross-category level vision regression tasks, namely object discovery and pose
estimation of unprecedented complexity in the meta-learning domain for computer
vision. To this end, we (i) exhaustively evaluate common meta-learning
techniques on these tasks, and (ii) quantitatively analyze the effect of
various deep learning techniques commonly used in recent meta-learning
algorithms in order to strengthen the generalization capability: data
augmentation, domain randomization, task augmentation and meta-regularization.
Finally, we (iii) provide some insights and practical recommendations for
training meta-learning algorithms on vision regression tasks. \emph{Second}, we
propose the addition of functional contrastive learning (FCL) over the task
representations in Conditional Neural Processes (CNPs) and train in an
end-to-end fashion. The experimental results show that the results of prior
work are misleading as a consequence of a poor choice of the loss function as
well as too small meta-training sets. Specifically, we find that CNPs
outperform MAML on most tasks without fine-tuning. Furthermore, we observe that
naive task augmentation without a tailored design results in underfitting.


title: Tracking single atoms in a liquid environment

abstract:
  The chemical behaviour of single metal atoms largely depends on the local
coordination environment, including interactions with the substrate and with
the surrounding gas or liquid. However, the key instrumentation for studying
such systems at the atomic scale generally requires high vacuum conditions,
limiting the degree to which the aforementioned environmental parameters can be
investigated. Here we develop a new platform for transmission electron
microscopy investigation of single metal atoms in liquids and study the dynamic
behaviour of individual platinum atoms on the surface of a single layer MoS2
crystal in water. To achieve the record single atom resolution, we introduce a
double liquid cell based on a 2D material heterostructure, which allows us to
submerge an atomically thin membrane with liquid on both sides while
maintaining the total specimen thickness of only ~ 70 nm. By comparison with an
identical specimen imaged under high vacuum conditions, we reveal drastic
differences in the single atom resting sites and atomic hopping behaviour,
demonstrating that in situ imaging conditions are essential to gain complete
understanding of the chemical activity of individual atoms. These findings pave
the way for in situ liquid imaging of chemical processes with single atom
precision.


title: Pose Guided Multi-person Image Generation From Text

abstract:
  Transformers have recently been shown to generate high quality images from
texts. However, existing methods struggle to create high fidelity full-body
images, especially multiple people. A person's pose has a high degree of
freedom that is difficult to describe using words only; this creates errors in
the generated image, such as incorrect body proportions and pose. We propose a
pose-guided text-to-image model, using pose as an additional input constraint.
Using the proposed Keypoint Pose Encoding (KPE) to encode human pose into low
dimensional representation, our model can generate novel multi-person images
accurately representing the pose and text descriptions provided, with minimal
errors. We demonstrate that KPE is invariant to changes in the target image
domain and image resolution; we show results on the Deepfashion dataset and
create a new multi-person Deepfashion dataset to demonstrate the
multi-capabilities of our approach.


title:
Rethinking data-driven point spread function modeling with a
  differentiable optical model

abstract:
  In astronomy, upcoming space telescopes with wide-field optical instruments
have a spatially varying point spread function (PSF). Certain scientific goals
require a high-fidelity estimation of the PSF at target positions where no
direct measurement of the PSF is provided. Even though observations of the PSF
are available at some positions of the field of view (FOV), they are
undersampled, noisy, and integrated in wavelength in the instrument's passband.
PSF modeling requires building a model from these observations that can infer a
super-resolved PSF at any wavelength and any position in the FOV. Current
data-driven PSF models can tackle spatial variations and super-resolution, but
are not capable of capturing chromatic variations. Our model, coined WaveDiff,
proposes a paradigm shift in the data-driven modeling of the point spread
function field of telescopes. By adding a differentiable optical forward model
into the modeling framework, we change the data-driven modeling space from the
pixels to the wavefront. The proposed model relies on efficient automatic
differentiation technology as well as modern stochastic first-order
optimization techniques recently developed by the thriving machine-learning
community. Our framework paves the way to building powerful models that are
physically motivated and do not require special calibration data. This paper
demonstrates the WaveDiff model on a simplified setting of a space telescope.
The proposed framework represents a performance breakthrough with respect to
existing data-driven approaches. The pixel reconstruction errors decrease
6-fold at observation resolution and 44-fold for a 3x super-resolution. The
ellipticity errors are reduced by a factor of at least 20 and the size error by
a factor of more than 250. By only using noisy broad-band in-focus
observations, we successfully capture the PSF chromatic variations due to
diffraction.


title: Study of two color QCD on large lattices

abstract:
  We study two colors lattice QCD (QC$_2$D) with two flavors of staggered
fermions on $40^4$ and $32^4$ lattices with lattice spacing $a =0.048$~fm in
the wide range of the quark chemical potential $\mu_q$. Our focus is on the
confinement-deconfinement transition in this theory. Thus we compute the string
tension from the Wilson loops and the static quark free energy from the
Polyakov loops. We find that the deconfinement transition found earlier in the
range $\mu_q \approx 800 - 1000$ MeV is shifted to higher values. This shift is
attributed to decreasing of the lattice spacing used in our simulations in
comparison with the earlier study.


title:
BaM: A Case for Enabling Fine-grain High Throughput GPU-Orchestrated
  Access to Storage

abstract:
  Accelerators like Graphics Processing Units (GPUs) have been increasingly
deployed in modern data centers because of their compute capabilities and
memory bandwidth. These accelerators have traditionally relied on the
"application host code" and the OS running on the CPU to orchestrate their
access to the data storage devices. CPU orchestration of storage data accesses
works well for classic GPU applications, like dense neural network training,
where data access patterns are predefined, regular, dense, and independent of
the data values, enabling the CPU to partition the storage data into
coarse-grain chunks and coordinate the storage device accesses and data
transfers to the accelerators. Unfortunately, such a CPU-centric strategy
causes excessive CPU-GPU synchronization overhead and/or I/O traffic
amplification, diminishing the effective storage bandwidth for emerging
applications with fine-grain data-dependent access patterns like graph and data
analytics, recommender systems, and graph neural networks. In this work, we
make a case for enabling GPUs to orchestrate high-throughput, fine-grain
accesses into NVMe Solid State Drives (SSDs) in a new system architecture
called BaM. BaM mitigates the I/O traffic amplification by enabling the GPU
threads to read or write small amounts of data on-demand, as determined by the
compute. We show that (1) the BaM infrastructure software running on GPUs can
identify and communicate the fine-grain accesses at a sufficiently high rate to
fully utilize the underlying storage devices, (2) even with consumer-grade
SSDs, a BaM system can support application performance that is competitive
against a much more expensive DRAM-only solution, and (3) the reduction in I/O
amplification can yield significant performance benefit.


title:
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken
  Question Answering

abstract:
  Spoken Question Answering (SQA) is to find the answer from a spoken document
given a question, which is crucial for personal assistants when replying to the
queries from the users. Existing SQA methods all rely on Automatic Speech
Recognition (ASR) transcripts. Not only does ASR need to be trained with
massive annotated data that are time and cost-prohibitive to collect for
low-resourced languages, but more importantly, very often the answers to the
questions include name entities or out-of-vocabulary words that cannot be
recognized correctly. Also, ASR aims to minimize recognition errors equally
over all words, including many function words irrelevant to the SQA task.
Therefore, SQA without ASR transcripts (textless) is always highly desired,
although known to be very difficult.
  This work proposes Discrete Spoken Unit Adaptive Learning (DUAL), leveraging
unlabeled data for pre-training and fine-tuned by the SQA downstream task. The
time intervals of spoken answers can be directly predicted from spoken
documents. We also release a new SQA benchmark corpus, NMSQA, for data with
more realistic scenarios. We empirically showed that DUAL yields results
comparable to those obtained by cascading ASR and text QA model and robust to
real-world data. Our code and model will be open-sourced.


title:
Comparing Classical-Quantum Portfolio Optimization with Enhanced
  Constraints

abstract:
  One of the problems frequently mentioned as a candidate for quantum advantage
is that of selecting a portfolio of financial assets to maximize returns while
minimizing risk. In this paper we formulate several real-world constraints for
use in a Quantum Annealer (QA), extending the scenarios in which the algorithm
can be implemented. Specifically, we show how to add fundamental analysis to
the portfolio optimization problem, adding in asset-specific and global
constraints based on chosen balance sheet metrics. We also expand on previous
work in improving the constraint to enforce investment bands in sectors and
limiting the number of assets to invest in, creating a robust and flexible
solution amenable to QA.
  Importantly, we analyze the current state-of-the-art algorithms for solving
such a problem using D-Wave's Quantum Processor and compare the quality of the
solutions obtained to commercially-available optimization software. We explore
a variety of traditional and new constraints that make the problem
computationally harder to solve and show that even with these additional
constraints, classical algorithms outperform current hybrid solutions in the
static portfolio optimization model.


title:
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep
  Classifiers

abstract:
  Algorithmic fairness is frequently motivated in terms of a trade-off in which
overall performance is decreased so as to improve performance on disadvantaged
groups where the algorithm would otherwise be less accurate. Contrary to this,
we find that applying existing fairness approaches to computer vision improve
fairness by degrading the performance of classifiers across all groups (with
increased degradation on the best performing groups).
  Extending the bias-variance decomposition for classification to fairness, we
theoretically explain why the majority of fairness classifiers designed for low
capacity models should not be used in settings involving high-capacity models,
a scenario common to computer vision. We corroborate this analysis with
extensive experimental support that shows that many of the fairness heuristics
used in computer vision also degrade performance on the most disadvantaged
groups. Building on these insights, we propose an adaptive augmentation
strategy that, uniquely, of all methods tested, improves performance for the
disadvantaged groups.


title: Interfacial water asymmetry at ideal electrochemical interfaces

abstract:
  Controlling electrochemical reactivity requires a detailed understanding of
the charging behavior and thermodynamics of the electrochemical interface.
Experiments can independently probe the overall charge response of the
electrochemical double layer by capacitance measurements, and the
thermodynamics of the inner layer with potential of maximum entropy (PME)
measurements. Relating these properties by computational modeling of the
electrochemical interface has so far been challenging due to the low accuracy
of classical molecular dynamics (MD) for capacitance and the limited time and
length scales of \emph{ab initio} MD (AIMD). Here, we combine large ensembles
of long-time-scale classical MD simulations with charge response from
electronic density functional theory (DFT) to predict the potential-dependent
capacitance of a family of ideal aqueous electrochemical interfaces with
different peak capacitances. We show that, while the potential of maximum
capacitance varies, this entire family exhibits an electrode charge of maximum
capacitance (CMC) between -3.7 $\mu$C/cm$^2$ and -3.3 $\mu$C/cm$^2$, regardless
of details in the electronic response. Simulated heating of the same interfaces
reveals that the entropy peaks at a charge of maximum entropy (CME) of $-6.4
\pm 0.7~\mu$C/cm$^2$, in agreement with experimental findings for metallic
electrodes. The CME and CMC both indicate asymmetric response of interfacial
water that is stronger for negatively charged electrodes, while the difference
between CME and CMC illustrates the richness in behavior of even the ideal
electrochemical interface.


title: A novel method for adaptive control of deformable mirrors

abstract:
  For sufficiently wide ranges of applied control signals (control voltages),
MEMS and piezoelectric Deformable Mirrors (DMs), exhibit nonlinear behavior.
The nonlinear behavior manifests itself in nonlinear actuator couplings,
nonlinear actuator deformation characteristics, and in the case of
piezoelectric DMs, hysteresis. Furthermore, in a number of situations, DM
behavior can change over time, and this requires a procedure for updating the
DM models on the basis of the observed data. If not properly modeled and if not
taken into account when designing control algorithms, nonlinearities, and
time-varying DM behavior, can significantly degrade the achievable closed-loop
performance of Adaptive Optics (AO) systems. Widely used approaches for DM
control are based on pre-estimated linear time-invariant DM models in the form
of influence matrices. Often, these models are not being updated during system
operation. Consequently, when the nonlinear DM behavior is excited by control
signals with wide operating ranges, or when the DM behavior changes over time,
the state-of-the-art DM control approaches relying upon linear control methods,
might not be able to produce a satisfactory closed-loop performance of an AO
system. Motivated by these key facts, we present a novel method for data-driven
DM control. Our approach combines a simple open-loop control method with a
recursive least squares method for dynamically updating the DM model. The DM
model is constantly being updated on the basis of the dynamically changing DM
operating points. That is, the proposed method updates both the control actions
and the DM model during the system operation. We experimentally verify this
approach on a Boston Micromachines MEMS DM with 140 actuators. Preliminary
experimental results reported in this manuscript demonstrate good potential for
using the developed method for DM control.


title:
Monitoring Time Series With Missing Values: a Deep Probabilistic
  Approach

abstract:
  Systems are commonly monitored for health and security through collection and
streaming of multivariate time series. Advances in time series forecasting due
to adoption of multilayer recurrent neural network architectures make it
possible to forecast in high-dimensional time series, and identify and classify
novelties early, based on subtle changes in the trends. However, mainstream
approaches to multi-variate time series predictions do not handle well cases
when the ongoing forecast must include uncertainty, nor they are robust to
missing data. We introduce a new architecture for time series monitoring based
on combination of state-of-the-art methods of forecasting in high-dimensional
time series with full probabilistic handling of uncertainty. We demonstrate
advantage of the architecture for time series forecasting and novelty
detection, in particular with partially missing data, and empirically evaluate
and compare the architecture to state-of-the-art approaches on a real-world
data set.


title: Anosov Flows and Dynamical Zeta Functions (Errata)

abstract:
  This errata fixes a mistake in the part of Giulietti, P.; Liverani, C.;
Pollicott, M. Anosov flows and dynamical zeta functions. Ann. of Math. (2) {\bf
178} (2013), no. 2, 687--773, which proves a spectral gap for contact Anosov
flows with respect to the measure of maximal entropy (Section 7). However, the
first part of the paper, in which it is proved that the Ruelle zeta function is
meromorphic, is unaffected.


title: Bouncing Cosmology in Fourth-Order Gravity

abstract:
  The Big Bang initial singularity problem can be solved by means of bouncing
solutions. In the context of extended theories of gravity, we will look for
covariant effective actions whose field equations contain up to fourth-order
derivatives of the metric tensor. In finding such bouncing solutions, we will
make use of an order reduction technique based on a perturbative approach.
Reducing the order of the field equations to second-order, we are able to find
solutions which are perturbatively close to General Relativity. We will build
the covariant effective actions of the resulting order reduced theories.


title: Eigenvalue spacing for 1D singular Schr\"odinger operators

abstract:
  The aim of this paper is to provide uniform estimates for the eigenvalue
spacings of one-dimensional semiclassical Schr\"odinger operators with singular
potentials on the half-line. We introduce a new development of semiclassical
measures related to families of Schr\"odinger operators that provides a means
of establishing uniform non-concentration estimates within that class of
operators. This dramatically simplifies analysis that would typically require
detailed WKB expansions near the turning point, near the singular point and
several gluing type results to connect various regions in the domain.


title:
Why Interpretable Causal Inference is Important for High-Stakes Decision
  Making for Critically Ill Patients and How To Do It

abstract:
  Many fundamental problems affecting the care of critically ill patients lead
to similar analytical challenges: physicians cannot easily estimate the effects
of at-risk medical conditions or treatments because the causal effects of
medical conditions and drugs are entangled. They also cannot easily perform
studies: there are not enough high-quality data for high-dimensional
observational causal inference, and RCTs often cannot ethically be conducted.
However, mechanistic knowledge is available, including how drugs are absorbed
into the body, and the combination of this knowledge with the limited data
could potentially suffice -- if we knew how to combine them. In this work, we
present a framework for interpretable estimation of causal effects for
critically ill patients under exactly these complex conditions: interactions
between drugs and observations over time, patient data sets that are not large,
and mechanistic knowledge that can substitute for lack of data. We apply this
framework to an extremely important problem affecting critically ill patients,
namely the effect of seizures and other potentially harmful electrical events
in the brain (called epileptiform activity -- EA) on outcomes. Given the high
stakes involved and the high noise in the data, interpretability is critical
for troubleshooting such complex problems. Interpretability of our matched
groups allowed neurologists to perform chart reviews to verify the quality of
our causal analysis. For instance, our work indicates that a patient who
experiences a high level of seizure-like activity (75% high EA burden) and is
untreated for a six-hour window, has, on average, a 16.7% increased chance of
adverse outcomes such as severe brain damage, lifetime disability, or death. We
find that patients with mild but long-lasting EA (average EA burden >= 50%)
have their risk of an adverse outcome increased by 11.2%.


title:
The Severity Prediction of The Binary And Multi-Class Cardiovascular
  Disease -- A Machine Learning-Based Fusion Approach

abstract:
  In today's world, a massive amount of data is available in almost every
sector. This data has become an asset as we can use this enormous amount of
data to find information. Mainly health care industry contains many data
consisting of patient and disease-related information. By using the machine
learning technique, we can look for hidden data patterns to predict various
diseases. Recently CVDs, or cardiovascular disease, have become a leading cause
of death around the world. The number of death due to CVDs is frightening. That
is why many researchers are trying their best to design a predictive model that
can save many lives using the data mining model. In this research, some fusion
models have been constructed to diagnose CVDs along with its severity. Machine
learning(ML) algorithms like artificial neural network, SVM, logistic
regression, decision tree, random forest, and AdaBoost have been applied to the
heart disease dataset to predict disease. Randomoversampler was implemented
because of the class imbalance in multiclass classification. To improve the
performance of classification, a weighted score fusion approach was taken. At
first, the models were trained. After training, two algorithms' decision was
combined using a weighted sum rule. A total of three fusion models have been
developed from the six ML algorithms. The results were promising in the
performance parameter. The proposed approach has been experimented with
different test training ratios for binary and multiclass classification
problems, and for both of them, the fusion models performed well. The highest
accuracy for multiclass classification was found as 75%, and it was 95% for
binary. The code can be found in :
https://github.com/hafsa-kibria/Weighted_score_fusion_model_heart_disease_prediction


title:
On the effect of fast rotation and vertical viscosity on the lifespan of
  the $3D$ primitive equations

abstract:
  We study the effect of the fast rotation and vertical viscosity on the
lifespan of solutions to the three-dimensional primitive equations (also known
as the hydrostatic Navier-Stokes equations) with impermeable and stress-free
boundary conditions. Firstly, for a short time interval, independent of the
rate of rotation $|\Omega|$, we establish the local well-posedness of solutions
with initial data that is analytic in the horizontal variables and only $L^2$
in the vertical variable. Moreover, it is shown that the solutions immediately
become analytic in all the variables with increasing-in-time (at least
linearly) radius of analyticity in the vertical variable for as long as the
solutions exist. On the other hand, the radius of analyticity in the horizontal
variables might decrease with time, but as long as it remains positive the
solution exists. Secondly, with fast rotation, i.e., large $|\Omega|$, we show
that the existence time of the solution can be prolonged, with "well-prepared"
initial data. Finally, in the case of two spatial dimensions with $\Omega=0$,
we establish the global well-posedness provided that the initial data is small
enough. The smallness condition on the initial data depends on the vertical
viscosity and the initial radius of analyticity in the horizontal variables.