title:
Rotation measure and synchrotron emission signatures in simulations of
  magnetized galactic discs

abstract:
  We analyse observational signatures of magnetic fields for simulations of a
Milky-Way like disc with supernova-driven interstellar turbulence and
self-consistent chemical processes. In particular, we post-process two
simulations data sets of the SILCC Project for two initial amplitudes of the
magnetic field, $B_0 = $ 3 and 6 $\mu$G, to study the evolution of Faraday
rotation measures (RM) and synchrotron luminosity. For calculating the RM,
three different models of the electron density $n_e$ are considered. A constant
electron density, and two estimations based on the density of ionized species
and the fraction of the total gas, respectively. Our results show that the RM
profiles are extremely sensitive to the $n_e$ models, which assesses the
importance of accurate electron distribution observations/estimations for the
magnetic fields to be probed using Faraday RMs. As a second observable of the
magnetic field, we estimate the synchrotron luminosity in the simulations using
a semi-analytical cosmic ray model. We find that the synchrotron luminosity
decreases over time, which is connected to the decay of magnetic energy in the
simulations. The ratios between the magnetic, the cosmic ray, and the thermal
energy density indicate that the assumption of equipartition does not hold for
most regions of the ISM. In particular, for the ratio of the cosmic ray to the
magnetic energy the assumption of equipatition could lead to a wrong
interpretation of the observed synchrotron emission.


title:
Patchy Kinetic Sunyaev-Zel'dovich Effect with Controlled Reionization
  History and Morphology

abstract:
  Using the novel semi-numerical code for reionization AMBER, we model the
patchy kinetic Sunyaev-Zel'dovich (kSZ) effect by directly specifying the
reionization history with the redshift midpoint $z_\mathrm{mid}$, duration
$\Delta_\mathrm{z}$, and asymmetry $A_\mathrm{z}$. We further control the
ionizing sources and radiation through the minimum halo mass $M_\mathrm{h}$ and
the radiation mean free path $\lambda_\mathrm{mfp}$. AMBER reproduces the free
electron number density and the patchy kSZ power spectrum of
radiation-hydrodynamic simulations at the target resolution ($1\,{\rm Mpc}/h$)
with matched reionization parameters. With a suite of $(2\,{\rm Gpc}/h)^3$
simulations using AMBER, we first constrain the redshift midpoint $6.0<z_{\rm
mid}<8.9$ using the Planck2018 Thomson optical depth result (95\% CL). Then,
assuming $z_{\rm mid}=8$, we find that the amplitude of $D^{\rm
pkSZ}_{\ell=3000}$ scales linearly with the duration of reionization
$\Delta_z$, and is consistent with the $1\sigma$ upper limit from the South
Pole Telescope (SPT) results up to $\Delta_z<5.1$ ($\Delta_z$ encloses $5\%$ to
$95\%$ ionization). Moreover, a shorter $\lambda_{\rm mfp}$ can lead to a $\sim
10\%$ lower $D^{\rm pkSZ}_{\ell=3000}$ and a flatter slope in the
$\Delta_z-D^{\rm pkSZ}_{\ell=3000}$ scaling relation, thereby affecting the
constraints on $\Delta_z$ at $\ell=3000$. Allowing $z_{\rm mid}$ and
$\lambda_{\rm mfp}$ to vary simultaneously, we get spectra consistent with the
SPT result ($95\%$ CL) up to $\Delta_z=12.8$ (but $A_z>8$ is needed to ensure
an end of reionization before $z=5.5$). We show that constraints on the
asymmetry require $\sim 0.1\,\mu k^2$ measurement accuracy at multipoles other
than $\ell=3000$. Finally, we find that the amplitude and shape of the kSZ
spectrum are only weakly sensitive to $M_h$ under a fixed reionization history
and radiation mean-free path.


title:
Experimental Realization of a Measurement-Induced Entanglement Phase
  Transition on a Superconducting Quantum Processor

abstract:
  Ergodic quantum many-body systems undergoing unitary dynamics evolve towards
increasingly entangled states characterized by an extensive scaling of
entanglement entropy with system volume. At the other extreme, quantum systems
repeatedly measured may be stabilized in a measurement eigenstate, a phenomenon
known as the quantum Zeno effect. Recently, the intermediate regime in which
unitary evolution is interspersed with quantum measurements has become of
interest. Numerical studies have reported the existence of distinct phases
characterized by volume- and area-law entanglement entropy scaling for
infrequent and frequent measurement rates, respectively, separated by a
critical measurement rate. The experimental investigation of these dynamic
quantum phases of matter on near-term quantum hardware is challenging due to
the need for repeated high-fidelity mid-circuit measurements and fine control
over the evolving unitaries. Here, we report the realization of a
measurement-induced entanglement transition on superconducting quantum
processors with mid-circuit readout capability. We directly observe extensive
and sub-extensive scaling of entanglement entropy in the volume- and area-law
phases, respectively, by varying the rate of projective measurements. We
further establish the critical nature of the entanglement transition by
extracting the critical exponents. Our work paves the way for the use of
mid-circuit measurement as an effective resource for quantum simulation on
near-term quantum computers, for instance by facilitating the study of dynamic
and long-range entangled quantum phases.


title:
Replicated Entanglement Negativity for Disjoint Intervals in the Ising
  and Free Compact Boson Conformal Field Theories

abstract:
  We calculate the $N$ interval replicated negativity for the Ising and free
compact boson conformal field theory using correlation functions of branch
point twist fields. For some subset $P\subset N$, this is a calculation of
$\rm{Tr}(\rho_A^{T_P})^n$ where $n$ is the replica index. This can be
reformulated as a calculation of partition functions over superelliptic Riemann
surfaces, and for the models in question, this partition function can be
expressed in terms of the period matrix of this surface. We detail how to
construct the period matrices for these surfaces, giving an analytic expression
for $\rm{Tr}(\rho_A^{T_P})^n$. The results are expressed such that when $P =
\emptyset$, which corresponds to calculating the R\'enyi entropy, the formulas
aligns with known results for the $N$ interval R\'enyi entropy.


title: Modular Parity Quantum Approximate Optimization

abstract:
  The parity transformation encodes spin models in the low-energy subspace of a
larger Hilbert-space with constraints on a planar lattice. Applying the Quantum
Approximate Optimization Algorithm (QAOA), the constraints can either be
enforced explicitly, by energy penalties, or implicitly, by restricting the
dynamics to the low-energy subspace via the driver Hamiltonian. While the
explicit approach allows for parallelization with a system-size-independent
circuit depth, the implicit approach shows better QAOA performance. Here we
combine the two approaches in order to improve the QAOA performance while
keeping the circuit parallelizable. In particular, we introduce a modular
parallelization method that partitions the circuit into clusters of subcircuits
with fixed maximal circuit depth, relevant for scaling up to large system
sizes.


title:
Change-point Detection and Segmentation of Discrete Data using Bayesian
  Context Trees

abstract:
  A new Bayesian modelling framework is introduced for piece-wise homogeneous
variable-memory Markov chains, along with a collection of effective algorithmic
tools for change-point detection and segmentation of discrete time series.
Building on the recently introduced Bayesian Context Trees (BCT) framework, the
distributions of different segments in a discrete time series are described as
variable-memory Markov chains. Inference for the presence and location of
change-points is then performed via Markov chain Monte Carlo sampling. The key
observation that facilitates effective sampling is that, using one of the BCT
algorithms, the prior predictive likelihood of the data can be computed
exactly, integrating out all the models and parameters in each segment. This
makes it possible to sample directly from the posterior distribution of the
number and location of the change-points, leading to accurate estimates and
providing a natural quantitative measure of uncertainty in the results.
Estimates of the actual model in each segment can also be obtained, at
essentially no additional computational cost. Results on both simulated and
real-world data indicate that the proposed methodology performs better than or
as well as state-of-the-art techniques.


title:
Non-ideal fields solve the injection problem in relativistic
  reconnection

abstract:
  Magnetic reconnection in relativistic plasmas is well established as a fast
and efficient particle accelerator, capable of explaining the most dramatic
astrophysical flares. With particle-in-cell simulations, we demonstrate the
importance of non-ideal fields for the early stages ("injection") of particle
acceleration. Most of the particles ending up with high energies (near or above
the mean magnetic energy per particle) must have passed through non-ideal
regions where the assumptions of ideal magnetohydrodynamics are broken (i.e.,
regions with $E>B$ or nonzero $E_\parallel={\bf E}\cdot {\bf B}/B$), whereas
particles that do not experience non-ideal fields end up with Lorentz factors
of order unity. Thus, injection by non-ideal fields is a necessary prerequisite
for further acceleration. Our results have important implications for the
origin of nonthermal particles in high-energy astrophysical sources.


title:
Multi-Center Magnon Excitations Open the Entire Brillouin Zone to
  Terahertz Magnetometry of Quantum Magnets

abstract:
  Due to the small photon momentum, optical spectroscopy commonly probes
magnetic excitations only at the center of the Brillouin zone; however, there
are ways to override this restriction. In the case of the distorted kagome
quantum magnet Y-kapellasite, Y$_3$Cu$_9$(OH)$_{19}$Cl$_8$, under scrutiny
here, the magnon density of states can be accessed over the entire Brillouin
zone through three-center magnon excitations. This mechanism is aided by the
three different magnetic sublattices and strong short-range correlations in the
distorted kagome lattice. The results of THz time-domain experiments agree
remarkably well with linear spin-wave theory. Relaxing the conventional
zone-center constraint of photons gives a new aspect to probe magnetism in
matter.


title: Accretion of the magnetized neutrino-cooled torus

abstract:
  Neutrino-cooled accretion flow around a black hole, produced by a compact
binary merger, is a promising scenario for jet formation and magnetic-driven
winds to explain short duration gamma ray bursts (GRBs) central engine and
kilonovae based on GW170817 gravitational wave observation. Magnetorotational
instability (MRI) turbulence and Blandford-Znajek (BZ) mechanism are expected
to play key roles in the thermal equilibrium of the disk (balancing neutrino
cooling) and in driving accretion and creating jets. Using the open-source
GRMHD HARM-COOL code, we study the magnetically-driven evolution of an
accretion disk with realistic equation of state in the fixed curved space-time
background. We identify the effects of the neutrino cooling and the magnetic
field, paying particular attention to the dynamical, thermal and composition
evolution of the disk and outflows.


title: On the regular 2-connected 2-path Hamiltonian graphs

abstract:
  A graph $G$ is $l$-path Hamiltonian if every path of length not exceeding $l$
is contained in a Hamiltonian cycle. It is well known that a 2-connected,
$k$-regular graph $G$ on at most $3k-1$ vertices is edge-Hamiltonian if for
every edge $uv$ of $G$, $\{u,v\}$ is not a cut-set. Thus $G$ is 1-path
Hamiltonian if $G\setminus \{u,v\}$ is connected for every edge $uv$ of $G$.
Let $P=uvz$ be a 2-path of a 2-connected, $k$-regular graph $G$ on at most $2k$
vertices. In this paper, we show that there is a Hamiltonian cycle containing
the 2-path $P$ if $G\setminus V(P)$ is connected. Therefore, the work implies a
condition for a 2-connected, $k$-regular graph to be 2-path Hamiltonian. An
example shows that the $2k$ is almost sharp, i.e., the number is at most
$2k+1$.


title: Unique continuation for $\bar\partial$ with square-integrable potentials

abstract:
  In this paper, we investigate the unique continuation property for the
inequality $|\bar\partial u| \le V|u|$, where $u$ is a vector-valued function
from a domain in $\mathbb C^n$ to $\mathbb C^N$, and the potential $V\in L^2$.
We show that the strong unique continuation property holds when $n=1$, and the
weak unique continuation property holds when $n\ge 2$. In both cases, the $L^2$
integrability condition on the potential is optimal.


title: An accurate IoT Intrusion Detection Framework using Apache Spark

abstract:
  The internet has caused tremendous changes since its appearance in the 1980s,
and now, the Internet of Things (IoT) seems to be doing the same. The potential
of IoT has made it the center of attention for many people, but, where some see
an opportunity to contribute, others may see IoT networks as a target to be
exploited. The high number of IoT devices makes them the perfect setup for
staging denial-of-service attacks (DoS) that can have devastating consequences.
This renders the need for cybersecurity measures such as intrusion detection
systems (IDSs) evident. The aim of this paper is to build an IDS using the big
data platform, Apache Spark. Apache Spark was used along with its ML library
(MLlib) and the BoT-IoT dataset. The IDS was then tested and evaluated based on
F-Measure (f1), as was the standard when evaluating imbalanced data. Two rounds
of tests were performed, a partial dataset for minimizing bias, and the full
BoT-IoT dataset for exploring big data and ML capabilities in a security
setting. For the partial dataset, the Random Forest algorithm had the highest
performance for binary classification at an average f1 measure of 99.7%, as
well as 99.6% for main category classification, and an 88.5% f1 measure for sub
category classification. As for the complete dataset, the Decision Tree
algorithm scored the highest f1 measures for all conducted tests; 97.9% for
binary classification, 79% for main category classification, and 77% for sub
category classification.


title:
Feasibility Guaranteed Traffic Merging Control Using Control Barrier
  Functions

abstract:
  We consider the merging control problem for Connected and Automated Vehicles
(CAVs) aiming to jointly minimize travel time and energy consumption while
providing speed-dependent safety guarantees and satisfying velocity and
acceleration constraints. Applying the joint optimal control and control
barrier function (OCBF) method, a controller that optimally tracks the
unconstrained optimal control solution while guaranteeing the satisfaction of
all constraints is efficiently obtained by transforming the optimal tracking
problem into a sequence of quadratic programs (QPs). However, these QPs can
become infeasible, especially under tight control bounds, thus failing to
guarantee safety constraints. We solve this problem by deriving a
control-dependent feasibility constraint corresponding to each CBF constraint
which is added to each QP and we show that each such modified QP is guaranteed
to be feasible. Extensive simulations of the merging control problem illustrate
the effectiveness of this feasibility guaranteed controller.


title: Three-point energy correlator in $\mathcal{N}=4$ super Yang-Mills Theory

abstract:
  An analytic formula is given for the three-point energy correlator (EEEC) at
leading order (LO) in maximally supersymmetric Yang-Mills theory
($\mathcal{N}=4$ sYM). This is the first analytic calculation of a
three-parameter event shape observable, which provides valuable data for
various studies ranging from conformal field theories to jet substructure. The
associated class of functions define a new type of single-valued polylogarithms
characterized by 16 alphabet letters, which manifest a $D_6 \times Z_2$
dihedral symmetry of the event shape. With the unexplored simplicity in the
perturbative structure of EEEC, all kinematic regions including collinear,
squeezed and coplanar limits are now available.


title: Beam Search for Feature Selection

abstract:
  In this paper, we present and prove some consistency results about the
performance of classification models using a subset of features. In addition,
we propose to use beam search to perform feature selection, which can be viewed
as a generalization of forward selection. We apply beam search to both
simulated and real-world data, by evaluating and comparing the performance of
different classification models using different sets of features. The results
demonstrate that beam search could outperform forward selection, especially
when the features are correlated so that they have more discriminative power
when considered jointly than individually. Moreover, in some cases
classification models could obtain comparable performance using only ten
features selected by beam search instead of hundreds of original features.


title: Riemann-Roch theorems in monoidal 2-categories

abstract:
  We prove a generalized 2-categorical Riemann-Roch theorem that recovers those
of Shklyarov and Petit as well as spectral generalizations. The theorem is a
compatibility result between traces and pairings under certain 2-categorical
dualizability and compactness conditions. One can view the result as an
instantiation of a 2-dimensional generalized cobordism hypothesis. This
continues the authors' program of recognizing structural results about
Hochschild homology as arising quite generally from traces in 2-category
theory.


title: TTML: tensor trains for general supervised machine learning

abstract:
  This work proposes a novel general-purpose estimator for supervised machine
learning (ML) based on tensor trains (TT). The estimator uses TTs to
parametrize discretized functions, which are then optimized using Riemannian
gradient descent under the form of a tensor completion problem. Since this
optimization is sensitive to initialization, it turns out that the use of other
ML estimators for initialization is crucial. This results in a competitive,
fast ML estimator with lower memory usage than many other ML estimators, like
the ones used for the initialization.


title: Unrolled Primal-Dual Networks for Lensless Cameras

abstract:
  Conventional image reconstruction models for lensless cameras often assume
that each measurement results from convolving a given scene with a single
experimentally measured point-spread function. These image reconstruction
models fall short in simulating lensless cameras truthfully as these models are
not sophisticated enough to account for optical aberrations or scenes with
depth variations. Our work shows that learning a supervised primal-dual
reconstruction method results in image quality matching state of the art in the
literature without demanding a large network capacity. This improvement stems
from our primary finding that embedding learnable forward and adjoint models in
a learned primal-dual optimization framework can even improve the quality of
reconstructed images (+5dB PSNR) compared to works that do not correct for the
model error. In addition, we built a proof-of-concept lensless camera prototype
that uses a pseudo-random phase mask to demonstrate our point. Finally, we
share the extensive evaluation of our learned model based on an open dataset
and a dataset from our proof-of-concept lensless camera prototype.


title: Theory of entanglement and measurement in high harmonic generation

abstract:
  Quantum information science and intense laser matter interaction are two
apparently unrelated fields. However, the recent developments of the quantum
optical description of the intense laser driven process of high harmonic
generation allow to conceive new light engineering protocols. Here, we
introduce the notion of quantum information theory to intense laser driven
processes by providing the quantum mechanical description of measurement
protocols for high harmonic generation in atoms. We explicitly evaluate
conditioning experiments on individual optical field modes, and provide the
corresponding quantum operation for coherent states. The associated positive
operator-valued measures are obtained, and give rise to the quantum theory of
measurement for the generation of high dimensional entangled states, and
coherent state superposition with controllable non-classical features on the
attosecond timescale.


title: Cavity-Enhanced Raman Scattering from 2D Hybrid Perovskites

abstract:
  Two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs) are
promising candidates for optoelectronic applications due to their efficient
light emission properties, and strong dielectric confinement effects. Raman
spectroscopy is a versatile, non-contact and often non-destructive technique,
widely used to characterize crystalline materials. However, the inherently weak
phonon scattering, strong background, and complex nature of the Raman signals
from HOIPs present challenges in obtaining reliable signals. Further, the
fragile nature of the 2D HOIP crystals results in rapid degradation upon
exposure to heat, light and moisture, which presents further difficulty in
enhancing Raman scattered photon signals. Herein, we report a novel approach to
enhance the weak Raman scattering signals in Ruddlesden-Popper (RP) phase HOIPs
by introducing an open-cavity comprising HOIP crystals on a gold substrate. We
observe 15x enhancement of the Raman signals due to the Purcell effect inside
the high-index HOIP crystals. Our simple approach can be extended to enhance
the study of phonon scattering in other HOIP and van der Waals layered
crystals.


title:
Which side are you on? Insider-Outsider classification in
  conspiracy-theoretic social media

abstract:
  Social media is a breeding ground for threat narratives and related
conspiracy theories. In these, an outside group threatens the integrity of an
inside group, leading to the emergence of sharply defined group identities:
Insiders -- agents with whom the authors identify and Outsiders -- agents who
threaten the insiders. Inferring the members of these groups constitutes a
challenging new NLP task: (i) Information is distributed over many
poorly-constructed posts; (ii) Threats and threat agents are highly contextual,
with the same post potentially having multiple agents assigned to membership in
either group; (iii) An agent's identity is often implicit and transitive; and
(iv) Phrases used to imply Outsider status often do not follow common negative
sentiment patterns. To address these challenges, we define a novel
Insider-Outsider classification task. Because we are not aware of any
appropriate existing datasets or attendant models, we introduce a labeled
dataset (CT5K) and design a model (NP2IO) to address this task. NP2IO leverages
pretrained language modeling to classify Insiders and Outsiders. NP2IO is shown
to be robust, generalizing to noun phrases not seen during training, and
exceeding the performance of non-trivial baseline models by $20\%$.


title:
Solar H{\alpha} excess during Solar Cycle 24 from full-disk filtergrams
  of the Chromospheric Telescope

abstract:
  The chromospheric H-alpha spectral line is a strong line in the spectrum of
the Sun and other stars. In the stellar regime, this spectral line is already
used as a powerful tracer of stellar activity. For the Sun, other tracers (i.e,
CaII K) are typically used to monitor solar activity. We used observations of
full-disk H-alpha filtergrams of the Chromospheric Telescope (ChroTel) to
extract the imaging H-alpha excess and deficit, which are related to bright
features (plage regions) and dark absorption features (filaments and sunspots),
respectively. The aim of this study is to introduce the imaging H-alpha excess
and deficit as tracers of solar activity and compare them to other established
indicators: the relative sunspot number, the F10.7cm radio flux, and the MgII
index. The H-alpha excess and deficit follow the behavior of the solar activity
over the course of the cycle, whereby the peak of the H-alpha deficit is
shortly after the solar maximum. The H-alpha excess is closely correlated to
the chromospheric MgII index. The highest correlation of the H-alpha deficit is
found with the F10.7cm radio flux. The H-alpha deficit reflects the cyclic
behavior of polar crown filaments and their disappearance shortly before the
solar maximum. We investigated the mean intensity distribution for H-alpha
excess regions for solar minimum and maximum, whereby the shape of the
distributions is very similar, but with different amplitudes. Furthermore, we
investigate whether the area coverage fraction or the changing H-alpha excess
in the active regions dominates temporal variability in solar H-alpha
observations. The area coverage fraction and the H-alpha excess are strongly
correlated, whereas the weak correlation between the area coverage fraction and
mean intensity leaves us pessimistic that the degeneracy between these two
quantities can be broken for the modeling of unresolved stellar surfaces.


title: ARcall: Real-Time AR Communication using Smartphones and Smartglasses

abstract:
  Augmented Reality (AR) smartglasses are increasingly regarded as the next
generation personal computing platform. However, there is a lack of
understanding about how to design communication systems using them. We present
ARcall, a novel Augmented Reality-based real-time communication system that
enables an immersive, delightful, and privacy-preserving experience between a
smartphone user and a smartglasses wearer. ARcall allows a remote friend
(Friend) to send and project AR content to a smartglasses wearer (Wearer). The
ARcall system was designed with the practical limits of existing AR glasses in
mind, including shorter battery life and a reduced field of view. We conduct a
qualitative evaluation of the three main components of ARcall: Drop-In,
ARaction, and Micro-Chat. Our results provide novel insights for building
future AR-based communication methods, including, the importance of context
priming, user control over AR content placement, and the feeling of co-presence
while conversing.


title:
The Effect of Intrinsic Quantum Fluctuations on the Phase Diagram of
  Anisotropic Dipolar Magnets

abstract:
  The rare-earth material $\mathrm{LiHoF_4}$ is believed to be an experimental
realization of the celebrated (dipolar) Ising model, and upon the inclusion of
a transverse field $B_x$, an archetypal quantum Ising model. Moreover, by
substituting the magnetic Ho ions by non-magnetic Y ions, disorder can be
introduced into the system giving rise to a dipolar disordered magnet and at
high disorders to a spin-glass. Indeed, this material has been scrutinized
experimentally, numerically and theoretically over many decades with the aim of
understanding various collective magnetic phenomena. One of the to-date open
questions is the discrepancy between the experimental and theoretical $B_x -T$
phase diagram at low-fields and high temperatures. Here we propose a mechanism,
backed by numerical results, that highlights the importance of quantum
fluctuations induced by the off-diagonal dipolar terms, in determining the
critical temperature of anisotropic dipolar magnets in the presence and in the
absence of a transverse field. We thus show that the description as a simple
Ising system is insufficient to quantitatively describe the full phase diagram
of $\mathrm{LiHoF_4}$, for the pure as well as for the dilute system.


title:
Mixed inequalities of Fefferman-Stein type for singular integral
  operators

abstract:
  We give Feffermain-Stein type inequalities related to mixed estimates for
Calder\'on-Zygmund operators. More precisely, given $\delta>0$, $q>1$,
$\varphi(z)=z(1+\log^+z)^\delta$, a nonnegative and locally integrable function
$u$ and $v\in \mathrm{RH}_\infty\cap A_q$, we prove that the inequality
  \[uv\left(\left\{x\in \mathbb{R}^n:
\frac{|T(fv)(x)|}{v(x)}>t\right\}\right)\leq
\frac{C}{t}\int_{\mathbb{R}^n}|f|\left(M_{\varphi,
v^{1-q'}}u\right)M(\Psi(v))\]
  holds with
$\Psi(z)=z^{p'+1-q'}\mathcal{X}_{[0,1]}(z)+z^{p'}\mathcal{X}_{[1,\infty)}(z)$,
for every $t>0$ and every $p>\max\{q,1+1/\delta\}$. This inequality provides a
more general version of mixed estimates for Calder\'on-Zygmund operators proved
in \cite{CruzUribe-Martell-Perez}. It also generalizes the Fefferman-Stein
estimates given in \cite{P94} for the same operators.
  We further get similar estimates for operators of convolution type with
kernels satisfying an $L^\Phi-$H\"ormander condition, generalizing some
previously known results which involve mixed estimates and Fefferman-Stein
inequalities for these operators.


title: High Fidelity Quantum State Transfer by Pontryagin Maximum Principle

abstract:
  High fidelity quantum state transfer is an essential part of quantum
information processing. In this regard, we address the problem of maximizing
the fidelity in a quantum state transformation process satisfying the
Liouville-von Neumann equation. By introducing fidelity as the performance
index, we aim at maximizing the similarity of the final state density operator
with the one of the desired target state. Optimality conditions in the form of
a Maximum Principle of Pontryagin are given for the matrix-valued dynamic
control systems propagating the probability density function. These provide a
complete set of relations enabling the computation of the optimal control
strategy.


title: The Sobolev Wavefront Set of the Causal Propagator in Finite Regularity

abstract:
  In this paper we estimate the Sobolev wavefront set for the causal propagator
$K_G$ of the Klein-Gordon equation in an ultrastatic spacetime when the
regularity of the metric is $C^\tau$ with $\tau>2$ and $C^{1,1}$. Our main
tools are a propagation of singularities result for non-smooth
pseudodifferential operators and eigenvalue asymptotics for elliptic operators
of low regularity.


title:
On the Fitness Landscapes of Interdependency Models in the Travelling
  Thief Problem

abstract:
  Since its inception in 2013, the Travelling Thief Problem (TTP) has been
widely studied as an example of problems with multiple interconnected
sub-problems. The dependency in this model arises when tying the travelling
time of the "thief" to the weight of the knapsack. However, other forms of
dependency as well as combinations of dependencies should be considered for
investigation, as they are often found in complex real-world problems. Our goal
is to study the impact of different forms of dependency in the TTP using a
simple local search algorithm. To achieve this, we use Local Optima Networks, a
technique for analysing the fitness landscape.


title:
Signal model and event reconstruction for the radio detection of
  inclined air showers

abstract:
  The radio emission from very inclined air showers (with zenith angles $\theta
\gtrsim 65^\circ$) illuminates large footprints from a few up to hundreds of
square kilometers. Inclined air showers can be detected with kilometer-spaced
radio-antenna arrays, suitable to measure cosmic rays up to the highest
energies ($10^{18.4}\,\text{eV} \lesssim E \sim 10^{20}\,\text{eV}$). Radio
antennas are sensitive to the electromagnetic particle cascades of the showers,
which are absorbed in the atmosphere before reaching the ground. Hence,
combining radio antennas with particle detectors, which, in turn, measure the
muons reaching the ground, offers the unique potential to discriminate between
showers initiated by light and heavy nuclei and study hadronic interactions. A
precise understanding and description of the radio-emission footprint of
inclined air showers is indispensable for any analyses of radio data. Such a
model has to master the description of complex asymmetry patterns imprinted in
the radio-emission. We propose a model that describes the radio-emission from
inclined air showers by explicitly describing the dominant, rotationally
symmetric geomagnetic emission in combination with additional effects which
disturb the symmetric emission pattern. We use a comprehensive set of CoREAS
simulations to exploit correlations between the radio emission and air shower
properties. We find a description which is based on two observables only: the
geometrical distance $d_\mathrm{max}$ between the shower core at the ground and
the shower maximum and the geomagnetic radiation energy $E_\mathrm{geo}$.
Hence, this model can be used in a robust and efficient reconstruction
algorithms. We demonstrate that with this model, the electromagnetic shower
energy can be reconstructed by sparse antenna arrays with an intrinsic
resolution of only 5\% and a negligible bias.


title: Canonical binary $\Delta$-matroids

abstract:
  The handle slide operation, originally defined for ribbon graphs, was
extended to delta-matroids by I. Moffatt and E. Mphako-Bandab, who show that,
using a delta-matroid analogue of handle slides, every binary delta-matroid in
which the empty set is feasible can be written in a canonical form analogous to
the canonical form for one-vertex maps on a surface. We provide a canonical
form for binary delta-matroids without restriction on the feasibility of the
empty set.


title: It's AI Match: A Two-Step Approach for Schema Matching Using Embeddings

abstract:
  Since data is often stored in different sources, it needs to be integrated to
gather a global view that is required in order to create value and derive
knowledge from it. A critical step in data integration is schema matching which
aims to find semantic correspondences between elements of two schemata. In
order to reduce the manual effort involved in schema matching, many solutions
for the automatic determination of schema correspondences have already been
developed.
  In this paper, we propose a novel end-to-end approach for schema matching
based on neural embeddings. The main idea is to use a two-step approach
consisting of a table matching step followed by an attribute matching step. In
both steps we use embeddings on different levels either representing the whole
table or single attributes. Our results show that our approach is able to
determine correspondences in a robust and reliable way and compared to
traditional schema matching approaches can find non-trivial correspondences.


title: The widths of magnetised filaments in molecular clouds

abstract:
  Filaments are an ubiquitous feature of molecular clouds, and appear to play a
critical role in assembling the material to form stars. The dominant filaments
are observed to have a rather narrow range of widths around $\sim 0.1$ pc, and
to be preferentially aligned perpendicularly to the direction of the local
magnetic field. We have previously argued that the observed filament widths can
be explained if filaments are formed by converging, mildly supersonic flows,
resulting from large-scale turbulent motions in the parent molecular cloud.
Here we demonstrate that the introduction of a magnetic field perpendicular to
the filament long axis does not greatly alter this conclusion, as long as the
mass-to-flux ratio is supercritical. The distribution of widths for
supercritical magnetised filaments formed via this mechanism is peaked at
slightly higher values, and is slightly broader, than for non-magnetised
filaments, but still reproduces the basic properties of the width distributions
derived from far-infrared observations of molecular clouds. In contrast,
subcritical filaments have width distributions with a fundamentally different
shape, and typically have much larger widths than those observed. Both
subcritical and supercritical filaments are consistent with the observed lack
of correlation between filament widths and filament surface densities.


title:
Deep Learning Neural Networks for Emotion Classification from Text:
  Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss

abstract:
  Accurate emotion classification for online reviews is vital for business
organizations to gain deeper insights into markets. Although deep learning has
been successfully implemented in this area, accuracy and processing time are
still major problems preventing it from reaching its full potential. This paper
proposes an Enhanced Leaky Rectified Linear Unit activation and Weighted Loss
(ELReLUWL) algorithm for enhanced text emotion classification and faster
parameter convergence speed. This algorithm includes the definition of the
inflection point and the slope for inputs on the left side of the inflection
point to avoid gradient saturation. It also considers the weight of samples
belonging to each class to compensate for the influence of data imbalance.
Convolutional Neural Network (CNN) combined with the proposed algorithm to
increase the classification accuracy and decrease the processing time by
eliminating the gradient saturation problem and minimizing the negative effect
of data imbalance, demonstrated on a binary sentiment problem. The results show
that the proposed solution achieves better classification performance in
different data scenarios and different review types. The proposed model takes
less convergence time to achieve model optimization with seven epochs against
the current convergence time of 11.5 epochs on average. The proposed solution
improves accuracy and reduces the processing time of text emotion
classification. The solution provides an average class accuracy of 96.63%
against a current average accuracy of 91.56%. It also provides a processing
time of 23.3 milliseconds compared to the current average processing time of
33.2 milliseconds. Finally, this study solves the issues of gradient saturation
and data imbalance. It enhances overall average class accuracy and decreases
processing time.


title: Element-wise Estimation Error of Generalized Fused Lasso

abstract:
  The main result of this article is that we obtain an elementwise error bound
for the Fused Lasso estimator for any general convex loss function $\rho$. We
then focus on the special cases when either $\rho$ is the square loss function
(for mean regression) or is the quantile loss function (for quantile
regression) for which we derive new pointwise error bounds. Even though error
bounds for the usual Fused Lasso estimator and its quantile version have been
studied before; our bound appears to be new. This is because all previous works
bound a global loss function like the sum of squared error, or a sum of Huber
losses in the case of quantile regression in Padilla and Chatterjee (2021).
Clearly, element wise bounds are stronger than global loss error bounds as it
reveals how the loss behaves locally at each point. Our element wise error
bound also has a clean and explicit dependence on the tuning parameter
$\lambda$ which informs the user of a good choice of $\lambda$. In addition,
our bound is nonasymptotic with explicit constants and is able to recover
almost all the known results for Fused Lasso (both mean and quantile
regression) with additional improvements in some cases.


title: New Coresets for Projective Clustering and Applications

abstract:
  $(j,k)$-projective clustering is the natural generalization of the family of
$k$-clustering and $j$-subspace clustering problems. Given a set of points $P$
in $\mathbb{R}^d$, the goal is to find $k$ flats of dimension $j$, i.e., affine
subspaces, that best fit $P$ under a given distance measure. In this paper, we
propose the first algorithm that returns an $L_\infty$ coreset of size
polynomial in $d$. Moreover, we give the first strong coreset construction for
general $M$-estimator regression. Specifically, we show that our construction
provides efficient coreset constructions for Cauchy, Welsch, Huber,
Geman-McClure, Tukey, $L_1-L_2$, and Fair regression, as well as general
concave and power-bounded loss functions. Finally, we provide experimental
results based on real-world datasets, showing the efficacy of our approach.


title:
Deep Learning for Sleep Stages Classification: Modified Rectified Linear
  Unit Activation Function and Modified Orthogonal Weight Initialisation

abstract:
  Background and Aim: Each stage of sleep can affect human health, and not
getting enough sleep at any stage may lead to sleep disorder like parasomnia,
apnea, insomnia, etc. Sleep-related diseases could be diagnosed using
Convolutional Neural Network Classifier. However, this classifier has not been
successfully implemented into sleep stage classification systems due to high
complexity and low accuracy of classification. The aim of this research is to
increase the accuracy and reduce the learning time of Convolutional Neural
Network Classifier. Methodology: The proposed system used a modified Orthogonal
Convolutional Neural Network and a modified Adam optimisation technique to
improve the sleep stage classification accuracy and reduce the gradient
saturation problem that occurs due to sigmoid activation function. The proposed
system uses Leaky Rectified Linear Unit (ReLU) instead of sigmoid activation
function as an activation function. Results: The proposed system called
Enhanced Sleep Stage Classification system (ESSC) used six different databases
for training and testing the proposed model on the different sleep stages.
These databases are University College Dublin database (UCD), Beth Israel
Deaconess Medical Center MIT database (MIT-BIH), Sleep European Data Format
(EDF), Sleep EDF Extended, Montreal Archive of Sleep Studies (MASS), and Sleep
Heart Health Study (SHHS). Our results show that the gradient saturation
problem does not exist anymore. The modified Adam optimiser helps to reduce the
noise which in turn result in faster convergence time. Conclusion: The
convergence speed of ESSC is increased along with better classification
accuracy compared to the state of art solution.


title: Average Response Curves for Treatment Time in the Emergency Department

abstract:
  We estimate average responses curves for treatment time in the Emergency
Department (ED). Extending treatment time is considered a promising solution
for improving admission decisions. Providing empirical support for this
solution, however, is difficult because this intervention (treatment) is a
continuous time-to-event; is strongly influenced by unmeasured patient health
needs and is jointly determined with the admission decision (admit vs.
discharge); and may be only modifiable up to a shift in the realized time. We
formalize the admission process as a directed acyclic graph and show that
average responses curves for treatment time cannot be identified
nonparametrically due to unmeasured confounding from patient health needs. We
thus use a parametric model that includes a latent variable for health needs
and a threshold regression model for the admission process. We fit this model
to observational data (n = 28,862) from abdominal pain patients at a large
tertiary teaching hospital. We estimate that fixing ED treatment time to 2
hours rather than 1 hour decreases admission rates from 41.6% (95% CI: [40.6,
42.7]) to 32.7% (95% CI: [32.2, 33.2]) and that increasing the realized
treatment time by 30 minutes can reduce admission rates by 1.1% (95% CI: [-1.1,
3.2]), with little change to 30-day revisit and readmission rates.


title:
Sensitivity Analysis under the $f$-Sensitivity Models: Definition,
  Estimation and Inference

abstract:
  The digitization of the economy has witnessed an explosive growth of
available observational data across a variety of domains, which has provided an
exciting opportunity for causal inference that aims to discern the
effectiveness of different treatment options. However, unlike in experimental
data -- where the designer has the full control to randomize treatment choices
based on observable covariates -- causal inference using observational data
faces the key challenge of confoundedness: there might exist unobservable
covariates that affect both the treatment choice and the outcome.
  In this paper, we aim to add to the emerging line of tools for sensitivity
analysis that assesses the robustness of causal conclusions against potential
unmeasured confounding. We propose a new sensitivity model where, in contrast
to uniform bounds on the selection bias in the literature, we assume the
selection bias is bounded "on average". It generalizes the widely used
$\Gamma$-marginal selection model of Tan (2006) and is particularly suitable
for situations where the selection bias grows unbounded locally but, due to the
impact of such region being small overall, is controlled at the population
level. We study the partial identification of treatment effects under the new
sensitivity model. From a distributional-shift perspective, we represent the
bounds on counterfactual means via certain distributionally robust optimization
programs. We then design procedures to estimate these bounds, and show that our
procedure is doubly robust to the estimation error of nuisance components and
remains valid even when the optimization step is off. In addition, we establish
the Wald-type inference guarantee of our procedure that is again robust to the
optimization step. Finally, we demonstrate our method and verify its validity
with numerical experiments.


title:
Code Red: The Business Impact of Code Quality -- A Quantitative Study of
  39 Proprietary Production Codebases

abstract:
  Code quality remains an abstract concept that fails to get traction at the
business level. Consequently, software companies keep trading code quality for
time-to-market and new features. The resulting technical debt is estimated to
waste up to 42% of developers' time. At the same time, there is a global
shortage of software developers, meaning that developer productivity is key to
software businesses. Our overall mission is to make code quality a business
concern, not just a technical aspect. Our first goal is to understand how code
quality impacts 1) the number of reported defects, 2) the time to resolve
issues, and 3) the predictability of resolving issues on time. We analyze 39
proprietary production codebases from a variety of domains using the CodeScene
tool based on a combination of source code analysis, version-control mining,
and issue information from Jira. By analyzing activity in 30,737 files, we find
that low quality code contains 15 times more defects than high quality code.
Furthermore, resolving issues in low quality code takes on average 124% more
time in development. Finally, we report that issue resolutions in low quality
code involve higher uncertainty manifested as 9 times longer maximum cycle
times. This study provides evidence that code quality cannot be dismissed as a
technical concern. With 15 times fewer defects, twice the development speed,
and substantially more predictable issue resolution times, the business
advantage of high quality code should be unmistakably clear.


title:
Tsallis and Kaniadakis holographic dark energy with Complex Quintessence
  theory in Brans-Dicke cosmology

abstract:
  In this paper, by considering the complex form of the quintessence model, we
study two different dynamic structures of holographic dark energy as Tsallis
and Kaniadakis in the framework Brans-Dicke cosmology. In each setup, we employ
non-interacting and interacting cases and calculate some cosmological
parameters such as the equation of state $\omega$. We also discuss the $
\omega- \omega '$ behavior. By modifying the potential and studying the scalar
field dynamics, we examine the complex quintessence cosmology.
  In addition, considering the two parts of the quintessence field effects,
i.e., real and complex, and considering the fractional energy density $
\Omega_{D} $, we examine whether it can describe a real universe or not.
  We also specify that the fractional energy density can not be arbitrary
between 0 and 1. In other words, it depends on the Tsallis, Kaniadakis, and
Brans-Dicke cosmology free parameters.
  We create a relationship between the fractional energy density and other
parameters introduced in the text such as $\delta$, $b^{2}$, $\alpha$ and
$\beta$ for each model separately. Finally, we compare the obtained results of
models to each other and the latest observable data.


title:
Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT
  Networks

abstract:
  Federated Edge Learning (FEEL) is a promising distributed learning technique
that aims to train a shared global model while reducing communication costs and
promoting users' privacy. However, the training process might significantly
occupy a long time due to the nature of the used data for training, which leads
to higher energy consumption and therefore impacts the model convergence. To
tackle this issue, we propose a data-driven federated edge learning scheme that
tends to select suitable participating nodes based on quality data and energy.
First, we design an unsupervised data-aware splitting scheme that partitions
the node's local data into diverse samples used for training. We incorporate a
similarity index to select quality data that enhances the training performance.
Then, we propose a heuristic participating nodes selection scheme to minimize
the communication and computation energy consumption, as well as the amount of
communication rounds. The obtained results show that the proposed scheme
substantially outperforms the vanilla FEEL in terms of energy consumption and
the number of communication rounds.


title: Long mmWave Backhaul Connectivity Using Fixed-Wing UAVs

abstract:
  This paper discusses the analysis of a fixed-wing unmanned aerial vehicle
(UAV)-based millimeter wave (mmWave) backhaul link, that is offered as a
cost-effective and easy deploy the solution to connect a disaster or remote
area to the nearest core network. We present the optimal design of a relay
system based on fixed-wing UAV, taking into account the actual channel
parameters such as the UAV vibrations, tracking error, real 3GPP antenna
pattern, UAV's height, flight path, and the effect of physical obstacles. The
performance of the considered system is evaluated in terms of outage
probability and the channel capacity while taking into account the impact of
the system parameters such as optimal selection of UAV flight path and antenna
patterns.


title:
Logic-based AI for Interpretable Board Game Winner Prediction with
  Tsetlin Machine

abstract:
  Hex is a turn-based two-player connection game with a high branching factor,
making the game arbitrarily complex with increasing board sizes. As such,
top-performing algorithms for playing Hex rely on accurate evaluation of board
positions using neural networks. However, the limited interpretability of
neural networks is problematic when the user wants to understand the reasoning
behind the predictions made. In this paper, we propose to use propositional
logic expressions to describe winning and losing board game positions,
facilitating precise visual interpretation. We employ a Tsetlin Machine (TM) to
learn these expressions from previously played games, describing where pieces
must be located or not located for a board position to be strong. Extensive
experiments on $6\times6$ boards compare our TM-based solution with popular
machine learning algorithms like XGBoost, InterpretML, decision trees, and
neural networks, considering various board configurations with $2$ to $22$
moves played. On average, the TM testing accuracy is $92.1\%$, outperforming
all the other evaluated algorithms. We further demonstrate the global
interpretation of the logical expressions and map them down to particular board
game configurations to investigate local interpretability. We believe the
resulting interpretability establishes building blocks for accurate assistive
AI and human-AI collaboration, also for more complex prediction tasks.


title:
An insensitizing control problem for a linear Stabilized
  Kuramoto-Sivashinsky system

abstract:
  In this work, we address the existence of insensitizing controls for a
coupled system of fourth- and second-order parabolic equations known as the
stabilized Kuramoto-Sivashinsky model. The main idea is to look for control
functions such that some functional of the state is locally insensitive to the
perturbations of initial data. Let $\mathcal{O}$ be a nonempty observation set
for the solution component(s) w.r.t. the $L^2$-norm(s) and $\omega$ be another
nonempty set where the interior controls are acting. Then, we study the
associated insensitizing control problem under the assumption $\mathcal{O} \cap
\omega \neq \emptyset$ and as usual, it can be shown that this is equivalent to
a null-controllability problem for a cascade system where the number of
equations are doubled. This new problem is studied by means of the classical
duality arguments and Carleman estimates, but unlike other insensitizing
problems for scalar systems, the election of the Carleman tools depends on the
number of components of the system to be insensitized.


title:
On finite time Type I singularities of the K\"ahler-Ricci flow on
  compact K\"ahler surfaces

abstract:
  We show that the underlying complex manifold of a complete non-compact
two-\linebreak dimensional shrinking gradient K\"ahler-Ricci soliton
$(M,\,g,\,X)$ with soliton metric $g$ with bounded scalar curvature
$\operatorname{R}_{g}$ whose soliton vector field $X$ has an integral curve
along which $\operatorname{R}_{g}\not\to0$ is biholomorphic to either
$\mathbb{C}\times\mathbb{P}^{1}$ or to the blowup of this manifold at one
point. Assuming the existence of such a soliton on this latter manifold, we
show that it is toric and unique. We also identify the corresponding soliton
vector field. Given these possibilities, we then prove a strong form of the
Feldman-Ilmanen-Knopf conjecture for finite time Type I singularities of the
K\"ahler-Ricci flow on compact K\"ahler surfaces, leading to a classification
of the bubbles of such singularities in this dimension.


title:
Formation Control of Nonlinear Multi-Agent Systems Using Three-Layer
  Neural Networks

abstract:
  This paper considers a leader-following formation control problem for
heterogeneous, second-order, uncertain, input-affine, nonlinear multi-agent
systems modeled by a directed graph. A tunable, three-layer neural network (NN)
is proposed with an input layer, two hidden layers, and an output layer to
approximate an unknown nonlinearity. Unlike commonly used trial and error
efforts to select the number of neurons in a conventional NN, in this case an
\textit{a priori} knowledge allows one to set up the number of neurons in each
layer. The NN weights tuning laws are derived using the Lyapunov theory. The
leader-following and formation control problems are addressed by a robust
integral of the sign of the error (RISE) feedback and a NN-based control. The
RISE feedback term compensates for unknown leader dynamics and the unknown,
bounded disturbance in the agent error dynamics. The NN-based term compensates
for the unknown nonlinearity in the dynamics of multi-agent systems, and
semi-global asymptotic tracking results are rigorously proven using the
Lyapunov stability theory. The results of the paper are compared with two
previous results to evaluate the efficiency and performance of the proposed
method.


title:
Regularized Training of Intermediate Layers for Generative Models for
  Inverse Problems

abstract:
  Generative Adversarial Networks (GANs) have been shown to be powerful and
flexible priors when solving inverse problems. One challenge of using them is
overcoming representation error, the fundamental limitation of the network in
representing any particular signal. Recently, multiple proposed inversion
algorithms reduce representation error by optimizing over intermediate layer
representations. These methods are typically applied to generative models that
were trained agnostic of the downstream inversion algorithm. In our work, we
introduce a principle that if a generative model is intended for inversion
using an algorithm based on optimization of intermediate layers, it should be
trained in a way that regularizes those intermediate layers. We instantiate
this principle for two notable recent inversion algorithms: Intermediate Layer
Optimization and the Multi-Code GAN prior. For both of these inversion
algorithms, we introduce a new regularized GAN training algorithm and
demonstrate that the learned generative model results in lower reconstruction
errors across a wide range of under sampling ratios when solving compressed
sensing, inpainting, and super-resolution problems.


title:
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid
  COVID-19

abstract:
  The COVID-19 pandemic has significantly impacted the tourism and hospitality
sector. Public policies such as travel restrictions and stay-at-home orders had
significantly affected tourist activities and service businesses' operations
and profitability. To this end, it is essential to develop an interpretable
forecast model that supports managerial and organizational decision-making. We
developed DemandNet, a novel deep learning framework for predicting time series
data under the influence of the COVID-19 pandemic. The framework starts by
selecting the top static and dynamic features embedded in the time series data.
Then, it includes a nonlinear model which can provide interpretable insight
into the previously seen data. Lastly, a prediction model is developed to
leverage the above characteristics to make robust long-term forecasts. We
evaluated the framework using daily hotel demand and revenue data from eight
cities in the US. Our findings reveal that DemandNet outperforms the
state-of-art models and can accurately predict the impact of the COVID-19
pandemic on hotel demand and revenues.


title: On generative models as the basis for digital twins

abstract:
  A framework is proposed for generative models as a basis for digital twins or
mirrors of structures. The proposal is based on the premise that deterministic
models cannot account for the uncertainty present in most structural modelling
applications. Two different types of generative models are considered here. The
first is a physics-based model based on the stochastic finite element (SFE)
method, which is widely used when modelling structures that have material and
loading uncertainties imposed. Such models can be calibrated according to data
from the structure and would be expected to outperform any other model if the
modelling accurately captures the true underlying physics of the structure. The
potential use of SFE models as digital mirrors is illustrated via application
to a linear structure with stochastic material properties. For situations where
the physical formulation of such models does not suffice, a data-driven
framework is proposed, using machine learning and conditional generative
adversarial networks (cGANs). The latter algorithm is used to learn the
distribution of the quantity of interest in a structure with material
nonlinearities and uncertainties. For the examples considered in this work, the
data-driven cGANs model outperform the physics-based approach. Finally, an
example is shown where the two methods are coupled such that a hybrid model
approach is demonstrated.


title:
Geometric Assumptions in Hydrodynamic Modeling of Coronal and Flaring
  Loops

abstract:
  In coronal loop modeling, it is commonly assumed that the loops are
semi-circular with a uniform cross-sectional area. However, observed loops are
rarely semi-circular, and extrapolations of the magnetic field show that the
field strength decreases with height, implying that the cross-sectional area
should expand with height. In this work, we examine these two assumptions
directly to understand how they affect the hydrodynamic and radiative response
to strong, impulsive heating events. Both the magnitude and rate of area
expansion impact the dynamics directly, and we show that an expanding
cross-section significantly lengthens the time for a loop to cool and drain,
increases upflow durations, and suppresses sound waves. An increase in the
eccentricity of loops, on the other hand, only increases the draining
timescale, and is a minor effect in general. Spectral line intensities are also
strongly impacted by the variation in the cross-sectional area since they
depend on both the volume of the emitting region as well as the density and
ionization state. With a larger expansion, the density is reduced, so the lines
at all heights are relatively reduced in intensity and, because of the increase
of cooling times, the hottest lines remain bright for significantly longer.
Future modeling work needs to include area expansion for an accurate picture of
the hydrodynamics, and future observations are needed to provide tighter
constraints on the magnitude, rate, and location of the expansion or lack
thereof.


title:
Model-free feature selection to facilitate automatic discovery of
  divergent subgroups in tabular data

abstract:
  Data-centric AI encourages the need of cleaning and understanding of data in
order to achieve trustworthy AI. Existing technologies, such as AutoML, make it
easier to design and train models automatically, but there is a lack of a
similar level of capabilities to extract data-centric insights. Manual
stratification of tabular data per a feature (e.g., gender) is limited to scale
up for higher feature dimension, which could be addressed using automatic
discovery of divergent subgroups. Nonetheless, these automatic discovery
techniques often search across potentially exponential combinations of features
that could be simplified using a preceding feature selection step. Existing
feature selection techniques for tabular data often involve fitting a
particular model in order to select important features. However, such
model-based selection is prone to model-bias and spurious correlations in
addition to requiring extra resource to design, fine-tune and train a model. In
this paper, we propose a model-free and sparsity-based automatic feature
selection (SAFS) framework to facilitate automatic discovery of divergent
subgroups. Different from filter-based selection techniques, we exploit the
sparsity of objective measures among feature values to rank and select
features. We validated SAFS across two publicly available datasets (MIMIC-III
and Allstate Claims) and compared it with six existing feature selection
methods. SAFS achieves a reduction of feature selection time by a factor of 81x
and 104x, averaged cross the existing methods in the MIMIC-III and Claims
datasets respectively. SAFS-selected features are also shown to achieve
competitive detection performance, e.g., 18.3% of features selected by SAFS in
the Claims dataset detected divergent samples similar to those detected by
using the whole features with a Jaccard similarity of 0.95 but with a 16x
reduction in detection time.


title: A Study of Multihop mmW Aerial Backhaul Links

abstract:
  The main contribution of this paper is to analyze a long networked flying
platform (NFP)-based millimeter wave (mmWave) backhaul link that is offered as
a cost effective and easy to deploy solution to connect a disaster or remote
area to the nearest core network. For this aim, we characterize the backhaul
channel as a function of realistic physical parameters such as heights and
distances of obstacles along the route, flight altitude and the intensity of
NFPs' vibrations, the real 3D antenna pattern provided by 3GPP, etc. For the
characterized channel, we derive an analytical closed-form expression for the
outage probability. Finally, using the obtained results, we provide a fast
algorithm for the optimal parameter design of the considered system that
minimizes the cost.


title: Inverse engineering of fast state transfer among coupled oscillators

abstract:
  We design faster-than-adiabatic state transfers (switching of quantum
numbers) in time-dependent coupled-oscillator Hamiltonians. The manipulation to
drive the process is found using a two-dimensional invariant recently proposed
in S. Simsek and F. Mintert, Quantum 5 (2021) 409, and involves both rotation
and transient scaling of the principal axes of the potential in a Cartesian
representation. Importantly, this invariant is degenerate except for the
subspace spanned by its ground state. Such degeneracy, in general, allows for
infidelities of the final states with respect to ideal target eigenstates.
However, the value of a single control parameter can be chosen so that the
state switching is perfect for arbitrary (not necessarily known) initial
eigenstates. Additional 2D linear invariants are used to find easily the
parameter values needed and to provide generic expressions for the final states
and final energies. In particular we find time-dependent transformations of a
two-dimensional harmonic trap for a particle (such as an ion or neutral atom)
so that the final trap is rotated with respect to the initial one, and
eigenstates of the initial trap are converted into rotated replicas at final
time, in some chosen time and rotation angle.


title:
Metaplastic and Energy-Efficient Biocompatible Graphene Artificial
  Synaptic Transistors for Enhanced Accuracy Neuromorphic Computing

abstract:
  CMOS-based computing systems that employ the von Neumann architecture are
relatively limited when it comes to parallel data storage and processing. In
contrast, the human brain is a living computational signal processing unit that
operates with extreme parallelism and energy efficiency. Although numerous
neuromorphic electronic devices have emerged in the last decade, most of them
are rigid or contain materials that are toxic to biological systems. In this
work, we report on biocompatible bilayer graphene-based artificial synaptic
transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices
leverage a dry ion-selective membrane, enabling long-term potentiation, with
~50 aJ/m^2 switching energy efficiency, at least an order of magnitude lower
than previous reports on two-dimensional material-based artificial synapses.
The devices show unique metaplasticity, a useful feature for generalizable deep
neural networks, and we demonstrate that metaplastic BLASTs outperform ideal
linear synapses in classic image classification tasks. With switching energy
well below the 1 fJ energy estimated per biological synapse, the proposed
devices are powerful candidates for bio-interfaced online learning, bridging
the gap between artificial and biological neural networks.


title: Structural Learning of Simple Staged Trees

abstract:
  Bayesian networks faithfully represent the symmetric conditional
independences existing between the components of a random vector. Staged trees
are an extension of Bayesian networks for categorical random vectors whose
graph represents non-symmetric conditional independences via vertex coloring.
However, since they are based on a tree representation of the sample space, the
underlying graph becomes cluttered and difficult to visualize as the number of
variables increases. Here we introduce the first structural learning algorithms
for the class of simple staged trees, entertaining a compact coalescence of the
underlying tree from which non-symmetric independences can be easily read. We
show that data-learned simple staged trees often outperform Bayesian networks
in model fit and illustrate how the coalesced graph is used to identify
non-symmetric conditional independences.


title:
Adaptive Machine Learning for Time-Varying Systems: Towards 6D Phase
  Space Diagnostics of Short Intense Charged Particle Beams

abstract:
  As charged particle bunches become shorter and more intense, the effects of
nonlinear intra-bunch collective interactions such as space charge forces and
bunch-to-bunch influences such as wakefields and coherent synchrotron radiation
also increase. Shorter more intense bunches are also more difficult to
accurately image because their dimensions are beyond the resolution of existing
diagnostics and they may be destructive to intercepting diagnostics. The
limited availability of detailed diagnostics for intense high energy beams is a
fundamental challenge for the accelerator community because both beams and
accelerators are time-varying systems that change in unpredictable ways. The
detailed 6D (x,y,z,px,py,pz) distributions of beams emerging from sources vary
with time due to factors such as evolving photocathode laser intensity profiles
and the quantum efficiency of photocathodes. Accelerator magnets, RF
amplifiers, and control systems are perturbed by external disturbances,
beam-loading effects, temperature variations, and misalignments. Although
machine learning (ML) methods have grown in popularity in the accelerator
community in recently years, they are fundamentally limited when it comes to
time-varying systems for which most current approaches are to simply collect
large new data sets and perform re-training, something which is not feasible
for busy accelerator user facilities because detailed beam measurements usually
interrupt operations. New adaptive machine learning (AML) methods designed for
time-varying systems are needed to aid in the diagnostics and control of
high-intensity, ultrashort beams by combining deep learning tools such as
convolutional neural network-based encoder-decoder architectures,
model-independent feedback, physics constraints, and online models with real
time non-invasive beam data, to provide a detailed virtual view of intense
bunch dynamics.


title: Tetravalent vertex-transitive graphs of order $6p$

abstract:
  A graph is vertex-transitive if its automorphism group acts transitively on
vertices of the graph. A vertex-transitive graph is a Cayley graph if its
automorphism group contains a subgroup acting regularly on its vertices. In
this paper, the tetravalent vertex-transitive non-Cayley graphs of order $6p$
are classified for each prime $p$.


title:
Remarks about weighted energy integrals over Minkowski spectral
  functions from Euclidean lattice data

abstract:
  I make some simple observations about the calculation of weighted averages
over energy of Minkowski space spectral densities from weighted averages over
time of Euclidean space correlation functions, measured in latice simulations.
The correlator of two vector currents is used as an example, where it appears
that a determination of a weighted average of the spectral function near the
rho pole at the five per cent level is possible from lattice simulations.


title:
Quantifying Daily Evolution of Mobile Software Based on Memory Allocator
  Churn

abstract:
  The pace and volume of code churn necessary to evolve modern software systems
present challenges for analyzing the performance impact of any set of code
changes. Traditional methods used in performance analysis rely on extensive
data collection and profiling, which often takes days. For large organizations
utilizing Continuous Integration (CI) and Continuous Deployment (CD), these
traditional techniques often fail to provide timely and actionable data. A
different impact analysis method that allows for more efficient detection of
performance regressions is needed. We propose the utilization of user mode
memory allocator churn as a novel approach to performance engineering. User
mode allocator churn acts as a proxy metric to evaluate the relative change in
the cost of specific tasks. We prototyped the memory allocation churn
methodology while engaged in performance engineering for an iOS version of
Facebook. We find that calculating and analyzing memory allocator churn (a)
results in deterministic measurements, (b) is efficient for determining the
presence of both individual performance regressions and general
performance-related trends, and (c) is a suitable alternative to measuring the
task completion time.


title: Equivalences of Geometric Ergodicity of Markov Chains

abstract:
  This paper gathers together different conditions which are all equivalent to
geometric ergodicity of time-homogeneous Markov chains on general state spaces.
A total of 33 different conditions are presented (26 for general chains plus 7
just for reversible chains), some old and some new, in terms of such notions as
convergence bounds, drift conditions, spectral properties, etc., with different
assumptions about the distance metric used, finiteness of function moments,
initial distribution, uniformity of bounds, and more. Proofs of the connections
between the different conditions are provided, mostly self-contained but using
some results from the literature where appropriate.


title:
Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based
  Heart Rate Monitoring

abstract:
  Photoplethysmography (PPG) sensors allow for non-invasive and comfortable
heart-rate (HR) monitoring, suitable for compact wrist-worn devices.
Unfortunately, Motion Artifacts (MAs) severely impact the monitoring accuracy,
causing high variability in the skin-to-sensor interface. Several data fusion
techniques have been introduced to cope with this problem, based on combining
PPG signals with inertial sensor data. Until know, both commercial and
reasearch solutions are computationally efficient but not very robust, or
strongly dependent on hand-tuned parameters, which leads to poor generalization
performance. % In this work, we tackle these limitations by proposing a
computationally lightweight yet robust deep learning-based approach for
PPG-based HR estimation. Specifically, we derive a diverse set of Temporal
Convolutional Networks (TCN) for HR estimation, leveraging Neural Architecture
Search (NAS). Moreover, we also introduce ActPPG, an adaptive algorithm that
selects among multiple HR estimators depending on the amount of MAs, to improve
energy efficiency. We validate our approaches on two benchmark datasets,
achieving as low as 3.84 Beats per Minute (BPM) of Mean Absolute Error (MAE) on
PPGDalia, which outperforms the previous state-of-the-art. Moreover, we deploy
our models on a low-power commercial microcontroller (STM32L4), obtaining a
rich set of Pareto optimal solutions in the complexity vs. accuracy space.


title:
Observations on the Angular Statistics of the Indoor Sub-THz Radio
  Channel at 158 GHz

abstract:
  This paper presents selected results from a channel measurement campaign
conducted in a shopping mall scenario at 158 GHz. The focus is on the
statistical analysis of the collected measurement data in terms of directional
channel gain. Although most power is received from the line-of-sight (LOS)
direction, significant multipaths arrive from all measured azimuth directions.
The median of the sorted directional channel gain can be approximated by a
linear curve showing an offset of about 10 dB with respect to the LOS
direction.


title:
Window Filtering Algorithm for Pulsed Light Coherent Combining of Low
  Repetition Frequency

abstract:
  The multi-dithering method has been well verified in phase locking of
polarization coherent combination experiment. However, it is hard to apply to
low repetition frequency pulsed lasers, since there exists an overlap frequency
domain between pulse laser and the amplitude phase noise and traditional
filters cannot effectively separate phase noise. Aiming to solve the problem in
this paper, we propose a novel method of pulse noise detection, identification,
and filtering based on the autocorrelation characteristics between noise
signals. In the proposed algorithm, a self-designed window algorithm is used to
identify the pulse, and then the pulse signal group in the window is replaced
by interpolation, which effectively filter the pulse signal doped in the phase
noise within 0.1 ms. After filtering the pulses in the phase noise, the phase
difference of two pulsed beams (10 kHz) is successfully compensated to zero in
1 ms, and the coherent combination of closed-loop phase lock is realized. At
the same time, the phase correction times are few, the phase lock effect is
stable, and the final light intensity increases to the ideal value (0.9 Imax).


title:
Multi-Scale Single-Bit RP-EMS Synthesis for Advanced Propagation
  Manipulation through System-by-Design

abstract:
  A new method for synthesizing Single-Bit Reconfigurable Passive
Electromagnetic Skins (1RP-EMSs) featuring advanced beam shaping capabilities
is proposed. By using single-bit unit cells, the multi-scale problem of
controlling 1RP-EMSs is formulated as a two-phase process. First, the
macro-scale synthesis of the discrete surface current that radiates the
electromagnetic (EM) field fitting user-designed requirements is performed by
means of an innovative quantized version of the iterative projection method
(QIPM). Successively, the meta-atom states of the 1RP-EMS are optimized with a
customized implementation of the System-by-Design paradigm to yield a 1RP-EMS
that supports such a feasible reference current. A representative set of
numerical results is reported to assess the effectiveness of the proposed
approach in designing and controlling single-bit meta-atom RP-EMSs that enable
complex wave manipulations.


title: Multi-Objective System-by-Design for mm-Wave Automotive Radar Antennas

abstract:
  The computationally-efficient solution of multi-objective optimization
problems (MOPs) arising in the design of modern electromagnetic (EM) microwave
devices is addressed. Towards this end, a novel System-by-Design (SbD) method
is developed to effectively explore the solution space and to provide the
decision maker with a set of optimal trade-off solutions minimizing multiple
and (generally) contrasting objectives. The proposed MO-SbD method proves a
high computational efficiency, with a remarkable time saving with respect to a
competitive state-of-the-art MOP solution strategy, thanks to the "smart"
integration of evolutionary-inspired concepts and operators with artificial
intelligence (AI) and machine learning (ML) techniques. Representative
numerical results are reported to provide the interested users with useful
insights and guidelines on the proposed optimization method as well as to
assess its effectiveness in designing mm-wave automotive radar antennas.


title:
KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High
  Solar Scenarios

abstract:
  With the expected rise in behind-the-meter solar penetration within the
distribution networks, there is a need to develop time-series forecasting
methods that can reliably predict the net-load, accurately quantifying its
uncertainty and variability. This paper presents a deep learning method to
generate probabilistic forecasts of day-ahead net-load at 15-min resolution, at
various solar penetration levels. Our proposed deep-learning based architecture
utilizes the dimensional reduction, from a higher-dimensional input to a
lower-dimensional latent space, via a convolutional Autoencoder (AE). The
extracted features from AE are then utilized to generate probability
distributions across the latent space, by passing the features through a
kernel-embedded Perron-Frobenius (kPF) operator. Finally, long short-term
memory (LSTM) layers are used to synthesize time-series probability
distributions of the forecasted net-load, from the latent space distributions.
The models are shown to deliver superior forecast performance (as per several
metrics), as well as maintain superior training efficiency, in comparison to
existing benchmark models. Detailed analysis is carried out to evaluate the
model performance across various solar penetration levels (up to 50\%),
prediction horizons (e.g., 15\,min and 24\,hr ahead), and aggregation level of
houses, as well as its robustness against missing measurements.


title:
High Noise Immune Time-domain Inversion via Cascade Network (TICaN) for
  Complex Scatterers

abstract:
  In this paper, a high noise immune time-domain inversion cascade network
(TICaN) is proposed to reconstruct scatterers from the measured electromagnetic
fields. The TICaN is comprised of a denoising block aiming at improving the
signal-to-noise ratio, and an inversion block to reconstruct the
electromagnetic properties from the raw time-domain measurements. The
scatterers investigated in this study include complicated geometry shapes and
high contrast, which cover the stratum layer, lossy medium and hyperfine
structure, etc. After being well trained, the performance of the TICaN is
evaluated from the perspective of accuracy, noise-immunity, computational
acceleration, and generalizability. It can be proven that the proposed
framework can realize high-precision inversion under high-intensity noise
environments. Compared with traditional reconstruction methods, TICaN avoids
the tedious iterative calculation by utilizing the parallel computing ability
of GPU and thus significantly reduce the computing time. Besides, the proposed
TICaN has certain generalization ability in reconstructing the unknown
scatterers such as the famous Austria rings. Herein, it is confident that the
proposed TICaN will serve as a new path for real-time quantitative microwave
imaging for various practical scenarios.


title:
Blessing of Dependence: Identifiability and Geometry of Discrete Models
  with Multiple Binary Latent Variables

abstract:
  Identifiability of discrete statistical models with latent variables is known
to be challenging to study, yet crucial to a model's interpretability and
reliability. This work presents a general algebraic technique to investigate
identifiability of complicated discrete models with latent and graphical
components. Specifically, motivated by diagnostic tests collecting multivariate
categorical data, we focus on discrete models with multiple binary latent
variables. In the considered model, the latent variables can have arbitrary
dependencies among themselves while the latent-to-observed measurement graph
takes a "star-forest" shape. We establish necessary and sufficient graphical
criteria for identifiability, and reveal an interesting and perhaps surprising
phenomenon of blessing-of-dependence geometry: under the minimal conditions for
generic identifiability, the parameters are identifiable if and only if the
latent variables are not statistically independent. Thanks to this theory, we
can perform formal hypothesis tests of identifiability in the boundary case by
testing certain marginal independence of the observed variables. Our results
give new understanding of statistical properties of graphical models with
latent variables. They also entail useful implications for designing diagnostic
tests or surveys that measure binary latent traits.


title:
Sub-THz Channel Measurements at 158 GHz and 300 GHz in a Street Canyon
  Environment

abstract:
  This paper presents first results of a channel measurement campaign performed
in an urban micro (UMi) street canyon scenario at 158 GHz and 300 GHz.


title: Art-Attack: Black-Box Adversarial Attack via Evolutionary Art

abstract:
  Deep neural networks (DNNs) have achieved state-of-the-art performance in
many tasks but have shown extreme vulnerabilities to attacks generated by
adversarial examples. Many works go with a white-box attack that assumes total
access to the targeted model including its architecture and gradients. A more
realistic assumption is the black-box scenario where an attacker only has
access to the targeted model by querying some input and observing its predicted
class probabilities. Different from most prevalent black-box attacks that make
use of substitute models or gradient estimation, this paper proposes a
gradient-free attack by using a concept of evolutionary art to generate
adversarial examples that iteratively evolves a set of overlapping transparent
shapes. To evaluate the effectiveness of our proposed method, we attack three
state-of-the-art image classification models trained on the CIFAR-10 dataset in
a targeted manner. We conduct a parameter study outlining the impact the number
and type of shapes have on the proposed attack's performance. In comparison to
state-of-the-art black-box attacks, our attack is more effective at generating
adversarial examples and achieves a higher attack success rate on all three
baseline models.


title: Routing with Privacy for Drone Package Delivery Systems

abstract:
  Unmanned aerial vehicles (UAVs), or drones, are increasingly being used to
deliver goods from vendors to customers. To safely conduct these operations at
scale, drones are required to broadcast position information as codified in
remote identification (remote ID) regulations. However, location broadcast of
package delivery drones introduces a privacy risk for customers using these
delivery services: Third-party observers may leverage broadcast drone
trajectories to link customers with their purchases, potentially resulting in a
wide range of privacy risks. We propose a probabilistic definition of privacy
risk based on the likelihood of associating a customer to a vendor given a
package delivery route. Next, we quantify these risks, enabling drone operators
to assess privacy risks when planning delivery routes. We then evaluate the
impacts of various factors (e.g., drone capacity) on privacy and consider the
trade-offs between privacy and delivery wait times. Finally, we propose
heuristics for generating routes with privacy guarantees to avoid exhaustive
enumeration of all possible routes and evaluate their performance on several
realistic delivery scenarios.


title:
Controlling the volume fraction of glass-forming colloidal suspensions
  using thermosensitive host `mesogels'

abstract:
  The key parameter controlling the glass transition of colloidal suspensions
is $\varphi$, the fraction of the sample volume occupied by the particles.
Unfortunately, changing $\varphi$ by varying an external parameter,
\textit{e.g.} temperature $T$ as in molecular glass formers, is not possible,
unless one uses thermosensitive colloidal particles, like the popular
poly(N-isopropylacrylamide) (PNiPAM) microgels. These however have several
drawbacks, including high deformability, osmotic deswelling and
interpenetration, which complicate their use as a model system to study the
colloidal glass transition. Here, we propose a new system consisting of a
colloidal suspension of non-deformable spherical silica nanoparticles, in which
PNiPAM hydrogel spheres of ~$100-200 \mu m$ size are suspended. These
non-colloidal `mesogels' allow for controlling the sample volume effectively
available to the silica nanoparticles and hence their $\varphi$, thanks to the
$T$-induced change in mesogels volume. Using optical microscopy, we first show
that the mesogels retain their ability to change size with $T$ when suspended
in Ludox suspensions, similarly as in water. We then show that their size is
independent of the sample thermal history, such that a well-defined, reversible
relationship between $T$ and $\varphi$ may be established. Finally, we use
space-resolved dynamic light scattering to demonstrate that, upon varying $T$,
our system exhibits a broad range of dynamical behaviors across the glass
transition and beyond, comparable with those exhibited by a series of distinct
silica nanoparticle suspensions of various $\varphi$.


title: iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Models

abstract:
  Error analysis in NLP models is essential to successful model development and
deployment. One common approach for diagnosing errors is to identify
subpopulations in the dataset where the model produces the most errors.
However, existing approaches typically define subpopulations based on
pre-defined features, which requires users to form hypotheses of errors in
advance. To complement these approaches, we propose iSEA, an Interactive
Pipeline for Semantic Error Analysis in NLP Models, which automatically
discovers semantically-grounded subpopulations with high error rates in the
context of a human-in-the-loop interactive system. iSEA enables model
developers to learn more about their model errors through discovered
subpopulations, validate the sources of errors through interactive analysis on
the discovered subpopulations, and test hypotheses about model errors by
defining custom subpopulations. The tool supports semantic descriptions of
error-prone subpopulations at the token and concept level, as well as
pre-defined higher-level features. Through use cases and expert interviews, we
demonstrate how iSEA can assist error understanding and analysis.


title:
A machine learning accelerated inverse design of underwater acoustic
  polyurethane coatings with cylindrical voids

abstract:
  Here, we report the development of a detailed "Materials Informatics"
framework for the design of acoustic coatings for underwater sound attenuation
through integrating Machine Learning (ML) and statistical optimization
algorithms with a Finite Element Model (FEM). The finite element models were
developed to simulate the realistic performance of the acoustic coatings based
on polyurethane (PU) elastomers with embedded cylindrical voids. The FEM
results revealed that the frequency-dependent viscoelastic behavior of the
polyurethane matrix has a significant impact on the magnitude and frequency of
the absorption peak associated with the cylinders at low frequencies, which has
been commonly ignored in previous studies on similar systems. The data
generated from the FEM was used to train a Deep Neural Network (DNN) to
accelerate the design process, and subsequently, was integrated with a Genetic
Algorithm (GA) to determine the optimal geometric parameters of the cylinders
to achieve maximized, broadband, low-frequency waterborne sound attenuation. A
significant, broadband, low-frequency attenuation is achieved by optimally
configuring the layers of cylindrical voids and using attenuation mechanisms,
including Fabry-P\'erot resonance and Bragg scattering of the layers of voids.
Integration of the machine learning technique into the optimization algorithm
further accelerated the exploration of the high dimensional design space for
the targeted performance. The developed DNN exhibited significantly increased
speed (by a factor of $4.5\times 10^3$ ) in predicting the absorption
coefficient compared to the conventional FEM(s). Therefore, the acceleration
brought by the materials informatics framework brings a paradigm shift to the
design and development of acoustic coatings compared to the conventional
trial-and-error practices.


title:
OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market
  Simulation

abstract:
  This paper presents OpenGridGym, an open-source Python-based package that
allows for seamless integration of distribution market simulation with
state-of-the-art artificial intelligence (AI) decision-making algorithms. We
present the architecture and design choice for the proposed framework,
elaborate on how users interact with OpenGridGym, and highlight its value by
providing multiple cases to demonstrate its use. Four modules are used in any
simulation: (1) the physical grid, (2) market mechanisms, (3) a set of
trainable agents which interact with the former two modules, and (4)
environment module that connects and coordinates the above three. We provide
templates for each of those four, but they are easily interchangeable with
custom alternatives. Several case studies are presented to illustrate the
capability and potential of this toolkit in helping researchers address key
design and operational questions in distribution electricity markets.


title:
Cuban natural palygorskite nanoclays for the removal of sulfamethoxazole
  from aqueous solutions

abstract:
  Water pollution with pharmaceutical and personal care products has become a
serious environmental. A reasonable strategy to mitigate the problem involves
absorbent materials. In particular, the use of natural clays is an advantageous
alternative considering their high adsorption capacity and compatibility with
the environment. In the present work, the efficacy of a Cuban natural clay
(palygorskite, Pal) as support of sulfamethoxazole (SMX), an antibiotic
considered an emerging contaminant (EC), was evaluated. The amount of SMX
incorporated onto clay was determined by UV spectroscopy. The resulting
composite material was characterized by infrared spectroscopy (IR), X-ray
diffraction (XRD), thermogravimetric analysis (TG/DTG), zeta potential (ZP),
nitrogen adsorption measurements and transmission electron microscopy (TEM).
The drug desorption studies in aqueous solution indicated the reversibility of
the incorporation process, suggesting the potential use of the Pal nanoclay as
an effective support of SMX and hence, a good prospect for water
decontamination.


title:
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness
  against Adversarial Patches

abstract:
  Adversarial patches are optimized contiguous pixel blocks in an input image
that cause a machine-learning model to misclassify it. However, their
optimization is computationally demanding, and requires careful hyperparameter
tuning, potentially leading to suboptimal robustness evaluations. To overcome
these issues, we propose ImageNet-Patch, a dataset to benchmark
machine-learning models against adversarial patches. It consists of a set of
patches, optimized to generalize across different models, and readily
applicable to ImageNet data after preprocessing them with affine
transformations. This process enables an approximate yet faster robustness
evaluation, leveraging the transferability of adversarial perturbations. We
showcase the usefulness of this dataset by testing the effectiveness of the
computed patches against 127 models. We conclude by discussing how our dataset
could be used as a benchmark for robustness, and how our methodology can be
generalized to other domains. We open source our dataset and evaluation code at
https://github.com/pralab/ImageNet-Patch.


title:
Score matching enables causal discovery of nonlinear additive noise
  models

abstract:
  This paper demonstrates how to recover causal graphs from the score of the
data distribution in non-linear additive (Gaussian) noise models. Using score
matching algorithms as a building block, we show how to design a new generation
of scalable causal discovery methods. To showcase our approach, we also propose
a new efficient method for approximating the score's Jacobian, enabling to
recover the causal graph. Empirically, we find that the new algorithm, called
SCORE, is competitive with state-of-the-art causal discovery methods while
being significantly faster.


title: Bayesian Calibration for Activity Based Models

abstract:
  We consider the problem of calibration and uncertainty analysis for
activity-based transportation simulators. ABMs rely on statistical models of
traveler's behavior to predict travel patterns in a metropolitan area. Input
parameters are typically estimated from traveler's surveys using maximum
likelihood. We develop an approach that uses Gaussian process emulator to
calibrate an activity-based model of a metropolitan transplantation system. Our
approach extends traditional emulators to handle high-dimensional and
non-stationary nature of the transportation simulator. Our methodology is
applied to transportation simulator of Bloomington, Illinois. We calibrate key
parameters of the model and compare to the ad-hoc calibration process.


title: Practical cognitive speech compression

abstract:
  This paper presents a new neural speech compression method that is practical
in the sense that it operates at low bitrate, introduces a low latency, is
compatible in computational complexity with current mobile devices, and
provides a subjective quality that is comparable to that of standard
mobile-telephony codecs. Other recently proposed neural vocoders also have the
ability to operate at low bitrate. However, they do not produce the same level
of subjective quality as standard codecs. On the other hand, standard codecs
rely on objective and short-term metrics such as the segmental signal-to-noise
ratio that correlate only weakly with perception. Furthermore, standard codecs
are less efficient than unsupervised neural networks at capturing speech
attributes, especially long-term ones. The proposed method combines a
cognitive-coding encoder that extracts an interpretable unsupervised
hierarchical representation with a multi stage decoder that has a GAN-based
architecture. We observe that this method is very robust to the quantization of
representation features. An AB test was conducted on a subset of the Harvard
sentences that are commonly used to evaluate standard mobile-telephony codecs.
The results show that the proposed method outperforms the standard AMR-WB codec
in terms of delay, bitrate and subjective quality.


title: Sample-Based Output-Feedback Navigation with Bearing Measurements

abstract:
  We consider the problem of sample-based feedback-based motion planning from
bearing (direction-only) measurements. We build on our previous work that
defines a cell decomposition of the environment using RRT*, and finds an output
feedback controller to navigate through each cell toward a goal location using
duality, Control Lyapunov and Barrier Functions (CLF, CBF), and Linear
Programming. In this paper, we propose a novel strategy that uses relative
bearing measurements with respect to a set of landmarks in the environment, as
opposed to full relative displacements. The main advantage is then that the
measurements can be obtained using a simple monocular camera. We test the
proposed algorithm in the simulation, and then in an experimental environment
to evaluate the performance of our approach with respect to practical issues
such as mismatches in the dynamical model of the robot, and measurements
acquired with a camera with a limited field of view.


title:
Grid Value Analysis of Medium Voltage Back-to-Back Converter on DER
  Hosting Enhancement

abstract:
  This paper presents an analysis of the value that can be realized by
medium-voltage back-to-back (MVB2B) converters in terms of increased
utilization rate of distributed energy resource (DER) and the improvement in
operational conditions. A systematic, transferrable, and scalable methodology
has been designed to analyze and quantify the increased DER value from three
perspectives: 1) curtailment reduction of the DER generation, 2) size reduction
of the energy storage needed to otherwise realize DER hosting levels, and 3)
hosting capacity improvement of DER compared to base distribution circuit
capability. In the case study, the proposed methodology is applied to two
utility distribution systems for analysis and quantification of the grid value
of the MVB2B converter, installed in the distribution circuit, and provided to
the solar photovoltaic (PV) DERs. The analysis results demonstrate that the
MVB2B converter can deliver significant value to PV hosting enhancement of two
adjacent distribution systems when they are connected by the MVB2B converter.
Based on this case study, this paper analyzes and summarizes the approximate
realized grid value of the MVB2B converter for distribution systems dominated
by different shares of customer classes.