title:
Photonic reinforcement learning based on optoelectronic reservoir
  computing

abstract:
  Reinforcement learning has been intensively investigated and developed in
artificial intelligence in the absence of training data, such as autonomous
driving vehicles, robot control, internet advertising, and elastic optical
networks. However, the computational cost of reinforcement learning with deep
neural networks is extremely high and reducing the learning cost is a
challenging issue. We propose a photonic on-line implementation of
reinforcement learning using optoelectronic delay-based reservoir computing,
both experimentally and numerically. In the proposed scheme, we accelerate
reinforcement learning at a rate of several megahertz because there is no
required learning process for the internal connection weights in reservoir
computing. We perform two benchmark tasks, CartPole-v0 and MountanCar-v0 tasks,
to evaluate the proposed scheme. Our results represent the first hardware
implementation of reinforcement learning based on photonic reservoir computing
and pave the way for fast and efficient reinforcement learning as a novel
photonic accelerator.


title: Alternative ideas in cosmology

abstract:
  Some remarkable examples of alternative cosmological theories are reviewed
here, ranging from a compilation of variations on the Standard Model through
the more distant quasi-steady-state cosmology, plasma cosmology, or universe
models as a hypersphere, to the most exotic cases including static models.
  The present-day standard model of cosmology, Lambda-CDM, gives us a
representation of a cosmos whose dynamics is dominated by gravity with a finite
lifetime, large scale homogeneity, expansion and a hot initial state, together
with other dark elements necessary to avoid certain inconsistencies with
observations. There are however some models with characteristics that are close
to those of the standard model but differing in some minor aspects: different
considerations on CP violation, inflation, number of neutrino species,
quark-hadron phase transition, baryonic or non-baryonic dark-matter, dark
energy, nucleosynthesis scenarios, large-scale structure formation scenarios;
or major variations like a inhomogeneous universe, Cold Big Bang, varying
physical constants or gravity law, zero-active mass (also called `R_h=ct'),
Milne, and cyclical models.
  At the most extreme distance from the standard model, the static models, a
non-cosmological redshift includes `tired-light' hypotheses, which assume that
the photon loses energy owing to an intrinsic property or an interaction with
matter or light as it travels some distance, or other non-standard ideas.
  Our impression is that none of the alternative models has acquired the same
level of development as Lambda-CDM in offering explanations of available
cosmological observations. One should not, however, judge any theory in terms
of the number of observations that it can successfully explain (ad hoc in many
cases) given the much lower level of development of the alternative ones.


title:
Interface between the long-term propagation and the destructive re-entry
  phases exploiting the overshoot boundary

abstract:
  In recent years, due to the constant increase of the density of satellites in
the space environment, several studies have been focused on the development of
active and passive strategies to remove and mitigate space debris. This work
investigates the feasibility of developing a reliable and fast approach to
analyze the re-entry of a satellite. The numerical model interfaces the
long-term orbit propagation obtained through semi-analytical methods with the
atmospheric destructive re-entry phase exploiting the concept of overshoot
boundary, highlighting the effect that an early break-off of the solar panels
can have on the re-entry prediction. The re-entry of ESA's INTEGRAL mission is
chosen as a test case to demonstrate the efficiency of the model in producing a
complete simulation of the re-entry. The simulation of the destructive re-entry
phase is produced using an object-oriented approach, paying attention to the
demisability process of the most critical components of the space system.


title: Simulating Glueball Production in $N_f = 0$ QCD

abstract:
  In an $SU(N_c)$ gauge theory with zero light quark flavours $N_f = 0$, the
only hadronic states that form below the confinement scale are composite gluon
states called glueballs. These minimal confining sectors arise in many Hidden
Valley extensions of the Standard Model, including scenarios that could hold
the solution to the dark matter question and the hierarchy problem.
Quantitative study of dark glueball phenomenology requires an understanding of
pure glue hadronization, which to date is severely lacking. In this work we
show that significant progress can be made by combining a perturbative pure
glue parton shower with a self-consistent and physically motivated
parameterization of the unknown non-perturbative physics, thanks to the modest
hierarchy between the glueball mass and the confinement scale. We make our
simulation code available as the public GlueShower package, the first glueball
generator for Hidden Valley theories, and perform preliminary studies of
several glueball production observables, with theoretical uncertainties that
take the full range of possible hadronization scenarios into account. We hope
this will enable new studies of dark sector phenomenology that were previously
inaccessible.


title: Towards excluding a light $Z^\prime$ explanation of $b\to s\ell^+\ell^-$

abstract:
  The discrepancies between $b\to s\ell^+\ell^-$ data and the corresponding
Standard Model predictions constitute the most significant hints for new
physics (at the TeV scale or below) currently available. In fact, many
scenarios that can account for these anomalies have been proposed in the
literature. However, only a single light new physics explanation, i.e. with a
mass below the $B$ meson scale, is possible: a light $Z^\prime$ boson. In this
article, we point out that improved limits on $B\to K^{(*)}\nu\nu$, including
the experimental sensitivities required for a proper treatment of the
necessarily sizable $Z^\prime$ width, together with the forthcoming Belle~II
analyses of $e^+e^-\to\mu^+\mu^-+{\rm invisible}$, can rule out a $Z^\prime$
explanation of $b\to s\ell^+\ell^-$ data with a mass below $\approx4\,$GeV.
Importantly, such a light $Z^\prime$ is the only viable single particle
solution to the $b\to s\ell^+\ell^-$ anomalies predicting $R(K^{(*)})>0$ in
high $q^2$ bins, therefore providing an essential consistency test of data.


title:
Jet Launching from Binary Neutron Star Mergers: Incorporating Neutrino
  Transport and Magnetic Fields

abstract:
  We perform general relativistic, magnetohydrodynamic (GRMHD) simulations of
merging binary neutron stars incorporating neutrino transport and magnetic
fields. Our new radiative transport module for neutrinos adopts a general
relativistic, truncated-moment (M1) formalism. The binaries consist of two
identical, irrotational stars modeled by the SLy nuclear equation of state
(EOS). They are initially in quasicircular orbit and threaded with a poloidal
magnetic field that extends from the stellar interior into the exterior, as in
typical pulsars. We insert neutrino processes shortly after the merger and
focus on the role of neutrinos in launching a jet following the collapse of the
hypermassive neutron star (HMNS) remnant to a spinning black hole (BH). We
treat two microphysical versions: one (a "warm-up") evolving a single neutrino
species and considering only charged-current processes, and the other evolving
three species $(\nu_e, \bar{\nu}_e, \nu_{\rm x})$ and related processes. We
trace the evolution until the system reaches a quasiequilibrium state after BH
formation. We find that the BH + disk remnant eventually launches an incipient
jet. The electromagnetic Poynting luminosity is $\sim 10^{53} \rm \, erg\,
s^{-1}$, consistent with that of typical short gamma-ray bursts (sGRBs). The
effect of neutrino cooling shortens the lifetime of the HMNS, and lowers the
amplitude of the major peak of the gravitational wave (GW) power spectrum
somewhat. After BH formation, neutrinos help clear out the matter near the BH
poles, resulting in lower baryon-loaded surrounding debris. The neutrino
luminosity resides in the range $\sim 10^{52-53} \rm \,erg\,s^{-1}$ once
quasiequilibrium is achieved. Comparing with the neutrino-free models, we
observe that the inclusion of neutrinos yields similar ejecta masses and is
inefficient in carrying off additional angular momentum.


title:
Slow convection and fast rotation in crystallization-driven white dwarf
  dynamos

abstract:
  It has been recently suggested that white dwarfs generate magnetic fields in
a process analogous to the Earth. The crystallization of the core creates a
compositional inversion that drives convection, and combined with rotation,
this can sustain a magnetic dynamo. We reanalyse the dynamo mechanism, arising
from the slow crystallization of the core, and find convective turnover times
$t_{\rm conv}$ of weeks to months - longer by orders of magnitude than
previously thought. With white dwarf spin periods $P\ll t_{\rm conv}$,
crystallization-driven dynamos are almost always in the fast rotating regime,
where the magnetic field $B$ is at least in equipartition with the convective
motion and is possibly further enhanced by a factor of $B\propto (t_{\rm
conv}/P)^{1/2}$, depending on the assumed dynamo scaling law. We track the
growth of the crystallized core using MESA and compute the magnetic field
$B(T_{\rm eff})$ as a function of the white dwarf's effective temperature
$T_{\rm eff}$. We compare this prediction with observations and show that
crystallization-driven dynamos can explain some - but not all - of the $\sim$MG
magnetic fields measured for single white dwarfs, as well as the stronger
fields measured for white dwarfs in cataclysmic variables, which were spun up
by mass accretion to short $P$. Our $B(T_{\rm eff})$ curves might also explain
the clustering of white dwarfs with Balmer emission lines around $T_{\rm
eff}\approx 7500\textrm{ K}$.


title:
A white dwarf accreting planetary material determined from X-ray
  observations

abstract:
  The atmospheres of a large proportion of white dwarf stars are polluted by
heavy elements that are expected to sink out of visible layers on short
timescales. This has been interpreted as a signature of ongoing accretion of
debris from asteroids, comets, and giant planets. This scenario is supported by
the detection of debris discs and transits of planetary fragments around some
white dwarfs. However, photospheric metals are only indirect evidence for
ongoing accretion, and the inferred accretion rates and parent body
compositions heavily depend on models of diffusion and mixing processes within
the white dwarf atmosphere. Here we report a 4.4$\sigma$ detection of X-rays
from a polluted white dwarf, G29$-$38, using a 106 ks exposure with the Chandra
X-ray Observatory, demonstrating directly that the star is currently accreting.
From the measured X-ray luminosity, we find an instantaneous accretion rate of
$\dot{M_{\rm X}}=1.63^{+1.29}_{-0.40}\times 10^{9}\mathrm{\,g\,s^{-1}}$. This
is the first direct measurement of the accretion rate onto the white dwarf,
which is independent of stellar atmosphere models. This rate exceeds estimates
based on past studies of the photospheric abundances by more than a factor two,
and implies that convective overshoot has to be accounted for in modelling the
spectra of debris-accreting white dwarfs. We measure a low plasma temperature
of $kT=0.5\pm0.2\,\mathrm{keV}$, corroborating the predicted bombardment
solution for white dwarfs accreting at low accretion rates. Offering a new
method for studying evolved planetary systems, these observations provide the
opportunity to independently measure the instantaneous accretion rate of
planetary material, and therefore investigate the timescale of accretion onto
white dwarfs, and the evolution and replenishment of debris disks.


title:
Investigating the Disk-Jet Structure in M87 through Flux Separation in
  the Linear and Circular Polarization Images

abstract:
  For testing different electron temperature ($T_{\rm e}$) prescriptions in
general relativistic magnetohydrodynamics (GRMHD) simulations through
observations, we propose to utilize linear polarization (LP) and circular
polarization (CP) images. We calculate the polarization images based on a
semi-Magnetically Arrested Disk (MAD) GRMHD model for various $T_{\rm e}$
parameters, bearing M87 in mind. We find an LP-CP separation in the images of
the low-$T_{\rm e}$ disk cases at 230 GHz; namely, the LP flux mainly
originates from downstream of the jet and the CP flux comes from the
counter-side jet, while the total intensity is maximum at the jet base. This
can be understood as follows: although the LP flux is generated through
synchrotron emission widely around the black hole, most of the LP flux from the
jet base does not reach the observer, since it undergoes Faraday rotation
($\propto T_{\rm e}^{-2}$) when passing through the outer cold disk and is thus
depolarized. Hence, only the LP flux from the downstream (not passing the cold
dense plasmas) can survive. Meanwhile, the CP flux is generated from the LP
flux by Faraday conversion ($\propto T_{\rm e}$) in the inner hot region.
Stronger CP flux is thus observed from the counter-side jet. Moreover, the
LP-CP separation is more enhanced at a lower frequency such as 86 GHz but is
rather weak at 43 GHz, since the media in the latter case is optically thick
for synchrotron self-absorption so that all the fluxes should come from the
photosphere. The same is true for cases with higher mass accretion rates and/or
larger inclination angles.


title:
Measurement-induced power law negativity in an open monitored quantum
  circuit

abstract:
  Generic many-body systems coupled to an environment lose their quantum
entanglement due to decoherence and evolve to a mixed state with only classical
correlations. Here, we show that measurements can stabilize quantum
entanglement within open quantum systems. Specifically, in random unitary
circuits with dephasing at the boundary, we find both numerically and
analytically that projective measurements performed at a small nonvanishing
rate results in a steady state with an $L^{1/3}$ power-law scaling entanglement
negativity within the system. Using an analytical mapping to a statistical
mechanics model of directed polymers in a random environment, we show that the
power-law negativity scaling can be understood as Kardar-Parisi-Zhang (KPZ)
fluctuations due to the random measurement locations. Further increasing the
measurement rate leads to a phase transition into an area-law negativity phase,
which is of the same universality as the entanglement transition in monitored
random circuits without decoherence.


title: Ultraviolet Freeze-in with a Time-dependent Inflaton Decay

abstract:
  It is typically assumed that during reheating the inflaton decays with a
constant decay width. However, this is not guaranteed and can have a strong
impact on the dark matter (DM) genesis. In the context of the ultraviolet (UV)
freeze-in mechanism, if the operators connecting the dark and visible sectors
are of sufficiently high mass dimension, the bulk of the DM abundance is
produced during and not after reheating. We study here the impact of a
time-dependent decay width of the inflaton on the DM abundance, emphasizing the
differences with respect to the cases where the decay is either instantaneous
or constant. We also provide concrete examples for DM production via UV
freeze-in, e.g., from 2-to-2 scatterings of standard model particles, or from
inflaton scatterings or decays, elucidating how the time-dependence influences
the DM yield.


title: Origin of Neutrino Masses on the Convex Cone of Positivity Bounds

abstract:
  We exhibit the geometric structure of the convex cone in the linear space of
the Wilson coefficients for the dimension-8 operators involving the left-handed
lepton doublet $L$ and the Higgs doublet $H$ in the Standard Model effective
field theory (SMEFT). The boundary of the convex cone gives rise to the
positivity bounds on the Wilson coefficients, while the extremal ray
corresponds to the unique particle state in the theory of ultra-violet
completion. Among three types of canonical seesaw models for neutrino masses,
we discover that only right-handed neutrinos in the type-I seesaw model show up
as one of extremal rays, whereas the heavy particles in the type-II and
type-III seesaw models live inside the cone. The experimental determination of
the relevant Wilson coefficients close to the extremal ray of type-I seesaw
model will unambiguously pin down or rule out the latter as the origin of
neutrino masses. This discovery offers a novel way to distinguish the most
popular seesaw model from others, and also strengthens the SMEFT as an
especially powerful tool to probe new physics beyond the Standard Model.


title: Prethermalization in confined spin chains

abstract:
  Unconventional nonequilibrium phases with restricted correlation spreading
and slow entanglement growth have been proposed to emerge in systems with
confined excitations, calling their thermalization dynamics into question.
Here, we investigate the many-body dynamics of a confined Ising spin chain, in
which domain walls in the ordered phase form bound states reminiscent of
mesons. We show that the thermalization dynamics after a quantum quench
exhibits multiple stages with well separated time scales. The system first
relaxes towards a prethermal state, described by a Gibbs ensemble with
conserved meson number. The prethermal state arises from rare events in which
mesons are created in close vicinity, leading to an avalanche of scattering
events. Only at much later times a true thermal equilibrium is achieved in
which the meson number conservation is violated by a mechanism akin to the
Schwinger effect.

categories: cond-mat.stat-mech cond-mat.str-el hep-th quant-ph


title:
Unboundedness of the first and the last Betti numbers of Numerical
  Semigroups Generated by Concatenation

abstract:
  We show that the minimal number of generators and the Cohen-Macaulay type of
a family of numerical semigroups generated by concatenation of arithmetic
sequences is unbounded.


title:
Simulating spectroscopic detection of Majorana zero modes with a
  superconducting quantum computer

abstract:
  We detect the remnants of the topological phase boundary of a two site Kitaev
chain with Coulomb interactions using a spectroscopic eigensolver algorithm on
a quantum computer. Along the boundary, even- and odd-parity states are
degenerate and Majorana zero modes become spatially separated. The
spectroscopic eigensolver interacts with a quantum register which simulates the
evolution of the Hamiltonian system via an auxiliary "probe" qubit. The probe
qubit is weakly coupled to the system so that it responds to the resonant
transitions of the system.


title: Illustrating galaxy-halo connection in the DESI era with IllustrisTNG

abstract:
  We employ the hydrodynamical simulation IllustrisTNG to inform the
galaxy-halo connection of the Luminous Red Galaxy (LRG) and Emission Line
Galaxy (ELG) samples of the Dark Energy Spectroscopic Instrument (DESI) survey
at redshift z ~ 0.8. Specifically, we model the galaxy colors of IllustrisTNG
and apply sliding DESI color-magnitude cuts, matching the DESI target
densities. We study the halo occupation distribution model (HOD) of the
selected samples by matching them to their corresponding dark matter halos in
the IllustrisTNG dark matter run. We find the HOD of both the LRG and ELG
samples to be consistent with their respective baseline models, but also we
find important deviations from common assumptions about the satellite
distribution, velocity bias, and galaxy secondary biases. We identify strong
evidence for concentration-based and environment-based occupational variance in
both samples, an effect known as "galaxy assembly bias". The central and
satellite galaxies have distinct dependencies on secondary halo properties,
showing that centrals and satellites have distinct evolutionary trajectories
and should be modelled separately. These results serve to inform the necessary
complexities in modeling galaxy-halo connection for DESI analyses and also
prepare for building high-fidelity mock galaxies. Finally, we present a
shuffling-based clustering analysis that reveals a 10-15% excess in the LRG
clustering of modest statistical significance due to secondary galaxy biases.
We also find a similar excess signature for the ELGs, but with much lower
statistical significance. When a larger hydrodynamical simulation volume
becomes available, we expect our analysis pipeline to pinpoint the exact
sources of such excess clustering signatures.


title:
SGL: Symbolic Goal Learning in a Hybrid, Modular Framework for Human
  Instruction Following

abstract:
  This paper investigates robot manipulation based on human instruction with
ambiguous requests. The intent is to compensate for imperfect natural language
via visual observations. Early symbolic methods, based on manually defined
symbols, built modular framework consist of semantic parsing and task planning
for producing sequences of actions from natural language requests. Modern
connectionist methods employ deep neural networks to automatically learn visual
and linguistic features and map to a sequence of low-level actions, in an
endto-end fashion. These two approaches are blended to create a hybrid, modular
framework: it formulates instruction following as symbolic goal learning via
deep neural networks followed by task planning via symbolic planners.
Connectionist and symbolic modules are bridged with Planning Domain Definition
Language. The vision-and-language learning network predicts its goal
representation, which is sent to a planner for producing a task-completing
action sequence. For improving the flexibility of natural language, we further
incorporate implicit human intents with explicit human instructions. To learn
generic features for vision and language, we propose to separately pretrain
vision and language encoders on scene graph parsing and semantic textual
similarity tasks. Benchmarking evaluates the impacts of different components
of, or options for, the vision-and-language learning model and shows the
effectiveness of pretraining strategies. Manipulation experiments conducted in
the simulator AI2THOR show the robustness of the framework to novel scenarios.


title: The evolution of scientific literature as metastable knowledge states

abstract:
  The problem of identifying common concepts in the sciences and deciding when
new ideas have emerged is an open one. Metascience researchers have sought to
formalize principles underlying stages in the life-cycle of scientific
research, determine how knowledge is transferred between scientists and
stakeholders, and understand how new ideas are generated and take hold. Here,
we model the state of scientific knowledge immediately preceding new directions
of research as a metastable state and the creation of new concepts as
combinatorial innovation. We find that, through the combined use of natural
language clustering and citation graph analysis, we can predict the evolution
of ideas over time and thus connect a single scientific article to past and
future concepts in a way that goes beyond traditional citation and reference
connections.


title:
Constraining Type Ia supernova explosions and early flux excesses with
  the Zwicky Transient Factory

abstract:
  In the new era of time-domain surveys Type Ia supernovae are being caught
sooner after explosion, which has exposed significant variation in their early
light curves. Two driving factors for early time evolution are the distribution
of nickel in the ejecta and the presence of flux excesses of various causes. We
perform an analysis of the largest young SN Ia sample to date. We compare 115
SN Ia light curves from the Zwicky Transient Facility to the turtls model grid
containing light curves of Chandrasekhar-mass explosions with a range of nickel
masses, nickel distributions and explosion energies. We find that the majority
of our observed light curves are well reproduced by Chandrasekhar-mass
explosion models with a preference for highly extended nickel distributions. We
identify six SNe Ia with an early-time flux excess in our g- and r-band data
(four `blue' and two `red' flux excesses). We find an intrinsic rate of 18+/-11
per cent of early flux excesses in SNe Ia at z < 0.07, based on three detected
flux excesses out of 30 (10 per cent) observed SNe Ia with a simulated
efficiency of 57 per cent. This is comparable to rates of flux excesses in the
literature but also accounts for detection efficiencies. Two of these events
are mostly consistent with CSM interaction, while the other four have longer
lifetimes in agreement with companion interaction and nickel-clump models. We
find a higher frequency of flux excesses in 91T/99aa-like events (44+/-13 per
cent).


title: Designing Trustworthy User Interfaces

abstract:
  Interface design can directly influence trustworthiness of a software.
Thereby, it affects users' intention to use a tool. Previous research on user
trust has not comprehensively addressed user interface design, though. We lack
an understanding of what makes interfaces trustworthy (1), as well as
actionable measures to improve trustworthiness (2). We contribute to this by
addressing both gaps. Based on a systematic literature review, we give a
thorough overview over the theory on user trust and provide a taxonomy of
factors influencing user interface trustworthiness. Then, we derive concrete
measures to address these factors in interface design. We use the results to
create a proof of concept interface. In a preliminary evaluation, we compare a
variant designed to elicit trust with one designed to reduce it. Our results
show that the measures we apply can be effective in fostering trust in users.


title:
Incremental Inference on Higher-Order Probabilistic Graphical Models
  Applied to Constraint Satisfaction Problems

abstract:
  Probabilistic graphical models (PGMs) are tools for solving complex
probabilistic relationships. However, suboptimal PGM structures are primarily
used in practice. This dissertation presents three contributions to the PGM
literature. The first is a comparison between factor graphs and cluster graphs
on graph colouring problems such as Sudokus - indicating a significant
advantage for preferring cluster graphs. The second is an application of
cluster graphs to a practical problem in cartography: land cover classification
boosting. The third is a PGMs formulation for constraint satisfaction problems
and an algorithm called purge-and-merge to solve such problems too complex for
traditional PGMs.


title: Learning English with Peppa Pig

abstract:
  Attempts to computationally simulate the acquisition of spoken language via
grounding in perception have a long tradition but have gained momentum in the
past few years. Current neural approaches exploit associations between the
spoken and visual modality and learn to represent speech and visual data in a
joint vector space. A major unresolved issue from the point of ecological
validity is the training data, typically consisting of images or videos paired
with spoken descriptions of what is depicted. Such a setup guarantees an
unrealistically strong correlation between speech and the visual world. In the
real world the coupling between the linguistic and the visual is loose, and
often contains confounds in the form of correlations with non-semantic aspects
of the speech signal. The current study is a first step towards simulating a
naturalistic grounding scenario by using a dataset based on the children's
cartoon Peppa Pig. We train a simple bi-modal architecture on the portion of
the data consisting of naturalistic dialog between characters, and evaluate on
segments containing descriptive narrations. Despite the weak and confounded
signal in this training data our model succeeds at learning aspects of the
visual semantics of spoken language.


title: On the complexity and modeling of the electric vehicle sharing problem

abstract:
  We introduce the electric vehicle sharing problem (EVSP), a problem that
arises from the planning and operation of electric car-sharing systems which
allow one-way rental of vehicles. The problem aims at finding the maximum total
daily rental time in which customers' demands are assigned to the existing
fleet. In addition, either all of the customer's demands are completely
fulfilled or the customer does not use the system at all. We show that the EVSP
is NP-hard, and we provide four mixed-integer linear programming formulations
based on space-time network flow models, along with some theoretical results.
We perform a comprehensive computational study of the behavior of the proposed
formulations using two benchmark sets, one of which is based on real-world data
from an electric car-sharing system located in Fortaleza, Brazil. The results
show that our best formulation is effective in solving instances where each
customer has only one demand. In general, we were able to optimally solve at
least 55% of the instances within the time limit of one hour.


title:
An analytical method to quantify the statistics of energy landscapes in
  random solid solutions

abstract:
  Recent studies of concentrated solid solutions have highlighted the role of
varied solute interactions in the determination of a wide variety of mesoscale
properties. These solute interactions emerge as spatial fluctuations in
potential energy, which arise from local variations in the chemical
environment. Although observations of potential energy fluctuations are well
documented in the literature, there remains a paucity of methods to determine
their statistics. Here, we present a set of analytical equations to quantify
the statistics of potential energy landscapes in randomly arranged solid
solutions. Our approach is based on a reparameterization of the relations of
the embedded atom method in terms of the solute coordination environment. The
final equations are general and can be applied to different crystal lattices
and energy landscapes, provided the systems of interest can be described by
sets of coordination relations. We leverage these statistical relations to
study the cohesive energy and generalized planar fault energy landscapes of
several different solid solutions. Analytical predictions are validated using
molecular statics simulations, which find excellent agreement in most cases.
The outcomes of this analysis provide new insights into phase stability and the
interpretation of local planar fault energies in solid solutions, which are
topics of ongoing discussion within the community.


title: Modelling Magnetic Multipolar Phases in Density Functional Theory

abstract:
  Multipolar magnetic phases in correlated insulators represent a great
challenge for Density Functional Theory (DFT) due to the coexistence of
intermingled interactions, typically spin-orbit coupling, crystal field and
complex non-collinear and high-rank inter-site exchange, creating a complected
configurational space with multiple minima. Though the +U correction to DFT
allows, in principle, the modelling of such magnetic ground states, its results
strongly depend on the initially symmetry breaking, constraining the nature of
order parameter in the converged DFT+U solution. As a rule, DFT+U calculations
starting from a set of initial on-site magnetic moments result in a
conventional dipolar order. A more sophisticated approach is clearly needed in
the case of magnetic multipolar ordering, which is revealed by a null integral
of the magnetization density over spheres centered on magnetic atoms, but with
non-zero local contributions. Here we show how such phases can be efficiently
captured using an educated constrained initialisation of the onsite density
matrix, which is derived from the multipolar-ordered ground state of an ab
initio effective Hamiltonian. Various properties of such exotic ground states,
like their one-electron spectra, become therefore accessible by all-electron
DFT+U methods. We assess the reliability of this procedure on the
Ferro-Octupolar ground state recently predicted in Ba$_2$MOsO$_6$ (M = Ca, Mg,
Zn) [Phys. Rev. Lett. 127, 237201 (2021)]


title: Refining Self-Supervised Learning in Imaging: Beyond Linear Metric

abstract:
  We introduce in this paper a new statistical perspective, exploiting the
Jaccard similarity metric, as a measure-based metric to effectively invoke
non-linear features in the loss of self-supervised contrastive learning.
Specifically, our proposed metric may be interpreted as a dependence measure
between two adapted projections learned from the so-called latent
representations. This is in contrast to the cosine similarity measure in the
conventional contrastive learning model, which accounts for correlation
information. To the best of our knowledge, this effectively non-linearly fused
information embedded in the Jaccard similarity, is novel to self-supervision
learning with promising results. The proposed approach is compared to two
state-of-the-art self-supervised contrastive learning methods on three image
datasets. We not only demonstrate its amenable applicability in current ML
problems, but also its improved performance and training efficiency.


title: Conformal capacity and polycircular domains

abstract:
  We study numerical conformal mapping of multiply connected planar domains
with boundaries consisting of unions of finitely many circular arcs, so called
polycircular domains. We compute the conformal capacities of condensers defined
by polycircular domains. Experimental error estimates are provided for the
computed capacity and, when possible, the rate of convergence under refinement
of discretisation is analysed. The main ingredients of the computation are two
computational methods, on one hand the boundary integral equation method
combined with the fast multipole method and on the other hand the $hp$-FEM
method. The results obtained with these two methods agree with high accuracy.


title:
The product formula for regularized Fredholm determinants: two new
  proofs

abstract:
  For an $m$-summable operator $A$ in a separable Hilbert space the higher
regularized Fredholm determinant $\det\nolimits_m(I+A)$ generalizes the
classical Fredholm determinant. Recently, Britz et al presented a proof of a
product formula \[ \det\nolimits_m\bigl( (I+A)\cdot(I+B) \bigr)
  = \det\nolimits_m (I+A) \cdot \det\nolimits_m (I+B) \cdot
\exp\operatorname{Tr}\bigl(X_m(A,B)\bigr), \] where $X_m(A,B)$ is an explicit
polynomial in $A,B$ with values in the trace class operators. If $m=1$ then
$X_1(A,B)=0$, hence the formula generalizes the classical determinant product
formula.
  The purpose of this note is to present two very simple alternative proofs of
the formula. The first proof is analytic and makes use of the fact that
$z\mapsto \det\nolimits_m(I+zA)$ is holomorphic, while the second proof is
completely algebraic. The algebraic proof has, in our opinion, some interesting
aspects in its own about the trace and commutators.


title: CAFQA: Clifford Ansatz For Quantum Accuracy

abstract:
  Variational Quantum Algorithms (VQAs) rely upon the iterative optimization of
a parameterized unitary circuit with respect to an objective function. Since
quantum machines are noisy and expensive resources, it is imperative to choose
a VQA's ansatz appropriately and its initial parameters to be close to optimal.
  This work tackles the problem of finding initial ansatz parameters by
proposing CAFQA, a Clifford ansatz for quantum accuracy. The CAFQA ansatz is a
hardware-efficient circuit built with only Clifford gates. In this ansatz, the
initial parameters for the tunable gates are chosen by searching efficiently
through the Clifford parameter space via classical simulation, thereby
producing a suitable stabilizer state. The stabilizer states produced are shown
to always equal or outperform traditional classical initialization (e.g.,
Hartree-Fock), and often produce high accuracy estimations prior to quantum
exploration. Furthermore, the technique is classically suited since a) Clifford
circuits can be exactly simulated classically in polynomial time and b) the
discrete Clifford space, while scaling exponentially in the number of qubits,
is searched efficiently via Bayesian Optimization.
  For the Variational Quantum Eigensolver (VQE) task of molecular ground state
energy estimation up to 20 qubits, CAFQA's Clifford Ansatz achieves a mean
accuracy of near 99%, recovering as much as 99.99% of the correlation energy
over Hartree-Fock. Notably, the scalability of the approach allows for
preliminary ground state energy estimation of the challenging Chromium dimer
with an accuracy greater than Hartree-Fock. With CAFQA's initialization, VQA
convergence is accelerated by a factor of 2.5x.
  In all, this work shows that stabilizer states are an accurate ansatz
initialization for VQAs. Furthermore, it highlights the potential for
quantum-inspired classical techniques to support VQAs.


title:
NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical
  Molecular Dynamics in the Condensed Phase

abstract:
  Accurate and efficient methods to simulate nonadiabatic and quantum nuclear
effects in high-dimensional and dissipative systems are crucial for the
prediction of chemical dynamics in condensed phase. To facilitate effective
development, code sharing and uptake of newly developed dynamics methods, it is
important that software implementations can be easily accessed and built upon.
Using the Julia programming language, we have developed the NQCDynamics.jl
package which provides a framework for established and emerging methods for
performing semiclassical and mixed quantum-classical dynamics in condensed
phase. The code provides several interfaces to existing atomistic simulation
frameworks, electronic structure codes, and machine learning representations.
In addition to the existing methods, the package provides infrastructure for
developing and deploying new dynamics methods which we hope will benefit
reproducibility and code sharing in the field of condensed phase quantum
dynamics. Herein, we present our code design choices and the specific Julia
programming features from which they benefit. We further demonstrate the
capabilities of the package on two examples of chemical dynamics in condensed
phase: the population dynamics of the spin-boson model as described by a wide
variety of semi-classical and mixed quantum-classical nonadiabatic methods and
the reactive scattering of H2 on Ag(111) using the Molecular Dynamics with
Electronic Friction method. Together, they exemplify the broad scope of the
package to study effective model Hamiltonians and realistic atomistic systems.


title: Towards Funnel MPC for nonlinear systems with relative degree two

abstract:
  Funnel MPC, a novel Model Predictive Control (MPC) scheme, allows guaranteed
output tracking of smooth reference signals with prescribed error bounds for
nonlinear multi-input multi-output systems. To this end, the stage cost
resembles the high-gain idea of funnel control. However, rigorous proofs for
initial and recursive feasibility without incorporating additional output
constraints in the Funnel MPC scheme are only available for systems with
relative degree one and stable internal dynamics. In this paper, we extend
these results to systems with relative degree two by incorporating also a term
based on the idea of a derivative funnel in the stage cost.


title:
A blob method for inhomogeneous diffusion with applications to
  multi-agent control and sampling

abstract:
  As a counterpoint to classical stochastic particle methods for linear
diffusion equations, we develop a deterministic particle method for the
weighted porous medium equation (WPME) and prove its convergence on bounded
time intervals. This generalizes related work on blob methods for unweighted
porous medium equations. From a numerical analysis perspective, our method has
several advantages: it is meshfree, preserves the gradient flow structure of
the underlying PDE, converges in arbitrary dimension, and captures the correct
asymptotic behavior in simulations.
  That our method succeeds in capturing the long time behavior of WPME is
significant from the perspective of related problems in quantization. Just as
the Fokker-Planck equation provides a way to quantize a probability measure
$\bar{\rho}$ by evolving an empirical measure according to stochastic Langevin
dynamics so that the empirical measure flows toward $\bar{\rho}$, our particle
method provides a way to quantize $\bar{\rho}$ according to deterministic
particle dynamics approximating WMPE. In this way, our method has natural
applications to multi-agent coverage algorithms and sampling probability
measures.
  A specific case of our method corresponds exactly to the mean-field dynamics
of training a two-layer neural network for a radial basis function activation
function. From this perspective, our convergence result shows that, in the over
parametrized regime and as the variance of the radial basis functions goes to
zero, the continuum limit is given by WPME. This generalizes previous results,
which considered the case of a uniform data distribution, to the more general
inhomogeneous setting. As a consequence of our convergence result, we identify
conditions on the target function and data distribution for which convexity of
the energy landscape emerges in the continuum limit.


title:
Supersymmetric explanation of the muon g-2 anomaly with and without
  stable neutralino

abstract:
  In this paper we explore the possibility of explaining the muon $g-2$ anomaly
in various types of supersymmetric extensions of the Standard Model. In
particular, we investigate and compare the phenomenological constraints in the
MSSM with stable neutralino and the other types of scenarios where the
neutralino is unstable. For the latter case we study the Gauge Mediated SUSY
Breaking (GMSB) scenario with very light gravitino and the $UDD$-type R-Parity
Violating (RPV) scenario. In the MSSM with stable neutralino, the parameter
region favoured by the $(g-2)_\mu$ is strongly constrained by the neutralino
relic abundance and the dark matter direct detection experiments, as well as by
the LHC searches in the lepton plus missing transverse energy channel. On the
other hand, the scenarios without stable neutralino are free from the dark
matter constraints, while the LHC constraints depend strongly on the decay of
the neutralino. We find that in GMSB the entire parameter region favoured by
the muon $g-2$ is already excluded if the Next Lightest SUSY Particle (NLSP) is
the neutralino, while some regions are still allowed if the NLSP is stau. In
the RPV scenario, the LHC constraints are much weaker than the other scenarios
and a wide region of the parameter space is still open for the muon $g-2$.


title:
OptGAN: Optimizing and Interpreting the Latent Space of the Conditional
  Text-to-Image GANs

abstract:
  Text-to-image generation intends to automatically produce a photo-realistic
image, conditioned on a textual description. It can be potentially employed in
the field of art creation, data augmentation, photo-editing, etc. Although many
efforts have been dedicated to this task, it remains particularly challenging
to generate believable, natural scenes. To facilitate the real-world
applications of text-to-image synthesis, we focus on studying the following
three issues: 1) How to ensure that generated samples are believable, realistic
or natural? 2) How to exploit the latent space of the generator to edit a
synthesized image? 3) How to improve the explainability of a text-to-image
generation framework? In this work, we constructed two novel data sets (i.e.,
the Good & Bad bird and face data sets) consisting of successful as well as
unsuccessful generated samples, according to strict criteria. To effectively
and efficiently acquire high-quality images by increasing the probability of
generating Good latent codes, we use a dedicated Good/Bad classifier for
generated images. It is based on a pre-trained front end and fine-tuned on the
basis of the proposed Good & Bad data set. After that, we present a novel
algorithm which identifies semantically-understandable directions in the latent
space of a conditional text-to-image GAN architecture by performing independent
component analysis on the pre-trained weight values of the generator.
Furthermore, we develop a background-flattening loss (BFL), to improve the
background appearance in the edited image. Subsequently, we introduce linear
interpolation analysis between pairs of keywords. This is extended into a
similar triangular `linguistic' interpolation in order to take a deep look into
what a text-to-image synthesis model has learned within the linguistic
embeddings. Our data set is available at
https://zenodo.org/record/6283798#.YhkN_ujMI2w.


title:
Modulation and signal class labelling using active learning and
  classification using machine learning

abstract:
  Supervised learning in machine learning (ML) requires labelled data set.
Further real-time data classification requires an easily available methodology
for labelling. Wireless modulation and signal classification find their
application in plenty of areas such as military, commercial and electronic
reconaissance and cognitive radio. This paper mainly aims to solve the problem
of real-time wireless modulation and signal class labelling with an active
learning framework. Further modulation and signal classification is performed
with machine learning algorithms such as KNN, SVM, Naive bayes. Active learning
helps in labelling the data points belonging to different classes with the
least amount of data samples trained. An accuracy of 86 percent is obtained by
the active learning algorithm for the signal with SNR 18 dB. Further, KNN based
model for modulation and signal classification performs well over range of SNR,
and an accuracy of 99.8 percent is obtained for 18 dB signal. The novelty of
this work exists in applying active learning for wireless modulation and signal
class labelling. Both modulation and signal classes are labelled at a given
time with help of couplet formation from the data samples.


title: Reflexivity of a Banach Space with a Countable Vector Space Basis

abstract:
  All most all the function spaces over real or complex domains and spaces of
sequences, that arise in practice as examples of normed complete linear spaces
(Banach spaces), are reflexive. These Banach spaces are dual to their
respective spaces of continuous linear functionals over the corresponding
Banach spaces. For each of these Banach spaces, a countable vector space basis
exists, which is responsible for their reflexivity. In this paper, a specific
criterion for reflexivity of a Banach space with a countable vector space basis
is presented.


title:
Capturing Actionable Dynamics with Structured Latent Ordinary
  Differential Equations

abstract:
  End-to-end learning of dynamical systems with black-box models, such as
neural ordinary differential equations (ODEs), provides a flexible framework
for learning dynamics from data without prescribing a mathematical model for
the dynamics. Unfortunately, this flexibility comes at the cost of
understanding the dynamical system, for which ODEs are used ubiquitously.
Further, experimental data are collected under various conditions (inputs),
such as treatments, or grouped in some way, such as part of sub-populations.
Understanding the effects of these system inputs on system outputs is crucial
to have any meaningful model of a dynamical system. To that end, we propose a
structured latent ODE model that explicitly captures system input variations
within its latent representation. Building on a static latent variable
specification, our model learns (independent) stochastic factors of variation
for each input to the system, thus separating the effects of the system inputs
in the latent space. This approach provides actionable modeling through the
controlled generation of time-series data for novel input combinations (or
perturbations). Additionally, we propose a flexible approach for quantifying
uncertainties, leveraging a quantile regression formulation. Experimental
results on challenging biological datasets show consistent improvements over
competitive baselines in the controlled generation of observational data and
prediction of biologically meaningful system inputs.


title:
Evaluation of CMIP models with IOMB: Rates of contemporary ocean carbon
  uptake linked with vertical temperature gradients and transport to the ocean
  interior

abstract:
  The International Ocean Model Benchmarking (IOMB) software package is a new
community resource used here to evaluate surface and upper ocean variables from
CMIP5 and CMIP6 Earth System Models (ESMs) Our analysis reveals general
improvement in the multi-model mean of CMIP6 compared to CMIP5 for most of the
variables we examined including surface nutrients, temperature, and salinity.
We find that both CMIP5 and CMIP6 ocean models underestimate anthropogenic
carbon dioxide uptake after the 1970s. For the period of 1994 to 2007, the
multi-model mean from CMIP6 yields a mean cumulative carbon uptake of 27.2
+-2.2 Pg C, which is about 15% lower than the 32.0+-5.7 Pg C estimate derived
from two sets of observations. Negative biases in the change in anthropogenic
carbon inventory exist in the northern North Atlantic and at mid-latitudes in
the southern hemisphere (30-60{\deg}S). For the few models that provided
simulations of chlorofluorocarbon (CFC), we demonstrate that regions with
negative anthropogenic DIC biases coincide with regions that have a negative
bias in CFC concentrations. This relationship suggests that underestimates of
anthropogenic carbon storage in some models originates, in part, from weak
transport between the surface and interior ocean. To examine the robustness of
this attribution across the full suite of CMIP5 and CMIP6 models, we examined
the vertical temperature gradient between 200 and 1000m as a metric for
stratification and exchange between the surface and deeper waters. On a global
scale across different models and different MIPs we find a linear relationship
between the bias of vertical temperature gradients and the bias in
anthropogenic carbon uptake, consistent with the hypothesis that model biases
in the ocean carbon sink are related to biases in surface-to-interior
transport.


title:
Accelerating Neural Architecture Exploration Across Modalities Using
  Genetic Algorithms

abstract:
  Neural architecture search (NAS), the study of automating the discovery of
optimal deep neural network architectures for tasks in domains such as computer
vision and natural language processing, has seen rapid growth in the machine
learning research community. While there have been many recent advancements in
NAS, there is still a significant focus on reducing the computational cost
incurred when validating discovered architectures by making search more
efficient. Evolutionary algorithms, specifically genetic algorithms, have a
history of usage in NAS and continue to gain popularity versus other
optimization approaches as a highly efficient way to explore the architecture
objective space. Most NAS research efforts have centered around computer vision
tasks and only recently have other modalities, such as the rapidly growing
field of natural language processing, been investigated in depth. In this work,
we show how genetic algorithms can be paired with lightly trained objective
predictors in an iterative cycle to accelerate multi-objective architectural
exploration in a way that works in the modalities of both machine translation
and image classification.


title:
Semi-Supervised Learning and Data Augmentation in Wearable-based
  Momentary Stress Detection in the Wild

abstract:
  Physiological and behavioral data collected from wearable or mobile sensors
have been used to estimate self-reported stress levels. Since the stress
annotation usually relies on self-reports during the study, a limited amount of
labeled data can be an obstacle in developing accurate and generalized stress
predicting models. On the other hand, the sensors can continuously capture
signals without annotations. This work investigates leveraging unlabeled
wearable sensor data for stress detection in the wild. We first applied data
augmentation techniques on the physiological and behavioral data to improve the
robustness of supervised stress detection models. Using an auto-encoder with
actively selected unlabeled sequences, we pre-trained the supervised model
structure to leverage the information learned from unlabeled samples. Then, we
developed a semi-supervised learning framework to leverage the unlabeled data
sequences. We combined data augmentation techniques with consistency
regularization, which enforces the consistency of prediction output based on
augmented and original unlabeled data. We validated these methods using three
wearable/mobile sensor datasets collected in the wild. Our results showed that
combining the proposed methods improved stress classification performance by
7.7% to 13.8% on the evaluated datasets, compared to the baseline supervised
learning models.


title:
Automated Parkinson's Disease Detection and Affective Analysis from
  Emotional EEG Signals

abstract:
  While Parkinson's disease (PD) is typically characterized by motor disorder,
there is evidence of diminished emotion perception in PD patients. This study
examines the utility of affective Electroencephalography (EEG) signals to
understand emotional differences between PD vs Healthy Controls (HC), and for
automated PD detection. Employing traditional machine learning and deep
learning methods, we explore (a) dimensional and categorical emotion
recognition, and (b) PD vs HC classification from emotional EEG signals. Our
results reveal that PD patients comprehend arousal better than valence, and
amongst emotion categories, \textit{fear}, \textit{disgust} and
\textit{surprise} less accurately, and \textit{sadness} most accurately.
Mislabeling analyses confirm confounds among opposite-valence emotions with PD
data. Emotional EEG responses also achieve near-perfect PD vs HC recognition.
{Cumulatively, our study demonstrates that (a) examining \textit{implicit}
responses alone enables (i) discovery of valence-related impairments in PD
patients, and (ii) differentiation of PD from HC, and (b) emotional EEG
analysis is an ecologically-valid, effective, facile and sustainable tool for
PD diagnosis vis-\'a-vis self reports, expert assessments and resting-state
analysis.}


title:
An Evaluation of the EEG alpha-to-theta and theta-to-alpha band Ratios
  as Indexes of Mental Workload

abstract:
  Many research works indicate that EEG bands, specifically the alpha and theta
bands, have been potentially helpful cognitive load indicators. However,
minimal research exists to validate this claim. This study aims to assess and
analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on
supporting the creation of models capable of discriminating self-reported
perceptions of mental workload. A dataset of raw EEG data was utilized in which
48 subjects performed a resting activity and an induced task demanding exercise
in the form of a multitasking SIMKAP test. Band ratios were devised from
frontal and parietal electrode clusters. Building and model testing was done
with high-level independent features from the frequency and temporal domains
extracted from the computed ratios over time. Target features for model
training were extracted from the subjective ratings collected after resting and
task demand activities. Models were built by employing Logistic Regression,
Support Vector Machines and Decision Trees and were evaluated with performance
measures including accuracy, recall, precision and f1-score. The results
indicate high classification accuracy of those models trained with the
high-level features extracted from the alpha-to-theta ratios and theta-to-alpha
ratios. Preliminary results also show that models trained with logistic
regression and support vector machines can accurately classify self-reported
perceptions of mental workload. This research contributes to the body of
knowledge by demonstrating the richness of the information in the temporal,
spectral and statistical domains extracted from the alpha-to-theta and
theta-to-alpha EEG band ratios for the discrimination of self-reported
perceptions of mental workload.


title:
Assessing the State of Self-Supervised Human Activity Recognition using
  Wearables

abstract:
  The emergence of self-supervised learning in the field of wearables-based
human activity recognition (HAR) has opened up opportunities to tackle the most
pressing challenges in the field, namely to exploit unlabeled data to derive
reliable recognition systems from only small amounts of labeled training
samples. Furthermore, self-supervised methods enable a host of new application
domains such as, for example, domain adaptation and transfer across sensor
positions, activities etc. As such, self-supervision, i.e., the paradigm of
'pretrain-then-finetune' has the potential to become a strong alternative to
the predominant end-to-end training approaches, let alone the classic activity
recognition chain with hand-crafted features of sensor data. Recently a number
of contributions have been made that introduced self-supervised learning into
the field of HAR, including, Multi-task self-supervision, Masked
Reconstruction, CPC to name but a few. With the initial success of these
methods, the time has come for a systematic inventory and analysis of the
potential self-supervised learning has for the field. This paper provides
exactly that. We assess the progress of self-supervised HAR research by
introducing a framework that performs a multi-faceted exploration of model
performance. We organize the framework into three dimensions, each containing
three constituent criteria, and utilize it to assess state-of-the-art
self-supervised learning methods in a large empirical study on a curated set of
nine diverse benchmarks. This exploration leads us to the formulation of
insights into the properties of these techniques and to establish their value
towards learning representations for diverse scenarios. Based on our findings
we call upon the community to join our efforts and to contribute towards
shaping the evaluation of the ongoing paradigm change in modeling human
activities from body-worn sensor data.


title:
Automated Extraction of Energy Systems Information from Remotely Sensed
  Data: A Review and Analysis

abstract:
  High quality energy systems information is a crucial input to energy systems
research, modeling, and decision-making. Unfortunately, precise information
about energy systems is often of limited availability, incomplete, or only
accessible for a substantial fee or through a non-disclosure agreement.
Recently, remotely sensed data (e.g., satellite imagery, aerial photography)
have emerged as a potentially rich source of energy systems information.
However, the use of these data is frequently challenged by its sheer volume and
complexity, precluding manual analysis. Recent breakthroughs in machine
learning have enabled automated and rapid extraction of useful information from
remotely sensed data, facilitating large-scale acquisition of critical energy
system variables. Here we present a systematic review of the literature on this
emerging topic, providing an in-depth survey and review of papers published
within the past two decades. We first taxonomize the existing literature into
ten major areas, spanning the energy value chain. Within each research area, we
distill and critically discuss major features that are relevant to energy
researchers, including, for example, key challenges regarding the accessibility
and reliability of the methods. We then synthesize our findings to identify
limitations and trends in the literature as a whole, and discuss opportunities
for innovation.


title:
Fully-integrated multipurpose microwave frequency identification system
  on a single chip

abstract:
  We demonstrate a fully-integrated multipurpose microwave frequency
identification system on silicon-on-insulator platform. Thanks to its
multipurpose features, the chip is able to identify different types of
microwave signals, including single-frequency, multiple-frequency, chirped and
frequency-hopping microwave signals, as well as discriminate instantaneous
frequency variation among the frequency-modulated signals. This demonstration
exhibits fully integrated solution and fully functional microwave frequency
identification, which can meet the requirements in reduction of size, weight
and power for future advanced microwave photonic processor.


title:
Digital Signal Analysis based on Convolutional Neural Networks for
  Active Target Time Projection Chambers

abstract:
  An algorithm for digital signal analysis using convolutional neural networks
(CNN) was developed in this work. The main objective of this algorithm is to
make the analysis of experiments with active target time projection chambers
more efficient. The code is divided in three steps: baseline correction, signal
deconvolution and peak detection and integration. The CNNs were able to learn
the signal processing models with relative errors of less than 6\%. The
analysis based on CNNs provides the same results as the traditional
deconvolution algorithms, but considerably more efficient in terms of computing
time (about 65 times faster). This opens up new possibilities to improve
existing codes and to simplify the analysis of the large amount of data
produced in active target experiments.

categories: eess.SP cs.LG nucl-ex physics.comp-ph physics.data-an physics.ins-det


title: Quadratic phase wave packet transform

abstract:
  The quadratic phase Fourier transform has gained much popularity in recent
years because of its applications in image and signal processing. However, the
QPFT is inadequate for localizing the quadratic phase spectrum which is
required in some applications. In this paper, the quadratic phase wave packet
transform QP WPT is proposed to address this problem, based on the wave packet
transform WPT and QPFT. Firstly, we propose the definition of the QP WPT and
gave its relation with windowed Fourier transform WFT. Secondly, several
notable inequalities and important properties of newly defined QP WPT, such as
boundedness, reconstruction formula, Moyals formula, Reproducing kernel are
derived. Finally, we formulate several classes of uncertainty inequalities such
as Leibs uncertainty principle, logarithmic uncertainty inequality and the
Heisenberg uncertainty inequality.


title:
Arrhythmia Classifier Using Convolutional Neural Network with Adaptive
  Loss-aware Multi-bit Networks Quantization

abstract:
  Cardiovascular disease (CVDs) is one of the universal deadly diseases, and
the detection of it in the early stage is a challenging task to tackle.
Recently, deep learning and convolutional neural networks have been employed
widely for the classification of objects. Moreover, it is promising that lots
of networks can be deployed on wearable devices. An increasing number of
methods can be used to realize ECG signal classification for the sake of
arrhythmia detection. However, the existing neural networks proposed for
arrhythmia detection are not hardware-friendly enough due to a remarkable
quantity of parameters resulting in memory and power consumption.
  In this paper, we present a 1-D adaptive loss-aware quantization, achieving a
high compression rate that reduces memory consumption by 23.36 times. In order
to adapt to our compression method, we need a smaller and simpler network. We
propose a 17 layer end-to-end neural network classifier to classify 17
different rhythm classes trained on the MIT-BIH dataset, realizing a
classification accuracy of 93.5%, which is higher than most existing methods.
Due to the adaptive bitwidth method making important layers get more attention
and offered a chance to prune useless parameters, the proposed quantization
method avoids accuracy degradation. It even improves the accuracy rate, which
is 95.84%, 2.34% higher than before. Our study achieves a 1-D convolutional
neural network with high performance and low resources consumption, which is
hardware-friendly and illustrates the possibility of deployment on wearable
devices to realize a real-time arrhythmia diagnosis.


title: Domain wall between the Dirac sea and the "anti-Dirac sea"

abstract:
  It was shown in work \cite{vergeles2021note} that in the theory of gravity
coupled with the Dirac field, each state $|\lambda\rangle$ has its own twin
$|\lambda;PT\rangle$, which is obtained by a discrete PT transformation. If in
the state $|\lambda\rangle$ the Dirac sea is filled, then in the state
$|\lambda;PT\rangle$ there is an "anti-Dirac" filling (in terms of the state
$|\lambda\rangle$). It is important that the energies of these states are the
same. Therefore, there may be domains with different filling of the Dirac sea.
Here we study a domain wall connecting two such adjacent domains.


title:
Generalized form of fixed-point theorems in generalized Banach algebra
  relative to the weak topology with an application

abstract:
  In this paper, a general hybrid fixed point theorem for the contractive
mappings in generalized Banach spaces is proved via measure of weak
non-compactness and it is further applied to fractional integral equations for
proving the existence results for the solutions under mixed Lipschitz and
weakly sequentially continuous conditions. Finally, an example is given to
illustrate the result.


title: A contagion process with self-exciting jumps in credit risk applications

abstract:
  The modeling of the probability of joint default or total number of defaults
among the firms is one of the crucial problems to mitigate the credit risk
since the default correlations significantly affect the portfolio loss
distribution and hence play a significant role in allocating capital for
solvency purposes. In this article, we derive a closed-form expression for the
probability of default of a single firm and the probability of the total number
of defaults by any time $t$ in a homogeneous portfolio of firms. We use a
contagion process to model the arrival of credit events that causes the default
and develop a framework that allows firms to have resistance against default
unlike the standard intensity-based models. We assume the point process driving
the credit events to be composed of a systematic and an idiosyncratic
component, whose intensities are independently specified by a mean-reverting
affine jump-diffusion process with self-exciting jumps. The proposed framework
is competent of capturing the feedback effect, an empirically observed
phenomenon in the default events. We further demonstrate how the proposed
framework can be used to price synthetic collateralized debt obligation (CDO)
and obtain a closed-form solution for tranche spread. Finally, we present the
sensitivity analysis to demonstrate the effect of different parameters
governing the contagion effect on the spread of tranches and the expected loss
of the CDO.


title:
A Therapeutic Stress Ball to Monitor Hand Dexterity and Electrodermal
  Activity

abstract:
  This work presents a triboelectric nanogenerator-based (TENG) therapeutic
stress ball to provide gesture monitoring and physiological data on patients
requiring physical therapy of various degrees. The device utilizes a 5-layer
stack of silicone and braided silver-coated nylon rope electrodes to create a
sensor network that monitors 40-points across the surface of a semi-spherical
prototype. A modified version of a standard ECG circuit was utilized to provide
proper loading, noise rejection, filtering, and phase of the TENG signals along
with multiplexing of the many electrodes. All system components were selected
with a final embedded system in mind. Testing of the device was conducted
utilizing an Arduino Uno and an EVAL-AD5940BIOZ evaluation board for
electrodermal activity for stress and/or pain after exercise. An accelerometer
was included for device activation and hand tremor detection. Upon testing, the
self-powered TENG sensors produce positive impulses upon contact and negative
impulses upon release of contact from the surface of the ball. Furthermore,
finger removal detection was demonstrated by capturing the associated negative
impulse by maintaining the bipolar signal in our conditioning circuit. EDA
results indicate silver-coated nylon as a potentially good dry-electrode which
can be used with even more electrodes for bio-impedance or ECG capture to
further expand the device functionality. A MATLAB-based GUI was designed to
provide the user with data tracking and visual monitoring of the data via
serial communication from the microcontrollers. Finally, it should be noted
that this provides a means for low-cost low-power gesture tracking without the
use of flexible capacitive grid arrays and provides the user with a pleasant
tactile experience that one expects form a stress ball due to its unique
material design.


title:
DAGAM: A Domain Adversarial Graph Attention Model for Subject
  Independent EEG-Based Emotion Recognition

abstract:
  One of the most significant challenges of EEG-based emotion recognition is
the cross-subject EEG variations, leading to poor performance and
generalizability. This paper proposes a novel EEG-based emotion recognition
model called the domain adversarial graph attention model (DAGAM). The basic
idea is to generate a graph to model multichannel EEG signals using biological
topology. Graph theory can topologically describe and analyze relationships and
mutual dependency between channels of EEG. Then, unlike other graph
convolutional networks, self-attention pooling is applied to benefit salient
EEG feature extraction from the graph, which effectively improves the
performance. Finally, after graph pooling, the domain adversarial based on the
graph is employed to identify and handle EEG variation across subjects,
efficiently reaching good generalizability. We conduct extensive evaluations on
two benchmark datasets (SEED and SEED IV) and obtain state-of-the-art results
in subject-independent emotion recognition. Our model boosts the SEED accuracy
to 92.59% (4.69% improvement) with the lowest standard deviation of 3.21%
(2.92% decrements) and SEED IV accuracy to 80.74% (6.90% improvement) with the
lowest standard deviation of 4.14% (3.88% decrements) respectively.


title:
Multi-View Fusion Transformer for Sensor-Based Human Activity
  Recognition

abstract:
  As a fundamental problem in ubiquitous computing and machine learning,
sensor-based human activity recognition (HAR) has drawn extensive attention and
made great progress in recent years. HAR aims to recognize human activities
based on the availability of rich time-series data collected from multi-modal
sensors such as accelerometers and gyroscopes. However, recent deep learning
methods are focusing on one view of the data, i.e., the temporal view, while
shallow methods tend to utilize the hand-craft features for recognition, e.g.,
the statistics view. In this paper, to extract a better feature for advancing
the performance, we propose a novel method, namely multi-view fusion
transformer (MVFT) along with a novel attention mechanism. First, MVFT encodes
three views of information, i.e., the temporal, frequent, and statistical views
to generate multi-view features. Second, the novel attention mechanism uncovers
inner- and cross-view clues to catalyze mutual interactions between three views
for detailed relation modeling. Moreover, extensive experiments on two datasets
illustrate the superiority of our methods over several state-of-the-art
methods.


title:
2021 BEETL Competition: Advancing Transfer Learning for Subject
  Independence & Heterogenous EEG Data Sets

abstract:
  Transfer learning and meta-learning offer some of the most promising avenues
to unlock the scalability of healthcare and consumer technologies driven by
biosignal data. This is because current methods cannot generalise well across
human subjects' data and handle learning from different heterogeneously
collected data sets, thus limiting the scale of training data. On the other
side, developments in transfer learning would benefit significantly from a
real-world benchmark with immediate practical application. Therefore, we pick
electroencephalography (EEG) as an exemplar for what makes biosignal machine
learning hard. We design two transfer learning challenges around diagnostics
and Brain-Computer-Interfacing (BCI), that have to be solved in the face of low
signal-to-noise ratios, major variability among subjects, differences in the
data recording sessions and techniques, and even between the specific BCI tasks
recorded in the dataset. Task 1 is centred on the field of medical diagnostics,
addressing automatic sleep stage annotation across subjects. Task 2 is centred
on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across
both subjects and data sets. The BEETL competition with its over 30 competing
teams and its 3 winning entries brought attention to the potential of deep
transfer learning and combinations of set theory and conventional machine
learning techniques to overcome the challenges. The results set a new
state-of-the-art for the real-world BEETL benchmark.


title: Model Attribution of Face-swap Deepfake Videos

abstract:
  AI-created face-swap videos, commonly known as Deepfakes, have attracted wide
attention as powerful impersonation attacks. Existing research on Deepfakes
mostly focuses on binary detection to distinguish between real and fake videos.
However, it is also important to determine the specific generation model for a
fake video, which can help attribute it to the source for forensic
investigation. In this paper, we fill this gap by studying the model
attribution problem of Deepfake videos. We first introduce a new dataset with
DeepFakes from Different Models (DFDM) based on several Autoencoder models.
Specifically, five generation models with variations in encoder, decoder,
intermediate layer, input resolution, and compression ratio have been used to
generate a total of 6,450 Deepfake videos based on the same input. Then we take
Deepfakes model attribution as a multiclass classification task and propose a
spatial and temporal attention based method to explore the differences among
Deepfakes in the new dataset. Experimental evaluation shows that most existing
Deepfakes detection methods failed in Deepfakes model attribution, while the
proposed method achieved over 70% accuracy on the high-quality DFDM dataset.


title: Superconductivity in cuprates governed by topological constraints

abstract:
  The remarkable universality of the cuprate $T_c$ dome suggests a very
fundamental unifying principle. Moreover, the superconducting gap is known to
persist above $T_c$ in the pseudogap phase of all cuprates. So, contrary to
BCS, the gap cannot be the order parameter of the transition.
  In this work, we show that both the $T_c$-dome and the pseudogap line
$T^*(p)$ arise from a unique and identifiable principle: the interaction of
localized `pairons' on an antiferromagnetic square lattice. The topological
constraints on such preformed pairons give rise to both the $T_c$ dome and the
pairing energy {\it simultaneously}. It also provides a natural explanation for
the critical doping points of the phase diagram.
  The model matches perfectly both the $T^*$ and $T_c$ experimental lines, with
only one adjustable parameter.


title: Optimal finite-time heat engines under constrained control

abstract:
  We optimise a broad class of finite-time `breathing' stochastic heat engines
under experimentally motivated constraints on control parameters, such as trap
frequencies. We present a general geometric argument showing that the
corresponding maximum-efficiency protocols are piecewise constant. When the
control parameters are restricted to a small range, these protocols maximize
also output power. Furthermore, for overdamped Brownian heat engines, maximum
powers for maximum-efficiency and maximum-power protocols are close, suggesting
the former can provide a good compromise between power and efficiency, which is
for power law Hamiltonians and slow isothermal strokes given by the
Curzon-Ahlborn expression. These specific situations are typical for colloidal
heat engines, e.g., realised with optical tweezers.


title:
A Hardware-Aware System for Accelerating Deep Neural Network
  Optimization

abstract:
  Recent advances in Neural Architecture Search (NAS) which extract specialized
hardware-aware configurations (a.k.a. "sub-networks") from a hardware-agnostic
"super-network" have become increasingly popular. While considerable effort has
been employed towards improving the first stage, namely, the training of the
super-network, the search for derivative high-performing sub-networks is still
largely under-explored. For example, some recent network morphism techniques
allow a super-network to be trained once and then have hardware-specific
networks extracted from it as needed. These methods decouple the super-network
training from the sub-network search and thus decrease the computational burden
of specializing to different hardware platforms. We propose a comprehensive
system that automatically and efficiently finds sub-networks from a pre-trained
super-network that are optimized to different performance metrics and hardware
configurations. By combining novel search tactics and algorithms with
intelligent use of predictors, we significantly decrease the time needed to
find optimal sub-networks from a given super-network. Further, our approach
does not require the super-network to be refined for the target task a priori,
thus allowing it to interface with any super-network. We demonstrate through
extensive experiments that our system works seamlessly with existing
state-of-the-art super-network training methods in multiple domains. Moreover,
we show how novel search tactics paired with evolutionary algorithms can
accelerate the search process for ResNet50, MobileNetV3 and Transformer while
maintaining objective space Pareto front diversity and demonstrate an 8x faster
search result than the state-of-the-art Bayesian optimization WeakNAS approach.


title: Spatially distributed computation in cortical circuits

abstract:
  The traditional view of neural computation in the cerebral cortex holds that
sensory neurons are specialized, i.e., selective for certain dimensions of
sensory stimuli. This view was challenged by evidence of contextual
interactions between stimulus dimensions in which a neuron's response to one
dimension strongly depends on other dimensions. Here we use methods of
mathematical modeling, psychophysics, and electrophysiology to address
shortcomings of the traditional view. Using a model of a generic cortical
circuit, we begin with the simple demonstration that cortical responses are
always distributed among neurons, forming characteristic waveforms, which we
call neural waves. When stimulated by patterned stimuli, circuit responses
arise by interference of neural waves. Resulting patterns of interference
depend on interaction between stimulus dimensions. Comparison of these modeled
responses with responses of biological vision makes it clear that the framework
of neural wave interference provides a useful alternative to the standard
concept of neural computation.


title:
Agile requirements engineering practices: a survey in Brazilian software
  development companies

abstract:
  Requirements Engineering (RE) is one of the prime areas in software
development. Since agile software development englobes several emerging
techniques and advocates for continuous improvement, it urges the question of
which agile RE practices are currently most used, their characteristics, and
the challenges in their employment. The aim of this work is to investigate and
categorize the collection and specification of agile requirements practices
based on how professionals perceive their importance for a software project
that applies agile methodologies. Thus, a survey was carried out with forty-six
(46) Brazilian software development professionals, inquiring which methods are
used for the collection and specification of agile requirements, as well as the
features, benefits, and difficulties when employing the methods. The responses
allowed us to perform data analysis and identify the relationships between the
respondents' experience and the viewpoints on the collection methods and the
agile requirements specification. In addition, it was noted that the adoption
of these methods is still very recent. They have mainly been used for less than
five years. Moreover, it was noted that, for most respondents, there are yet
significant challenges and advances to be made for better efficiency in
applying the informed methods.


title: Deep Neural Network for Automatic Assessment of Dysphonia

abstract:
  The purpose of this work is to contribute to the understanding and
improvement of deep neural networks in the field of vocal quality. A neural
network that predicts the perceptual assessment of overall severity of
dysphonia in GRBAS scale is obtained. The design focuses on amplitude
perturbations, frequency perturbations, and noise. Results are compared with
performance of human raters on the same data. Both the precision and the mean
absolute error of the neural network are close to human intra-rater
performance, exceeding inter-rater performance.


title: Off-Policy Evaluation with Policy-Dependent Optimization Response

abstract:
  The intersection of causal inference and machine learning for decision-making
is rapidly expanding, but the default decision criterion remains an
\textit{average} of individual causal outcomes across a population. In
practice, various operational restrictions ensure that a decision-maker's
utility is not realized as an \textit{average} but rather as an \textit{output}
of a downstream decision-making problem (such as matching, assignment, network
flow, minimizing predictive risk). In this work, we develop a new framework for
off-policy evaluation with a \textit{policy-dependent} linear optimization
response: causal outcomes introduce stochasticity in objective function
coefficients. In this framework, a decision-maker's utility depends on the
policy-dependent optimization, which introduces a fundamental challenge of
\textit{optimization} bias even for the case of policy evaluation. We construct
unbiased estimators for the policy-dependent estimand by a perturbation method.
We also discuss the asymptotic variance properties for a set of plug-in
regression estimators adjusted to be compatible with that perturbation method.
Lastly, attaining unbiased policy evaluation allows for policy optimization,
and we provide a general algorithm for optimizing causal interventions. We
corroborate our theoretical results with numerical simulations.


title:
Image reconstruction algorithms in radio interferometry: from
  handcrafted to learned denoisers

abstract:
  We introduce a new class of iterative image reconstruction algorithms for
radio interferometry, at the interface of convex optimization and deep
learning, inspired by plug-and-play methods. The approach consists in learning
a prior image model by training a deep neural network (DNN) as a denoiser, and
substituting it for the handcrafted proximal regularization operator of an
optimization algorithm. The proposed AIRI ("AI for Regularization in
Radio-Interferometric Imaging") framework, for imaging complex intensity
structure with diffuse and faint emission, inherits the robustness and
interpretability of optimization, and the learning power and speed of networks.
Our approach relies on three steps. Firstly, we design a low dynamic range
database for supervised training from optical intensity images. Secondly, we
train a DNN denoiser with basic architecture ensuring positivity of the output
image, at a noise level inferred from the signal-to-noise ratio of the data. We
use either $\ell_2$ or $\ell_1$ training losses, enhanced with a
nonexpansiveness term ensuring algorithm convergence, and including on-the-fly
database dynamic range enhancement via exponentiation. Thirdly, we plug the
learned denoiser into the forward-backward optimization algorithm, resulting in
a simple iterative structure alternating a denoising step with a
gradient-descent data-fidelity step. The resulting AIRI-$\ell_2$ and
AIRI-$\ell_1$ were validated against CLEAN and optimization algorithms of the
SARA family, propelled by the "average sparsity" proximal regularization
operator. Simulation results show that these first AIRI incarnations are
competitive in imaging quality with SARA and its unconstrained
forward-backward-based version uSARA, while providing significant acceleration.
CLEAN remains faster but offers lower reconstruction quality.


title:
Quantum vacuum under mixed boundary conditions: the case for curved
  spacetime

abstract:
  Influence of gravity on the quantum vacuum of a massless minimally coupled
scalar field under Robin boundary conditions on parallel plates is
investigated. We introduce the detailed calculation of the volume energy for
the case the gravitational background is weak in its most general form for a
static spacetime. It founds that the quantum vacuum usually reacts to the
gravitational field by decreasing the Casimir energy. In addition, we find
sufficient conditions under which the Casimir force increases. Interestingly,
the first order perturbation corrections, are present in the obtained formula
for the volume energy. We show that for some specific choices of parameters,
the energy is independent of Robin coefficients. Consistency with the
literature is shown in some limiting cases and well-known examples are
presented for both an increase or decrease in the volume energy.


title: Exploiting Prior Function Evaluations in Derivative-Free Optimization

abstract:
  A derivative-free optimization (DFO) algorithm is presented. The
distinguishing feature of the algorithm is that it allows for the use of
function values that have been made available through prior runs of a DFO
algorithm for solving prior related optimization problems. Applications in
which sequences of related optimization problems are solved such that the
proposed algorithm is applicable include certain least-squares and
simulation-based optimization problems. A convergence guarantee of a generic
algorithmic framework that exploits prior function evaluations is presented,
then a particular instance of the framework is proposed and analyzed. The
results of numerical experiments when solving engineering test problems show
that the algorithm gains advantage over a state-of-the-art DFO algorithm when
prior function values are available.


title:
How Do Mothers and Fathers Talk About Parenting to Different Audiences?:
  Stereotypes and Audience Effects: An Analysis of r/Daddit, r/Mommit, and
  r/Parenting Using Topic Modelling

abstract:
  While major strides have been made towards gender equality in public life,
serious inequality remains in the domestic sphere, especially around parenting.
The present study analyses discussions about parenting on Reddit to explore
audience effects and gender stereotypes. It suggests a novel method to study
topical variation in individuals' language when interacting with different
audiences. Comments posted in 2020 were collected from three parenting
subreddits, described as being for fathers (r/Daddit), mothers (r/Mommit), and
all parents (r/Parenting). Users posting on r/Parenting and r/Daddit or on
r/Parenting and r/Mommit were assumed to identify as fathers or mothers,
respectively, allowing gender comparison. Users' comments on r/Parenting (to a
mixed-gender audience) were compared with their comments to single-gender
audiences on r/Daddit or r/Mommit using LDA topic modelling. Results showed
that the most discussed topic among parents is about education and family
advice, a topic mainly discussed in the mixed-gender subreddit and more by
fathers than mothers. Regarding the basic needs of children (sleep, food, and
medical care), mothers seemed to be more concerned regardless of the audience.
In contrast, topics such as birth and pregnancy announcements and physical
appearance were more discussed by fathers in the father-centric subreddit.
Overall, findings seem to show that mothers are generally more concerned about
the practical sides of parenting while fathers' expressed concerns are more
contextual: with other fathers, there seems to be a desire to show their
fatherhood and be recognized for it while they discuss education with mothers.
These results demonstrate that concerns expressed by parents on Reddit are
context-sensitive but also consistent with gender stereotypes, potentially
reflecting a persistent gendered and unequal division of labour in parenting.


title: Glueball-Meson Mixing In Holographic QCD

abstract:
  Top-down holographic QCD models often work in the "probe" (or "quenched")
limit, which assumes that the number of colors is much greater than the number
of flavors. Relaxing this limit is essential to a fuller understanding of
holography and more accurate phenomenological predictions. In this work, we
focus on a mixing of glueball and meson mass eigenstates that arises from the
DBI action as a finite Nf/Nc effect. For concreteness, we work in the
Witten-Sakai-Sugimoto model, and show that this mixing must be treated in
conjunction with the backreaction of the flavor branes onto the background
geometry. Including the backreaction with the simplification that it is
"smeared out" over the compact transverse direction, we derive a corrected
effective action for the vector glueball and scalar states. Along the way, we
observe a Stueckelberg-like mechanism that restores translation invariance in
the transverse direction. We also derive a general technique, that lends itself
easily to numerics, for finding mass eigenstates of Lagrangians with
vector-scalar mixing. We then calculate the first order corrections to the mass
spectra of both the vector and scalar particles, and show that the term that
explicitly mixes vector and scalar states is the most significant correction to
the masses of low-lying scalar mesons.


title:
Skyrmion crystals in centrosymmetric triangular magnets under hexagonal
  and trigonal single-ion anisotropy

abstract:
  We theoretically report an instability toward a skyrmion crystal in
centrosymmetric magnets under hexagonal and trigonal single-ion anisotropy. The
results are obtained for a minimal spin model with a crystal-dependent
single-ion anisotropy on a triangular lattice by performing simulated
annealing. By constructing magnetic phase diagrams in the presence of six
different types of single-ion anisotropy while changing the amplitudes of
external magnetic field and single-ion anisotropy in a systematic way, we find
that the hexagonal and trigonal single-ion anisotropy becomes a source of the
skyrmion crystal depending on the magnetic-field direction. We show that the
skyrmion crystal is stabilized by the uniaxial-type or trigonal-type single-ion
anisotropy under the out-of-plane magnetic field, while it is stabilized by the
hexagonal-type inplane single-ion anisotropy under the inplane magnetic field.
We also find various multiple-$Q$ spin states depending on the types of the
single-ion anisotropy.


title: Quantum Persistent Homology

abstract:
  Persistent homology is a powerful mathematical tool that summarizes useful
information about the shape of data allowing one to detect persistent
topological features while one adjusts the resolution. However, the computation
of such topological features is often a rather formidable task necessitating
the subsampling the underlying data. To remedy this, we develop an efficient
quantum computation of persistent Betti numbers, which track topological
features of data across different scales. Our approach employs a persistent
Dirac operator whose square yields the persistent combinatorial Laplacian, and
in turn the underlying persistent Betti numbers which capture the persistent
features of data. We also test our algorithm on point cloud data.


title: A geometric take on Kostant's Convexity Theorem

abstract:
  Given a compact Lie group $G$ and an orthogonal $G$-representation $V$, we
give a purely metric criterion for a closed subset of the orbit space $V/G$ to
have convex pre-image in $V$. In fact, this also holds with the natural
quotient map $V\to V/G$ replaced with an arbitrary submetry $V\to X$.
  In this context, we introduce a notion of "fat section" which generalizes
polar representations, representations of non-trivial copolarity, and
isoparametric foliations.
  We show that Kostant's Convexity Theorem partially generalizes from polar
representations to submetries with a fat section, and give examples
illustrating that it does not fully generalize to this situation.


title:
Exploring with Sticky Mittens: Reinforcement Learning with Expert
  Interventions via Option Templates

abstract:
  Environments with sparse rewards and long horizons pose a significant
challenge for current reinforcement learning algorithms. A key feature enabling
humans to learn challenging control tasks is that they often receive expert
intervention that enables them to understand the high-level structure of the
task before mastering low-level control actions. We propose a framework for
leveraging expert intervention to solve long-horizon reinforcement learning
tasks. We consider option templates, which are specifications encoding a
potential option that can be trained using reinforcement learning. We formulate
expert intervention as allowing the agent to execute option templates before
learning an implementation. This enables them to use an option, before
committing costly resources to learning it. We evaluate our approach on three
challenging reinforcement learning problems, showing that it outperforms state
of-the-art approaches by an order of magnitude. Project website at
https://sites.google.com/view/stickymittens


title: Does Label Differential Privacy Prevent Label Inference Attacks?

abstract:
  Label differential privacy (LDP) is a popular framework for training private
ML models on datasets with public features and sensitive private labels.
Despite its rigorous privacy guarantee, it has been observed that in practice
LDP does not preclude label inference attacks (LIAs): Models trained with LDP
can be evaluated on the public training features to recover, with high
accuracy, the very private labels that it was designed to protect. In this
work, we argue that this phenomenon is not paradoxical and that LDP merely
limits the advantage of an LIA adversary compared to predicting training labels
using the Bayes classifier. At LDP $\epsilon=0$ this advantage is zero, hence
the optimal attack is to predict according to the Bayes classifier and is
independent of the training labels. Finally, we empirically demonstrate that
our result closely captures the behavior of simulated attacks on both synthetic
and real world datasets.


title:
On abelian covers of the projective line with fixed gonality and many
  rational points

abstract:
  A smooth geometrically connected curve over the finite field $\mathbb{F}_q$
with gonality $\gamma$ has at most ${\gamma(q+1)}$ rational points. The first
author and Grantham conjectured that there exist curves of every sufficiently
large genus with gonality $\gamma$ that achieve this bound. In this paper, we
show that this bound can be achieved for an infinite sequence of genera using
abelian covers of the projective line. We also argue that abelian covers will
not suffice to prove the full conjecture.


title: Critical Infrastructure Cybersecurity Challenges: IoT In Perspective

abstract:
  A technology platform that is gradually bridging the gap between object
visibility and remote accessibility is the Internet of Things (IoT). Rapid
deployment of this application can significantly transform the health, housing,
and power (distribution and generation) sectors, etc. It has considerably
changed the power sector regarding operations, services optimization, power
distribution, asset management and aided in engaging customers to reduce energy
consumption. Despite its societal opportunities and the benefits it presents,
the power generation sector is bedeviled with many security challenges on the
critical infrastructure. This review discusses the security challenges posed by
IoT in power generation and critical infrastructure. To achieve this, the
authors present the various IoT applications, particularly on the grid
infrastructure, from an empirical literature perspective. The authors concluded
by discussing how the various entities in the sector can overcome these
security challenges to ensure an exemplary future IoT implementation on the
power critical infrastructure value chain.


title:
Grain boundary segregation in steels: Towards engineering the design of
  internal interfaces

abstract:
  Solute decoration at grain boundaries (GB) leads to a number of phenomenon
such as changes in interface structure,mobility,cohesion etc.Recent
experimental investigations on interfacial segregation in steels are based on
microstructural characterisation using two correlative methodologies,
namely,Transmission Electron Microscopy-Atom Probe Tomography (APT) and
Electron Backscatter Diffraction-APT.Considering the growing interest in this
avenue,the present review is aimed at addressing the common adsorption
isotherms used for quantifying interfacial segregation and providing an
overview of the present state of experimental research in the area of GB
segregation in steels.The areas where an understanding of GB segregation may be
utilised have also been highlighted with a focus on the experimental challenges
associated with understanding GB segregation in steels.