title: Dark State Induced Heat Rectification

abstract:
  Heat and noise control is essential for the continued development of quantum
technologies. For this purpose, a particular powerful tool is the heat
rectifier, which allows for heat transport in one configuration of two baths
but not the reverse. Here we propose a class of rectifiers that exploits the
unidirectionality of a low temperature bath to force the system into a dark
state thus blocking heat transport in one configuration of the two baths.
However, if the two baths are switched around, a heat current is observed. An
implementation using a qutrit coupled to two harmonic oscillators is proposed
and rectification values beyond $10^3$ is achieved for realistic parameter
values. Furthermore, we show that the heat current can be amplified by an order
of magnitude through external driving without diminishing the diode
functionality. The heat rectification effect is seen for a large range of
parameters, and it is robust towards both decay and dephasing.


title: Spinning correlators in large-charge CFTs

abstract:
  We systematically study correlators of a generic conformal field theory with
a global O(2) symmetry in a sector of large global charge. We focus in
particular on three- and four-point correlators with conserved current
insertions sandwiched between spinful excited states corresponding to phonons
over the large-charge vacuum. We also discuss loop corrections to the scaling
dimensions and observe multiple logarithms in even dimensions.


title: Schwarzschild-like Topological Solitons

abstract:
  We construct the first class of topological solitons in gravity that are
supported by internal electromagnetic flux with vanishing net charges. The
solutions are obtained in a six-dimensional Einstein-Maxwell theory with a
three-form flux, and admit an uplift to type IIB supergravity on T$^4$. They
are asymptotic to a torus fibration over four-dimensional Minkowski spacetime.
An interesting class corresponds to solitons with a BPS particle and its
anti-BPS partner held apart by a vacuum bubble. In type IIB, they correspond to
bound states of BPS and anti-BPS D1-D5 extremal black holes. These metrics are
a particular limit of a larger class of axially symmetric metrics that we
construct and that describe smooth horizonless topological solitons. They
correspond to bound states of three non-BPS bubbles on a line. An important
achievement is that the outer bubbles can carry arbitrary D1-D5 charges that we
can tune to vanishing net charges. We discuss their properties and compare them
to a four-dimensional Schwarzschild black hole of the same mass. We show that
they have a long throat with a large redshift, and that they are ultra-compact
with a characteristic size of 1.52 times the Schwarzschild radius.


title:
Contribution to the 2022 Cosmology session of the 56th Rencontres de
  Moriond: Reionization constraints from HERA 21cm power spectrum limits

abstract:
  I present our analysis of the 21cm power spectrum upper limits from the HERA
radio interferometer, published in HERA Collaboration et al. 2022. We use the
recent limits to constrain reionization and the properties of the IGM and
galaxies in the early universe. I focus in particular on the possibility of a
radio background in addition to the CMB (e.g. produced by early galaxies) which
can lead to a stronger 21cm signal and is thus easier to constrain. I show what
limits the HERA observations can put on these models and the IGM, and how this
compares to existing constraints on the radio and X-ray background.


title:
First Light And Reionisation Epoch Simulations (FLARES) IV: The size
  evolution of galaxies at $z\geq5$

abstract:
  We present the intrinsic and observed sizes of galaxies at $z\geq5$ in the
First Light And Reionisation Epoch Simulations (FLARES). We employ the large
effective volume of FLARES to produce a sizeable sample of high redshift
galaxies with intrinsic and observed luminosities and half light radii in a
range of rest frame UV and visual photometric bands. This sample contains a
significant number of intrinsically ultra-compact galaxies in the far-UV (1500
angstrom), leading to a negative intrinsic far-UV size-luminosity relation.
However, after the inclusion of the effects of dust these same compact galaxies
exhibit observed sizes that are as much as 50 times larger than those measured
from the intrinsic emission, and broadly agree with a range of observational
samples. This increase in size is driven by the concentration of dust in the
core of galaxies, heavily attenuating the intrinsically brightest regions. At
fixed luminosity we find a galaxy size redshift evolution with a slope of
$m=1.21-1.87$ depending on the luminosity sample in question, and we
demonstrate the wavelength dependence of the size-luminosity relation which
will soon be probed by the Webb Space Telescope.


title: The dark side of the proton

abstract:
  We study the sensitivity of the High-Luminosity LHC to a light baryonic dark
photon B, primarily coupled to quarks, as a constituent of the proton. This is
achieved by allowing for a dark photon parton distribution function (PDF) in
the PDF evolution equations. Depending on the mass and coupling of the dark
photon, the evolution of standard quark and gluon PDFs is distorted to varying
degrees. By analysing the effect of the dark photon on the tails of Drell-Yan
invariant mass distributions, we demonstrate the potential of the LHC in
determining competitive bounds on dark photon parameter space.


title: Random Field $\phi^3$ Model and Parisi-Sourlas Supersymmetry

abstract:
  We use the RG framework set up in arXiv:2009.10087 to explore the $\phi^3$
theory with a random field interaction. According to the Parisi-Sourlas
conjecture this theory admits a fixed point with emergent supersymmetry which
is related to the pure Lee-Yang CFT in two less dimensions. We study the model
using replica trick and Cardy variables in $d=8-\epsilon$ where the RG flow is
perturbative. Allowed perturbations are singlets under the $S_n$ symmetry that
permutes the $n$ replicas. These are decomposed into operators with different
scaling dimensions: the lowest dimensional part, `leader', controls the RG flow
in the IR; the other operators, `followers', can be neglected. The leaders are
classified into: susy-writable, susy-null and non-susy-writable according to
their mixing properties. We construct low lying leaders and compute the
anomalous dimensions of a number of them. We argue that there is no operator
that can destabilize the SUSY RG flow in $d\le 8$. This agrees with the well
known numerical result for critical exponents of Branched Polymers (which are
in the same universality class as the random field $\phi^3$ model) that match
the ones of the pure Lee-Yang fixed point according to dimensional reduction in
all $2\le d\le 8$. Hence this is a second strong check of the RG framework that
was previously shown to correctly predict loss of dimensional reduction in
random field Ising model.


title: Hadrophilic Light Dark Matter from the Atmosphere

abstract:
  Light sub-GeV dark matter (DM) constitutes an underexplored target, beyond
the optimized sensitivity of typical direct DM detection experiments. We
comprehensively investigate hadrophilic light DM produced from cosmic-ray
collisions with the atmosphere. The resulting relativistic DM, originating from
meson decays, can be efficiently observed in variety of experiments, such as
XENON1T. We include for the first time decays of $\eta$, $\eta^{\prime}$ and
$K^+$ mesons, leading to improved limits for DM masses above few hundred MeV.
We incorporate an exact treatment of the DM attenuation in Earth and
demonstrate that nuclear form factor effects can significantly impact the
resulting testable DM parameter space. Further, we establish projections for
upcoming experiments, such as DARWIN, over a wide range of DM masses below the
GeV scale.


title: Global anomalies in 8d supergravity

abstract:
  We study gauge and gravitational anomalies of fermions and 2-form fields on
eight-dimensional spin manifolds. Possible global gauge anomalies are
classified by spin bordism groups $\Omega^{\text{spin}}_9(BG)$ which we
determine by spectral sequence techniques, and we also identify their explicit
generator manifolds. It turns out that a fermion in the adjoint representation
of any simple Lie group, and a gravitino in $8d$ $\mathcal{N}=1$ supergravity
theory, have anomalies. We discuss how a 2-form field, which also appears in
supergravity, produces anomalies which cancel against these fermion anomalies
in a certain class of supergravity theories. In another class of theories, the
anomaly of the gravitino is not cancelled by the 2-form field, but by
topological degrees of freedom. It gives a restriction on the topology of
spacetime manifolds which is not visible at the level of differential-form
analysis.


title: $\varepsilon'/\varepsilon$ in the Standard Model and Beyond: 2021

abstract:
  The present status of the ratio $\varepsilon'/\varepsilon$ in the Standard
Model (SM) is summarized. In particular we stress the differences between three
approaches that attempt to calculate $\varepsilon'/\varepsilon$ within the SM:
Lattice QCD, Dual QCD and Chiral Perturbation Theory. As presently there is
still a significant room left for new physics (NP) contributions, we summarize
the present status of the technology that has been recently developed with the
goal to analyze $\varepsilon'/\varepsilon$ and its correlations with other
observables within the Weak Effective Theory (WET) and the Standard Model
Effective Field Theory (SMEFT). We also make a few comments on the
interpretation of the main dynamics behind the $\Delta I=1/2$ rule in
$K\to\pi\pi$ decays.


title:
Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary
  Optimization

abstract:
  We present a hybrid classical-quantum framework based on the Frank-Wolfe
algorithm, Q-FW, for solving quadratic, linearly-constrained, binary
optimization problems on quantum annealers (QA). The computational premise of
quantum computers has cultivated the re-design of various existing vision
problems into quantum-friendly forms. Experimental QA realizations can solve a
particular non-convex problem known as the quadratic unconstrained binary
optimization (QUBO). Yet a naive-QUBO cannot take into account the restrictions
on the parameters. To introduce additional structure in the parameter space,
researchers have crafted ad-hoc solutions incorporating (linear) constraints in
the form of regularizers. However, this comes at the expense of a
hyper-parameter, balancing the impact of regularization. To date, a true
constrained solver of quadratic binary optimization (QBO) problems has lacked.
Q-FW first reformulates constrained-QBO as a copositive program (CP), then
employs Frank-Wolfe iterations to solve CP while satisfying linear (in)equality
constraints. This procedure unrolls the original constrained-QBO into a set of
unconstrained QUBOs all of which are solved, in a sequel, on a QA. We use
D-Wave Advantage QA to conduct synthetic and real experiments on two important
computer vision problems, graph matching and permutation synchronization, which
demonstrate that our approach is effective in alleviating the need for an
explicit regularization coefficient.


title: Applications of physics informed neural operators

abstract:
  We present an end-to-end framework to learn partial differential equations
that brings together initial data production, selection of boundary conditions,
and the use of physics-informed neural operators to solve partial differential
equations that are ubiquitous in the study and modeling of physics phenomena.
We first demonstrate that our methods reproduce the accuracy and performance of
other neural operators published elsewhere in the literature to learn the 1D
wave equation and the 1D Burgers equation. Thereafter, we apply our
physics-informed neural operators to learn new types of equations, including
the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we
show that our approach is also applicable to learn the physics of the 2D linear
and nonlinear shallow water equations, which involve three coupled partial
differential equations. We release our artificial intelligence surrogates and
scientific software to produce initial data and boundary conditions to study a
broad range of physically motivated scenarios. We provide the source code, an
interactive website to visualize the predictions of our physics informed neural
operators, and a tutorial for their use at the Data and Learning Hub for
Science.

categories: physics.comp-ph astro-ph.IM cs.AI cs.LG gr-qc


title:
The velocity structure of the Intracluster Medium of the Centaurus
  cluster

abstract:
  There are few direct measurements of ICM velocity structure, despite its
importance for understanding clusters. We present a detailed analysis of the
velocity structure of the Centaurus cluster using XMM-Newton observations.
Using a new EPIC-pn energy scale calibration, which uses the Cu Ka instrumental
line as reference, we are able to obtain velocity measurements with
uncertainties down to $\Delta v \sim 79$ km/s. We create 2D spectral maps for
the velocity, metallicity, temperature, density, entropy and pressure with an
spatial resolution of 0.25'. We have found that the velocity structure of the
ICM is similar to the velocity structure of the main galaxies while the cold
fronts are likely moving in a plane perpendicular to our line of sight with low
velocity. Finally, we have found a contribution from the kinetic component of
<25\% to the total energetic budget for radius $>30$ kpc.


title: Anti-brane uplift instability from goldstino condensation

abstract:
  We investigate the possible appearance of composite states of the goldstino
in models with four-dimensional non-linear supersymmetry and we provide a
description of their dynamics in terms of a K\"ahler potential and a
superpotential. Our analysis shows that the critical point corresponding to the
Volkov-Akulov model is unstable. Similarly, we find that the uplifted stable de
Sitter critical point of the KKLT model is shifted and acquires a tachyonic
instability. Our findings indicate the existence of a potentially dangerous
instability shared by all anti-brane uplifts.


title: Asynchronous Collaborative Learning Across Data Silos

abstract:
  Machine learning algorithms can perform well when trained on large datasets.
While large organisations often have considerable data assets, it can be
difficult for these assets to be unified in a manner that makes training
possible. Data is very often 'siloed' in different parts of the organisation,
with little to no access between silos. This fragmentation of data assets is
especially prevalent in heavily regulated industries like financial services or
healthcare. In this paper we propose a framework to enable asynchronous
collaborative training of machine learning models across data silos. This
allows data science teams to collaboratively train a machine learning model,
without sharing data with one another. Our proposed approach enhances
conventional federated learning techniques to make them suitable for this
asynchronous training in this intra-organisation, cross-silo setting. We
validate our proposed approach via extensive experiments.


title: Bounded subgroups of relatively finitely presented groups

abstract:
  Given a finitely generated group $G$ that is relatively finitely presented
with respect to a collection of peripheral subgroups, we prove that every
infinite subgroup $H$ of $G$ that is bounded in the relative Cayley graph of
$G$ is conjugate into a peripheral subgroup. As an application, we obtain a
trichotomy for subgroups of relatively hyperbolic groups. Moreover we prove the
existence of the relative exponential growth rate for all subgroups of limit
groups.


title:
The complex evolution of supermassive black holes in cosmological
  simulations

abstract:
  We present here self-consistent zoom-in simulations of massive galaxies
forming in a full cosmological setting. The simulations are run with an updated
version of the KETJU code, which is able to resolve the gravitational dynamics
of their supermassive black holes, while simultaneously modelling the
large-scale astrophysical processes in the surrounding galaxies, such as gas
cooling, star formation and stellar and AGN feedback. The KETJU code is able to
accurately model the complex behaviour of multiple SMBHs, including dynamical
friction, stellar scattering and gravitational wave emission, and also to
resolve Lidov-Kozai oscillations that naturally occur in hierarchical triplet
SMBH systems. In general most of the SMBH binaries form at moderately high
eccentricities, with typical values in the range of e =0.6-0.95, meaning that
the circular binary models that are commonly used in the literature are
insufficient for capturing the typical binary evolution.


title: Torsion and the Probability of Inflation

abstract:
  We revisit the problem of the "probability of inflation" from the point of
view of the Einstein-Cartan theory, where torsion can be present off-shell even
in the absence of spinorial currents. An informal estimate suggests that the
barrier for tunneling from "nothing" into a classical universe becomes thinner
and lower, should torsion be present even if only off-shell. This is confirmed
by a detailed calculation, where the usual assumptions are re-evaluated and
repurposed in our situation. Interestingly some approximations used in the
literature (such as the WKB approximation) are not needed in general, and in
particular in our case. When we consider wave packets centered around zero
torsion, however, the conclusion depends crucially how these are built. With a
Klein-Gordon current prescription for the measure and probability, for small
torsion variance, {\sigma}_c, we recover the plane wave results. Nonetheless,
for large {\sigma}c, higher order corrections could reverse this conclusion.


title:
A Starry Byte -- proton beam measurements of single event upsets and
  other radiation effects in ABCStar ASIC Versions 0 and 1 for the ITk strip
  tracker

abstract:
  Single Event Effects (SEEs) - predominately bit-flips in electronics caused
by particle interactions - are a major concern for ASICs operated in high
radiation environments such as ABCStar ASICs, which are designed to be used in
the future ATLAS ITk strip tracker. The chip design is therefore optimised to
protect it from SEEs by implementing triplication techniques such as Triple
Modular Redundancy (TMR). In order to verify the radiation protection
mechanisms of the chip design, the cross-section for Single Event Upsets
(SEUs), a particular class of SEEs, is measured by exposing the chip to
high-intensity particle beams while monitoring it for observed SEUs. This study
presents the setup, the performed measurements, and the results from SEU tests
performed using the latest version of the ABCStar ASIC (ABCStar V1) using a 480
MeV proton beam.


title: pyobs -- An observatory control system forrobotic telescopes

abstract:
  We present a Python-based framework for the complete operation of a robotic
telescope observatory. It provides out-of-the-box support for many popular
camera types while other hardware like telescopes, domes, and weather stations
can easily be added via a thin abstraction layer to existing code. Common
functionality like focusing, acquisition, auto-guiding, sky-flat acquisition,
and pipeline calibration are ready for use. A remote-control interface, a
"mastermind" for truly robotic operations as well as an interface to the Las
Cumbres Observatory observation portal is included. The whole system is fully
configurable and easily extendable. We are currently running pyobs successfully
on three different types of telescopes, of which one is a siderostat for
observing the Sun. pyobs uses open standards and open software wherever
possible and is itself freely available.


title: Universal graphs for the topological minor relation

abstract:
  We prove that the class $\mathcal{P}$ of all countable planar graphs does not
contain a topological minor-universal graph, i.e. there is no graph in
$\mathcal{P}$ which contains a subdivision of every graph in $\mathcal{P}$.
This answers a question of Diestel and K\"uhn and strengthens a result of Pach
stating that there is no subgraph-universal graph in $\mathcal{P}$.
Furthermore, we characterise for which subdivided stars $T$ there is a
topological minor-universal graph in the class of all countable $T$-free
graphs.


title: Linearizing Transformer with Key-Value Memory Bank

abstract:
  Transformer has brought great success to a wide range of natural language
processing tasks. Nevertheless, the computational overhead of the vanilla
transformer scales quadratically with sequence length. Many efforts have been
made to develop more efficient transformer variants. A line of work (e.g.,
Linformer) projects the input sequence into a low-rank space, achieving linear
time complexity. However, Linformer does not suit well for text generation
tasks as the sequence length must be pre-specified. We propose MemSizer, an
approach also projects the source sequence into lower dimension representation
but can take input with dynamic length, with a different perspective of the
attention mechanism. MemSizer not only achieves the same linear time complexity
but also enjoys efficient recurrent-style autoregressive generation, which
yields constant memory complexity and reduced computation at inference. We
demonstrate that MemSizer provides an improved tradeoff between efficiency and
accuracy over the vanilla transformer and other linear variants in language
modeling and machine translation tasks, revealing a viable direction towards
further inference efficiency improvement.


title: What it takes to measure Reionization with Fast Radio Bursts

abstract:
  Fast Radio Bursts (FRBs) are recently discovered extra-galactic radio
transients which are now used as novel cosmological probes. We show how the
Bursts' Dispersion Measure can model-independently probe the history of
Hydrogen reionization. Using a FlexKnot free-form parameterization to
reconstruct the reionization history we predict an 11% accuracy constraint on
the CMB optical depth, and 4% accuracy on the midpoint of reionization, to be
achieved with 100 FRBs originating from redshifts z>5.


title: CRGC -- A Practical Framework for Constructing Reusable Garbled Circuits

abstract:
  In this work, we introduce two schemes to construct reusable garbled circuits
(RGCs) in the semi-honest setting. Our completely reusable garbled circuit
(CRGC) scheme allows the generator (party A) to construct and send an
obfuscated boolean circuit along with an encoded input to the evaluator (Party
B). In contrast to Yao's garbled circuit protocol, the evaluator can securely
evaluate the same CRGC with an arbitrary number of inputs. As a tradeoff, CRGCs
come with predictable input bit leakage. We also propose a partially reusable
garbled circuit (PRGC) scheme that divides a circuit into reusable and
non-reusable sections. PRGCs do not leak input bits of the generator. We
benchmark our CRGC implementation against the state-of-the-art Yao's Garbled
Circuit libraries EMP SH2PC and TinyGarble2. Using our framework, evaluating a
CRGC is up to twenty times faster than evaluating an equivalent Yao's Garbled
Circuit constructed by the two existing libraries. Our open-source library can
convert any C++ function to an RGC at approx. 80 million gates per second and
repeatedly evaluate a CRGC at approx. 350 million gates per second.
Additionally, a compressed CRGC is approx. 75% smaller in file size than the
unobfuscated boolean circuit.


title:
Robust Onboard Localization in Changing Environments Exploiting Text
  Spotting

abstract:
  Robust localization in a given map is a crucial component of most autonomous
robots. In this paper, we address the problem of localizing in an indoor
environment that changes and where prominent structures have no correspondence
in the map built at a different point in time. To overcome the discrepancy
between the map and the observed environment caused by such changes, we exploit
human-readable localization cues to assist localization. These cues are readily
available in most facilities and can be detected using RGB camera images by
utilizing text spotting. We integrate these cues into a Monte Carlo
localization framework using a particle filter that operates on 2D LiDAR scans
and camera data. By this, we provide a robust localization solution for
environments with structural changes and dynamics by humans walking. We
evaluate our localization framework on multiple challenging indoor scenarios in
an office environment. The experiments suggest that our approach is robust to
structural changes and can run on an onboard computer. We release an open
source implementation of our approach (upon paper acceptance), which uses
off-the-shelf text spotting, written in C++ with a ROS wrapper.


title:
Large-eddy simulations to define building-specific similarity
  relationships for natural ventilation flow rates

abstract:
  Natural ventilation can play an important role towards preventing the spread
of airborne diseases in indoor environments. However, quantifying natural
ventilation flow rates is a challenging task due to significant variability in
the boundary conditions that drive the flow. In the current study, we propose
and validate an efficient strategy for using computational fluid dynamics (CFD)
to assess natural ventilation flow rates under variable conditions, considering
the test case of a single-room home in a dense urban slum. The method
characterizes the dimensionless ventilation rate as a function of the
dimensionless ventilation Richardson number and the wind direction. First, the
high-fidelity large-eddy simulation predictions are validated against
full-scale ventilation rate measurements. Next, simulations with identical
Richardson numbers, but varying dimensional wind speeds and temperatures, are
compared to verify the proposed similarity relationship. Last, the functional
form of the similarity relationship is determined based on 32 LES. Validation
of the surrogate model against full-scale measurements demonstrates that the
proposed strategy can efficiently inform accurate building-specific similarity
relationships for natural ventilation flow rates in complex urban environments.


title: Reflectivity spectra as absorption ones -- is it correct ?

abstract:
  Approximate expressions for X-ray resonant and M\"ossbauer reflectivity in
the total external reflection region are developed for the limiting cases of a
semiinfinite mirror with a small resonant addition to the total susceptibility
and for the case of an ultrathin resonant layer. It is shown that in this
region the reflectivity can really show the linear dependence on the imaginary
part of resonant susceptibility, therefore in these cases the consideration of
reflectivity spectra (R-spectra) as absorption resonant spectra, undertaken in
several experimental works, can be justified. However, we have selected several
effects producing essential dispersive distortions of the R-spectrum shape even
for very small grazing angles. It has been shown that the dispersive
corrections to the R-spectrum shape are mostly essential if the nonresonant
absorption is relatively large. Model calculations demonstrate that the
quantitative spectroscopic information extracted from R-spectra by using the
software developed for absorption spectra can be inaccurate.


title: Limit-Periodic Dirac Operators with Thin Spectra

abstract:
  We prove that limit-periodic Dirac operators generically have spectra of zero
Lebesgue measure and that a dense set of them have spectra of zero Hausdorff
dimension. The proof combines ideas of Avila from a Schr\"odinger setting with
a new commutation argument for generating open spectral gaps. This overcomes an
obstacle previously observed in the literature; namely, in Schr\"odinger-type
settings, translation of the spectral measure corresponds to small
$L^\infty$-perturbations of the operator data, but this is not true for Dirac
or CMV operators. The new argument is much more model-independent. To
demonstrate this, we also apply the argument to prove generic zero-measure
spectrum for CMV matrices with limit-periodic Verblunsky coefficients.


title: Predicting the location of shear band initiation in a metallic glass

abstract:
  We report atomistic simulation results which indicate that the location of
shear banding in a metallic glass (MG) can be ascertained with reasonably high
accuracy solely from the undeformed static structure. Correlation is observed
between the location of the initiation of shear bands in a simulated MG and the
initial distribution of the density of fertile sites (DFS) for stress-driven
shear transformations identified a priori based on a deep learning model
devised in our recent work [Fan and Ma, Nat. Commun. 12, 1506 (2021)]. In
addition, we demonstrated that one can judge whether a glass is brittle or
ductile solely based upon its initial DFS distribution. These validate that
shear bands in MG arise from non-linear instabilities, and that the as-quenched
glass structure contains inhomogeneities that influence these instabilities.
This work also reveals an important subtlety regarding the non-deterministic
nature of athermal quasistatic shear simulations.


title: Reclaiming saliency: rhythmic precision-modulated action and perception

abstract:
  Computational models of visual attention in artificial intelligence and
robotics have been inspired by the concept of a saliency map. These models
account for the mutual information between the (current) visual information and
its estimated causes. However, they fail to consider the circular causality
between perception and action. In other words, they do not consider where to
sample next, given current beliefs. Here, we reclaim salience as an active
inference process that relies on two basic principles: uncertainty minimisation
and rhythmic scheduling. For this, we make a distinction between attention and
salience. Briefly, we associate attention with precision control, i.e., the
confidence with which beliefs can be updated given sampled sensory data, and
salience with uncertainty minimisation that underwrites the selection of future
sensory data. Using this, we propose a new account of attention based on
rhythmic precision-modulation and discuss its potential in robotics, providing
numerical experiments that showcase advantages of precision-modulation for
state and noise estimation, system identification and action selection for
informative path planning.


title:
Iterative Implicit Gradients for Nonconvex Optimization with Variational
  Inequality Constraints

abstract:
  We propose an implicit gradient based scheme for a constrained optimization
problem with nonconvex loss function, which can be potentially used to analyze
a variety of applications in machine learning, including meta-learning,
hyperparameter optimization, and reinforcement learning. The proposed algorithm
is based on the iterative differentiation (ITD) strategy. We extend the
convergence and rate analysis of the current literature of bilevel optimization
to a constrained bilevel structure with the motivation of learning under
constraints. For addressing bilevel optimization using any first-order scheme,
it involves the gradient of the inner-level optimal solution with respect to
the outer variable (implicit gradient). In this paper, taking into account of a
possible large-scale structure, we propose an efficient way of obtaining the
implicit gradient. We further provide error bounds with respect to the true
gradients. Further, we provide nonasymptotic rate results.


title:
Impact of event activity variable on the ratio of observables in isobar
  collisions

abstract:
  The STAR isobar data of $^{96}$Ru+$^{96}$Ru and $^{96}$Zr+$^{96}$Zr
collisions at $\sqrt{s_{\mathrm{NN}}}= 200$ GeV show that ratios of observables
($R_{\mathcal{O}}$) such as the multiplicity distribution,
$p(N_{\mathrm{ch}})$, and the harmonic flow, $v_n$, deviate from unity, when
presented as a function of centrality, $c$. These deviations have been
attributed to the differences in the shape and radial profiles between
$^{96}$Ru and $^{96}$Zr nuclei. In addition, the ratios $R_{\mathcal{O}}(x)$
depend on the choice of the event activity variable $x$, which could be either
$N_{\mathrm{ch}}$ or centrality. We estimate the difference $\Delta R$ between
these two choices, based on the published $p(N_{\mathrm{ch}})$, as well as
those from a multiphase transport (AMPT) model with varied nuclear structure
parameters: nuclear radius ($R_0$), surface diffuseness ($a_0$), quadrupole
deformation ($\beta_2$), and octupole deformation ($\beta_3$). In contrary to
$R_{v_n}(c)$, $R_{v_n}(N_{\mathrm{ch}})$ is nearly independent of the analysis
approaches, suggesting that nonflow effects are better controlled by
$N_{\mathrm{ch}}$ than $c$. The ratios of observables sensitive to the chiral
magnetic effect (CME) are also much closer to unity for $x=N_{\mathrm{ch}}$
than $x=c$, indicating that the ratios calculated at the same $N_{\mathrm{ch}}$
provide a better baseline for the non-CME background. According to the AMPT
results, the dominant parameter for $\Delta R$ is $a_0$, while $R_0$ and
$\beta_n$ are only important in central collisions. The published
$p(N_{\mathrm{ch}})$ is also used to estimate $\Delta R_{\left\langle
p_{\mathrm{T}}\right\rangle}$ for mean transverse momentum, which is
non-negligible compared with $R_{\left\langle p_{\mathrm{T}}\right\rangle}-1$.


title: Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels

abstract:
  We propose a novel scene flow method that captures 3D motions from point
clouds without relying on ground-truth scene flow annotations. Due to the
irregularity and sparsity of point clouds, it is expensive and time-consuming
to acquire ground-truth scene flow annotations. Some state-of-the-art
approaches train scene flow networks in a self-supervised learning manner via
approximating pseudo scene flow labels from point clouds. However, these
methods fail to achieve the performance level of fully supervised methods, due
to the limitations of point cloud such as sparsity and lacking color
information. To provide an alternative, we propose a novel approach that
utilizes monocular RGB images and point clouds to generate pseudo scene flow
labels for training scene flow networks. Our pseudo label generation module
infers pseudo scene labels for point clouds by jointly leveraging rich
appearance information in monocular images and geometric information of point
clouds. To further reduce the negative effect of noisy pseudo labels on the
training, we propose a noisy-label-aware training scheme by exploiting the
geometric relations of points. Experiment results show that our method not only
outperforms state-of-the-art self-supervised approaches, but also outperforms
some supervised approaches that use accurate ground-truth flows.


title: Correlation spectroscopy with multi-qubit-enhanced phase estimation

abstract:
  Precision spectroscopy on trapped-ion crystals subject to correlated
dephasing can reveal a multitude of information in the absence of any
single-particle coherences. We use correlation spectroscopy for measuring
ion-ion distances and transition frequency shifts in one- and two-dimensional
ion crystals by analyzing multi-particle correlations of up to $N=91$ qubits,
each of which is encoded in a single ion. Additionally we carry out single-shot
measurements of the laser-ion detuning and of path-length fluctuations. We show
that the information contained in $N$-particle correlations reduces the
measurement uncertainty as compared to the case where only two-particle
correlations are analyzed. We derive quantum precision limits for this problem.
While for a pair of qubits entanglement can reduce the measurement uncertainty,
entanglement-free correlation spectroscopy becomes asymptotically optimal in
the limit of $N\rightarrow\infty$ when the measurement goal is the
determination of all phase differences between the qubits.


title: A Case Study on Nutek India Limited, Regarding Deep Fall in Share Price

abstract:
  Manipulating the security price is an act of artificially inflating or
deflating the price of a security. Generally, manipulation is defined as a
series of transactions designed to raise or lower a price of a security or to
give the appearance of trading for the purpose of inducing others to buy or
sell. In essence, a manipulation is intentional interference with the free
forces of supply and demand. In this paper we have tried to study the reasons
behind drastic fall in share price of Nutek India Limited.


title: Computed Tomography Reconstruction using Generative Energy-Based Priors

abstract:
  In the past decades, Computed Tomography (CT) has established itself as one
of the most important imaging techniques in medicine. Today, the applicability
of CT is only limited by the deposited radiation dose, reduction of which
manifests in noisy or incomplete measurements. Thus, the need for robust
reconstruction algorithms arises. In this work, we learn a parametric
regularizer with a global receptive field by maximizing it's likelihood on
reference CT data. Due to this unsupervised learning strategy, our trained
regularizer truly represents higher-level domain statistics, which we
empirically demonstrate by synthesizing CT images. Moreover, this regularizer
can easily be applied to different CT reconstruction problems by embedding it
in a variational framework, which increases flexibility and interpretability
compared to feed-forward learning-based approaches. In addition, the
accompanying probabilistic perspective enables experts to explore the full
posterior distribution and may quantify uncertainty of the reconstruction
approach. We apply the regularizer to limited-angle and few-view CT
reconstruction problems, where it outperforms traditional reconstruction
algorithms by a large margin.


title:
A Supervised Machine Learning Approach for Sequence Based
  Protein-protein Interaction (PPI) Prediction

abstract:
  Computational protein-protein interaction (PPI) prediction techniques can
contribute greatly in reducing time, cost and false-positive interactions
compared to experimental approaches. Sequence is one of the key and primary
information of proteins that plays a crucial role in PPI prediction. Several
machine learning approaches have been applied to exploit the characteristics of
PPI datasets. However, these datasets greatly influence the performance of
predicting models. So, care should be taken on both dataset curation as well as
design of predictive models. Here, we have described our submitted solution
with the results of the SeqPIP competition whose objective was to develop
comprehensive PPI predictive models from sequence information with high-quality
bias-free interaction datasets. A training set of 2000 positive and 2000
negative interactions with sequences was given to us. Our method was evaluated
with three independent high-quality interaction test datasets and with other
competitors solutions.


title: Towards a Deep Learning Model for Hadronization

abstract:
  Hadronization is a complex quantum process whereby quarks and gluons become
hadrons. The widely-used models of hadronization in event generators are based
on physically-inspired phenomenological models with many free parameters. We
propose an alternative approach whereby neural networks are used instead. Deep
generative models are highly flexible, differentiable, and compatible with
Graphical Processing Unit (GPUs). We make the first step towards a data-driven
machine learning-based hadronization model by replacing a compont of the
hadronization model within the Herwig event generator (cluster model) with a
Generative Adversarial Network (GAN). We show that a GAN is capable of
reproducing the kinematic properties of cluster decays. Furthermore, we
integrate this model into Herwig to generate entire events that can be compared
with the output of the public Herwig simulator as well as with $e^+e^-$ data.


title: Adjoint characteristics for Eulerian two-dimensional supersonic flow

abstract:
  The formal expressions, in terms of local flow variables, defining the
adjoint characteristic curves, and the associated compatibility relationships
satisfied along them, are formally derived in the case of a supersonic flow
governed by the compressible Euler equations in two dimensions. These findings
extend their well-known counterparts for the direct system, and should serve
analytical and possibly numerical studies of the perfect-flow model with
respect to adjoint fields or sensitivity questions. Beside the analytical
theory, the results are demonstrate by the numerical integration over a very
fine grid of the compatibility relationships for discrete flow-fields and
dual-consistent adjoint fields.


title: Spiking Neural Streaming Binary Arithmetic

abstract:
  Boolean functions and binary arithmetic operations are central to standard
computing paradigms. Accordingly, many advances in computing have focused upon
how to make these operations more efficient as well as exploring what they can
compute. To best leverage the advantages of novel computing paradigms it is
important to consider what unique computing approaches they offer. However, for
any special-purpose co-processor, Boolean functions and binary arithmetic
operations are useful for, among other things, avoiding unnecessary I/O
on-and-off the co-processor by pre- and post-processing data on-device. This is
especially true for spiking neuromorphic architectures where these basic
operations are not fundamental low-level operations. Instead, these functions
require specific implementation. Here we discuss the implications of an
advantageous streaming binary encoding method as well as a handful of circuits
designed to exactly compute elementary Boolean and binary operations.


title: CorpusVis: Visual Analysis of Digital Sheet Music Collections

abstract:
  Manually investigating sheet music collections is challenging for music
analysts due to the magnitude and complexity of underlying features,
structures, and contextual information. However, applying sophisticated
algorithmic methods would require advanced technical expertise that analysts do
not necessarily have. Bridging this gap, we contribute CorpusVis, an
interactive visual workspace, enabling scalable and multi-faceted analysis. Our
proposed visual analytics dashboard provides access to computational methods,
generating varying perspectives on the same data. The proposed application uses
metadata including composers, type, epoch, and low-level features, such as
pitch, melody, and rhythm. To evaluate our approach, we conducted a pair
analytics study with nine participants. The qualitative results show that
CorpusVis supports users in performing exploratory and confirmatory analysis,
leading them to new insights and findings. In addition, based on three
exemplary workflows, we demonstrate how to apply our approach to different
tasks, such as exploring musical features or comparing composers.


title:
Optimal quantization for mixed distributions generated by two uniform
  distributions

abstract:
  In this paper, for mixed distributions generated by two uniform distributions
we investigate the optimal sets of $n$-means and the $n$th quantization errors
for all positive integers $n$. Some conjectures and open problems are also
given.


title:
The most general structure of graphs with hamiltonian or hamiltonian
  connected square

abstract:
  On the basis of recent results on hamiltonicity and hamiltonian connectedness
in the square of a 2-block, we determine the most general block-cutvertex
structure a graph $G$ may have in order to guarantee that $G^2$ is hamiltonian,
hamiltonian connected, respectively. Such an approach was already developed for
hamiltonian total graphs.


title:
Diagrammatic Monte Carlo for electronic correlation in molecules:
  high-order many-body perturbation theory with low scaling

abstract:
  We present a low-scaling diagrammatic Monte Carlo approach to molecular
correlation energies. Using combinatorial graph theory to encode many-body
Hugenholtz diagrams, we sample the M{\o}ller-Plesset (MPn) perturbation series,
obtaining accurate correlation energies up to n = 5, with quadratic scaling in
the number of basis functions. Our technique reduces the computational
complexity of the molecular many-fermion correlation problem, opening up the
possibility of low-scaling, accurate stochastic computations for a wide class
of many-body systems described by Hugenholtz diagrams.


title:
Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future
  Directions

abstract:
  A long-term goal of AI research is to build intelligent agents that can
communicate with humans in natural language, perceive the environment, and
perform real-world tasks. Vision-and-Language Navigation (VLN) is a fundamental
and interdisciplinary research topic towards this goal, and receives increasing
attention from natural language processing, computer vision, robotics, and
machine learning communities. In this paper, we review contemporary studies in
the emerging field of VLN, covering tasks, evaluation metrics, methods, etc.
Through structured analysis of current progress and challenges, we highlight
the limitations of current VLN and opportunities for future work. This paper
serves as a thorough reference for the VLN research community.


title: Pseudo Label Is Better Than Human Label

abstract:
  State-of-the-art automatic speech recognition (ASR) systems are trained with
tens of thousands of hours of labeled speech data. Human transcription is
expensive and time consuming. Factors such as the quality and consistency of
the transcription can greatly affect the performance of the ASR models trained
with these data. In this paper, we show that we can train a strong teacher
model to produce high quality pseudo labels by utilizing recent self-supervised
and semi-supervised learning techniques. Specifically, we use JUST (Joint
Unsupervised/Supervised Training) and iterative noisy student teacher training
to train a 600 million parameter bi-directional teacher model. This model
achieved 4.0% word error rate (WER) on a voice search task, 11.1% relatively
better than a baseline. We further show that by using this strong teacher model
to generate high-quality pseudo labels for training, we can achieve 13.6%
relative WER reduction (5.9% to 5.1%) for a streaming model compared to using
human labels.


title:
A Machine Learning Approach to Correct for Mass Resolution Effects in
  Simulated Halo Clustering Statistics

abstract:
  The increase in the observed volume in cosmological surveys imposes various
challenges on simulation preparations. Firstly, the volume of the simulations
required increases proportionally to the observations. However, large-volume
simulations are quickly becoming computationally intractable. Secondly,
on-going and future large-volume survey are targeting smaller objects, e.g.
emission line galaxies, compared to the earlier focus, i.e. luminous red
galaxies. They require the simulations to have higher mass resolutions. In this
work we present a machine learning (ML) approach to calibrate the halo
catalogue of a low-resolution (LR) simulation by training with a paired
high-resolution (HR) simulation with the same background white noise, thus we
can build the training data by matching HR haloes to LR haloes in a one-to-one
fashion. After training, the calibrated LR halo catalogue reproduces the
mass-clustering relation for mass down to $2.5\times 10^{11}~h^{-1}M_\odot$
within $5~{\rm per~cent}$ at scales $k<1~h\,\rm Mpc^{-1}$. We validate the
performance of different statistics including halo mass function, power
spectrum, two-point correlation function, and bispectrum in both real and
redshift space. Our approach generates high-resolution-like halo catalogues
($>200$ particles per halo) from low-resolution catalogues ($>25$ particles per
halo) containing corrected halo masses for each object. This allows to bypass
the computational burden of a large-volume real high-resolution simulation
without much compromise in the mass resolution of the result. The cost of our
ML approach ($\sim 1$ CPU-hour) is negligible compared to the cost of a
$N$-body simulation (e.g. millions of CPU-hours), The required computing time
is cut a factor of 8.


title: Competency Assessment for Autonomous Agents using Deep Generative Models

abstract:
  For autonomous agents to act as trustworthy partners to human users, they
must be able to reliably communicate their competency for the tasks they are
asked to perform. Towards this objective, we develop probabilistic world models
based on deep generative modelling that allow for the simulation of agent
trajectories and accurate calculation of tasking outcome probabilities. By
combining the strengths of conditional variational autoencoders with recurrent
neural networks, the deep generative world model can probabilistically forecast
trajectories over long horizons to task completion. We show how these
forecasted trajectories can be used to calculate outcome probability
distributions, which enable the precise assessment of agent competency for
specific tasks and initial settings.


title:
SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of
  Academic Performance

abstract:
  Comprehensively evaluating and comparing researchers' academic performance is
complicated due to the intrinsic complexity of scholarly data. Different
scholarly evaluation tasks often require the publication and citation data to
be investigated in various manners. In this paper, we present an interactive
visualization framework, SD2, to enable flexible data partition and composition
to support various analysis requirements within a single system. SD2 features
the hierarchical histogram, a novel visual representation for flexibly slicing
and dicing the data, allowing different aspects of scholarly performance to be
studied and compared. We also leverage the state-of-the-art set visualization
technique to select individual researchers or combine multiple scholars for
comprehensive visual comparison. We conduct multiple rounds of expert
evaluation to study the effectiveness and usability of SD2 and revise the
design and system implementation accordingly. The effectiveness of SD2 is
demonstrated via multiple usage scenarios with each aiming to answer a
specific, commonly raised question.


title: Deep Learning based Data Prefetching in CPU-GPU Unified Virtual Memory

abstract:
  Unified Virtual Memory (UVM) relieves the developers from the onus of
maintaining complex data structures and explicit data migration by enabling
on-demand data movement between CPU memory and GPU memory. However, on-demand
paging soon becomes a performance bottleneck of UVM due to the high latency
caused by page table walks and data migration over interconnect. Prefetching is
considered a promising solution to this problem given its ability to leverage
the locality of program memory access patterns. However, existing
locality-based prefetching schemes can not handle all the situations. %Data
structures like arrays tend to be stored in contiguous blocks, and accessed
repeatedly. An ideal prefetcher should not only look at narrow regions of the
requested address space but also capture global context to deliver a good
prediction of the memory access pattern.
  This paper proposes a novel approach for page prefetching for UVM through
deep learning. We first show that a powerful Transformer learning model can
provide high accuracy for UVM page prefetching. We then perform analysis to
interpret this Transformer model and derive several insights that allow us to
design a simpler model to match the unconstrained model's accuracy with orders
of magnitude lower cost. We evaluate this simplified model on a set of 11
memory-intensive benchmarks from popular benchmark suites. Our solution
outperforms the state-of-the-art UVM framework, improving the performance by
10.89%, improving the device memory page hit rate by 16.98% (89.02% vs. 76.10%
for prior art), and reducing the CPU-GPU interconnect traffic by 11.05%.
According to our proposed unified metric, which combines the accuracy,
coverage, and page hit rate, our solution is approaching the ideal prefetching
scheme more than the state-of-the-art design (0.90 vs. 0.85, with the perfect
prefetcher of 1.0).


title: Decision-making of Emergent Incident based on P-MADDPG

abstract:
  In recent years, human casualties and damage to resources caused by emergent
incidents have become a serious problem worldwide. In this paper, we model the
emergency decision-making problem and use Multi-agent System (MAS) to solve the
problem that the decision speed cannot keep up with the spreading speed. MAS
can play an important role in the automated execution of these tasks to reduce
mission completion time. In this paper, we propose a P-MADDPG algorithm to
solve the emergency decision-making problem of emergent incidents, which
predicts the nodes where an incident may occur in the next time by GRU model
and makes decisions before the incident occurs, thus solving the problem that
the decision speed cannot keep up with the spreading speed. A simulation
environment was established for realistic scenarios, and three scenarios were
selected to test the performance of P-MADDPG in emergency decision-making
problems for emergent incidents: unmanned storage, factory assembly line, and
civil airport baggage transportation. Simulation results using the P-MADDPG
algorithm are compared with the greedy algorithm and the MADDPG algorithm, and
the final experimental results show that the P-MADDPG algorithm converges
faster and better than the other algorithms in scenarios of different sizes.
This shows that the P-MADDP algorithm is effective for emergency
decision-making in emergent incident.


title:
A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks
  with Transient Data

abstract:
  The Internet of Things (IoT) has been continuously rising in the past few
years, and its potentials are now more apparent. However, transient data
generation and limited energy resources are the major bottlenecks of these
networks. Besides, minimum delay and other conventional quality of service
measurements are still valid requirements to meet. An efficient caching policy
can help meet the standard quality of service requirements while bypassing IoT
networks' specific limitations. Adopting deep reinforcement learning (DRL)
algorithms enables us to develop an effective caching scheme without the need
for any prior knowledge or contextual information. In this work, we propose a
DRL-based caching scheme that improves the cache hit rate and reduces energy
consumption of the IoT networks, in the meanwhile, taking data freshness and
limited lifetime of IoT data into account. To better capture the
regional-different popularity distribution, we propose a hierarchical
architecture to deploy edge caching nodes in IoT networks. The results of
comprehensive experiments show that our proposed method outperforms the
well-known conventional caching policies and an existing DRL-based solution in
terms of cache hit rate and energy consumption of the IoT networks by
considerable margins.


title:
MMES: Mixture Model based Evolution Strategy for Large-Scale
  Optimization

abstract:
  This work provides an efficient sampling method for the covariance matrix
adaptation evolution strategy (CMA-ES) in large-scale settings. In contract to
the Gaussian sampling in CMA-ES, the proposed method generates mutation vectors
from a mixture model, which facilitates exploiting the rich variable
correlations of the problem landscape within a limited time budget. We analyze
the probability distribution of this mixture model and show that it
approximates the Gaussian distribution of CMA-ES with a controllable accuracy.
We use this sampling method, coupled with a novel method for mutation strength
adaptation, to formulate the mixture model based evolution strategy (MMES) -- a
CMA-ES variant for large-scale optimization. The numerical simulations show
that, while significantly reducing the time complexity of CMA-ES, MMES
preserves the rotational invariance, is scalable to high dimensional problems,
and is competitive against the state-of-the-arts in performing global
optimization.


title: Multiparameter quantum critical metrology

abstract:
  Single parameter estimation is known to benefit from extreme sensitivity to
parameter changes in quantum critical systems. However, the simultaneous
estimation of multiple parameters is generally limited due to the
incompatibility arising from the quantum nature of the underlying system. A key
question is whether quantum criticality may also play a positive role in
reducing the incompatibility in the simultaneous estimation of multiple
parameters. We argue that this is generally the case and verify this prediction
in paradigmatic quantum many-body systems close to first and second order phase
transitions. The antiferromagnetic and ferromagnetic 1-D Ising chain with both
transverse and longitudinal fields are analysed across different regimes and
close to criticality.


title: Vision-Based Manipulators Need to Also See from Their Hands

abstract:
  We study how the choice of visual perspective affects learning and
generalization in the context of physical manipulation from raw sensor
observations. Compared with the more commonly used global third-person
perspective, a hand-centric (eye-in-hand) perspective affords reduced
observability, but we find that it consistently improves training efficiency
and out-of-distribution generalization. These benefits hold across a variety of
learning algorithms, experimental settings, and distribution shifts, and for
both simulated and real robot apparatuses. However, this is only the case when
hand-centric observability is sufficient; otherwise, including a third-person
perspective is necessary for learning, but also harms out-of-distribution
generalization. To mitigate this, we propose to regularize the third-person
information stream via a variational information bottleneck. On six
representative manipulation tasks with varying hand-centric observability
adapted from the Meta-World benchmark, this results in a state-of-the-art
reinforcement learning agent operating from both perspectives improving its
out-of-distribution generalization on every task. While some practitioners have
long put cameras in the hands of robots, our work systematically analyzes the
benefits of doing so and provides simple and broadly applicable insights for
improving end-to-end learned vision-based robotic manipulation.


title: Piecewise scalable frames

abstract:
  In this paper we define "piecewise scalable frames". This new scaling process
allows us to alter many frames to Parseval frames which is impossible by the
previous standard scaling. We give necessary and sufficient conditions for a
frame to be piecewise scalable. We show that piecewise scalability is preserved
under unitary transformations. Unlike standard scaling, we show that all frames
in $\RR^2$ and $\RR^3$ are piecewise scalable. We also show that if the frame
vectors are close to each other, then they might not be piecewise scalable.
Several properties of scaling constants are also presented.


title:
Sample-efficient Iterative Lower Bound Optimization of Deep Reactive
  Policies for Planning in Continuous MDPs

abstract:
  Recent advances in deep learning have enabled optimization of deep reactive
policies (DRPs) for continuous MDP planning by encoding a parametric policy as
a deep neural network and exploiting automatic differentiation in an end-to-end
model-based gradient descent framework. This approach has proven effective for
optimizing DRPs in nonlinear continuous MDPs, but it requires a large number of
sampled trajectories to learn effectively and can suffer from high variance in
solution quality. In this work, we revisit the overall model-based DRP
objective and instead take a minorization-maximization perspective to
iteratively optimize the DRP w.r.t. a locally tight lower-bounded objective.
This novel formulation of DRP learning as iterative lower bound optimization
(ILBO) is particularly appealing because (i) each step is structurally easier
to optimize than the overall objective, (ii) it guarantees a monotonically
improving objective under certain theoretical conditions, and (iii) it reuses
samples between iterations thus lowering sample complexity. Empirical
evaluation confirms that ILBO is significantly more sample-efficient than the
state-of-the-art DRP planner and consistently produces better solution quality
with lower variance. We additionally demonstrate that ILBO generalizes well to
new problem instances (i.e., different initial states) without requiring
retraining.


title: The $k$-Cap Process on Geometric Random Graphs

abstract:
  The $k$-cap (or $k$-winners-take-all) process on a graph works as follows: in
each iteration, exactly $k$ vertices of the graph are in the cap (i.e.,
winners); the next round winners are the vertices that have the highest total
degree to the current winners, with ties broken randomly. This natural process
is a simple model of firing activity in the brain. We study its convergence on
geometric random graphs, revealing rather surprising behavior.


title:
Spectral Projected Subgradient Method for Nonsmooth Convex Optimization
  Problems

abstract:
  We consider constrained optimization problems with a nonsmooth objective
function in the form of mathematical expectation. The Sample Average
Approximation (SAA) is used to estimate the objective function and variable
sample size strategy is employed. The proposed algorithm combines an SAA
subgradient with the spectral coefficient in order to provide a suitable
direction which improves the performance of the first order method as shown by
numerical results. The step sizes are chosen from the predefined interval and
the almost sure convergence of the method is proved under the standard
assumptions in stochastic environment. To enhance the performance of the
proposed algorithm, we further specify the choice of the step size by
introducing an Armijo-like procedure adapted to this framework. Considering the
computational cost on machine learning problems, we conclude that the line
search improves the performance significantly. Numerical experiments conducted
on finite sums problems also reveal that the variable sample strategy
outperforms the full sample approach.


title:
High Gain Array Antenna With FSS for Vital Sign Monitoring Through the
  Wall

abstract:
  In this research, a 2.4 GHz planar array antenna 2X2 combined with frequency
selective surface (FSS) is proposed to obtain a high gain and high efficiency
for improving the performance of vital sign monitoring through the wall using
Self-injection-locked radar systems. The FSS is designed on a double-layer with
unit cells of 9X7 for each layer. Modified Jerusalem cross-shaped of FSS is
proposed to enhance the gain. The antenna array and additional FSS can obtain a
gain of 15.2 dB and 82% of efficiency. the vital sign monitoring of the subject
behind the wall has been successfully carried out.


title: Revisiting Multi-Scale Feature Fusion for Semantic Segmentation

abstract:
  It is commonly believed that high internal resolution combined with expensive
operations (e.g. atrous convolutions) are necessary for accurate semantic
segmentation, resulting in slow speed and large memory usage. In this paper, we
question this belief and demonstrate that neither high internal resolution nor
atrous convolutions are necessary. Our intuition is that although segmentation
is a dense per-pixel prediction task, the semantics of each pixel often depend
on both nearby neighbors and far-away context; therefore, a more powerful
multi-scale feature fusion network plays a critical role. Following this
intuition, we revisit the conventional multi-scale feature space (typically
capped at P5) and extend it to a much richer space, up to P9, where the
smallest features are only 1/512 of the input size and thus have very large
receptive fields. To process such a rich feature space, we leverage the recent
BiFPN to fuse the multi-scale features. Based on these insights, we develop a
simplified segmentation model, named ESeg, which has neither high internal
resolution nor expensive atrous convolutions. Perhaps surprisingly, our simple
method can achieve better accuracy with faster speed than prior art across
multiple datasets. In real-time settings, ESeg-Lite-S achieves 76.0% mIoU on
CityScapes [12] at 189 FPS, outperforming FasterSeg [9] (73.1% mIoU at 170
FPS). Our ESeg-Lite-L runs at 79 FPS and achieves 80.1% mIoU, largely closing
the gap between real-time and high-performance segmentation models.


title: Triple Higgs Couplings in the 2HDM: The Complete Picture

abstract:
  The measurement of the triple Higgs coupling is one of the main tasks of the
(HL-)LHC and future lepton colliders. Similarly, triple Higgs couplings
involving BSM Higgs bosons are of high interest. Within the framework of Two
Higgs Doublet Models (2HDM) we investigate the allowed ranges for all triple
Higgs couplings involving at least one light, SM-like Higgs boson. We present
newly the allowed ranges for 2HDM type~III and~IV and update the results within
the type~I and~II. We take into account theoretical constraints from unitarity
and stability, experimental constraints from direct BSM Higgs-boson searches,
measurements of the rates of the SM-like Higgs-boson at the LHC, as well as
flavor observables and electroweak precision data. For the SM-type triple Higgs
coupling w.r.t. its SM value, $\lambda_{hhh}/\lambda_{hhh}^\mathrm{SM}$, we
find allowed intervals of $\sim [-0.5,1.3]$ in type~I and $\sim [0.5,1.0]$ in
the other Yukawa types. These allowed ranges have important implications for
the experimental determination of this coupling at future collider experiments.
We find the coupling $\lambda_{hhH}$ between $\sim -1.5$ and $\sim +1.5$ in the
four Yukawa types. For the triple Higgs couplings involving two heavy neutral
Higgs bosons, $\lambda_{hHH}$ and $\lambda_{hAA}$ we find values between $\sim
-0.5$ and $\sim 16$, and between $\sim -1$ and $\sim 32$ for
$\lambda_{hH^+H^-}$. These potentially large values could lead to strongly
enhanced production of two Higgs-bosons at the HL-LHC or high-energy lepton
colliders.


title:
Prospects of measuring a metallicity trend and spread in globular
  clusters from low-resolution spectroscopy

abstract:
  The metallicity spread, or the metallicity trend along the evolutionary
sequence of a globular cluster, is a rich source of information to help
understand the cluster physics (e.g. multiple populations) and stellar physics
(e.g. atomic diffusion). Low-resolution integral-field-unit spectroscopy in the
optical with the MUSE is an attractive prospect if it can provide these
diagnostics because it allows us to extract spectra of a large fraction of the
cluster stars. We investigate the possibilities of full-spectrum fitting to
derive stellar parameters and chemical abundances at low spectral resolution
(R~2000). We reanalysed 1584 MUSE spectra of 1061 stars above the turn-off of
NGC 6397 using FERRE and employing two different synthetic libraries. We derive
the equivalent iron abundance \fehe for fixed values of \afe. We find that (i)
the interpolation schema and grid mesh are not critical for the precision,
metallicity spread, and trend; (ii) with the two grids, \fehe increases by ~0.2
dex along the sub-giant branch, starting from the turn-off of the main
sequence; (iii) restricting the wavelength range to the optical decreases the
precision significantly; and (iv) the precision obtained with the synthetic
libraries is lower than the precision obtained previously with empirical
libraries. Full-spectrum fitting provides reproducible results that are robust
to the choice of the reference grid of synthetic spectra and to the details of
the analysis. The \fehe increase along the sub-giant branch is in stark
contrast with the nearly constant iron abundance previously found with
empirical libraries. The precision of the measurements (0.05 dex on \fehe) is
currently not sufficient to assess the intrinsic chemical abundance spreads,
but this may change with deeper observations. Improvements of the synthetic
spectra are still needed to deliver the full possibilities of full-spectrum
fitting.


title: Possibility Before Utility: Learning And Using Hierarchical Affordances

abstract:
  Reinforcement learning algorithms struggle on tasks with complex hierarchical
dependency structures. Humans and other intelligent agents do not waste time
assessing the utility of every high-level action in existence, but instead only
consider ones they deem possible in the first place. By focusing only on what
is feasible, or "afforded", at the present moment, an agent can spend more time
both evaluating the utility of and acting on what matters. To this end, we
present Hierarchical Affordance Learning (HAL), a method that learns a model of
hierarchical affordances in order to prune impossible subtasks for more
effective learning. Existing works in hierarchical reinforcement learning
provide agents with structural representations of subtasks but are not
affordance-aware, and by grounding our definition of hierarchical affordances
in the present state, our approach is more flexible than the multitude of
approaches that ground their subtask dependencies in a symbolic history. While
these logic-based methods often require complete knowledge of the subtask
hierarchy, our approach is able to utilize incomplete and varying symbolic
specifications. Furthermore, we demonstrate that relative to
non-affordance-aware methods, HAL agents are better able to efficiently learn
complex tasks, navigate environment stochasticity, and acquire diverse skills
in the absence of extrinsic supervision -- all of which are hallmarks of human
learning.


title: Trust in AI and Its Role in the Acceptance of AI Technologies

abstract:
  As AI-enhanced technologies become common in a variety of domains, there is
an increasing need to define and examine the trust that users have in such
technologies. Given the progress in the development of AI, a correspondingly
sophisticated understanding of trust in the technology is required. This paper
addresses this need by explaining the role of trust on the intention to use AI
technologies. Study 1 examined the role of trust in the use of AI voice
assistants based on survey responses from college students. A path analysis
confirmed that trust had a significant effect on the intention to use AI, which
operated through perceived usefulness and participants' attitude toward voice
assistants. In study 2, using data from a representative sample of the U.S.
population, different dimensions of trust were examined using exploratory
factor analysis, which yielded two dimensions: human-like trust and
functionality trust. The results of the path analyses from Study 1 were
replicated in Study 2, confirming the indirect effect of trust and the effects
of perceived usefulness, ease of use, and attitude on intention to use.
Further, both dimensions of trust shared a similar pattern of effects within
the model, with functionality-related trust exhibiting a greater total impact
on usage intention than human-like trust. Overall, the role of trust in the
acceptance of AI technologies was significant across both studies. This
research contributes to the advancement and application of the TAM in
AI-related applications and offers a multidimensional measure of trust that can
be utilized in the future study of trustworthy AI.


title: Orientation Control of the Bouncing Ball

abstract:
  Control of a hybrid dynamical system can manifest in one of two main ways:
either through the continuous or the discrete dynamics. An example of controls
influencing the continuous dynamics is legged locomotion, where the joints are
actuated but the location and nature of the impacts are uncontrolled. In
contrast, an example of discrete control would be in tennis; the player can
only influence the trajectory of the ball through striking it.
  This work examines the latter case with two key emphases. The first is that
controls manifest through changing the location of the guard (as opposed to
changing only the reset). The second is that the location of the guard is
described by "external variables" while the goal is to control "internal
variables." As a simple test of this theory, orientation control of a bouncing
ball is explored; the ball is only controlled during impacts which are
exclusively position-dependent.


title:
Towards Scalable Risk Analysis for Stochastic Systems Using Extreme
  Value Theory

abstract:
  We aim to analyze the behaviour of a finite-time stochastic system, whose
model is not available, in the context of more rare and harmful outcomes.
Standard estimators are not effective in predicting the magnitude of such
outcomes due to their rarity. Instead, we leverage Extreme Value Theory (EVT),
the study of the long-term behaviour of normalized maxima of random variables.
In this letter, we quantify risk using the upper-semideviation
$E(\max\{Y-E(Y),0\})$, which is the expected exceedance of a random variable
$Y$ above its mean $E(Y)$. To assess more rare and harmful outcomes, we propose
an EVT-based estimator for this functional in a given fraction of the worst
cases. We show that our estimator enjoys a closed-form representation in terms
of a popular functional called the conditional value-at-risk. In experiments,
we illustrate the extrapolation power of our estimator when a small number of
i.i.d. samples is available ($<$50). Our approach is useful for estimating the
risk of finite-time systems when models are inaccessible and data collection is
expensive. The numerical complexity does not grow with the size of the state
space. This letter initiates a broader pathway for estimating the risk of
large-scale systems.


title: A New Class of Magnetic Topological Materials with Hypervalent Bi Chains

abstract:
  The link between crystal and electronic structure is crucial for
understanding structure-property relations in solid-state chemistry. In
particular, it has been instrumental in understanding topological materials,
where electrons behave differently than they would in conventional solids.
Herein, we identify 1D Bi chains as a structural motif of interest for
topological materials. We focus on Sm$_3$ZrBi$_5$, a new quasi-one-dimensional
(1D) compound in the Ln$_3$MPn$_5$ (Ln = lanthanide; M = metal; Pn = pnictide)
family that crystallizes in the P$6_{3}$/mcm space group. Density functional
theory calculations indicate a complex, topologically non-trivial electronic
structure that changes significantly in the presence of spin-orbit coupling.
Magnetic measurements show a quasi-1D antiferromagnetic structure with two
magnetic transitions at 11.7 and 10.7 K that are invariant to applied field up
to 9 T, indicating magnetically frustrated spins. Heat capacity, electrical,
and thermal transport measurements support this claim and suggest complex
scattering behavior in Sm$_3$ZrBi$_5$. This work highlights 1D chains as an
unexplored structural motif for identifying topological materials, as well as
the potential for rich physical phenomena in the Ln$_3$MPn$_5$ family.


title: Learning to generate line drawings that convey geometry and semantics

abstract:
  This paper presents an unpaired method for creating line drawings from
photographs. Current methods often rely on high quality paired datasets to
generate line drawings. However, these datasets often have limitations due to
the subjects of the drawings belonging to a specific domain, or in the amount
of data collected. Although recent work in unsupervised image-to-image
translation has shown much progress, the latest methods still struggle to
generate compelling line drawings. We observe that line drawings are encodings
of scene information and seek to convey 3D shape and semantic meaning. We build
these observations into a set of objectives and train an image translation to
map photographs into line drawings. We introduce a geometry loss which predicts
depth information from the image features of a line drawing, and a semantic
loss which matches the CLIP features of a line drawing with its corresponding
photograph. Our approach outperforms state-of-the-art unpaired image
translation and line drawing generation methods on creating line drawings from
arbitrary photographs. For code and demo visit our webpage
carolineec.github.io/informative_drawings


title: Affective Feedback Synthesis Towards Multimodal Text and Image Data

abstract:
  In this paper, we have defined a novel task of affective feedback synthesis
that deals with generating feedback for input text & corresponding image in a
similar way as humans respond towards the multimodal data. A feedback synthesis
system has been proposed and trained using ground-truth human comments along
with image-text input. We have also constructed a large-scale dataset
consisting of image, text, Twitter user comments, and the number of likes for
the comments by crawling the news articles through Twitter feeds. The proposed
system extracts textual features using a transformer-based textual encoder
while the visual features have been extracted using a Faster region-based
convolutional neural networks model. The textual and visual features have been
concatenated to construct the multimodal features using which the decoder
synthesizes the feedback. We have compared the results of the proposed system
with the baseline models using quantitative and qualitative measures. The
generated feedbacks have been analyzed using automatic and human evaluation.
They have been found to be semantically similar to the ground-truth comments
and relevant to the given text-image input.


title: Enhancing Classifier Conservativeness and Robustness by Polynomiality

abstract:
  We illustrate the detrimental effect, such as overconfident decisions, that
exponential behavior can have in methods like classical LDA and logistic
regression. We then show how polynomiality can remedy the situation. This,
among others, leads purposefully to random-level performance in the tails, away
from the bulk of the training data. A directly related, simple, yet important
technical novelty we subsequently present is softRmax: a reasoned alternative
to the standard softmax function employed in contemporary (deep) neural
networks. It is derived through linking the standard softmax to Gaussian
class-conditional models, as employed in LDA, and replacing those by a
polynomial alternative. We show that two aspects of softRmax, conservativeness
and inherent gradient regularization, lead to robustness against adversarial
attacks without gradient obfuscation.


title: The Carleman-contraction method to solve quasi-linear elliptic equations

abstract:
  We propose a globally convergent numerical method to compute solutions to a
general class of quasi-linear PDEs. Combining the quasi-reversibility method
and a suitable Carleman weight function, we define a map of which fixed point
is the solution to the PDE under consideration. To find this fixed point, we
define a recursive sequence with an arbitrary initial term using the same
manner as in the proof of the contraction principle. Applying a Carleman
estimate, we show that the sequence above converges to the desired solution. On
the other hand, we also show that our method delivers reliable solutions even
when the given data are noisy. Numerical examples are presented.


title: Probing cage relaxation in concentrated protein solutions by XPCS

abstract:
  Diffusion of proteins on length scales of their own diameter in highly
concentrated solutions is essential for understanding the cellular machinery of
living cells, but its experimental characterization remains a challenge. While
X-ray photon correlation spectroscopy (XPCS) is currently the only technique
that in principle allows for a measurement of long-time collective diffusion on
these length scales for such systems, its application to protein solutions is
seriously hampered by radiation damage caused by the highly intense X-ray beams
required for such experiments. Here we apply an experimental design and an
analysis strategy that allow us to successfully use XPCS experiments in order
to measure collective long-time cage relaxation in highly crowded solutions of
the eye lens protein {\alpha}-crystallin close to and beyond dynamical arrest.
We also address the problem of radiation-induced damage in such experiments. We
demonstrate that radiation effects depend both on the total dose as well as the
dose rate of the absorbed radiation, and discuss possible processes and
mechanism responsible for the observed radiation effects as well as their
consequences for future applications of XPCS in biological systems.


title:
Rocks, Water and Noble Liquids: Unfolding the Flavor Contents of
  Supernova Neutrinos

abstract:
  Measuring core-collapse supernova neutrinos, both from individual supernovae
within the Milky Way and from past core collapses throughout the Universe (the
diffuse supernova neutrino background, or DSNB), is one of the main goals of
current and next generation neutrino experiments. Detecting the heavy-lepton
flavor (muon and tau types, collectively $\nu_x$) component of the flux is
particularly challenging due to small statistics and large backgrounds. While
the next galactic neutrino burst will be observed in a plethora of neutrino
channels, allowing to measure a small number of $\nu_x$ events, only upper
limits are anticipated for the diffuse $\nu_x$ flux even after decades of data
taking with conventional detectors. However, paleo-detectors could measure the
time-integrated flux of neutrinos from galactic core-collapse supernovae via
flavor-blind neutral current interactions. In this work, we show how combining
a measurement of the average galactic core-collapse supernova flux with paleo
detectors and measurements of the DSNB electron-type neutrino fluxes with the
next-generation water Cherenkov detector Hyper-Kamiokande and the liquid noble
gas detector DUNE will allow to determine the mean supernova $\nu_x$ flux
parameters with precision of order ten percent.


title: What is Software Quality for AI Engineers? Towards a Thinning of the Fog

abstract:
  It is often overseen that AI-enabled systems are also software systems and
therefore rely on software quality assurance (SQA). Thus, the goal of this
study is to investigate the software quality assurance strategies adopted
during the development, integration, and maintenance of AI/ML components and
code. We conducted semi-structured interviews with representatives of ten
Austrian SMEs that develop AI-enabled systems. A qualitative analysis of the
interview data identified 12 issues in the development of AI/ML components.
Furthermore, we identified when quality issues arise in AI/ML components and
how they are detected. The results of this study should guide future work on
software quality assurance processes and techniques for AI/ML components.


title: Polarization and Media Bias

abstract:
  This paper presents a model of partisan media trying to persuade a
sophisticated and heterogeneous audience. We base our analysis on a Bayesian
persuasion framework where receivers have heterogeneous preferences and
beliefs. We identify an intensive-versus-extensive-margin trade-off that drives
the media's choice of slant: Biasing the news garners more support from the
audience who follows the media but reduces the size of the audience. The
media's slant and target audience are qualitatively different in polarized and
unimodal (or non-polarized) societies. When the media's agenda becomes more
popular, the media become more biased. When society becomes more polarized, the
media become less biased. Thus, polarization may have an unexpected
consequence: It may compel partisan media to be less biased and more
informative.


title: Quantum Coherence Bounds the Distributed Discords

abstract:
  Establishing quantum correlations between two remote parties by sending an
information carrier is an essential step of many protocols in quantum
information processing. We obtain trade-off relations between discords and
coherence within a bipartite system. Then we study the distribution of
coherence in a bipartite quantum state by using the relations of relative
entropy and mutual information. We show that the increase of the relative
entropy of discord between two remote parties is bounded by the nonclassical
correlations quantified by the relative entropy of coherence between the
carrier and two remote parties, providing an optimal protocol for discord
distribution and showing that quantum correlations are the essential resource
for such tasks.


title:
Effect of grain boundary state and grain size on the microstructure and
  mechanical properties of alumina obtained by SPS: A case of the amorphous
  layer on particle surface

abstract:
  The effect of temperature modes and heating rates (Vh) on the shrinkage
kinetics of submicron and fine aluminum oxide powders has been studied. The
objects of research comprised (i) submicron alfa-Al2O3 powder, (ii) submicron
alfa-Al2O3 powder with an amorphous layer on particle surface, (iii) fine
alfa-Al2O3 powder. The alumina ceramic specimens were produced by Spark Plasma
Sintering (SPS). Equally fine powders (i) and (ii) were used to analyze the
effect of an amorphous layer on sintering kinetics. Powders (i) and (iii) were
used to analyze the effect of the initial particle size on shrinkage kinetics.
Shrinkage curves were analyzed using the Young-Cutler and Coble models. It has
been shown that sintering kinetics is determined by the intensity of grain
boundary diffusion for submicron powders and by simultaneous lattice and grain
boundary diffusion for fine powders. It has been determined that an amorphous
layer on the surface of submicron alfa-Al2O3 powder affects grain boundary
migration rate and the Coble equation parameters at SPS final stages. It has
been suggested that abnormal characteristics of the alumina ceramics sintered
from a submicron powder with an amorphous layer on the particle surface are
associated with an increased concentration of defects at grain boundaries that
were formed during crystallization of the amorphous layer.


title:
On Understanding the Influence of Controllable Factors with a Feature
  Attribution Algorithm: a Medical Case Study

abstract:
  Feature attribution XAI algorithms enable their users to gain insight into
the underlying patterns of large datasets through their feature importance
calculation. Existing feature attribution algorithms treat all features in a
dataset homogeneously, which may lead to misinterpretation of consequences of
changing feature values. In this work, we consider partitioning features into
controllable and uncontrollable parts and propose the Controllable fActor
Feature Attribution (CAFA) approach to compute the relative importance of
controllable features. We carried out experiments applying CAFA to two existing
datasets and our own COVID-19 non-pharmaceutical control measures dataset.
Experimental results show that with CAFA, we are able to exclude influences
from uncontrollable features in our explanation while keeping the full dataset
for prediction.


title: Modelling the galaxy-halo connection with semi-recurrent neural networks

abstract:
  We present an artificial neural network design in which past and present-day
properties of dark matter halos and their local environment are used to predict
time-resolved star formation histories and stellar metallicity histories of
central and satellite galaxies. Using data from the IllustrisTNG simulations,
we train a TensorFlow-based neural network with two inputs: a standard layer
with static properties of the dark matter halo, such as halo mass and starting
time; and a recurrent layer with variables such as overdensity and halo mass
accretion rate, evaluated at multiple time steps from 0 < z < 20. This
constitutes a model which can be applied to data from a pure dark matter
simulation, emulating galaxy formation histories in much greater volumes than
what is currently feasible with cosmohydrodynamical simulations. The model
successfully reproduces key features of the galaxy halo connection, such as the
stellar-to-halo mass relation, downsizing, and colour bimodality. With temporal
measures of mass growth, subhalo infall and dark matter concentration, we
identify mass accretion history as crucial in determining the geometry of the
star formation history and trends with halo mass such as downsizing, while
environmental variables are important indicators of chemical enrichment. We use
these outputs to compute optical spectral energy distributions, and find that
they are well matched to the equivalent results in IllustrisTNG, recovering
observational statistics such as colour bimodality and mass-magnitude diagrams.


title: Randomized Benchmarking Beyond Groups

abstract:
  Randomized benchmarking (RB) is the gold standard for experimentally
evaluating the quality of quantum operations. The current framework for RB is
centered on groups and their representations, but this can be problematic. For
example, Clifford circuits need up to $O(n^2)$ gates, and thus Clifford RB
cannot scale to larger devices. Attempts to remedy this include new schemes
such as linear cross-entropy benchmarking (XEB), cycle benchmarking, and
non-uniform RB, but they do not fall within the group-based RB framework. In
this work, we formulate the universal randomized benchmarking (URB) framework
which does away with the group structure and also replaces the recovery gate
plus measurement component with a general "post-processing" POVM. Not only does
this framework cover most of the existing benchmarking schemes, but it also
gives the language for and helps inspire the formulation of new schemes. We
specifically consider a class of URB schemes called twirling schemes. For
twirling schemes, the post-processing POVM approximately factorizes into an
intermediate channel, inverting maps, and a final measurement. This leads us to
study the twirling map corresponding to the gate ensemble specified by the
scheme. We prove that if this twirling map is strictly within unit distance of
the Haar twirling map in induced diamond norm, the probability of measurement
as a function of gate length is a single exponential decay up to small error
terms. The core technical tool we use is the matrix perturbation theory of
linear operators on quantum channels.


title: The Average of Some Irreducible Character Degrees

abstract:
  We are interested in determining the bound of the average of the degrees of
the irreducible characters whose degrees are not divisible by some prime $p$
that guarantees a finite group $G$ of odd order is $p$-nilpotent. We find a
bound that depends on the prime $p$. If we further restrict our average by
fixing a subfield $k$ of the complex numbers and then compute the average of
the degrees of the irreducible characters whose degrees are not divisible by
$p$ and have values in $k$, then we will see that we obtain a bound that
depends on both $p$ and $k$. Moreover, we find examples that make those bounds
best possible.


title: RILI: Robustly Influencing Latent Intent

abstract:
  When robots interact with human partners, often these partners change their
behavior in response to the robot. On the one hand this is challenging because
the robot must learn to coordinate with a dynamic partner. But on the other
hand -- if the robot understands these dynamics -- it can harness its own
behavior, influence the human, and guide the team towards effective
collaboration. Prior research enables robots to learn to influence other robots
or simulated agents. In this paper we extend these learning approaches to now
influence humans. What makes humans especially hard to influence is that -- not
only do humans react to the robot -- but the way a single user reacts to the
robot may change over time, and different humans will respond to the same robot
behavior in different ways. We therefore propose a robust approach that learns
to influence changing partner dynamics. Our method first trains with a set of
partners across repeated interactions, and learns to predict the current
partner's behavior based on the previous states, actions, and rewards. Next, we
rapidly adapt to new partners by sampling trajectories the robot learned with
the original partners, and then leveraging those existing behaviors to
influence the new partner dynamics. We compare our resulting algorithm to
state-of-the-art baselines across simulated environments and a user study where
the robot and participants collaborate to build towers. We find that our
approach outperforms the alternatives, even when the partner follows new or
unexpected dynamics. Videos of the user study are available here:
https://youtu.be/lYsWM8An18g


title: Gauge theory models on $\kappa$-Minkowski space: Results and prospects

abstract:
  Recent results obtained in $\kappa$-Poincar\'e invariant gauge theories on
$\kappa$-Minkowski space are reviewed and commented. A Weyl quantization
procedure can be applied to convolution algebras to derive a convenient star
product. For such a star product, gauge invariant polynomial action functional
depending on the curvature exists only in 5 dimensions. The corresponding
noncommutative differential calculus and the related connection are twisted
together with the BRST structure linked to the gauge invariance.
Phenomenological consequences stemming from the existence of one extra
dimension are commented. Some consequences of the appearance of a non-vanishing
one-loop tadpole upon BRST gauge-fixing are discussed.


title:
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image
  Translation

abstract:
  Unpaired image-to-image translation (I2I) is an ill-posed problem, as an
infinite number of translation functions can map the source domain distribution
to the target distribution. Therefore, much effort has been put into designing
suitable constraints, e.g., cycle consistency (CycleGAN), geometry consistency
(GCGAN), and contrastive learning-based constraints (CUTGAN), that help better
pose the problem. However, these well-known constraints have limitations: (1)
they are either too restrictive or too weak for specific I2I tasks; (2) these
methods result in content distortion when there is a significant spatial
variation between the source and target domains. This paper proposes a
universal regularization technique called maximum spatial perturbation
consistency (MSPC), which enforces a spatial perturbation function (T ) and the
translation operator (G) to be commutative (i.e., TG = GT ). In addition, we
introduce two adversarial training components for learning the spatial
perturbation function. The first one lets T compete with G to achieve maximum
perturbation. The second one lets G and T compete with discriminators to align
the spatial variations caused by the change of object size, object distortion,
background interruptions, etc. Our method outperforms the state-of-the-art
methods on most I2I benchmarks. We also introduce a new benchmark, namely the
front face to profile face dataset, to emphasize the underlying challenges of
I2I for real-world applications. We finally perform ablation experiments to
study the sensitivity of our method to the severity of spatial perturbation and
its effectiveness for distribution alignment.


title:
Synthesis of memristive one-port circuits with piecewise-smooth
  characteristics

abstract:
  A generalized approach for the implementation of memristive two-terminal
circuits with piesewise-smooth characteristics is proposed on the example of a
multifunctional circuit based on a transistor switch. Two versions of the
circuit are taken into consideration: an experimental model of the
piecewise-smooth memristor (Chua's memristor) and a piecewise-smooth memristive
capacitor. Physical experiments are combined with numerical modelling of the
discussed circuit models. Thus, it is demonstrated that the considered circuit
is a flexible solution for synthesis of a wide range of memristive systems with
tuneable characteristics.


title:
Adversarial Training for Improving Model Robustness? Look at Both
  Prediction and Interpretation

abstract:
  Neural language models show vulnerability to adversarial examples which are
semantically similar to their original counterparts with a few words replaced
by their synonyms. A common way to improve model robustness is adversarial
training which follows two steps-collecting adversarial examples by attacking a
target model, and fine-tuning the model on the augmented dataset with these
adversarial examples. The objective of traditional adversarial training is to
make a model produce the same correct predictions on an original/adversarial
example pair. However, the consistency between model decision-makings on two
similar texts is ignored. We argue that a robust model should behave
consistently on original/adversarial example pairs, that is making the same
predictions (what) based on the same reasons (how) which can be reflected by
consistent interpretations. In this work, we propose a novel feature-level
adversarial training method named FLAT. FLAT aims at improving model robustness
in terms of both predictions and interpretations. FLAT incorporates variational
word masks in neural networks to learn global word importance and play as a
bottleneck teaching the model to make predictions based on important words.
FLAT explicitly shoots at the vulnerability problem caused by the mismatch
between model understandings on the replaced words and their synonyms in
original/adversarial example pairs by regularizing the corresponding global
word importance scores. Experiments show the effectiveness of FLAT in improving
the robustness with respect to both predictions and interpretations of four
neural network models (LSTM, CNN, BERT, and DeBERTa) to two adversarial attacks
on four text classification tasks. The models trained via FLAT also show better
robustness than baseline models on unforeseen adversarial examples across
different attacks.