title:
Radar-based Materials Classification Using Deep Wavelet Scattering
  Transform: A Comparison of Centimeter vs. Millimeter Wave Units

abstract:
  Radar-based materials detection received significant attention in recent
years for its potential inclusion in consumer and industrial applications like
object recognition for grasping and manufacturing quality assurance and
control. Several radar publications were developed for material classification
under controlled settings with specific materials' properties and shapes.
Recent literature has challenged the earlier findings on radars-based materials
classification claiming that earlier solutions are not easily scaled to
industrial applications due to a variety of real-world issues. Published
experiments on the impact of these factors on the robustness of the extracted
radar-based traditional features have already demonstrated that the application
of deep neural networks can mitigate, to some extent, the impact to produce a
viable solution. However, previous studies lacked an investigation of the
usefulness of lower frequency radar units, specifically <10GHz, against the
higher range units around and above 60GHz. This research considers two radar
units with different frequency ranges: Walabot-3D (6.3-8 GHz) cm-wave and
IMAGEVK-74 (62-69 GHz) mm-wave imaging units by Vayyar Imaging. A comparison is
presented on the applicability of each unit for material classification. This
work extends upon previous efforts, by applying deep wavelet scattering
transform for the identification of different materials based on the reflected
signals. In the wavelet scattering feature extractor, data is propagated
through a series of wavelet transforms, nonlinearities, and averaging to
produce low-variance representations of the reflected radar signals. This work
is unique in comparison of the radar units and algorithms in material
classification and includes real-time demonstrations that show strong
performance by both units, with increased robustness offered by the cm-wave
radar unit.


title: Efficacy of Transformer Networks for Classification of Raw EEG Data

abstract:
  With the unprecedented success of transformer networks in natural language
processing (NLP), recently, they have been successfully adapted to areas like
computer vision, generative adversarial networks (GAN), and reinforcement
learning. Classifying electroencephalogram (EEG) data has been challenging and
researchers have been overly dependent on pre-processing and hand-crafted
feature extraction. Despite having achieved automated feature extraction in
several other domains, deep learning has not yet been accomplished for EEG. In
this paper, the efficacy of the transformer network for the classification of
raw EEG data (cleaned and pre-processed) is explored. The performance of
transformer networks was evaluated on a local (age and gender data) and a
public dataset (STEW). First, a classifier using a transformer network is built
to classify the age and gender of a person with raw resting-state EEG data.
Second, the classifier is tuned for mental workload classification with open
access raw multi-tasking mental workload EEG data (STEW). The network achieves
an accuracy comparable to state-of-the-art accuracy on both the local (Age and
Gender dataset; 94.53% (gender) and 87.79% (age)) and the public (STEW dataset;
95.28% (two workload levels) and 88.72% (three workload levels)) dataset. The
accuracy values have been achieved using raw EEG data without feature
extraction. Results indicate that the transformer-based deep learning models
can successfully abate the need for heavy feature-extraction of EEG data for
successful classification.


title:
Image classification using collective optical modes of an array of
  nanolasers

abstract:
  Recent advancements in nanolaser design and manufacturing open up
unprecedented perspectives in terms of high integration densities and ultra-low
power consumption, making these devices ideal for high-performance optical
computing systems. In this work we exploit the symmetry properties of the
collective modes of a nanolaser array for binary image classification. The
implementation is based on a 8x8 array, and relies on the activation of a
collective optical mode of the array, the so-called "zero mode", under
spatially modulated pump patterns. We demonstrate that a simple training
strategy allows us to achieve an overall success rate of 98% in binary image
recognition.


title:
Study of the four-body decays $B^{0}_{S} \rightarrow \pi\pi\pi\pi$ in
  the perturbative QCD approach

abstract:
  In this work, we analyse the CP-averaged branching ratios and direct
CP-violating asymmetries of the four-body decays B_{S} \rightarrow \pi\pi\pi\pi
decay from the S-wave resonances, f_{0}(980) and f_{0}(500) and P-wave
resonances, \rho(770) by introducing the S-wave and P-wave \pi\pi distribution
amplitudes within the framework of the perturbative QCD approach. We also
calculate branching ratios of the two-body decays
B^{0}_{S}\rightarrow\rho^{0}\rho^{0}, B^{0}_{S}\rightarrow\rho^{+}\rho^{-} from
the corresponding quasi-two-body decays models and compare our results with
those obtained in previous perturbative QCD approach, QCD factorization
approach and FAT approach, it is found that the predictions are consistent with
present data within errors. The branching ratios of our calculations for the
four-body decays B_{S}\rightarrow \pi\pi\pi\pi are at the order of the 10^{-7}.
For the CP-violating asymmetries, we found that CP-violating asymmetry can be
enhanced largely by the \rho-\omega mixing resonances when \pi\pi pairs masses
are in the vicinity of \omega resonance.


title:
Spectroscopic Signatures of the Dynamical Hydrophobic Solvation Shell
  Formation

abstract:
  When a hydrophilic solute in water is suddenly turned into a hydrophobic
species, for instance, by photoionization, a layer of hydrated water molecules
forms around the solute on a time scale of a few picoseconds. We study the
dynamic build-up of the hydration shell around a hydrophobic solute on the
basis of a time-dependent dielectric continuum model. Information about the
solvent is spectroscopically extracted from the relaxation dynamics of a test
dipole inside a static Onsager sphere in the nonequilibrium solvent. The growth
process is described phenomenologically within two approaches. First, we
consider a time-dependent thickness of the hydration layer which grows from
zero to a finite value over a finite time. Second, we assume a time-dependent
complex permittivity within a finite layer region around the Onsager sphere.
The layer is modeled as a continuous dielectric with a much slower fluctuation
dynamics. We find a time-dependent frequency shift down to the blue of the
resonant absorption of the dipole, together with a dynamically decreasing line
width, as compared to bulk water. The blue shift reflects the work performed
against the hydrogen bonded network of the bulk solvent and is a directly
measurable quantity. Our results are in agreement with an experiment on the
hydrophobic solvation of iodine in water.


title:
Dust Mass Associated with the Supernova Remnant IC 443 when Emission
  Meets Extinction

abstract:
  The dust mass of the well-known supernova remnant (SNR) IC 443 is estimated
from both the infrared emission and the visual extinction. With photometry to
the images taken by \emph{Spitzer}, \emph{WISE}, \emph{IRAS}, \emph{AKARI} and
\emph{Planck}, the spectral energy distribution (SED) of the dust is obtained
after subtracting the synchrotron radiation and considering the spectral line
emission. The dust mass is derived from fitting the SED by a two-component
model, which results in a warm component of the temperature of $\sim$ 53 K and
the mass of 0.1 $M_\odot$, and a cold component of the temperature of $\sim 17$
K and the mass of 46 $M_\odot$. On the other hand, the dust mass is derived to
be $\sim$ 66 $M_\odot$ from the visual extinction of IC 443 which is identified
from the 3D Bayestar extinction map and its coincidence with the infrared
emission morphology. Roughly the dust mass derived from the infrared emission
and the extinction agree mutually. However, the dust mass derived from the
infrared emission can be adjusted to be more consistent with that from the
extinction by using different dust opacity property or considering optically
thick radiation. In addition, the distribution of dust temperature and mass is
analyzed by fitting the SED pixel by pixel.


title:
Application of the Affinity Propagation Clustering Technique to obtain
  traffic accident clusters at macro, meso, and micro levels

abstract:
  Accident grouping is a crucial step in identifying accident-prone locations.
Among the different accident grouping modes, clustering methods present
excellent performance for discovering different distributions of accidents in
space. This work introduces the Affinity Propagation Clustering (APC) approach
for grouping traffic accidents based on criteria of similarity and
dissimilarity between distributions of data points in space. The APC provides
more realistic representations of the distribution of events from similarity
matrices between instances. The results showed that when representative data
samples obtain, the preference parameter of similarity provides the necessary
performance to calibrate the model and generate clusters according to the
desired characteristics. In addition, the study demonstrates that the
preference parameter as a continuous parameter facilitates the calibration and
control of the model's convergence, allowing the discovery of clustering
patterns with less effort and greater control of the results


title: Evaluating the impact of quarantine measures on COVID-19 spread

abstract:
  During the early stage of the COVID-19 pandemic, many countries implemented
non-pharmaceutical interventions (NPIs) to control the transmission of
SARS-CoV-2, the causative pathogen of COVID-19. Among those NPIs, quarantine
measures were widely adopted and enforced through stay-at-home and
shelter-in-place orders. Understanding the effectiveness of quarantine measures
can inform decision-making and control planning during the ongoing COVID-19
pandemic and for future disease outbreaks. In this study, we use mathematical
models to evaluate the impact of quarantine measures on COVID-19 spread in four
cities that experienced large-scale outbreaks in the spring of 2020: Wuhan, New
York, Milan, and London. We develop a susceptible-exposed-infected-removed
(SEIR)-type model with a component of quarantine and couple this disease
transmission model with a data assimilation method. By calibrating the model to
case data, we estimate key epidemiological parameters before lockdown in each
city. We further examine the impact of quarantine rates on COVID-19 spread
after lockdown using model simulations. Results indicate that quarantine of
susceptible and exposed individuals and undetected infections is necessary to
contain the outbreak; however, the quarantine rates for these populations can
be reduced through faster isolation of confirmed cases. We generate
counterfactual simulations to estimate effectiveness of quarantine measures.
Without quarantine measures, the cumulative confirmed cases could be 73, 22, 43
and 93 times higher than reported numbers within 40 days after lockdown in
Wuhan, New York, Milan, and London. Our findings underscore the essential role
of quarantine during the early phase of the pandemic.


title:
Automated Atrial Fibrillation Classification Based on Denoising Stacked
  Autoencoder and Optimized Deep Network

abstract:
  The incidences of atrial fibrillation (AFib) are increasing at a daunting
rate worldwide. For the early detection of the risk of AFib, we have developed
an automatic detection system based on deep neural networks. For achieving
better classification, it is mandatory to have good pre-processing of
physiological signals. Keeping this in mind, we have proposed a two-fold study.
First, an end-to-end model is proposed to denoise the electrocardiogram signals
using denoising autoencoders (DAE). To achieve denoising, we have used three
networks including, convolutional neural network (CNN), dense neural network
(DNN), and recurrent neural networks (RNN). Compared the three models and CNN
based DAE performance is found to be better than the other two. Therefore, the
signals denoised by the CNN based DAE were used to train the deep neural
networks for classification. Three neural networks' performance has been
evaluated using accuracy, specificity, sensitivity, and signal to noise ratio
(SNR) as the evaluation criteria.
  The proposed end-to-end deep learning model for detecting atrial fibrillation
in this study has achieved an accuracy rate of 99.20%, a specificity of 99.50%,
a sensitivity of 99.50%, and a true positive rate of 99.00%. The average
accuracy of the algorithms we compared is 96.26%, and our algorithm's accuracy
is 3.2% higher than this average of the other algorithms. The CNN
classification network performed better as compared to the other two.
Additionally, the model is computationally efficient for real-time
applications, and it takes approx 1.3 seconds to process 24 hours ECG signal.
The proposed model was also tested on unseen dataset with different proportions
of arrhythmias to examine the model's robustness, which resulted in 99.10% of
recall and 98.50% of precision.


title: Semidirect Product Key Exchange: the State of Play

abstract:
  In this report we survey the various proposals of the key exchange protocol
known as semidirect product key exchange (SDPKE). We discuss the various
platforms proposed and give an overview of the main cryptanalytic ideas
relevant to each scheme.


title:
Diffusion-induced anisotropic cancer invasion: a novel experimental
  method based on tumour spheroids

abstract:
  Tumour invasion is strongly influenced by microenvironment and, among other
parameters, chemical stimuli play an important role. An innovative methodology
for the quantitative investigation of chemotaxis in vitro by live imaging of
morphology of cell spheroids, in 3D collagen gel, is presented here. The assay
was performed by using a chemotactic chamber to impose a controlled gradients
of nutrients (glucose) on spheroids, mimicking the chemotactic stimuli
naturally occurring in the proximity of blood vessels. Different tumoral cell
lines (PANC-1 and HT-1080) are compared to non-tumoral ones (NIH/3T3).
Morphology response is observed by means a Time-lapse workstation equipped with
an incubating system and quantified by image analysis techniques. Description
of invasion phenomena was based on an engineering approach, based on transport
phenomena concepts. As expected, NIH/3T3 spheroids are characterized by a
limited tendency of cells to invade the surrounding tissue, unlike PANC-1 and
HT-1080 that show relatively stronger response to gradients.


title:
Remarks on the paper "On Gromov's dihedral extremality and rigidity
  conjectures" by Jinmin Wang, Zhizhang Xie and Guoliang Yu

abstract:
  The recent article "On Gromov's dihedral extremality and rigidity
conjectures" by Jinmin Wang, Zhizhang Xie and Guoliang Yu makes a number of
claims for self-adjoint extensions of Dirac type operators on manifolds with
corners under local boundary conditions. We construct a counterexample to an
index computation in that paper which affects the proof of its main result
stating a generalisation of Gromov's dihedral extremality conjecture.


title: Amplifying spin waves along N\'eel domain wall by spin-orbit torque

abstract:
  Traveling spin waves in magnonic waveguides undergo severe attenuation, which
tends to result in a finite propagation length of spin waves, even in magnetic
materials with the accessible lowest damping constant, heavily restricting the
development of magnonic devices. Compared with the spin waves in traditional
waveguides, propagating spin waves along strip domain wall are expected to
exhibit enhanced transmission. Here, we demonstrate, theoretically and through
micromagnetic simulations, that spin-orbit torque associated with a
ferromagnet/heavy metal bilayer can efficiently control the attenuation of spin
waves along a N\'eel-type strip domain wall, despite the complexity in the
ground-state magnetization configuration. The direction of the electric current
applied to the heavy-metal layer determines whether these spin waves are
amplified or further attenuated otherwise. Remarkably, our simulations reveal
that the effective current densities required to efficiently tune the decay of
such spin waves are just ~10^10 Am-2, roughly an order smaller than those
required in conventional spin waveguides. Our results will enrich the toolset
for magnonic technologies.


title: Remote Contextual Bandits

abstract:
  We consider a remote contextual multi-armed bandit (CMAB) problem, in which
the decision-maker observes the context and the reward, but must communicate
the actions to be taken by the agents over a rate-limited communication
channel. This can model, for example, a personalized ad placement application,
where the content owner observes the individual visitors to its website, and
hence has the context information, but must convey the ads that must be shown
to each visitor to a separate entity that manages the marketing content. In
this remote CMAB (R-CMAB) problem, the constraint on the communication rate
between the decision-maker and the agents imposes a trade-off between the
number of bits sent per agent and the acquired average reward. We are
particularly interested in characterizing the rate required to achieve
sub-linear regret. Consequently, this can be considered as a policy compression
problem, where the distortion metric is induced by the learning objectives. We
first study the fundamental information theoretic limits of this problem by
letting the number of agents go to infinity, and study the regret achieved when
Thompson sampling strategy is adopted. In particular, we identify two distinct
rate regions resulting in linear and sub-linear regret behavior, respectively.
Then, we provide upper bounds on the achievable regret when the decision-maker
can reliably transmit the policy without distortion.


title: Discovering Quantum Phase Transitions with Fermionic Neural Networks

abstract:
  Deep neural networks have been extremely successful as highly accurate wave
function ans\"atze for variational Monte Carlo calculations of molecular ground
states. We present an extension of one such ansatz, FermiNet, to calculations
of the ground states of periodic Hamiltonians, and study the homogeneous
electron gas. FermiNet calculations of the ground-state energies of small
electron gas systems are in excellent agreement with previous initiator full
configuration interaction quantum Monte Carlo and diffusion Monte Carlo
calculations. We investigate the spin-polarized homogeneous electron gas and
demonstrate that the same neural network architecture is capable of accurately
representing both the delocalized Fermi liquid state and the localized Wigner
crystal state. The network is given no \emph{a priori} knowledge that a phase
transition exists, but converges on the translationally invariant ground state
at high density and spontaneously breaks the symmetry to produce the
crystalline ground state at low density.

categories: physics.comp-ph cond-mat.other cond-mat.str-el cs.LG


title: Fuchsian DPW potentials for Lawson surfaces

abstract:
  The Lawson surfaces $\xi_{1,g}$ of genus $g$ are constructed by rotating and
reflecting the Plateau solution $f_t$ with respect to a particular geodesic
$4$-gon $\Gamma_t$ along its boundary, where $t= \tfrac{1}{2g+2}$ is an angle
of $\Gamma_t$. In this paper we combine the existence and regularity of the
Plateau solution $f_t$ in $t \in (0, \tfrac{1}{4})$ with topological
information about the moduli space of Fuchsian systems on the 4-puncture sphere
to obtain existence of a Fuchsian DPW potential $\eta_t$ for every $f_t$ with
$t\in(0, \tfrac{1}{4}]$. Moreover, the coefficients of $\eta_t$ are shown to
depend real analytically on $t$. This implies that the Taylor approximation of
the DPW potential $\eta_t$ and of the area obtained at $t=0$ found in
\cite{HHT2} determines these quantities for all $\xi_{1,g}$. In particular,
this leads to an algorithm to conformally parametrize all Lawson surfaces
$\xi_{1,g}$.


title:
Pok\'emon GO to Pok\'emon STAY: How Covid-19 Affected Pok\'emon GO
  Players

abstract:
  Since its creation, the Location-Based Game (LBG), Pok\'emon GO, has been
embraced by a community of fans across the world. Due to its recency, the
impact of COVID-19 on the community of Pok\'emon GO players is underexplored.
We address how COVID-19 has impacted the players of Pok\'emon GO by building
upon existing work focusing on player gratifications and impacts in Pok\'emon
GO. Through semi-structured interviews, we provide a snapshot of the state of
LBG play during unprecedented times. These player testimonies demonstrate (1)
the importance of in-person socialization to LBG, (2) additional ways players
use the game as a coping mechanism, and (3) how intentionality mediates player
perceptions of people-place relationships. In demonstrating these behaviors, we
provide a glimpse of how a game that forces players to explore the world around
them changed when going outside with friends became a source of danger.


title: Fair allocation of a multiset of indivisible items

abstract:
  We study the problem of allocating a set $M$ of $m$ ${indivisible}$ items
among $n$ agents in a fair manner. We consider two well-studied notions of
fairness: envy-freeness (EF), and envy-freeness up to any good (EFX). While it
is known that complete EF allocations do not always exist, it is not known if
complete EFX allocations exist besides a few cases. In this work, we
reformulate the problem to allow $M$ to be a multiset. Specifically, we
introduce a parameter $t$ for the number of distinct ${types}$ of items, and
study allocations of multisets that contain items of these $t$ types. We show
the following:
  1. For arbitrary $n$, $t$, a complete EF allocation exists when agents have
distinct additive valuations, and there are ${enough}$ items of each type.
  2. For arbitrary $n$, $m$, $t$, a complete EFX allocation exists when agents
have additive valuations with identical ${preferences}$.
  3. For arbitrary $n$, $m$, and $t\le2$, a complete EFX allocation exists when
agents have additive valuations.
  For 2 and 3, our approach is constructive; we give a polynomial-time
algorithm to find a complete EFX allocation.


title:
Adults as Augmentations for Children in Facial Emotion Recognition with
  Contrastive Learning

abstract:
  Emotion recognition in children can help the early identification of, and
intervention on, psychological complications that arise in stressful situations
such as cancer treatment. Though deep learning models are increasingly being
adopted, data scarcity is often an issue in pediatric medicine, including for
facial emotion recognition in children. In this paper, we study the application
of data augmentation-based contrastive learning to overcome data scarcity in
facial emotion recognition for children. We explore the idea of ignoring
generational gaps, by adding abundantly available adult data to pediatric data,
to learn better representations. We investigate different ways by which adult
facial expression images can be used alongside those of children. In
particular, we propose to explicitly incorporate within each mini-batch adult
images as augmentations for children's. Out of $84$ combinations of learning
approaches and training set sizes, we find that supervised contrastive learning
with the proposed training scheme performs best, reaching a test accuracy that
typically surpasses the one of the second-best approach by 2% to 3%. Our
results indicate that adult data can be considered to be a meaningful
augmentation of pediatric data for the recognition of emotional facial
expression in children, and open up the possibility for other applications of
contrastive learning to improve pediatric care by complementing data of
children with that of adults.


title: A candidate short-period sub-Earth orbiting Proxima Centauri

abstract:
  Proxima Centauri is the closest star to the Sun. This small, low-mass, mid M
dwarf is known to host an Earth-mass exoplanet with an orbital period of 11.2
days within the habitable zone, as well as a long-period planet candidate with
an orbital period of close to 5 years. We report on the analysis of a large set
of observations taken with the ESPRESSO spectrograph at the VLT aimed at a
thorough evaluation of the presence of a third low-mass planetary companion,
which started emerging during a previous campaign. Radial velocities (RVs) were
calculated using both a cross-correlation function (CCF) and a template
matching approach. The RV analysis includes a component to model Proxima's
activity using a Gaussian process (GP). We use the CCF's full width at half
maximum to help constrain the GP, and we study other simultaneous observables
as activity indicators in order to assess the nature of any potential RV
signals. We detect a signal at 5.12 $\pm$ 0.04 days with a semi-amplitude of 39
$\pm$ 7 cm/s. The analysis of subsets of the ESPRESSO data, the activity
indicators, and chromatic RVs suggest that this signal is not caused by stellar
variability but instead by a planetary companion with a minimum mass of 0.26
$\pm$ 0.05 $M_\oplus$ (about twice the mass of Mars) orbiting at 0.029 au from
the star. The orbital eccentricity is well constrained and compatible with a
circular orbit.


title: Understanding Rare Spurious Correlations in Neural Networks

abstract:
  Neural networks are known to use spurious correlations for classification;
for example, they commonly use background information to classify objects. But
how many examples does it take for a network to pick up these correlations?
This is the question that we empirically investigate in this work. We introduce
spurious patterns correlated with a specific class to a few examples and find
that it takes only a handful of such examples for the network to pick up on the
spurious correlation. Through extensive experiments, we show that (1) spurious
patterns with a larger $\ell_2$ norm are learnt to correlate with the specified
class more easily; (2) network architectures that are more sensitive to the
input are more susceptible to learning these rare spurious correlations; (3)
standard data deletion methods, including incremental retraining and influence
functions, are unable to forget these rare spurious correlations through
deleting the examples that cause these spurious correlations to be learnt. Code
available at https://github.com/yangarbiter/rare-spurious-correlation.


title: Sgoldstino signal at FASER: prospects in searches for supersymmetry

abstract:
  We investigate FASER@LHC perspectives in searches for light ($0.1-5$ GeV)
sgoldstinos in models with low energy ($10-10^4$ TeV) supersymmetry breaking.
We consider flavor conserving and flavor violating couplings of sgoldstinos to
Standard Model fermions and find the both options to be testable at FASER. Even
the first FASER run allows one to probe interesting patches in the model
parameter space, while the second run, FASER-II, with significantly larger
detector fiducial volume, gives a possibility to thoroughly explore a wide
class of supersymmetric extensions of particle physics complementary to those
probed at LHC with ATLAS and CMS detectors.


title: Low-energy effective description of dark $Sp(4)$ theories

abstract:
  Strongly interacting massive particles are viable dark matter candidates. We
consider a dark $Sp(4)$ gauge theory with $N_f=2$ fermions in the pseudo-real
fundamental representation and construct the chiral low-energy effective
theory. We determine the flavor multiplet structure and the chiral Lagrangian,
including the Wess-Zumino-Witten term for mass-degenerate and non-degenerate
flavors. We then study the possible charge assignments under a $U(1)'$ gauge
symmetry, emphasizing on dark state stability, and provide the full Lagrangian
description for Goldstone bosons and vector resonances, including the
Wess-Zumino-Witten term. Finally, we use dedicated lattice simulations to
determine the chiral low-energy effective theory's validity and low-energy
constants. This work represents a self-consistent study of this non-Abelian
theory. It thereby provides a framework for future phenomenological exploration
in connection to the dark matter problem.


title: von Mises-Fisher distributions and their statistical divergence

abstract:
  The von Mises-Fisher family is a parametric family of distributions on the
surface of the unit ball, summarised by a concentration parameter and a mean
direction. As a quasi-Bayesian prior, the von Mises-Fisher distribution is a
convenient and parsimonious choice when parameter spaces are isomorphic to the
hypersphere (e.g., maximum score estimation in semi-parametric discrete choice,
estimation of single-index treatment assignment rules via empirical welfare
maximisation, under-identifying linear simultaneous equation models). Despite a
long history of application, measures of statistical divergence have not been
analytically characterised for von Mises-Fisher distributions. This paper
provides analytical expressions for the $f$-divergence of a von Mises-Fisher
distribution from another, distinct, von Mises-Fisher distribution in
$\mathbb{R}^p$ and the uniform distribution over the hypersphere. This paper
also collect several other results pertaining to the von Mises-Fisher family of
distributions, and characterises the limiting behaviour of the measures of
divergence that we consider.


title:
Bayes Optimal Algorithm is Suboptimal in Frequentist Best Arm
  Identification

abstract:
  We consider the fixed-budget best arm identification problem with Normal
rewards. In this problem, the forecaster is given $K$ arms (treatments) and $T$
time steps. The forecaster attempts to find the best arm in terms of the
largest mean via an adaptive experiment conducted with an algorithm. The
performance of the algorithm is measured by the simple regret, or the quality
of the estimated best arm. It is known that the frequentist simple regret can
be exponentially small to $T$ for any fixed parameters, whereas the Bayesian
simple regret is $\Theta(T^{-1})$ over a continuous prior distribution. This
paper shows that Bayes optimal algorithm, which minimizes the Bayesian simple
regret, does not have an exponential simple regret for some parameters. This
finding contrasts with the many results indicating the asymptotic equivalence
of Bayesian and frequentist algorithms in fixed sampling regimes. While the
Bayes optimal algorithm is described in terms of a recursive equation that is
virtually impossible to compute exactly, we pave the way to an analysis by
introducing a key quantity that we call the expected Bellman improvement.


title: Robust and fair work allocation

abstract:
  In today's digital world, interaction with online platforms is ubiquitous,
and thus content moderation is important for protecting users from content that
do not comply with pre-established community guidelines. Having a robust
content moderation system throughout every stage of planning is particularly
important. We study the short-term planning problem of allocating human content
reviewers to different harmful content categories. We use tools from fair
division and study the application of competitive equilibrium and leximin
allocation rules. Furthermore, we incorporate, to the traditional Fisher market
setup, novel aspects that are of practical importance. The first aspect is the
forecasted workload of different content categories. We show how a formulation
that is inspired by the celebrated Eisenberg-Gale program allows us to find an
allocation that not only satisfies the forecasted workload, but also fairly
allocates the remaining reviewing hours among all content categories. The
resulting allocation is also robust as the additional allocation provides a
guardrail in cases where the actual workload deviates from the predicted
workload. The second practical consideration is time dependent allocation that
is motivated by the fact that partners need scheduling guidance for the
reviewers across days to achieve efficiency.
  To address the time component, we introduce new extensions of the various
fair allocation approaches for the single-time period setting, and we show that
many properties extend in essence, albeit with some modifications. Related to
the time component, we additionally investigate how to satisfy markets' desire
for smooth allocation (e.g., partners for content reviewers prefer an
allocation that does not vary much from time to time, to minimize staffing
switch). We demonstrate the performance of our proposed approaches through
real-world data obtained from Meta.


title: Uncovering Instabilities in Variational-Quantum Deep Q-Networks

abstract:
  Deep Reinforcement Learning (RL) has considerably advanced over the past
decade. At the same time, state-of-the-art RL algorithms require a large
computational budget in terms of training time to converge. Recent work has
started to approach this problem through the lens of quantum computing, which
promises theoretical speed-ups for several traditionally hard tasks. In this
work, we examine a class of hybrid quantumclassical RL algorithms that we
collectively refer to as variational quantum deep Q-networks (VQ-DQN). We show
that VQ-DQN approaches are subject to instabilities that cause the learned
policy to diverge, study the extent to which this afflicts reproduciblity of
established results based on classical simulation, and perform systematic
experiments to identify potential explanations for the observed instabilities.
Additionally, and in contrast to most existing work on quantum reinforcement
learning, we execute RL algorithms on an actual quantum processing unit (an IBM
Quantum Device) and investigate differences in behaviour between simulated and
physical quantum systems that suffer from implementation deficiencies. Our
experiments show that, contrary to opposite claims in the literature, it cannot
be conclusively decided if known quantum approaches, even if simulated without
physical imperfections, can provide an advantage as compared to classical
approaches. Finally, we provide a robust, universal and well-tested
implementation of VQ-DQN as a reproducible testbed for future experiments.


title:
On the definition of heat current for periodic systems and its
  implications for simulations of thermal conductivity in solids

abstract:
  We re-derive the expression for the heat current for a classical system
subject to periodic boundary conditions and show that it can be written as a
sum of two terms. The first term is a time derivative of the first moment of
the system energy density while the second term is expressed through the energy
transfer rate through the periodic boundary. We show that in solids the second
term alone leads to the same thermal conductivity as the full expression for
the heat current when used in the Green-Kubo approach. More generally, energy
passing though any surface formed by translation of the original periodic
boundary can be used to calculate thermal conductivity. These statements are
verified for two systems: crystalline argon and crystals of argon and krypton
forming an interface.


title: Large-N limit of spontaneous superradiance

abstract:
  We investigate the thermodynamic limit of Dicke superradiance. We find an
expression for the system's density matrix that we can prove is exact in the
limit of large atom numbers N. This is in contrast to previously known
solutions whose accuracy has only been established numerically and that are
valid only for a range of times. We also introduce an asymptotically exact
solution when the system is subject to additional incoherent decay of
excitations as this is a common occurrence in experiment.


title:
P-split formulations: A class of intermediate formulations between big-M
  and convex hull for disjunctive constraints

abstract:
  We develop a class of mixed-integer formulations for disjunctive constraints
intermediate to the big-M and convex hull formulations in terms of relaxation
strength. The main idea is to capture the best of both the big-M and convex
hull formulations: a computationally light formulation with a tight relaxation.
The "$P$-split" formulations are based on a lifted transformation that splits
convex additively separable constraints into $P$ partitions and forms the
convex hull of the linearized and partitioned disjunction. We analyze the
continuous relaxation of the $P$-split formulations and show that, under
certain assumptions, the formulations form a hierarchy starting from a big-M
equivalent and converging to the convex hull. The goal of the $P$-split
formulations is to form a strong approximation of the convex hull through a
computationally simpler formulation. We computationally compare the $P$-split
formulations against big-M and convex hull formulations on 320 test instances.
The test problems include K-means clustering, P_ball problems, and optimization
over trained ReLU neural networks. The computational results show promising
potential of the $P$-split formulations. For many of the test problems,
$P$-split formulations are solved with a similar number of explored nodes as
the convex hull formulation, while reducing the solution time by an order of
magnitude and outperforming big-M both in time and number of explored nodes.


title:
A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction
  Tracking in Ultrasound Images

abstract:
  Biomechanical and clinical gait research observes muscles and tendons in
limbs to study their functions and behaviour. Therefore, movements of distinct
anatomical landmarks, such as muscle-tendon junctions, are frequently measured.
We propose a reliable and time efficient machine-learning approach to track
these junctions in ultrasound videos and support clinical biomechanists in gait
analysis. In order to facilitate this process, a method based on deep-learning
was introduced. We gathered an extensive dataset, covering 3 functional
movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3
different ultrasound systems, and providing a total of 66864 annotated
ultrasound images in our network training. Furthermore, we used data collected
across independent laboratories and curated by researchers with varying levels
of experience. For the evaluation of our method a diverse test-set was selected
that is independently verified by four specialists. We show that our model
achieves similar performance scores to the four human specialists in
identifying the muscle-tendon junction position. Our method provides
time-efficient tracking of muscle-tendon junctions, with prediction times of up
to 0.078 seconds per frame (approx. 100 times faster than manual labeling). All
our codes, trained models and test-set were made publicly available and our
model is provided as a free-to-use online service on https://deepmtj.org/.


title:
Visual Servoing for Pose Control of Soft Continuum Arm in a Structured
  Environment

abstract:
  For soft continuum arms, visual servoing is a popular control strategy that
relies on visual feedback to close the control loop. However, robust visual
servoing is challenging as it requires reliable feature extraction from the
image, accurate control models and sensors to perceive the shape of the arm,
both of which can be hard to implement in a soft robot. This letter circumvents
these challenges by presenting a deep neural network-based method to perform
smooth and robust 3D positioning tasks on a soft arm by visual servoing using a
camera mounted at the distal end of the arm. A convolutional neural network is
trained to predict the actuations required to achieve the desired pose in a
structured environment. Integrated and modular approaches for estimating the
actuations from the image are proposed and are experimentally compared. A
proportional control law is implemented to reduce the error between the desired
and current image as seen by the camera. The model together with the
proportional feedback control makes the described approach robust to several
variations such as new targets, lighting, loads, and diminution of the soft
arm. Furthermore, the model lends itself to be transferred to a new environment
with minimal effort.


title: Control of systems by parallel actuators

abstract:
  We describe a particular control method for a system controlled by several
actuators with the same control constants. We show under certain assumptions
that the control constants for the whole system can be obtained immediately
from the control constants for a single actuator, and that no gain scheduling
is required.


title: The topology of Calabi-Yau threefolds

abstract:
  We ask about the simply connected compact smooth 6-manifolds which can
support structures of Calabi-Yau threefolds. In particular, we study the
interesting case of Calabi-Yau threefolds $X$ with second betti number 3. We
have a cup-product cubic form on the second integral cohomology, a linear form
given by the second Chern class, and the integral middle cohomology, and if $X$
is simply connected with torsion free homology this information determines
precisely the diffeomorphism class of the underlying 6-manifold by a result of
Wall. For simplicity, we assume that the cubic form defines a smooth real
elliptic curve whose Hessian is also smooth. Under a further relatively mild
assumption that there are no non-movable surfaces $E$ on $X$ with $1 \le E^3
\le 8$, we prove that the real elliptic curve must have two connected
components rather than one, and that the K\"ahler cone is contained in the open
positive cone on the bounded component; we show moreover that the second Chern
class is also positive on this open cone. Using Wall's result, for any given
third Betti number we therefore have an abundance of examples of smooth compact
oriented 6-manifolds which support no Calabi-Yau structures, both in the cases
when the cubic defines a real elliptic curve with one or two connected
components.


title:
Field theoretical approach to open quantum systems and the Lindblad
  equation

abstract:
  We develop a field theoretical approach to open quantum systems based on
condensed matter many body methods. The time evolution of the reduced density
matrix for the open system is determined by a transmission matrix. Invoking
Wick's theorem in connection with a Caldeira-Leggett quantum oscillator
environment the transmission matrix satisfies a Dyson equation characterised by
an irreducible kernel. The Dyson equation is equivalent to a general master
equation for the reduced density matrix. We consider the Born approximation for
the kernel. Applying a condensed matter "quasi-particle-type" approximation we
obtain a Master equation of the Markov type. Imposing the rotating wave
approximation we obtain a Markov master equation of the Lindblad form. As a
standard example we discuss the case of a single qubit coupled to a thermal
heat bath.


title:
Towards Predicting Fine Finger Motions from Ultrasound Images via
  Kinematic Representation

abstract:
  A central challenge in building robotic prostheses is the creation of a
sensor-based system able to read physiological signals from the lower limb and
instruct a robotic hand to perform various tasks. Existing systems typically
perform discrete gestures such as pointing or grasping, by employing
electromyography (EMG) or ultrasound (US) technologies to analyze the state of
the muscles. In this work, we study the inference problem of identifying the
activation of specific fingers from a sequence of US images when performing
dexterous tasks such as keyboard typing or playing the piano. While estimating
finger gestures has been done in the past by detecting prominent gestures, we
are interested in classification done in the context of fine motions that
evolve over time. We consider this task as an important step towards higher
adoption rates of robotic prostheses among arm amputees, as it has the
potential to dramatically increase functionality in performing daily tasks. Our
key observation, motivating this work, is that modeling the hand as a robotic
manipulator allows to encode an intermediate representation wherein US images
are mapped to said configurations. Given a sequence of such learned
configurations, coupled with a neural-network architecture that exploits
temporal coherence, we are able to infer fine finger motions. We evaluated our
method by collecting data from a group of subjects and demonstrating how our
framework can be used to replay music played or text typed. To the best of our
knowledge, this is the first study demonstrating these downstream tasks within
an end-to-end system.


title: Interior control of waves on time dependent domains

abstract:
  We obtain a novel interior control result for wave equations on time
dependent domains. This is done by deriving a suitable Carleman estimate and
proving the corresponding observability inequality. We consider the wave
equation with time dependent lower order coefficients, without any time
analyticity assumptions. Moreover, we obtain improved control regions when
compared with standard Carleman methods.


title:
Zero Shot Learning for Predicting Energy Usage of Buildings in
  Sustainable Design

abstract:
  The 2030 Challenge is aimed at making all new buildings and major renovations
carbon neutral by 2030. One of the potential solutions to meet this challenge
is through innovative sustainable design strategies. For developing such
strategies it is important to understand how the various building factors
contribute to energy usage of a building, right at design time. The growth of
artificial intelligence (AI) in recent years provides an unprecedented
opportunity to advance sustainable design by learning complex relationships
between building factors from available data. However, rich training datasets
are needed for AI-based solutions to achieve good prediction accuracy.
Unfortunately, obtaining training datasets are time consuming and expensive in
many real-world applications. Motivated by these reasons, we address the
problem of accurately predicting the energy usage of new or unknown building
types, i.e., those building types that do not have any training data. We
propose a novel approach based on zero-shot learning (ZSL) to solve this
problem. Our approach uses side information from building energy modeling
experts to predict the closest building types for a given new/unknown building
type. We then obtain the predicted energy usage for the k-closest building
types using the models learned during training and combine the predicted values
using a weighted averaging function. We evaluated our approach on a dataset
containing five building types generated using BuildSimHub, a popular platform
for building energy modeling. Our approach achieved better average accuracy
than a regression model (based on XGBoost) trained on the entire dataset of
known building types.


title:
Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem
  Provers

abstract:
  Verification of neural networks is currently a hot topic in automated theorem
proving. Progress has been rapid and there are now a wide range of tools
available that can verify properties of networks with hundreds of thousands of
nodes. In theory this opens the door to the verification of larger control
systems that make use of neural network components. However, although work has
managed to incorporate the results of these verifiers to prove larger
properties of individual systems, there is currently no general methodology for
bridging the gap between verifiers and interactive theorem provers (ITPs).
  In this paper we present Vehicle, our solution to this problem. Vehicle is
equipped with an expressive domain specific language for stating neural network
specifications which can be compiled to both verifiers and ITPs. It overcomes
previous issues with maintainability and scalability in similar ITP
formalisations by using a standard ONNX file as the single canonical
representation of the network. We demonstrate its utility by using it to
connect the neural network verifier Marabou to Agda and then formally verifying
that a car steered by a neural network never leaves the road, even in the face
of an unpredictable cross wind and imperfect sensors. The network has over
20,000 nodes, and therefore this proof represents an improvement of 3 orders of
magnitude over prior proofs about neural network enhanced systems in ITPs.


title:
Parasitic-free gate: A protected switch between idle and entangled
  states

abstract:
  We propose a protected 2-qubit gate composed of two sub-gates that switches
between idling and entangling modes. These sub-gates are separated by a large
energy barrier. In superconducting circuits, this gate is derived by harnessing
microwave driving and coupling modulation. The idle mode decouples qubits and
leaves them with highly localized wave functions. The entangled mode, however,
makes them interact in absence of the adversarial $ZZ$ interactions. Our
theoretical results show that this gate is a promising way to switch on and off
a fast and high fidelity 2-qubit interaction.


title:
Improving Automatic Speech Recognition for Non-Native English with
  Transfer Learning and Language Model Decoding

abstract:
  ASR systems designed for native English (L1) usually underperform on
non-native English (L2). To address this performance gap, \textbf{(i)} we
extend our previous work to investigate fine-tuning of a pre-trained wav2vec
2.0 model \cite{baevski2020wav2vec,xu2021self} under a rich set of L1 and L2
training conditions. We further \textbf{(ii)} incorporate language model
decoding in the ASR system, along with the fine-tuning method. Quantifying
gains acquired from each of these two approaches separately and an error
analysis allows us to identify different sources of improvement within our
models. We find that while the large self-trained wav2vec 2.0 may be
internalizing sufficient decoding knowledge for clean L1 speech
\cite{xu2021self}, this does not hold for L2 speech and accounts for the
utility of employing language model decoding on L2 data.


title: Recollections of the Feynman Lectures in Physics

abstract:
  Brief recollections by the author about how he contributed to the production
of the Feynman Lectures in Physics


title:
Behavior-Semantic Scenery Description (BSSD) of Road Networks for
  Automated Driving

abstract:
  The safety approval of Highly Automated Vehicles (HAV) is economically
infeasible with current approaches. For verification and validation, it is
essential to describe the intended behavior of an HAV in the development
process in order to prove safety. The demand for this behavior comes from the
traffic rules which are instantiated by the present scenery around the vehicle
(e.g. traffic signs or road markings). The Operational Design Domain (ODD)
specifies the scenery in which an HAV may operate, but current descriptions
fail to explicitly represent the associated behavioral demand of the scenery.
We propose a new approach for a Behavior-Semantic Scenery Description (BSSD) in
order to describe the behavior space of a present scenery. A behavior space
represents the delimitation of the legally possible behavior. The BSSD
explicitly links the scenery with the behavioral demand for HAV. Based on
identified goals and challenges for such an approach, we derive requirements
for a generic structure of the description for complete road networks. All
required elements to represent the behavior space of the scenery are
identified. Within real world examples, we present an instance of the BSSD
integrated into the HD-map framework Lanelet2 to prove the applicability of the
description. The presented approach supports development, test and operation of
HAV by closing the knowledge gap of where a vehicle has to behave in which
limits within an ODD.


title:
On the number and sums of eigenvalues of Schr\"odinger-type operators
  with degenerate kinetic energy

abstract:
  We estimate sums of functions of negative eigenvalues of Schr\"odinger-type
operators whose kinetic energy vanishes on a codimension one submanifold. Our
main technical tool is the Stein-Tomas theorem and some of its generalizations.


title:
Exact solution of the macroscopic fluctuation theory for the symmetric
  exclusion process

abstract:
  We present the first exact solution for the time dependent equations of the
macroscopic fluctuation theory (MFT) for the symmetric simple exclusion process
by combining a generalization of the canonical Cole-Hopf transformation with
the inverse scattering method. For the step initial condition with two
densities, the associated Riemann-Hilbert problem is solved to determine
exactly the optimal density profile and the response field which produce a
required fluctuation, both at initial and final times. The large deviation
function of the current is derived and coincides with the formula obtained
previously by microscopic calculations. This provides the first analytic
confirmation of the validity of the MFT for an interacting model in the time
dependent regime.


title:
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity
  Guarantees for Langevin Monte Carlo

abstract:
  For the task of sampling from a density $\pi \propto \exp(-V)$ on
$\mathbb{R}^d$, where $V$ is possibly non-convex but $L$-gradient Lipschitz, we
prove that averaged Langevin Monte Carlo outputs a sample with
$\varepsilon$-relative Fisher information after $O( L^2 d^2/\varepsilon^2)$
iterations. This is the sampling analogue of complexity bounds for finding an
$\varepsilon$-approximate first-order stationary points in non-convex
optimization and therefore constitutes a first step towards the general theory
of non-log-concave sampling. We discuss numerous extensions and applications of
our result; in particular, it yields a new state-of-the-art guarantee for
sampling from distributions which satisfy a Poincar\'e inequality.


title: The square of a Hamilton cycle in randomly perturbed graphs

abstract:
  We investigate the appearance of the square of a Hamilton cycle in the model
of randomly perturbed graphs, which is, for a given $\alpha \in (0,1)$, the
union of any $n$-vertex graph with minimum degree $\alpha n$ and the binomial
random graph $G(n,p)$. This is known when $\alpha > 1/2$, and we determine the
exact perturbed threshold probability in all the remaining cases, i.e., for
each $\alpha \le 1/2$. We demonstrate that, as $\alpha$ ranges over the
interval $(0,1)$, the threshold performs a countably infinite number of
`jumps'. Our result has implications on the perturbed threshold for
$2$-universality, where we also fully address all open cases.


title:
Dynamical quantum phase transition in diamond: applications in quantum
  metrology

abstract:
  Nonequilibrium dynamics is a paramount scenario for studying quantum systems.
The emergence of new features with no equilibrium counterpart, such as
dynamical quantum phase transition (DQPT), has attracted wide attention. In
this work, we depart from the well known Ising model and showcase an
experimentally accessible configuration of a negatively charged
Nitrogen-Vacancy center that interacts with nearby Carbon-13 nuclear spins. We
provide new insights into this system in the context of DQPT. We show that
nuclear spins undergo DQPT by appropriately choosing the relation between the
transverse and longitudinal components of an external magnetic field.
Furthermore, we can steer the DQPT via a time-dependent longitudinal magnetic
field and apply this control to enhance the estimation of the coupling strength
between the nuclear spins. Moreover, we propose a novel quenched dynamics that
originates from the rotation of the central electron spin, which controls the
DQPT relying on the anisotropy of the hyperfine coupling.


title:
Initial State Dependent Dynamics Across Many-body Localization
  Transition

abstract:
  We investigate quench dynamics across many-body localization (MBL) transition
in an interacting one dimensional system of spinless fermions with aperiodic
potential. We consider a large number of initial states characterized by the
number of kinks, $N_{kinks}$, in the density profile. On the delocalized side
of the MBL transition the dynamics becomes faster with increase in $N_{kinks}$
such that the decay exponent, $\gamma$, in the density imbalance increases with
increase in $N_{kinks}$. The growth exponent of the mean square displacement
which shows a power-law behaviour $\langle x^2(t) \rangle \sim t^\beta$ in the
long time limit is much larger than the exponent $\gamma$ for 1-kink and other
low kink states though $\beta \sim 2\gamma$ for a charge density wave state. As
the disorder strength increases $\gamma_{N_{kink}} \rightarrow 0$ at some
critical disorder, $h_{N_{kinks}}$ which is a monotonically increasing function
of $N_{kinks}$. A 1-kink state always underestimates the value of disorder at
which the MBL transition takes place but $h_{1-kink}$ coincides with the onset
of the sub-diffusive phase preceding the MBL phase. This is consistent with the
dynamics of interface broadening for the 1-kink state. We show that the
bipartite entanglement entropy has a logarithmic growth $a \ln(Vt)$ not only in
the MBL phase but also in the delocalised phase and in both the phases the
coefficient $a$ increases with $N_{kinks}$ as well as with the interaction
strength $V$. We explain this dependence of dynamics on the number of kinks in
terms of the normalized participation ratio of initial states in the eigenbasis
of the interacting Hamiltonian.


title: Pynguin: Automated Unit Test Generation for Python

abstract:
  Automated unit test generation is a well-known methodology aiming to reduce
the developers' effort of writing tests manually. Prior research focused mainly
on statically typed programming languages like Java. In practice, however,
dynamically typed languages have received a huge gain in popularity over the
last decade. This introduces the need for tools and research on test generation
for these languages, too. We introduce Pynguin, an extendable test-generation
framework for Python, which generates regression tests with high code coverage.
Pynguin is designed to be easily usable by practitioners; it is also extensible
to allow researchers to adapt it for their needs and to enable future research.
We provide a demo of Pynguin at https://youtu.be/UiGrG25Vts0; further
information, documentation, the tool, and its source code are available at
https://www.pynguin.eu.


title:
Solving integral equations in free-space with inverse-designed ultrathin
  optical metagratings

abstract:
  As standard microelectronic technology approaches fundamental limitations in
speed and power consumption, novel computing strategies are strongly needed.
Analog optical computing enables processing large amounts of data at a
negligible energy cost and high speeds. Based on these principles, ultrathin
optical metasurfaces have been recently explored to process large images in
real-time, in particular for edge detection. By incorporating feedback, it has
also been recently shown that metamaterials can be tailored to solve complex
mathematical problems in the analog domain, although these efforts have so far
been limited to guided-wave systems and bulky setups. Here, we present an
ultrathin Si metasurface-based platform for analog computing that is able to
solve Fredholm integral equations of the second kind using free-space visible
radiation. A Si-based metagrating was inverse-designed to implement the
scattering matrix synthesizing a prescribed Kernel corresponding to the
mathematical problem of interest. Next, a semi-transparent mirror was
incorporated into the sample to provide adequate feedback and thus perform the
required Neumann series, solving the corresponding equation in the analog
domain at the speed of light. Visible wavelength operation enables a highly
compact, ultrathin device that can be interrogated from free-space, implying
high processing speeds and the possibility of on-chip integration.


title:
Privacy Protection, Measurement Error, and the Integration of Remote
  Sensing and Socioeconomic Survey Data

abstract:
  When publishing socioeconomic survey data, survey programs implement a
variety of statistical methods designed to preserve privacy but which come at
the cost of distorting the data. We explore the extent to which spatial
anonymization methods to preserve privacy in the large-scale surveys supported
by the World Bank Living Standards Measurement Study - Integrated Surveys on
Agriculture (LSMS-ISA) introduce measurement error in econometric estimates
when that survey data is integrated with remote sensing weather data. Guided by
a pre-analysis plan, we produce 90 linked weather-household datasets that vary
by the spatial anonymization method and the remote sensing weather product. By
varying the data along with the econometric model we quantify the magnitude and
significance of measurement error coming from the loss of accuracy that results
from protect privacy measures. We find that spatial anonymization techniques
currently in general use have, on average, limited to no impact on estimates of
the relationship between weather and agricultural productivity. However, the
degree to which spatial anonymization introduces mismeasurement is a function
of which remote sensing weather product is used in the analysis. We conclude
that care must be taken in choosing a remote sensing weather product when
looking to integrate it with publicly available survey data.


title: Signs behaviour of sums of weighted numbers of partitions

abstract:
  Let $A$ be a subset of positive integers. By $A$-partition of $n$ we
understand the representation of $n$ as a sum of elements from the set $A$. For
given $i, n\in\N$, by $c_{A}(i,n)$ we denote the number of $A$-partitions of
$n$ with exactly $i$ parts. In the paper we obtain several result concerning
sign behaviour of the sequence
$S_{A,k}(n)=\sum_{i=0}^{n}(-1)^{i}i^{k}c_{A}(i,n)$, where $k\in\N$ is fixed. In
particular, we prove that for a broad class $\cal{A}$ of subsets of $\N_{+}$ we
have that for each $A\in \cal{A}$ we have $(-1)^{n}S_{A,k}(n)\geq 0$ for each
$n, k\in\N$.


title: Cold gas removal from the centre of a galaxy by a low-luminosity jet

abstract:
  The energy emitted by active galactic nuclei (AGN) may provide a
self-regulating process (AGN feedback) that shapes the evolution of galaxies.
This is believed to operate along two modes: on galactic scales by clearing the
interstellar medium via outflows, and on circumgalactic scales by preventing
the cooling and accretion of gas onto the host galaxy. Radio jets associated
with radiatively-inefficient AGN are known to contribute to the latter mode of
feedback. However, such jets could also play a role on circum-nuclear and
galactic scales, blurring the distinction between the two modes. We have
discovered a spatially-resolved, massive molecular outflow, carrying $\sim75\%$
of the gas in the central region of the host galaxy of a
radiatively-inefficient AGN. The outflow coincides with the radio jet 540 pc
offset from the core, unambiguously pointing to the jet as the driver of this
phenomenon. The modest luminosity of the radio source ($L\rm_{1.4 GHz}=2.1
\times 10\rm^{23}~\rm W~\rm Hz^{-1}$) confirms predictions of simulations that
jets of low-luminosity radio sources carry enough power to drive such outflows.
Including kpc-scale feedback from such sources -- comprising of the majority of
the radio AGN population -- in cosmological simulations may assist in resolving
some of their limitations.


title: Reliabuild: Searching for High-Fidelity Builds Using Active Learning

abstract:
  Modern software is incredibly complex. A typical application may comprise
hundreds or thousands of reusable components. Automated package managers can
help to maintain a consistent set of dependency versions, but ultimately the
solvers in these systems rely on constraints generated by humans. At scale,
small errors add up, and it becomes increasingly difficult to find
high-fidelity configurations. We cannot test all configurations, because the
space is combinatorial, so exhaustive exploration is infeasible.
  In this paper, we present Reliabuild, an auto-tuning framework that
efficiently explores the build configuration space and learns which package
versions are likely to result in a successful configuration. We implement two
models in Reliabuild to rank the different configurations and use adaptive
sampling to select good configurations with fewer samples. We demonstrate
Reliabuild's effectiveness by evaluating 31,186 build configurations of 61
packages from the Extreme-scale Scientific Software Stack(E4S). Reliabuild
selects good configurations efficiently. For example, Reliabuild selects 3X the
number of good configurations in comparison to random sampling for several
packages including Abyss, Bolt, libnrm, OpenMPI. Our framework is also able to
select all the high-fidelity builds in half the number of samples required by
random sampling for packages such as Chai, OpenMPI, py-petsc4py, and slepc. We
further use the model to learn statistics about the compatibility of different
packages, which will enable package solvers to better select high-fidelity
build configurations automatically.


title:
On the almost sure scattering for the energy-critical cubic wave
  equation with supercritical data

abstract:
  In this article we study the defocusing energy-critical nonlinear wave
equation on $\mathbb{R}^4$ with scaling supercritical data. We prove almost
sure scattering for randomized initial data in $H^s(\mathbb{R}^4) \times
H^{s-1}(\mathbb{R}^4)$ with $\frac{5}{6} < s < 1$. The proof relies on new
probabilistic estimates for the linear flow of the wave equation with
randomized data, where the randomization is based on a unit-scale decomposition
in frequency space, a decomposition in the angular variable, and a unit-scale
decomposition of physical space. In particular, we show that the solution to
the linear wave equation with randomized data almost surely belongs to $L^1_t
L^\infty_x$.


title: On the Identity Problem for Unitriangular Matrices of Dimension Four

abstract:
  We show that the Identity Problem is decidable in polynomial time for
finitely generated sub-semigroups of the group $\mathsf{UT}(4, \mathbb{Z})$ of
$4 \times 4$ unitriangular integer matrices. As a byproduct of our proof, we
also show the polynomial-time decidability of several subset reachability
problems in $\mathsf{UT}(4, \mathbb{Z})$.


title: Deadwooding: Robust Global Pruning for Deep Neural Networks

abstract:
  The ability of Deep Neural Networks to approximate highly complex functions
is the key to their success. This benefit, however, often comes at the cost of
a large model size, which challenges their deployment in resource-constrained
environments. To limit this issue, pruning techniques can introduce sparsity in
the models, but at the cost of accuracy and adversarial robustness. This paper
addresses these critical issues and introduces Deadwooding, a novel pruning
technique that exploits a Lagrangian Dual method to encourage model sparsity
while retaining accuracy and ensuring robustness. The resulting model is shown
to significantly outperform the state-of-the-art studies in measures of
robustness and accuracy.


title:
A Novel Four-DOF Lagrangian Approach to Attitude Tracking for Rigid
  Spacecraft

abstract:
  This paper presents a novel Lagrangian approach to attitude tracking for
rigid spacecraft using unit quaternions, where the motion equations of a
spacecraft are described by a four degrees of freedom Lagrangian dynamics
subject to a holonomic constraint imposed by the norm of a unit quaternion. The
basic energy-conservation property as well as some additional useful properties
of the Lagrangian dynamics are explored, enabling to develop quaternion-based
attitude tracking controllers by taking full advantage of a broad class of
tracking control designs for mechanical systems based on energy-shaping
methodology. Global tracking of a desired attitude on the unit sphere is
achieved by designing control laws that render the tracking error on the
four-dimensional Euclidean space to converge to the origin. The topological
constraints for globally exponentially tracking by a quaternion-based
continuous controller and singularities in controller designs based on any
three-parameter representation of the attitude are then avoided. Using this
approach, a full-state feedback controller is first developed, and then several
important issues, such as robustness to noise in quaternion measurements,
unknown on-orbit torque disturbances, uncertainty in the inertial matrix, and
lack of angular-velocity measurements are addressed progressively, by designing
a hybrid state-feedback controller, an adaptive hybrid state-feedback
controller, and an adaptive hybrid attitude-feedback controller. Global
asymptotic stability is established for each controller. Simulations are
included to illustrate the theoretical results.


title: Entanglement catalysis for quantum states and noisy channels

abstract:
  Many applications of the emerging quantum technologies, such as quantum
teleportation and quantum key distribution, require singlets, maximally
entangled states of two quantum bits. It is thus of utmost importance to
develop optimal procedures for establishing singlets between remote parties. As
has been shown very recently, singlets can be obtained from other quantum
states by using a quantum catalyst, an entangled quantum system which is not
changed in the procedure. In this work we put this idea further, investigating
properties of entanglement catalysis and its role for quantum communication.
For transformations between bipartite pure states we prove the existence of a
universal catalyst, which can enable all possible transformations in this
setup. We demonstrate the advantage of catalysis in asymptotic settings, going
beyond the typical assumption of independent and identically distributed
systems. We further develop methods to estimate the number of singlets which
can be established via a noisy quantum channel when assisted by entangled
catalysts. For various types of quantum channels our results lead to optimal
protocols, allowing to establish the maximal number of singlets with a single
use of the channel.


title: How rare are the properties of binary relations?

abstract:
  Knoblauch (2014) and Knoblauch (2015) investigate the relative size of the
collection of binary relations with desirable features as compared to the set
of all binary relations using symmetric difference metric (Cantor) topology and
Hausdorff metric topology. We consider Ellentuck and doughnut topologies to
further this line of investigation. We report the differences among the size of
the useful binary relations in Cantor, Ellentuck and doughnut topologies. It
turns out that the doughnut topology admits binary relations with more general
properties in contrast to the other two. We further prove that among the
induced Cantor and Ellentuck topologies, the latter captures the relative size
of partial orders among the collection of all quasi-orders. Finally we show
that the class of ethical binary relations is small in Ellentuck (and therefore
in Cantor) topology but is not small in doughnut topology. In essence, the
Ellentuck topology fares better compared to Cantor topology in capturing the
relative size of collections of binary relations.


title:
Integral Fourier transforms and the integral Hodge conjecture for
  one-cycles on abelian varieties

abstract:
  We prove the integral Hodge conjecture for one-cycles on a principally
polarized complex abelian variety whose minimal class is algebraic. In
particular, the Jacobian of a smooth proper complex curve satisfies the
integral Hodge conjecture for one-cycles. The main ingredient is a lift of the
Fourier transform to integral Chow groups. Similarly, we prove the integral
Tate conjecture for one-cycles on Jacobians of smooth proper curves over the
separable closure of a finitely generated field. Furthermore, abelian varieties
over the complex numbers (respectively an algebraically closed field of
positive characteristic) satisfying the integral Hodge (respectively integral
Tate) conjecture for one-cycles are dense in their moduli space.


title:
Landau parameters and entrainment matrix of cold stellar matter: effect
  of the symmetry energy and strong magnetic fields

abstract:
  Nuclear matter properties based on a relativistic approach suitable for the
description of multi-component systems are calculated. We use a set of nuclear
relativistic mean-field models that satisfy acceptable nuclear matter
properties and neutron star observations. The effects of the density dependence
of the symmetry energy and of the Landau quantization due to the presence of a
strong external magnetic field are discussed. Properties such as the proton
fraction, the Landau mass, Landau parameters and entrainment matrix, the
adiabatic index and speed of sound are calculated for cold $\beta$-equilibrium
matter. A large dispersion on the calculated properties is obtained at two to
three times saturation density $\rho_0 $. The proton Landau mass can be as low
as one third of the vacuum nucleon mass at 2-3$~\rho_0 $. Similar effects are
obtained for the Landau parameters, in particular, the ones involving protons,
where the relative dispersion of $F^0_{pp}$ and $F^1_{pp}$ is as high as 30\%
to 50\% at 2-3$~\rho_0 $. These parameters are particularly sensitive to the
symmetry energy. The effect of the magnetic field on the nuclear properties is
small for fields as high as 10$^{18}$G except for a small range of densities
just above the crust-core transition. Tables with the EoS, and the parameters,
are provided in the Supplementary Material section.


title: Matching with Transfers under Distributional Constraints

abstract:
  We study two-sided many-to-one matching markets with transferable utilities,
e.g., labor and rental housing markets, in which money can exchange hands
between agents, subject to distributional constraints on the set of feasible
allocations. In such markets, we establish the efficiency of equilibrium
arrangements, specified by an assignment and transfers between agents on the
two sides of the market, and study the conditions on the distributional
constraints and agent preferences under which equilibria exist and can be
computed efficiently. To this end, we first consider the setting when the
number of institutions (e.g., firms in a labor market) is one and show that
equilibrium arrangements exist irrespective of the nature of the constraint
structure or the agents' preferences. However, equilibrium arrangements may not
exist in markets with multiple institutions even when agents on each side have
linear (or additively separable) preferences over agents on the other side.
Thus, for markets with linear preferences, we study sufficient conditions on
the constraint structure that guarantee the existence of equilibria using
linear programming duality. Our linear programming approach not only
generalizes that of Shapley and Shubik (1971) in the one-to-one matching
setting to the many-to-one matching setting under distributional constraints
but also provides a method to compute market equilibria efficiently.


title: Game Theoretic Analysis of an Adversarial Status Updating System

abstract:
  We investigate the game theoretic equilibrium points of a status updating
system with an adversary that jams the updates in the downlink. We consider the
system models with and without diversity. The adversary can jam up to $\alpha$
proportion of the entire communication window. In the model without diversity,
in each time slot, the base station schedules a user from $N$ users according
to a stationary distribution. The adversary blocks (jams) $\alpha T$ time slots
of its choosing out of the total $T$ time slots. For this system, we show that
a Nash equilibrium does not exist, however, a Stackelberg equilibrium exists
when the scheduling algorithm of the base station acts as the leader and the
adversary acts as the follower. In the model with diversity, in each time slot,
the base station schedules a user from $N$ users and chooses a sub-carrier from
$N_{sub}$ sub-carriers to transmit update packets to the scheduled user
according to a stationary distribution. The adversary blocks $\alpha T$ time
slots of its choosing out of $T$ time slots at the sub-carriers of its
choosing. For this system, we show that a Nash equilibrium exists and identify
the Nash equilibrium.


title:
Precision study of the continuum SU(3) Yang-Mills theory: how to use
  parallel tempering to improve on supercritical slowing down for first order
  phase transitions

abstract:
  We perform large scale simulations to characterize the transition in quenched
QCD. It is shown by a rigorous finite size scaling that the transition is of
first order. After this qualitative feature quantitative results are obtained
with unprecedented precision: we calculate the transition temperature
$w_0T_c$=0.25384(23), -- which is the first per-mill accurate result in QCD
thermodynamics -- and the latent heat $\Delta E/T_c^4$=1.025(21)(27) in both
cases carrying out controlled continuum and infinite volume extrapolations. As
it is well known the cost of lattice simulations explodes in the vicinity of
phase transitions, a phenomenon called critical slowing down for second order
phase transitions and supercritical slowing down for first order phase
transitions. We show that a generalization of the parallel tempering algorithm
of Marinari and Parisi [Europhys. Lett. 19, 451 (1992)] originally for spin
systems can efficiently overcome these difficulties even if the transition is
of first order, like in the case of QCD without quarks, or with very heavy
quarks. We also report on our investigations on the autocorrelation times and
other details.


title: Potential energy of totally positive algebraic integers

abstract:
  Given positive real numbers, we prove two inequalities involving their
potential energy and their power sums. We also prove an inequality involving
the energy and the discriminant and apply it to deduce a result on totally
positive irreducible polynomials.


title: Learnable Nonlinear Compression for Robust Speaker Verification

abstract:
  In this study, we focus on nonlinear compression methods in spectral features
for speaker verification based on deep neural network. We consider different
kinds of channel-dependent (CD) nonlinear compression methods optimized in a
data-driven manner. Our methods are based on power nonlinearities and dynamic
range compression (DRC). We also propose multi-regime (MR) design on the
nonlinearities, at improving robustness. Results on VoxCeleb1 and VoxMovies
data demonstrate improvements brought by proposed compression methods over both
the commonly-used logarithm and their static counterparts, especially for ones
based on power function. While CD generalization improves performance on
VoxCeleb1, MR provides more robustness on VoxMovies, with a maximum relative
equal error rate reduction of 21.6%.


title: Severe testing of Benford's law

abstract:
  This paper contributes to the debate on Benford's law testing by proposing a
method to overcome the "excess power problem" critique, so widespread in this
literature. In particular we suggest using the notion of severity testing (Mayo
& Spanos, 2006) in the framework of tests for conformity with Benford's law. To
do so, we also derive the asymptotic distribution of Nigrini's mean absolute
deviation statistic (MAD, Nigrini, 2012) and an asymptotic standard normal
test. Finally, the severity testing principle is applied to six controversial
data sets to assess their "Benford-ness".


title: Counting Various Classes of Tournament Score Sequences

abstract:
  Based on known methods for computing the number of distinct score sequences
for $n$-vertex tournaments, we develop algorithms for computing the number of
distinct score sequences for self-complementary tournaments, strong
tournaments, and tournaments that are both self-complementary and strong.


title: F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

abstract:
  Neural network quantization is a promising compression technique to reduce
memory footprint and save energy consumption, potentially leading to real-time
inference. However, there is a performance gap between quantized and
full-precision models. To reduce it, existing quantization approaches require
high-precision INT32 or full-precision multiplication during inference for
scaling or dequantization. This introduces a noticeable cost in terms of
memory, speed, and required energy. To tackle these issues, we present F8Net, a
novel quantization framework consisting of only fixed-point 8-bit
multiplication. To derive our method, we first discuss the advantages of
fixed-point multiplication with different formats of fixed-point numbers and
study the statistical behavior of the associated fixed-point numbers. Second,
based on the statistical and algorithmic analysis, we apply different
fixed-point formats for weights and activations of different layers. We
introduce a novel algorithm to automatically determine the right format for
each layer during training. Third, we analyze a previous quantization algorithm
-- parameterized clipping activation (PACT) -- and reformulate it using
fixed-point arithmetic. Finally, we unify the recently proposed method for
quantization fine-tuning and our fixed-point approach to show the potential of
our method. We verify F8Net on ImageNet for MobileNet V1/V2 and ResNet18/50.
Our approach achieves comparable and better performance, when compared not only
to existing quantization techniques with INT32 multiplication or floating-point
arithmetic, but also to the full-precision counterparts, achieving
state-of-the-art performance.


title: ChemicalX: A Deep Learning Library for Drug Pair Scoring

abstract:
  In this paper, we introduce ChemicalX, a PyTorch-based deep learning library
designed for providing a range of state of the art models to solve the drug
pair scoring task. The primary objective of the library is to make deep drug
pair scoring models accessible to machine learning researchers and
practitioners in a streamlined framework.The design of ChemicalX reuses
existing high level model training utilities, geometric deep learning, and deep
chemistry layers from the PyTorch ecosystem. Our system provides neural network
layers, custom pair scoring architectures, data loaders, and batch iterators
for end users. We showcase these features with example code snippets and case
studies to highlight the characteristics of ChemicalX. A range of experiments
on real world drug-drug interaction, polypharmacy side effect, and combination
synergy prediction tasks demonstrate that the models available in ChemicalX are
effective at solving the pair scoring task. Finally, we show that ChemicalX
could be used to train and score machine learning models on large drug pair
datasets with hundreds of thousands of compounds on commodity hardware.


title: Thermal Emission of Gravitational Waves from Weak to Strong Coupling

abstract:
  We study the production of gravitational waves by a thermalized plasma of
$\mathcal N $=4 Supersymmetric Yang Mills matter. We focus on the large number
of colors limit, $N_c\rightarrow \infty$, and compute the spectrum of
gravitational waves both for infinitely large and infinitesimally small values
of the t'Hoft coupling constant $\lambda$. In the $\lambda\rightarrow \infty$
limit we employ the gauge/gravity duality to compute the emission rate via the
analysis of Energy-Momentum tensor thermal correlators. In the $\lambda
\rightarrow 0$ limit we employ state-of-the-art perturbative analyses to
calculate the complete leading order emission rate. By comparing these extreme
limits, we bracket the magnitude of the spectrum induced by this source of
gravitational waves. Embedding our results in a cosmological evolution model,
we find qualitative and quantitative similarities between the strong coupling
spectrum and the extrapolation of the perturbative results up to an
intermediate value of the coupling, after an appropriate re-scaling of the
effective number of degrees of freedom. We comment on how our results can help
better understand the contribution of thermalized matter to the stochastic
spectrum of gravitational waves.


title:
Spin Supersolidity in Nearly Ideal Easy-axis Triangular Quantum
  Antiferromagnet Na$_2$BaCo(PO$_4$)$_2$

abstract:
  Prototypical models and their material incarnations are cornerstones to the
understanding of quantum magnetism. Here we show theoretically that the
recently synthesized magnetic compound Na$_2$BaCo(PO$_4$)$_2$ (NBCP) is a rare,
nearly ideal material realization of the $S=1/2$ triangular lattice
antiferromagnet with significant easy-axis spin exchange anisotropy. By
combining the automatic parameter searching and tensor-network quantum
many-body simulations, we establish a microscopic model description of this
material with realistic model parameters. The resulted model can not only fit
well the experimental thermodynamic data but also reproduce the measured
magnetization curves without further adjustment of parameters. As a
consequence, the NBCP hosts a spin supersolid state that breaks both the
lattice translational and spin U(1) symmetries. The NBCP therefore represents
an ideal material platform to explore the field-tunable quantum phase
transitions and Berezinskii-Kosterlitz-Thouless melting of the spin
supersolidity.


title: Dust diffusion in SPH simulations of an isolated galaxy

abstract:
  We compute the evolution of the grain size distribution (GSD) in a suite of
numerical simulations of an isolated Milky-Way-like galaxy using the
$N$-body/smoothed-particle-hydrodynamics code {\sc Gadget-4}. The full GSD is
sampled on a logarithmically spaced grid with 30 bins, and its evolution is
calculated self-consistently with the hydrodynamical and chemical evolution of
the galaxy using a state-of-the-art star formation and feedback model. In
previous versions of this model, the GSD tended to be slightly biased towards
larger grains and the extinction curve had a tendency to be flatter than the
observations. This work addresses these issues by considering the diffusion of
dust and metals through turbulence on subgrid scales and introducing a
multi-phase subgrid model that enables a smoother transition from diffuse to
dense gas. We show that diffusion can significantly enhance the production of
small grains and improve the agreement with the observed dust extinction curve
in the Milky Way.


title:
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy
  Transfer

abstract:
  A popular paradigm in robotic learning is to train a policy from scratch for
every new robot. This is not only inefficient but also often impractical for
complex robots. In this work, we consider the problem of transferring a policy
across two different robots with significantly different parameters such as
kinematics and morphology. Existing approaches that train a new policy by
matching the action or state transition distribution, including imitation
learning methods, fail due to optimal action and/or state distribution being
mismatched in different robots. In this paper, we propose a novel method named
$REvolveR$ of using continuous evolutionary models for robotic policy transfer
implemented in a physics simulator. We interpolate between the source robot and
the target robot by finding a continuous evolutionary change of robot
parameters. An expert policy on the source robot is transferred through
training on a sequence of intermediate robots that gradually evolve into the
target robot. Experiments show that the proposed continuous evolutionary model
can effectively transfer the policy across robots and achieve superior sample
efficiency on new robots using a physics simulator. The proposed method is
especially advantageous in sparse reward settings where exploration can be
significantly reduced.


title:
Benign-Overfitting in Conditional Average Treatment Effect Prediction
  with Linear Regression

abstract:
  We study the benign overfitting theory in the prediction of the conditional
average treatment effect (CATE), with linear regression models. As the
development of machine learning for causal inference, a wide range of
large-scale models for causality are gaining attention. One problem is that
suspicions have been raised that the large-scale models are prone to
overfitting to observations with sample selection, hence the large models may
not be suitable for causal prediction. In this study, to resolve the
suspicious, we investigate on the validity of causal inference methods for
overparameterized models, by applying the recent theory of benign overfitting
(Bartlett et al., 2020). Specifically, we consider samples whose distribution
switches depending on an assignment rule, and study the prediction of CATE with
linear models whose dimension diverges to infinity. We focus on two methods:
the T-learner, which based on a difference between separately constructed
estimators with each treatment group, and the inverse probability weight
(IPW)-learner, which solves another regression problem approximated by a
propensity score. In both methods, the estimator consists of interpolators that
fit the samples perfectly. As a result, we show that the T-learner fails to
achieve the consistency except the random assignment, while the IPW-learner
converges the risk to zero if the propensity score is known. This difference
stems from that the T-learner is unable to preserve eigenspaces of the
covariances, which is necessary for benign overfitting in the overparameterized
setting. Our result provides new insights into the usage of causal inference
methods in the overparameterizated setting, in particular, doubly robust
estimators.


title: Monotone Learning

abstract:
  The amount of training-data is one of the key factors which determines the
generalization capacity of learning algorithms. Intuitively, one expects the
error rate to decrease as the amount of training-data increases. Perhaps
surprisingly, natural attempts to formalize this intuition give rise to
interesting and challenging mathematical questions. For example, in their
classical book on pattern recognition, Devroye, Gyorfi, and Lugosi (1996) ask
whether there exists a {monotone} Bayes-consistent algorithm. This question
remained open for over 25 years, until recently Pestov (2021) resolved it for
binary classification, using an intricate construction of a monotone
Bayes-consistent algorithm.
  We derive a general result in multiclass classification, showing that every
learning algorithm A can be transformed to a monotone one with similar
performance. Further, the transformation is efficient and only uses a black-box
oracle access to A. This demonstrates that one can provably avoid non-monotonic
behaviour without compromising performance, thus answering questions asked by
Devroye et al (1996), Viering, Mey, and Loog (2019), Viering and Loog (2021),
and by Mhammedi (2021).
  Our transformation readily implies monotone learners in a variety of
contexts: for example it extends Pestov's result to classification tasks with
an arbitrary number of labels. This is in contrast with Pestov's work which is
tailored to binary classification.
  In addition, we provide uniform bounds on the error of the monotone
algorithm. This makes our transformation applicable in distribution-free
settings. For example, in PAC learning it implies that every learnable class
admits a monotone PAC learner. This resolves questions by Viering, Mey, and
Loog (2019); Viering and Loog (2021); Mhammedi (2021).

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


title: Spectral dimensions of Krein-Feller operators in higher dimensions

abstract:
  We study the spectral dimensions of Krein-Feller operators for finite Borel
measures $\nu$ on the $d$-dimensional unit cube via a form approach. We
introduce the notion of the spectral partition function of $\nu$ and, assuming
that the lower $\infty$-dimension of $\nu$ exceeds $d-2$, we show that the
upper Neumann spectral dimension coincides with the unique zero of the spectral
partition function. We show that if the lower $\infty$-dimension of $\nu$ is
strictly less than $d-2$, the form approach breaks down. Examples are given for
the critical case, that is the lower $\infty$-dimension of $\nu$ equals $d-2$,
such that for one case the form approach breaks down, another case, where the
operator is well defined but we have no discrete set of eigenvalues, and for
the third case, where the spectral dimension exists. We provide additional
regularity assumptions on the spectral partition function, guaranteeing that
the Neumann spectral dimension exists and coincides with the Dirichlet spectral
dimension. The significance of our new approach is illustrated by several
prominent examples -- namely absolutely continuous measures, Ahlfors-David
regular measure, and self-conformal measures with or without overlaps -- for
which the spectral partition function is essentially given by its
$L^{q}$-spectrum and both the Dirichlet and Neumann spectral dimensions exist.
Moreover, we provide general bounds for the upper Neumann spectral dimension in
terms of the upper Minkowski dimension of the support of $\nu$ and its lower
$\infty$-dimension. Finally, we give an example for which the spectral
dimension does not exist.


title:
Extracting high-order cosmological information in galaxy surveys with
  power spectra

abstract:
  The reconstruction method was proposed more than a decade ago to boost the
signal of baryonic acoustic oscillations measured in galaxy redshift surveys,
which is one of key probes for dark energy. After moving observed galaxies in
galaxy surveys back to their initial position, the reconstructed density field
is closer to a linear Gaussian field, with higher-order information moved back
into the power spectrum. By combining power spectra measured from the pre- and
post-reconstructed galaxy samples, higher-order information beyond the 2-point
power spectrum can be efficiently extracted, as power spectra can be measured
more quickly than direct measurements of the bispectrum and other higher order
functions. This opens a new window to easily use higher-order information when
constraining cosmological models.


title: Buying Opinions

abstract:
  A principal hires an agent to acquire a distribution over unverifiable
posteriors before reporting to the principal, who can contract on the realized
state. An agent's optimal learning and truthful disclosure completely specify
the relative incentives the principal must provide, which radically simplifies
the principal's problem. When the agent i. is risk neutral, and iia. has a
sufficiently high outside option, or iib. can face sufficiently large
penalties, the principal can attain the first-best outcome. We also explore in
detail the general problem of cheaply implementing distributions over
posteriors with limited liability constraints and a risk-averse agent.


title: Adaptive and Robust Multi-task Learning

abstract:
  We study the multi-task learning problem that aims to simultaneously analyze
multiple datasets collected from different sources and learn one model for each
of them. We propose a family of adaptive methods that automatically utilize
possible similarities among those tasks while carefully handling their
differences. We derive sharp statistical guarantees for the methods and prove
their robustness against outlier tasks. Numerical experiments on synthetic and
real datasets demonstrate the efficacy of our new methods.


title:
Differentiable Phonon Simulations To Optimize Thermal Transport in
  Nanostructures

abstract:
  We introduce a methodology for large-scale optimization of non-Fourier
thermal transport in nanostructures, based upon the forward and adjoint phonon
Boltzmann transport equation (BTE) and density-based topology optimization. To
this end, we also develop the transmission interpolation model (TIM), an
interface-based method that allows for smooth interpolation between void and
solid regions. We first use our approach to tailor the effective thermal
conductivity tensor of a periodic nanomaterial; then, we maximize classical
phonon size effects under constrained diffusive transport, obtaining more than
a four-fold degradation in the thermal conductivity with respect to
commonly-employed configurations. Our method enables systematic optimization of
materials for heat management and conversion, and, more broadly, the design of
devices where diffusive transport is not valid.


title:
SA-HMTS: A Secure and Adaptive Hierarchical Multi-timescale Framework
  for Resilient Load Restoration Using A Community Microgrid

abstract:
  Distribution system integrated community microgrids (CMGs) can partake in
restoring loads during extended duration outages. At such times, the CMG is
challenged with limited resource availability, absence of robust grid support,
and heightened demand-supply uncertainty. This paper proposes a secure and
adaptive three-stage hierarchical multi-timescale framework for scheduling and
real-time (RT) dispatch of CMGs with hybrid PV systems to address these
challenges. The framework enables the CMG to dynamically expand its boundary to
support the neighboring grid sections and is adaptive to the changing forecast
error impacts. The first stage solves a stochastic extended duration scheduling
(EDS) problem to obtain referral plans for optimal resource rationing. The
intermediate near-real-time (NRT) scheduling stage updates the EDS schedule
closer to the dispatch time using newly obtained forecasts, followed by the RT
dispatch stage. To make the dispatch decisions more secure and robust against
forecast errors, a novel concept called delayed recourse is proposed. The
methodology is evaluated via numerical simulations on a modified IEEE 123-bus
system and validated using OpenDSS/hardware-in-loop simulations. The results
show superior performance in maximizing load supply and continuous secure CMG
operation under numerous operating scenarios.