Abstract:
  Gold nanoparticles are unique electrocatalysts for oxygen reduction, carbon
dioxide reduction, and alcohol oxidation. Electrocatalytic processes are
influenced by the interaction with the solvent, yet the direct investigation of
the solvation of nanoparticles is scarce. Here we select gold nanoparticles as
10 nm sized solutes of which we can control the charge. We perform mid-infrared
and terahertz spectroscopy to compare the behavior of the water in solution
with micelles loaded with neutral and positive gold nanoparticles. We find
indications that the hydration water around the gold-loaded micelles is
characterized by weaker hydrogen bonds than bulk water. A positive nanoparticle
charge is observed to result in a larger blue shift of the OH stretch, quenches
the intensity of the collective translational mode, and increases the
absorption by librating water molecules. Water at the interface of a positive
gold nanoparticle could experience a stiffer potential energy surface which, in
turn, might unveil local thermodynamic properties.


Abstract:
  Lossy compression algorithms are typically designed to achieve the lowest
possible distortion at a given bit rate. However, recent studies show that
pursuing high perceptual quality would lead to increase of the lowest
achievable distortion (e.g., MSE). This paper provides nontrivial results
theoretically revealing that, \textit{1}) the cost of achieving perfect
perception quality is exactly a doubling of the lowest achievable MSE
distortion, \textit{2}) an optimal encoder for the "classic" rate-distortion
problem is also optimal for the perceptual compression problem, \textit{3})
distortion loss is unnecessary for training a perceptual decoder. Further, we
propose a novel training framework to achieve the lowest MSE distortion under
perfect perception constraint at a given bit rate. This framework uses a GAN
with discriminator conditioned on an MSE-optimized encoder, which is superior
over the traditional framework using distortion plus adversarial loss.
Experiments are provided to verify the theoretical finding and demonstrate the
superiority of the proposed training framework.


Abstract:
  This paper describes energy distribution in a block medium simulated by a
one-dimensional chain of masses joined by springs and dampers. Equations
describing the motion of masses are solved by the methods of the theory of
ordinary differential equations. The effect of the block medium parameters on
energy dissipation is investigated. An approximate analytical solution is
obtained that describes the total energy of a block medium at large values of
time.


Abstract:
  The ellipses model is a continuum percolation process in which ellipses with
random orientation and eccentricity are placed in the plane according to a
Poisson point process. A parameter $\alpha$ controls the tail distribution of
the major axis' distribution and we focus on the regime $\alpha \in (1,2)$ for
which there exists a unique infinite cluster of ellipses and this cluster
fulfills the so called highway property. We prove that the distance within this
infinite cluster behaves asymptotically like the (unrestricted) Euclidean
distance in the plane. We also show that the chemical distance between points
$x$ and $y$ behaves roughly as $c \log\log |x-y|$.


Abstract:
  Automatic evaluations for natural language generation (NLG) conventionally
rely on token-level or embedding-level comparisons with the text references.
This is different from human language processing, for which visual imaginations
often improve comprehension. In this work, we propose ImaginE, an
imagination-based automatic evaluation metric for natural language generation.
With the help of CLIP and DALL-E, two cross-modal models pre-trained on
large-scale image-text pairs, we automatically generate an image as the
embodied imagination for the text snippet and compute the imagination
similarity using contextual embeddings. Experiments spanning several text
generation tasks demonstrate that adding imagination with our ImaginE displays
great potential in introducing multi-modal information into NLG evaluation, and
improves existing automatic metrics' correlations with human similarity
judgments in many circumstances.


Abstract:
  Black-hole (BH) accretion disks formed in compact-object mergers or
collapsars may be major sites of the rapid-neutron-capture (r-)process, but the
conditions determining the electron fraction (Y_e) remain uncertain given the
complexity of neutrino transfer and angular-momentum transport. After
discussing relevant weak-interaction regimes, we study the role of neutrino
absorption for shaping Y_e using an extensive set of simulations performed with
two-moment neutrino transport and again without neutrino absorption. We vary
the torus mass, BH mass and spin, and examine the impact of rest-mass and
weak-magnetism corrections in the neutrino rates. We also test the dependence
on the angular-momentum transport treatment by comparing axisymmetric models
using the standard alpha-viscosity with viscous models assuming constant
viscous length scales (l_t) and three-dimensional magnetohydrodynamic (MHD)
simulations. Finally, we discuss the nucleosynthesis yields and basic kilonova
properties. We find that absorption pushes Y_e towards ~0.5 outside the torus,
while inside increasing the equilibrium value Y_e^eq by ~0.05-0.2.
Correspondingly, a substantial ejecta fraction is pushed above Y_e=0.25,
leading to a reduced lanthanide fraction and a brighter, earlier, and bluer
kilonova than without absorption. More compact tori with higher neutrino
optical depth, tau, tend to have lower Y_e^eq up to tau~1-10, above which
absorption becomes strong enough to reverse this trend. Disk ejecta are less
(more) neutron-rich when employing an l_t=const. viscosity (MHD treatment). The
solar-like abundance pattern found for our MHD model marginally supports
collapsar disks as major r-process sites, although a strong r-process may be
limited to phases of high mass-infall rates, Mdot>~ 2 x 10^(-2) Msun/s.


Abstract:
  We present a general framework for the estimation of corporate default based
on a firm's capital structure, when its assets are assumed to follow a pure
jump L\'evy processes; this setup provides a natural extension to usual default
metrics defined in diffusion (log-normal) models, and allows to capture extreme
market events such as sudden drops in asset prices, which are closely linked to
default occurrence. Within this framework, we introduce several pure jump
processes featuring negative jumps only and derive practical closed formulas
for equity prices, which enable us to use a moment-based algorithm to calibrate
the parameters from real market data and to estimate the associated default
metrics. A notable feature of these models is the redistribution of credit risk
towards shorter maturity: this constitutes an interesting improvement to
diffusion models, which are known to underestimate short term default
probabilities. We also provide extensions to a model featuring both positive
and negative jumps and discuss qualitative and quantitative features of the
results. For readers convenience, practical tools for model implementation and
GitHub links are also included.


Abstract:
  The widespread proliferation of data-driven decision-making has ushered in a
recent interest in the design of privacy-preserving algorithms. In this paper,
we consider the ubiquitous problem of gaussian process (GP) bandit optimization
from the lens of privacy-preserving statistics. We propose a solution for
differentially private GP bandit optimization that combines a uniform kernel
approximator with random perturbations, providing a generic framework to create
differentially-private (DP) Gaussian process bandit algorithms. For two
specific DP settings - joint and local differential privacy, we provide
algorithms based on efficient quadrature Fourier feature approximators, that
are computationally efficient and provably no-regret for popular stationary
kernel functions. Our algorithms maintain differential privacy throughout the
optimization procedure and critically do not rely explicitly on the sample path
for prediction, making the parameters straightforward to release as well.


Abstract:
  We report anomalous heating in a colloidal system, the first observation of
the inverse Mpemba effect, where an initially cold system heats up faster than
an identical warm system coupled to the same thermal bath. For an overdamped,
Brownian colloidal particle moving in a tilted double-well potential, we find a
non-monotonic dependence of the heating times on the initial temperature of the
system, as predicted by an eigenfunction expansion of the associated
Fokker-Planck equation. By carefully tuning parameters, we also observe a
"strong" version of anomalous heating, where a cold system heats up
exponentially faster than systems prepared under slightly different conditions


Abstract:
  We begin with proving a formula relating the Hilbert series of a graded
algebra $A$ and the Poincar\'{e} series for $A$ in two variables. This gives
the Fr\"oberg formula in the case where the bigraded $Tor^A(k,k)$ is
concentrated on the diagonal, which we take as definition of $A$ being
"Koszul". We look at a resolution in the commutative case obtained from the
Koszul complex in the "trivially Golod" case. The algebra structure of
$Ext_A(k,k)$ is introduced in different ways. Its subalgebra generated by the
one-dimensional elements is by definition the "Koszul" dual of $A$. We define
the"generalized Koszul complex" and construct a minimal resolution in the case
where the cube of the augmentation ideal of $A$ is zero. The above results are
at least 45 years old and most of it can be found in my thesis. In the second
part graded Lie algebras are defined. Free Lie algebras and enveloping algebras
are introduced. The Koszul dual is looked upon as the enveloping algebra of a
Lie algebra in the graded commutative case with examples. The
Poincar\'{e}-Birkhoff-Witt theorem is stated giving a formula for the Hilbert
series of the enveloping algebra. The homotopy Lie algebra is defined in
different ways, in particular the Lie subalgebra generated by the
one-dimensional elements. Examples of homotopy Lie algebras are given for
complete intersections, Golod rings and rings with the cube of the augmentation
ideal equal to zero. A logarithmic formula for the dimensions of a Lie algebra
given the Hilbert series for its enveloping algebra is given. Examples of
periodic Lie subalgebras are given, yielding irrational Poincar\'{e} series.
The "holonomy" Lie algebra of a hyperplane arrangement is defined examplified
by the graphical arrangement $K_4$.


Abstract:
  This research is comparing learning environments to students dropout
intentions. While using statistics I looked at data and the correlations
between two articles to see how the two studies looked side to side. Learning
environments and dropout intentions can both have vary effects on students.
They can both determine if a student does well, or bad in school especially
math.


Abstract:
  We consider the probabilistic planning problem where the agent (called Player
1, or P1) can jointly plan the control actions and sensor queries in a sensor
network and an attacker (called player 2, or P2) can carry out attacks on the
sensors. We model such an adversarial interaction using a formal model -- a
reachability game with partially controllable observation functions. The main
contribution of this paper is to assess the cost of P1's unawareness: Suppose
P1 misinterprets the sensor failures as probabilistic node failures due to
unreliable network communication, and P2 is aware of P1's misinterpretation in
addition to her partial observability. Then, from which states can P2 carry out
sensor attacks to ensure, with probability one, that P1 will not be able to
complete her reachability task even though, due to misinterpretation, P1
believes that she can almost-surely achieve her task. We develop an algorithm
to solve the almost-sure winning sensor-attack strategy given P1's
observation-based strategy. Our attack analysis could be used for attack
detection in wireless communication networks and the design of provably secured
attack-aware sensor allocation in decision-theoretic models for cyber-physical
systems.


Abstract:
  This work is entirely devoted to compare the largest claims from two
heterogeneous portfolios. It is assumed that the claim amounts in an insurance
portfolio are nonnegative absolutely continuous random variables and belong to
a general family of distributions. The largest claims have been compared based
on various stochastic orderings. The established sufficient conditions are
associated with the matrices and vectors of model parameters. Applications of
the results are provided for the purpose of illustration.


Abstract:
  In order to handle modern convolutional neural networks (CNNs) efficiently, a
hardware architecture of CNN inference accelerator is proposed to handle
depthwise convolutions and regular convolutions, which are both essential
building blocks for embedded-computer-vision algorithms. Different from related
works, the proposed architecture can support filter kernels with different
sizes with high flexibility since it does not require extra costs for
intra-kernel parallelism, and it can generate convolution results faster than
the architecture of the related works. The experimental results show the
importance of supporting depthwise convolutions and dilated convolutions with
the proposed hardware architecture. In addition to depthwise convolutions with
large-kernels, a new structure called DDC layer, which includes the combination
of depthwise convolutions and dilated convolutions, is also analyzed in this
paper. For face detection, the computational costs decrease by 30%, and the
model size decreases by 20% when the DDC layers are applied to the network. For
image classification, the accuracy is increased by 1% by simply replacing $3
\times 3$ filters with $5 \times 5$ filters in depthwise convolutions.


Abstract:
  Binaries play a critical role in the formation, evolution, and fundamental
properties of planets, stars, and stellar associations. Observational studies
in these areas often include a mix of observations aimed at detecting or ruling
out the presence of stellar companions. Rarely can non-detections rule out all
possible binary configurations. Here we present MOLUSC, our framework for
constraining the range of properties of unseen companions using astrometric,
imaging, and velocity information. We showcase the use of MOLUSC on a number of
systems, ruling out stellar false positives in the signals of HIP67522b, and DS
Tuc Ab. We also demonstrate how MOLUSC could be used to predict the number of
missing companions in a stellar sample using the ZEIT sample of young planet
hosts. Although our results are not significant, with a larger sample MOLUSC
could be used to see if close-in planets are less common in young binary
systems, as is seen for their older counterparts.


Abstract:
  Thermostability is an important prerequisite for enzymes employed for
industrial applications. Several machine learning based models have thus been
formulated for protein classification based on this particular trait. These
models have employed features derived from sequences, structures or both
resulting in a >93% accuracy based on a 10-fold cross-validation. Besides using
various proteins from a wide range of organisms, such studies also rely on
hundreds of features. In the present study, an enzyme specific classification
model was created using significantly less number of features that provides a
similar accuracy of classification for thermophilic and non-thermophilic enzyme
serine proteases. For building the classifier, 219 thermophilic and 200
mesophilic bacterial genomes were mined for their respective serine protease
sequences. Features were extracted for 800 sequences followed by feature
selection. We deployed a random forest based classifier that identified
thermophilic and non-thermophilic serine proteases with an accuracy of 95.71%.
Knowledge of thermostability along with amino acid positional shifts can be
vital for downstream protein engineering techniques. Thus, to emphasize the
real time application of the enzyme specific classification model, a web
platform has been designed. Combining the sequence data and the classification
model, this prototype can allow users to align their query serine protease
sequence against the custom database and identify its thermophilic nature.


Abstract:
  Network slicing to support multi-tenancy plays a key role in improving the
performance of 5G networks. In this paper, we propose a two time-scale
framework for the reservation-based network slicing in the backhaul and Radio
Access Network (RAN). In the proposed two time-scale scheme, a subset of
network slices is activated via a novel sparse optimization framework in the
long time-scale with the goal of maximizing the expected utilities of tenants
while in the short time-scale the activated slices are reconfigured according
to the time-varying user traffic and channel states. Specifically, using the
statistics from users and channels and also considering the expected utility
from serving users of a slice and the reconfiguration cost, we formulate a
sparse optimization problem to update the configuration of a slice resources
such that the maximum isolation of reserved resources is enforced. The
formulated optimization problems for long and short time-scales are non-convex
and difficult to solve. We use the $\ell_q$-norm, $0<q<1$, and group LASSO
regularizations to iteratively find convex approximations of the optimization
problems. We propose a Frank-Wolfe algorithm to iteratively solve approximated
problems in long time-scales. To cope with the dynamical nature of traffic
variations, we propose a fast, distributed algorithm to solve the approximated
optimization problems in short time-scales. Simulation results demonstrate the
performance of our approaches relative to optimal solutions and the existing
state of the art method.


Abstract:
  In the last decade, the advancement of the Internet of Things (IoT) has
caused unlicensed radio spectrum, especially the 2.4 GHz ISM band, to be
immensely crowded with smart wireless devices that are used in a wide range of
application domains. Due to their diversity in radio resource use and channel
access techniques, when collocated, these wireless devices create interference
with each other, known as Cross-Technology Interference (CTI), which can lead
to increased packet losses and energy consumption. CTI is a significant problem
for low-power wireless networks, such as IEEE 802.15.4, as it decreases the
overall dependability of the wireless network.
  To improve the performance of low-power wireless networks under CTI
conditions, we propose a data-driven proactive receiver-aware MAC protocol,
LUCID, based on interference estimation and white space prediction. We leverage
statistical analysis of real-world traces from two indoor environments
characterised by varying channel conditions to develop CTI prediction methods.
The CTI models that generate accurate predictions of interference behaviour are
an intrinsic part of our solution. LUCID is thoroughly evaluated in realistic
simulations and we show that depending on the application data rate and the
network size, our solution achieves higher dependability, 1.2% increase in
packet delivery ratio and 0.02% decrease in duty-cycle under bursty indoor
interference than state of the art alternative methods.


Abstract:
  If $G$ is a group and $S$ a generating set, $G$ canonically embeds into the
automorphism group of its Cayley graph and it is natural to try to minimize,
over all generating sets, the index of this inclusion. This infimum is called
the Cayley index of the group. In a recent series of works, we have
characterized the infinite finitely generated groups with Cayley index $1$. We
complement this characterization by showing that the Cayley index is $2$ in the
remaining cases and is attained for a finite generating set.


Abstract:
  For a given pseudo-Anosov homeomorphism $\phi$ of a closed surface $S$, the
action of $\phi$ on the Teichm\"uller space $\mathcal T(S)$ is an automorphism
of the complex structure preserving the Weil-Petersson-Goldman symplectic form.
We give explicit formulae for two invariant functions $\mathcal T(S)\to \mathbb
R$ whose symplectic gradients generate autonomous Hamiltonian flows that
coincide with the action of $\phi$ at time one. We compute the Poisson bracket
of these two functions, which essentially amounts to computing the variation of
length of a H\"older cocyle on one lamination along the shear vector field
defined by another. For a measurably generic set of laminations, we prove that
the variation of length is expressed as the cosine of the angle between the two
laminations integrated against the product H\"older distribution, generalizing
a result of Kerckhoff. We also obtain rates of convergence for the supports of
germs of differentiable paths of measured laminations in the Hausdorff metric
on a hyperbolic surface, which may be of independent interest.


Abstract:
  Various deep neural network architectures (DNNs) maintain massive vital
records in computer vision. While drawing attention worldwide, the design of
the overall structure lacks general guidance. Based on the relationship between
DNN design and numerical differential equations, we performed a fair comparison
of the residual design with higher-order perspectives. We show that the widely
used DNN design strategy, constantly stacking a small design (usually 2-3
layers), could be easily improved, supported by solid theoretical knowledge and
with no extra parameters needed. We reorganise the residual design in
higher-order ways, which is inspired by the observation that many effective
networks can be interpreted as different numerical discretisations of
differential equations. The design of ResNet follows a relatively simple
scheme, which is Euler forward; however, the situation becomes complicated
rapidly while stacking. We suppose that stacked ResNet is somehow equalled to a
higher-order scheme; then, the current method of forwarding propagation might
be relatively weak compared with a typical high-order method such as
Runge-Kutta. We propose HO-ResNet to verify the hypothesis of widely used CV
benchmarks with sufficient experiments. Stable and noticeable increases in
performance are observed, and convergence and robustness are also improved. Our
stacking strategy improved ResNet-30 by 2.15 per cent and ResNet-58 by 2.35 per
cent on CIFAR-10, with the same settings and parameters. The proposed strategy
is fundamental and theoretical and can therefore be applied to any network as a
general guideline.


Abstract:
  In microbiome studies, one of the ways of studying bacterial abundances is to
estimate bacterial composition based on the sequencing read counts. Various
transformations are then applied to such compositional data for downstream
statistical analysis, among which the centered log-ratio (clr) transformation
is most commonly used.
  Due to limited sequencing depth and DNA dropouts, many rare bacterial taxa
might not be captured in the final sequencing reads, which results in many zero
counts. Naive composition estimation using count normalization leads to many
zero proportions, which makes clr transformation infeasible. This paper
proposes a multi-sample approach to estimation of the clr matrix directly in
order to borrow information across samples and across species. Empirical
results from real datasets suggest that the clr matrix over multiple samples is
approximately low rank, which motivates a regularized maximum likelihood
estimation with a nuclear norm penalty. An efficient optimization algorithm
using the generalized accelerated proximal gradient is developed. Theoretical
upper bounds of the estimation errors and of its corresponding singular
subspace errors are established. Simulation studies demonstrate that the
proposed estimator outperforms the naive estimators. The method is analyzed on
Gut Microbiome dataset and the American Gut project.


Abstract:
  Hidden Markov models with observations in a Euclidean space play an important
role in signal and image processing. Previous work extending to models where
observations lie in Riemannian manifolds based on the Baum-Welch algorithm
suffered from high memory usage and slow speed. Here we present an algorithm
that is online, more accurate, and offers dramatic improvements in speed and
efficiency.


Abstract:
  Preprint is a version of a scientific paper that is publicly distributed
preceding formal peer review. Since the launch of arXiv in 1991, preprints have
been increasingly distributed over the Internet as opposed to paper copies. It
allows open online access to disseminate the original research within a few
days, often at a very low operating cost. This work overviews how preprint has
been evolving and impacting the research community over the past thirty years
alongside the growth of the Web. In this work, we first report that the number
of preprints has exponentially increased 63 times in 30 years, although it only
accounts for 4% of research articles. Second, we quantify the benefits that
preprints bring to authors: preprints reach an audience 14 months earlier on
average and associate with five times more citations compared with a
non-preprint counterpart. Last, to address the quality concern of preprints, we
discover that 41% of preprints are ultimately published at a peer-reviewed
destination, and the published venues are as influential as papers without a
preprint version. Additionally, we discuss the unprecedented role of preprints
in communicating the latest research data during recent public health
emergencies. In conclusion, we provide quantitative evidence to unveil the
positive impact of preprints on individual researchers and the community.
Preprints make scholarly communication more efficient by disseminating
scientific discoveries more rapidly and widely with the aid of Web
technologies. The measurements we present in this study can help researchers
and policymakers make informed decisions about how to effectively use and
responsibly embrace a preprint culture.


Abstract:
  We study the metallicity dependence of the H/H$_2$ and C$^+$/C/CO
distributions in a self-regulated interstellar medium (ISM) across a broad
range of metallicities ($0.1 < Z/Z_\odot < 3$). To this end, we conduct
high-resolution (particle mass of $1\ {\rm M_\odot}$) hydrodynamical
simulations coupled with a time-dependent H$_2$ chemistry network. The results
are then post-processed with an accurate chemistry network to model the
associated C$^+$/C/CO abundances, based on the time-dependent non-steady-state
(``non-equilibrium'') H$_2$ abundances. We find that the time-averaged star
formation rate and the ISM structure are insensitive to metallicity. The column
densities relevant for molecular shielding appear correlated with the volume
densities in gravitationally unstable gas. As metallicity decreases, H$_2$
progressively deviates from steady state (``equilibrium'') and shows shallow
abundance profiles until they sharply truncate at the photodissociation fronts.
In contrast, the CO profile is sharp and controlled by photodissociation as CO
quickly reaches steady state. We construct effective one-dimensional cloud
models that successfully capture the time-averaged chemical distributions in
simulations. At low metallicities, the steady-state model significantly
overestimates the abundance of H$_2$ in the diffuse medium. The overestimated
H$_2$, however, has little impact on CO. Consequently, the mass fraction of
CO-dark H$_2$ gas is significantly lower than what a fully steady-state model
predicts. The mass ratios of H$_2$/C$^+$ and H$_2$/C both show a weaker
dependence on $Z^{\prime}$ than H$_2$/CO, which potentially indicates that
C$^+$ and C could be alternative tracers for H$_2$ at low $Z^{\prime}$ in terms
of mass budget. Our chemistry code for post-processing is publicly available.


Abstract:
  Several pathologies can alter the way people walk, i.e. their gait. Gait
analysis can therefore be used to detect impairments and help diagnose
illnesses and assess patient recovery. Using vision-based systems, diagnoses
could be done at home or in a clinic, with the needed computation being done
remotely. State-of-the-art vision-based gait analysis systems use deep
learning, requiring large datasets for training. However, to our best
knowledge, the biggest publicly available pathological gait dataset contains
only 10 subjects, simulating 4 gait pathologies. This paper presents a new
dataset called GAIT-IT, captured from 21 subjects simulating 4 gait
pathologies, with 2 severity levels, besides normal gait, being considerably
larger than publicly available gait pathology datasets, allowing to train a
deep learning model for gait pathology classification. Moreover, it was
recorded in a professional studio, making it possible to obtain nearly perfect
silhouettes, free of segmentation errors. Recognizing the importance of remote
healthcare, this paper proposes a prototype of a web application allowing to
upload a walking person's video, possibly acquired using a smartphone camera,
and execute a web service that classifies the person's gait as normal or across
different pathologies. The web application has a user friendly interface and
could be used by healthcare professionals or other end users. An automatic gait
analysis system is also developed and integrated with the web application for
pathology classification. Compared to state-of-the-art solutions, it achieves a
drastic reduction in the number of model parameters, which means significantly
lower memory requirements, as well as lower training and execution times.
Classification accuracy is on par with the state-of-the-art.


Abstract:
  What is the minimum time required to take the temperature? In this paper, we
solve this question for any process where temperature is inferred by measuring
a probe (the thermometer) weakly coupled to the sample of interest, so that the
probe's evolution is well described by a quantum Markovian master equation.
Considering the most general control strategy on the probe (adaptive
measurements, arbitrary control on the probe's state and Hamiltonian), we
provide bounds on the achievable measurement precision in a finite amount of
time, and show that in many scenarios these fundamental limits can be saturated
with a relatively simple experiment. We find that for a general class of
sample-probe interactions the scaling of the measurement uncertainty is
inversely proportional to the time of the process, a shot-noise like behaviour
that arises due to the dissipative nature of thermometry. As a side result, we
show that the Lamb shift induced by the probe-sample interaction can play a
relevant role in thermometry, allowing for finite measurement resolution in the
low-temperature regime (more precisely, the measurement uncertainty decays
polynomially with the temperature as $T\rightarrow 0$, in contrast to the usual
exponential decay with $T^{-1}$). We illustrate these general results for (i) a
qubit probe interacting with a bosonic sample, where the role of the Lamb shit
is highlighted, and (ii) a collective superradiant coupling between a $N$-qubit
probe and a sample, which enables a quadratic decay with $N^2$ of the
measurement uncertainty.


Abstract:
  Instance-based interpretation methods have been widely studied for supervised
learning methods as they help explain how black box neural networks predict.
However, instance-based interpretations remain ill-understood in the context of
unsupervised learning. In this paper, we investigate influence functions [20],
a popular instance-based interpretation method, for a class of deep generative
models called variational auto-encoders (VAE). We formally frame the
counter-factual question answered by influence functions in this setting, and
through theoretical analysis, examine what they reveal about the impact of
training samples on classical unsupervised learning methods. We then introduce
VAE-TracIn, a computationally efficient and theoretically sound solution based
on Pruthi et al., for VAEs. Finally, we evaluate VAE-TracIn on several real
world datasets with extensive quantitative and qualitative analysis.


Abstract:
  When an individual's DNA is sequenced, sensitive medical information becomes
available to the sequencing laboratory. A recently proposed way to hide an
individual's genetic information is to mix in DNA samples of other individuals.
We assume these samples are known to the individual but unknown to the
sequencing laboratory. Thus, these DNA samples act as "noise" to the sequencing
laboratory, but still allow the individual to recover their own DNA samples
afterward. Motivated by this idea, we study the problem of hiding a binary
random variable X (a genetic marker) with the additive noise provided by mixing
DNA samples, using mutual information as a privacy metric. This is equivalent
to the problem of finding a worst-case noise distribution for recovering X from
the noisy observation among a set of feasible discrete distributions. We
characterize upper and lower bounds to the solution of this problem, which are
empirically shown to be very close. The lower bound is obtained through a
convex relaxation of the original discrete optimization problem, and yields a
closed-form expression. The upper bound is computed via a greedy algorithm for
selecting the mixing proportions.


Abstract:
  We calculate the deflection angle, as well as the positions and
magnifications of the lensed images, in the case of covariant $f(T)$ gravity.
We first extract the spherically symmetric solutions for both the pure-tetrad
and the covariant formulation of the theory, since considering spherical
solutions the extension to the latter is crucial, in order for the results not
to suffer from frame-dependent artifacts. Applying the weak-field, perturbative
approximation we extract the deviations of the solutions comparing to General
Relativity. Furthermore, we calculate the deflection angle and then the
differences of the positions and magnifications in the lensing framework. This
effect of consistent $f(T)$ gravity on the lensing features can serve as an
observable signature in the realistic cases where $f(T)$ is expected to deviate
only slightly from General Relativity, since lensing scales in general are not
restricted as in the case of Solar System data, and therefore deviations from
General Relativity could be observed more easily.


Abstract:
  Let $D_N$ be the set of points around which a planar Brownian motion winds at
least $N$ times. We prove that the random measure on the plane with density $2
\pi N 1_{D_N}$ with respect to the Lebesgue measure converges almost surely
weakly, as $N$ tends to infinity, towards the occupation measure of the
Brownian motion.


Abstract:
  This paper presents the characterization measurements and related uncertainty
evaluation of a nonmagnetic material using the Vector Network Analyzer (VNA) at
microwave frequencies. The permittivity of the material under test is computed
from the scattering parameters (S-parameters). The aim of the work is to
highlight the different uncertainty contributions affecting the permittivity.


Abstract:
  Interactions govern the flow of information and the formation of correlations
in quantum systems, dictating the phases of matter found in nature and the
forms of entanglement generated in the laboratory. Typical interactions decay
with distance and thus produce a network of connectivity governed by geometry,
e.g., by the crystalline structure of a material or the trapping sites of atoms
in a quantum simulator. However, many envisioned applications in quantum
simulation and computation require richer coupling graphs including nonlocal
interactions, which notably feature in mappings of hard optimization problems
onto frustrated spin systems and in models of information scrambling in black
holes. Here, we report on the realization of programmable nonlocal interactions
in an array of atomic ensembles within an optical cavity, where photons carry
information between distant atomic spins. By programming the
distance-dependence of interactions, we access effective geometries where the
dimensionality, topology, and metric are entirely distinct from the physical
arrangement of atoms. As examples, we engineer an antiferromagnetic triangular
ladder, a Moebius strip with sign-changing interactions, and a treelike
geometry inspired by concepts of quantum gravity. The tree graph constitutes a
toy model of holographic duality, where the quantum system may be viewed as
lying on the boundary of a higher-dimensional geometry that emerges from
measured spin correlations. Our work opens broader prospects for simulating
frustrated magnets and topological phases, investigating quantum optimization
algorithms, and engineering new entangled resource states for sensing and
computation.


Abstract:
  Aggregating multiple sources of weak supervision (WS) can ease the
data-labeling bottleneck prevalent in many machine learning applications, by
replacing the tedious manual collection of ground truth labels. Current state
of the art approaches that do not use any labeled training data, however,
require two separate modeling steps: Learning a probabilistic latent variable
model based on the WS sources -- making assumptions that rarely hold in
practice -- followed by downstream model training. Importantly, the first step
of modeling does not consider the performance of the downstream model. To
address these caveats we propose an end-to-end approach for directly learning
the downstream model by maximizing its agreement with probabilistic labels
generated by reparameterizing previous probabilistic posteriors with a neural
network. Our results show improved performance over prior work in terms of end
model performance on downstream test sets, as well as in terms of improved
robustness to dependencies among weak supervision sources.


Abstract:
  The aim of this paper is to develop a Hamilton--Jacobi theory for contact
Hamiltonian systems. We find several forms for a suitable Hamilton-Jacobi
equation accordingly to the Hamiltonian and the evolution vector fields for a
given Hamiltonian function. We also analyze the corresponding formulation on
the symplectification of the contact Hamiltonian system, and establish the
relations between these two approaches. In the last section, some examples are
discussed.


Abstract:
  We establish the local Lipschitz continuity and the higher differentiability
of vector-valued local minimizers of a class of energy integrals of the
Calculus of Variations. The main novelty is that we deal with possibly
degenerate energy densities with respect to the x-variable.


Abstract:
  Measurement of charge configurations in few-electron quantum dots is a vital
technique for spin-based quantum information processing. While fast and
high-fidelity measurement is possible by using proximal quantum dot charge
sensors, their operating range is limited and prone to electrical disturbances.
Here we demonstrate realtime operation of a charge sensor in a feedback loop to
maintain its sensitivity suitable for fast charge sensing in a Si/SiGe double
quantum dot. Disturbances to the charge sensitivity, due to variation of gate
voltages for operating the quantum dot and $1/f$ charge fluctuation, are
compensated by a digital PID controller with the bandwidth of $\approx
100\,{\rm kHz}$. The rapid automated tuning of a charge sensor enables
unobstructed charge stability diagram measurement facilitating realtime quantum
dot tuning and submicrosecond single-shot spin readout without compromising the
performance of a charge sensor in time-consuming experiments for quantum
information processing.


Abstract:
  We address the problem of compiling defeasible theories to Datalog$^\neg$
programs. We prove the correctness of this compilation, for the defeasible
logic $DL(\partial_{||})$, but the techniques we use apply to many other
defeasible logics. Structural properties of $DL(\partial_{||})$ are identified
that support efficient implementation and/or approximation of the conclusions
of defeasible theories in the logic, compared with other defeasible logics. We
also use previously well-studied structural properties of logic programs to
adapt to incomplete Datalog$^\neg$ implementations.


Abstract:
  Magnetic exchange interactions (MEIs) define networks of coupled magnetic
moments and lead to a surprisingly rich variety of their magnetic properties.
Typically MEIs can be estimated by fitting experimental results. But how many
MEIs need to be included in the fitting process for a material is not clear a
priori, which limits the quality of results obtained by these conventional
methods. In this paper, based on linear spin-wave theory but without performing
matrix diagonalization, we show that for a general quadratic spin Hamiltonian,
there is a simple relation between the Fourier transform of MEIs and the sum of
square of magnon energies (SSME). We further show that according to the
real-space distance range within which MEIs are considered relevant, one can
obtain the corresponding relationships between SSME in momentum space. We also
develop a theoretical tool for tabulating the rule about SSME. By directly
utilizing these characteristics and the experimental magnon energies at only a
few high-symmetry k points in the Brillouin zone, one can obtain strong
constraints about the range of exchange path beyond which MEIs can be safely
neglected. Our methodology is also general applicable for other Hamiltonian
with quadratic Fermi or Boson operators.


Abstract:
  Galactic scale tests have proven to be powerful tools in constraining
fundamental physics in previously under-explored regions of parameter space.
The astrophysical regime which they probe is inherently complicated, and the
inference methods used to make these constraints should be robust to baryonic
effects. Previous analyses have assumed simple empirical models for
astrophysical noise without detailed calibration or justification. We outline a
framework for assessing the reliability of such methods by constructing and
testing more advanced baryonic models using cosmological hydrodynamical
simulations. As a case study, we use the Horizon-AGN simulation to investigate
warping of stellar disks and offsets between gas and stars within galaxies,
which are powerful probes of screened fifth forces. We show that the degree of
`U'-shaped warping of galaxies is well modelled by Gaussian random noise, but
that the magnitude of the gas-star offset is correlated with the virial radius
of the host halo. By incorporating this correlation we confirm recent results
ruling out astrophysically relevant Hu-Sawicki $f(R)$ gravity, and identify a
$\sim 30\%$ systematic uncertainty due to baryonic physics. Such an analysis
must be performed case-by-case for future galactic tests of fundamental
physics.


Abstract:
  The aim of the article is to study the Betti numbers of the tangent cone of
Gorenstein monomial curves in affine 4-space. If $C_S$ is a non-complete
intersection Gorenstein monomial curve whose tangent cone is Cohen-Macaulay, we
show that the possible Betti sequences are (1,5,5,1), (1,5,6,2) and (1,6,8,3).


Abstract:
  In this article we mainly aim to know what kind of asymptotic behavior of
typical orbits can display. For example, we show in any transitive system, the
emprical measures of a typical orbit can cover all emprical measures of dense
orbits and can intersect some physical-like measures. In particular, if the
union set of emprical measures of all dense orbits is not singleton, then the
typical orbit will display historic behavior simultaneously for typical
continuous functions and the limit set of ergodic average along every
continuous function equals to a closed interval composed by the union of limit
sets of ergodic average on all dense orbits. Moreover, if the union set of
emprical measures of all dense orbits contains all ergodic measures, the above
interval equals to the rotation set. These results are not only suitable for
systems with specification-like properties or minimal systems, but also
suitable for many other systems including all general (not assumed uniformly
hyperbolic) nontrivial homoclinic classes and Bowen eyes. Moreover, we
introduce a new property called m-g-product property weaker than classical
specification property and minimal property and nontrivial examples are
constructed.


Abstract:
  We consider the genus-one curves which arise in the cuts of the sunrise and
in the elliptic double-box Feynman integrals. We compute and compare invariants
of these curves in a number of ways, including Feynman parametrization,
lightcone and Baikov (in full and loop-by-loop variants). We find that the same
geometry for the genus-one curves arises in all cases, which lends support to
the idea that there exists an invariant notion of genus-one geometry,
independent on the way it is computed. We further indicate how to interpret
some previous results which found that these curves are related by isogenies
instead.


Abstract:
  This and companion papers by Harrington et al. and Blecic et al. present the
Bayesian Atmospheric Radiative Transfer (BART) code, an open-source,
open-development package to characterize extrasolar-planet atmospheres. BART
combines a thermochemical equilibrium abundances (TEA), a radiative-transfer
(Transit), and a Bayesian statistical (MC3) module to constrain atmospheric
temperatures and molecular abundances for given spectroscopic observations.
Here, we describe the Transit radiative-transfer package, an efficient
line-by-line radiative-transfer C code for one-dimensional atmospheres,
developed by P. Rojo and further modified by the UCF exoplanet group. This code
produces transmission and hemisphere-integrated emission spectra. Transit
handles line-by-line opacities from HITRAN, Partridge \& Schwenke ({\water}),
Schwenke (TiO), and Plez (VO); and collision-induced absorption from Borysow,
HITRAN, and ExoMol. Transit emission-spectra models agree with models from C.
Morley (priv. comm.) within a few percent. We applied BART to the {\Spitzer}
and {\Hubble} transit observations of the Neptune-sized planet HAT-P-11b. Our
results generally agree with those from previous studies, constraining the
{\water} abundance and finding an atmosphere enhanced in heavy elements.
Different conclusions start to emerge when we make different assumptions from
other studies. The BART source code and documentation are available at
https://github.com/exosports/BART.


Abstract:
  We construct a Darboux transformation for a class of two-dimensional Dirac
systems at zero energy. Our starting equation features a position-dependent
mass, a matrix potential, and an additional degree of freedom that can be
interpreted either as a magnetic field perpendicular to the plane or a
generalized Dirac oscillator interaction. We obtain a number of
Darbouxtransformed Dirac equations for which the zero energy solutions are
exactly known.


Abstract:
  One of the most influential results in neural network theory is the universal
approximation theorem [1, 2, 3] which states that continuous functions can be
approximated to within arbitrary accuracy by single-hidden-layer feedforward
neural networks. The purpose of this paper is to establish a result in this
spirit for the approximation of general discrete-time linear dynamical systems
- including time-varying systems - by recurrent neural networks (RNNs). For the
subclass of linear time-invariant (LTI) systems, we devise a quantitative
version of this statement. Specifically, measuring the complexity of the
considered class of LTI systems through metric entropy according to [4], we
show that RNNs can optimally learn - or identify in system-theory parlance -
stable LTI systems. For LTI systems whose input-output relation is
characterized through a difference equation, this means that RNNs can learn the
difference equation from input-output traces in a metric-entropy optimal
manner.


Abstract:
  The propagation of acoustic or elastic waves in artificial crystals,
including the case of phononic and sonic crystals, is inherently anisotropic.
As is known from the theory of periodic composites, anisotropy is directly
dictated by the space group of the unit cell of the crystal and the rank of the
elastic tensor. Here, we examine effective velocities in the long wavelength
limit of periodic acoustic and elastic composites as a function of the
direction of propagation. We derive explicit and efficient formulas for
estimating the effective velocity surfaces, based on second-order perturbation
theory, generalizing the Christofell equation for elastic waves in solids. We
identify strongly anisotropic sonic crystals for scalar acoustic waves and
strongly anisotropic phononic crystals for vector elastic waves. Furthermore,
we observe that under specific conditions, quasi-longitudinal waves can be made
much slower than shear waves propagating in the same direction.


Abstract:
  Confined nanoscale spaces, electric fields and tunneling currents make the
molecular electronic junction an experimental device for the discovery of new,
out-of-equilibrium chemical reactions. Reaction-rate theory for
current-activated chemical reactions is developed by combining a Keldysh
nonequilibrium Green's functions treatment of electrons, Fokker-Planck
description of the reaction coordinate, and Kramers' first-passage time
calculations. The NEGF provide an adiabatic potential as well as a diffusion
coefficient and temperature with local dependence on the reaction coordinate.
Van Kampen's Fokker-Planck equation, which describes a Brownian particle moving
in an external potential in an inhomogeneous medium with a position-dependent
friction and diffusion coefficient, is used to obtain an analytic expression
for the first-passage time. The theory is applied to several transport
scenarios: a molecular junction with a single, reaction coordinate dependent
molecular orbital, and a model diatomic molecular junction. We demonstrate the
natural emergence of Landauer's blowtorch effect as a result of the interplay
between the configuration dependent viscosity and diffusion coefficients. The
resultant localized heating in conjunction with the bond-deformation due to
current-induced forces are shown to be the determining factors when considering
chemical reaction rates; each of which result from highly tunable parameters
within the system.


Abstract:
  In recent years, there has been an increasing awareness of both the public
and scientific community that algorithmic systems can reproduce, amplify, or
even introduce unfairness in our societies. These lecture notes provide an
introduction to some of the core concepts in algorithmic fairness research. We
list different types of fairness-related harms, explain two main notions of
algorithmic fairness, and map the biases that underlie these harms upon the
machine learning development process.


Abstract:
  In this paper, using $I$-method, we establish $H^s_x\times H^s_x$ scattering
theories for the following Cauchy problem \begin{equation*} \left\{
\begin{array}{lll} iu_t+\Delta u=\lambda |v|^2u,\quad iv_t+\Delta
v=\mu|u|^2v,\quad x\in \mathbb{R}^3,\ t>0,\\ u(x,0)=u_0(x),\quad
v(x,0)=v_0(x),\quad x\in \mathbb{R}^3. \end{array}\right. \end{equation*} Here
$\lambda>0$, $\mu>0$, $(u_0,v_0)\in H^s_x(\mathbb{R}^3)\times
H^s_x(\mathbb{R}^3)$ and $\frac{1}{2}<s<1$.


Abstract:
  Hand segmentation is a crucial task in first-person vision. Since
first-person images exhibit strong bias in appearance among different
environments, adapting a pre-trained segmentation model to a new domain is
required in hand segmentation. Here, we focus on appearance gaps for hand
regions and backgrounds separately. We propose (i) foreground-aware image
stylization and (ii) consensus pseudo-labeling for domain adaptation of hand
segmentation. We stylize source images independently for the foreground and
background using target images as style. To resolve the domain shift that the
stylization has not addressed, we apply careful pseudo-labeling by taking a
consensus between the models trained on the source and stylized source images.
We validated our method on domain adaptation of hand segmentation from real and
simulation images. Our method achieved state-of-the-art performance in both
settings. We also demonstrated promising results in challenging multi-target
domain adaptation and domain generalization settings. Code is available at
https://github.com/ut-vision/FgSty-CPL.


Abstract:
  Embedded solitons are exceptional modes in nonlinear-wave systems with the
propagation constant falling in the system's propagation band. An especially
challenging topic is seeking for such modes in nonlinear dynamical lattices
(discrete systems). We address this problem for a system of coupled discrete
equations modeling the light propagation in an array of tunnel-coupled
waveguides with a combination of intrinsic quadratic
(second-harmonic-generating) and cubic nonlinearities. Solutions for discrete
embedded solitons (DESs) are constructed by means of two analytical
approximations, adjusted, severally, for broad and narrow DESs, and in a
systematic form with the help of numerical calculations. DESs of several types,
including ones with identical and opposite signs of their fundamental-frequency
and second-harmonic components, are produced. In the most relevant case of
narrow DESs, the analytical approximation produces very accurate results, in
comparison with the numerical findings. In this case, the DES branch extends
from the propagation band into a semi-infinite gap as a family of regular
discrete solitons. The study of stability of DESs demonstrates that, in
addition to ones featuring the well-known property of semi-stability, there are
linearly stable DESs which are genuinely robust modes.


Abstract:
  We revisit several aspects of the interaction of self-gravitating, slowly
varying sources with their own emitted radiation within the context of
post-Newtonian approximation to General Relativity. We discuss and clarify the
choice of boundary conditions of Green's functions used to determine
conservative potentials, and the interplay between the so-called near and far
zones, as well as the relation between far zone ultra-violet divergences and
emitted power. Both near and far zone contributions are required for the
computation of the conservative dynamics. Within a field-theory approach we
rederive far-zone self-energy processes, known as tail and memory effects,
generalising the calculation of their divergent part to arbitrary order in the
post-Newtonian expansion.


Abstract:
  In this work we explore the information processing inside neural networks
using logistic regression probes \cite{probes} and the saturation metric
\cite{featurespace_saturation}. We show that problem difficulty and neural
network capacity affect the predictive performance in an antagonistic manner,
opening the possibility of detecting over- and under-parameterization of neural
networks for a given task. We further show that the observed effects are
independent from previously reported pathological patterns like the ``tail
pattern'' described in \cite{featurespace_saturation}. Finally we are able to
show that saturation patterns converge early during training, allowing for a
quicker cycle time during analysis


Abstract:
  Millimeter wave (mmWave) is a practical solution to provide high data rate
for vehicle-to-everything (V2X) communications. This enables the future
autonomous vehicles to exchange big data with the base stations (BSs) such as
the velocity, and the location to enhance the safety for the advanced driving
assistance system (ADAS). To achieve this goal, we propose to develop a
situational rate map to characterize the distribution of the rates achieved
between the BSs and a uniform grid of vehicles. In this context, we consider a
mmWave 5G cellular system with two physical structures which are the
analog-only beamforming and hybrid precoding with limited feedback to
investigate the rate distribution for single and multiuser scenarios. We will
use the Ray-Tracing tool to construct the simulation environment and generate
the channels between the BSs and the grid of vehicles. Finally, we will study
the effects of the carrier frequency, the bandwidth, the BSs deployment, the
blockage, the codebook, the physical architectures and the number of served
users on the rate distribution maps. Moreover, we will present the rate
statistics to evaluate the coverage of served users for certain services
requirements and for various road shapes such as corners, intersections and
straightways.


Abstract:
  Voice-driven services (VDS) are being used in a variety of applications
ranging from smart home control to payments using digital assistants. The input
to such services is often captured via an open voice channel, e.g., using a
microphone, in an unsupervised setting. One of the key operational security
requirements in such setting is the freshness of the input speech. We present
AEOLUS, a security overlay that proactively embeds a dynamic acoustic nonce at
the time of user interaction, and detects the presence of the embedded nonce in
the recorded speech to ensure freshness. We demonstrate that acoustic nonce can
(i) be reliably embedded and retrieved, and (ii) be non-disruptive (and even
imperceptible) to a VDS user. Optimal parameters (acoustic nonce's operating
frequency, amplitude, and bitrate) are determined for (i) and (ii) from a
practical perspective. Experimental results show that AEOLUS yields 0.5% FRR at
0% FAR for speech re-use prevention upto a distance of 4 meters in three
real-world environments with different background noise levels. We also conduct
a user study with 120 participants, which shows that the acoustic nonce does
not degrade overall user experience for 94.16% of speech samples, on average,
in these environments. AEOLUS can therefore be used in practice to prevent
speech re-use and ensure the freshness of speech input.


Abstract:
  This paper proposes a neural network that performs audio transformations to
user-specified sources (e.g., vocals) of a given audio track according to a
given description while preserving other sources not mentioned in the
description. Audio Manipulation on a Specific Source (AMSS) is challenging
because a sound object (i.e., a waveform sample or frequency bin) is
`transparent'; it usually carries information from multiple sources, in
contrast to a pixel in an image. To address this challenging problem, we
propose AMSS-Net, which extracts latent sources and selectively manipulates
them while preserving irrelevant sources. We also propose an evaluation
benchmark for several AMSS tasks, and we show that AMSS-Net outperforms
baselines on several AMSS tasks via objective metrics and empirical
verification.


Abstract:
  We study gravitational wave (GW) emission in the strong-field regime by a
hierarchical triple system composed of a binary system placed in the vicinity
of a supermassive black hole (SMBH). The LIGO-Virgo collaboration recently
reported evidence for coalescences with this dynamical origin. These systems
are common in galactic centers and thus are a target for the space-based LISA
mission as well as other advanced detectors. Doppler shifts, aberration,
lensing and strong amplitude modulations are features present in the GW signal
from these systems, built into our framework and with no need for
phenomenological patches. We find that the binary can resonantly excite the
quasinormal modes of the SMBH, as in the resonant excitation of two tuning
forks with matching frequencies. The flux of energy crossing the SMBH horizon
can be significant, when compared with that from standard extreme-mass-ratio
inspirals. Therefore, these triple systems are an outstanding probe of
strong-field physics and of the BH nature of compact objects.


Abstract:
  Consider the elastic scattering of an incident wave by a rigid obstacle in
three dimensions, which is formulated as an exterior problem for the Navier
equation. By constructing a Dirichlet-to-Neumann (DtN) operator and introducing
a transparent boundary condition, the scattering problem is reduced
equivalently to a boundary value problem in a bounded domain. The discrete
problem with the truncated DtN operator is solved by using the a posteriori
error estimate based adaptive finite element method. The estimate takes account
of both the finite element approximation error and the truncation error of the
DtN operator, where the latter is shown to converge exponentially with respect
to the truncation parameter. Moreover, the generalized Woodbury matrix identity
is utilized to solve the resulting linear system efficiently. Numerical
experiments are presented to demonstrate the superior performance of the
proposed method.


Abstract:
  We compute the leading-logarithmic correction to the neutrino mass matrix in
the Standard Model effective field theory (SMEFT) to dimension seven. In the
limit of negligible lepton and down-type quark Yukawa couplings, it receives
contributions from the Weinberg dimension-five operator as well as from 11
dimension-six and five dimension-seven independent interactions. Two of the
main implications we derive from this result are the following. First, we find
dimension-seven operators which, despite violating lepton number, do not
renormalise neutrino masses at one loop. And second, we demonstrate that the
presence of dimension-six operators around the TeV scale can modify the
Standard Model prediction by up to $\mathcal{O}(50\,\%)$. Our result comprises
also one step forward towards the renormalisation of the SMEFT to order
$v^3/\Lambda^3$.


Abstract:
  Accurate prediction of epileptic seizures allows patients to take preventive
measures in advance to avoid possible injuries. In this work, a novel
convolutional neural network (CNN) is proposed to analyze time, frequency, and
channel information of electroencephalography (EEG) signals. The model uses
three-dimensional (3D) kernels to facilitate the feature extraction over the
three dimensions. The application of multiscale dilated convolution enables the
3D kernel to have more flexible receptive fields. The proposed CNN model is
evaluated with the CHB-MIT EEG database, the experimental results indicate that
our model outperforms the existing state-of-the-art, achieves 80.5% accuracy,
85.8% sensitivity and 75.1% specificity.


Abstract:
  The phenomenon of adversarial examples illustrates one of the most basic
vulnerabilities of deep neural networks. Among the variety of techniques
introduced to surmount this inherent weakness, adversarial training has emerged
as the most effective strategy to achieve robustness. Typically, this is
achieved by balancing robust and natural objectives. In this work, we aim to
further optimize the trade-off between robust and standard accuracy by
enforcing a domain-invariant feature representation. We present a new
adversarial training method, Domain Invariant Adversarial Learning (DIAL),
which learns a feature representation that is both robust and domain invariant.
DIAL uses a variant of Domain Adversarial Neural Network (DANN) on the natural
domain and its corresponding adversarial domain. In the case where the source
domain consists of natural examples and the target domain is the adversarially
perturbed examples, our method learns a feature representation constrained not
to discriminate between the natural and adversarial examples, and can therefore
achieve a more robust representation. Our experiments indicate that our method
improves both robustness and standard accuracy, when compared to other
state-of-the-art adversarial training methods.


Abstract:
  In this paper we develop a stochastic analysis for marked binomial processes,
that can be viewed as the discrete analogues of marked Poisson processes. The
starting point is the statement of a chaotic expansion for square-integrable
(marked binomial) functionals, prior to the elaboration of a Markov-Malliavin
structure within this framework. We take advantage of the new formalism to deal
with two main applications. First, we revisit the Chen-Stein method for the
(compound) Poisson approximation which we perform in the paradigm of the built
Markov-Malliavin structure, before studying in the second one the problem of
portfolio optimisation in the trinomial model.


Abstract:
  Ehrenfest urns with interaction that are connected in a ring is considered as
a paradigm model for non-equilibrium thermodynamics and is shown to exhibit two
distinct non-equilibrium steady states (NESS) of uniform and non-uniform
particle distributions. As the inter-particle attraction varies, a first order
non-equilibrium phase transition occurs between these two NESSs characterized
by a coexistence regime. The phase boundaries, the NESS particle distributions
near saddle points and the associated particle fluxes, average urn population
fractions, and the relaxational dynamics to the NESSs are obtained analytically
and verified numerically. A generalized non-equilibrium thermodynamics law is
also obtained, which explicitly identifies the heat, work, energy and entropy
of the system.


Abstract:
  In this article, we provide a review of the current state of the research of
the black hole shadow, focusing on analytical (as opposed to numerical and
observational) studies. We start with particular attention to the definition of
the shadow and its relation to the often used concepts of escape cone, critical
impact parameter and particle cross-section. For methodological purposes, we
present the derivation of the angular size of the shadow for an arbitrary
spherically symmetric and static space-time, which allows one to calculate the
shadow for an observer at arbitrary distance from the center. Then we discuss
the calculation of the shadow of a Kerr black hole, for an observer anywhere
outside of the black hole. For observers at large distances we present and
compare two methods used in the literature. Special attention is given to
calculating the shadow in space-times which are not asymptotically flat.
Shadows of wormholes and other black-hole impostors are reviewed. Then we
discuss the calculation of the black hole shadow in an expanding universe as
seen by a comoving observer. The influence of a plasma on the shadow of a black
hole is also considered.


Abstract:
  For indications where only unstable reference treatments are available and
use of placebo is ethically justified, three-arm `gold standard' designs with
an experimental, reference and placebo arm are recommended for non-inferiority
trials. In such designs, the demonstration of efficacy of the reference or
experimental treatment is a requirement. They have the disadvantage that only
little can be concluded from the trial if the reference fails to be
efficacious. To overcome this, we investigate novel single-stage, adaptive test
strategies where non-inferiority is tested only if the reference shows
sufficient efficacy and otherwise $\delta$-superiority of the experimental
treatment over placebo is tested. With a properly chosen superiority margin,
$\delta$-superiority indirectly shows non-inferiority. We optimize the sample
size for several decision rules and find that the natural, data driven test
strategy, which tests non-inferiority if the reference's efficacy test is
significant, leads to the smallest overall and placebo sample sizes. We proof
that under specific constraints on the sample sizes, this procedure controls
the family-wise error rate. All optimal sample sizes are found to meet this
constraint. We finally show how to account for a relevant placebo drop-out rate
in an efficient way and apply the new test strategy to a real life data set.


Abstract:
  Camera trapping is increasingly used to monitor wildlife, but this technology
typically requires extensive data annotation. Recently, deep learning has
significantly advanced automatic wildlife recognition. However, current methods
are hampered by a dependence on large static data sets when wildlife data is
intrinsically dynamic and involves long-tailed distributions. These two
drawbacks can be overcome through a hybrid combination of machine learning and
humans in the loop. Our proposed iterative human and automated identification
approach is capable of learning from wildlife imagery data with a long-tailed
distribution. Additionally, it includes self-updating learning that facilitates
capturing the community dynamics of rapidly changing natural systems. Extensive
experiments show that our approach can achieve a ~90% accuracy employing only
~20% of the human annotations of existing approaches. Our synergistic
collaboration of humans and machines transforms deep learning from a relatively
inefficient post-annotation tool to a collaborative on-going annotation tool
that vastly relieves the burden of human annotation and enables efficient and
constant model updates.


Abstract:
  Model compression techniques allow to significantly reduce the computational
cost associated with data processing by deep neural networks with only a minor
decrease in average accuracy. Simultaneously, reducing the model size may have
a large effect on noisy cases or objects belonging to less frequent classes. It
is a crucial problem from the perspective of the models' safety, especially for
object detection in the autonomous driving setting, which is considered in this
work. It was shown in the paper that the sensitivity of compressed models to
different distortion types is nuanced, and some of the corruptions are heavily
impacted by the compression methods (i.e., additive noise), while others (blur
effect) are only slightly affected. A common way to improve the robustness of
models is to use data augmentation, which was confirmed to positively affect
models' robustness, also for highly compressed models. It was further shown
that while data imbalance methods brought only a slight increase in accuracy
for the baseline model (without compression), the impact was more striking at
higher compression rates for the structured pruning. Finally, methods for
handling data imbalance brought a significant improvement of the pruned models'
worst-detected class accuracy.


Abstract:
  In this paper, we consider mixed-integer nonsmooth constrained optimization
problems whose objective/constraint functions are available only as the output
of a black-box zeroth-order oracle (i.e., an oracle that does not provide
derivative information) and we propose a new derivative-free linesearch-based
algorithmic framework to suitably handle those problems. We first describe a
scheme for bound constrained problems that combines a dense sequence of
directions (to handle the nonsmoothness of the objective function) with
primitive directions (to handle discrete variables). Then, we embed an exact
penalty approach in the scheme to suitably manage nonlinear (possibly
nonsmooth) constraints. We analyze the global convergence properties of the
proposed algorithms toward stationary points and we report the results of an
extensive numerical experience on a set of mixed-integer test problems.


Abstract:
  As the use of spectral/$hp$ element methods, and high-order finite element
methods in general, continues to spread, community efforts to create efficient,
optimized algorithms associated with fundamental high-order operations have
grown. Core tasks such as solution expansion evaluation at quadrature points,
stiffness and mass matrix generation, and matrix assembly have received
tremendousattention. With the expansion of the types of problems to which
high-order methods are applied, and correspondingly the growth in types of
numerical tasks accomplished through high-order methods, the number and types
of these core operations broaden. This work focuses on solution expansion
evaluation at arbitrary points within an element. This operation is core to
many postprocessing applications such as evaluation of streamlines and
pathlines, as well as to field projection techniques such as mortaring. We
expand barycentric interpolation techniques developed on an interval to 2D
(triangles and quadrilaterals) and 3D (tetrahedra, prisms, pyramids, and
hexahedra) spectral/$hp$ element methods. We provide efficient algorithms for
their implementations, and demonstrate their effectiveness using the
spectral/$hp$ element library Nektar++.


Abstract:
  Infrared absorption spectroscopy study of endohedral water molecule in a
solid mixture of H$_2$O@C$_{60}$ and C$_{60}$ was carried out at liquid helium
temperature. From the evolution of the spectra during the ortho-para conversion
process, the spectral lines were identified as para- and ortho-water
transitions. Eight vibrational transitions with rotational side peaks were
observed in the mid-infrared: $\omega_1$, $\omega_2$, $\omega_3$, $2\omega_1$,
$2\omega_2$, $\omega_1 +\omega_3$, $\omega_2 +\omega_3$, and
$2\omega_2+\omega_3$. The vibrational frequencies $\omega_2$ and 2$\omega_2$
are lower by 1.6\% and the rest by 2.4\%, as compared to free \water/. A model
consisting of a rovibrational Hamiltonian with the dipole and quadrupole
moments of water interacting with the crystal field was used to fit the
infrared absorption spectra. The electric quadrupole interaction with the
crystal field lifts the degeneracy of the rotational levels. The finite
amplitudes of the pure $v_1$ and $v_2$ vibrational transitions are consistent
with the interaction of the water molecule dipole moment with a lattice-induced
electric field. The permanent dipole moment of encapsulated \water/ is found to
be $0.5\pm 0.1$ D as determined from the far-infrared rotational line
intensities. The translational mode of the quantized center of mass motion of
\water/ in the molecular cage of C$_{60}$ was observed at 110cm$^{-1}$
(13.6meV).


Abstract:
  We systematically develop a theory of stratification in the context of tensor
triangular geometry and apply it to classify the localizing tensor-ideals of
certain categories of spectral $G$-Mackey functors for all finite groups $G$.
Our theory of stratification is based on the approach of Stevenson which uses
the Balmer-Favi notion of big support for tensor-triangulated categories whose
Balmer spectrum is weakly noetherian. We clarify the role of the
local-to-global principle and establish that the Balmer-Favi notion of support
provides the universal approach to weakly noetherian stratification. This
provides a uniform new perspective on existing classifications in the
literature and clarifies the relation with the theory of Benson-Iyengar-Krause.
Our systematic development of this approach to stratification, involving a
reduction to local categories and the ability to pass through finite \'{e}tale
extensions, may be of independent interest. Moreover, we strengthen the
relationship between stratification and the telescope conjecture. The starting
point for our equivariant applications is the recent computation by
Patchkoria-Sanders-Wimmer of the Balmer spectrum of the category of derived
Mackey functors, which was found to capture precisely the height $0$ and height
$\infty$ chromatic layers of the spectrum of the equivariant stable homotopy
category. We similarly study the Balmer spectrum of the category of
$E(n)$-local spectral Mackey functors noting that it bijects onto the height
$\le n$ chromatic layers of the spectrum of the equivariant stable homotopy
category; conjecturally the topologies coincide. Despite our incomplete
knowledge of the topology of the Balmer spectrum, we are able to completely
classify the localizing tensor-ideals of these categories of spectral Mackey
functors.


Abstract:
  In this paper, we use large scale structure observations to test the cosmic
distance duality relation (CDDR), $D_{\rm L}(1+z)^{-2}/D_{\rm A}=\eta=1 $, with
$D_{\rm L}$ and $D_{\rm A}$, being the luminosity and angular diameter
distances, respectively. In order to perform the test, the following data set
are considered: strong lensing systems and galaxy cluster measurements (gas
mass fractions). No specific cosmological model is adopted, only a flat
universe is assumed. By considering two $\eta(z)$ parametrizations, we obtained
the validity of the CDDR within $1.5\sigma$ which is in full agreement with
other recent tests involving cosmological data. It is worth to comment that our
results are independent of the baryon budget of galaxy clusters.


Abstract:
  Target networks are at the core of recent success in Reinforcement Learning.
They stabilize the training by using old parameters to estimate the $Q$-values,
but this also limits the propagation of newly-encountered rewards which could
ultimately slow down the training. In this work, we propose an alternative
training method based on functional regularization which does not have this
deficiency. Unlike target networks, our method uses up-to-date parameters to
estimate the target $Q$-values, thereby speeding up training while maintaining
stability. Surprisingly, in some cases, we can show that target networks are a
special, restricted type of functional regularizers. Using this approach, we
show empirical improvements in sample efficiency and performance across a range
of Atari and simulated robotics environments.


Abstract:
  Intermediate-mass black holes (IMBHs) span the approximate mass range
$100$--$10^5\,M_\odot$, between black holes (BHs) formed by stellar collapse
and the supermassive BHs at the centers of galaxies. Mergers of IMBH binaries
are the most energetic gravitational-wave sources accessible by the terrestrial
detector network. Searches of the first two observing runs of Advanced LIGO and
Advanced Virgo did not yield any significant IMBH binary signals. In the third
observing run (O3), the increased network sensitivity enabled the detection of
GW190521, a signal consistent with a binary merger of mass $\sim
150\,M_\odot\,$ providing direct evidence of IMBH formation. Here we report on
a dedicated search of O3 data for further IMBH binary mergers, combining both
modelled (matched filter) and model independent search methods. We find some
marginal candidates, but none are sufficiently significant to indicate
detection of further IMBH mergers. We quantify the sensitivity of the
individual search methods and of the combined search using a suite of IMBH
binary signals obtained via numerical relativity, including the effects of
spins misaligned with the binary orbital axis, and present the resulting upper
limits on astrophysical merger rates. Our most stringent limit is for equal
mass and aligned spin BH binary of total mass $200\,M_\odot$ and effective
aligned spin 0.8 at $0.056\,Gpc^{-3} yr^{-1}$ (90 $\%$ confidence), a factor of
3.5 more constraining than previous LIGO-Virgo limits. We also update the
estimated rate of mergers similar to GW190521 to $0.08\, Gpc^{-3}yr^{-1}$.


Abstract:
  User response prediction, which aims to predict the probability that a user
will provide a predefined positive response in a given context such as clicking
on an ad or purchasing an item, is crucial to many industrial applications such
as online advertising, recommender systems, and search ranking. However, due to
the high dimensionality and super sparsity of the data collected in these
tasks, handcrafting cross features is inevitably time expensive. Prior studies
in predicting user response leveraged the feature interactions by enhancing
feature vectors with products of features to model second-order or high-order
cross features, either explicitly or implicitly. Nevertheless, these existing
methods can be hindered by not learning sufficient cross features due to model
architecture limitations or modeling all high-order feature interactions with
equal weights. This work aims to fill this gap by proposing a novel
architecture Deep Cross Attentional Product Network (DCAP), which keeps cross
network's benefits in modeling high-order feature interactions explicitly at
the vector-wise level. Beyond that, it can differentiate the importance of
different cross features in each network layer inspired by the multi-head
attention mechanism and Product Neural Network (PNN), allowing practitioners to
perform a more in-depth analysis of user behaviors. Additionally, our proposed
model can be easily implemented and train in parallel. We conduct comprehensive
experiments on three real-world datasets. The results have robustly
demonstrated that our proposed model DCAP achieves superior prediction
performance compared with the state-of-the-art models. Public codes are
available at https://github.com/zachstarkk/DCAP.


Abstract:
  Continuous integration of renewable energy sources into power networks is
causing a paradigm shift in energy generation and distribution with regards to
trading and control; the intermittent nature of renewable sources affects
pricing of energy sold or purchased; the networks are subject to operational
constraints, voltage limits at each node, rated capacities for the power
electronic devices, current bounds for distribution lines. These economic and
technical constraints coupled with intermittent renewable injection may pose a
threat to system stability and performance. We propose a novel holistic
approach to energy trading composed of a distributed predictive control
framework to handle physical interactions, i,e., voltage constraints and power
dispatch, together with a negotiation framework to determine pricing policies
for energy transactions. We study the effect of forecasting generation and
consumption on the overall network's performance and market behaviours. We
provide a rigorous convergence analysis for both the negotiation framework and
the distributed control. Lastly, we assess the impact of forecasting in the
proposed system with the aid of testing scenarios.


Abstract:
  Word embeddings trained on large corpora have shown to encode high levels of
unfair discriminatory gender, racial, religious and ethnic biases.
  In contrast, human-written dictionaries describe the meanings of words in a
concise, objective and an unbiased manner.
  We propose a method for debiasing pre-trained word embeddings using
dictionaries, without requiring access to the original training resources or
any knowledge regarding the word embedding algorithms used.
  Unlike prior work, our proposed method does not require the types of biases
to be pre-defined in the form of word lists, and learns the constraints that
must be satisfied by unbiased word embeddings automatically from dictionary
definitions of the words.
  Specifically, we learn an encoder to generate a debiased version of an input
word embedding such that it
  (a) retains the semantics of the pre-trained word embeddings,
  (b) agrees with the unbiased definition of the word according to the
dictionary, and
  (c) remains orthogonal to the vector space spanned by any biased basis
vectors in the pre-trained word embedding space.
  Experimental results on standard benchmark datasets show that the proposed
method can accurately remove unfair biases encoded in pre-trained word
embeddings, while preserving useful semantics.


Abstract:
  Design erosion is a persistent problem within the software engineering
discipline. Software designs tend to deteriorate over time and there is a need
for tools and techniques that support software architects when dealing with
legacy systems. This paper presents an evaluation of a Search Based Software
Engineering (SBSE) approach intended to recover high-level architecture designs
of software systems by structuring low-level artefacts into high-level
architecture artefact configurations. In particular , this paper describes the
performance evaluation of a number of metaheuristic search algorithms applied
to architecture reconstruction problems with high dimensionality in terms of
objectives. These problems have been selected as representative of the typical
challenges faced by software architects dealing with legacy systems and the
results inform the ongoing developed of a software tool that supports the
analysis of trade-offs between different reconstructed architectures.


Abstract:
  This paper develops a generalization of the Hansen-Jagannathan bound that
incorporates information beyond the mean and variance of returns. The resulting
bound compares the physical and risk neutral distribution for every
$\tau$-quantile, where $\tau \in (0,1)$. An empirical application with S\&P500
return data shows that the new bound is stronger than the Hansen-Jagannathan
bound for small values of $\tau$. The long run risk model cannot reconcile this
feature of the data, due to the absence of disaster risk. I extend this finding
using conditioning information and document that disaster risk is time-varying,
using a semiparametric approach. I also propose a new measure of quantile
forecastability and show that many stylized facts about the equity premium
carry over to the quantile setting.


Abstract:
  The use of an infinity of fluctuating paths of least time that are compatible
with the quantum mechanics indeterminacy provides a new interpretation in
geometrical optic of the interference pattern of Young's double slit
experiment, which suggests that the wave behavior of matter and radiation is
dictated by the space-time geodesics. Moreover, the association of a wave
function to each path of least time as a probability amplitude together with an
uncertainty for momentum and position allows to derive the Schrodinger's
equation starting from the geodesic's characteristics. A new insight is
obtained regarding the Van Der Waals torque as well as Casimir
attraction/repulsion mechanism.


Abstract:
  Bayesian nonparametric estimates of Australian mental health distributions
are obtained to assess how the mental health status of the population has
changed over time and to compare the mental health status of female/male and
indigenous/non-indigenous population subgroups. First- and second-order
stochastic dominance are used to compare distributions, with results presented
in terms of the posterior probability of dominance and the posterior
probability of no dominance. Our results suggest mental health has deteriorated
in recent years, that males mental health status is better than that of
females, and non-indigenous health status is better than that of the indigenous
population.


Abstract:
  We study the possibility to use line-intensity mapping (LIM) to seek photons
from the radiative decay of neutrinos in the cosmic neutrino background. The
Standard Model prediction for the rate for these decays is extremely small, but
it can be enhanced if new physics increases the neutrino electromagnetic
moments. The decay photons will appear as an interloper of astrophysical
spectral lines. We propose that the neutrino-decay line can be identified with
anisotropies in LIM clustering and also with the voxel intensity distribution.
Ongoing and future LIM experiments will have -- depending on the neutrino
hierarchy, transition and experiment considered -- a sensitivity to an
effective electromagnetic transition moment $\sim 10^{-12}\, -\,10^{-8}\,
(m_ic^2/{0.1 \rm eV})^{3/2}\mu_{\rm B}$, where $m_i$ is the mass of the
decaying neutrino and $\mu_{\rm B}$ is the Bohr magneton. This will be
significantly more sensitive than cosmic microwave background spectral
distortions, and it will be competitive with stellar cooling studies. As a
byproduct, we also report an analytic form of the one-point probability
distribution function for neutrino-density fluctuations, obtained from the
Quijote simulations using symbolic regression.


Abstract:
  Traditionally, the main focus of image super-resolution techniques is on
recovering the most likely high-quality images from low-quality images, using a
one-to-one low- to high-resolution mapping. Proceeding that way, we ignore the
fact that there are generally many valid versions of high-resolution images
that map to a given low-resolution image. We are tackling in this work the
problem of obtaining different high-resolution versions from the same
low-resolution image using Generative Adversarial Models. Our learning approach
makes use of high frequencies available in the training high-resolution images
for preserving and exploring in an unsupervised manner the structural
information available within these images. Experimental results on the CelebA
dataset confirm the effectiveness of the proposed method, which allows the
generation of both realistic and diverse high-resolution images from
low-resolution images.


Abstract:
  Magnetic Resonance Imaging (MRI) is a valuable clinical diagnostic modality
for spine pathologies with excellent characterization for infection, tumor,
degenerations, fractures and herniations. However in surgery, image-guided
spinal procedures continue to rely on CT and fluoroscopy, as MRI slice
resolutions are typically insufficient. Building upon state-of-the-art single
image super-resolution, we propose a reference-based, unpaired multi-contrast
texture-transfer strategy for deep learning based in-plane and across-plane MRI
super-resolution. We use the scattering transform to relate the texture
features of image patches to unpaired reference image patches, and additionally
a loss term for multi-contrast texture. We apply our scheme in different
super-resolution architectures, observing improvement in PSNR and SSIM for 4x
super-resolution in most of the cases.


Abstract:
  The Riccati equation method is used to establish an oscillatory and a non
oscillatory criteria for nonhomogeneous linear systems of two first-order
ordinary differential equations. It is shown that the obtained oscillatory
criterion is a generalization of J. S. W. Wong's oscillatory criterion.


Abstract:
  We investigate the quantum dynamics of a spin chain that implements a quantum
analog of Conway's game of life. We solve the time-dependent Schr\"odinger
equation starting with initial separable states and analyse the evolution of
quantum correlations across the lattice. We report examples of evolutions
leading to all-entangled chains and/or to time oscillating entangling
structures and characterize them by means of entanglement and network measures.
The quantum patterns result to be quite different from the classical ones, even
in the dynamics of local observables. A peculiar instance is a structure
behaving as the quantum analog of a blinker, but that has no classical
counterpart.


Abstract:
  Many complex activities of production cycles, such as quality control or
fault analysis, require highly experienced specialists to perform various
operations on (semi)finished products using different tools. In practical
scenarios, the selection of a next operation is complicated, since each expert
has only a local view on the total set of operations to be performed. As a
result, decisions made by the specialists are suboptimal and might cause
significant costs. In this paper, we consider a Multi-resource Partial-ordering
Flexible Job-shop Scheduling (MPF-JSS) problem where partially-ordered
sequences of operations must be scheduled on multiple required resources, such
as tools and specialists. The resources are flexible and can perform one or
more operations depending on their properties. The problem is modeled using
Answer Set Programming (ASP) in which the time assignments are efficiently done
using Difference Logic. Moreover, we suggest two multi-shot solving strategies
aiming at the identification of the time bounds allowing for a solution of the
schedule optimization problem. Experiments conducted on a set of instances
extracted from a medium-sized semiconductor fault analysis lab indicate that
our approach can find schedules for 87 out of 91 considered real-world
instances.


Abstract:
  Electrons and ions trapped with electromagnetic fields have long served as
important high-precision metrological instruments, and more recently have also
been proposed as a platform for quantum information processing. Here we point
out that these systems can also be used as highly sensitive detectors of
passing charged particles, due to the combination of their extreme
charge-to-mass ratio and low-noise quantum readout and control. In particular,
these systems can be used to detect energy depositions many orders of magnitude
below typical ionization scales. As illustrations, we suggest some applications
in particle physics. We outline a non-destructive time-of-flight measurement
capable of sub-eV energy resolution for slowly moving, collimated particles. We
also show that current devices can be used to provide competitive sensitivity
to models where ambient dark matter particles carry small electric millicharges
$\ll e$. Our calculations may also be useful in the characterization of noise
in quantum computers coming from backgrounds of charged particles.


Abstract:
  Turbulence generally arises in shear flows if velocities and hence inertial
forces are sufficiently large. In striking contrast, viscoelastic fluids can
exhibit disordered motion even at vanishing inertia. Intermediate between these
cases, a novel state of chaotic motion, `elasto-inertial turbulence' (EIT), has
been observed in a narrow Reynolds number interval. We here determine the
origin of EIT in experiments and show that characteristic EIT structures can be
detected across an unexpectedly wide range of parameters. Close to onset a
pattern of chevron shaped streaks emerges in excellent agreement with linear
theory. However, the instability can be traced to far lower Reynolds numbers
than permitted by theory. For increasing inertia, a secondary instability gives
rise to a wall mode composed of inclined near wall streaks and shear layers.
This mode persists to what is known as the `maximum drag reduction limit' and
overall EIT is found to dominate viscoelastic flows across more than three
orders of magnitude in Reynolds number.


Abstract:
  Edge computing is emerging as a new paradigm to allow processing data at the
edge of the network, where data is typically generated and collected, by
exploiting multiple devices at the edge collectively. However, exploiting the
potential of edge computing is challenging mainly due to the heterogeneous and
time-varying nature of edge devices. Coded computation, which advocates mixing
data in sub-tasks by employing erasure codes and offloading these sub-tasks to
other devices for computation, is recently gaining interest, thanks to its
higher reliability, smaller delay, and lower communication cost. In this paper,
our focus is on characterizing the cost-benefit trade-offs of coded computation
for practical edge computing systems, and develop an adaptive coded computation
framework. In particular, we focus on matrix multiplication as a
computationally intensive task, and develop an adaptive coding for matrix
multiplication (ACM^2) algorithm by taking into account the heterogeneous and
time varying nature of edge devices. ACM^2 dynamically selects the best coding
policy by taking into account the computing time, storage requirements as well
as successful decoding probability. We show that ACM^2 improves the task
completion delay significantly as compared to existing coded matrix
multiplication algorithms.


Abstract:
  We construct stable Adams operations on the cohomology theory Tmf of
topological modular forms. This is done by extending the sheaf defining Tmf to
be functorial with respect to isogenies of generalised elliptic curves of
invertible degree. These Adams operations are then calculated on the homotopy
groups of Tmf using a combination of descent spectral sequences, the theory of
synthetic spectra, and Anderson duality.


Abstract:
  We develop a sophisticated model of FRB observations, accounting for the
intrinsic cosmological gas distribution and host galaxy contributions, and give
the most detailed account yet of observational biases due to burst width,
dispersion measure, and the exact telescope beamshape. Our results offer a
significant increase in both accuracy and precision beyond those previously
obtained. Using results from ASKAP and Parkes, we present our best-fit FRB
population parameters in a companion paper. Here, we consider in detail the
expected and fitted distributions in redshift, dispersion measure, and
signal-to-noise. We estimate that the unlocalised ASKAP FRBs arise from
$z<0.5$, with between a third and a half within $z<0.1$. Our predicted
source-counts ("logN--logS") distribution confirms previous indications of a
steepening index near the Parkes detection threshold of $1$\,Jy\,ms. We find no
evidence for a minimum FRB energy, and rule out $E_{\rm min} > 10^{38.5}$\,erg
at 90\% C.L. Importantly, we find that above a certain DM, observational biases
cause the Macquart (DM--z) relation to become inverted, implying that the
highest-DM events detected in the unlocalised Parkes and ASKAP samples are
unlikely to be the most distant. We do not expect our quantitative estimates in
this region to be accurate until it is directly probed with localised FRBs.
Since the cause of this effect is a well-understood observational bias however,
it is guaranteed to be present to some degree. Works assuming a 1--1 DM--z
relation may therefore derive erroneous results.


Abstract:
  Background: Studies on intravoxel incoherent motion (IVIM) imaging are
carried out with different acquisition protocols. Purpose: Investigate the
dependence of IVIM parameters on the B_0field strength when using a bi- or
triexponential model. Field Strength/Sequence: 20 volunteers were examined at
two field strengths (1.5 and 3T). Diffusion-weighted images of the abdomen were
acquired at 24 b-values ranging from 0.2 to 500 s/mm2. Assessment: ROIs were
manually drawn in the liver. Data were fitted with a bi- and a triexponential
IVIM model. Resulting parameters were compared between both field strengths.
Results: At b-values below 6s/mm2, the triexponential model provided better
agreement with the data than the biexponential model. The average tissue
diffusivity was D=1.22/1.00 um/ms at 1.5/3T. The average pseudo-diffusion
coefficients for the biexponential model were D*=308/260um/ms at 1.5/3T; and
for the triexponential model D1*=81.3/65.9um/ms , D2*=2453/2333um/ms at 1.5/3T.
The average perfusion fractions for the biexponential model were f=0.286/0.303
at 1.5/3T; and for the triexponential model f1=0.161/0.174 and f2=0.152/0.159
at 1.5/3T. A significant B0 dependence was only found for the biexponential
pseudo-diffusion coefficient (ANOVA/KW p=0.037/0.0453) and tissue diffusivity
(ANOVA/KW: p<0.001). Conclusion: Our experimental results suggest that
triexponential pseudo-diffusion coefficients and perfusion fractions obtained
at different field strengths could be compared across different studies using
different B0. However, it is recommendable to take the field strength into
account when comparing tissue diffusivities or using the biexponential IVIM
model. Considering published values for oxygenation-dependent transversal
relaxation times of blood, it is unlikely that the two blood compartments of
the triexponential model represent venous and arterial blood.