Abstract:
  The dataset described in this paper contains daily data about COVID-19 cases
that occurred in Italy over the period from Jan. 28, 2020 to March 20, 2021,
divided into ten age classes of the population, the first class being 0-9
years, the tenth class being 90 years and over. The dataset contains eight
columns, namely: date (day), age class, number of new cases, number of newly
hospitalized patients, number of patients entering intensive care, number of
deceased patients, number of recovered patients, number of active infected
patients. This data has been officially released for research purposes by the
Italian authority for COVID-19 epidemiologic surveillance (Istituto Superiore
di Sanit\`a - ISS), upon formal request by the authors, in accordance with the
Ordonnance of the Chief of the Civil Protection Department n. 691 dated Aug. 4
2020. A separate file contains the numerosity of the population in each age
class, according to the National Institute of Statistics (ISTAT) data of the
resident population of Italy as of Jan. 2020. This data has potential use, for
instance, in epidemiologic studies of the effects of the COVID-19 contagion in
Italy, in mortality analysis by age class, and in the development and testing
of dynamical models of the contagion.


Abstract:
  Optimal transport distances have become a classic tool to compare probability
distributions and have found many applications in machine learning. Yet,
despite recent algorithmic developments, their complexity prevents their direct
use on large scale datasets. To overcome this challenge, a common workaround is
to compute these distances on minibatches i.e. to average the outcome of
several smaller optimal transport problems. We propose in this paper an
extended analysis of this practice, which effects were previously studied in
restricted cases. We first consider a large variety of Optimal Transport
kernels. We notably argue that the minibatch strategy comes with appealing
properties such as unbiased estimators, gradients and a concentration bound
around the expectation, but also with limits: the minibatch OT is not a
distance. To recover some of the lost distance axioms, we introduce a debiased
minibatch OT function and study its statistical and optimisation properties.
Along with this theoretical analysis, we also conduct empirical experiments on
gradient flows, generative adversarial networks (GANs) or color transfer that
highlight the practical interest of this strategy.


Abstract:
  Fast scramblers are dynamical quantum systems that produce many-body
entanglement on a timescale that grows logarithmically with the system size
$N$. We propose and investigate a family of deterministic, fast scrambling
quantum circuits realizable in near-term experiments with arrays of neutral
atoms. We show that three experimental tools -- nearest-neighbour Rydberg
interactions, global single-qubit rotations, and shuffling operations
facilitated by an auxiliary tweezer array -- are sufficient to generate
nonlocal interaction graphs capable of scrambling quantum information using
only $O(\log N)$ parallel applications of nearest-neighbor gates. These tools
enable direct experimental access to fast scrambling dynamics in a highly
controlled and programmable way, and can be harnessed to produce highly
entangled states with varied applications.


Abstract:
  Anomalies in the flux-ratios of the images of quadruply-lensed quasars have
been used to constrain the nature of dark matter. Assuming these lensing
perturbations are caused by dark matter haloes, it is possible to constrain the
mass of a hypothetical Warm Dark Matter (WDM) particle to be $m_\chi > 5.2$
keV. However, the assumption that perturbations are only caused by DM haloes
might not be correct as other structures, such as filaments and pancakes, exist
and make up a significant fraction of the mass in the universe, ranging between
5$\%$ -- 50$\%$ depending on the dark matter model. Using novel
fragmentation-free simulations of 1 and 3keV WDM cosmologies we study these
"non-halo" structures and estimate their impact on flux-ratio observations. We
find that these structures display sharp density gradients with short
correlation lengths, and can contribute more to the lensing signal than all
haloes up to the half-mode mass combined, thus reducing the differences
expected among WDM models. We estimate that this becomes especially important
for any flux-ratio based constraint sensitive to haloes of mass $M \sim 10^8
M_\odot$. We conclude that accounting for all types structures in
strong-lensing observations is required to improve the accuracy of current and
future constraints.


Abstract:
  The source of diffuse aurora has been widely studied and linked to electron
cyclotron harmonic (ECH) and upper-band chorus (UBC) waves. It is known that
these waves scatter 100s of eV to 10s of keV electrons from the plasma sheet,
but the relative contribution of each wave type is still an open question. In
this paper, we report on an interesting and unusual auroral feature observed on
March 15, 2002. We believe that these observations could help further our
understanding of waves associated with diffuse aurora. This diffuse auroral
feature is characterized by four phases: (1) the initial phase exhibiting
regular diffuse aurora, (2) the brightening phase, where an east-west auroral
stripe rapidly brightens, (3) the eraser phase, where the stripe dims to below
its initial state, and (4) the recovery phase, where the diffuse aurora returns
to its original brightness. Using a superposed epoch analysis of 22 events, we
calculate the average recovery phase time to be 20 seconds, although this
varies widely between events. We hypothesize that the process responsible for
these auroral eraser events could be the result of chorus waves modulating
diffuse aurora.


Abstract:
  We provide sufficient conditions for the occurrence of time-periodic Hopf
bifurcation for the coupled system constituted by a rigid sphere, $\mathscr S$,
freely moving under gravity in a Navier-Stokes liquid. Since the region of flow
is unbounded (namely, the whole space outside $\mathscr S$), the main
difficulty consists in finding the appropriate functional setting where general
theory may apply. In this regard, we are able to show that the problem can be
formulated as a suitable system of coupled operator equations in Banach spaces,
where the relevant operators are Fredholm of index 0. In such a way, we can use
the theory recently introduced by the author, and give sufficient conditions
for time-periodic bifurcation to take place.


Abstract:
  Purpose of this research is to forecast the development of sand bodies in
productive sediments based on well log data and seismic attributes. The object
of the study is the productive intervals of Achimov sedimentary complex in the
part of oil field located in Western Siberia. The research shows a
technological stack of machine learning algorithms, methods for enriching the
source data with synthetic ones and algorithms for creating new features. The
result was the model of regression relationship between the values of natural
radioactivity of rocks and seismic wave field attributes with an acceptable
prediction quality. Acceptable quality of the forecast is confirmed both by
model cross validation, and by the data obtained following the results of new
well.


Abstract:
  American football is a leading sport for contact-related injuries such as
cervical spine injuries, some of which result from an unforeseen hit. The use
of a feedback mechanism to alert an athlete of a potential hit may mitigate the
risk for sport-related concussion and catastrophic injury by allowing athletes
to effectively brace for an otherwise unforeseen impact. In this project, we
created a proximity sensor system using radio frequency identification (RFID)
technology to send haptic feedback to the wearer before a potential impact. Our
pilot test consisted of three participants who wore our novel sensor system,
who ran simulated routes to represent player-to-player contact. Additionally,
we used simulated data from a Network Live Football (NFL) league game to
further validate our contact prediction algorithm. Results show that our method
can predict players' collisions with less than 14% false-alarms.


Abstract:
  The direct and indirect boundary element methods, accelerated via the fast
multipole method, are applied to numerical simulation of room acoustics for
large rooms of volume $\sim 150$ $m^{3}$ and frequencies up to 5 kHz on a
workstation. As the parameter $kD$ (wavenumber times room diameter) is large,
stabilization of the previously developed FMM algorithms is required for
accuracy. A stabilization scheme is one of the key contribution of this paper.
The computations are validated using well-known image source solutions for
shoebox shaped rooms. Computations for L-shaped rooms are performed to
illustrate the ability to capture diffractions. The ability to model in-room
baffles, and boundary openings (doors/windows) is also demonstrated. The
largest case has $kD>1100$ with a discretization of size 6 million elements.
The performance of different boundary integral formulations was compared, and
their rates of convergence using a preconditioned flexible GMRES were found to
be substantially different. These promising results suggest a path to efficient
simulations of room acoustics via high performance boundary element methods.


Abstract:
  One of the most used priors in Bayesian clustering is the Dirichlet prior. It
can be expressed as a Chinese Restaurant Process. This process allows
nonparametric estimation of the number of clusters when partitioning datasets.
Its key feature is the "rich-get-richer" property, which assumes a cluster has
an a priori probability to get chosen linearly dependent on population. In this
paper, we show that such prior is not always the best choice to model data. We
derive the Powered Chinese Restaurant process from a modified version of the
Dirichlet-Multinomial distribution to answer this problem. We then develop some
of its fundamental properties (expected number of clusters, convergence).
Unlike state-of-the-art efforts in this direction, this new formulation allows
for direct control of the importance of the "rich-get-richer" prior.


Abstract:
  We study the well-posedness and the spatial behavior at infinity of perfect
fluid flows on $\R^d$ with initial data in a scale of weighted Sobolev spaces
that allow spatial growth/decay at infinity as $|x|^\beta$ with $\beta<1/2$. In
particular, we show that the solution of the Euler equation generically
develops an asymptotic expansion at infinity with non-vanishing asymptotic
terms that depend analytically on time and the initial data. We identify the
evolution space for initial data in the Schwartz class with a certain space of
symbols.


Abstract:
  In this work we study microlocal regularity of hyperfunctions defining in
this context a class of generalized FBI transforms first introduced for
distributions by Berhanu and Hounie. Using a microlocal decomposition of a
hyperfunction and the generalized FBI transforms we were able to characterize
the wave-front set of hyperfunctions according several types of regularity. The
microlocal decomposition allowed us to recover and generalize both classical
and recent results and, in particular, we proved for differential operators
with real-analytic coefficients that if the elliptic regularity theorem
regarding any reasonable regularity holds for distributions, then it is
automatically true for hyperfunctions.


Abstract:
  We report generation of ultrashort UV pulses by soliton self-compression in
kagom\'e-style hollow-core photonic crystal fiber filled with ambient air. Pump
pulses with energy 2.6 uJ and duration 54 fs at 400 nm were compressed
temporally by a factor of 5, to a duration of ~11 fs. The experimental results
are supported by numerical simulations, showing that both Raman and Kerr
effects play a role in the compression dynamics. The convenience of using
ambient air, and the absence of glass windows that would distort the compressed
pulses, makes the setup highly attractive as the basis of an efficient
table-top UV pulse compressor.


Abstract:
  Transition-edge sensors (TESs) are capable of highly accurate single particle
energy measurement. TESs have been used for a wide range of photon detection
applications, particularly in astronomy, but very little consideration has been
given to their capabilities as electron calorimeters. Existing electron
spectrometers require electron filtering optics to achieve energy
discrimination, but this step discards the vast majority of electrons entering
the instrument. TESs require no such energy filtering, meaning they could
provide orders of magnitude improvement in measurement rate. To investigate the
capabilities of TESs in electron spectroscopy, a simulation pipeline has been
devised. The pipeline allows the results of a simulated experiment to be
compared with the actual spectrum of the incident beam, thereby allowing
measurement accuracy and efficiency to be studied. Using Fisher information,
the energy resolution of the simulated detectors was also calculated, allowing
the intrinsic limitations of the detector to be separated from the specific
data analysis method used. The simulation platform has been used to compare the
performance of TESs with existing X-ray photoelectron spectroscopy (XPS)
analysers. TESs cannot match the energy resolution of XPS analysers for
high-precision measurements but have comparable or better resolutions for high
count rate applications. The measurement rate of a typical XPS analyser can be
matched by an array of 10 TESs with 120 microsecond response times and there is
significant scope for improvement, without compromising energy resolution, by
increasing array size.


Abstract:
  A key functionality of emerging connected autonomous systems such as smart
cities, smart transportation systems, and the industrial Internet-of-Things, is
the ability to process and learn from data collected at different physical
locations. This is increasingly attracting attention under the terms of
distributed learning and federated learning. However, in connected autonomous
systems, data transfer takes place over communication networks with often
limited resources. This paper examines algorithms for communication-efficient
learning for linear regression tasks by exploiting the informativeness of the
data. The developed algorithms enable a tradeoff between communication and
learning with theoretical performance guarantees and efficient practical
implementations.


Abstract:
  In this paper we introduce OperA, a transformer-based model that accurately
predicts surgical phases from long video sequences. A novel attention
regularization loss encourages the model to focus on high-quality frames during
training. Moreover, the attention weights are utilized to identify
characteristic high attention frames for each surgical phase, which could
further be used for surgery summarization. OperA is thoroughly evaluated on two
datasets of laparoscopic cholecystectomy videos, outperforming various
state-of-the-art temporal refinement approaches.


Abstract:
  In this paper, we consider the Cauchy problem to the 3D MHD equations. We
show that the Serrin--type conditions imposed on one component of the velocity
$u_{3}$ and one component of magnetic fields $b_{3}$ with $$ u_{3} \in
L^{p_{0},1}(-1,0;L^{q_{0}}(B(2))),\ b_{3} \in
L^{p_{1},1}(-1,0;L^{q_{1}}(B(2))), $$
$\frac{2}{p_{0}}+\frac{3}{q_{0}}=\frac{2}{p_{1}}+\frac{3}{q_{1}}=1$ and
$3<q_{0},q_{1}<+\infty$ imply that the suitable weak solution is regular at
$(0,0)$. The proof is based on the new local energy estimates introduced by
Chae-Wolf (Arch. Ration. Mech. Anal. 2021) and Wang-Wu-Zhang
(arXiv:2005.11906).


Abstract:
  We have searched for axion-like resonance states by colliding optical photons
in a focused laser field (creation beam) by adding another laser field
(inducing beam) for stimulation of the resonance decays, where
frequency-converted signal photons can be created as a result of stimulated
photon-photon scattering via exchanges of axion-like resonances. A
quasi-parallel collision system (QPS) in such a focused field allows access to
the sub-eV mass range of resonance particles. In past searches in QPS, for
simplicity, we interpreted the scattering rate based on an analytically
calculable symmetric collision geometry in both incident angles and incident
energies by partially implementing the asymmetric nature to meet the actual
experimental conditions. In this paper, we present new search results based on
a complete parameterization including fully asymmetric collisional geometries.
In particular, we combined a linearly polarized creation laser and a circularly
polarized inducing laser to match the new parameterization. A 0.10 mJ / 31 fs
Ti:sapphire laser pulse and a 0.20 mJ / 9 ns Nd:YAG laser pulse were
spatiotemporally synchronized by sharing a common optical axis and focused into
the vacuum system. Under a condition in which atomic background processes were
completely negligible, no significant scattering signal was observed at the
vacuum pressure of $2.6 \times 10^{-5}$ Pa, thereby providing upper bounds on
the coupling-mass relation by assuming exchanges of scalar and pseudoscalar
fields at a 95 % confidence level in the sub-eV mass range.


Abstract:
  Small scale jet-induced erosion experiments are useful for identifying the
scaling of erosion with respect to the various physical parameters (gravity,
grain size, gas velocity, gas density, grain density, etc.), and because they
provide a data set for benchmarking numerical flow codes. We have performed
experiments varying the physical parameters listed above (e.g., gravity was
varied in reduced gravity aircraft flights). In all these experiments, a
subsonic jet of gas impinges vertically on a bed of sand or lunar soil simulant
forming a localized scour hole beneath the jet. Videography captures the
erosion and scour hole formation processes, and analysis of these videos
post-test identifies the scaling of these processes. This has produced
important new insights into the physics of erosion. Based on these insights, we
have developed an erosion rate model that can be applied to generalized
situations, such as the erosion of soil beneath a horizontal gas flow on a
planetary surface. This is important to lunar exploration because the rate of
erosion beneath the rocket exhaust plume of a landing spacecraft will determine
the amount of sand-blasting damage that can be inflicted upon surrounding
hardware. Although the rocket exhaust plume at the exit of the nozzle is
supersonic, the boundary layer on the lunar surface where erosion occurs is
subsonic. The model has been benchmarked through comparison with the Apollo
landing videos, which show the blowing lunar soil, and computational fluid
dynamics simulations of those landings.


Abstract:
  In the traditional quantum theory, one-dimensional quantum spin models
possess a factorization surface where the ground states are fully separable
having vanishing bipartite as well as multipartite entanglement. We report that
in the non-Hermitian counterpart of these models, these factorization surfaces
either can predict the exceptional points where the unbroken to the broken
transition occurs or can guarantee the reality of the spectrum, thereby
proposing a procedure to reveal the unbroken phase. We first analytically
demonstrate it for the nearest-neighbor rotation-time RT-symmetric XY model
with uniform and alternating transverse magnetic fields, referred to as the
iATXY model. Exact diagonalization techniques are then employed to establish
this fact for the RT-symmetric XYZ model with short- and long-range
interactions as well as for the variable-ranged iATXY model. Moreover, we show
that although the factorization surface prescribes the unbroken phase of the
non-Hermitian model, the bipartite nearest-neighbor entanglement at the
exceptional point is nonvanishing.


Abstract:
  Usually, the realizations of the noncommutative Snyder model lead to a
nonassociative star product. However, it has been shown that this problem can
be avoided by adding to the spacetime coordinates new tensorial degrees of
freedom. The model so obtained, called extended Snyder model, can be subject to
a $\kappa$-deformation, giving rise to a unification of the Snyder and the
$\kappa$-Poincar\'e algebras in the formalism of extended spacetime. In this
paper we review this construction and consider the generic realizations of the
$\kappa$-deformed extended Snyder model, calculating the associated star
product, coproduct and twist in a perturbative setting. We also introduce a
representation of the Lorentz algebra in the extended space and speculate on
possible interpretations of the tensorial degrees of freedom.


Abstract:
  In the present paper, we study the distribution of the return points in the
fibers for a RDS (random dynamical systems) nonuniformly expanding preserving
an ergodic probability, we also show the abundance of nonlacunarity of
hyperbolic times that are obtained along the orbits through the fibers. We
conclude that any ergodic measure with positive Lyapunov exponents satisfies
the nonuniform specification property between fibers. As consequences, we prove
that any expanding measure is the limit of probability measure whose measures
of disintegration on the fibers are supported by a finite number of return
points and we prove that the average of the measures on the fibers
corresponding to a disintegration, along an orbit in the base dynamics is the
limit of Dirac measures supported in return orbits on the fibers.


Abstract:
  In forensic investigations it is often of value to establish whether two
phones were used by the same person during a given time period. We present a
method that uses time and location of cell tower registrations of mobile phones
to assess the strength of evidence that any pair of phones were used by the
same person. The method is transparent as it uses logistic regression to
discriminate between the hypotheses of same and different user, and a standard
kernel density estimation to quantify the weight of evidence in terms of a
likelihood ratio. We further add to previous theoretical work by training and
validating our method on real world data, paving the way for application in
practice. The method shows good performance under different modeling choices
and robustness under lower quantity or quality of data. We discuss practical
usage in court.


Abstract:
  Viral fusion proteins are attached to the membrane of enveloped viruses (a
group that includes Coronaviruses, Dengue, HIV and Influenza) and catalyze
fusion between the viral and host membranes, enabling the virus to insert its
genetic material into the host cell. Given the importance of these
biomolecules, this work presents a centralized database containing the most
relevant information on viral fusion proteins, available through a free-to-use
web server accessible through the URL https://viralfp.bio.di.uminho.pt/. This
web application contains several bioinformatic tools, such as Clustal sequence
alignment and Weblogo, including as well a machine learning-based tool capable
of predicting the location of fusion peptides (the component of fusion proteins
that inserts into the host's cell membrane) within the fusion protein sequence.
Given the crucial role of these proteins in viral infection, their importance
as natural targets of our immune system and their potential as therapeutic
targets, this web application aims to foster our ability to fight pathogenic
viruses.


Abstract:
  We define an involution on the space of compact tempered unipotent
representations of inner twists of a split simple $p$-adic group $G$ and
investigate its behaviour with respect to restrictions to reductive quotients
of maximal compact open subgroups. In particular, we formulate a precise
conjecture about the relation with a version of Lusztig's nonabelian Fourier
transform on the space of unipotent representations of the (possibly
disconnected) reductive quotients of maximal compact subgroups. We give
evidence of the conjecture, including proofs for $\mathsf{SL}_n$ and
$\mathsf{PGL}_n$.


Abstract:
  In recent years, resistive RAM often referred to as memristor is actively
pursued as a replacement for nonvolatile-flash memory due to its superior
characteristics such as high density, scalability, low power operation, high
endurance, and fast operating speed. However, one of the challenges that need
to be overcome is the loss of retention for both ON- and OFF-states; the
retention loss. While various models are proposed to explain the retention loss
in memristors consisting of a switching layer, in this paper, we propose that
the nucleation of clusters made of electrical charges, charge-clusters, in the
switching layer acts as a potential root cause for the retention loss. The
nucleation results from localized electric-field produced intermittently during
cyclic switching operations. We use the phase-field method to illustrate how
the nucleation of charge-clusters gives rise to the retention loss. Our results
suggest that the degree at which the retention loss arises is linked to the
number of cyclic switching operations since the probability at which nucleation
centers form increases with the number of cycle switching operations, which is
consistent with a range of experimental findings previously reported.


Abstract:
  Recent advances in protecting node privacy on graph data and attacking graph
neural networks (GNNs) gain much attention. The eye does not bring these two
essential tasks together yet. Imagine an adversary can utilize the powerful
GNNs to infer users' private labels in a social network. How can we
adversarially defend against such privacy attacks while maintaining the utility
of perturbed graphs? In this work, we propose a novel research task,
adversarial defenses against GNN-based privacy attacks, and present a graph
perturbation-based approach, NetFense, to achieve the goal. NetFense can
simultaneously keep graph data unnoticeability (i.e., having limited changes on
the graph structure), maintain the prediction confidence of targeted label
classification (i.e., preserving data utility), and reduce the prediction
confidence of private label classification (i.e., protecting the privacy of
nodes). Experiments conducted on single- and multiple-target perturbations
using three real graph data exhibit that the perturbed graphs by NetFense can
effectively maintain data utility (i.e., model unnoticeability) on targeted
label classification and significantly decrease the prediction confidence of
private label classification (i.e., privacy protection). Extensive studies also
bring several insights, such as the flexibility of NetFense, preserving local
neighborhoods in data unnoticeability, and better privacy protection for
high-degree nodes.


Abstract:
  Formation of quantum scars in many-body systems provides a novel mechanism
for enhancing coherence of weakly entangled states. At the same time, coherence
of edge modes in certain symmetry protected topological (SPT) phases can
persist away from the ground state. In this work we show the existence of
many-body scars and their implications on bulk coherence in such an SPT phase.
To this end, we study the eigenstate properties and the dynamics of an
interacting spin-$1/2$ chain with three-site "cluster" terms hosting a
$\mathbb{Z}_2 \times \mathbb{Z}_2$ SPT phase. Focusing on the weakly
interacting regime, we find that eigenstates with volume-law entanglement
coexist with area-law entangled eigenstates throughout the spectrum. We show
that a subset of the latter can be constructed by virtue of repeated cluster
excitations on the even or odd sublattice of the chain, resulting in an
equidistant "tower" of states, analogous to the phenomenology of quantum
many-body scars. We further demonstrate that these scarred eigenstates support
nonthermal expectation values of local cluster operators in the bulk and
exhibit signatures of topological order even at finite energy densities.
Studying the dynamics for out-of-equilibrium states drawn from the
noninteracting "cluster basis", we unveil that nonthermalizing bulk dynamics
can be observed on long time scales if clusters on odd and even sites are
energetically detuned. In this case, cluster excitations remain essentially
confined to one of the two sublattices such that inhomogeneous cluster
configurations cannot equilibrate and thermalization of the full system is
impeded. Our work sheds light on the role of quantum many-body scars in
preserving SPT order at finite temperature and the possibility of coherent bulk
dynamics in models with SPT order beyond the existence of long-lived edge
modes.


Abstract:
  Many emerging reconfigurable optical systems are limited by routing
complexity when producing dynamic, two-dimensional (2D) electric fields. Using
a gradient-based inverse designed, static phase-mask doublet, we propose an
optical system to produce 2D intensity wavefronts using a one-dimensional (1D)
intensity Spatial Light Modulator (SLM). We show the capability of mapping each
point in a 49 element 1D array to a distinct 7x7 2D spatial distribution. Our
proposed method will significantly relax the routing complexity of 2D
sub-wavelength SLMs, possibly enabling next-generation SLMs to leverage novel
pixel architectures and new materials.


Abstract:
  Permalloy Ni$_{80}$Fe$_{20}$ is one of the key magnetic materials in the
field of magnonics. Its potential would be further unveiled if it could be
deposited in three dimensional (3D) architectures of sizes down to the
nanometer. Atomic Layer Deposition, ALD, is the technique of choice for
covering arbitrary shapes with homogeneous thin films. Early successes with
ferromagnetic materials include nickel and cobalt. Still, challenges in
depositing ferromagnetic alloys reside in the synthesis via decomposing the
consituent elements at the same temperature and homogeneously. We report
plasma-enhanced ALD to prepare permalloy Ni$_{80}$Fe$_{20}$ thin films and
nanotubes using nickelocene and iron(III) tert-butoxide as metal precursors,
water as the oxidant agent and an in-cycle plasma enhanced reduction step with
hydrogen. We have optimized the ALD cycle in terms of Ni:Fe atomic ratio and
functional properties. We obtained a Gilbert damping of 0.013, a resistivity of
28 $\mu\Omega$cm and an anisotropic magnetoresistance effect of 5.6 $\%$ in the
planar thin film geometry. We demonstrate that the process also works for
covering GaAs nanowires, resulting in permalloy nanotubes with high aspect
ratios and diameters of about 150 nm. Individual nanotubes were investigated in
terms of crystal phase, composition and spin-dynamic response by microfocused
Brillouin Light Scattering. Our results enable NiFe-based 3D spintronics and
magnonic devices in curved and complex topology operated in the GHz frequency
regime.


Abstract:
  Extensive works have tackled Language Identification (LID) in the speech
domain, however their application to the singing voice trails and performances
on Singing Language Identification (SLID) can be improved leveraging recent
progresses made in other singing related tasks. This work presents a modernized
phonotactic system for SLID on polyphonic music: phoneme recognition is
performed with a Connectionist Temporal Classification (CTC)-based acoustic
model trained with multilingual data, before language classification with a
recurrent model based on the phonemes estimation. The full pipeline is trained
and evaluated with a large and publicly available dataset, with unprecedented
performances. First results of SLID with out-of-set languages are also
presented.


Abstract:
  We present full-sky maps of the Integrated Sachs-Wolfe effect (ISW) for the
MICE Grand Challenge lightcone simulation up to redshift 1.4. The maps are
constructed in the linear regime using spherical Bessel transforms. We compare
and contrast this procedure against analytical approximations found in the
literature. By computing the ISW in the linear regime, we remove the
substantial computing and storage resources required to calculate the
non-linear Rees-Sciama effect. Since the linear ISW at low redshift $z\leq1$,
at large angular scales, and after matter domination is $\sim 10^{2}\times$
larger in $\Delta T/T$, this has a negligible impact on the maps produced and
only becomes relevant on scales which are dominated by cosmic microwave
background (CMB) anisotropies. The MICE simulation products have been
extensively used for studies involving current and future galaxy surveys. The
availability of these maps will allow MICE to be used for future galaxy and CMB
cross-correlation studies, ISW reconstruction studies, and ISW void-stacking
studies probed by galaxy surveys such as Dark Energy Survey, Dark Energy
Spectroscopic Instrument, Euclid, and Rubin Legacy Survey of Space and Time.
The pipeline developed in this study is provided as a public Python package
pyGenISW. This could be used in the future studies for constructing the ISW
from existing and future simulation suites probing vast sets of cosmological
parameters and models.


Abstract:
  Using a lifecycle framework with Epstein-Zin (1989) utility and a
mixed-integer optimization approach, we compute the optimal age to claim Social
Security benefits. Taking advantage of homogeneity, a sufficient statistic is
the ratio of wealth to the primary insurance amount (PIA). If the investor's
wealth to PIA ratio exceeds a certain threshold, individuals should defer
Social Security for at least a year. The optimal threshold depends on mortality
assumptions and an individual's utility preferences, but is less sensitive to
capital market assumptions. The threshold wealth to PIA ratio increases from
5.5 for men and 5.2 for women at age 62 to 11.1 for men and 10.4 for women at
age 69. Below the threshold wealth to PIA ratio, individuals claim Social
Security to raise consumption. Above this level, investors can afford to fund
consumption out of wealth for at least one year, and then claim a higher
benefit.


Abstract:
  We study a class of optimization problems including matrix scaling, matrix
balancing, multidimensional array scaling, operator scaling, and tensor scaling
that arise frequently in theory and in practice. Some of these problems, such
as matrix and array scaling, are convex in the Euclidean sense, but others such
as operator scaling and tensor scaling are geodesically convex on a different
Riemannian manifold. Trust region methods, which include box-constrained
Newton's method, are known to produce high precision solutions very quickly for
matrix scaling and matrix balancing (Cohen et. al., FOCS 2017, Allen-Zhu et.
al. FOCS 2017), and result in polynomial time algorithms for some geodesically
convex problems like operator scaling (Garg et. al. STOC 2018, B\"urgisser et.
al. FOCS 2019). One is led to ask whether these guarantees also hold for
multidimensional array scaling and tensor scaling.
  We show that this is not the case by exhibiting instances with exponential
diameter bound: we construct polynomial-size instances of 3-dimensional array
scaling and 3-tensor scaling whose approximate solutions all have doubly
exponential condition number. Moreover, we study convex-geometric notions of
complexity known as margin and gap, which are used to bound the running times
of all existing optimization algorithms for such problems. We show that margin
and gap are exponentially small for several problems including array scaling,
tensor scaling and polynomial scaling. Our results suggest that it is
impossible to prove polynomial running time bounds for tensor scaling based on
diameter bounds alone. Therefore, our work motivates the search for analogues
of more sophisticated algorithms, such as interior point methods, for
geodesically convex optimization that do not rely on polynomial diameter
bounds.


Abstract:
  In the present work, we investigate the hidden-strangeness production process
in the $S=+1$ channel via $K^+p\to K^+\phi\,p$, focussing on the exotic
\textit{pentaquark} molecular $K^*\Sigma$ bound state, assigned by
$P^+_s(2071,3/2^-)$. For this purpose, we employ the effective Lagrangian
approach in the tree-level Born approximation. Using the experimental and
theoretical inputs for the exotic state and for the ground-state hadron
interactions, the numerical results show a small but obvious peak structure
from $P^+_s$ with the signal-to-background ratio $\approx1.7\,\%$, and it is
enhanced in the backward-scattering region of the outgoing $K^+$ in the
center-of-mass frame. We also find that the contribution from the
$K^*(1680,1^-)$ meson plays an important role to reproduce the data. The
proton-spin polarizations are taken into account to find a way to reduce the
background. The effects of the possible $27$-plet pentaquark $\Theta^{++}_{27}$
is discussed as well.


Abstract:
  Single image pose estimation is a fundamental problem in many vision and
robotics tasks, and existing deep learning approaches suffer by not completely
modeling and handling: i) uncertainty about the predictions, and ii) symmetric
objects with multiple (sometimes infinite) correct poses. To this end, we
introduce a method to estimate arbitrary, non-parametric distributions on
SO(3). Our key idea is to represent the distributions implicitly, with a neural
network that estimates the probability given the input image and a candidate
pose. Grid sampling or gradient ascent can be used to find the most likely
pose, but it is also possible to evaluate the probability at any pose, enabling
reasoning about symmetries and uncertainty. This is the most general way of
representing distributions on manifolds, and to showcase the rich expressive
power, we introduce a dataset of challenging symmetric and nearly-symmetric
objects. We require no supervision on pose uncertainty -- the model trains only
with a single pose per example. Nonetheless, our implicit model is highly
expressive to handle complex distributions over 3D poses, while still obtaining
accurate pose estimation on standard non-ambiguous environments, achieving
state-of-the-art performance on Pascal3D+ and ModelNet10-SO(3) benchmarks.


Abstract:
  This report bases itself on the idea of using concrete examples to verify
conceptual schemas, and in particular cardinality constraints. When novice ORM
modellers model domains, the selection of proper cardinality constraints for
relationship types is quite often prone to errors. In this report we propose a
mechanism for the generation of significant examples for selected subschemas.
The generated examples are significant in the sense that they illustrate the
possible combinations of instances that are allowed with respect to the
cardinality constraints on the involved relationship types. In this report we
firstly provide a brief informal discussion of the basic idea. Then we present
a syntactic mechanism to select the subschema for which example instances are
to be generated. This is followed by the actual example generation algorithm
itself. We will also present, as a {\em spin-off}, an algorithm that allows us
to detect possible flaws in the conceptual schema by calculating the number of
instances that can be used to populate the types in the schema.


Abstract:
  Over the last few years, neural network derived word embeddings became
popular in the natural language processing literature. Studies conducted have
mostly focused on the quality and application of word embeddings trained on
public available corpuses such as Wikipedia or other news and social media
sources. However, these studies are limited to generic text and thus lack
technical and scientific nuances such as domain specific vocabulary,
abbreviations, or scientific formulas which are commonly used in academic
context. This research focuses on the performance of word embeddings applied to
a large scale academic corpus. More specifically, we compare quality and
efficiency of trained word embeddings to TFIDF representations in modeling
content of scientific articles. We use a word2vec skip-gram model trained on
titles and abstracts of about 70 million scientific articles. Furthermore, we
have developed a benchmark to evaluate content models in a scientific context.
The benchmark is based on a categorization task that matches articles to
journals for about 1.3 million articles published in 2017. Our results show
that content models based on word embeddings are better for titles (short text)
while TFIDF works better for abstracts (longer text). However, the slight
improvement of TFIDF for larger text comes at the expense of 3.7 times more
memory requirement as well as up to 184 times higher computation times which
may make it inefficient for online applications. In addition, we have created a
2-dimensional visualization of the journals modeled via embeddings to
qualitatively inspect embedding model. This graph shows useful insights and can
be used to find competitive journals or gaps to propose new journals.


Abstract:
  Due to the increasing connectivity of modern vehicles, collected data is no
longer only stored in the vehicle itself but also transmitted to car
manufacturers and vehicle assistant apps. This development opens up new
possibilities for digital forensics in criminal investigations involving modern
vehicles. This paper deals with the digital forensic analysis of vehicle
assistant apps of eight car manufacturers. We reconstruct the driver's
activities based on the data stored on the smartphones and in the
manufacturer's backend.
  For this purpose, data of the Android and iOS apps of the car manufacturers
Audi, BMW, Ford, Mercedes, Opel, Seat, Tesla, and Volkswagen were extracted
from the smartphone and examined using digital forensic methods in accordance
with lawful government-approved forensics guidelines. Additionally,
manufacturer data was retrieved using Subject Access Requests. Using the
extensive data gathered, we successfully reconstruct trips and refueling
processes, determine parking positions and duration, and track the locking and
unlocking of the vehicle.
  These findings show that the digital forensic investigation of smartphone
applications is a useful addition to vehicle forensics and should therefore be
taken into account in the strategic preparation of future digital forensic
investigations.


Abstract:
  Collaborative Filtering (CF) has become the standard approach to solve
recommendation systems (RS) problems. Collaborative Filtering algorithms try to
make predictions about interests of a user by collecting the personal interests
from multiple users. There are multiple CF algorithms, each one of them with
its own biases. It is the Machine Learning practitioner that has to choose the
best algorithm for each task beforehand. In Recommender Systems, different
algorithms have different performance for different users within the same
dataset. Meta Learning (MtL) has been used to choose the best algorithm for a
given problem. Meta Learning is usually applied to select algorithms for a
whole dataset. Adapting it to select the to the algorithm for a single user in
a RS involves several challenges. The most important is the design of the
metafeatures which, in typical meta learning, characterize datasets while here,
they must characterize a single user. This work presents a new meta-learning
based framework named $\mu$-cf2vec to select the best algorithm for each user.
We propose using Representation Learning techniques to extract the
metafeatures. Representation Learning tries to extract representations that can
be reused in other learning tasks. In this work we also implement the framework
using different RL techniques to evaluate which one can be more useful to solve
this task. In the meta level, the meta learning model will use the metafeatures
to extract knowledge that will be used to predict the best algorithm for each
user. We evaluated an implementation of this framework using MovieLens 20M
dataset. Our implementation achieved consistent gains in the meta level,
however, in the base level we only achieved marginal gains.


Abstract:
  We study four-dimensional gauge theories with arbitrary simple gauge group
with $1$-form global center symmetry and $0$-form parity or discrete chiral
symmetry. We canonically quantize on $\mathbb{T}^3$, in a fixed background
field gauging the $1$-form symmetry. We show that the mixed $0$-form/$1$-form
't Hooft anomaly results in a central extension of the global-symmetry operator
algebra. We determine this algebra in each case and show that the anomaly
implies degeneracies in the spectrum of the Hamiltonian at any finite-size
torus. We discuss the consistency of these constraints with both older and
recent semiclassical calculations in $SU(N)$ theories, with or without adjoint
fermions, as well as with their conjectured infrared phases.


Abstract:
  We introduce a novel method to aggregate Bipolar Argumentation (BA)
Frameworks expressing opinions by different parties in debates. We use Bipolar
Assumption-based Argumentation (ABA) as an all-encompassing formalism for BA
under different semantics. By leveraging on recent results on judgement
aggregation in Social Choice Theory, we prove several preservation results,
both positive and negative, for relevant properties of Bipolar ABA.


Abstract:
  We show that the $n$th derivative with maximal domain is a chaotic operator
in the spaces $C[a,b]$ and $L_p(a,b)$ ($-\infty<a<b<\infty$, $1\le p<\infty$)
for each $n\in \mathbb{N}$.


Abstract:
  We study the multi-user Bayesian persuasion game between one encoder and two
decoders, where the first decoder is better informed than the second decoder.
We consider two perfect links, one to the first decoder only, and the other to
both decoders. We consider that the encoder and both decoders are endowed with
distinct and arbitrary distortion functions. We investigate the strategic
source coding problem in which the encoder commits to an encoding while the
decoders select the sequences of symbols that minimize their respective
distortion functions. We characterize the optimal encoder distortion value by
considering successive refinement coding with respect to a specific probability
distribution which involves two auxiliary random variables, and captures the
incentives constraints of both decoders.


Abstract:
  This paper addresses the problem of picking up only one object at a time
avoiding any entanglement in bin-picking. To cope with a difficult case where
the complex-shaped objects are heavily entangled together, we propose a
topology-based method that can generate non-tangle grasp positions on a single
depth image. The core technique is entanglement map, which is a feature map to
measure the entanglement possibilities obtained from the input image. We use
the entanglement map to select probable regions containing graspable objects.
The optimum grasping pose is detected from the selected regions considering the
collision between robot hand and objects. Experimental results show that our
analytic method provides a more comprehensive and intuitive observation of
entanglement and exceeds previous learning-based work in success rates.
Especially, our topology-based method does not rely on any object models or
time-consuming training process, so that it can be easily adapted to more
complex bin-picking scenes.


Abstract:
  Searching for network width is an effective way to slim deep neural networks
with hardware budgets. With this aim, a one-shot supernet is usually leveraged
as a performance evaluator to rank the performance \wrt~different width.
Nevertheless, current methods mainly follow a manually fixed weight sharing
pattern, which is limited to distinguish the performance gap of different
width. In this paper, to better evaluate each width, we propose a locally free
weight sharing strategy (CafeNet) accordingly. In CafeNet, weights are more
freely shared, and each width is jointly indicated by its base channels and
free channels, where free channels are supposed to loCAte FrEely in a local
zone to better represent each width. Besides, we propose to further reduce the
search space by leveraging our introduced FLOPs-sensitive bins. As a result,
our CafeNet can be trained stochastically and get optimized within a min-min
strategy. Extensive experiments on ImageNet, CIFAR-10, CelebA and MS COCO
dataset have verified our superiority comparing to other state-of-the-art
baselines. For example, our method can further boost the benchmark NAS network
EfficientNet-B0 by 0.41\% via searching its width more delicately.


Abstract:
  Modern neural networks obtain information about the problem and calculate the
output solely from the input values. We argue that it is not always optimal,
and the network's performance can be significantly improved by augmenting it
with a query mechanism that allows the network to make several solution trials
at run time and get feedback on the loss value on each trial. To demonstrate
the capabilities of the query mechanism, we formulate an unsupervised (not
dependant on labels) loss function for Boolean Satisfiability Problem (SAT) and
theoretically show that it allows the network to extract rich information about
the problem. We then propose a neural SAT solver with a query mechanism called
QuerySAT and show that it outperforms the neural baseline on a wide range of
SAT tasks and the classical baselines on SHA-1 preimage attack and 3-SAT task.


Abstract:
  In this work, we consider networks of so-called geometrically exact beams,
namely, shearable beams that may undergo large motions. The corresponding
mathematical model, commonly written in terms of displacements and rotations
expressed in a fixed basis (Geometrically Exact Beam model, or GEB), has a
quasilinear governing system. However, the model may also be written in terms
of intrinsic variables expressed in a moving basis attached to the beam
(Intrinsic GEB model, or IGEB) and while the number of equations is then
doubled, the latter model has the advantage of being of first-order, hyperbolic
and only semilinear. First, for any network, we show the existence and
uniqueness of semi-global in time classical solutions to the IGEB model (i.e.,
for arbitrarily large time intervals, provided that the data are small enough).
Then, for a specific network containing a cycle, we address the problem of
local exact controllability of nodal profiles for the IGEB model -- we steer
the solution to satisfy given profiles at one of the multiple nodes by means of
controls applied at the simple nodes -- by using the constructive method of
Zhuang, Leugering and Li [Exact boundary controllability of nodal profile for
Saint-Venant system on a network with loops, in J. Math. Pures Appl., 2018].
Afterwards, for any network, we show that the existence of a unique classical
solution to the IGEB network implies the same for the corresponding GEB
network, by using that these two models are related by a nonlinear
transformation. In particular, this allows us to give corresponding existence,
uniqueness and controllability results for the GEB network.


Abstract:
  In the current picture of cosmology and astrophysics, the formation and
evolution of galaxies is closely linked to that of their dark matter haloes.
The best representation of this galaxy-dark matter halo co-evolution is the M*
- Mhalo relation. In this study we investigate how the radio-mode feedback from
active galactic nuclei (AGN) affects the M* - Mhalo relation at redshifts 0.08
< z < 1.53. We use a set of 111 radio-selected AGN at 3 GHz VLA-COSMOS within
the X-ray galaxy groups in the COSMOS field. We compare these results to the
ones of 171 star-forming galaxies (SFGs), using the theoretical relation of
Moster et al. (2013). We find that AGN agree within 1% with the Moster et al.
(2013) relation, SFGs show an offset of 37%, suggesting that the radio-mode
feedback from AGN at a median redshift of ~ 0.5 still plays a significant role
in the M* - Mhalo relation.


Abstract:
  Ride Hailing Services (RHS) have become a popular means of transportation,
and with its popularity comes the concerns of privacy of riders and drivers.
ORide is a privacy-preserving RHS proposed at the USENIX Security Symposium
2017 and uses Somewhat Homomorphic Encryption (SHE). In their protocol, a rider
and all drivers in a zone send their encrypted coordinates to the RHS Service
Provider (SP) who computes the squared Euclidean distances between them and
forwards them to the rider. The rider decrypts these and selects the optimal
driver with least Euclidean distance. In this work, we demonstrate a
location-harvesting attack where an honest-but-curious rider, making only a
single ride request, can determine the exact coordinates of about half the
number of responding drivers even when only the distance between the rider and
drivers are given. The significance of our attack lies in inferring locations
of other drivers in the zone, which are not (supposed to be) revealed to the
rider as per the protocol. We validate our attack by running experiments on
zones of varying sizes in arbitrarily selected big cities. Our attack is based
on enumerating lattice points on a circle of sufficiently small radius and
eliminating solutions based on conditions imposed by the application scenario.
Finally, we propose a modification to ORide aimed at thwarting our attack and
show that this modification provides sufficient driver anonymity while
preserving ride matching accuracy.


Abstract:
  We associate with each convex optimization problem posed on some locally
convex space with an infinite index set T, and a given non-empty family H
formed by finite subsets of T, a suitable Lagrangian-Haar dual problem. We
provide reverse H-strong duality theorems, H-Farkas type lemmas and optimality
theorems. Special attention is addressed to infinite and semi-infinite linear
optimization problems.


Abstract:
  We derive a Doi-Peliti field theory for free active Ornstein-Uhlenbeck
particles, or, equivalently, free inertial Brownian particles, and present a
way to diagonalise the Gaussian part of the action and calculate the
propagator. Unlike previous course-grained approaches this formulation
correctly tracks particle identity and yet can easily be expanded to include
potentials and arbitrary reactions.


Abstract:
  Domain adaptation is an important problem and often needed for real-world
applications. In this problem, instead of i.i.d. datapoints, we assume that the
source (training) data and the target (testing) data have different
distributions. With that setting, the empirical risk minimization training
procedure often does not perform well, since it does not account for the change
in the distribution. A common approach in the domain adaptation literature is
to learn a representation of the input that has the same distributions over the
source and the target domain. However, these approaches often require
additional networks and/or optimizing an adversarial (minimax) objective, which
can be very expensive or unstable in practice. To tackle this problem, we first
derive a generalization bound for the target loss based on the training loss
and the reverse Kullback-Leibler (KL) divergence between the source and the
target representation distributions. Based on this bound, we derive an
algorithm that minimizes the KL term to obtain a better generalization to the
target domain. We show that with a probabilistic representation network, the KL
term can be estimated efficiently via minibatch samples without any additional
network or a minimax objective. This leads to a theoretically sound alignment
method which is also very efficient and stable in practice. Experimental
results also suggest that our method outperforms other representation-alignment
approaches.


Abstract:
  Nonlinear terms in standard models of ship maneuvering entail continuous
non-smooth terms so that bifurcations of straight motion are not amenable to
standard center manifold reduction. In this paper, we determine the criticality
of Hopf bifurcations that arise in stabilizing the straight motion based on a
recently developed analytical approach. For such a 3 DOF model of ship motion
with yaw damping and yaw restoring control, we present a detailed analysis of
the possibilities to stabilize the straight motion, and the resulting nonlinear
effects. To facilitate the analysis, we consider a combination of rudder and
propeller forces into an effective thruster force. We identify existence,
location and geometry of the stability boundary in terms of the controls,
including the dependence on the propeller diameter and the thruster position.
We find that `safe' supercritical Andronov-Hopf bifurcations are predominant
and by means of numerical continuation we provide a global bifurcation
analysis, which identifies the arrangement and relative location of stable and
unstable equilibria and periodic orbits. We illustrate the resulting stable
quasi-periodic ship motions in Earth-fixed coordinates and present some direct
numerical simulations.


Abstract:
  Secure and scalable device provisioning is a notorious challenge in Wi-Fi.
WPA2/WPA3 solutions take user interaction and a strong passphrase for granted.
However, the often weak passphrases are subject to guessing attacks. Notably,
there has been a significant rise of cyberattacks on Wi-Fi home or small office
networks during the COVID-19 pandemic. This paper addresses the device
provisioning problem in Wi-Fi (personal mode) and proposes ComPass protocol to
supplement WPA2/WPA3. ComPass replaces the pre-installed or user-selected
passphrases with automatically generated ones. For this, ComPass employs
Physical Layer Security and extracts credentials from common random physical
layer parameters between devices. Two major features make ComPass unique and
superior compared to previous proposals: First, it employs phase information
(rather than amplitude or signal strength) to generate the passphrase so that
it is robust, scaleable, and impossible to guess. Our analysis showed that
ComPass generated passphrases have 3 times more entropy than human generated
passphrases (113-bits vs. 34-bits). Second, ComPass selects parameters such
that two devices bind only within a certain proximity (less than 3m), hence
providing practically useful in-build PLS-based authentiation. ComPass is
available as a kernel module or as full firmware.


Abstract:
  Templated copying is the central operation by which biology produces complex
molecules. Cells copy sequence information from DNA to RNA and on into
proteins, which are the molecules responsible for the function and regulation
of cellular systems. In the templated copying process the template catalyses
the formation of a second molecule carrying the same sequence. Traditionally,
people have ignored the separation of the template and copy at the end of the
process, but separation is necessary and fundamentally changes the
thermodynamics of the process. In general, creating an accurate polymer costs
free energy. Omitting separation, this cost can be compensated for by the extra
free energy released by "correct" copy/template bonds. Separation requires
these bonds be broken, so true copying requires an input of free energy.
Equally the fact that copy/template bonds are temporary means there is no
thermodynamic bias towards accuracy, instead copying relies only on kinetic
effects to promote accuracy. In general, transducing energy can only happen
reversibly when the transduction process is quasistatic and time varying;
something that cannot be true when you are relying on kinetic discrimination.
  Copying is a far from equilibrium process. This thesis explores the
consequences of this observation. We start in the limit of infinite length
copies where the costs of accuracy represent hard thermodynamic bounds and then
moves to the finite length limit where these same limits can be understood as
kinetic barriers. We then discuss copying systems as non-equilibrium steady
states, which can be analysed as information engines moving free energy between
out-of-equilibrium baths.


Abstract:
  The few-shot multi-speaker multi-style voice cloning task is to synthesize
utterances with voice and speaking style similar to a reference speaker given
only a few reference samples. In this work, we investigate different speaker
representations and proposed to integrate pretrained and learnable speaker
representations. Among different types of embeddings, the embedding pretrained
by voice conversion achieves the best performance. The FastSpeech 2 model
combined with both pretrained and learnable speaker representations shows great
generalization ability on few-shot speakers and achieved 2nd place in the
one-shot track of the ICASSP 2021 M2VoC challenge.


Abstract:
  Knowledge distillation is a method of transferring the knowledge from a
pretrained complex teacher model to a student model, so a smaller network can
replace a large teacher network at the deployment stage. To reduce the
necessity of training a large teacher model, the recent literatures introduced
a self-knowledge distillation, which trains a student network progressively to
distill its own knowledge without a pretrained teacher network. While
Self-knowledge distillation is largely divided into a data augmentation based
approach and an auxiliary network based approach, the data augmentation
approach looses its local information in the augmentation process, which
hinders its applicability to diverse vision tasks, such as semantic
segmentation. Moreover, these knowledge distillation approaches do not receive
the refined feature maps, which are prevalent in the object detection and
semantic segmentation community. This paper proposes a novel self-knowledge
distillation method, Feature Refinement via Self-Knowledge Distillation
(FRSKD), which utilizes an auxiliary self-teacher network to transfer a refined
knowledge for the classifier network. Our proposed method, FRSKD, can utilize
both soft label and feature-map distillations for the self-knowledge
distillation. Therefore, FRSKD can be applied to classification, and semantic
segmentation, which emphasize preserving the local information. We demonstrate
the effectiveness of FRSKD by enumerating its performance improvements in
diverse tasks and benchmark datasets. The implemented code is available at
https://github.com/MingiJi/FRSKD.


Abstract:
  The colocation of individuals in different environments is an important
prerequisite for exposure to infectious diseases on a social network. Standard
epidemic models fail to capture the potential complexity of this scenario by
(1) neglecting the higher-order structure of contacts which typically occur
through environments like workplaces, restaurants, and households; and by (2)
assuming a linear relationship between the exposure to infected contacts and
the risk of infection. Here, we leverage a hypergraph model to embrace the
heterogeneity of environments and the heterogeneity of individual participation
in these environments. We find that combining heterogeneous exposure with the
concept of minimal infective dose induces a universal nonlinear relationship
between infected contacts and infection risk. Under nonlinear infection
kernels, conventional epidemic wisdom breaks down with the emergence of
discontinuous transitions, super-exponential spread, and hysteresis.


Abstract:
  We make use of Hecke operators and arithmetic of imaginary quadratic fields
to derive an explicit version of a special case of Siegel's mass formula.


Abstract:
  In this paper, we prove both of the Titchmarsh theorems associated with
Two-Sided Quaternionic Fourier Transform and we conclude about Short-Time
Two-Sided Quaternionic Fourier Transform.


Abstract:
  This article presents the results of a study involving the translation of a
fictional story from English into Catalan in three modalities:
machine-translated (MT), post-edited (MTPE) and translated without aid (HT).
Each translation was analysed to evaluate its creativity. Subsequently, a
cohort of 88 Catalan participants read the story in a randomly assigned
modality and completed a survey. The results show that HT presented a higher
creativity score if compared to MTPE and MT. HT also ranked higher in narrative
engagement, and translation reception, while MTPE ranked marginally higher in
enjoyment. HT and MTPE show no statistically significant differences in any
category, whereas MT does in all variables tested. We conclude that creativity
is highest when professional translators intervene in the process, especially
when working without any aid. We hypothesize that creativity in translation
could be the factor that enhances reading engagement and the reception of
translated literary texts.


Abstract:
  Lithium metal batteries are seen as a critical piece towards electrifying
aviation. During charging, plating of lithium metal, a critical failure
mechanism, has been studied and mitigation strategies have been proposed. For
electric aircraft, high discharge power requirements necessitate stripping of
lithium metal in an uniform way and recent studies have identified the
evolution of surface voids and pits as a potential failure mechanism. In this
work, using density functional theory calculations and thermodynamic analysis,
we investigate the discharge process on lithium metal surfaces. In particular,
we calculate the tendency for vacancy congregation on lithium metal surfaces,
which constitutes the first step in the formation of voids and pits. We find
that among the low Miller index surfaces, the (111) surface is the least likely
to exhibit pitting issues. Our analysis suggests that faceting control during
electrodeposition could be a key pathway towards simultaneously enabling both
fast charge and fast discharge.


Abstract:
  Pastures are a class of field-like algebraic objects which include both
partial fields hyperfields and have nice categorical properties. We prove
several lift theorems for representations of matroids over pastures, including
a generalization of Pendavingh and van Zwam's Lift Theorem for partial fields.
By embedding the earlier theory into a more general framework, we are able to
establish new results even in the case of lifts of partial fields, for example
the conjecture of Pendavingh--van Zwam that their lift construction is
idempotent. We give numerous applications to matroid representations, e.g. we
show that, up to projective equivalence, every pair consisting of a hexagonal
representation and an orientation lifts uniquely to a near-regular
representation. The proofs are different from the arguments used by Pendavingh
and van Zwam, relying instead on a result of Gelfand-Rybnikov-Stone inspired by
Tutte's homotopy theorem.


Abstract:
  The latent knowledge in the emotions and the opinions of the individuals that
are manifested via social networks are crucial to numerous applications
including social management, dynamical processes, and public security.
Affective computing, as an interdisciplinary research field, linking artificial
intelligence to cognitive inference, is capable to exploit emotion-oriented
knowledge from brief contents. The textual contents convey hidden information
such as personality and cognition about corresponding authors that can
determine both correlations and variations between users. Emotion recognition
from brief contents should embrace the contrast between authors where the
differences in personality and cognition can be traced within emotional
expressions. To tackle this challenge, we devise a framework that, on the one
hand, infers latent individual aspects, from brief contents and, on the other
hand, presents a novel ensemble classifier equipped with dynamic dropout
convnets to extract emotions from textual context. To categorize short text
contents, our proposed method conjointly leverages cognitive factors and
exploits hidden information. We utilize the outcome vectors in a novel
embedding model to foster emotion-pertinent features that are collectively
assembled by lexicon inductions. Experimental results show that compared to
other competitors, our proposed model can achieve a higher performance in
recognizing emotion from noisy contents.


Abstract:
  Can a neural network minimizing cross-entropy learn linearly separable data?
Despite progress in the theory of deep learning, this question remains
unsolved. Here we prove that SGD globally optimizes this learning problem for a
two-layer network with Leaky ReLU activations. The learned network can in
principle be very complex. However, empirical evidence suggests that it often
turns out to be approximately linear. We provide theoretical support for this
phenomenon by proving that if network weights converge to two weight clusters,
this will imply an approximately linear decision boundary. Finally, we show a
condition on the optimization that leads to weight clustering. We provide
empirical results that validate our theoretical analysis.


Abstract:
  Rule ensembles are designed to provide a useful trade-off between predictive
accuracy and model interpretability. However, the myopic and random search
components of current rule ensemble methods can compromise this goal: they
often need more rules than necessary to reach a certain accuracy level or can
even outright fail to accurately model a distribution that can actually be
described well with a few rules. Here, we present a novel approach aiming to
fit rule ensembles of maximal predictive power for a given ensemble size (and
thus model comprehensibility). In particular, we present an efficient
branch-and-bound algorithm that optimally solves the per-rule objective
function of the popular second-order gradient boosting framework. Our main
insight is that the boosting objective can be tightly bounded in linear time of
the number of covered data points. Along with an additional novel pruning
technique related to rule redundancy, this leads to a computationally feasible
approach for boosting optimal rules that, as we demonstrate on a wide range of
common benchmark problems, consistently outperforms the predictive performance
of boosting greedy rules.


Abstract:
  Temporal aggregation is an intuitively appealing approach to deal with demand
uncertainty. There are two types of temporal aggregation: non-overlapping and
overlapping. Most of the supply chain forecasting literature has focused so far
on the former and there is no research that analyses the latter for
auto-correlated demands. In addition, most of the analytical research to-date
assumes infinite demand series' lengths whereas, in practice, forecasting is
based on finite demand histories. The length of the demand history is an
important determinant of the comparative performance of the two approaches but
has not been given sufficient attention in the literature. In this paper we
examine the effectiveness of temporal aggregation for forecasting finite
auto-correlated demand. We do so by means of an analytical study of the
forecast accuracy of aggregation and non-aggregation approaches based on mean
squared error. We complement this with a numerical analysis to explore the
impact of demand parameters and the length of the series on (comparative)
performance. We also conduct an empirical evaluation to validate the analytical
results using monthly time series of the M4-competition dataset. We find the
degree of auto-correlation, the forecast horizon and the length of the series
to be important determinants of forecast accuracy. We discuss the merits of
each approach and highlight their implications for real world practices.


Abstract:
  Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep
learning model for representation learning on graphs. However, it remains
notoriously challenging to train and inference GCNs over large graph datasets,
limiting their application to large real-world graphs and hindering the
exploration of deeper and more sophisticated GCN graphs. This is because as the
graph size grows, the sheer number of node features and the large adjacency
matrix can easily explode the required memory and data movements. To tackle the
aforementioned challenges, we explore the possibility of drawing lottery
tickets when sparsifying GCN graphs, i.e., subgraphs that largely shrink the
adjacency matrix yet are capable of achieving accuracy comparable to or even
better than their full graphs. Specifically, we for the first time discover the
existence of graph early-bird (GEB) tickets that emerge at the very early stage
when sparsifying GCN graphs, and propose a simple yet effective detector to
automatically identify the emergence of such GEB tickets. Furthermore, we
advocate graph-model co-optimization and develop a generic efficient GCN
early-bird training framework dubbed GEBT that can significantly boost the
efficiency of GCN training by (1) drawing joint early-bird tickets between the
GCN graphs and models and (2) enabling simultaneously sparsification of both
the GCN graphs and models. Experiments on various GCN models and datasets
consistently validate our GEB finding and the effectiveness of our GEBT, e.g.,
our GEBT achieves up to 80.2% ~ 85.6% and 84.6% ~ 87.5% savings of GCN training
and inference costs while offering a comparable or even better accuracy as
compared to state-of-the-art methods. Our source code and supplementary
appendix are available at https://github.com/RICE-EIC/Early-Bird-GCN.


Abstract:
  Many high-energy astrophysical systems contain magnetized collisionless
plasmas with relativistic particles, in which turbulence can be driven by an
arbitrary mixture of solenoidal and compressive motions. For example,
turbulence in hot accretion flows may be driven solenoidally by the
magnetorotational instability or compressively by spiral shock waves. It is
important to understand the role of the driving mechanism on kinetic turbulence
and the associated particle energization. In this work, we compare
particle-in-cell simulations of solenoidally driven turbulence with similar
simulations of compressively driven turbulence. We focus on plasma that has an
initial beta of unity, relativistically hot electrons, and varying ion
temperature. Apart from strong large-scale density fluctuations in the
compressive case, the turbulence statistics are similar for both drives, and
the bulk plasma is described reasonably well by an isothermal equation of
state. We find that nonthermal particle acceleration is more efficient when
turbulence is driven compressively. In the case of relativistically hot ions,
both driving mechanisms ultimately lead to similar power-law particle energy
distributions, but over a different duration. In the case of non-relativistic
ions, there is significant nonthermal particle acceleration only for
compressive driving. Additionally, we find that the electron-to-ion heating
ratio is less than unity for both drives, but takes a smaller value for
compressive driving. We demonstrate that this additional ion energization is
associated with the collisionless damping of large-scale compressive modes via
perpendicular electric fields.


Abstract:
  The spin-orbit entangled (SOE) Jeff-state has been a fertile ground to study
novel quantum phenomena. Contrary to the conventional weakly correlated
Jeff=1/2 state of 4d and 5d transition metal compounds, the ground state of
CuAl2O4 hosts a Jeff=1/2 state with a strong correlation of Coulomb U. Here, we
report that surprisingly Cu2+ ions of CuAl2O4 overcome the otherwise usually
strong Jahn-Teller distortion and instead stabilize the SOE state, although the
cuprate has relatively small spin-orbit coupling. From the x-ray absorption
spectroscopy and high-pressure x-ray diffraction studies, we obtained definite
evidence of the Jeff=1/2 state with a cubic lattice at ambient pressure. We
also found the pressure-induced structural transition to a compressed
tetragonal lattice consisting of the spin-only S=1/2 state for pressure higher
than Pc=8 GPa. This phase transition from the Mott insulating Jeff=1/2 to the
S=1/2 states is a unique phenomenon and has not been reported before. Our study
offers a rare example of the SOE Jeff-state under strong electron correlation
and its pressure-induced transition to the S=1/2 state.


Abstract:
  Genetic Algorithm (GA) -- motivated by the natural evolutionary process -- is
a robust method to estimate the optimal solutions of problems involving one or
more objective functions. In this article, for the first time, we apply GA to
reconstruct the cleaned CMB temperature anisotropy map over large angular
scales of the sky using (internal) linear combination (ILC) of observations
from the final-year WMAP and Planck satellite missions. To avoid getting
trapped into a local minimum, we implement the GA with generous diversity in
the populations. This is achieved by introducing a small but significant amount
of mutation of genes during crossover and selecting pairs with diverse fitness
coefficients. We find that the new GA-ILC method produces a cleaned CMB map
which agrees very well with the CMB map obtained using the exact and analytical
expression of weights in the ILC method. By performing extensive Monte Carlo
simulations of the CMB reconstruction using the GA-ILC algorithm we find that
residual foregrounds in the cleaned map are minimal and mostly tend to occupy
localized regions along the central galactic plane. The CMB angular power
spectrum shows no indication of any bias in the entire multipole range $2 \leq
\ell \leq 32$ studied in this work. The error in the CMB angular power spectrum
is minimal as well and given entirely by the cosmic variance induced error. Our
results agree well with those obtained by various other reconstruction methods
by different research groups. This problem-independent robust GA-ILC method
provides a flexible way towards the complex and challenging task of CMB
component reconstruction in cosmology.


Abstract:
  This paper deals with the preparation and characterization of conductive and
flexible sheets based on graphite nanoplates (GNP) and polydimethylsiloxane
(PDMS). Highly porous GNP nanopaper were prepared by filtration from a GNP
suspension in solvent. Subsequently, the impregnation strategy was pursued to
obtain a composite material. By varying the concentration of the solution to be
impregnated and the deposition time, PDMS/GNP nanopapers are produced with a
range of PDMS content, porosity and density. Thermal diffusivity of the films
(both in-plane and cross-plane) were measured and correlated with the structure
of the nanopapers. Based on the results found and considering the restraints,
three formulations were further investigated for their physical, thermal and
mechanical properties. Highly flexible, tough and conductive (up to 25 W/mK)
nanopapers were obtained, for possible application in modern electronic
devices.


Abstract:
  It has been recently shown that the halo near the Sun contains several
kinematic substructures associated to past accretion events. For the more
distant halo, there is evidence of large-scale density variations -- in the
form of stellar clouds or overdensities. We study the link between the local
halo kinematic groups and three of these stellar clouds: the Hercules-Aquila
cloud, the Virgo Overdensity, and the Eridanus-Phoenix overdensity. We perform
orbital integrations in a standard Milky Way potential of a local halo sample
extracted from Gaia eDR3, with the goal of predicting the location of the
merger debris elsewhere in the Galaxy. We specifically focus on the regions
occupied by the three stellar clouds and compare their kinematic and distance
distributions with those predicted from the orbits of the nearby debris. We
find that the local halo substructures have families of orbits that tend to
pile up in the regions where the stellar clouds have been found. The distances
and velocities of the cloud's member stars are in good agreement with those
predicted from the orbit integrations, particularly for Gaia-Enceladus stars.
This is the dominant contributor of all three overdensities, with a minor part
stemming from the Helmi streams and to an even smaller extent from Sequoia. The
orbital integrations predict no asymmetries in the sky distribution of halo
stars, and they pinpoint where additional debris associated with the local halo
substructures may be located.


Abstract:
  We consider a simple theoretical model to investigate the impact of
inheritances on the wealth distribution. Wealth is described as a finite
resource, which remains constant over different generations and is divided
equally among offspring. All other sources of wealth are neglected. We consider
different societies characterized by a different offspring probability
distribution. We find that, if the population remains constant, the society
reaches a stationary wealth distribution. We show that inequality emerges every
time the number of children per family is not always the same. For realistic
offspring distributions from developed countries, the model predicts a Gini
coefficient of $G\approx 0.3$. If we divide the society into wealth classes and
set the probability of getting married to depend on the distance between
classes, the stationary wealth distribution crosses over from an exponential to
a power-law regime as the number of wealth classes and the level of class
distinction increase.


Abstract:
  Successfully attaining consensus in the absence of a centralized coordinator
is a fundamental problem in distributed multi-agent systems. We analyze
progress in the Synod consensus protocol -- which does not assume a unique
leader -- under the assumptions of asynchronous communication and potential
agent failures. We identify a set of sufficient conditions under which it is
possible to guarantee that a set of agents will eventually attain consensus.
First, a subset of the agents must behave correctly and not permanently fail
until consensus is reached, and second, at least one proposal must be
eventually uninterrupted by higher-numbered proposals. To formally reason about
agent failures, we introduce a failure-aware actor model (FAM). Using FAM, we
model the identified conditions and provide a formal proof of eventual progress
in Synod. Our proof has been mechanically verified using the Athena proof
assistant and, to the best of our knowledge, it is the first machine-checked
proof of eventual progress in Synod.


Abstract:
  Two automatic threshold selection (TS) methods for Extreme Value analysis
under a peaks-over-threshold (POT) approach are presented and evaluated, both
built on: fitting the Generalized Pareto distribution (GPd) to excesses'
samples over candidate levels ; the GPd-specific relation between L-skewness
and L-kurtosis; the asymptotic behaviour of the matching L-statistics.
Performance is illustrated on significant wave heights data sets and compared
to the L-moment-based heuristic in [10], which is found to be favorable.
  PUBLISHED VERSION AVAILABLE AT:
https://www.spestatistica.pt/storage/app/uploads/public/609/28f/6d0/60928f6d08a0c016386627.pdf


Abstract:
  Medical steerable needles can move along 3D curvilinear trajectories to avoid
anatomical obstacles and reach clinically significant targets inside the human
body. Automating steerable needle procedures can enable physicians and patients
to harness the full potential of steerable needles by maximally leveraging
their steerability to safely and accurately reach targets for medical
procedures such as biopsies and localized therapy delivery for cancer. For the
automation of medical procedures to be clinically accepted, it is critical from
a patient care, safety, and regulatory perspective to certify the correctness
and effectiveness of the motion planning algorithms involved in procedure
automation. In this paper, we take an important step toward creating a
certifiable motion planner for steerable needles. We introduce the first motion
planner for steerable needles that offers a guarantee, under clinically
appropriate assumptions, that it will, in finite time, compute an exact,
obstacle-avoiding motion plan to a specified target, or notify the user that no
such plan exists. We present an efficient, resolution-complete motion planner
for steerable needles based on a novel adaptation of multi-resolution planning.
Compared to state-of-the-art steerable needle motion planners (none of which
provide any completeness guarantees), we demonstrate that our new
resolution-complete motion planner computes plans faster and with a higher
success rate.


Abstract:
  We prove that Cheng maximal diameter theorem for hypergraphs with positive
coarse Ricci curvature.


Abstract:
  The observations of high redshifts quasars at $z\gtrsim 6$ have revealed that
supermassive black holes (SMBHs) of mass $\sim 10^9\,\mathrm{M_{\odot}}$ were
already in place within the first $\sim$ Gyr after the Big Bang. Supermassive
stars (SMSs) with masses $10^{3-5}\,\mathrm{M_{\odot}}$ are potential seeds for
these observed SMBHs. A possible formation channel of these SMSs is the
interplay of gas accretion and runaway stellar collisions inside dense nuclear
star clusters (NSCs). However, mass loss due to stellar winds could be an
important limitation for the formation of the SMSs and affect the final mass.
In this paper, we study the effect of mass loss driven by stellar winds on the
formation and evolution of SMSs in dense NSCs using idealised N-body
simulations. Considering different accretion scenarios, we have studied the
effect of the mass loss rates over a wide range of metallicities
$Z_\ast=[.001-1]\mathrm{Z_{\odot}}$ and Eddington factors $f_{\rm
Edd}=L_\ast/L_{\mathrm{Edd}}=0.5,0.7,\,\&\, 0.9$. For a high accretion rate of
$10^{-4}\,\mathrm{M_{\odot}yr^{-1}}$, SMSs with masses $\gtrsim 10^3\MSun$
could be formed even in a high metallicity environment. For a lower accretion
rate of $10^{-5}\,\mathrm{M_{\odot}yr^{-1}}$, SMSs of masses $\sim
10^{3-4}\,\mathrm{M_{\odot}}$ can be formed for all adopted values of $Z_\ast$
and $f_{\rm Edd}$, except for $Z_\ast=\mathrm{Z_{\odot}}$ and $f_{\rm Edd}=0.7$
or 0.9. For Eddington accretion, SMSs of masses $\sim 10^3\,\mathrm{M_{\odot}}$
can be formed in low metallicity environments with $Z_\ast\lesssim
0.01\mathrm{Z_{\odot}}$. The most massive SMSs of masses $\sim
10^5\,\mathrm{M_{\odot}}$ can be formed for Bondi-Hoyle accretion in
environments with $Z_\ast \lesssim 0.5\mathrm{Z_{\odot}}$.


Abstract:
  With the rapid growth of data, it is becoming increasingly difficult to train
or improve deep learning models with the right subset of data. We show that
this problem can be effectively solved at an additional labeling cost by
targeted data subset selection(TSS) where a subset of unlabeled data points
similar to an auxiliary set are added to the training data. We do so by using a
rich class of Submodular Mutual Information (SMI) functions and demonstrate its
effectiveness for image classification on CIFAR-10 and MNIST datasets. Lastly,
we compare the performance of SMI functions for TSS with other state-of-the-art
methods for closely related problems like active learning. Using SMI functions,
we observe ~20-30% gain over the model's performance before re-training with
added targeted subset; ~12% more than other methods.


Abstract:
  The loading of resources from third-parties has evoked new security and
privacy concerns about the current world wide web. Building on the concepts of
forced and implicit trust, this paper examines cross-domain transmission
control protocol (TCP) connections that are initiated to domains other than the
domain queried with a web browser. The dataset covers nearly ten thousand
domains and over three hundred thousand TCP connections initiated by querying
popular Finnish websites and globally popular sites. According to the results,
(i) cross-domain connections are extremely common in the current Web. (ii) Most
of these transmit encrypted content, although mixed content delivery is
relatively common; many of the cross-domain connections deliver unencrypted
content at the same time. (iii) Many of the cross-domain connections are
initiated to known web advertisement domains, but a much larger share traces to
social media platforms and cloud infrastructures. Finally, (iv) the results
differ slightly between the Finnish web sites sampled and the globally popular
sites. With these results, the paper contributes to the ongoing work for better
understanding cross-domain connections and dependencies in the world wide web.


Abstract:
  In this paper we analyze the problem of optimal task scheduling for data
centers. Given the available resources and tasks, we propose a fast distributed
iterative algorithm which operates over a large scale network of nodes and
allows each of the interconnected nodes to reach agreement to an optimal
solution in a finite number of time steps. More specifically, the algorithm (i)
is guaranteed to converge to the exact optimal scheduling plan in a finite
number of time steps and, (ii) once the goal of task scheduling is achieved, it
exhibits distributed stopping capabilities (i.e., it allows the nodes to
distributely determine whether they can terminate the operation of the
algorithm). Furthermore, the proposed algorithm operates exclusively with
quantized values (i.e., the information stored, processed and exchanged between
neighboring agents is subject to deterministic uniform quantization) and relies
on event-driven updates (e.g., to reduce energy consumption, communication
bandwidth, network congestion, and/or processor usage). We also provide
examples to illustrate the operation, performance, and potential advantages of
the proposed algorithm. Finally, by using extensive empirical evaluations
through simulations we show that the proposed algorithm exhibits
state-of-the-art performance.


Abstract:
  Within Density Functional Theory, we have studied the interplay between
vortex arrays and capillary waves in spinning prolate He-4 droplets made of
several thousands of helium atoms. Surface capillary waves are ubiquitous in
prolate superfluid He-4 droplets and, depending on the size and angular
momentum of the droplet, they may coexist with vortex arrays. We have found
that the equilibrium configuration of small prolate droplets is vortex-free,
evolving towards vortex-hosting as the droplet size increases. This result is
in agreement with a recent experiment [S.M. O'Connell et al., Phys. Rev. Lett.
124, 215301 (2020)], where it has been disclosed that vortex arrays and
capillary waves coexist in the equilibrium configuration of very large drops.
Contrarily to viscous droplets executing rigid body rotation, the stability
phase diagram of spinning He-4 droplets cannot be universally described in
terms of dimensionless angular momentum and angular velocity variables:
instead, the rotational properties of superfluid helium droplets display a
clear dependence on the droplet size and the number of vortices they host.


Abstract:
  This editorial article partially informs the algorithmic trading community
about launching of the new journal "Algorithmic Trading and Controls" (ATC).
ATC is an online open-access journal that publishes novel works on algorithmic
trading and its control methodologies. In this inaugural article, we discuss
nine major challenges that contemporary Algo trading faces. There is nothing
superstitiously magical about the number "nine," but so is any other one.
Several of these challenges are at the strategy level, including for example,
trading of illiquid securities or optimal portfolio execution. Others are more
at the level of risk management and controls, such as on how to develop
automated controls, testing and simulations. The editorial views could be
inevitably personal and biased, but have been explored with the most innocent
intention of contributing to this important field in modern financial services
and technologies.


Abstract:
  Prophet inequalities are a useful tool for designing online allocation
procedures and comparing their performance to the optimal offline allocation.
In the basic setting of $k$-unit prophet inequalities, the magical procedure of
Alaei (2011) with its celebrated performance guarantee of
$1-\frac{1}{\sqrt{k+3}}$ has found widespread adoption in mechanism design and
other online allocation problems in online advertising, healthcare scheduling,
and revenue management. Despite being commonly used for implementing online
allocation, the tightness of Alaei's procedure for a given $k$ has remained
unknown. In this paper we resolve this question, characterizing the tight bound
by identifying the structure of the optimal online implementation, and
consequently improving the best-known guarantee for $k$-unit prophet
inequalities for all $k>1$. We also consider a more general online stochastic
knapsack problem where each individual allocation can consume an arbitrary
fraction of the initial capacity. We introduce a new "best-fit" procedure for
implementing a fractionally-feasible knapsack solution online, with a
performance guarantee of $\frac{1}{3+e^{-2}}\approx0.319$, which we also show
is tight. This improves the previously best-known guarantee of 0.2 for online
knapsack. Our analysis differs from existing ones by eschewing the need to
split items into "large" or "small" based on capacity consumption, using
instead an invariant for the overall utilization on different sample paths.
Finally, we refine our technique for the unit-density special case of knapsack,
and improve the guarantee from 0.321 to 0.3557 in the multi-resource
appointment scheduling application of Stein et al. (2020). All in all, our
results imply \textit{tight} Online Contention Resolution Schemes for
$k$-uniform matroids and the knapsack polytope, respectively, which has further
implications in mechanism design.


Abstract:
  Coherent interconversion of signals between optical and mechanical domains is
enabled by optomechanical interactions. Extreme light-matter coupling produced
by confining light to nanoscale mode volumes can then access single
mid-infrared (MIR) photon sensitivity. Here we utilise the infrared absorption
and Raman activity of molecular vibrations in plasmonic nanocavities to
demonstrate frequency upconversion. We convert {\lambda}~10 {\mu}m incoming
light to visible via surface-enhanced Raman scattering (SERS) in
doubly-resonant antennas that enhance upconversion by >10^10. We show >200%
amplification of the SERS antiStokes emission when a MIR pump is tuned to a
molecular vibrational frequency, obtaining lowest detectable powers ~1
{\mu}W/{\mu}m^2 at room temperature. These results have potential for low-cost
and large-scale infrared detectors and spectroscopic techniques, and bring
single-molecule sensing into the infrared


Abstract:
  Searchable Symmetric Encryption (SSE) allows a data owner to securely
outsource its encrypted data to a cloud server while maintaining the ability to
search over it and retrieve matched documents. Most existing SSE schemes leak
which documents are accessed per query, i.e., the so-called access pattern, and
thus are vulnerable to attacks that can recover the database or the queried
keywords. Current techniques that fully hide access patterns, such as ORAM or
PIR, suffer from heavy communication or computational costs, and are not
designed with search capabilities in mind. Recently, Chen et al. (INFOCOM'18)
proposed an obfuscation framework for SSE that protects the access pattern in a
differentially private way with a reasonable utility cost. However, this scheme
leaks the so-called search pattern, i.e., how many times a certain query is
performed. This leakage makes the proposal vulnerable to certain database and
query recovery attacks.
  In this paper, we propose OSSE (Obfuscated SSE), an SSE scheme that
obfuscates the access pattern independently for each query performed. This in
turn hides the search pattern and makes our scheme resistant against attacks
that rely on this leakage. Under certain reasonable assumptions, our scheme has
smaller communication overhead than ORAM-based SSE. Furthermore, our scheme
works in a single communication round and requires very small constant
client-side storage. Our empirical evaluation shows that OSSE is highly
effective at protecting against different query recovery attacks while keeping
a reasonable utility level. Our protocol provides significantly more protection
than the proposal by Chen et al.~against some state-of-the-art attacks, which
demonstrates the importance of hiding search patterns in designing effective
privacy-preserving SSE schemes.


Abstract:
  In ML-aided decision-making tasks, such as fraud detection or medical
diagnosis, the human-in-the-loop, usually a domain-expert without technical ML
knowledge, prefers high-level concept-based explanations instead of low-level
explanations based on model features. To obtain faithful concept-based
explanations, we leverage multi-task learning to train a neural network that
jointly learns to predict a decision task based on the predictions of a
precedent explainability task (i.e., multi-label concepts). There are two main
challenges to overcome: concept label scarcity and the joint learning. To
address both, we propose to: i) use expert rules to generate a large dataset of
noisy concept labels, and ii) apply two distinct multi-task learning strategies
combining noisy and golden labels. We compare these strategies with a fully
supervised approach in a real-world fraud detection application with few golden
labels available for the explainability task. With improvements of 9.26% and of
417.8% at the explainability and decision tasks, respectively, our results show
it is possible to improve performance at both tasks by combining labels of
heterogeneous quality.


Abstract:
  Data augmentation has attracted a lot of research attention in the deep
learning era for its ability in alleviating data sparseness. The lack of
labeled data for unseen evaluation databases is exactly the major challenge for
cross-domain text-to-SQL parsing. Previous works either require human
intervention to guarantee the quality of generated data, or fail to handle
complex SQL queries. This paper presents a simple yet effective data
augmentation framework. First, given a database, we automatically produce a
large number of SQL queries based on an abstract syntax tree grammar. For
better distribution matching, we require that at least 80% of SQL patterns in
the training data are covered by generated queries. Second, we propose a
hierarchical SQL-to-question generation model to obtain high-quality natural
language questions, which is the major contribution of this work. Finally, we
design a simple sampling strategy that can greatly improve training efficiency
given large amounts of generated data. Experiments on three cross-domain
datasets, i.e., WikiSQL and Spider in English, and DuSQL in Chinese, show that
our proposed data augmentation framework can consistently improve performance
over strong baselines, and the hierarchical generation component is the key for
the improvement.