Abstract:
  Magnetic field source localization and imaging happen at different scales.
The sensing baseline ranges from meter scale such as magnetic anomaly
detection, centimeter scale such as brain field imaging to nanometer scale such
as the imaging of magnetic skyrmion and single cell. Here we show how atomic
vapor cell can be used to realize a baseline of 109.6 {\mu}m with a magnetic
sensitivity of 10pT/sqrt(Hz)@0.6-100Hz and a dynamic range of 2062-4124nT.We
use free induction decay (FID) scheme to suppress low-frequency noise and avoid
scale factor variation for different domains due to light non-uniformity. The
measurement domains are scanned by digital micro-mirror device (DMD). The
currents of 22mA, 30mA, 38mA and 44mA are applied in the coils to generate
different fields along the pumping axis which are measured respectively by
fitting the FID signals of the probe light. The residual fields of every domain
are obtained from the intercept of linearly-fitting of the measurement data
corresponding to these four currents. The coil-generated fields are calculated
by deducting the residual fields from the total fields. The results demonstrate
that the hole of shield affects both the residual and the coil-generated field
distribution. The potential impact of field distribution measurement with an
outstanding comprehensive properties of spatial resolution, sensitivity and
dynamic range is far-reaching. It could lead to capability of 3D magnetography
for small stuffs and/or organs in millimeter or even smaller scale.


Abstract:
  This paper presents libtxsize, a library to estimate the size requirements of
arbitrary Bitcoin transactions. To account for different use cases, the library
provides estimates in bytes, virtual bytes, and weight units. In addition to
all currently existing input, output, and witness types, the library also
supports estimates for the anticipated Pay-to-Taproot transaction type, so that
estimates can be used as input for models attempting to quantify the impact of
Taproot on Bitcoin's scalability. libtxsize is based on analytic models, whose
credibility is established through first-principle analysis of transaction
types as well as exhaustive empirical validation. Consequently, the paper can
also serve as reference for different Bitcoin data and transaction types, their
semantics, and their size requirements (both from an analytic and empirical
point of view).


Abstract:
  We present a new approach for fast calculation of gravitational lensing
properties, including the lens potential, deflection angles, convergence, and
shear, of elliptical Navarro-Frenk-White (NFW) and Hernquist density profiles,
by approximating them by superpositions of elliptical density profiles for
which simple analytic expressions of gravitational lensing properties are
available. This model achieves high fractional accuracy better than $10^{-4}$
in the range of the radius normalized by the scale radius of $10^{-4}-10^3$.
These new approximations are $\sim 300$ times faster in solving the lens
equation for a point source compared with the traditional approach resorting to
expensive numerical integrations, and are implemented in {\tt glafic} software.


Abstract:
  There are situations where data relevant to a machine learning problem are
distributed among multiple locations that cannot share the data due to
regulatory, competitiveness, or privacy reasons. For example, data present in
users' cellphones, manufacturing data of companies in a given industrial
sector, or medical records located at different hospitals. Federated Learning
(FL) provides an approach to learn a joint model over all the available data
across silos. In many cases, participating sites have different data
distributions and computational capabilities. In these heterogeneous
environments previous approaches exhibit poor performance: synchronous FL
protocols are communication efficient, but have slow learning convergence;
conversely, asynchronous FL protocols have faster convergence, but at a higher
communication cost. Here we introduce a novel Semi-Synchronous Federated
Learning protocol that mixes local models periodically with minimal idle time
and fast convergence. We show through extensive experiments that our approach
significantly outperforms previous work in data and computationally
heterogeneous environments.


Abstract:
  Measures of algorithmic fairness often do not account for human perceptions
of fairness that can substantially vary between different sociodemographics and
stakeholders. The FairCeptron framework is an approach for studying perceptions
of fairness in algorithmic decision making such as in ranking or
classification. It supports (i) studying human perceptions of fairness and (ii)
comparing these human perceptions with measures of algorithmic fairness. The
framework includes fairness scenario generation, fairness perception
elicitation and fairness perception analysis. We demonstrate the FairCeptron
framework by applying it to a hypothetical university admission context where
we collect human perceptions of fairness in the presence of minorities. An
implementation of the FairCeptron framework is openly available, and it can
easily be adapted to study perceptions of algorithmic fairness in other
application contexts. We hope our work paves the way towards elevating the role
of studies of human fairness perceptions in the process of designing
algorithmic decision making systems.


Abstract:
  For a fixed graph $H$ and for arbitrarily large host graphs $G$, the number
of homomorphisms from $H$ to $G$ and the number of subgraphs isomorphic to $H$
contained in $G$ have been extensively studied in extremal graph theory and
graph limits theory when the host graphs are allowed to be dense. This paper
addresses the case when the host graphs are robustly sparse and proves a
general theorem that solves a number of open questions proposed since 1990s and
strengthens a number of results in the literature.
  We prove that for any graph $H$ and any set ${\mathcal H}$ of homomorphisms
from $H$ to members of a hereditary class ${\mathcal G}$ of graphs, if
${\mathcal H}$ satisfies a natural and mild condition, and contracting disjoint
subgraphs of radius $O(\lvert V(H) \rvert)$ in members of ${\mathcal G}$ cannot
create a graph with large edge-density, then an obvious lower bound for the
size of ${\mathcal H}$ gives a good estimation for the size of ${\mathcal H}$.
This result determines the maximum number of $H$-homomorphisms, the maximum
number of $H$-subgraphs, and the maximum number $H$-induced subgraphs in graphs
in any hereditary class with bounded expansion up to a constant factor; it also
determines the exact value of the asymptotic logarithmic density for
$H$-homomorphisms, $H$-subgraphs and $H$-induced subgraphs in graphs in any
hereditary nowhere dense class. Hereditary classes with bounded expansion
include (topological) minor-closed families and many classes of graphs with
certain geometric properties; nowhere dense classes are the most general sparse
classes in sparsity theory. Our machinery also allows us to determine the
maximum number of $H$-subgraphs in the class of all $d$-degenerate graphs with
any fixed $d$.


Abstract:
  The multiplicative and additive compounds of a matrix play an important role
in several fields of mathematics including geometry, multi-linear algebra,
combinatorics, and the analysis of nonlinear time-varying dynamical systems.
There is a growing interest in applications of these compounds, and their
generalizations, in systems and control theory. This tutorial paper provides a
gentle introduction to these topics with an emphasis on the geometric
interpretation of the compounds, and surveys some of their recent applications.


Abstract:
  Gaussian distributions can be generalized from Euclidean space to a wide
class of Riemannian manifolds. Gaussian distributions on manifolds are harder
to make use of in applications since the normalisation factors, which we will
refer to as partition functions, are complicated, intractable integrals in
general that depend in a highly non-linear way on the mean of the given
distribution. Nonetheless, on Riemannian symmetric spaces, the partition
functions are independent of the mean and reduce to integrals over finite
dimensional vector spaces. These are generally still hard to compute
numerically when the dimension (more precisely the rank $N$) of the underlying
symmetric space gets large. On the space of positive definite Hermitian
matrices, it is possible to compute these integrals exactly using methods from
random matrix theory and the so-called Stieltjes-Wigert polynomials. In other
cases of interest to applications, such as the space of symmetric positive
definite (SPD) matrices or the Siegel domain (related to block-Toeplitz
covariance matrices), these methods seem not to work quite as well.
Nonetheless, it remains possible to compute leading order terms in a large $N$
limit, which provide increasingly accurate approximations as $N$ grows. This
limit is inspired by realizing a given partition function as the partition
function of a zero-dimensional quantum field theory or even Chern-Simons
theory. From this point of view the large $N$ limit arises naturally and
saddle-point methods, Feynman diagrams, and certain universalities that relate
different spaces emerge.


Abstract:
  A neoclassically optimized compact stellarator with simple coils has been
designed. The magnetic field of the new stellarator is generated by only four
planar coils including two interlocking coils of elliptical shape and two
circular poloidal field coils. The interlocking coil topology is the same as
that of the Columbia Non-neutral Torus (CNT). The new configuration was
obtained by minimizing the effective helical ripple directly via the shape of
the two interlocking coils. The optimized compact stellarator has very low
effective ripple in the plasma core implying excellent neoclassical
confinement. This is confirmed by the results of the drift-kinetic code SFINCS
showing that the particle diffusion coefficient of the new configuration is one
order of magnitude lower than CNT's.


Abstract:
  Given a compact Riemann surface $\Sigma$ of genus $g_\Sigma\, \geq\, 2$, and
an effective divisor $D\, =\, \sum_i n_i x_i$ on $\Sigma$ with
$\text{degree}(D)\, <\, 2(g_\Sigma -1)$, there is a unique cone metric on
$\Sigma$ of constant negative curvature $-4$ such that the cone angle at each
$x_i$ is $2\pi n_i$ (see McOwen and Troyanov [McO,Tr]). We describe the Higgs
bundle corresponding to this uniformization associated to the above conical
metric. We also give a family of Higgs bundles on $\Sigma$ parametrized by a
nonempty open subset of $H^0(\Sigma,\,K_\Sigma^{\otimes 2}\otimes{\mathcal
O}_\Sigma(-2D))$ that correspond to conical metrics of the above type on moving
Riemann surfaces. These are inspired by Hitchin's results in [Hi1], for the
case $D\,=\, 0$.


Abstract:
  Package-to-group recommender systems recommend a set of unified items to a
group of people. Different from conventional settings, it is not easy to
measure the utility of group recommendations because it involves more than one
user. In particular, fairness is crucial in group recommendations. Even if some
members in a group are substantially satisfied with a recommendation, it is
undesirable if other members are ignored to increase the total utility. Many
methods for evaluating and applying the fairness of group recommendations have
been proposed in the literature. However, all these methods maximize the score
and output only one package. This is in contrast to conventional recommender
systems, which output several (e.g., top-$K$) candidates. This can be
problematic because a group can be dissatisfied with the recommended package
owing to some unobserved reasons, even if the score is high. To address this
issue, we propose a method to enumerate fair packages efficiently. Our method
furthermore supports filtering queries, such as top-$K$ and intersection, to
select favorite packages when the list is long. We confirm that our algorithm
scales to large datasets and can balance several aspects of the utility of the
packages.


Abstract:
  Milwaukee's 53206 ZIP code, located on the city's near North Side, has drawn
considerable attention for its poverty and incarceration rates, as well as for
its large proportion of vacant properties. As a result, it has benefited from
targeted policies at the city level. Keeping in mind that ZIP codes are often
not the most effective unit of geographic analysis, this study investigates
Milwaukee's socioeconomic conditions at the block group level. These smaller
areas' statistics are then compared with those of their corresponding ZIP
codes. The 53206 ZIP code is compared against others in Milwaukee for eight
socioeconomic variables and is found to be near the extreme end of most
rankings. This ZIP code would also be among Chicago's most extreme areas, but
would lie near the middle of the rankings if located in Detroit. Parts of other
ZIP codes, which are often adjacent, are statistically similar to 53206,
however--suggesting that a focus solely on ZIP codes, while a convenient
shorthand, might overlook neighborhoods that have similar need for investment.
A multivariate index created for this study performs similarly to a standard
multivariate index of economic deprivation if spatial correlation is taken into
account, confirming that poverty and other socioeconomic stresses are
clustered, both in the 53206 ZIP code and across Milwaukee.


Abstract:
  Coulomb fission mechanism may take place if the maximum Coulomb-excitation
energy transfer in a reaction exceeds the fission barrier of either the
projectile or target. This condition is satisfied by all the reactions used for
the earlier blocking measurements except one reaction 208 Pb + Natural Ge
crystal, where the measured timescale was below the measuring limit of the
blocking measurements < 1 as. Hence, inclusion of the Coulomb fission in the
data analysis of the blocking experiments leads us to interpret that the
measured time longer than a few attoseconds (about 2-2.5 as) is nothing but
belonging to the Coulomb fission timescale and shorter than 1 as are due to the
quasifission. Consequently, this finding resolves the critical discrepancies
between the fission timescale measurements using the nuclear and blocking
techniques. This, in turn, validates the fact that the quasifission timescale
is indeed of the order of zeptoseconds in accordance with the nuclear
experiments and theories. It thus provides a radical input in understanding the
reaction mechanism for heavy element formation via fusion evaporation processes


Abstract:
  Consider the family of bounded degree graphs in any minor-closed family (such
as planar graphs). Let d be the degree bound and n be the number of vertices of
such a graph. Graphs in these classes have hyperfinite decompositions, where,
for a sufficiently small \e > 0, one removes \edn edges to get connected
components of size independent of n. An important tool for sublinear algorithms
and property testing for such classes is the partition oracle, introduced by
the seminal work of Hassidim-Kelner-Nguyen-Onak (FOCS 2009). A partition oracle
is a local procedure that gives consistent access to a hyperfinite
decomposition, without any preprocessing. Given a query vertex v, the partition
oracle outputs the component containing v in time independent of n. All the
answers are consistent with a single hyperfinite decomposition. The partition
oracle of Hassidim et al. runs in time d^poly(d/\e) per query. They pose the
open problem of whether poly(d/\e)-time partition oracles exist. Levi-Ron
(ICALP 2013) give a refinement of the previous approach, to get a partition
oracle that runs in time d^{\log(d/\e)-per query. In this paper, we resolve
this open problem and give \poly(d/\e)-time partition oracles for bounded
degree graphs in any minor-closed family. Unlike the previous line of work
based on combinatorial methods, we employ techniques from spectral graph
theory. We build on a recent spectral graph theoretical toolkit for
minor-closed graph families, introduced by the authors to develop efficient
property testers. A consequence of our result is a poly(d/\e)-query tester for
any monotone and additive property of minor-closed families (such as bipartite
planar graphs). Our result also gives poly(d/\e)-query algorithms for additive
{\e}n-approximations for problems such as maximum matching, minimum vertex
cover, maximum independent set, and minimum dominating set for these graph
families.


Abstract:
  This arXiv report provides a short introduction to the information-theoretic
measure proposed by Chen and Golan in 2016 for analyzing machine- and
human-centric processes in data intelligence workflows. This introduction was
compiled based on several appendices written to accompany a few research papers
on topics of data visualization and visual analytics. Although the original
2016 paper and the follow-on papers were mostly published in the field of
visualization and visual analytics, the cost-benefit measure can help explain
the informative trade-off in a wide range of data intelligence phenomena
including machine learning, human cognition, language development, and so on.
Meanwhile, there is an ongoing effort to improve its mathematical properties in
order to make it more intuitive and usable in practical applications as a
measurement tool.


Abstract:
  Face recognition (FR) using deep convolutional neural networks (DCNNs) has
seen remarkable success in recent years. One key ingredient of DCNN-based FR is
the appropriate design of a loss function that ensures discrimination between
various identities. The state-of-the-art (SOTA) solutions utilise normalised
Softmax loss with additive and/or multiplicative margins. Despite being
popular, these Softmax+margin based losses are not theoretically motivated and
the effectiveness of a margin is justified only intuitively. In this work, we
utilise an alternative framework that offers a more direct mechanism of
achieving discrimination among the features of various identities. We propose a
novel loss that is equivalent to a triplet loss with proxies and an implicit
mechanism of hard-negative mining. We give theoretical justification that
minimising the proposed loss ensures a minimum separability between all
identities. The proposed loss is simple to implement and does not require heavy
hyper-parameter tuning as in the SOTA solutions. We give empirical evidence
that despite its simplicity, the proposed loss consistently achieves SOTA
performance in various benchmarks for both high-resolution and low-resolution
FR tasks.


Abstract:
  Bimetallic nanoparticles (BNPs) exhibit diverse morphologies such as
core-shell, Janus, onion-like, quasi-Janus, and homogeneous structures.
Although extensive effort has been directed towards understanding the
equilibrium configurations of BNPs, kinetic mechanisms involved in their
development have not been explored systematically. Since these systems often
contain a miscibility gap, experimental studies have alluded to spinodal
decomposition (SD) as a likely mechanism for the formation of such structures.
We present a novel phase-field model for confined (embedded)systems to study
SD-induced morphological evolution within a BNP. It initiates with the
formation of compositionally modulated rings as a result of surface-directed SD
and eventually develops into core-shell or Janus structures due to
coarsening/breakdown of the rings. The final configuration depends crucially on
contact angle and particle size -Janus is favored at smaller sizes and higher
contact angles. Our simulations also illustrate the formation of metastable,
kinetically trapped structures as a result of competition between capillarity
and diffusion.


Abstract:
  We consider the vector space $E_{\rho,p}$ of entire functions of finite
order, whose types are not more than $p>0$, endowed with Frechet topology,
which is generated by a sequence of weighted norms. We call a function $f\in
E_{\rho,p}$ {\it typical} if it is surjective and has an infinite number
critical points such that each of them is non-degenerate and all the values of
$f$ at these points are pairwise different. We prove that the set of all
typical functions contains a set which is $G_\delta$ and dense in $E_{\rho,p}$.
Furthermore, we show that inverse to any typical function has Riemann surface
whose monodromy group coincides with finitary symmetric group of permutations
of naturals, which is unsolvable in the following strong sense: it does not
have a normal tower of subgroups, whose factor groups are or abelian or finite.
As a consequence from these facts and Topological Galois Theory, we obtain that
generically (in the above sense) for $f\in E_{\rho,p}$ the solution of equation
$f(w)=z$ cannot be represented via $z$ and complex constants by a finite number
of the following actions: algebraic operations (i.e., rational ones and
solutions of polynomial equations) and quadratures (in particular,
superpositions with elementary functions).


Abstract:
  The standard diffusive spreading, characterized by a Gaussian distribution
with mean square displacement that grows linearly with time, can break down,
for instance, under the presence of correlations and heterogeneity. In this
work, we consider the spread of a population of fractional (long-time
correlated) Brownian walkers, with time-dependent and heterogeneous
diffusivity. We aim to obtain the possible scenarios related to these
individual-level features from the observation of the temporal evolution of the
population spatial distribution. We develop and discuss the possibility and
limitations of this connection for the broad class of self-similar diffusion
processes. Our results are presented in terms of a general framework, which is
then used to address well-known processes, such as Laplace diffusion, nonlinear
diffusion, and their extensions.


Abstract:
  The existence of massive compact stars $(M\gtrsim 2.1 M_{\odot})$ implies
that the conformal limit of the speed of sound $c_s^2=1/3$ is violated if those
stars have a crust of ordinary nuclear matter. Here we show that, if the most
massive objects are strange quark stars, i.e. stars entirely composed of
quarks, the conformal limit can be respected while observational limits on
those objects are also satisfied. By using astrophysical data associated with
those massive stars, derived from electromagnetic and gravitational wave
signals, we show, within a Bayesian analysis framework and by adopting a
constant speed of sound equation of state, that the posterior distribution of
$c_s^2$ is peaked around 0.3, and the maximum mass of the most probable
equation of state is $\sim 2.13 M_{\odot}$. We discuss which new data would
require a violation of the conformal limit even when considering strange quark
stars, in particular we analyze the possibility that the maximum mass of
compact stars is larger than $2.5M_{\odot}$, as it would be if the secondary
component of GW190814 is a compact star and not a black hole. Finally, we
discuss how the new data for PSR J0740+6620 obtained by the NICER collaboration
compare with our analysis (not based on them) and with other possible
interpretations.


Abstract:
  From Swift monitoring of a sample of active galactic nuclei (AGN) we found a
transient X-ray obscuration event in Seyfert-1 galaxy NGC 3227, and thus
triggered our joint XMM-Newton, NuSTAR, and Hubble Space Telescope (HST)
observations to study this event. Here in the first paper of our series we
present the broadband continuum modelling of the spectral energy distribution
(SED) for NGC 3227, extending from near infrared (NIR) to hard X-rays. We use
our new spectra taken with XMM-Newton, NuSTAR, and HST/COS in 2019, together
with archival unobscured XMM-Newton, NuSTAR, and HST/STIS data, in order to
disentangle various spectral components of NGC 3227 and recover the underlying
continuum. We find the observed NIR-optical-UV continuum is explained well by
an accretion disk blackbody component (Tmax = 10 eV), which is internally
reddened by E(B-V) = 0.45 with a Small Magellanic Cloud (SMC) extinction law.
We derive the inner radius (12 Rg) and the accretion rate (0.1 solar mass per
year) of the disk by modelling the thermal disk emission. The internal
reddening in NGC 3227 is most likely associated with outflows from the dusty
AGN torus. In addition, an unreddened continuum component is also evident,
which likely arises from scattered radiation, associated with the extended
narrow-line region (NLR) of NGC 3227. The extreme ultraviolet (EUV) continuum,
and the 'soft X-ray excess', can be explained with a 'warm Comptonisation'
component. The hard X-rays are consistent with a power-law and a neutral
reflection component. The intrinsic bolometric luminosity of the AGN in NGC
3227 is about 2.2e+43 erg/s in 2019, corresponding to 3% Eddington luminosity.
Our continuum modelling of the new triggered data of NGC 3227 requires the
presence of a new obscuring gas with column density NH = 5e+22 cm^-2, partially
covering the X-ray source (Cf = 0.6).


Abstract:
  No quantum circuit can turn a completely unknown unitary gate into its
coherently controlled version. Yet, coherent control of unknown gates has been
realised in experiments, making use of a different type of initial resources.
Here, we formalise the task achieved by these experiments, extending it to the
control of arbitrary noisy channels, and to more general types of control
involving higher dimensional control systems. For the standard notion of
coherent control, we identify the information-theoretic resource for
controlling an arbitrary quantum channel on a $d$-dimensional system:
specifically, the resource is an extended quantum channel acting as the
original channel on a $d$-dimensional sector of a $(d+1)$-dimensional system.
Using this resource, arbitrary controlled channels can be built with a
universal circuit architecture. We then extend the standard notion of control
to more general notions, including control of multiple channels with possibly
different input and output systems. Finally, we develop a theoretical
framework, called supermaps on routed channels, which provides a compact
representation of coherent control as an operation performed on the extended
channels, and highlights the way the operation acts on different sectors.


Abstract:
  This paper presents a taxonomy that allows defining the fault tolerance
regimes fail-operational, fail-degraded, and fail-safe in the context of
automotive systems. Fault tolerance regimes such as these are widely used in
recent publications related to automated driving, yet without definitions. This
largely holds true for automotive safety standards, too. We show that fault
tolerance regimes defined in scientific publications related to the automotive
domain are partially ambiguous as well as taxonomically unrelated. The
presented taxonomy is based on terminology stemming from ISO 26262 as well as
from systems engineering. It uses four criteria to distinguish fault tolerance
regimes. In addition to fail-operational, fail-degraded, and fail-safe, the
core terminology consists of operational and fail-unsafe. These terms are
supported by definitions of available performance, nominal performance,
functionality, and a concise definition of the safe state. For verification, we
show by means of two examples from the automotive domain that the taxonomy can
be applied to hierarchical systems of different complexity.


Abstract:
  We propose Preferential MoE, a novel human-ML mixture-of-experts model that
augments human expertise in decision making with a data-based classifier only
when necessary for predictive performance. Our model exhibits an interpretable
gating function that provides information on when human rules should be
followed or avoided. The gating function is maximized for using human-based
rules, and classification errors are minimized. We propose solving a coupled
multi-objective problem with convex subproblems. We develop approximate
algorithms and study their performance and convergence. Finally, we demonstrate
the utility of Preferential MoE on two clinical applications for the treatment
of Human Immunodeficiency Virus (HIV) and management of Major Depressive
Disorder (MDD).


Abstract:
  The rapid expansion of distributed energy resources (DERs) is one of the most
significant changes to electricity systems around the world. Examples of DERs
include solar panels, small natural gas-fueled generators, combined heat and
power plants, etc. Due to the small supply capacities of these DERs, it is
impractical for them to participate directly in the wholesale electricity
market. We study in this paper an efficient aggregation model where a
profit-maximizing aggregator procures electricity from DERs, and sells them in
the wholesale market. The interaction between the aggregator and the DER owners
is modeled as a Stackelberg game: the aggregator adopts two-part pricing by
announcing a participation fee and a per-unit price of procurement for each DER
owner, and the DER owner responds by choosing her payoff-maximizing energy
supplies. We show that our proposed model preserves full market efficiency,
i.e., the social welfare achieved by the aggregation model is the same as that
when DERs participate directly in the wholesale market. We also note that
two-part pricing is critical for market efficiency, and illustrate via an
example that with one-part pricing, there will be an efficiency loss from DER
aggregation, due to the profit-seeking behavior of the aggregator.


Abstract:
  Learning and reasoning over graphs is increasingly done by means of
probabilistic models, e.g. exponential random graph models, graph embedding
models, and graph neural networks. When graphs are modeling relations between
people, however, they will inevitably reflect biases, prejudices, and other
forms of inequity and inequality. An important challenge is thus to design
accurate graph modeling approaches while guaranteeing fairness according to the
specific notion of fairness that the problem requires. Yet, past work on the
topic remains scarce, is limited to debiasing specific graph modeling methods,
and often aims to ensure fairness in an indirect manner.
  We propose a generic approach applicable to most probabilistic graph modeling
approaches. Specifically, we first define the class of fair graph models
corresponding to a chosen set of fairness criteria. Given this, we propose a
fairness regularizer defined as the KL-divergence between the graph model and
its I-projection onto the set of fair models. We demonstrate that using this
fairness regularizer in combination with existing graph modeling approaches
efficiently trades-off fairness with accuracy, whereas the state-of-the-art
models can only make this trade-off for the fairness criterion that they were
specifically designed for.


Abstract:
  This paper reports a comprehensive study on the applicability of ultra-scaled
ferroelectric FinFETs with 6 nm thick hafnium zirconium oxide layer for
neuromorphic computing in the presence of process variation, flicker noise, and
device aging. An intricate study has been conducted about the impact of such
variations on the inference accuracy of pre-trained neural networks consisting
of analog, quaternary (2-bit/cell) and binary synapse. A pre-trained neural
network with 97.5% inference accuracy on the MNIST dataset has been adopted as
the baseline. Process variation, flicker noise, and device aging
characterization have been performed and a statistical model has been developed
to capture all these effects during neural network simulation. Extrapolated
retention above 10 years have been achieved for binary read-out procedure. We
have demonstrated that the impact of (1) retention degradation due to the oxide
thickness scaling, (2) process variation, and (3) flicker noise can be abated
in ferroelectric FinFET based binary neural networks, which exhibits superior
performance over quaternary and analog neural network, amidst all variations.
The performance of a neural network is the result of coalesced performance of
device, architecture and algorithm. This research corroborates the
applicability of deeply scaled ferroelectric FinFETs for non-von Neumann
computing with proper combination of architecture and algorithm.


Abstract:
  The holographic light-front QCD framework provides a unified nonperturbative
description of the hadron mass spectrum, form factors and quark distributions.
In this article we extend holographic QCD in order to describe the gluonic
distribution in both the proton and pion from the coupling of the metric
fluctuations induced by the spin-two Pomeron with the energy momentum tensor in
anti--de Sitter space, together with constraints imposed by the Veneziano
model{\color{blue},} without additional free parameters. The gluonic and quark
distributions are shown to have significantly different effective QCD scales.


Abstract:
  Filinski constructed a symmetric lambda-calculus consisting of expressions
and continuations which are symmetric, and functions which have duality. In his
calculus, functions can be encoded to expressions and continuations using
primitive operators. That is, the duality of functions is not derived in the
calculus but adopted as a principle of the calculus. In this paper, we propose
a simple symmetric lambda-calculus corresponding to the negation-free natural
deduction based bilateralism in proof-theoretic semantics. In our calculus,
continuation types are represented as not negations of formulae but formulae
with negative polarity. Function types are represented as the implication and
but-not connectives in intuitionistic and paraconsistent logics, respectively.
Our calculus is not only simple but also powerful as it includes a call-value
calculus corresponding to the call-by-value dual calculus invented by Wadler.
We show that mutual transformations between expressions and continuations are
definable in our calculus to justify the duality of functions. We also show
that every typable function has dual types. Thus, the duality of function is
derived from bilateralism.


Abstract:
  For a pair of polynomials with real or complex coefficients, given in any
particular basis, the problem of finding their GCD is known to be ill-posed. An
answer is still desired for many applications, however. Hence, looking for a
GCD of so-called approximate polynomials where this term explicitly denotes
small uncertainties in the coefficients has received significant attention in
the field of hybrid symbolic-numeric computation. In this paper we give an
algorithm, based on one of Victor Ya. Pan, to find an approximate GCD for a
pair of approximate polynomials given in a Lagrange basis. More precisely, we
suppose that these polynomials are given by their approximate values at
distinct known points. We first find each of their roots by using a Lagrange
basis companion matrix for each polynomial, cluster the roots of each
polynomial to identify multiple roots, and then "marry" the two polynomials to
find their GCD. At no point do we change to the monomial basis, thus preserving
the good conditioning properties of the original Lagrange basis. We discuss
advantages and drawbacks of this method. The computational cost is dominated by
the rootfinding step; unless special-purpose eigenvalue algorithms are used,
the cost is cubic in the degrees of the polynomials. In principle, this cost
could be reduced but we do not do so here.


Abstract:
  The diffusive behaviour of simple random-walk proposals of many Markov Chain
Monte Carlo (MCMC) algorithms results in slow exploration of the state space
making inefficient the convergence to a target distribution. Hamiltonian/Hybrid
Monte Carlo (HMC), by introducing fictious momentum variables, adopts
Hamiltonian dynamics, rather than a probability distribution, to propose future
states in the Markov chain. Splitting schemes are numerical integrators for
Hamiltonian problems that may advantageously replace the St\"ormer-Verlet
method within HMC methodology. In this paper a family of stable methods for
univariate and multivariate Gaussian distributions, taken as guide-problems for
more realistic situations, is proposed. Differently from similar methods
proposed in the recent literature, the considered schemes are featured by null
expectation of the random variable representing the energy error. The
effectiveness of the novel procedures is shown for bivariate and multivariate
test cases taken from the literature.


Abstract:
  This article is an introduction to machine learning for financial
forecasting, planning and analysis (FP\&A). Machine learning appears well
suited to support FP\&A with the highly automated extraction of information
from large amounts of data. However, because most traditional machine learning
techniques focus on forecasting (prediction), we discuss the particular care
that must be taken to avoid the pitfalls of using them for planning and
resource allocation (causal inference). While the naive application of machine
learning usually fails in this context, the recently developed double machine
learning framework can address causal questions of interest. We review the
current literature on machine learning in FP\&A and illustrate in a simulation
study how machine learning can be used for both forecasting and planning. We
also investigate how forecasting and planning improve as the number of data
points increases.


Abstract:
  We show that the Weil representation associated with any discriminant form
admits a basis in which the action of the representation involves algebraic
integers. The action of a general element of
$\operatorname{SL}_{2}(\mathbb{Z})$ on many parts of these bases is simple an
explicit, a fact that we use for determining the dimension of the space of
invariants for some families of discriminant forms.


Abstract:
  We give an overview of the work done during the past ten years on the Casimir
interaction in electronic topological materials, our focus being solids which
possess surface or bulk electronic band structures with nontrivial topologies,
which can be evinced through optical properties that are characterizable in
terms of nonzero topological invariants. The examples we review are
three-dimensional magnetic topological insulators, two-dimensional Chern
insulators, graphene monolayers exhibiting the relativistic quantum Hall
effect, and time reversal symmetry-broken Weyl semimetals, which are
fascinating systems in the context of Casimir physics, firstly for the reason
that they possess electromagnetic properties characterizable by axial vectors
(because of time reversal symmetry breaking), and depending on the mutual
orientation of a pair of such axial vectors, two systems can experience a
repulsive Casimir-Lifshitz force even though they may be dielectrically
identical. Secondly, the repulsion thus generated is potentially robust against
weak disorder, as such repulsion is associated with a Hall conductivity which
is topologically protected in the zero-frequency limit. Finally, the far-field
low-temperature behavior of the Casimir force of such systems can provide
signatures of topological quantization.


Abstract:
  In this paper we study dynamical systems generated by a gonosomal evolution
operator of a bisexual population. We find explicitly all (uncountable set) of
fixed points of the operator. It is shown that each fixed point has eigenvalues
less or equal to 1. Moreover, we show that each trajectory converges to a fixed
point, i.e. the operator is reqular. There are uncountable family of invariant
sets each of which consisting unique fixed point. Thus there is one-to-one
correspondence between such invariant sets and the set of fixed points. Any
trajectory started at a point of the invariant set converges to the
corresponding fixed point.


Abstract:
  Although routinely utilized in literature, orthogonal waveforms may lose
orthogonality in distributed multi-input multi-output (MIMO) radar with
spatially separated transmit (TX) and receive (RX) antennas, as the waveforms
may experience distinct delays and Doppler frequency offsets unique to
different TX-RX propagation paths. In such cases, the output of each
waveform-specific matched filter (MF), employed to unravel the waveforms at the
RXs, contains both an \auto term and multiple cross terms, i.e., the filtered
response of the desired and, respectively, undesired waveforms. We consider the
impact of non-orthogonal waveforms and their cross terms on target detection
with or without timing, frequency, and phase errors. To this end, we present a
general signal model for distributed MIMO radar, examine target detection using
existing coherent/non-coherent detectors and two new detectors, including a
hybrid detector that requires phase coherence locally but not across
distributed antennas, and provide a statistical analysis leading to closed-form
expressions of false alarm and detection probabilities for all detectors. Our
results show that cross terms can behave like foes or allies, respectively, if
they and the auto term add destructively or constructively, depending on the
propagation delay, frequency, and phase offsets. Regarding sync errors, we show
that phase errors affect only coherent detectors, frequency errors degrade all
but the non-coherent detector, while all are impacted by timing errors, which
result in a loss in the signal-to-noise ratio (SNR).


Abstract:
  We establish well-posedness conclusions for the Cauchy problem associated to
the dispersion generalized Zakharov-Kutnesov equation in bi-periodic Sobolev
spaces $H^{s}\left(\mathbb{T}^{2}\right)$,
$s>(\frac{3}{2}-\frac{1}{2^{\alpha+2}})(\frac{3}{2}-\frac{\beta}{4})$.


Abstract:
  In this comment we show untenability of key points of the recent article of
N. Biancacci, E. Metral and M. Migliorati [Phys. Rev. Accel. Beams 23, 124402
(2020)], hereafter the Article and the Authors. Specifically, the main Eqs.
(23), suggested to describe mode coupling, are shown to be unacceptable even as
an approximation. The Article claims the solution of this pair of equations to
be in "excellent agreement" with the pyHEADTAIL simulations for CERN PS, which
is purportedly demonstrated by Fig. 6. Were it really so, it would be a signal
of a mistake in the code. However, the key part of the simulation results is
not actually shown, and the demonstrated agreement has all the features of an
illusion.


Abstract:
  Let $\mathcal{F}$ and $\mathcal{K}$ be commuting $C^\infty$ diffeomorphisms
of the cylinder $\mathbb{T}\times\mathbb{R}$ that are, respectively, close to
$\mathcal{F}_0 (x, y)=(x+\omega(y), y)$ and $T_\alpha (x, y)=(x+\alpha, y)$,
where $\omega(y)$ is non-degenerate and $\alpha$ is Diophantine. Using the KAM
iterative scheme for the group action we show that $\mathcal{F}$ and
$\mathcal{K}$ are simultaneously $C^\infty$-linearizable if $\mathcal{F}$ has
the intersection property (including the exact symplectic maps) and
$\mathcal{K}$ satisfies a semi-conjugacy condition. We also provide examples
showing necessity of these conditions. As a consequence, we get local rigidity
of certain elliptic $\mathbb{Z}^2$-actions on the cylinder.


Abstract:
  Recently, Haynes, Hedetniemi and Henning published the book Topics in
Domination in Graphs, which comprises 16 contributions that present advanced
topics in graph domination, featuring open problems, modern techniques, and
recent results. One of these contributions is the chapter Multiple Domination,
by Hansberg and Volkmann, where they put into context all relevant research
results on multiple domination that have been found up to 2020. In this note,
we show how to improve some results on double domination that are included in
the book.


Abstract:
  The recent discovery of a Galactic fast radio burst (FRB) occurring
simultaneously with an X-ray burst (XRB) from the Galactic magnetar SGR
J1935+2154 implies that at least some FRBs arise from magnetar activities. We
propose that FRBs are triggered by crust fracturing of magnetars, with the
burst event rate depending on the magnetic field strength in the crust. Since
the crust fracturing rate is relatively higher in polar regions, FRBs are
preferred to be triggered near the directions of multipolar magnetic poles.
Crust fracturing produces Alfv\'en waves, forming a charge starved region in
the magnetosphere and leading to non-stationary pair plasma discharges. An FRB
is produced by coherent plasma radiation due to nonuniform pair production
across magnetic field lines. Meanwhile, the FRB-associated XRB is produced by
the rapid relaxation of the external magnetic field lines. In this picture, the
sharp-peak hard X-ray component in association with FRB 200428 is from a region
between adjacent trapped fireballs, and its spectrum with a high cutoff energy
is attributed to resonant Compton scattering. The persistent X-ray emission is
from a hot spot heated by the magnetospheric activities, and its temperature
evolution is dominated by magnetar surface cooling. Within this picture,
magnetars with stronger fields tend to produce brighter and more frequent
repeated bursts.


Abstract:
  In the Priority $k$-Center problem, the input consists of a metric space
$(X,d)$, an integer $k$ and for each point $v \in X$ a priority radius $r(v)$.
The goal is to choose $k$-centers $S \subseteq X$ to minimize $\max_{v \in X}
\frac{1}{r(v)} d(v,S)$. If all $r(v)$'s were uniform, one obtains the classical
$k$-center problem. Plesn\'ik [Plesn\'ik, Disc. Appl. Math. 1987] introduced
this problem and gave a $2$-approximation algorithm matching the best possible
algorithm for vanilla $k$-center. We show how the problem is related to two
different notions of fair clustering [Harris et al., NeurIPS 2018; Jung et al.,
FORC 2020]. Motivated by these developments we revisit the problem and, in our
main technical contribution, develop a framework that yields constant factor
approximation algorithms for Priority $k$-Center with outliers. Our framework
extends to generalizations of Priority $k$-Center to matroid and knapsack
constraints, and as a corollary, also yields algorithms with fairness
guarantees in the lottery model of Harris et al.


Abstract:
  In this study, a geometric version of an NP-hard problem ("Almost $2-SAT$"
problem) is introduced which has potential applications in clustering,
separation axis, binary sensor networks, shape separation, image processing,
etc. Furthermore, it has been illustrated that the new problem known as "Two
Disjoint Convex Hulls" can be solved in polynomial time due to some
combinatorial aspects and geometric properties. For this purpose, an $O(n^2)$
algorithm has also been presented which employs the Separating Axis Theorem
(SAT) and the duality of points/lines.


Abstract:
  How does the chromatic number of a graph chosen uniformly at random from all
graphs on $n$ vertices behave? This quantity is a random variable, so one can
ask (i) for upper and lower bounds on its typical values, and (ii) for bounds
on how much it varies: what is the width (e.g., standard deviation) of its
distribution?
  On (i) there has been considerable progress over the last 45 years; on (ii),
which is our focus here, remarkably little. One would like both upper and lower
bounds on the width of the distribution, and ideally a description of the
(appropriately scaled) limiting distribution. There is a well known upper bound
of Shamir and Spencer of order $\sqrt{n}$, improved slightly by Alon to
$\sqrt{n}/\log n$, but no non-trivial lower bound was known until 2019, when
the first author proved that the width is at least $n^{1/4-o(1)}$ for
infinitely many $n$, answering a longstanding question of Bollob\'as.
  In this paper we have two main aims: first, we shall prove a much stronger
lower bound on the width. We shall show unconditionally that, for some values
of $n$, the width is at least $n^{1/2-o(1)}$, matching the upper bounds up to
the error term. Moreover, conditional on a recently announced sharper explicit
estimate for the chromatic number, we improve the lower bound to order
$\sqrt{n} \log \log n /\log^3 n$, within a logarithmic factor of the upper
bound.
  Secondly, we will describe a number of conjectures as to what the true
behaviour of the variation in $\chi(G_{n,1/2})$ is, and why. The first form of
this conjecture arises from recent work of Bollob\'as, Heckel, Morris,
Panagiotou, Riordan and Smith. We will also give much more detailed
conjectures, suggesting that the true width, for the worst case $n$, matches
our lower bound up to a constant factor. These conjectures also predict a
Gaussian limiting distribution.


Abstract:
  Over the last few years, ReS2 has generated a myriad of unattended queries
regarding its structure, the concomitant thickness dependent electronic
properties and apparently contrasting experimental optical response. In this
work, with elaborate first-principles investigations, using density functional
theory (DFT) and time-dependent DFT (TDDFT), we identify the structure of ReS2,
which is capable of reproducing and analyzing the layer-dependent optical
response. The theoretical results are further validated by an in-depth
structural, chemical, optical and optoelectronic analysis of the large-area
ReS2 thin films, grown by chemical vapor deposition (CVD) process. Micro-Raman
(MR), X-ray photoelectron spectroscopy (XPS), cross-sectional transmission
electron microscopy (TEM) and energy-dispersive X-ray analysis (EDAX) have
enabled the optimization of the uniform growth of the CVD films. The
correlation between the layer-dependent optical and electronic properties of
the excited states was established by static photoluminescence (PL) and
transient absorption (TA) measurements. Sulfur vacancy-induced localized
mid-gap states render a significantly long life-time of the excitons in these
films. The ionic gel top-gated photo-detectors, fabricated from the as-prepared
CVD films, exhibit a large photo-response of ~ 5 A/W and a remarkable
detectivity of ~ 1011 Jones. The outcome of the present work will be useful to
promote the application of vertically grown large-area films in the field of
optics and opto-electronics.


Abstract:
  The interest in offensive content identification in social media has grown
substantially in recent years. Previous work has dealt mostly with post level
annotations. However, identifying offensive spans is useful in many ways. To
help coping with this important challenge, we present MUDES, a multilingual
system to detect offensive spans in texts. MUDES features pre-trained models, a
Python API for developers, and a user-friendly web-based interface. A detailed
description of MUDES' components is presented in this paper.


Abstract:
  Context: Backsourcing is the process of insourcing previously outsourced
activities. When companies experience environmental or strategic changes, or
challenges with outsourcing, backsourcing can be a viable alternative. While
outsourcing and related processes have been extensively studied in software
engineering, few studies report experiences with backsourcing. Objectives: We
intend to summarize the results of the research literature on the backsourcing
of IT, with a focus on software development. By identifying practical relevance
experience, we aim to present findings that may help companies considering
backsourcing. In addition, we aim to identify gaps in the current research
literature and point out areas for future work. Method: Our systematic
literature review (SLR) started with a search for empirical studies on the
backsourcing of software development. From each study we identified the
contexts in which backsourcing occurs, the factors leading to the decision to
backsource, the backsourcing process itself, and the outcomes of backsourcing.
We employed inductive coding to extract textual data from the papers identified
and qualitative cross-case analysis to synthesize the evidence from
backsourcing experiences. Results: We identified 17 papers that reported 26
cases of backsourcing, six of which were related to software development. The
cases came from a variety of contexts. The most common reasons for backsourcing
were improving quality, reducing costs, and regaining control of outsourced
activities. The backsourcing process can be described as containing five
sub-processes: change management, vendor relationship management, competence
building, organizational build-up, and transfer of ownership. Furthermore, ...


Abstract:
  The unbound proton-rich nuclei $^{16}$F and $^{15}$F are investigated
experimentally and theoretically. Several experiments using the resonant
elastic scattering method were performed at GANIL with radioactive beams to
determine the properties of the low lying states of these nuclei. Strong
asymmetry between $^{16}$F-$^{16}$N and $^{15}$F-$^{15}$C mirror nuclei is
observed. The strength of the $nucleon-nucleon$ effective interaction involving
the loosely bound proton in the $s_{1/2}$ orbit is significantly modified with
respect to their mirror nuclei $^{16}$N and $^{15}$C. The reduction of the
effective interaction is estimated by calculating the interaction energies with
a schematic zero-range force. It is found that, after correcting for the
effects due to changes in the radial distribution of the single-particle wave
functions, the mirror symmetry of the $n-p$ interaction is preserved between
$^{16}$F and $^{16}$N, while a difference of 63\% is measured between the $p-p$
versus $n-n$ interactions in the second excited state of $^{15}$F and $^{15}$C
nuclei. Several explanations are proposed.


Abstract:
  We extend to Segal-Piatetski-Shapiro sequences previous results on the
Luca-Schinzel question over integral valued polynomial sequences. Namely, we
prove that for any real $c$ larger than $1$ the sequence $(\sum_{m\le n}
\varphi(\lfloor m^c \rfloor) /\lfloor m^c \rfloor)_n$ is dense modulo $1$,
where $\varphi$ denotes Euler's totient function. The main part of the proof
consists in showing that when $R$ is a large integer, the sequence of the
residues of $\lfloor m^c \rfloor$ modulo $R$ contains blocks of consecutive
values which are in an arithmetic progression.


Abstract:
  A decentralized feedback controller for multi-agent systems, inspired by
vehicle platooning, is proposed. The closed-loop resulting from the
decentralized control action has three distinctive features: the generation of
collision-free trajectories, flocking of the system towards a consensus state
in velocity, and asymptotic convergence to a prescribed pattern of distances
between agents. For each feature, a rigorous dynamical analysis is provided,
yielding a characterization of the set of parameters and initial configurations
where collision avoidance, flocking, and pattern formation is guaranteed.
Numerical tests assess the theoretical results presented.


Abstract:
  The extended state observer (ESO) is an inherent element of robust
observer-based control systems that allows estimating the impact of disturbance
on system dynamics. Proper tuning of ESO parameters is necessary to ensure a
good quality of estimated quantities and impacts the overall performance of the
robust control structure. In this paper, we propose a neural network (NN) based
tuning procedure that allows the user to prioritize between selected quality
criteria such as the control and observation errors and the specified features
of the control signal. The designed NN provides an accurate assessment of the
control system performance and returns a set of ESO parameters that delivers a
near-optimal solution to the user-defined cost function. The proposed tuning
procedure, using an estimated state from the single closed-loop experiment
produces near-optimal ESO gains within seconds.


Abstract:
  We explicitly construct the Dirichlet series
$$L_{\mathrm{Tam}}(s):=\sum_{m=1}^{\infty}\frac{P_{\mathrm{Tam}}(m)}{m^s},$$
where $P_{\mathrm{Tam}}(m)$ is the proportion of elliptic curves $E/\mathbb{Q}$
in short Weierstrass form with Tamagawa product $m.$ Although there are no
$E/\mathbb{Q}$ with everywhere good reduction, we prove that the proportion
with trivial Tamagawa product is $P_{\mathrm{Tam}}(1)=0.5053\dots.$ As a
corollary, we find that $L_{\mathrm{Tam}}(-1)=1.8193\dots$ is the average
Tamagawa product for elliptic curves over $\mathbb{Q}.$ We give an application
of these results to canonical and Weil heights.


Abstract:
  This article focuses on a preaveraging description of polymer nonequilibrium
stretching, where a single polymer undergoes a transient process from
equilibrium to nonequilibrium steady state by pulling one chain end. The
preaveraging method combined with mode analysis reduces the original Langevin
equation to a simplified form for both a stretched steady state and an
equilibrium state, even in the presence of self-avoiding repulsive interactions
spanning a long range. However, the transient stretching process exhibits
evolution of a hierarchal regime structure, which means a qualitative temporal
change in probabilistic distributions assumed in preaveraging. We investigate
the preaveraging method for evolution of the regime structure with
consideration of the nonequilibrium work relations and deviations from the
fluctuation-dissipation relation.


Abstract:
  We consider an extension of the classical capital accumulation model, and
present an example in which the Hamilton-Jacobi-Bellman (HJB) equation is
neither necessary nor sufficient for a function to be the value function. Next,
we present assumptions under which the HJB equation becomes a necessary and
sufficient condition for a function to be the value function, and using this
result, we propose a new method for solving the original problem using the
solution of the HJB equation. Our assumptions are so mild that many
macroeconomic growth models satisfy them. Therefore, our results ensure that
the solution of the HJB equation is rigorously the value function in many
macroeconomic models, and present a new solving method for these models.


Abstract:
  We present, here, advanced DFT-NEGF techniques that we have implemented in
our ATOmistic MOdelling Solver, ATOMOS, to explore transport in novel materials
and devices and in particular in van-der-Waals heterojunction transistors. We
describe our methodologies using plane-wave DFT, followed by a Wannierization
step, and linear combination of atomic orbital DFT, that leads to an orthogonal
and non-orthogonal NEGF model, respectively. We then describe in detail our
non-orthogonal NEGF implementation including the Sancho-Rubio and
electron-phonon scattering within a non-orthogonal framework. We also present
our methodology to extract electron-phonon coupling from first principle and
include them in our transport simulations. Finally, we apply our methods
towards the exploration of novel 2D materials and devices. This includes 2D
material selection and the Dynamically-Doped FET for ultimately scaled MOSFETS,
the exploration of vdW TFETs, in particular the HfS2/WSe2 TFET that could
achieve high on-current levels, and the study of Schottky-barrier height and
transport through a metal-semiconducting WTe2/WS2 VDW junction transistor.


Abstract:
  Typicality arguments attempt to use the Copernican Principle to draw
conclusions about the cosmos and presently unknown conscious beings within it.
The most notorious is the Doomsday Argument, which purports to constrain
humanity's future from its current lifespan alone. These arguments rest on a
likelihood calculation that penalizes models in proportion to the number of
distinguishable observers. I argue that such reasoning leads to solipsism, the
belief that one is the only being in the world, and is therefore unacceptable.
Using variants of the "Sleeping Beauty" thought experiment as a guide, I
present a framework for evaluating observations in a large cosmos: Fine
Graining with Auxiliary Indexicals (FGAI). FGAI requires the construction of
specific models of physical outcomes and observations. Valid typicality
arguments then emerge from the combinatorial properties of third-person
physical microhypotheses. Indexical (observer-relative) facts do not directly
constrain physical theories. Instead they serve to weight different provisional
evaluations of credence. These weights define a probabilistic reference class
of locations. As indexical knowledge changes, the weights shift. I show that
the self-applied Doomsday Argument fails in FGAI, even though it can work for
an external observer. I also discuss how FGAI could handle observations in
large universes with Boltzmann brains.


Abstract:
  We propose a novel keypoint voting scheme based on intersecting spheres, that
is more accurate than existing schemes and allows for a smaller set of more
disperse keypoints. The scheme is based upon the distance between points, which
as a 1D quantity can be regressed more accurately than the 2D and 3D vector and
offset quantities regressed in previous work, yielding more accurate keypoint
localization. The scheme forms the basis of the proposed RCVPose method for 6
DoF pose estimation of 3D objects in RGB-D data, which is particularly
effective at handling occlusions. A CNN is trained to estimate the distance
between the 3D point corresponding to the depth mode of each RGB pixel, and a
set of 3 disperse keypoints defined in the object frame. At inference, a sphere
centered at each 3D point is generated, of radius equal to this estimated
distance. The surfaces of these spheres vote to increment a 3D accumulator
space, the peaks of which indicate keypoint locations. The proposed radial
voting scheme is more accurate than previous vector or offset schemes, and is
robust to disperse keypoints. Experiments demonstrate RCVPose to be highly
accurate and competitive, achieving state-of-the-art results on the LINEMOD
99.7% and YCB-Video 97.2% datasets, notably scoring +7.9% higher (71.1%) than
previous methods on the challenging Occlusion LINEMOD dataset.


Abstract:
  The ability to label and track physical objects that are assets in digital
representations of the world is foundational to many complex systems. Simple,
yet powerful methods such as bar- and QR-codes have been highly successful,
e.g. in the retail space, but the lack of security, limited information content
and impossibility of seamless integration with the environment have prevented a
large-scale linking of physical objects to their digital twins. This paper
proposes to link digital assets created through BIM with their physical
counterparts using fiducial markers with patterns defined by Cholesteric
Spherical Reflectors (CSRs), selective retroreflectors produced using liquid
crystal self-assembly. The markers leverage the ability of CSRs to encode
information that is easily detected and read with computer vision while
remaining practically invisible to the human eye. We analyze the potential of a
CSR-based infrastructure from the perspective of BIM, critically reviewing the
outstanding challenges in applying this new class of functional materials, and
we discuss extended opportunities arising in assisting autonomous mobile robots
to reliably navigate human-populated environments, as well as in augmented
reality.


Abstract:
  During the preparatory phase of the International Linear Collider (ILC)
project, all technical development and engineering design needed for the start
of ILC construction must be completed, in parallel with intergovernmental
discussion of governance and sharing of responsibilities and cost. The ILC
Preparatory Laboratory (Pre-lab) is conceived to execute the technical and
engineering work and to assist the intergovernmental discussion by providing
relevant information upon request. It will be based on a worldwide partnership
among laboratories with a headquarters hosted in Japan. This proposal, prepared
by the ILC International Development Team and endorsed by the International
Committee for Future Accelerators, describes an organisational framework and
work plan for the Pre-lab. Elaboration, modification and adjustment should be
introduced for its implementation, in order to incorporate requirements arising
from the physics community, laboratories, and governmental authorities
interested in the ILC.


Abstract:
  Music source separation (MSS) is the task of separating a music piece into
individual sources, such as vocals and accompaniment. Recently, neural network
based methods have been applied to address the MSS problem, and can be
categorized into spectrogram and time-domain based methods. However, there is a
lack of research of using complementary information of spectrogram and
time-domain inputs for MSS. In this article, we propose a CatNet framework that
concatenates a UNet separation branch using spectrogram as input and a WavUNet
separation branch using time-domain waveform as input for MSS. We propose an
end-to-end and fully differentiable system that incorporate spectrogram
calculation into CatNet. In addition, we propose a novel mix-audio data
augmentation method that randomly mix audio segments from the same source as
augmented audio segments for training. Our proposed CatNet MSS system achieves
a state-of-the-art vocals separation source distortion ratio (SDR) of 7.54 dB,
outperforming MMDenseNet of 6.57 dB evaluated on the MUSDB18 dataset.


Abstract:
  Laser Doppler holography was introduced as a full-field imaging technique to
measure blood flow in the retina and choroid with an as yet unrivaled temporal
resolution. We here investigate separating the different contributions to the
power Doppler signal in order to isolate the flow waveforms of vessels in the
posterior pole of the human eye. Distinct flow behaviors are found in retinal
arteries and veins with seemingly interrelated waveforms. We demonstrate a full
field mapping of the local resistivity index, and the possibility to perform
unambiguous identification of retinal arteries and veins on the basis of their
systolodiastolic variations. Finally we investigate the arterial flow waveforms
in the retina and choroid and find synchronous and similar waveforms, although
with a lower pulsatility in choroidal vessels. This work demonstrates the
potential held by laser Doppler holography to study ocular hemodynamics in
healthy and diseased eyes.


Abstract:
  Recent worldwide events shed light on the need of human-centered systems
engineering in the healthcare domain. These systems must be prepared to evolve
quickly but safely, according to unpredicted environments and ever-changing
pathogens that spread ruthlessly. Such scenarios suffocate hospitals'
infrastructure and disable healthcare systems that are not prepared to deal
with unpredicted environments without costly re-engineering. In the face of
these challenges, we offer the SA-BSN -- Self-Adaptive Body Sensor Network --
prototype to explore the rather dynamic patient's health status monitoring. The
exemplar is focused on self-adaptation and comes with scenarios that hinder an
interplay between system reliability and battery consumption that is available
after each execution. Also, we provide: (i) a noise injection mechanism, (ii)
file-based patient profiles' configuration, (iii) six healthcare sensor
simulations, and (iv) an extensible/reusable controller implementation for
self-adaptation. The artifact is implemented in ROS (Robot Operating System),
which embraces principles such as ease of use and relies on an active open
source community support.


Abstract:
  This paper investigates the theory of robustness against adversarial attacks.
We focus on randomized classifiers (\emph{i.e.} classifiers that output random
variables) and provide a thorough analysis of their behavior through the lens
of statistical learning theory and information theory. To this aim, we
introduce a new notion of robustness for randomized classifiers, enforcing
local Lipschitzness using probability metrics. Equipped with this definition,
we make two new contributions. The first one consists in devising a new upper
bound on the adversarial generalization gap of randomized classifiers. More
precisely, we devise bounds on the generalization gap and the adversarial gap
(\emph{i.e.} the gap between the risk and the worst-case risk under attack) of
randomized classifiers. The second contribution presents a yet simple but
efficient noise injection method to design robust randomized classifiers. We
show that our results are applicable to a wide range of machine learning models
under mild hypotheses. We further corroborate our findings with experimental
results using deep neural networks on standard image datasets, namely CIFAR-10
and CIFAR-100. All robust models we trained models can simultaneously achieve
state-of-the-art accuracy (over $0.82$ clean accuracy on CIFAR-10) and enjoy
\emph{guaranteed} robust accuracy bounds ($0.45$ against $\ell_2$ adversaries
with magnitude $0.5$ on CIFAR-10).


Abstract:
  We study the performance of quantum annealing for two sets of problems,
namely, 2-satisfiability (2-SAT) problems represented by Ising-type
Hamiltonians, and nonstoquastic problems which are obtained by adding extra
couplings to the 2-SAT problem Hamiltonians. In addition, we add to the
transverse Ising-type Hamiltonian used for quantum annealing a third term, the
trigger Hamiltonian with ferromagnetic or antiferromagnetic couplings, which
vanishes at the beginning and end of the annealing process. We also analyze
some problem instances using the energy spectrum, average energy or overlap of
the state during the evolution with the instantaneous low lying eigenstates of
the Hamiltonian, and identify some non-adiabatic mechanisms which can enhance
the performance of quantum annealing.


Abstract:
  We analyze the citation time-series of manuscripts in three different fields
of science; physics, social science and technology. The evolution of the
time-series of the yearly number of citations, namely the citation
trajectories, diffuse anomalously, their variance scales with time $\propto
t^{2H}$, where $H\neq 1/2$. We provide detailed analysis of the various factors
that lead to the anomalous behavior: non-stationarity, long-ranged correlations
and a fat-tailed increment distribution. The papers exhibit high degree of
heterogeneity, across the various fields, as the statistics of the highest
cited papers is fundamentally different from that of the lower ones. The
citation data is shown to be highly correlated and non-stationary; as all the
papers except the small percentage of them with high number of citations, die
out in time.


Abstract:
  The Ensemble Kalman inversion (EKI), proposed by Iglesias et al. for the
solution of Bayesian inverse problems of type $y=A u^\dagger +\varepsilon$,
with $u^\dagger$ being an unknown parameter and $y$ a given datum, is a
powerful tool usually derived from a sequential Monte Carlo point of view. It
describes the dynamics of an ensemble of particles $\{u^j(t)\}_{j=1}^J$, whose
initial empirical measure is sampled from the prior, evolving over an
artificial time $t$ towards an approximate solution of the inverse problem.
Using spectral techniques, we provide a complete description of the
deterministic dynamics of EKI and their asymptotic behavior in parameter space.
In particular, we analyze the dynamics of deterministic EKI and mean-field EKI.
We demonstrate that the Bayesian posterior can only be recovered with the
mean-field limit and not with finite sample sizes or deterministic EKI.
Furthermore, we show that -- even in the deterministic case -- residuals in
parameter space do not decrease monotonously in the Euclidean norm and suggest
a problem-adapted norm, where monotonicity can be proved. Finally, we derive a
system of ordinary differential equations governing the spectrum and
eigenvectors of the covariance matrix.


Abstract:
  The LIGO and Virgo gravitational-wave detectors carried out the first half of
their third observing run from April through October 2019. During this period,
they detected 39 new signals from the coalescence of black holes or neutron
stars, more than quadrupling the total number of detected events. These
detections included some unprecedented sources, like a pair of black holes with
unequal masses (GW190412), a massive pair of neutron stars (GW190425), a black
hole potentially in the supernova pair-instability mass gap (GW190521), and
either the lightest black hole or the heaviest neutron star known to date
(GW190814). Collectively, the full set of signals provided astrophysically
valuable information about the distributions of compact objects and their
evolution throughout cosmic history. It also enabled more constraining and
diverse tests of general relativity, including new probes of the fundamental
nature of black holes. This review summarizes the highlights of these results
and their implications.


Abstract:
  Spin sum rules depend on the choice of a pivot, i.e. the point about which
the angular momentun is defined, usually identified with the center of the
nucleon. The latter is however not unique in a relativistic theory and has led
to apparently contradictory results in the literature. Using the recently
developed phase-space approach, we compute for the first time the contribution
associated with the motion of the center of the nucleon, and we derive a
general spin sum rule which reduces to established results after appropriate
choices for the pivot and the spin component.


Abstract:
  Recently developed large pre-trained language models, e.g., BERT, have
achieved remarkable performance in many downstream natural language processing
applications. These pre-trained language models often contain hundreds of
millions of parameters and suffer from high computation and latency in
real-world applications. It is desirable to reduce the computation overhead of
the models for fast training and inference while keeping the model performance
in downstream applications. Several lines of work utilize knowledge
distillation to compress the teacher model to a smaller student model. However,
they usually discard the teacher's knowledge when in inference. Differently, in
this paper, we propose RefBERT to leverage the knowledge learned from the
teacher, i.e., facilitating the pre-computed BERT representation on the
reference sample and compressing BERT into a smaller student model. To
guarantee our proposal, we provide theoretical justification on the loss
function and the usage of reference samples. Significantly, the theoretical
result shows that including the pre-computed teacher's representations on the
reference samples indeed increases the mutual information in learning the
student model. Finally, we conduct the empirical evaluation and show that our
RefBERT can beat the vanilla TinyBERT over 8.1\% and achieves more than 94\% of
the performance of $\BERTBASE$ on the GLUE benchmark. Meanwhile, RefBERT is
7.4x smaller and 9.5x faster on inference than BERT$_{\rm BASE}$.


Abstract:
  As machine learning (ML) models become more widely deployed in high-stakes
applications, counterfactual explanations have emerged as key tools for
providing actionable model explanations in practice. Despite the growing
popularity of counterfactual explanations, a deeper understanding of these
explanations is still lacking. In this work, we systematically analyze
counterfactual explanations through the lens of adversarial examples. We do so
by formalizing the similarities between popular counterfactual explanation and
adversarial example generation methods identifying conditions when they are
equivalent. We then derive the upper bounds on the distances between the
solutions output by counterfactual explanation and adversarial example
generation methods, which we validate on several real-world data sets. By
establishing these theoretical and empirical similarities between
counterfactual explanations and adversarial examples, our work raises
fundamental questions about the design and development of existing
counterfactual explanation algorithms.


Abstract:
  Mahalanobis distance between treatment group and control group covariate
means is often adopted as a balance criterion when implementing a
rerandomization strategy. However, this criterion may not work well for
high-dimensional cases because it balances all orthogonalized covariates
equally. Here, we propose leveraging principal component analysis (PCA) to
identify proper subspaces in which Mahalanobis distance should be calculated.
Not only can PCA effectively reduce the dimensionality for high-dimensional
cases while capturing most of the information in the covariates, but it also
provides computational simplicity by focusing on the top orthogonal components.
We show that our PCA rerandomization scheme has desirable theoretical
properties on balancing covariates and thereby on improving the estimation of
average treatment effects. We also show that this conclusion is supported by
numerical studies using both simulated and real examples.


Abstract:
  We prove that if a family of compact connected sets in the plane has the
property that every three members of it are intersected by a line, then there
are three lines intersecting all the sets in the family. This answers a
question of Eckhoff from 1993, who proved that, under the same condition, there
are four lines intersecting all the sets. In fact, we prove a colorful version
of this result, under weakened conditions on the sets.
  A triple of sets $A,B,C$ in the plane is said to be a {\em tight} if
$\textrm{conv}(A\cup B)\cap \textrm{conv}(A\cup C)\cap \textrm{conv}(B\cap
C)\neq \emptyset.$ This notion was first introduced by Holmsen, where he showed
that if $\mathcal{F}$ is a family of compact convex sets in the plane in which
every three sets form a tight triple, then there is a line intersecting at
least $\frac{1}{8}|\mathcal{F}|$ members of $\mathcal{F}$. Here we prove that
if $\mathcal{F}_1,\dots,\mathcal{F}_6$ are families of compact connected sets
in the plane such that every three sets, chosen from three distinct families
$\mathcal{F}_i$, form a tight triple, then there exists $1\le j\le 6$ and three
lines intersecting every member of $\mathcal{F}_j$. In particular, this
improves $\frac{1}{8}$ to $\frac{1}{3}$ in Holmsen's result.


Abstract:
  This paper describes a machine learning approach for annotating and analyzing
data curation work logs at ICPSR, a large social sciences data archive. The
systems we studied track curation work and coordinate team decision-making at
ICPSR. Repository staff use these systems to organize, prioritize, and document
curation work done on datasets, making them promising resources for studying
curation work and its impact on data reuse, especially in combination with data
usage analytics. A key challenge, however, is classifying similar activities so
that they can be measured and associated with impact metrics. This paper
contributes: 1) a schema of data curation activities; 2) a computational model
for identifying curation actions in work log descriptions; and 3) an analysis
of frequent data curation activities at ICPSR over time. We first propose a
schema of data curation actions to help us analyze the impact of curation work.
We then use this schema to annotate a set of data curation logs, which contain
records of data transformations and project management decisions completed by
repository staff. Finally, we train a text classifier to detect the frequency
of curation actions in a large set of work logs. Our approach supports the
analysis of curation work documented in work log systems as an important step
toward studying the relationship between research data curation and data reuse.


Abstract:
  In a recent article we presented a model for hadronic rescattering, and some
results were shown for pp collisions at LHC energies. In order to extend the
studies to pA and AA collisions, the Angantyr model for heavy-ion collisions is
taken as the starting point. Both these models are implemented within the
general-purpose Monte Carlo event generator Pythia, which makes the matching
reasonably straightforward, and allows for detailed studies of the full
space--time evolution. The rescattering rate is significantly higher than in
pp, especially for central AA collisions, where the typical primary hadron
rescatters several times. We study the impact of rescattering on a number of
distributions, such as pT and eta spectra, and the space--time evolution of the
whole collision process. Notably rescattering is shown to give a significant
contribution to elliptic flow in XeXe and PbPb, and to give a nontrivial impact
on charm production.


Abstract:
  In passive linear systems, complete combining of powers carried by waves from
several input channels into a single output channel is forbidden by the energy
conservation law. Here, we demonstrate that complete combination of both
coherent and incoherent plane waves can be achieved using metasurfaces with
properties varying in space and time. The proposed structure reflects waves of
the same frequency but incident at different angles towards a single direction.
The frequencies of the output waves are shifted by the metasurface, ensuring
perfect incoherent power combining. The proposed concept of power combining is
general and can be applied for electromagnetic waves from the microwave to
optical domains, as well as for waves of other physical nature.


Abstract:
  In recent years, there has been significant growth of distributed energy
resources (DERs) penetration in the power grid. The stochastic and intermittent
features of variable DERs such as roof top photovoltaic (PV) bring substantial
uncertainties to the grid on the consumer end and weaken the grid reliability.
In addition, the fact that numerous DERs are widespread in the grid makes it
hard to monitor and manage DERs. To address this challenge, this paper proposes
a novel real-time grid-supporting energy management (GSEM) strategy for
grid-supporting microgrid (MG). This strategy can not only properly manage DERs
in a MG but also enable DERs to provide grid services, which enables a MG to be
grid-supporting via flexible trading power. The proposed GSEM strategy is based
on a 2-step optimization which includes a routine economic dispatch (ED) step
and an acceptable trading power range determination step. Numerical simulations
demonstrate the performance of the proposed GSEM strategy which enables the
grid operator to have a dispatch choice of trading power with MG and enhance
the reliability and resilience of the main grid.


Abstract:
  The approximation of solutions to second order Hamilton--Jacobi--Bellman
(HJB) equations by deep neural networks is investigated. It is shown that for
HJB equations that arise in the context of the optimal control of certain
Markov processes the solution can be approximated by deep neural networks
without incurring the curse of dimension. The dynamics is assumed to depend
affinely on the controls and the cost depends quadratically on the controls.
The admissible controls take values in a bounded set.


Abstract:
  Creating and destroying threads on modern Linux systems incurs high latency,
absent concurrency, and fails to scale as we increase concurrency. To address
this concern we introduce a process-local cache of idle threads. Specifically,
instead of destroying a thread when it terminates, we cache and then recycle
that thread in the context of subsequent thread creation requests. This
approach shows significant promise in various applications and benchmarks that
create and destroy threads rapidly and illustrates the need for and potential
benefits of improved concurrency infrastructure. With caching, the cost of
creating a new thread drops by almost an order of magnitude. As our experiments
demonstrate, this results in significant performance improvements for multiple
applications that aggressively create and destroy numerous threads.


Abstract:
  An intense activity is nowadays devoted to the definition of models capturing
the properties of complex networks. Among the most promising approaches, it has
been proposed to model these graphs via their clique incidence bipartite
graphs. However, this approach has, until now, severe limitations resulting
from its incapacity to reproduce a key property of this object: the overlapping
nature of cliques in complex networks. In order to get rid of these limitations
we propose to encode the structure of clique overlaps in a network thanks to a
process consisting in iteratively factorising the maximal bicliques between the
upper level and the other levels of a multipartite graph. We show that the most
natural definition of this factorising process leads to infinite series for
some instances. Our main result is to design a restriction of this process that
terminates for any arbitrary graph. Moreover, we show that the resulting
multipartite graph has remarkable combinatorial properties and is closely
related to another fundamental combinatorial object. Finally, we show that, in
practice, this multipartite graph is computationally tractable and has a size
that makes it suitable for complex network modelling.


Abstract:
  In a conventional domain adaptation person Re-identification (Re-ID) task,
both the training and test images in target domain are collected under the
sunny weather. However, in reality, the pedestrians to be retrieved may be
obtained under severe weather conditions such as hazy, dusty and snowing, etc.
This paper proposes a novel Interference Suppression Model (ISM) to deal with
the interference caused by the hazy weather in domain adaptation person Re-ID.
A teacherstudent model is used in the ISM to distill the interference
information at the feature level by reducing the discrepancy between the clear
and the hazy intrinsic similarity matrix. Furthermore, in the distribution
level, the extra discriminator is introduced to assist the student model make
the interference feature distribution more clear. The experimental results show
that the proposed method achieves the superior performance on two synthetic
datasets than the stateof-the-art methods. The related code will be released
online https://github.com/pangjian123/ISM-ReID.


Abstract:
  The mobile data traffic has been exponentially growing during the last
decades, which has been enabled by the densification of the network
infrastructure in terms of increased cell density (i.e., ultra-dense network
(UDN)) and/or increased number of active antennas per access point (AP) (i.e.,
massive multiple-input multiple-output (mMIMO)). However, neither UDN nor mMIMO
will meet the increasing data rate demands of the sixth generation (6G)
wireless communications due to the inter-cell interference and large
quality-of-service variations, respectively. Cell-free (CF) mMIMO, which
combines the best aspects of UDN and mMIMO, is viewed as a key solution to this
issue. In such systems, each user equipment (UE) is served by a preferred set
of surrounding APs cooperatively. In this paper, we provide a survey of the
state-of-the-art literature on CF mMIMO. As a starting point, the significance
and the basic properties of CF mMIMO are highlighted. We then present the
canonical framework, where the essential details (i.e., transmission procedure
and mathematical system model) are discussed. Next, we provide a deep look at
the resource allocation and signal processing problems related to CF mMIMO and
survey the up-to-date schemes and algorithms. After that, we discuss the
practical issues when implementing CF mMIMO. Potential future directions are
then pointed out. Finally, we conclude this paper with a summary of the key
lessons learned in this field. This paper aims to provide a starting point for
anyone who wants to conduct research on CF mMIMO for future wireless networks.


Abstract:
  Autonomous exploration is an application of growing importance in robotics. A
promising strategy is ergodic trajectory planning, whereby an agent spends in
each area a fraction of time which is proportional to its probability
information density function. In this paper, a decentralized ergodic
multi-agent trajectory planning algorithm featuring limited communication
constraints is proposed. The agents' trajectories are designed by optimizing a
weighted cost encompassing ergodicity, control energy and close-distance
operation objectives. To solve the underlying optimal control problem, a
second-order descent iterative method coupled with a projection operator in the
form of an optimal feedback controller is used. Exhaustive numerical analyses
show that the multi-agent solution allows a much more efficient exploration in
terms of completion task time and control energy distribution by leveraging
collaboration among agents.


Abstract:
  Federated averaging (FedAvg) is a communication efficient algorithm for the
distributed training with an enormous number of clients. In FedAvg, clients
keep their data locally for privacy protection; a central parameter server is
used to communicate between clients. This central server distributes the
parameters to each client and collects the updated parameters from clients.
FedAvg is mostly studied in centralized fashions, which requires massive
communication between server and clients in each communication. Moreover,
attacking the central server can break the whole system's privacy. In this
paper, we study the decentralized FedAvg with momentum (DFedAvgM), which is
implemented on clients that are connected by an undirected graph. In DFedAvgM,
all clients perform stochastic gradient descent with momentum and communicate
with their neighbors only. To further reduce the communication cost, we also
consider the quantized DFedAvgM. We prove convergence of the (quantized)
DFedAvgM under trivial assumptions; the convergence rate can be improved when
the loss function satisfies the P{\L} property. Finally, we numerically verify
the efficacy of DFedAvgM.


Abstract:
  Since the evolution of digital computers, the storage of data has always been
in terms of discrete bits that can store values of either 1 or 0. Hence, all
computer programs (such as MATLAB), convert any input continuous signal into a
discrete dataset. Applying this to oscillating signals, such as audio, opens a
domain for processing as well as editing. The Fourier transform, which is an
integral over infinite limits, for the use of signal processing is discrete.
The essential feature of the Fourier transform is to decompose any signal into
a combination of multiple sinusoidal waves that are easy to deal with. The
discrete Fourier transform (DFT) can be represented as a matrix, with each data
point acting as an orthogonal point, allowing one to perform complicated
transformations on individual frequencies. Due to this formulation, all the
concepts of linear algebra and linear transforms prove to be extremely useful
here. In this paper, we first explain the theoretical basis of audio processing
using linear algebra, and then focus on a simulation coded in MATLAB, to
process and edit various audio samples. The code is open ended and easily
expandable by just defining newer matrices which can transform over the
original audio signal. Finally, this paper attempts to highlight and briefly
explain the results that emerge from the simulation


Abstract:
  Resistive Random-Access-Memory (ReRAM) crossbar is a promising technique for
deep neural network (DNN) accelerators, thanks to its in-memory and in-situ
analog computing abilities for Vector-Matrix Multiplication-and-Accumulations
(VMMs). However, it is challenging for crossbar architecture to exploit the
sparsity in the DNN. It inevitably causes complex and costly control to exploit
fine-grained sparsity due to the limitation of tightly-coupled crossbar
structure. As the countermeasure, we developed a novel ReRAM-based DNN
accelerator, named Sparse-Multiplication-Engine (SME), based on a hardware and
software co-design framework. First, we orchestrate the bit-sparse pattern to
increase the density of bit-sparsity based on existing quantization methods.
Second, we propose a novel weigh mapping mechanism to slice the bits of a
weight across the crossbars and splice the activation results in peripheral
circuits. This mechanism can decouple the tightly-coupled crossbar structure
and cumulate the sparsity in the crossbar. Finally, a superior squeeze-out
scheme empties the crossbars mapped with highly-sparse non-zeros from the
previous two steps. We design the SME architecture and discuss its use for
other quantization methods and different ReRAM cell technologies. Compared with
prior state-of-the-art designs, the SME shrinks the use of crossbars up to 8.7x
and 2.1x using Resent-50 and MobileNet-v2, respectively, with less than 0.3%
accuracy drop on ImageNet.


Abstract:
  The current world challenges include issues such as infectious disease
pandemics, environmental health risks, food safety, and crime prevention.
Through this article, a special emphasis is given to one of the main challenges
in the healthcare sector during the COVID-19 pandemic, the cyber risk. Since
the beginning of the Covid-19 pandemic, the World Health Organization has
detected a dramatic increase in the number of cyber-attacks. For instance, in
Italy the COVID-19 emergency has heavily affected cybersecurity; from January
to April 2020, the total of attacks, accidents, and violations of privacy to
the detriment of companies and individuals has doubled. Using a systematic and
rigorous approach, this paper aims to analyze the literature on the cyber risk
in the healthcare sector to understand the real knowledge on this topic. The
findings highlight the poor attention of the scientific community on this
topic, except in the United States. The literature lacks research contributions
to support cyber risk management in subject areas such as Business, Management
and Accounting; Social Science; and Mathematics. This research outlines the
need to empirically investigate the cyber risk, giving a practical solution to
health facilities. Keywords: cyber risk; cyber-attack; cybersecurity; computer
security; COVID-19; coronavirus;information technology risk; risk management;
risk assessment; health facilities; healthcare sector;systematic literature
review; insurance


Abstract:
  The paper presents a solution for the problem of choosing a method for
analytical determining of weight factors for a genetic algorithm additive
fitness function. This algorithm is the basis for an evolutionary process,
which forms a stable and effective query population in a search engine to
obtain highly relevant results. The paper gives a formal description of an
algorithm fitness function, which is a weighted sum of three heterogeneous
criteria. The selected methods for analytical determining of weight factors are
described in detail. It is noted that expert assessment methods are impossible
to use. The authors present a research methodology using the experimental
results from earlier in the discussed project "Data Warehouse Support on the
Base Intellectual Web Crawler and Evolutionary Model for Target Information
Selection". There is a description of an initial dataset with data ranges for
calculating weights. The calculation order is illustrated by examples. The
research results in graphical form demonstrate the fitness function behavior
during the genetic algorithm operation using various weighting options.


Abstract:
  Saccadic eye movements allow animals to bring different parts of an image
into high-resolution. During free viewing, inhibition of return incentivizes
exploration by discouraging previously visited locations. Despite this
inhibition, here we show that subjects make frequent return fixations. We
systematically studied a total of 44,328 return fixations out of 217,440
fixations across different tasks, in monkeys and humans, and in static images
or egocentric videos. The ubiquitous return fixations were consistent across
subjects, tended to occur within short offsets, and were characterized by
longer duration than non-return fixations. The locations of return fixations
corresponded to image areas of higher saliency and higher similarity to the
sought target during visual search tasks. We propose a biologically-inspired
computational model that capitalizes on a deep convolutional neural network for
object recognition to predict a sequence of fixations. Given an input image,
the model computes four maps that constrain the location of the next saccade: a
saliency map, a target similarity map, a saccade size map, and a memory map.
The model exhibits frequent return fixations and approximates the properties of
return fixations across tasks and species. The model provides initial steps
towards capturing the trade-off between exploitation of informative image
locations combined with exploration of novel image locations during scene
viewing.


Abstract:
  This note relies mainly on a refined version of the main results of the paper
by F. Catrina and D. Costa (J. Differential Equations 2009). We provide very
short and self-contained proofs. Our results are sharp and minimizers are
obtained in suitable functional spaces. As main tools we use the so-called
\textit{expand of squares} method to establish sharp weighted
$L^{2}$-Caffarelli-Kohn-Nirenberg (CKN) inequalities and density arguments.


Abstract:
  In this paper we study quantum group deformations of the infinite dimensional
symmetry algebra of asymptotically AdS spacetimes in three dimensions. Building
on previous results in the finite dimensional subalgebras we classify all
possible Lie bialgebra structures and for selected examples, we explicitly
construct the related Hopf algebras. Using cohomological arguments we show that
this construction can always be performed by a so-called twist deformation. The
resulting structures can be compared to the well-known $\kappa$-Poincar\'e Hopf
algebras constructed on the finite dimensional Poincar\'e or (anti) de Sitter
algebra. The dual $\kappa$ Minkowski spacetime is supposed to describe a
specific non-commutative geometry. Importantly, we find that some incarnations
of the $\kappa$-Poincar\'e can not be extended consistently to the infinite
dimensional algebras. Furthermore, certain deformations can have potential
physical applications if subalgebras are considered. Since the conserved
charges associated with asymptotic symmetries in 3-dimensional form a centrally
extended algebra we also discuss briefly deformations of such algebras. The
presence of the full symmetry algebra might have observable consequences that
could be used to rule out these deformations. }