Abstract:
  Let $F(z)$ be an arbitrary complex polynomial. We introduce the local root
clustering problem, to compute a set of natural $\varepsilon$-clusters of roots
of $F(z)$ in some box region $B_0$ in the complex plane. This may be viewed as
an extension of the classical root isolation problem. Our contribution is
two-fold: we provide an efficient certified subdivision algorithm for this
problem, and we provide a bit-complexity analysis based on the local geometry
of the root clusters.
  Our computational model assumes that arbitrarily good approximations of the
coefficients of $F$ are provided by means of an oracle at the cost of reading
the coefficients. Our algorithmic techniques come from a companion paper
(Becker et al., 2018) and are based on the Pellet test, Graeffe and Newton
iterations, and are independent of Sch\"onhage's splitting circle method. Our
algorithm is relatively simple and promises to be efficient in practice.


Abstract:
  Recently, Mutual Information (MI) has attracted attention in bounding the
generalization error of Deep Neural Networks (DNNs). However, it is intractable
to accurately estimate the MI in DNNs, thus most previous works have to relax
the MI bound, which in turn weakens the information theoretic explanation for
generalization. To address the limitation, this paper introduces a
probabilistic representation of DNNs for accurately estimating the MI.
Leveraging the proposed MI estimator, we validate the information theoretic
explanation for generalization, and derive a tighter generalization bound than
the state-of-the-art relaxations.


Abstract:
  Background: Network Analysis (NA) is a method that has been used in various
disciplines such as Social sciences and Ecology for decades. So far, NA has not
been used extensively in studies of medication use. Only a handful of papers
have used NA in Drug Prescription Networks (DPN). We provide an introduction to
NA terminology alongside a guide to creating and extracting results from the
medication networks. Objective: To introduce the readers to NA as a tool to
study medication use by demonstrating how to apply different NA measures on 3
generated medication networks. Methods: We used the Norwegian Prescription
Database (NorPD) to create a network that describes the co-medication in
elderly persons in Norway on January 1, 2013. We used the Norwegian Electronic
Prescription Support System (FEST) to create another network of severe
drug-drug interactions (DDIs). Lastly, we created a network combining the two
networks to show the actual use of drugs with severe DDIs. We used these
networks to elucidate how to apply and interpret different network measures in
medication networks. Results: Interactive network graphs are made available
online, Stata and R syntaxes are provided. Various useful network measures for
medication networks were applied such as network topological features,
modularity analysis and centrality measures. Edge lists data used to generate
the networks are openly available for readers in an open data repository to
explore and use. Conclusion: We believe that NA can be a useful tool in
medication use studies. We have provided information and hopefully inspiration
for other researchers to use NA in their own projects. While network analyses
are useful for exploring and discovering structures in medication use studies,
it also has limitations. It can be challenging to interpret and it is not
suitable for hypothesis testing.


Abstract:
  To address the dynamic nature of real-world networks, we generalize
competitive diffusion games and Voronoi games from static to temporal graphs,
where edges may appear or disappear over time. This establishes a new direction
of studies in the area of graph games, motivated by applications such as
influence spreading. As a first step, we investigate the existence of Nash
equilibria in competitive diffusion and Voronoi games on different temporal
graph classes. Even when restricting our studies to temporal paths and cycles,
this turns out to be a challenging undertaking, revealing significant
differences between the two games in the temporal setting. Notably, both games
are equivalent on static paths and cycles. Our two main technical results are
(algorithmic) proofs for the existence of Nash equilibria in temporal
competitive diffusion and temporal Voronoi games when the edges are restricted
not to disappear over time.


Abstract:
  In this paper, we present a new control policy parametrization for the
finite-horizon covariance steering problem for discrete-time Gaussian linear
systems (DTGLS) which can reduce the latter stochastic optimal control problem
to a tractable optimization problem. The covariance steering problem seeks for
a feedback control policy that will steer the state covariance of a DTGLS to a
desired positive definite matrix in finite time. We consider two different
formulations of the covariance steering problem, one with hard terminal LMI
constraints (hard-constrained covariance steering) and another one with soft
terminal constraints in the form of a terminal cost which corresponds to the
squared Wasserstein distance between the actual terminal state (Gaussian)
distribution and the desired one (soft-constrained covariance steering). We
propose a solution approach that relies on the affine disturbance feedback
parametrization for both problem formulations. We show that this particular
parametrization allows us to reduce the hard-constrained covariance steering
problem into a semi-definite program (SDP) and the soft-constrained covariance
steering problem into a difference of convex functions program(DCP). Finally,
we show the advantages of our approach over other covariance steering
algorithms in terms of computational complexity and computation time by means
of theoretical analysis and numerical simulations.


Abstract:
  The minimum sum-of-squares clustering problem (MSSC) consists of partitioning
$n$ observations into $k$ clusters in order to minimize the sum of squared
distances from the points to the centroid of their cluster. In this paper, we
propose an exact algorithm for the MSSC problem based on the branch-and-bound
technique. The lower bound is computed by using a cutting-plane procedure where
valid inequalities are iteratively added to the Peng-Wei SDP relaxation. The
upper bound is computed with the constrained version of k-means where the
initial centroids are extracted from the solution of the SDP relaxation. In the
branch-and-bound procedure, we incorporate instance-level must-link and
cannot-link constraints to express knowledge about which data points should or
should not be grouped together. We manage to reduce the size of the problem at
each level preserving the structure of the SDP problem itself. The obtained
results show that the approach allows to successfully solve for the first time
real-world instances up to 4000 data points.


Abstract:
  Recently, orthogonal recurrent neural networks (RNNs) have emerged as
state-of-the-art models for learning long-term dependencies. This class of
models mitigates the exploding and vanishing gradients problem by design. In
this work, we employ tools and insights from differential geometry to offer a
novel perspective on orthogonal RNNs. We show that orthogonal RNNs may be
viewed as optimizing in the space of divergence-free vector fields.
Specifically, based on a well-known result in differential geometry that
relates vector fields and linear operators, we prove that every divergence-free
vector field is related to a skew-symmetric matrix. Motivated by this
observation, we study a new recurrent model, which spans the entire space of
vector fields. Our method parameterizes vector fields via the directional
derivatives of scalar functions. This requires the construction of latent inner
product, gradient, and divergence operators. In comparison to state-of-the-art
orthogonal RNNs, our approach achieves comparable or better results on a
variety of benchmark tasks.


Abstract:
  Attention mechanisms, especially self-attention, have played an increasingly
important role in deep feature representation for visual tasks. Self-attention
updates the feature at each position by computing a weighted sum of features
using pair-wise affinities across all positions to capture the long-range
dependency within a single sample. However, self-attention has quadratic
complexity and ignores potential correlation between different samples. This
paper proposes a novel attention mechanism which we call external attention,
based on two external, small, learnable, shared memories, which can be
implemented easily by simply using two cascaded linear layers and two
normalization layers; it conveniently replaces self-attention in existing
popular architectures. External attention has linear complexity and implicitly
considers the correlations between all data samples. We further incorporate the
multi-head mechanism into external attention to provide an all-MLP
architecture, external attention MLP (EAMLP), for image classification.
Extensive experiments on image classification, object detection, semantic
segmentation, instance segmentation, image generation, and point cloud analysis
reveal that our method provides results comparable or superior to the
self-attention mechanism and some of its variants, with much lower
computational and memory costs.


Abstract:
  In 1977, Gauduchon proved that on every compact hermitian manifold $(X,
\omega)$ there exists a conformally equivalent hermitian metric
$\omega_{\mathrm{G}}$ which satisfies $\mathrm{dd}^c \omega_{\mathrm{G}}^{n-1}
= 0$. In this note, we extend this result to irreducible compact singular
hermitian varieties which admit a smoothing.


Abstract:
  Modern hydrodynamical simulations reproduce many properties of the real
universe. These simulations model various physical processes, but many of these
are included using `subgrid models' due to resolution limits. Although
different subgrid models have been successful in modelling the effects of
supernovae (SNe) feedback on galactic properties, it remains unclear if, and by
how much, these differing implementations affect observable halo gas
properties. In this work, we use `zoom-in' cosmological initial conditions of
two volumes selected to resemble the Local Group (LG) evolved with both the
Auriga and EAGLE galaxy formation models. While the subgrid physics models in
both simulations reproduce realistic stellar components of $L^\star$ galaxies,
they exhibit different gas properties. Namely, Auriga predicts that the Milky
Way (MW) is almost baryonically closed, whereas EAGLE suggests that only half
of the expected baryons reside within the halo. Furthermore, EAGLE predicts
that this baryon deficiency extends to the LG, ($r \leq 1 \mathrm{~Mpc}$). The
baryon deficiency in EAGLE is likely due to SNe feedback at high redshift,
which generates halo-wide outflows, with high covering fractions and radial
velocities, which both eject baryons and significantly impede cosmic gas
accretion. Conversely, in Auriga, gas accretion is almost unaffected by
feedback. These differences appear to be the result of the different energy
injection methods from SNe to gas. Our results suggest that both quasar
absorption lines and fast radio burst dispersion measures could constrain these
two regimes with future observations.


Abstract:
  We develop a new method for the construction of one-dimensional integrable
Lindblad systems, which describe quantum many body models in contact with a
Markovian environment. We find several new models with interesting features,
such as annihilation-diffusion processes, a mixture of coherent and classical
particle propagation, and a rectified steady state current. We also find new
ways to represent known classical integrable stochastic equations by integrable
Lindblad operators. Our method can be extended to various other situations and
it establishes a structured approach to the study of solvable open quantum
systems.


Abstract:
  Electrohydrodynamic instabilities of fluid-fluid interfaces can be exploited
in various microfluidic applications in order to enhance mixing, replicate
well-controlled patterns or generate drops of a particular size. In this work,
we study the stability and dynamics of a system of three superimposed layers of
two immiscible fluids subject to a normal electric field. Following the
Taylor-Melcher leaky dielectric model, the bulk remains electroneutral while a
net charge accumulates on the interfaces. The interfacial charge dynamics is
captured by a conservation equation accounting for Ohmic conduction, advection
by the flow and finite charge relaxation. Using this model, we perform a linear
stability analysis and identify different modes of instability, and we
characterize the behavior of the system as a function of the relevant
dimensionless groups in each mode. Further, we perform numerical simulations
using the boundary element method in order to study the effect of
nonlinearities on long-time interfacial dynamics. We demonstrate how the
coupling of flow and surface charge transport in different modes of instability
can give rise to nonlinear phenomena such as tip streaming or pinching of the
film into droplets.


Abstract:
  We consider time-harmonic electromagnetic wave equations in composites of a
dispersive material surrounded by a classical material. In certain frequency
ranges this leads to sign-changing permittivity and/or permeability. Previously
meshing rules were reported, which guarantee the convergence of finite element
approximations to the related scalar source problems. Here we generalize these
results to the electromagnetic two dimensional vectorial equations and the
related holomorphic eigenvalue problems. Different than for the analysis on the
continuous level, we require an assumption on both contrasts of the
permittivity and the permeability. We confirm our theoretical results with
computational studies.


Abstract:
  In this short note, we consider the relative Gersten's complex for Milnor
$K$-theory over a regular Henselian domain $S$ and prove that in degrees $\geq
\dim S\geq 1$, the Gersten's complex of an essentially smooth Henselian local
$S$-scheme is exact.


Abstract:
  Results on unconditional convergence in the Maximum norm for ADI-type
methods, such as the Douglas method, applied to the time integration of
semilinear parabolic problems are quite difficult to get, mainly when the
number of space dimensions $m$ is greater than two. Such a result is obtained
here under quite general conditions on the PDE problem in case that
time-independent Dirichlet boundary conditions are imposed. To get these
bounds, a theorem that guarantees, in some sense, power-boundeness of the
stability function independently of both the space and time resolutions is
proved.


Abstract:
  Light fields are 4D scene representation typically structured as arrays of
views, or several directional samples per pixel in a single view. This highly
correlated structure is not very efficient to transmit and manipulate
(especially for editing), though. To tackle these problems, we present a novel
compact and editable light field representation, consisting of a set of visual
channels (i.e. the central RGB view) and a complementary meta channel that
encodes the residual geometric and appearance information. The visual channels
in this representation can be edited using existing 2D image editing tools,
before accurately reconstructing the whole edited light field back. We propose
to learn this representation via an autoencoder framework, consisting of an
encoder for learning the representation, and a decoder for reconstructing the
light field. To handle the challenging occlusions and propagation of edits, we
specifically designed an editing-aware decoding network and its associated
training strategy, so that the edits to the visual channels can be consistently
propagated to the whole light field upon reconstruction.Experimental results
show that our proposed method outperforms related existing methods in
reconstruction accuracy, and achieves visually pleasant performance in editing
propagation.


Abstract:
  The concept of Bohr radius for the class of bounded analytic functions was
introduced by Harald Bohr in 1914. His initial result received greater interest
and was sharpened-refined-generalized by several mathematicians in various
settings--which is now called Bohr phenomenon. Various generalization of Bohr's
classical theorem is now an active area of research and has been a source of
investigation in numerous other function spaces and including holomorphic
functions of several complex variables. Recently, a new generalization of
Bohr's ideas was introduced and investigated by Kayumov et al.. In this note,
we investigate and refine generalized Bohr's inequality for the class of
quasi-subordinations.


Abstract:
  This chapter outlines the relation between artificial intelligence (AI) /
machine learning (ML) algorithms and digital games. This relation is two-fold:
on one hand, AI/ML researchers can generate large, in-the-wild datasets of
human affective activity, player behaviour (i.e. actions within the game
world), commercial behaviour, interaction with graphical user interface
elements or messaging with other players, while games can utilise intelligent
algorithms to automate testing of game levels, generate content, develop
intelligent and responsive non-player characters (NPCs) or predict and respond
player behaviour across a wide variety of player cultures. In this work, we
discuss some of the most common and widely accepted uses of AI/ML in games and
how intelligent systems can benefit from those, elaborating on estimating
player experience based on expressivity and performance, and on generating
proper and interesting content for a language learning game.


Abstract:
  Hole spin qubits in planar Ge heterostructures are one of the frontrunner
platforms for scalable quantum computers. In these systems, the spin-orbit
interactions permit efficient all-electric qubit control. We propose a minimal
design modification of planar devices that enhances these interactions by
orders of magnitude and enables low power ultrafast qubit operations in the GHz
range. Our approach is based on an asymmetric potential that strongly squeezes
the quantum dot in one direction. This confinement-induced spin-orbit
interaction does not rely on microscopic details of the device such as growth
direction or strain, and could be turned on and off on demand in
state-of-the-art qubits.


Abstract:
  Two-dimensional (2D) materials have received considerable attention as
possible electrodes in Li-ion batteries (LIBs), although a deeper understanding
of the Li adsorption behavior as well as broad screening of the materials space
is still needed. In this work, we build a high-throughput screening scheme that
incorporates a learned interaction. First, density functional theory and graph
convolution networks are utilized to calculate minimum Li adsorption energies
for a small set of 2D metallic materials. The data is then used to find a
dependence of the minimum Li adsorption energies on the sum of ionization
potential, work function of the 2D metal, and coupling energy between Li+ and
substrate. Our results show that variances of elemental properties and density
are the most correlated features with coupling. To illustrate the applicability
of this approach, the model is employed to show that some fluorides and
chromium oxides are potential high-voltage materials with adsorption energies <
-7 eV, and the found physics is used as the design principle to enhance the Li
adsorption ability of graphene. This physics-driven approach shows higher
accuracy and transferability compared with purely data-driven models.


Abstract:
  We propose a new swampland conjecture stating that the limit of vanishing
gravitino mass corresponds to the massless limit of an infinite tower of states
and to the consequent breakdown of the effective field theory. We test our
proposal in large classes of models coming from compactification of string
theory to four dimensions, where we identify the Kaluza-Klein nature of the
tower of states becoming light. We point out a general relation between the
gravitino mass and abelian gauge coupling in models with extended
supersymmetry, which can survive also in examples with minimal supersymmetry.
This allows us to connect our conjecture to other well established swampland
conjectures, such as the weak gravity conjecture or the absence of global
symmetries in quantum gravity. We discuss phenomenological implications of our
conjecture in (quasi-)de Sitter backgrounds and extract a lower bound for the
gravitino mass in terms of the Hubble parameter.


Abstract:
  Efficient exploration in bandits is a fundamental online learning problem. We
propose a variant of Thompson sampling that learns to explore better as it
interacts with bandit instances drawn from an unknown prior. The algorithm
meta-learns the prior and thus we call it MetaTS. We propose several efficient
implementations of MetaTS and analyze it in Gaussian bandits. Our analysis
shows the benefit of meta-learning and is of a broader interest, because we
derive a novel prior-dependent Bayes regret bound for Thompson sampling. Our
theory is complemented by empirical evaluation, which shows that MetaTS quickly
adapts to the unknown prior.


Abstract:
  Let $X=\Lambda\backslash\mathbb{H}$ be a Schottky surface, that is, a
conformally compact hyperbolic surface of infinite area. Let $\delta$ denote
the Hausdorff dimension of the limit set of $\Lambda$.
  We prove that for any compact subset $\mathcal{K}
\subset\{\,s\,:\,\Re(s)>\frac{\delta}{2}\,\}$, if one picks a random degree $n$
cover $X_{n}$ of $X$ uniformly at random, then with probability tending to one
as $n\to\infty$, there are no resonances of $X_{n}$ in $\mathcal{K}$ other than
those already belonging to $X$ (and with the same multiplicity). This result is
conjectured to be the optimal one for bounded frequency resonances and is
analogous to both Alon's and Friedman's conjectures for random graphs, which
are now theorems due to Friedman and Bordenave-Collins, respectively.


Abstract:
  The aim of this work is to study the structure of bounded finite potent
endomorphisms on Hilbert spaces. In particular, for these operators, an answer
to the Invariant Subspace Problem is given and the main properties of its
adjoint operator are offered. Moreover, for every bounded finite potent
endomorphism we show that Tate's trace coincides with the Leray trace and with
the trace defined by R. Elliott for Riesz Trace Class operators.


Abstract:
  We describe the topology of singular real algebraic curves in a smooth
surface. We enumerate and bound in terms of the degree the number of
topological types of singular algebraic curves in the real projective plane.


Abstract:
  Speech sounds of spoken language are obtained by varying configuration of the
articulators surrounding the vocal tract. They contain abundant information
that can be utilized to better understand the underlying mechanism of human
speech production. We propose a novel deep neural network-based learning
framework that understands acoustic information in the variable-length sequence
of vocal tract shaping during speech production, captured by real-time magnetic
resonance imaging (rtMRI), and translate it into text. The proposed framework
comprises of spatiotemporal convolutions, a recurrent network, and the
connectionist temporal classification loss, trained entirely end-to-end. On the
USC-TIMIT corpus, the model achieved a 40.6% PER at sentence-level, much better
compared to the existing models. To the best of our knowledge, this is the
first study that demonstrates the recognition of entire spoken sentence based
on an individual's articulatory motions captured by rtMRI video. We also
performed an analysis of variations in the geometry of articulation in each
sub-regions of the vocal tract (i.e., pharyngeal, velar and dorsal, hard
palate, labial constriction region) with respect to different emotions and
genders. Results suggest that each sub-regions distortion is affected by both
emotion and gender.


Abstract:
  This paper investigates the impact of the charging level of high penetration
level of Electric Vehicles (EVs) on the power quality of the electricity
distribution network. The EV owners tend to charge their EVs as fast as
possible. The charging levels of EVs within the distribution network affect the
voltage profile of buses of the network. In this paper, an exact Second-Order
Cone Programming (SOCP) formulation of the full AC optimal power flow (ACOPF)
problem of the distribution network is presented. The network includes solar
generation units and EVs as Distributed Energy Resources (DERs). Different
charging levels are considered to analyze the impact of EVs on the distribution
network. The performance of the proposed model is illustrated for the modified
IEEE-33 bus system for different charging levels for EVs. Besides, the impact
of available solar power and battery degradation cost of EVs on the
distribution network is investigated. It is illustrated that how EV charging
will cause voltage deviation challenges for the distribution network.


Abstract:
  Multilingualism refers to the high degree of proficiency in two or more
languages in the written and oral communication modes. It often results in
language mixing, a.k.a. code-mixing, when a multilingual speaker switches
between multiple languages in a single utterance of a text or speech. This
paper discusses the current state of the NLP research, limitations, and
foreseeable pitfalls in addressing five real-world applications for social good
crisis management, healthcare, political campaigning, fake news, and hate
speech for multilingual societies. We also propose futuristic datasets, models,
and tools that can significantly advance the current research in multilingual
NLP applications for the societal good. As a representative example, we
consider English-Hindi code-mixing but draw similar inferences for other
language pairs


Abstract:
  We have used the central 44 antennas of the new 64-dish MeerKAT radio
telescope array to conduct a deep search for new pulsars in the core of nine
globular clusters. This has led to the discovery of eight new millisecond
pulsars in six different clusters. Two new binaries, 47 Tuc ac and 47 Tuc ad,
are eclipsing "spiders", featuring compact orbits ($\lesssim 0.32$ days), very
low-mass companions and regular occultations of their pulsed emission. The
other three new binary pulsars (NGC 6624G, M62G, and Ter 5 an) are in wider ($>
0.7$ days) orbits, with companions that are likely to be white dwarfs or
neutron stars. NGC 6624G has a large eccentricity of $e\simeq 0.38$, which
enabled us to detect the rate of advance of periastron. This suggests that the
system is massive, with a total mass of $M{\rm tot} = 2.65 \pm 0.07$
M$_{\odot}$. Likewise, for Ter 5 an, with $e \simeq 0.0066$, we obtain $M{\rm
tot}= 2.97 \pm 0.52$ M$_{\odot}$. The other three new discoveries (NGC 6522D,
NGC 6624H and NGC 6752F) are faint isolated pulsars. Finally, we have used the
whole MeerKAT array and synthesized 288 beams, covering an area of $\sim2$
arcmin in radius around the center of NGC 6624. This has allowed us to localize
many of the pulsars in the cluster, demonstrating the beamforming capabilities
of the TRAPUM software backend and paving the way for the upcoming MeerKAT
globular cluster pulsar survey.


Abstract:
  Intrusion Detection Systems (IDS) are key components for securing critical
infrastructures, capable of detecting malicious activities on networks or
hosts. The procedure of implementing a IDS for Internet of Things (IoT)
networks is not without challenges due to the variability of these systems and
specifically the difficulty in accessing data. The specifics of these very
constrained devices render the design of an IDS capable of dealing with the
varied attacks a very challenging problem and a very active research subject.
In the current state of literature, a number of approaches have been proposed
to improve the efficiency of intrusion detection, catering to some of these
limitations, such as resource constraints and mobility. In this article, we
review works on IDS specifically for these kinds of devices from 2008 to 2018,
collecting a total of 51 different IDS papers. We summarise the current themes
of the field, summarise the techniques employed to train and deploy the IDSs
and provide a qualitative evaluations of these approaches. While these works
provide valuable insights and solutions for sub-parts of these constraints, we
discuss the limitations of these solutions as a whole, in particular what kinds
of attacks these approaches struggle to detect and the setup limitations that
are unique to this kind of system. We find that although several paper claim
novelty of their approach little inter paper comparisons have been made, that
there is a dire need for sharing of datasets and almost no shared code
repositories, consequently raising the need for a thorough comparative
evaluation.


Abstract:
  The local existence of smooth solutions to the inviscid Hall-MHD system has
been obtained since Chae, Degond and Liu [Ann. Inst. H. Poincar\'e Anal. Non
Lin\'eaire, {31} (2014), 555--565]. However, as we known, how to construct the
global small solutions to the inviscid Hall-MHD system is still an open
problem. In the present paper, we give a positive answer in $ \mathbb{T}^3$
when the initial magnetic field is close to a background magnetic field
satisfying the Diophantine condition.


Abstract:
  Originating from neutron star-neutron star (NS-NS) or neutron star-black hole
(NS-BH) mergers, short gamma-ray bursts (SGRBs) are the first electromagnetic
emitters associated with gravitational waves. This association makes the
determination of SGRB formation rate (FR) a critical issue. We determine the
true SGRB FR and its relation to the cosmic star formation rate (SFR). This can
help in determining the expected Gravitation Wave (GW) rate involving small
mass mergers. We present non-parametric methods for the determination of the
evolutions of the luminosity function (LF) and the FR using SGRBs observed by
{\it Swift}, without any assumptions. These are powerful tools for small
samples, such as our sample of 68 SGRBs. We combine SGRBs with and without
extended emission (SEE), assuming that both descend from the same progenitor.
To overcome the incompleteness introduced by redshift measurements we use the
Kolmogorov-Smirnov (KS) test to find flux thresholds yielding a sample of
sources with a redshift drawn from the parent sample including all sources.
Using two subsamples of SGRBs with flux limits of $4.57 \times 10^{-7}$ and
$2.15 \times 10^{-7}$ erg cm$^{-2}$ s$^{-1}$ with respective KS {\it p=(1,
0.9)}, we find a 3 $\sigma$ evidence for luminosity evolution (LE), a broken
power-law LF with significant steepening at $L\sim 10^{50}$ erg s$^{-1}$, and a
FR evolution that decreases monotonically with redshift (independent of LE and
the thresholds). Thus, SGRBs may have been more luminous in the past with a FR
delayed relative to the SFR as expected in the merger scenario.


Abstract:
  Fuzzy dark matter (FDM) has been a promising alternative to standard cold
dark matter. The model consists of ultralight bosons with mass $m_b \sim
10^{-22}$ eV and features a quantum-pressure-supported solitonic core that
oscillates. In this work, we show that the soliton density oscillations persist
even after significant tidal stripping of the outer halo. We report two
intrinsic yet distinct timescales associated, respectively, with the
ground-state soliton wavefunction $\tau_{00}$ and the soliton density
oscillations $\tau_\text{soliton}$, obeying $\tau_\text{soliton} /\tau_{00}
\simeq 2.3$. The central star cluster (SC) in Eridanus II has a characteristic
timescale $\tau_\text{soliton} / \tau_\text{SC} \sim 2$ to $3$ that deviates
substantially from unity. As a result, we demonstrate, both analytically and
numerically with three-dimensional self-consistent FDM simulations, that the
gravitational heating of the SC owing to soliton density oscillations is
negligible irrespective of $m_b$. We also show that the subhalo mass function
to form Eridanus II does not place a strong constraint on $m_b$. These results
are contrary to the previous findings by Marsh & Niemeyer (2019).


Abstract:
  Machine learning may enable the automated generation of test oracles. We have
characterized emerging research in this area through a systematic literature
review examining oracle types, researcher goals, the ML techniques applied, how
the generation process was assessed, and the open research challenges in this
emerging field.
  Based on a sample of 22 relevant studies, we observed that ML algorithms
generated test verdict, metamorphic relation, and - most commonly - expected
output oracles. Almost all studies employ a supervised or semi-supervised
approach, trained on labeled system executions or code metadata - including
neural networks, support vector machines, adaptive boosting, and decision
trees. Oracles are evaluated using the mutation score, correct classifications,
accuracy, and ROC. Work-to-date show great promise, but there are significant
open challenges regarding the requirements imposed on training data, the
complexity of modeled functions, the ML algorithms employed - and how they are
applied - the benchmarks used by researchers, and replicability of the studies.
We hope that our findings will serve as a roadmap and inspiration for
researchers in this field.


Abstract:
  We present exact solutions for the size of the giant connected component
(GCC) of graphs composed of higher-order homogeneous cycles, including weak
cycles and cliques, following bond percolation. We use our theoretical result
to find the location of the percolation threshold of the model, providing
analytical solutions where possible. We expect the results derived here to be
useful to a wide variety of applications including graph theory, epidemiology,
percolation and lattice gas models as well as fragmentation theory. We also
examine the Erd\H{o}s-Gallai theorem as a necessary condition on the
graphicality of configuration model networks comprising higher-order clique
sub-graphs.


Abstract:
  We present an event generator for the process $pp\to \ell\nu_\ell\gamma$ at
next-to-next-to-leading order (NNLO) in QCD and matched to the PYTHIA8 parton
shower. The calculation makes use of the GENEVA framework, which combines a
resummed calculation obtained via Soft-Collinear Effective Theory (SCET) with
the fixed-order result. We validate the NNLO accuracy of our results by
comparing predictions for inclusive quantities with an independent fixed-order
calculation, and then present the resummed 0-jettiness spectrum at
next-to-next-to-leading logarithmic (NNLL$'$) accuracy. Finally, we compare our
predictions against data collected by the ATLAS experiment at the Large Hadron
Collider during its 7 TeV run, finding good agreement.


Abstract:
  The existence of the shortest confidence interval for Poisson mean is shown.
The method of obtaining such an interval is presented as well.


Abstract:
  In this document, we report an analysis of the Public MeSH Note field of the
new descriptors introduced in the MeSH thesaurus between 2006 and 2020. The aim
of this analysis was to extract information about the previous status of these
new descriptors as Supplementary Concept Records. The Public MeSH Note field
contains information in semi-structured text, meant to be read by humans.
Therefore, we adopted a semi-automated approach, based on regular expressions,
to extract information from it. In the large majority of cases, we managed to
minimize the required manual effort for extracting the previous state of a new
descriptor as a Supplementary Concept Record. The source code for this analysis
is openly available on GitHub.


Abstract:
  Inverse Compton scattering (ICS) can obtain quasi-monochromatic and
directional EUV radiation via a MeV-scale energy electron beam and a
micron-scale wavelength laser beam, which enables a dramatic reduction in
dimension and expense of the system, and makes it an attractive technology in
research, industry, medicine and homeland security. Here we propose an EUV
source based on high repetition ICS system. The scheme exploits the output from
the laser-electron interaction between a MW-ps laser at MHz repetition-rate and
a high quality electron beam with an energy of a few MeV at MHz
repetition-rate.


Abstract:
  We report on the expected sensitivity of dedicated scintillator-based
detectors at the LHC for elementary particles with charges much smaller than
the electron charge. The dataset provided by a prototype scintillator-based
detector is used to characterise the performance of the detector and provide an
accurate background projection. Detector designs, including a novel slab
detector configuration, are considered for the data taking period of the LHC to
start in 2022 (Run 3) and for the high luminosity LHC. With the Run 3 dataset,
the existence of new particles with masses between 10 MeV and 45 GeV could be
excluded at 95% confidence level for charges between 0.003e and 0.3e, depending
on their mass. With the high luminosity LHC dataset, the expected limits would
reach between 10 MeV and 80 GeV for charges between 0.0018e and 0.3e, depending
on their mass


Abstract:
  Recommender systems play a vital role in modern online services, such as
Amazon and Taobao. Traditional personalized methods, which focus on user-item
(UI) relations, have been widely applied in industrial settings, owing to their
efficiency and effectiveness. Despite their success, we argue that these
approaches ignore local information hidden in similar users. To tackle this
problem, user-based methods exploit similar user relations to make
recommendations in a local perspective. Nevertheless, traditional user-based
methods, like userKNN and matrix factorization, are intractable to be deployed
in the real-time applications since such transductive models have to be
recomputed or retrained with any new interaction. To overcome this challenge,
we propose a framework called self-complementary collaborative filtering~(SCCF)
which can make recommendations with both global and local information in real
time. On the one hand, it utilizes UI relations and user neighborhood to
capture both global and local information. On the other hand, it can identify
similar users for each user in real time by inferring user representations on
the fly with an inductive model. The proposed framework can be seamlessly
incorporated into existing inductive UI approach and benefit from user
neighborhood with little additional computation. It is also the first attempt
to apply user-based methods in real-time settings. The effectiveness and
efficiency of SCCF are demonstrated through extensive offline experiments on
four public datasets, as well as a large scale online A/B test in Taobao.


Abstract:
  Spectrum anomaly detection is of great importance in wireless communication
to secure safety and improve spectrum efficiency. However, spectrum anomaly
detection faces many difficulties, especially in unauthorized frequency bands.
For example, the composition of unauthorized frequency bands is very complex
and the abnormal usage patterns are unknown in prior. In this paper, a noise
attention method is proposed for unsupervised spectrum anomaly detection in
unauthorized bands. First of all, we theoretically prove that the anomalies in
unauthorized bands will raise the noise floor of spectrogram after VAE
reconstruction. Then, we introduce a novel anomaly metric named as noise
attention score to more effectively capture spectrum anomaly. The effectiveness
of the proposed method is experimentally verified in 2.4 GHz ISM band.
Leveraging the noise attention score, the AUC metric of anomaly detection is
increased by 0.193. The proposed method is beneficial to reliably detecting
abnormal spectrum while keeping low false alarm rate.


Abstract:
  We present analytical results for the distribution of first return (FR) times
of random walks (RWs) on random regular graphs (RRGs) consisting of $N$ nodes
of degree $c \ge 3$. Starting from a random initial node $i$ at time $t=0$, at
each time step $t \ge 1$ an RW hops into a random neighbor of its previous
node. We calculate the distribution $P ( T_{\rm FR} = t )$ of first return
times to the initial node $i$. We distinguish between first return trajectories
in which the RW retrocedes its own steps backwards all the way back to the
initial node $i$ and those in which the RW returns to $i$ via a path that does
not retrocede its own steps. In the retroceding scenario, each edge that
belongs to the RW trajectory is crossed the same number of times in the forward
and backward directions. In the non-retroceding scenario the subgraph that
consists of the nodes visited by the RW and the edges it has crossed between
these nodes includes at least one cycle. In the limit of $N \rightarrow \infty$
the RRG converges towards the Bethe lattice. The Bethe lattice exhibits a tree
structure, in which all the first return trajectories belong to the retroceding
scenario. Moreover, in the limit of $N \rightarrow \infty$ the trajectories of
RWs on RRGs are transient in the sense that they return to the initial node
with probability $<1$. In this sense they resemble the trajectories of RWs on
regular lattices of dimensions $d \ge 3$. The analytical results are found to
be in excellent agreement with the results obtained from computer simulations.


Abstract:
  Filter data structures over-approximate a set of hashable keys, i.e. set
membership queries may incorrectly come out positive. A filter with false
positive rate $f \in (0,1]$ is known to require $\ge \log_2(1/f)$ bits per key.
At least for larger $f \ge 2^{-4}$, existing practical filters require a space
overhead of at least 20% with respect to this information-theoretic bound.
  We introduce the Ribbon filter: a new filter for static sets with a broad
range of configurable space overheads and false positive rates with competitive
speed over that range, especially for larger $f \ge 2^{-7}$. In many cases,
Ribbon is faster than existing filters for the same space overhead, or can
achieve space overhead below 10% with some additional CPU time. An experimental
Ribbon design with load balancing can even achieve space overheads below 1%.
  A Ribbon filter resembles an Xor filter modified to maximize locality and is
constructed by solving a band-like linear system over Boolean variables. In
previous work, Dietzfelbinger and Walzer describe this linear system and an
efficient Gaussian solver. We present and analyze a faster, more adaptable
solving process we call "Rapid Incremental Boolean Banding ON the fly," which
resembles hash table construction. We also present and analyze an attractive
Ribbon variant based on making the linear system homogeneous, and describe
several more practical enhancements.


Abstract:
  We evolve PyDTNN, a framework for distributed parallel training of Deep
Neural Networks (DNNs), into an efficient inference tool for convolutional
neural networks. Our optimization process on multicore ARM processors involves
several high-level transformations of the original framework, such as the
development and integration of Cython routines to exploit thread-level
parallelism; the design and development of micro-kernels for the matrix
multiplication, vectorized with ARMs NEON intrinsics, that can accommodate
layer fusion; and the appropriate selection of several cache configuration
parameters tailored to the memory hierarchy of the target ARM processors. Our
experiments evaluate both inference throughput (measured in processed images/s)
and inference latency (i.e., time-to-response) as well as energy consumption
per image when varying the level of thread parallelism and the processor power
modes. The experiments with the new inference engine are reported for the
ResNet50 v1.5 model on the ImageNet dataset from the MLPerf suite using the ARM
v8.2 cores in the NVIDIA Jetson AGX Xavier board. These results show superior
performance compared with the well-spread TFLite from Google and slightly
inferior results when compared with ArmNN, the native library from ARM for DNN
inference.


Abstract:
  This paper explores how the current paradigm of vulnerability management
might adapt to include machine learning systems through a thought experiment:
what if flaws in machine learning (ML) were assigned Common Vulnerabilities and
Exposures (CVE) identifiers (CVE-IDs)? We consider both ML algorithms and model
objects. The hypothetical scenario is structured around exploring the changes
to the six areas of vulnerability management: discovery, report intake,
analysis, coordination, disclosure, and response. While algorithm flaws are
well-known in the academic research community, there is no apparent clear line
of communication between this research community and the operational
communities that deploy and manage systems that use ML. The thought experiments
identify some ways in which CVE-IDs may establish some useful lines of
communication between these two communities. In particular, it would start to
introduce the research community to operational security concepts, which
appears to be a gap left by existing efforts.


Abstract:
  In this work we introduce an implementation for which machine learning
techniques helped improve the overall performance of an evolutionary algorithm
for an optimization problem, namely a variation of robust minimum-cost path in
graphs. In this big data optimization problem, a path achieving a good cost in
most scenarios from an available set of scenarios (generated by a simulation
process) must be obtained. The most expensive task of our evolutionary
algorithm, in terms of computational resources, is the evaluation of candidate
paths: the fitness function must calculate the cost of the candidate path in
every generated scenario. Given the large number of scenarios, this task must
be implemented in a distributed environment. We implemented gradient boosting
decision trees to classify candidate paths in order to identify good
candidates. The cost of the not-so-good candidates is simply forecasted. We
studied the training process, gain performance, accuracy, and other variables.
Our computational experiments show that the computational performance was
significantly improved at the expense of a limited loss of accuracy.


Abstract:
  We present an efficient approach for doing approximate Bayesian inference
when only a limited number of noisy likelihood evaluations can be obtained due
to computational constraints, which is becoming increasingly common for
applications of complex models. Our main methodological innovation is to model
the log-likelihood function using a Gaussian process (GP) in a local fashion
and apply this model to emulate the progression that an exact
Metropolis-Hastings (MH) algorithm would take if it was applicable. New
log-likelihood evaluation locations are selected using sequential experimental
design strategies such that each MH accept/reject decision is done within a
pre-specified error tolerance. The resulting approach is conceptually simple
and sample-efficient as it takes full advantage of the GP model. It is also
more robust to violations of GP modelling assumptions and better suited for the
typical situation where the posterior is substantially more concentrated than
the prior, compared with various existing inference methods based on global GP
surrogate modelling. We discuss the probabilistic interpretations and central
theoretical aspects of our approach, and we then demonstrate the benefits of
the resulting algorithm in the context of likelihood-free inference for
simulator-based statistical models.


Abstract:
  Entanglement concurrence has been widely used for featuring entanglement in
quantum experiments. As an entanglement monotone it is related to specific
quantum Tsallis entropy. Our goal in this paper is to propose a new
parameterized bipartite entanglement monotone which is named as $q$-concurrence
inspired by general Tsallis entropy. We derive an analytical lower bound for
the $q$-concurrence of any bipartite quantum entanglement state by employing
positive partial transposition criterion and realignment criterion, which shows
an interesting relationship to the strong separability criteria. The new
entanglement monotone is used to characterize bipartite isotropic states.
Finally, we provide a computational method to estimate the $q$-concurrence for
any entanglement by superposing two bipartite pure states. It shows that the
superposition operations can at most increase one ebit for the $q$-concurrence
in the case that the two states being superposed are bi-orthogonal or one-sided
orthogonal. These results reveal a series of new phenomena about the
entanglement, which may be interesting in quantum communication and quantum
information processing.


Abstract:
  Underwater image enhancement is an important low-level computer vision task
for autonomous underwater vehicles and remotely operated vehicles to explore
and understand the underwater environments. Recently, deep convolutional neural
networks (CNNs) have been successfully used in many computer vision problems,
and so does underwater image enhancement. There are many deep-learning-based
methods with impressive performance for underwater image enhancement, but their
memory and model parameter costs are hindrances in practical application. To
address this issue, we propose a lightweight adaptive feature fusion network
(LAFFNet). The model is the encoder-decoder model with multiple adaptive
feature fusion (AAF) modules. AAF subsumes multiple branches with different
kernel sizes to generate multi-scale feature maps. Furthermore, channel
attention is used to merge these feature maps adaptively. Our method reduces
the number of parameters from 2.5M to 0.15M (around 94% reduction) but
outperforms state-of-the-art algorithms by extensive experiments. Furthermore,
we demonstrate our LAFFNet effectively improves high-level vision tasks like
salience object detection and single image depth estimation.


Abstract:
  The predominant reason for the damaging power of high-energy radiation is
multiple ionization of a molecule, either direct or via the decay of highly
excited intermediates, as e.g., in the case of X-ray irradiation. Consequently,
the molecule is irreparably damaged by the subsequent fragmentation in a
Coulomb explosion. In an aqueous environment, however, it has been observed
that irradiated molecules may be saved from fragmentation presumably by charge
and energy dissipation mechanisms. Here, we show that the protective effect of
the environment sets in even earlier than hitherto expected, namely immediately
after single inner-shell ionization. By combining coincidence measurements of
the fragmentation of X-ray-irradiated microsolvated pyrimidine molecules with
theoretical calculations, we identify direct intermolecular electronic decay as
the protective mechanism, outrunning the usually dominant Auger decay. Our
results demonstrate that such processes play a key role in charge
delocalization and have to be considered in investigations and models on
high-energy radiation damage in realistic environments.


Abstract:
  In this work we study the metric distortion problem in voting theory under a
limited amount of ordinal information. Our primary contribution is threefold.
First, we consider mechanisms which perform a sequence of pairwise comparisons
between candidates. We show that a widely-popular deterministic mechanism
employed in most knockout phases yields distortion $\mathcal{O}(\log m)$ while
eliciting only $m-1$ out of $\Theta(m^2)$ possible pairwise comparisons, where
$m$ represents the number of candidates. Our analysis for this mechanism
leverages a powerful technical lemma recently developed by Kempe
\cite{DBLP:conf/aaai/000120a}. We also provide a matching lower bound on its
distortion. In contrast, we prove that any mechanism which performs fewer than
$m-1$ pairwise comparisons is destined to have unbounded distortion. Moreover,
we study the power of deterministic mechanisms under incomplete rankings. Most
notably, when every agent provides her $k$-top preferences we show an upper
bound of $6 m/k + 1$ on the distortion, for any $k \in \{1, 2, \dots, m\}$.
Thus, we substantially improve over the previous bound of $12 m/k$ recently
established by Kempe \cite{DBLP:conf/aaai/000120a,DBLP:conf/aaai/000120b}, and
we come closer to matching the best-known lower bound. Finally, we are
concerned with the sample complexity required to ensure near-optimal distortion
with high probability. Our main contribution is to show that a random sample of
$\Theta(m/\epsilon^2)$ voters suffices to guarantee distortion $3 + \epsilon$
with high probability, for any sufficiently small $\epsilon > 0$. This result
is based on analyzing the sensitivity of the deterministic mechanism introduced
by Gkatzelis, Halpern, and Shah \cite{DBLP:conf/focs/Gkatzelis0020}.
Importantly, all of our sample-complexity bounds are distribution-independent.


Abstract:
  Satellite images are snapshots of the Earth surface. We propose to forecast
them. We frame Earth surface forecasting as the task of predicting satellite
imagery conditioned on future weather. EarthNet2021 is a large dataset suitable
for training deep neural networks on the task. It contains Sentinel 2 satellite
imagery at 20m resolution, matching topography and mesoscale (1.28km)
meteorological variables packaged into 32000 samples. Additionally we frame
EarthNet2021 as a challenge allowing for model intercomparison. Resulting
forecasts will greatly improve (>x50) over the spatial resolution found in
numerical models. This allows localized impacts from extreme weather to be
predicted, thus supporting downstream applications such as crop yield
prediction, forest health assessments or biodiversity monitoring. Find data,
code, and how to participate at www.earthnet.tech


Abstract:
  Numerous smart city testbeds and system deployments have surfaced around the
world, aiming to provide services over unified large heterogeneous IoT
infrastructures. Although we have achieved new scales in smart city
installations and systems, so far the focus has been to provide diverse sources
of data to smart city services consumers, while neglecting to provide ways to
simplify making good use of them. We believe that knowledge creation in smart
cities through data annotation, supported in both an automated and a
crowdsourced manner, is an aspect that will bring additional value to smart
cities. We present here our approach, aiming to utilize an existing smart city
deployment and the OrganiCity software ecosystem. We discuss key challenges
along with characteristic use cases, and report on our design and
implementation, along with preliminary results.


Abstract:
  Self- and mutually-exciting point processes are popular models in machine
learning and statistics for dependent discrete event data. To date, most
existing models assume stationary kernels (including the classical Hawkes
processes) and simple parametric models. Modern applications with complex event
data require more general point process models that can incorporate contextual
information of the events, called marks, besides the temporal and location
information. Moreover, such applications often require non-stationary models to
capture more complex spatio-temporal dependence. To tackle these challenges, a
key question is to devise a versatile influence kernel in the point process
model. In this paper, we introduce a novel and general neural network-based
non-stationary influence kernel with high expressiveness for handling complex
discrete events data while providing theoretical performance guarantees. We
demonstrate the superior performance of our proposed method compared with the
state-of-the-art on synthetic and real data.


Abstract:
  The problem for molecular identification knows many solutions which include
mass spectrometers whose mass sensitivity depends on the performance of the
detector involved. The purpose of this article is to show by means of molecular
dynamics simulations, how a laser-cooled ion cloud, confined in a linear
radio-frequency trap, can reach the ultimate sensitivity providing the
detection of individual charged heavy molecular ions. In our simulations, we
model the laser-cooled Ca + ions as two-level atoms, confined thanks to a set
of constant and time oscillating electrical fields. A singly-charged molecular
ion with a mass of 10 6 amu is propelled through the ion cloud. The induced
change in the fluorescence rate of the lather is used as the detection signal.
We show that this signal is due to a significant temperature variation
triggered by the Coulombian repulsion and amplified by the radio-frequency
heating induced by the trap itself. We identify the optimum initial energy for
the molecular ion to be detected and furthermore, we characterize the
performance of the detector for a large range of confinement voltages.


Abstract:
  In this paper we introduce general transfer operators between high-order and
low-order refined finite element spaces that can be used to couple high-order
and low-order simulations. Under natural restrictions on the low-order refined
space we prove that both the high-to-low-order and low-to-high-order linear
mappings are conservative, constant preserving and high-order accurate. While
the proofs apply to affine geometries, numerical experiments indicate that the
results hold for more general curved and mixed meshes. These operators also
have applications in the context of coarsening solution fields defined on
meshes with nonconforming refinement. The transfer operators for $H^1$ finite
element spaces require a globally coupled solve, for which robust and efficient
preconditioners are developed. We present several numerical results confirming
our analysis and demonstrate the utility of the new mappings in the context of
adaptive mesh refinement and conservative multi-discretization coupling.


Abstract:
  REBCO tape has remarkable performance for super-conducting technology, but it
is extremely expensive and it is a strongly anisotropic superconductor. The
design of a 3.5 T collider dipole is presented in which all turns of the body
winding are oriented so that the all turns of REBCO tape operate with maximum
current density. Each turn within the winding contains a stack of Cu-clad REBCO
tapes; all tapes within the turn are compressed to provide low-resistance Cu-Cu
contact, but the successive turns are electrically isolated. Each turn is
oriented so that the local magnetic field at the tapes is closely parallel to
the tape surface. Sextupole is controlled using a current-programmed turn to
provide excellent homogeneity over a 20:1 dynamic range. Issues of current
re-distribution during ramping, quench stability, AC losses, synchrotron
radiation, and cryogenics have been considered, and appear to provide favorable
properties for an ultimate-energy hadron collider.


Abstract:
  The paper presents some exact solutions of Bianchi types I, III and
Kantowski-Sachs cosmological models consisting of a dissipative fluid along
with an axial magnetic field. A barytropic equation of state between the
thermodynamic pressure and the matter density, together with a pair of linear
relations between the matter density, the shear scalar, and the expansion
scalar have been assumed for simplicity. The solutions are basically of two
different types, one for the Bianchi-I and the other for Bianchi-III and
Kantowski-Sachs type. The presence of the magnetic field, however, does not
change the fundamental nature of the initial singularity.


Abstract:
  The paper proposes an approach for the efficient model order reduction of
dynamic contact problems in linear elasticity. Instead of the augmented
Lagrangian method that is widely used for mechanical contact problems, we
prefer here the Linear Complementarity Programming (LCP) method as basic
methodology. It has the advantage of resulting in the much smaller dual problem
that is associated with the governing variational principle and that turns out
to be beneficial for the model order reduction. Since the shape of the contact
zone depends strongly on the acting outer forces, the LCP for the Lagrange
multipliers has to be solved in each time step. The model order reduction
scheme, on the other hand, is applied to the large linear system for the
displacements and computed in advance by means of an Arnoldi process. In terms
of computational effort the reduction scheme is very appealing because the
contact constraints are fully satisfied while the reduction acts only on the
displacements. As an extension of our approach, we furthermore take up the idea
of the Craig-Bampton method in order to distinguish between interior nodes and
the nodes in the contact zone. A careful performance analysis closes the paper.


Abstract:
  Electrochemical impedance spectroscopy (EIS) is a widely used tool for
characterization of fuel cells and other electrochemical conversion systems.
When applied to the on-line monitoring in the context of in-field applications,
the disturbances, drifts and sensor noise may cause severe distortions in the
evaluated spectra, especially in the low-frequency part. Failure to ignore the
random effects can result in misinterpreted spectra and, consequently, in
misleading diagnostic reasoning. This fact has not been often addressed in the
research so far. In this paper, we propose an approach to the quantification of
the spectral uncertainty, which relies on evaluating the uncertainty of the
equivalent circuit model (ECM). We apply the computationally efficient
variational Bayes (VB) method and compare the quality of the results with those
obtained with the Markov chain Monte Carlo (MCMC) algorithm. Namely, MCMC
algorithm returns accurate distributions of the estimated model parameters,
while VB approach provides the approximate distributions. By using simulated
and real data we show that approximate results provided by VB approach,
although slightly over-optimistic, are still close to the more realistic MCMC
estimates. A great advantage of the VB method for online monitoring is low
computational load, which is several orders of magnitude lower compared to
MCMC. The performance of VB algorithm is demonstrated on a case of ECM
parameters estimation in a 6 cell solid oxide fuel cell (SOFC) stack. The
complete numerical implementation for recreating the results can be found at
https://repo.ijs.si/lznidaric/variational-bayes-supplementary-material.


Abstract:
  Reply suggestion models help users process emails and chats faster. Previous
work only studies English reply suggestion. Instead, we present MRS, a
multilingual reply suggestion dataset with ten languages. MRS can be used to
compare two families of models: 1) retrieval models that select the reply from
a fixed set and 2) generation models that produce the reply from scratch.
Therefore, MRS complements existing cross-lingual generalization benchmarks
that focus on classification and sequence labeling tasks. We build a generation
model and a retrieval model as baselines for MRS. The two models have different
strengths in the monolingual setting, and they require different strategies to
generalize across languages. MRS is publicly available at
https://github.com/zhangmozhi/mrs.


Abstract:
  We show that universal rogue wave patterns exist in integrable systems. These
rogue patterns comprise fundamental rogue waves arranged in shapes such as
triangle, pentagon and heptagon, with a possible lower-order rogue wave at the
center. These patterns appear when one of the internal parameters in bilinear
expressions of rogue waves gets large. Analytically, these patterns are
determined by the root structures of the Yablonskii-Vorob'ev polynomial
hierarchy through a linear transformation. Thus, the induced rogue patterns in
the space-time plane are simply the root structures of Yablonskii-Vorob'ev
polynomials under actions such as dilation, rotation, stretch, shear and
translation. As examples, these universal rogue patterns are explicitly
determined and graphically illustrated for the generalized derivative nonlinear
Schr\"odinger equations, the Boussinesq equation, and the Manakov system.
Similarities and differences between these rogue patterns and those reported
earlier in the nonlinear Schr\"odinger equation are discussed.


Abstract:
  We propose to learn a generative model via entropy interpolation with a
Schr\"{o}dinger Bridge. The generative learning task can be formulated as
interpolating between a reference distribution and a target distribution based
on the Kullback-Leibler divergence. At the population level, this entropy
interpolation is characterized via an SDE on $[0,1]$ with a time-varying drift
term. At the sample level, we derive our Schr\"{o}dinger Bridge algorithm by
plugging the drift term estimated by a deep score estimator and a deep density
ratio estimator into the Euler-Maruyama method. Under some mild smoothness
assumptions of the target distribution, we prove the consistency of both the
score estimator and the density ratio estimator, and then establish the
consistency of the proposed Schr\"{o}dinger Bridge approach. Our theoretical
results guarantee that the distribution learned by our approach converges to
the target distribution. Experimental results on multimodal synthetic data and
benchmark data support our theoretical findings and indicate that the
generative model via Schr\"{o}dinger Bridge is comparable with state-of-the-art
GANs, suggesting a new formulation of generative learning. We demonstrate its
usefulness in image interpolation and image inpainting.


Abstract:
  Timely evacuation is crucial to disaster response, as people can avoid
suffering and loss of lives when a major disaster happens. With the development
of sharing economy, ridesharing has the advantage of reducing congestion,
saving travel time, and optimizing transportation mode to improve disaster
evacuation efficiency. The paper proposes to integrate the concept of
ridesharing into evacuation and develops a mixed-integer programming model for
this problem. A real-world case study based on Houston is used to validate the
proposed model. A series of instances are designed to compare the evacuation
efficiency using two indicators, evacuation percentage and average travel
distance. Results reveal that increasing the number of vehicles to help carless
individuals might not be the most efficient method in this model. Moreover,
this model offers a specific response strategy based on different disaster
scales, which not only develops a better evacuation plan for the people but
also provides relief agencies insights on resource utilization.


Abstract:
  Weakly supervised semantic segmentation is receiving great attention due to
its low human annotation cost. In this paper, we aim to tackle bounding box
supervised semantic segmentation, i.e., training accurate semantic segmentation
models using bounding box annotations as supervision. To this end, we propose
Affinity Attention Graph Neural Network ($A^2$GNN). Following previous
practices, we first generate pseudo semantic-aware seeds, which are then formed
into semantic graphs based on our newly proposed affinity Convolutional Neural
Network (CNN). Then the built graphs are input to our $A^2$GNN, in which an
affinity attention layer is designed to acquire the short- and long- distance
information from soft graph edges to accurately propagate semantic labels from
the confident seeds to the unlabeled pixels. However, to guarantee the
precision of the seeds, we only adopt a limited number of confident pixel seed
labels for $A^2$GNN, which may lead to insufficient supervision for training.
To alleviate this issue, we further introduce a new loss function and a
consistency-checking mechanism to leverage the bounding box constraint, so that
more reliable guidance can be included for the model optimization. Experiments
show that our approach achieves new state-of-the-art performances on Pascal VOC
2012 datasets (val: 76.5\%, test: 75.2\%). More importantly, our approach can
be readily applied to bounding box supervised instance segmentation task or
other weakly supervised semantic segmentation tasks, with state-of-the-art or
comparable performance among almot all weakly supervised tasks on PASCAL VOC or
COCO dataset. Our source code will be available at
https://github.com/zbf1991/A2GNN.


Abstract:
  In the PAC-Bayesian literature, the C-Bound refers to an insightful relation
between the risk of a majority vote classifier (under the zero-one loss) and
the first two moments of its margin (i.e., the expected margin and the voters'
diversity). Until now, learning algorithms developed in this framework minimize
the empirical version of the C-Bound, instead of explicit PAC-Bayesian
generalization bounds. In this paper, by directly optimizing PAC-Bayesian
guarantees on the C-Bound, we derive self-bounding majority vote learning
algorithms. Moreover, our algorithms based on gradient descent are scalable and
lead to accurate predictors paired with non-vacuous guarantees.


Abstract:
  We report spectropolarimetric observations of a supersonic downflow impacting
the lower atmosphere within a large sunspot umbra. This work is an extension of
Schad et al. 2016 using observations acquired in the He I 10830 Angstrom
triplet by the Facility Infrared Spectropolarimeter. Downflowing material
accelerating along a cooled coronal loop reaches peak speeds near 200 km
s$^{-1}$ and exhibits both high speed emission and absorption within the umbra,
which we determine to be a consequence of the strong height dependence of the
radiatively-controlled source function above the sunspot umbra. Strong emission
profiles close to the rest wavelengths but with long red-shifted tails are also
observed at the downflow terminus. From the polarized spectra, we infer
longitudinal magnetic field strengths of ${\sim}2.4$ kG in the core portion of
the He I strong emission, which we believe is the strongest ever reported in
this line. Photospheric field strengths along the same line-of-sight are
${\sim}2.8$ kG as inferred using the Ca I 10839 Angstrom spectral line. The
temperatures of the highest speed He I absorption and the near rest emission
are similar (${\sim}$10 kK), while a differential emission measure analysis
using SDO/AIA data indicates significant increases in radiative cooling for
temperatures between $\sim$0.5 and 1 MK plasma associated with the downflow
terminus. Combined we interpret these observations in the context of a strong
radiative shock induced by the supersonic downflow impacting the low sunspot
atmosphere.


Abstract:
  It has been recently discovered that some random processes may satisfy limit
theorems even though they exhibit intermittency, namely an unusual growth of
moments. In this paper we provide a deeper understanding of these intricate
limiting phenomena. We show that intermittent processes may exhibit a
multiscale behavior involving growth at different rates. To these rates
correspond different scales. In addition to a dominant scale, intermittent
processes may exhibit secondary scales. The probability of these scales
decreases to zero as a power function of time. For the analysis, we consider
large deviations of the rate of growth of the processes. Our approach is quite
general and covers different possible scenarios with special focus on the
so-called supOU processes.


Abstract:
  We report, for the first time, the long-awaited detection of diffuse gamma
rays with energies between 100 TeV and 1 PeV in the Galactic disk.
Particularly, all gamma rays above 398 TeV are observed apart from known TeV
gamma-ray sources and compatible with expectations from the hadronic emission
scenario in which gamma rays originate from the decay of $\pi^0$'s produced
through the interaction of protons with the interstellar medium in the Galaxy.
This is strong evidence that cosmic rays are accelerated beyond PeV energies in
our Galaxy and spread over the Galactic disk.


Abstract:
  We study the evolution of step bunches on vicinal surfaces using a
thermodynamically consistent step-flow model that (i) circumvents the
quasistatic approximation that prevails in the literature by accounting for the
dynamics of adatom diffusion on terraces and attachment-detachment at steps
(referred to as the dynamical effect), and (ii) generalizes the expression of
the step chemical potential by incorporating the necessary coupling between the
diffusion fields on adjacent terraces (referred to as the chemical effect).
Having previously shown that these effects can explain the onset of step
bunching without recourse to the inverse Ehrlich-Schwoebel (iES) barrier or
other extraneous mechanisms, we are here interested in the evolution of step
bunches beyond the linear-stability regime. In particular, the numerical
resolution of the step-flow problem yields a robust power-law coarsening of the
surface profile, with the bunch height growing in time as $H\sim t^{1/2}$ and
the minimal interstep distance as a function of the number of steps in the
bunch cell obeying $\ell_{min}\sim N^{-2/3}$. Although these exponents have
previously been reported, this is the first time such scaling laws are obtained
in the absence of an iES barrier or adatom electromigration. In order to
validate our simulations, we take the continuum limit of the discrete step-flow
system, leading to a novel nonlinear evolution equation for the surface height.
We investigate the existence of self-similar solutions of this equation and
confirm the 1/2 coarsening exponent obtained numerically for $H$. We highlight
the influence of the combined dynamical-chemical effect and show that it can be
interpreted as an effective iES barrier in the standard BCF theory. Finally, we
use a Pad\'e approximant to derive an analytical expression for the velocity of
steadily moving step bunches and compare it to numerical simulations.


Abstract:
  We present temporally abstract actor-critic (TAAC), a simple but effective
off-policy RL algorithm that incorporates closed-loop temporal abstraction into
the actor-critic framework. TAAC adds a second-stage binary policy to choose
between the previous action and a new action output by an actor. Crucially, its
"act-or-repeat" decision hinges on the actually sampled action instead of the
expected behavior of the actor. This post-acting switching scheme let the
overall policy make more informed decisions. TAAC has two important features:
a) persistent exploration, and b) a new compare-through Q operator for
multi-step TD backup, specially tailored to the action repetition scenario. We
demonstrate TAAC's advantages over several strong baselines across 14
continuous control tasks. Our surprising finding reveals that while achieving
top performance, TAAC is able to "mine" a significant number of repeated
actions with the trained policy even on continuous tasks whose problem
structures on the surface seem to repel action repetition. This suggests that
aside from encouraging persistent exploration, action repetition can find its
place in a good policy behavior. Code is available at
https://github.com/hnyu/taac.


Abstract:
  Scene classification, aiming at classifying a scene image to one of the
predefined scene categories by comprehending the entire image, is a
longstanding, fundamental and challenging problem in computer vision. The rise
of large-scale datasets, which constitute the corresponding dense sampling of
diverse real-world scenes, and the renaissance of deep learning techniques,
which learn powerful feature representations directly from big raw data, have
been bringing remarkable progress in the field of scene representation and
classification. To help researchers master needed advances in this field, the
goal of this paper is to provide a comprehensive survey of recent achievements
in scene classification using deep learning. More than 200 major publications
are included in this survey covering different aspects of scene classification,
including challenges, benchmark datasets, taxonomy, and quantitative
performance comparisons of the reviewed methods. In retrospect of what has been
achieved so far, this paper is also concluded with a list of promising research
opportunities.


Abstract:
  We present analytic threshold formulae applicable to both dispersive
(time-domain) and diffractive (pattern-forming) instabilities in Fabry-Perot
Kerr cavities of arbitrary finesse. We do so by extending the gain-circle
technique, recently developed for counter-propagating fields in
single-mirror-feedback systems, to allow for an input mirror. In time-domain
counter-propagating systems walk-off effects are known to suppress cross-phase
modulation contributions to dispersive instabilities. Applying the gain-circle
approach with appropriately-adjusted cross-phase couplings extends previous
results to arbitrary finesse, beyond mean-field approximations, and describes
Ikeda instabilities.


Abstract:
  We report intertwined Weyl phases, which come from superposing topological
phases by crystalline symmetry. In the intertwined Weyl phases, an
unconventional Weyl phase where Weyl points possess a higher charge (monopole
charge>1) due to rotation symmetry, and a higher-order topological phase
enforced by rotation symmetry, are superposed. The two phases are no longer
separable, but intertwine with each other, resulting in the novel phase.
Remarkably, the intertwining leads to a prominent characteristic feature of the
intertwined Weyl phases: $\textit{the change of Fermi-arc topology}$ in a
periodic pattern, i.e., the way how Fermi arcs connect to Weyl points changes
drastically with respect to surface orientation, which exhibits a periodic
pattern. Such a phenomenon is absent in any individual phase alone. Moreover,
we elaborate on how to emulate the intertwined double-Weyl phase in cold atoms.
Our theory is quite promising for generating new topological phases based on
existing ones.


Abstract:
  We consider a one-dimensional chain of N equidistantly spaced noninteracting
qubits embedded in an open waveguide. In the frame of single-excitation
subspace, we systematically study the evolution of qubits amplitudes if the
only qubit in the chain was initially excited. We show that the temporal
dynamics of qubits amplitudes crucially depend on the value of kd, where k is
the wave vector, d is a distance between neighbor qubits. If kd is equal to an
integer multiple of $\pi$, then the qubits are excited to a stationary level
which scales as SN^{-1}S. We show that in this case, it is the dark states
which prevent qubits from decaying to zero even though they do not contribute
to the output spectrum of photon emission. For other values of kd the
excitations of qubits have the form of damping oscillations, which represent
the vacuum Rabi oscillations in a multi-qubit system. In this case, the output
spectrum of photon radiation is defined by a subradiant state with the smallest
width.


Abstract:
  We present a catalog of eclipsing binaries in the northern Galactic Plane
from the Kiso Wide-Field Camera Intensive Survey of the Galactic Plane
(KISOGP). We visually identified 7055 eclipsing binaries spread across
$\sim$330 square degrees, including 4197 W Ursa Majoris/EW-, 1458 $\beta$
Lyrae/EB-, and 1400 Algol/EA-type eclipsing binaries. For all systems, $I$-band
light curves were used to obtain accurate system parameters. We derived the
distances and extinction values for the EW-type objects from their
period--luminosity relation. We also obtained the structure of the thin disk
from the distribution of our sample of eclipsing binary systems, combined with
those of high-mass star-forming regions and Cepheid tracers. We found that the
thin disk is inhomogeneous in number density as a function of Galactic
longitude. Using this new set of distance tracers, we constrain the detailed
structure of the thin disk. Finally, we report a global parallax zero-point
offset of $ \Delta \pi=-42.1\pm1.9\mbox{(stat.)}\pm12.9\mbox{(syst.)}$ $\mu$as
between our carefully calibrated EW-type eclipsing binary positions and those
provided by Gaia Early Data Release 3. Implementation of the officially
recommended parallax zero-point correction results in a significantly reduced
offset. Additionally, we provide a photometric characterization of our EW-type
eclipsing binaries that can be applied to further analyses.


Abstract:
  Taxonomies are an important ingredient of knowledge organization, and serve
as a backbone for more sophisticated knowledge representations in intelligent
systems, such as formal ontologies. However, building taxonomies manually is a
costly endeavor, and hence, automatic methods for taxonomy induction are a good
alternative to build large-scale taxonomies. In this paper, we propose TIEmb,
an approach for automatic unsupervised class subsumption axiom extraction from
knowledge bases using entity and text embeddings. We apply the approach on the
WebIsA database, a database of subsumption relations extracted from the large
portion of the World Wide Web, to extract class hierarchies in the Person and
Place domain.


Abstract:
  Crystalline defects can modify quantum interactions in solids, causing
unintuitive, even favourable, properties such as quantum Hall effect or
superconducting vortex pinning. Here we present another example of this notion
- an unexpected unidirectional Kondo scattering in single crystals of 2H-NbS2.
This manifests as a pronounced low-temperature enhancement in the out-of-plane
resistivity and thermopower below 40 K, hidden for the in-plane charge
transport. The anomaly can be suppressed by the c-axis-oriented magnetic field,
but is unaffected by field applied along the planes. The magnetic moments
originate from layers of 1T-NbS2, which inevitably form during the growth,
undergoing a charge-density-wave reconstruction with each superlattice cell
(David-star-shaped cluster of Nb atoms) hosting a localised spin. Our results
demonstrate the unique and highly anisotropic response of a spontaneously
formed Kondo lattice heterostructure, intercalated in a layered conductor.


Abstract:
  Outflows from super-massive black holes (SMBHs) play an important role in the
co-evolution of themselves, their host galaxies, and the larger scale
environments. Such outflows are often characterized by emission and absorption
lines in various bands and in a wide velocity range blueshifted from the
systematic redshift of the host quasar. In this paper, we report a strong broad
line region (BLR) outflow from the z~4.7 quasar BR 1202-0725 based on the
high-resolution optical spectrum taken with the Magellan Inamori Kyocera
Echelle (MIKE) spectrograph installed on the 6.5m Magellan/Clay telescope,
obtained from the `Probing the He II re-Ionization ERa via Absorbing C IV
Historical Yield' (HIERACHY) project. This rest-frame ultraviolet (UV) spectrum
is characterized by a few significantly blueshifted broad emission lines from
high ions; the most significant one is the C IV line at a velocity of -6500
km/s relative to the H{\alpha} emission line, which is among the highest
velocity BLR outflows in observed quasars at z > 4. The measured properties of
UV emission lines from different ions, except for O I and Ly{\alpha}, also
follow a clear trend that higher ions tend to be broader and outflow at higher
average velocities. There are multiple C IV and Si IV absorbing components
identified on the blue wings of the corresponding emission lines, which may be
produced by either the outflow or the intervening absorbers.


Abstract:
  Quantum nonlocality, one of the most important features of quantum mechanics,
is normally connected in experiments with the violation of Bell-Clauser-Horne
(Bell-CH) inequalities. We propose effective methods for the rearrangement and
linear inequality to prove a large variety of Bell-CH inequalities. We also
derive a set of Bell-CH inequalities by using these methods which can be
violated in some quantum entangled states.


Abstract:
  Some differential implications of classical Marx-Strohh\"acker theorem are
extended for multivalent functions. These results are also generalized for
functions with fixed second coefficient by using the theory of first order
differential subordination which in turn, corrects the results of Selvaraj and
Stelin [On multivalent functions associated with fixed second coefficient and
the principle of subordination, Int. J. Math. Anal. {\bf 9} (2015), no.~18,
883--895].


Abstract:
  Due to the limited smartness and abilities of machine intelligence, currently
autonomous vehicles are still unable to handle all kinds of situations and
completely replace drivers. Because humans exhibit strong robustness and
adaptability in complex driving scenarios, it is of great importance to
introduce humans into the training loop of artificial intelligence, leveraging
human intelligence to further advance machine learning algorithms. In this
study, a real-time human-guidance-based deep reinforcement learning (Hug-DRL)
method is developed for policy training of autonomous driving. Leveraging a
newly designed control transfer mechanism between human and automation, human
is able to intervene and correct the agent's unreasonable actions in real time
when necessary during the model training process. Based on this
human-in-the-loop guidance mechanism, an improved actor-critic architecture
with modified policy and value networks is developed. The fast convergence of
the proposed Hug-DRL allows real-time human guidance actions to be fused into
the agent's training loop, further improving the efficiency and performance of
deep reinforcement learning. The developed method is validated by
human-in-the-loop experiments with 40 subjects and compared with other
state-of-the-art learning approaches. The results suggest that the proposed
method can effectively enhance the training efficiency and performance of the
deep reinforcement learning algorithm under human guidance, without imposing
specific requirements on participant expertise and experience.


Abstract:
  For one 3-body and two 5-body planar choreographies on the same algebraic
Lemniscate by Bernoulli we found explicitly a maximal possible set of
(particular) Liouville integrals, 7 and 15, respectively, (including the total
angular momentum), which Poisson commute with the corresponding Hamiltonian
along the trajectory. Thus, these choreographies are particularly maximally
superintegrable. It is conjectured that the total number of (particular)
Liouville integrals is maximal possible for any odd number of bodies $(2n+1)$
moving choreographically (without collisions) along given algebraic Lemniscate,
thus, the corresponding trajectory is particularly, maximally superintegrable.
Some of these Liouville integrals are presented explicitly. The limit $n \rar
\infty$ is studied: it is predicted that one-dimensional liquid with
nearest-neighbor interactions occurs, it moves along algebraic Lemniscate and
it is characterized by infinitely-many constants of motion.


Abstract:
  Human resource management technologies have moved from biometric surveillance
to emotional artificial intelligence (AI) that monitor employees' engagement
and productivity, analyze video interviews and CVs of job applicants. The rise
of the US$20 billion emotional AI industry will transform the future workplace.
Yet, besides no international consensus on the principles or standards for such
technologies, there is a lack of cross-cultural research on future job seekers'
attitude toward such use of AI technologies. This study collects a
cross-sectional dataset of 1,015 survey responses of international students
from 48 countries and 8 regions worldwide. A majority of the respondents (52%)
are concerned about being managed by AI. Following the hypothetico-deductivist
philosophy of science, we use the MCMC Hamiltonian approach and conduct a
detailed comparison of 10 Bayesian network models with the PSIS-LOO method. We
consistently find having a higher income, being male, majoring in business,
and/or self-rated familiarity with AI correlate with a more positive view of
emotional AI in the workplace. There is also a stark cross-cultural and
cross-regional difference. Our analysis shows people from economically less
developed regions (Africa, Oceania, Central Asia) tend to exhibit less concern
for AI managers. And for East Asian countries, 64% of the Japanese, 56% of the
South Korean, and 42% of the Chinese professed the trusting attitude. In
contrast, an overwhelming majority of 75% of the European and Northern American
possesses the worrying/neutral attitude toward being managed by AI. Regarding
religion, Muslim students correlate with the most concern toward emotional AI
in the workplace. When religiosity is higher, the correlation becomes stronger
for Muslim and Buddhist students.


Abstract:
  Earlier studies using extreme-ultraviolet images and line-of-sight
magnetograms from the Solar Dynamics Observatory (SDO) have suggested that
active region (AR) plages and strong network concentrations often have small,
looplike features embedded within them, even though no minority-polarity flux
is visible in the corresponding magnetograms. Because of the unexpected nature
of these findings, we have searched the SDO database for examples of inverted-Y
structures rooted inside "unipolar" plages, with such jetlike structures being
interpreted as evidence for magnetic reconnection between small bipoles and the
dominant-polarity field. Several illustrative cases are presented from the
period 2013--2015, all of which are associated with transient outflows from AR
"moss." The triangular or dome-shaped bases have horizontal dimensions of
$\sim$2--4 Mm, corresponding to $\sim$1--3 granular diameters. We also note
that the spongy-textured Fe IX 17.1 nm moss is not confined to plages, but may
extend into regions where the photospheric field is relatively weak or even has
mixed polarity. We again find a tendency for bright coronal loops seen in the
17.1, 19.3, and 21.1 nm passbands to show looplike fine structure and compact
brightenings at their footpoints. These observations provide further
confirmation that present-day magnetograms are significantly underrepresenting
the amount of minority-polarity flux inside AR plages and again suggest that
footpoint reconnection and small-scale flux cancellation may play a major role
in coronal heating, both inside and outside ARs.


Abstract:
  The Artin stack $\mathcal M_n$ of $0$-dimensional sheaves of length $n$ on
$\mathbb A^3$ carries two natural d-critical structures in the sense of Joyce.
One comes from its description as a quotient stack
$[\textrm{crit}(f_n)/\textrm{GL}_n]$, another comes from derived deformation
theory of sheaves. We show that these d-critical structures agree. We use this
result to prove the analogous statement for the Quot scheme of points
$\textrm{Quot}_{\mathbb A^3}(\mathscr O^{\oplus r},n) =
\textrm{crit}(f_{r,n})$, which is a global critical locus for every $r>0$, and
also carries a derived-in-flavour d-critical structure besides the one induced
by the potential $f_{r,n}$. Again, we show these two d-critical structures
agree. Moreover, we prove that they locally model the d-critical structure on
$\textrm{Quot}_X(F,n)$, where $F$ is a locally free sheaf of rank $r$ on a
projective Calabi-Yau $3$-fold $X$.
  Finally, we prove that the perfect obstruction theory on
$\textrm{Hilb}^n\mathbb A^3=\textrm{crit}(f_{1,n})$ induced by the Atiyah class
of the universal ideal agrees with the \emph{critical} obstruction theory
induced by the Hessian of the potential $f_{1,n}$.


Abstract:
  The amount of biomedical data continues to grow rapidly. However, the ability
to analyze these data is limited due to privacy and regulatory concerns.
Machine learning approaches that require data to be copied to a single location
are hampered by the challenges of data sharing. Federated Learning is a
promising approach to learn a joint model over data silos. This architecture
does not share any subject data across sites, only aggregated parameters, often
in encrypted environments, thus satisfying privacy and regulatory requirements.
Here, we describe our Federated Learning architecture and training policies. We
demonstrate our approach on a brain age prediction model on structural MRI
scans distributed across multiple sites with diverse amounts of data and
subject (age) distributions. In these heterogeneous environments, our
Semi-Synchronous protocol provides faster convergence.


Abstract:
  Analog electronic and optical computing exhibit tremendous advantages over
digital computing for accelerating deep learning when operations are executed
at low precision. In this work, we derive a relationship between analog
precision, which is limited by noise, and digital bit precision. We propose
extending analog computing architectures to support varying levels of precision
by repeating operations and averaging the result, decreasing the impact of
noise. Such architectures enable programmable tradeoffs between precision and
other desirable performance metrics such as energy efficiency or throughput. To
utilize dynamic precision, we propose a method for learning the precision of
each layer of a pre-trained model without retraining network weights. We
evaluate this method on analog architectures subject to a variety of noise
sources such as shot noise, thermal noise, and weight noise and find that
employing dynamic precision reduces energy consumption by up to 89% for
computer vision models such as Resnet50 and by 24% for natural language
processing models such as BERT. In one example, we apply dynamic precision to a
shot-noise limited homodyne optical neural network and simulate inference at an
optical energy consumption of 2.7 aJ/MAC for Resnet50 and 1.6 aJ/MAC for BERT
with <2% accuracy degradation.


Abstract:
  This paper presents the findings about the impact of heat waves on a real
urban distribution system. A data-driven methodology is proposed to simulate
the portion of faults that can be associated to normal conditions (and hence to
reliability) and the portion correlated to the heat wave occurrence. Based on
real data collected in the years 2012-2017, the fault rates associated to
reliability and resilience have been calculated and then used to feed a Monte
Carlo simulation aiming to manage the uncertainty in the fault occurrence.
Finally, based on the Italian legislation, the benefits deriving by the
substitution of the faulted portion of the system have been calculated.


Abstract:
  In this note, we prove the Bogomolov's inequality over a reduced, compact,
irreducible, K\"ahler complex space that is smooth in codimension 2. The proof
is obtained by a reduction to the smooth case, using Hironaka's resolution of
singularities.


Abstract:
  The convergence of simultaneous and marginal predictive classifiers under
partition exchangeability in supervised classification is obtained. The result
shows the asymptotic convergence of these classifiers under infinite amount of
training or test data, such that after observing umpteen amount of data, the
differences between these classifiers would be negligible. This is an important
result from the practical perspective as under the presence of sufficiently
large amount of data, one can replace the simpler marginal classifier with
computationally more expensive simultaneous one.


Abstract:
  Based on results by recent surveys, the number of bright quasars at redshifts
z>3 is being constantly revised upwards. Current consensus is that at bright
magnitudes ($M_{1450}\le -27$) the number densities of such sources could have
been underestimated by a factor of 30-40%. In the framework of the QUBRICS
survey, we identified 58 bright QSOs at 3.6$\le z \le $4.2, with magnitudes
$i_{psf}\le$18, in an area of 12400$deg^{2}$. The uniqueness of our survey is
underlined by the fact that it allows us, for the first time, to extend the
sampled absolute magnitude range up to $M_{1450}= -29.5$. We derived a
bright-end slope of $\beta=-4.025$ and a space density at $<M_{1450}>=-28.75$
of 2.61$\times 10^{-10} Mpc^{-3}$ comoving, after taking into account the
estimated incompleteness of our observations. Taking into account the results
of fainter surveys, AGN brighter than $M_{1450}=-23$ could produce at least
half of the ionizing emissivity at z$\sim$4. Considering a mean escape fraction
of 0.7 for the QSO and AGN population, combined with a mean free path of 41.3
proper Mpc at $z=3.9$, we derive a photoionization rate of $Log(\Gamma
[s^{-1}])=-12.17^{+0.13}_{-0.07}$, produced by AGN at M$_{1450}<-18$, i.e.
~100% of the measured ionizing background at z~4.


Abstract:
  One of the main obstacles to 3D semantic segmentation is the significant
amount of endeavor required to generate expensive point-wise annotations for
fully supervised training. To alleviate manual efforts, we propose GIDSeg, a
novel approach that can simultaneously learn segmentation from sparse
annotations via reasoning global-regional structures and individual-vicinal
properties. GIDSeg depicts global- and individual- relation via a dynamic edge
convolution network coupled with a kernelized identity descriptor. The ensemble
effects are obtained by endowing a fine-grained receptive field to a
low-resolution voxelized map. In our GIDSeg, an adversarial learning module is
also designed to further enhance the conditional constraint of identity
descriptors within the joint feature distribution. Despite the apparent
simplicity, our proposed approach achieves superior performance over
state-of-the-art for inferencing 3D dense segmentation with only sparse
annotations. Particularly, with $5\%$ annotations of raw data, GIDSeg
outperforms other 3D segmentation methods.


Abstract:
  Jets constructed via clustering algorithms (e.g., anti-$k_T$, soft-drop) have
been proposed for many precision measurements, such as the strong coupling
$\alpha_s$ and the nucleon intrinsic dynamics. However, the theoretical
accuracy is affected by missing QCD corrections at higher orders for the jet
functions in the associated factorization theorems. Their calculation is
complicated by the jet clustering procedure. In this work, we propose a method
to evaluate jet functions at higher orders in QCD. The calculation involves the
phase space sector decomposition with suitable soft subtractions. As a concrete
example, we present the quark-jet function using the anti-$k_T$ algorithm with
E-scheme recombination at next-to-next-to-leading order.