Abstract:
  Equilibrating proteins and other biomacromolecules is cardinal for molecular
dynamics simulation of such biological systems in which they perform free
dynamics without any externally-applied mechanical constraint, until
thermodynamic equilibrium with the surrounding is attained. However, in some
important cases, we have to equilibrate the system of interest in the constant
presence of certain constraints, being referred to as constrained equilibration
in the present work. A clear illustration of this type is a single amyloid
\b{eta}-strand or RNA, when the reaction coordinate is defined as the distance
between the two ends of the strand and we are interested in carrying out
replica-exchange umbrella sampling to map the associated free energy profile as
the dependent quantity of interest. In such cases, each sample has to be
equilibrated with the two ends fixed. Here, we introduced a simulation trick to
perform this so-called constrained equilibration using steered molecular
dynamics. We then applied this method to equilibrate a single, stretched
\b{eta}-strand of an amyloid beta dodecamer fibril with fixed ends. Examining
the associated curves of the total energy and the force exerted on the
practically-fixed SMD atom over the total timespan broadly supported the
validity of this kind of equilibration.


Abstract:
  Machine Learning has been applied in a wide range of tasks throughout the
last years, ranging from image classification to autonomous driving and natural
language processing. Restricted Boltzmann Machine (RBM) has received recent
attention and relies on an energy-based structure to model data probability
distributions. Notwithstanding, such a technique is susceptible to adversarial
manipulation, i.e., slightly or profoundly modified data. An alternative to
overcome the adversarial problem lies in the Generative Adversarial Networks
(GAN), capable of modeling data distributions and generating adversarial data
that resemble the original ones. Therefore, this work proposes to artificially
generate RBMs using Adversarial Learning, where pre-trained weight matrices
serve as the GAN inputs. Furthermore, it proposes to sample copious amounts of
matrices and combine them into ensembles, alleviating the burden of training
new models'. Experimental results demonstrate the suitability of the proposed
approach under image reconstruction and image classification tasks, and
describe how artificial-based ensembles are alternatives to pre-training vast
amounts of RBMs.


Abstract:
  In this paper, our main focus is to explore different classes of nearly
normally torsion-free ideals. We first characterize all finite simple connected
graphs with nearly normally torsion-free cover ideals. Next, we characterize
all normally torsion-free $t$-spread principal Borel ideals that can also be
viewed as edge ideals of uniform multipartite hypergraphs.


Abstract:
  The interaction of quantum light with matter like that inside a cavity is
known to give rise to mixed light-matter states called polaritons. We discuss
the impact of rotation of the cavity on the polaritons. It is shown that the
number of polaritons increases due to this rotation. The structure of the
original polaritons is modified and new ones are induced by the rotation that
strongly depend on the angular velocity and the choice of axis of rotation. In
molecules the rotation can change the number of light-induced conical
intersections and their dimensionality and hence strongly impact their quantum
dynamics. General consequences are discussed.


Abstract:
  By the use of homotopy perturbation method-Pad\'e (HPM-Pad\'e) technique, a
new analytical approximation of luminosity distance in the flat universe is
proposed, which has the advantage of significant improvement for accuracy in
approximating luminosity distance over cosmological redshift range within
$0\leq z\leq 2.5$. Then we confront the analytical expression of luminosity
distance that is obtained by our new approach with the observational data, for
the purpose of checking whether it works well. In order to probe the robustness
of the proposed method, we also confront it to supernova type Ia and recent
data on the Hubble expansion rate $H(z)$. Markov Chain Monte Carlo (MCMC) code
emcee is used in the data fitting. The result indicates that it works fairly
well.


Abstract:
  We introduce Mini Slotted Threshold ALOHA (MiSTA), a slotted ALOHA
modification designed to minimize the network-wide time average Age of
Information (AoI). In MiSTA, sources whose ages are below a certain threshold
stay silent. When a node with age above the threshold has data to send, it
becomes active in the next time frame with a certain probability. The active
node first transmits a short control sequence in a mini-slot ahead of actual
data transmission, and if collision is sensed, it backs off with a certain
probability. We derive the steady state distribution of the number of active
sources and analyze its limiting behaviour. We show that MiSTA
probabilistically converges to a "thinned" slotted ALOHA, where the number of
active users at steady state adjusts to optimize age. With an optimal selection
of parameters, MiSTA achieves an AoI scaling with the number of sources, n, as
0.9641n, which is an improvement over the Threshold ALOHA policy proposed
earlier (for which the lowest possible scaling is 1.4169n). While achieving
this reduction in age, MiSTA also increases achievable throughput to
approximately 53%, from the 37% achievable by Threshold ALOHA and regular
slotted ALOHA.


Abstract:
  The transformation of late-type galaxies has been suggested as the origin of
early-type dwarf galaxies in galaxy clusters. Venhola et al. analysed
correlations between colour and surface brightness for galaxies in the Fornax
cluster binned by luminosity or stellar mass. In the bins with $M_\star<10^8
{\rm M}_\odot$, the authors identified a correlation of redness with fainter
surface brightness and interpreted it as a consequence of the quenching of star
formation by ram pressure stripping in the dwarf galaxies. We carry out a
corresponding analysis for the Virgo cluster and find great similarities in
these correlations between surface brightness and colour for the two clusters,
despite expected differences in the strength of the ram pressure. Furthermore,
we extend the analysis to a wider range of optical colours for both clusters
and contrast the results with expectations for fading and reddening stellar
populations. Overall the slopes of the surface brightness-colour relations are
consistent with these models. In addition the sizes of the early- and late-type
galaxies at these low masses are comparable. These two results are compatible
with a transformation scenario. However, when analysing early- and late-type
galaxies separately, the consistency of the slope of the surface
brightness-colour relations with the model expectations for fading and
reddening stellar population applies only to the late types. The lack of this
imprint for the early-type dwarfs calls for some additional explanation, for
which we discuss several possibilities. Finally, the Virgo cluster is an
atypical cluster with a low fraction of quiescent early-type galaxies at all
galaxy masses despite its large cluster mass. (abridged)


Abstract:
  Human body is a complex dynamic system composed of various sub-dynamic parts.
Especially, thoracic and abdominal organs have complex internal shape
variations with different frequencies by various reasons such as respiration
with fast motion and peristalsis with slower motion. CT protocols for abdominal
lesions are multi-phase scans for various tumor detection to use different
vascular contrast, however, they are not aligned well enough to visually check
the same area. In this paper, we propose a time-efficient and accurate
deformable registration algorithm for multi-phase CT scans considering
abdominal organ motions, which can be applied for differentiable or
non-differentiable motions of abdominal organs. Experimental results shows the
registration accuracy as 0.85 +/- 0.45mm (mean +/- STD) for pancreas within 1
minute for the whole abdominal region.


Abstract:
  An unsupervised clustering using a Gaussian mixture model is applied to Argo
profiles distributed over the entire ocean. We employ as the coordinate
components in feature space the path signature, which is a central notion in
rough path theory. This allows us to better identify the oceanic condition at
each horizontal point with distinct clusters, than by using conventional
temperature and salinity coordinate. To the best of my knowledge, it is the
first attempt at clustering almost all of the existing Argo profiles with the
full use of measured sequences of temperature, salinity, and pressure. We will
also discuss why the path signature is relevant to representing the property of
a profile.


Abstract:
  We recently discovered an extragalactic transient, AT2020caa, using the
community alert broker ANTARES. This transient apparently exhibited two
outbursts in a time span of a year (between 2020 and 2021). Based on a
decade-long historical light curve of the candidate host galaxy, we rule out an
activity from the galaxy nucleus to explain these outbursts. The measured peak
magnitudes (assuming the known spectroscopic redshift of the candidate host
galaxy) put AT2020caa in the realm of thermonuclear supernovae (SNe) or
luminous core-collapse SNe. A handful of the latter are known to show prior
outbursts (POs), thought to be linked to mass loss in massive stars. Using
Gemini/GMOS, we obtained a spectrum of the current outburst that shows it to be
a Type Ia supernova (SNIa). We examine the nature of AT2020caa's PO and
conclude that it is likely a separate SN within the same galaxy.


Abstract:
  This paper addresses the derivation of sufficient linear matrix inequality
conditions ensuring the stability of a coupled system composed of a
reaction-diffusion partial differential equation (PDE), with possibly
spatially-varying coefficients, and a finite-dimensional linear time invariant
ordinary differential equation (ODE). The coupling of the PDE with the ODE is
located at both left and right boundary conditions of the reaction-diffusion
equation and takes the form of the input and output of the ODE. We investigate
the four possible sets of left/right boundary couplings among Dirichlet and
Neumann traces. The adopted approach relies on the spectral reduction of the
problem by projecting the trajectory of the PDE into a Hilbert basis composed
of the eigenvectors of the underlying Sturm-Liouville operator. We propose
numerical examples, consisting of an unstable reaction-diffusion equation and
an unstable ODE, such that the application of the derived stability conditions
ensure the stability of the resulting coupled PDE-ODE system.


Abstract:
  Let $\mathbb{F}_{q}$ be the finite field with an odd prime power $q$. In this
paper, we construct a new isometric invariant of combinatorial type on
$(\mathbb{F}^{n}_{q},\text{dot}_{n})$, where
$\text{dot}_{n}(\mathbf{x}):=x_{1}^{2}+\cdots+x_{n}^{2}$. Additionally, using
counts from our new invariant, we give a new proof of Minkowski's formula on
the size of spheres over finite fields. We also show which types of quadratic
subspaces can be embedded in $(\mathbb{F}_{q}^{n},\text{dot}_{n})$.


Abstract:
  We present the first global model of prompt emission from a short gamma-ray
burst that consistently describes the evolution of the central black-hole (BH)
torus system, the propagation of the jet through multi-component merger ejecta,
the transition into free expansion, and the photospheric emission from the
relativistic jet. To this end, we perform a special relativistic
neutrino-hydrodynamics simulation of a viscous BH-torus system, which is formed
about 500ms after the merger and is surrounded by dynamical ejecta as well as
neutron star winds, along with a jet that is injected in the vicinity of the
central BH. In a post-processing step, we compute the photospheric emission
using a relativistic Monte-Carlo radiative transfer code. It is found that the
wind from the torus leaves a strong imprint on the jet as well as on the
emission causing narrow collimation and rapid time variability. The viewing
angle dependence of the emission gives rise to correlations among the spectral
peak energy, E_p, isotropic energy, E_iso, and peak luminosity, L_p, which may
provide natural explanations for the Amati- and Yonetoku-relations. We also
find that the degree of polarization is small for the emission from the jet
core (<2%), while it tends to increase with viewing angle outside of the core
and can become as high as ~10-40% for energies larger than the peak energy.
Finally, the comparison of our model with GRB170817A strongly disfavors the
photospheric emission scenario and therefore supports alternative scenarios,
such as the cocoon shock breakout.


Abstract:
  The increasing need for economic, safe, and sustainable smart manufacturing
combined with novel technological enablers, has paved the way for Artificial
Intelligence (AI) and Big Data in support of smart manufacturing. This implies
a substantial integration of AI, Industrial Internet of Things (IIoT),
Robotics, Big data, Blockchain, 5G communications, in support of smart
manufacturing and the dynamical processes in modern industries. In this paper,
we provide a comprehensive overview of different aspects of AI and Big Data in
Industry 4.0 with a particular focus on key applications, techniques, the
concepts involved, key enabling technologies, challenges, and research
perspective towards deployment of Industry 5.0. In detail, we highlight and
analyze how the duo of AI and Big Data is helping in different applications of
Industry 4.0. We also highlight key challenges in a successful deployment of AI
and Big Data methods in smart industries with a particular emphasis on
data-related issues, such as availability, bias, auditing, management,
interpretability, communication, and different adversarial attacks and security
issues. In a nutshell, we have explored the significance of AI and Big data
towards Industry 4.0 applications through panoramic reviews and discussions. We
believe, this work will provide a baseline for future research in the domain.


Abstract:
  I explore the implications of informational robustness under the assumptions
of common belief in rationality. That is, predictions for
incomplete-information games which are valid across all possible information
structures. First, I address this question from a global perspective and then
generalize the analysis to allow for localized informational robustness.


Abstract:
  Predicting multiple heterogeneous biological and medical targets is a
challenge for traditional deep learning models. In contrast to single-task
learning, in which a separate model is trained for each target, multi-task
learning (MTL) optimizes a single model to predict multiple related targets
simultaneously. To address this challenge, we propose the Multi-gate
Mixture-of-Experts with Exclusivity (MMoEEx). Our work aims to tackle the
heterogeneous MTL setting, in which the same model optimizes multiple tasks
with different characteristics. Such a scenario can overwhelm current MTL
approaches due to the challenges in balancing shared and task-specific
representations and the need to optimize tasks with competing optimization
paths. Our method makes two key contributions: first, we introduce an approach
to induce more diversity among experts, thus creating representations more
suitable for highly imbalanced and heterogenous MTL learning; second, we adopt
a two-step optimization [6, 11] approach to balancing the tasks at the gradient
level. We validate our method on three MTL benchmark datasets, including
Medical Information Mart for Intensive Care (MIMIC-III) and PubChem BioAssay
(PCBA).


Abstract:
  Optical flow estimation is a fundamental problem of computer vision and has
many applications in the fields of robot learning and autonomous driving. This
paper reveals novel geometric laws of optical flow based on the insight and
detailed definition of non-occlusion. Then, two novel loss functions are
proposed for the unsupervised learning of optical flow based on the geometric
laws of non-occlusion. Specifically, after the occlusion part of the images are
masked, the flowing process of pixels is carefully considered and geometric
constraints are conducted based on the geometric laws of optical flow. First,
neighboring pixels in the first frame will not intersect during the pixel
displacement to the second frame. Secondly, when the cluster containing
adjacent four pixels in the first frame moves to the second frame, no other
pixels will flow into the quadrilateral formed by them. According to the two
geometrical constraints, the optical flow non-intersection loss and the optical
flow non-blocking loss in the non-occlusion regions are proposed. Two loss
functions punish the irregular and inexact optical flows in the non-occlusion
regions. The experiments on datasets demonstrated that the proposed
unsupervised losses of optical flow based on the geometric laws in
non-occlusion regions make the estimated optical flow more refined in detail,
and improve the performance of unsupervised learning of optical flow. In
addition, the experiments training on synthetic data and evaluating on real
data show that the generalization ability of optical flow network is improved
by our proposed unsupervised approach.


Abstract:
  Recent discovery of superconductive rare earth/actinide superhydrides has
ushered in a new era of superconductivity research at high pressures. This
distinct type of clathrate metal hydrides was first proposed for
alkaline-earth-metal hydride CaH6 that, however, has long eluded experimental
synthesis, impeding an understanding of pertinent physics. Here, we report
successful synthesis of CaH6 and its measured superconducting critical
temperature Tc of 215 K at 172 GPa, which is evidenced by a sharp drop of
resistivity to zero and a characteristic decrease of Tc under a magnetic field
up to 9 T.An estimate based on the Werthamer-Helfand-Hohenberg model gives a
giant zero-temperature upper critical magnetic field of 203 T. These remarkable
benchmark superconducting properties place CaH6 among the most outstanding
high-Tc superhydrides, marking it as the hitherto only clathrate metal hydride
outside the family of rare earth/actinide hydrides. This exceptional case
raises great prospects of expanding the extraordinary class of high-Tc
superhydrides to a broader variety of compounds that possess more diverse
material features and physics characteristics.


Abstract:
  The analysis of second-order optimization methods based either on sampling,
randomization or sketching has two serious shortcomings compared to the
conventional Newton method. The first shortcoming is that the analysis of the
iterates is not scale-invariant, and even if it is, restrictive assumptions are
required on the problem structure. The second shortfall is that the fast
convergence rates of second-order methods have only been established by making
assumptions regarding the input data. In this paper, we close the theoretical
gap between the theoretical analysis of the conventional Newton method and
randomization-based second-order methods. We propose a Self-concordant
Iterative-minimization - Galerkin-based Multilevel Algorithm (SIGMA) and
establish its super-linear convergence rate using the well-established theory
of self-concordant functions. Our analysis is global and entirely independent
of unknown constants such as Lipschitz constants and strong convexity
parameters. Our analysis is based on the connections between multigrid
optimization methods, and the role of coarse-grained or reduced-order models in
the computation of search directions. We take advantage of the insights from
the analysis to significantly improve the performance of second-order methods
in machine learning applications. We report encouraging initial experiments
that suggest SIGMA significantly outperforms the state-of-the-art
sub-sampled/sketched Newton methods for both medium and large-scale problems.


Abstract:
  The study of superconductivity in compressed hydrides is of great interest
due to measurements of high critical temperatures (Tc) in the vicinity of room
temperature, beginning with the observations of LaH10 at 170-190 GPa. However,
the pressures required for synthesis of these high Tc superconducting hydrides
currently remain extremely high. Here we show the investigation of crystal
structures and superconductivity in the La-B-H system under pressure with
particle-swarm intelligence structure searches methods in combination with
first-principles calculations. Structures with six stoichiometries, LaBH,
LaBH3, LaBH4, LaBH6, LaBH7 and LaBH8, were predicted to become stable under
pressure. Remarkably, the hydrogen atoms in LaBH8 were found to bond with B
atoms in a manner that is similar to that in H3S. Lattice dynamics calculations
indicate that LaBH7 and LaBH8 become dynamically stable at pressures as low as
109.2 and 48.3 GPa, respectively. Moreover, the two phases were predicted to be
superconducting with a critical temperature (Tc) of 93 K and 156 K at 110 GPa
and 55 GPa, respectively. Our results provide guidance for future experiments
targeting new hydride superconductors with both low synthesis pressures and
high Tc.


Abstract:
  Micropolar continua are shown to be the generalisation of the nematic liquid
crystals through perspectives of order parameters, topological and geometrical
considerations. Micropolar continua and nematic liquid crystals are recognised
as the antipodals of $S^3$ and $S^2$ in projective geometry. We show that
position-dependent rotational axial fields in kinematic micropolar continua can
be considered as solutions of anisotropic Higgs fields, characterised by
integers N. We emphasise that the identical integers N are topological
invariants through homotopy classifications based on defects of order
parameters and a finite energy requirement. Magnetic monopoles and Skyrmions
are investigated based on the theories of defects of continua in Riemann-Cartan
manifolds.


Abstract:
  We compute the logarithmic contributions to the polarized massive Wilson
coefficients for deep-inelastic scattering in the asymptotic region $Q^2 \gg
m^2$ to 3-loop order in the fixed-flavor number scheme and present the
corresponding expressions for the polarized massive operator matrix elements
needed in the variable flavor number scheme. The calculation is performed in
the Larin scheme. For the massive operator matrix elements $A_{qq,Q}^{(3),\rm
PS}$ and $A_{qg,Q}^{(3),\rm S}$ the complete results are presented. The
expressions are given in Mellin-$N$ space and in momentum fraction $z$-space.


Abstract:
  Continual Learning (CL) refers to a learning setup where data is non
stationary and the model has to learn without forgetting existing knowledge.
The study of CL for sequential patterns revolves around trained recurrent
networks. In this work, instead, we introduce CL in the context of Echo State
Networks (ESNs), where the recurrent component is kept fixed. We provide the
first evaluation of catastrophic forgetting in ESNs and we highlight the
benefits in using CL strategies which are not applicable to trained recurrent
models. Our results confirm the ESN as a promising model for CL and open to its
use in streaming scenarios.


Abstract:
  In our model, the acceleration and propagation of particles from the
Bohm-like diffusion region inside the SNR to the Galactic diffusion region
outside the SNR are described through nonlinear diffusive shock acceleration
(NLDSA). The main content of our NLDSA model is to solve the hydrodynamic
equations numerically for gas density, gas velocity, gas pressure as well as
the equation for the quasi-isotropic particle momentum distribution. The
consequent multi-band non-thermal emission is from two different regions,
namely the acceleration region and the escaping region. Our model is capable of
explaining the multi-band photon emission via the dominant synchrotron
radiation of the electrons accelerated inside the SNR; and the photons with
energy of $\gtrsim$ GeV are naturally produced by the protons inside and
outside the SNR. Moreover, the photons in the energy range of $\sim 1 - \sim
100$ TeV are due to the interaction of escaped protons with dense molecular
clouds. For the photons with energy of $E_\gamma \gtrsim $ 1 GeV from SNR
G106.3+2.7, our results here favor a hadronic origin, where the photons in the
energy range of $\sim 1$ GeV to $\sim 1$ TeV are produced inside the SNR
through proton-proton interaction, while the photons with $E_\gamma \gtrsim 1$
TeV originate from the interaction of escaped protons with a dense molecular
cloud.


Abstract:
  Parametric and nonparametric inference for stochastic processes driven by a
fractional Brownian motion were investigated in Mishura (2008) and Prakasa
Rao(2010) among others. Similar problems for processes driven by an infinite
dimensional fractional Brownian motion were studied in Prakasa Rao (2004,2013),
Cialenco (2009) and others. Parametric estimation for processes driven by
infinite dimensional mixed fractional Brownian motion is discussed in this
article.


Abstract:
  Reward function, as an incentive representation that recognizes humans'
agency and rationalizes humans' actions, is particularly appealing for modeling
human behavior in human-robot interaction. Inverse Reinforcement Learning is an
effective way to retrieve reward functions from demonstrations. However, it has
always been challenging when applying it to multi-agent settings since the
mutual influence between agents has to be appropriately modeled. To tackle this
challenge, previous work either exploits equilibrium solution concepts by
assuming humans as perfectly rational optimizers with unbounded intelligence or
pre-assigns humans' interaction strategies a priori. In this work, we advocate
that humans are bounded rational and have different intelligence levels when
reasoning about others' decision-making process, and such an inherent and
latent characteristic should be accounted for in reward learning algorithms.
Hence, we exploit such insights from Theory-of-Mind and propose a new
multi-agent Inverse Reinforcement Learning framework that reasons about humans'
latent intelligence levels during learning. We validate our approach in both
zero-sum and general-sum games with synthetic agents and illustrate a practical
application to learning human drivers' reward functions from real driving data.
We compare our approach with two baseline algorithms. The results show that by
reasoning about humans' latent intelligence levels, the proposed approach has
more flexibility and capability to retrieve reward functions that explain
humans' driving behaviors better.


Abstract:
  A search for the flavor-changing neutral-current decay $B^{+}\to
K^{+}\nu\bar{\nu}$ is performed at the Belle II experiment at the SuperKEKB
asymmetric energy electron-positron collider. The results are based on a data
sample corresponding to an integrated luminosity of $63\,\mbox{fb}^{-1}$
collected at the $\Upsilon{(4S)}$ resonance and a sample of $9\,\mbox{fb}^{-1}$
collected at an energy $60\mathrm{\,Me\kern -0.1em V}$ below the resonance. A
novel measurement method is employed, which exploits topological properties of
the $B^{+}\to K^{+}\nu\bar{\nu}$ decay that differ from both generic
bottom-meson decays and light-quark pair production. This inclusive tagging
approach offers a higher signal efficiency compared to previous searches. No
significant signal is observed. An upper limit on the branching fraction of
$B^{+}\to K^{+}\nu\bar{\nu}$ of $4.1 \times 10^{-5}$ is set at the 90%
confidence level.


Abstract:
  We present the discovery and follow-up observations of two CCSNe that
occurred in the luminous infrared galaxy (LIRG), NGC3256. The first, SN2018ec,
was discovered using the ESO HAWK-I/GRAAL adaptive optics seeing enhancer, and
was classified as a Type Ic with a host galaxy extinction of
$A_V=2.1^{+0.3}_{-0.1}$ mag. The second, AT2018cux, was discovered during the
course of follow-up observations of SN2018ec, and is consistent with a
sub-luminous Type IIP classification with an $A_V=2.1 \pm 0.4$ mag of host
extinction. A third CCSN, PSNJ10275082-4354034 in NGC3256, has previously been
reported in 2014, and we recovered the source in late time archival HST
imaging. Based on template light-curve fitting, we favour a Type IIn
classification for it with modest host galaxy extinction of
$A_V=0.3^{+0.4}_{-0.3}$ mag. We also extend our study with follow-up data of
the recent Type IIb SN2019lqo and Type Ib SN2020fkb that occurred in the LIRG
system Arp299 with host extinctions of $A_V=2.1^{+0.1}_{-0.3}$ and
$A_V=0.4^{+0.1}_{-0.2}$ mag, respectively. Motivated by the above, we
inspected, for the first time, a sample of 29 CCSNe located within a projected
distance of 2.5 kpc from the host galaxy nuclei in a sample of 16 LIRGs. We
find that, if star formation within these galaxies is modelled assuming a
global starburst episode and normal IMF, there is evidence of a correlation
between the starburst age and the CCSN subtype. We infer that the two subgroups
of 14 H-poor (Type IIb/Ib/Ic/Ibn) and 15 H-rich (Type II/IIn) CCSNe have
different underlying progenitor age distributions, with the H-poor progenitors
being younger at 3$\sigma$ significance. However, we do note that the available
sample sizes of CCSNe and host LIRGs are so far small, and the statistical
comparisons between subgroups do not take into account possible systematic or
model errors related to the estimated starburst ages. (abridged)


Abstract:
  When modified theories of gravity are considered, at most six gravitational
wave polarization modes are allowed and classified in tensor modes, the only
ones predicted by General Relativity (GR), along with additional vector and
scalar modes. Therefore, gravitational waves represent a powerful tool to test
alternative theories of gravitation. In this paper, we forecast the sensitivity
of third-generation ground-based interferometers, Einstein Telescope and Cosmic
Explorer, to non-GR polarization modes focusing on the stochastic gravitational
wave background. We consider the latest technical specifications of the two
independent detectors and the full network in order to estimate both the
optimal signal-to-noise ratio and the detectable energy density limits relative
to all polarization modes in the stochastic background for several locations on
Earth and orientations of the two observatories. By considering optimal
detector configurations, we find that in 5 years of observation the detection
limit for tensor and extra polarization modes could reach
$h_0^2\Omega^{T,V,S}_{GW} \approx 10^{-12}-10^{-11}$, depending on the network
configuration and the stochastic background (i.e., if only one among vector and
scalar modes exists or both are present). This means that the network
sensitivity to different polarization modes can be approximately improved by a
factor $10^3$ with respect to second-generation interferometers. We finally
discuss the possibility of breaking the scalar modes degeneracy by considering
both detectors angular responses to sufficiently high gravitational wave
frequencies.


Abstract:
  We analyze the stochastic proximal subgradient descent in the case where the
objective functions are path differentiable and verify a Sard-type condition.
While the accumulation set may not be reduced to unique point, we show that the
time spent by the iterates to move from one accumulation point to another goes
to infinity. An oscillation-type behavior of the drift is established. These
results show a strong stability property of the proximal subgradient descent.
Using the theory of closed measures, Bolte, Pauwels and R\'ios-Zertuche
established this type of behavior for the deterministic subgradient descent.
Our technique of proof relies on the classical work of Bena\"im, Hofbauer and
Sorin, which allows us to extend results in the deterministic case to a
stochastic and proximal setting, as well as to treat these different cases in a
unified manner.


Abstract:
  We prove a Yau's type gradient estimate for positive $f$-harmonic functions
with the Dirichlet boundary condition on smooth metric measure spaces with
compact boundary when the infinite dimensional Bakry-Emery Ricci tensor and the
weighted mean curvature are bounded below. As an application, we give a
Liouville type result for bounded $f$-harmonic functions with the Dirichlet
boundary condition. Our results do not depend on any assumption on the
potential function $f$.


Abstract:
  We systematically investigate examples of non-hyperbolic dynamical systems
having irregular sets of full topological entropy and full Hausdorff dimension.
The examples include some partially hyperbolic systems and geometric Lorenz
flows. We also pose interesting questions for future research.


Abstract:
  Many black hole solutions of General Relativity are known to be linearly
exact. This opens a way to study them in gauge theories that apart from gravity
contain fields of higher spin $s>2$. Starting with a black brane in $AdS_4$ we
find its free field higher-spin generalization that respects static and planar
symmetry for all bosonic gauge fields $s\geq 0$. The solution is found for both
the higher-spin curvatures and potentials in the form suitable for further
non-linear analysis and satisfies the multi copy relation.


Abstract:
  This paper addresses the problem of a boundary control design for traffic
evolving in a large-scale urban network. The traffic state is described on a
macroscopic scale and corresponds to the vehicle density, whose dynamics are
governed by a two-dimensional conservation law. We aim at designing a boundary
control law such that the throughput of vehicles in a congested area is
maximized. Thereby, the only knowledge we use is the network's topology,
capacities of its roads and speed limits. In order to achieve this goal, we
treat a 2D equation as a set of 1D equations by introducing curvilinear
coordinates satisfying special properties. The theoretical results are verified
on a numerical example, where an initially fully congested area is driven to
the state with maximum possible througput.


Abstract:
  Media-based modulation (MBM) is a novel modulation technique that can improve
the spectral efficiency of the existing wireless systems. In MBM, multiple
radio frequency (RF) mirrors are placed near the transmit antenna(s) and are
switched ON/OFF to create different channel fade realizations. In such systems,
additional information is conveyed through the ON/OFF status of RF mirrors
along with conventional modulation symbols. A challenging task at the receiver
is to detect the transmitted information symbols and extract the additional
information from the channel fade realization used for transmission. In this
paper, we consider a massive MIMO (mMIMO) system where each user relies on MBM
for transmitting information to the base station, and investigate the problem
of symbol detection at the base station. First, we propose a mirror activation
pattern (MAP) selection based modified iterative sequential detection
algorithm. With the proposed algorithm, the most favorable MAP is selected,
followed by the detection of symbol corresponding to the selected MAP. Each
solution is subjected to the reliability check before getting the update. Next,
we introduce a $K$ favorable MAP search based iterative interference
cancellation (KMAP-IIC) algorithm. In particular, a selection rule is
introduced in KMAP-IIC for deciding the set of favorable MAPs over which
iterative interference cancellation is performed, followed by a greedy update
scheme for detecting the MBM symbols corresponding to each user. Simulation
results show that the proposed detection algorithms exhibit superior
performance-complexity trade-off over the existing detection techniques in
MBM-mMIMO systems.


Abstract:
  What is the "right" topological invariant of a large point cloud X? Prior
research has focused on estimating the full persistence diagram of X, a
quantity that is very expensive to compute, unstable to outliers, and far from
a sufficient statistic. We therefore propose that the correct invariant is not
the persistence diagram of X, but rather the collection of persistence diagrams
of many small subsets. This invariant, which we call "distributed persistence,"
is trivially parallelizable, more stable to outliers, and has a rich inverse
theory. The map from the space of point clouds (with the quasi-isometry metric)
to the space of distributed persistence invariants (with the
Hausdorff-Bottleneck distance) is a global quasi-isometry. This is a much
stronger property than simply being injective, as it implies that the inverse
of a small neighborhood is a small neighborhood, and is to our knowledge the
only result of its kind in the TDA literature. Moreover, the quasi-isometry
bounds depend on the size of the subsets taken, so that as the size of these
subsets goes from small to large, the invariant interpolates between a purely
geometric one and a topological one. Lastly, we note that our inverse results
do not actually require considering all subsets of a fixed size (an enormous
collection), but a relatively small collection satisfying certain covering
properties that arise with high probability when randomly sampling subsets.
These theoretical results are complemented by two synthetic experiments
demonstrating the use of distributed persistence in practice.


Abstract:
  We study the parity and time-reversal ($\mathcal{PT}$) symmetric quantum
physics in a non-Hermitian non-relativistic hydrogen molecule with local
(Hubbard type) Coulomb interaction. We consider non-Hermiticity generated from
both kinetic and orbital energies of the atoms and encounter the existence of
two different types of exceptional points (EPs) in pairs. These two kinds of EP
are characteristically different and depend differently on the interaction
strength. Our discovery may open the gates of a rich physics emerging out of a
simple Hamiltonian resembling a two-site Hubbard model.


Abstract:
  We study the stationary state of Hall devices composed of a load circuit
connected to the lateral edges of a Hall-bar. We follow the approach developed
in a previous work (Creff et al. J. Appl. Phys 2020) in which the stationary
state of a ideal Hall bar is defined by the minimum power dissipation
principle. The presence of both the lateral circuit and the magnetic field
induces the injection of a current: the so-called Hall current. Analytical
expressions for the longitudinal and the transverse currents are derived. It is
shown that the efficiency of the power injection into the lateral circuit is
quadratic in the Hall angle and obeys to the maximum transfer theorem. For
usual values of the Hall angle, the main contribution of this power injection
provides from the longitudinal current flowing along the edges, instead of the
transverse current crossing the Hall bar.


Abstract:
  Measurements carried out or reinterpreted within the framework of effective
field theory (EFT) will constitute a key component of the LHC legacy in the
quest towards physics beyond the standard model. Numerous EFT measurements were
already published by the CMS Collaboration. In the top quark sector, four
distinct approaches to EFT have been identified, each successfully applied in
several analyses. We review the pros and cons of each approach, while
illustrating them with recent analyses in which they were adopted.


Abstract:
  The second-largest order statistic is of special importance in reliability
theory since it represents the time to failure of a $2$-out-of-$n$ system.
Consider two $2$-out-of-$n$ systems with heterogeneous random lifetimes. The
lifetimes are assumed to follow heterogeneous general exponentiated
location-scale models. In this communication, the usual stochastic and reversed
hazard rate orders between the systems' lifetimes are established under two
cases. For the case of independent random lifetimes, the usual stochastic order
and the reversed hazard rate order between the second-largest order statistics
are obtained by using the concept of vector majorization and related orders.
For the dependent case, the conditions under which the usual stochastic order
between the second-largest order statistics holds are investigated. To
illustrate the theoretical findings, some special cases of the exponentiated
location-scale model are considered.


Abstract:
  The transmission of Lyman-{\alpha} (Ly{\alpha}) in the spectra of distant
quasars depends on the density, temperature, and ionization state of the
intergalactic medium (IGM). Therefore, high-redshift (z > 5) Ly{\alpha} forests
could be invaluable in studying the late stages of the epoch of reionization
(EoR), as well as properties of the sources that drive it. Indeed, high-quality
quasar spectra have now firmly established the existence of large-scale opacity
fluctuations at z > 5, whose physical origins are still debated. Here we
introduce a Bayesian framework capable of constraining the EoR and galaxy
properties by forward-modelling the high-z Ly{\alpha} forest. Using priors from
galaxy and CMB observations, we demonstrate that the final overlap stages of
the EoR (when >95% of the volume was ionized) should occur at z < 5.6, in order
to reproduce the large-scale opacity fluctuations seen in forest spectra.
However, it is the combination of patchy reionization and the inhomogeneous UV
background that produces the longest Gunn-Peterson troughs. Ly{\alpha} forest
observations tighten existing constraints on the characteristic ionizing escape
fraction of galaxies, with the combined observations suggesting f_{\rm esc}
\approx 7^4_3%, and disfavoring a strong evolution with the galaxy's halo (or
stellar) mass.


Abstract:
  In Budai et al. (2020) we argued that angular non-stationarities of gamma-ray
burst (GRB) jets can result in a statistical connection between the angle
values deduced from jet break times and the variabilities of prompt light
curves. The connection should be an anti-correlation if luminosity densities of
jets follow a power-law or a uniform profile, and a correlation if they have a
Gaussian profile. In this follow-up paper, we search for the connection by
measuring Spearman's rank correlation coefficient in a sample of 19 long GRBs
observed by the Swift satellite. Using 16 of the GRBs with well-defined angle
measurements, we find $\rho = -0.20_{-0.09}^{+0.09}$ and $p =
0.46_{-0.19}^{+0.23}$. Adding three more GRBs to the sample, each with a pair
of equally possible angle values, can strengthen the anti-correlation to
$\rho=-0.31_{-0.08}^{+0.07}$ and $p=0.19_{-0.09}^{+0.14}$. We show that these
results are incompatible with non-stationary jets having Gaussian profiles, and
that $\gtrsim\!100$ GRBs with observed afterglows would be needed to confirm
the potential existence of the angle-variability anti-correlation with
$3\sigma$ significance. If the connection is real, GRB jet angles would be
constrainable from prompt gamma light curves, without the need of afterglow
observations.


Abstract:
  We explore the relation between dust and several fundamental properties of
simulated galaxies using the Dusty SAGE semi-analytic model. In addition to
tracing the standard galaxy properties, Dusty SAGE also tracks cold dust mass
in the interstellar medium (ISM), hot dust mass in the halo and dust mass
ejected by feedback activity. Based on their ISM dust content, we divide our
galaxies into two categories: ISM dust-poor and ISM dust-rich. We split the ISM
dust-poor group into two subgroups: halo dust-rich and dust-poor (the latter
contains galaxies that lack dust in both the ISM and halo). Halo dust-rich
galaxies have high outflow rates of heated gas and dust and are more massive.
We divide ISM dust-rich galaxies based on their specific star formation rate
(sSFR) into star-forming and quenched subgroups. At redshift z=0, we find that
ISM dust-rich galaxies have a relatively high sSFR, low bulge-to-total (BTT)
mass ratio, and high gas metallicity. The high sSFR of ISM dust-rich galaxies
allows them to produce dust in the stellar ejecta. Their metal-rich ISM enables
dust growth via grain accretion. The opposite is seen in the ISM dust-poor
group. Furthermore, ISM dust-rich galaxies are typically late-types, while ISM
dust-poor galaxies resemble the early-type population, and we show how their
ISM content evolves from being dust-rich to dust-poor. Finally, we investigate
dust production from z=3 to z=0 and find that all groups evolve similarly,
except for the quenched ISM dust-rich group.


Abstract:
  The volume estimation of brain regions from MRI data is a key problem in many
clinical applications, where the acquisition of data at high spatial resolution
is desirable. While parallel MRI and constrained image reconstruction
algorithms can accelerate the scans, image reconstruction artifacts are
inevitable, especially at high acceleration factors. We introduce a novel image
domain deep-learning framework for calibrationless parallel MRI reconstruction,
coupled with a segmentation network to improve image quality and to reduce the
vulnerability of current segmentation algorithms to image artifacts resulting
from acceleration. The combination of the proposed image domain deep
calibrationless approach with the segmentation algorithm offers improved image
quality, while increasing the accuracy of the segmentations. The novel
architecture with an encoder shared between the reconstruction and segmentation
tasks is seen to reduce the need for segmented training datasets. In
particular, the proposed few-shot training strategy requires only 10% of
segmented datasets to offer good performance.


Abstract:
  We introduce concurrent quantum non-local games, quantum output mirror games
and concurrent classical-to-quantum non-local games, as quantum versions of
synchronous non-local games, and provide tracial characterisations of their
perfect strategies belonging to various correlation classes. We define
*-algebras and C*-algebras of concurrent classical-to-quantum and concurrent
quantum non-local games, and algebraic versions of the orthogonal rank of a
graph. We show that quantum homomorphisms of quantum graphs can be viewed as
entanglement assisted classical homomorphisms of the graphs, and give
descriptions of the perfect quantum commuting and the perfect approximately
quantum strategies for the quantum graph homomorphism game. We specialise the
latter results to the case where the inputs of the game are based on a
classical graph.


Abstract:
  Background/Aims: Standard Automated Perimetry (SAP) is the gold standard to
monitor visual field (VF) loss in glaucoma management, but is prone to
intra-subject variability. We developed and validated a deep learning (DL)
regression model that estimates pointwise and overall VF loss from unsegmented
optical coherence tomography (OCT) scans. Methods: Eight DL regression models
were trained with various retinal imaging modalities: circumpapillary OCT at
3.5mm, 4.1mm, 4.7mm diameter, and scanning laser ophthalmoscopy (SLO) en face
images to estimate mean deviation (MD) and 52 threshold values. This
retrospective study used data from patients who underwent a complete glaucoma
examination, including a reliable Humphrey Field Analyzer (HFA) 24-2 SITA
Standard VF exam and a SPECTRALIS OCT scan using the Glaucoma Module Premium
Edition. Results: A total of 1378 matched OCT-VF pairs of 496 patients (863
eyes) were included for training and evaluation of the DL models. Average
sample MD was -7.53dB (from -33.8dB to +2.0dB). For 52 VF threshold values
estimation, the circumpapillary OCT scan with the largest radius (4.7mm)
achieved the best performance among all individual models (Pearson r=0.77, 95%
CI=[0.72-0.82]). For MD, prediction averaging of OCT-trained models (3.5mm,
4.1mm, 4.7mm) resulted in a Pearson r of 0.78 [0.73-0.83] on the validation set
and comparable performance on the test set (Pearson r=0.79 [0.75-0.82]).
Conclusion: DL on unsegmented OCT scans accurately predicts pointwise and mean
deviation of 24-2 VF in glaucoma patients. Automated VF from unsegmented OCT
could be a solution for patients unable to produce reliable perimetry results.


Abstract:
  Many implicit inferences exist in text depending on how it is structured that
can critically impact the text's interpretation and meaning. One such
structural aspect present in text with chronology is the order of its
presentation. For narratives or stories, this is known as the narrative order.
Reordering a narrative can impact the temporal, causal, event-based, and other
inferences readers draw from it, which in turn can have strong effects both on
its interpretation and interestingness. In this paper, we propose and
investigate the task of Narrative Reordering (NAREOR) which involves rewriting
a given story in a different narrative order while preserving its plot. We
present a dataset, NAREORC, with human rewritings of stories within ROCStories
in non-linear orders, and conduct a detailed analysis of it. Further, we
propose novel task-specific training methods with suitable evaluation metrics.
We perform experiments on NAREORC using state-of-the-art models such as BART
and T5 and conduct extensive automatic and human evaluations. We demonstrate
that although our models can perform decently, NAREOR is a challenging task
with potential for further exploration. We also investigate two applications of
NAREOR: generation of more interesting variations of stories and serving as
adversarial sets for temporal/event-related tasks, besides discussing other
prospective ones, such as for pedagogical setups related to language skills
like essay writing and applications to medicine involving clinical narratives.


Abstract:
  In this brief note, it is shown (using the NP spin-coefficient formalism)
that, contrary to popular belief, the MOTS stability operator is self-adjoint
for a large class of geometric models in general relativity.


Abstract:
  We examine $3D$ flows $\mathbf{\dot{x}}=\mathbf{v}({\bf x})$ admitting vector
identity $M\mathbf{v} = \nabla \times \mathbf{A}$ for a multiplier $M$ and a
potential field $\mathbf{A}$. It is established that, for those systems, one
can complete the vector field $\mathbf{v}$ into a basis fitting an
$\mathfrak{sl}(2)$-algebra. Accordingly, in terms of covariant quantities, the
structure equations determine a set of equations in Maurer-Cartan form. This
realization permits one to obtain the potential field as well as to investigate
the (bi-)Hamiltonian character of the system. The latter occurs if the system
has a time-independent first integral. In order to exhibit the theoretical
results on some concrete cases, three examples are provided, namely the Gulliot
system, a system with a non-integrable potential, and the Darboux-Halphen
system in symmetric polynomials.


Abstract:
  We prove that if $M$ is a strictly stable complete minimal hypersurface in
Euclidean space with finite density at infinity and which lies on one side of a
minimal cylinder with cross-section a strictly stable area minimizing
hypercone, then $M$ must be cylindrical. Applications will be given in the
references [Sim20a], [Sim20b].


Abstract:
  It is proven that the ground state is unique in the Edwards-Anderson model
for almost all continuous random exchange interactions, and any excited state
with the overlap less than its maximal value has large energy in dimensions
higher than two with probability one. Since the spin overlap is shown to be
concentrated at its maximal value in the ground state, replica symmetry
breaking does not occur in the Edwards-Anderson model near zero temperature.


Abstract:
  The precise positioning of dopant atoms within bulk crystal lattices could
enable novel applications in areas including solid-state sensing and quantum
computation. Established scanning probe techniques are capable tools for the
manipulation of surface atoms, but at a disadvantage due to their need to bring
a physical tip into contact with the sample. This has prompted interest in
electron-beam techniques, followed by the first proof-of-principle experiment
of bismuth dopant manipulation in crystalline silicon. Here, we use first
principles modeling to discover a novel indirect exchange mechanism that allows
electron impacts to non-destructively move dopants with atomic precision within
the silicon lattice. However, this mechanism only works for the two heaviest
group V donors with split-vacancy configurations, Bi and Sb. We verify our
model by directly imaging these configurations for Bi, and by demonstrating
that the promising nuclear spin qubit Sb can be manipulated using a focused
electron beam.


Abstract:
  We introduce a new pretraining approach geared for multi-document language
modeling, incorporating two key ideas into the masked language modeling
self-supervised objective. First, instead of considering documents in
isolation, we pretrain over sets of multiple related documents, encouraging the
model to learn cross-document relationships. Second, we improve over recent
long-range transformers by introducing dynamic global attention that has access
to the entire input to predict masked tokens. We release CDLM (Cross-Document
Language Model), a new general language model for multi-document setting that
can be easily applied to downstream tasks. Our extensive analysis shows that
both ideas are essential for the success of CDLM, and work in synergy to set
new state-of-the-art results for several multi-text tasks. Code and models are
available at https://github.com/aviclu/CDLM.


Abstract:
  Making the gradients small is a fundamental optimization problem that has
eluded unifying and simple convergence arguments in first-order optimization,
so far primarily reserved for other convergence criteria, such as reducing the
optimality gap. We introduce a novel potential function-based framework to
study the convergence of standard methods for making the gradients small in
smooth convex optimization and convex-concave min-max optimization. Our
framework is intuitive and it provides a lens for viewing algorithms that make
the gradients small as being driven by a trade-off between reducing either the
gradient norm or a certain notion of an optimality gap. On the lower bounds
side, we discuss tightness of the obtained convergence results for the convex
setup and provide a new lower bound for minimizing norm of cocoercive operators
that allows us to argue about optimality of methods in the min-max setup.


Abstract:
  A class of high-order shock-capturing schemes, P$_n$T$_m$-BVD (Deng et al.,
J. Comp. Phys., 386:323-349, 2019; Comput. & Fluids, 200:104433, 2020.)
schemes, have been devised to solve the Euler equations with substantially
reduced numerical dissipation, which enable high-resolution simulations to
resolve flow structures of wider range scales. In such simulations with low
dissipation, errors of round-off level might grow and contaminate the numerical
solutions. A typical example of such problems is the loss of symmetry in the
numerical solutions for physical problems of symmetric configurations even if
the schemes are mathematically in line with the symmetry rules. In this study,
the mechanisms of symmetry-breaking in a finite volume framework with the
P$_4$T$_2$-BVD reconstruction scheme are thoroughly examined. Particular
attention has been paid to remove the possible causes due to the lack of
associativity in floating-point arithmetic which is associated with round-off
errors. Modifications and new techniques are proposed to completely remove the
possible causes for symmetry breaking in different components of the
P$_4$T$_2$-BVD finite volume solver. Benchmark tests that have symmetric
solution structures are used to verify the proposed methods. The numerical
results demonstrate the perfect symmetric solution structures.


Abstract:
  In this work we introduce and investigate the properties of the
"relativistic" Hopfield model endowed with temporally correlated patterns.
First, we review the "relativistic" Hopfield model and we briefly describe the
experimental evidence underlying correlation among patterns. Then, we face the
study of the resulting model exploiting statistical-mechanics tools in a
low-load regime. More precisely, we prove the existence of the thermodynamic
limit of the related free-energy and we derive the self-consistence equations
for its order parameters. These equations are solved numerically to get a phase
diagram describing the performance of the system as an associative memory as a
function of its intrinsic parameters (i.e., the degree of noise and of
correlation among patterns). We find that, beyond the standard retrieval and
ergodic phases, the relativistic system exhibits correlated and symmetric
regions -- that are genuine effects of temporal correlation -- whose width is,
respectively, reduced and increased with respect to the classical case.


Abstract:
  The excitations of nonlinear magnetosonic lump waves induced by orbiting
charged space debris objects in the Low Earth Orbital (LEO) plasma region are
investigated in presence of the ambient magnetic field. These nonlinear waves
are found to be governed by the forced Kadomtsev-Petviashvili (KP) type model
equation, where the forcing term signifies the source current generated by
different possible motions of charged space debris particles in the LEO plasma
region. Different analytic lump wave solutions that are stable for both slow
and fast magnetosonic waves in presence of charged space debris particles are
found for the first time. The dynamics of exact pinned accelerated lump waves
is explored in detail. Approximate lump wave solutions with time-dependent
amplitudes and velocities are analyzed through perturbation methods for
different types of localized space debris functions; yielding approximate
pinned accelerated lump wave solutions. These new results may pave new
direction in this field of research.


Abstract:
  Conductance signatures that signal the presence of Majorana zero modes in a
three terminal nanowire-topological superconductor hybrid system are analyzed
in detail, in both the clean nanowire limit and in the presence of non-coherent
dephasing interactions. In the coherent transport regime for a clean wire, we
point out contributions of the local Andreev reflection and the non-local
transmissions toward the total conductance lineshapes while clarifying the role
of contact broadening on the Majorana conductance lineshapes at the magnetic
field parity crossings. Interestingly, at larger $B$-field parity crossings,
the contribution of the Andreev reflection process decreases which is
compensated by the non-local processes in order to maintain the conductance
quantum regardless of contact coupling strength. In the non-coherent transport
regime, we include dephasing that is introduced by momentum randomization
processes, that allows one to smoothly transition to the diffusive limit. Here,
as expected, we note that while the Majorana character of the zero modes is
unchanged, there is a reduction in the conductance peak magnitude that scales
with the strength of the impurity scattering potentials. Important distinctions
between the effect of non-coherent dephasing processes and contact-induced
tunnel broadenings in the coherent regime on the conductance lineshapes are
elucidated. Most importantly our results reveal that the addition of dephasing
in the set up does not lead to any notable length dependence to the conductance
of the zero modes, contrary to what one would expect in a gradual transition to
the diffusive limit. We believe this work paves a way for a systematic
introduction of scattering processes into the realistic modeling of Majorana
nanowire hybrid devices and assessing topological signatures in such systems in
the presence of non-coherent scattering processes.


Abstract:
  We present a novel framework for the reconstruction of 1D composite signals
assumed to be a mixture of two additive components, one sparse and the other
smooth, given a finite number of linear measurements. We formulate the
reconstruction problem as a continuous-domain regularized inverse problem with
multiple penalties. We prove that these penalties induce reconstructed signals
that indeed take the desired form of the sum of a sparse and a smooth
component. We then discretize this problem using Riesz bases, which yields a
discrete problem that can be solved by standard algorithms. Our discretization
is exact in the sense that we are solving the continuous-domain problem over
the search space specified by our bases without any discretization error. We
propose a complete algorithmic pipeline and demonstrate its feasibility on
simulated data.


Abstract:
  Calculations of molecular spectral properties, like photodissociation rates
and absorption bands, rely on knowledge of the excited state energies of the
molecule of interest. Protocols based on the variational quantum eigensolver
(VQE) are promising candidates to calculate such energies on emerging noisy
intermediate scale quantum (NISQ) computers. The successful implementation of
these protocols on NISQ computers, relies on ans\"atze that can accurately
approximate the molecular states and that can be implemented by shallow quantum
circuits. In this paper, we introduce the excited qubit-excitation-based
adaptive (e-QEB-ADAPT)-VQE protocol to calculate molecular excited state
energies. The e-QEB-ADAPT-VQE constructs efficient problem-tailored ans\"atze
by iteratively appending evolutions of qubit excitation operators. The
e-QEB-ADAPT-VQE is an adaptation of the QEB-ADAPT-VQE protocol, which is
designed to be independent on the choice of an initial reference state. We
perform classical numerical simulations for LiH and BeH$_2$ to benchmark the
performance of the e-QEB-ADAPT-VQE. We demonstrate that the e-QEB-ADAPT-VQE can
construct highly accurate ans\"atze that require at least an order of magnitude
fewer $CNOT$s than standard fixed UCC ans\"atze, such as the UCCSD and the
GUCCSD.


Abstract:
  Modern compute nodes in high-performance computing provide a tremendous level
of parallelism and processing power. However, as arithmetic performance has
been observed to increase at a faster rate relative to memory and network
bandwidths, optimizing data movement has become critical for achieving strong
scaling in many communication-heavy applications. This performance gap has been
further accentuated with the introduction of graphics processing units, which
can provide by multiple factors higher throughput in data-parallel tasks than
central processing units. In this work, we explore the computational aspects of
iterative stencil loops and implement a generic communication scheme using
CUDA-aware MPI, which we use to accelerate magnetohydrodynamics simulations
based on high-order finite differences and third-order Runge-Kutta integration.
We put particular focus on improving intra-node locality of workloads. In
comparison to a theoretical performance model, our implementation exhibits
strong scaling from one to $64$ devices at $50\%$--$87\%$ efficiency in
sixth-order stencil computations when the problem domain consists of
$256^3$--$1024^3$ cells.


Abstract:
  Alfv\'{e}n waves as excited in black hole accretion disks and neutron star
magnetospheres are the building blocks of turbulence in relativistic,
magnetized plasmas. A large reservoir of magnetic energy is available in these
systems, such that the plasma can be heated significantly even in the weak
turbulence regime. We perform high-resolution three-dimensional simulations of
counter-propagating Alfv\'{e}n waves, showing that an $E_{B_{\perp}}(k_{\perp})
\propto k_{\perp}^{-2}$ energy spectrum develops as a result of the weak
turbulence cascade in relativistic magnetohydrodynamics and its infinitely
magnetized (force-free) limit. The plasma turbulence ubiquitously generates
current sheets, which act as locations where magnetic energy dissipates. We
show that current sheets form as a natural result of nonlinear interactions
between counter-propagating Alfv\'{e}n waves. These current sheets form due to
the compression of elongated eddies, driven by the shear induced by growing
higher order modes, and undergo a thinning process until they break-up into
small-scale turbulent structures. We explore the formation of {current sheets}
both in overlapping waves and in localized wave packet collisions. The
relativistic interaction of localized Alfv\'{e}n waves induces both Alfv\'{e}n
waves and fast waves and efficiently mediates the conversion and dissipation of
electromagnetic energy in astrophysical systems. Plasma energization through
reconnection in current sheets emerging during the interaction of Alfv\'{e}n
waves can potentially explain X-ray emission in black hole accretion coronae
and neutron star magnetospheres.


Abstract:
  All-optical switching of 77 fs pulses centered at 1560 nm, driven by 270 fs,
1030 nm pulses in a dual-core optical fiber exhibiting high index contrast is
presented. The fiber is made of a thermally matched pair of lead silicate and
borosilicate glasses used as core and cladding material, respectively. The
novel switching approach is based on nonlinear balancing of dual-core
asymmetry, by control pulse intensity induced group velocity reduction of the
fast fiber channel. Due to the fiber core made of soft glass with high
nonlinearity high switching contrast exceeding 20 dB is attained under
application of control pulses of only few nanojoule energy. The optimization of
the fiber length brought the best results at 14 mm, which is in good
correspondence with the calculated coupling length at the signal wavelength.
The results express significant progress in comparison to similar studies based
on self-switching of solitonic pulses in dual-core fibers and represent high
application potential.


Abstract:
  An exact low-dimensional system of mean-field equations for an infinite-size
network of pulse coupled integrate-and-fire neurons with a bimodal distribution
of an excitability parameter is derived. Bifurcation analysis of these
equations shows a rich variety of dynamic modes that do not exist with a
unimodal distribution of this parameter. New modes include multistable
equilibrium states with different levels of the spiking rate, collective
oscillations and chaos. All oscillatory modes coexist with stable equilibrium
states. The mean field equations are a good approximation to the solutions of a
microscopic model consisting of several thousand neurons.


Abstract:
  We continue the work done by the authors and before that by the second
author, Kanovei and koepke. We prove that for every set $A$ in a Magidor-Radin
generic extension using a coherent sequence such that
$o^{\vec{U}}(\kappa)<\kappa^+$, there is $C'\subseteq C_G$, such that
$V[A]=V[C']$. Also we prove that the supremum of a fresh set in a Prikry, tree
Prikry, Magidor, Radin-Magidor and Radin forcing, changes cofinality to
$\omega$.


Abstract:
  We introduce a simple, accurate, and extremely efficient method for
numerically solving the multi-marginal optimal transport (MMOT) problems
arising in density functional theory. The method relies on (i) the sparsity of
optimal plans [for $N$ marginals discretized by $\ell$ gridpoints each, general
Kantorovich plans require $\ell^N$ gridpoints but the support of optimizers is
of size $O(\ell\cdot N)$ [FV18]], (ii) the method of column generation (CG)
from discrete optimization which to our knowledge has not hitherto been used in
MMOT, and (iii) ideas from machine learning. The well-known bottleneck in CG
consists in generating new candidate columns efficiently; we prove that in our
context, finding the best new column is an NP-complete problem. To overcome
this bottleneck we use a genetic learning method tailormade for MMOT in which
the dual state within CG plays the role of an "adversary", in loose similarity
to Wasserstein GANs. On a sequence of benchmark problems with up to 120
gridpoints and up to 30 marginals, our method always found the exact
optimizers. Moreover, empirically the number of computational steps needed to
find them appears to scale only polynomially when both $N$ and $\ell$ are
simultaneously increased (while keeping their ratio fixed to mimic a
thermodynamic limit of the particle system).


Abstract:
  The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically
scheduling vehicles among multiple sites in order to minimize the cost when
delivery orders are not known a priori. Although DPDP plays an important role
in modern logistics and supply chain management, state-of-the-art DPDP
algorithms are still limited on their solution quality and efficiency. In
practice, they fail to provide a scalable solution as the numbers of vehicles
and sites become large. In this paper, we propose a data-driven approach,
Spatial-Temporal Aided Double Deep Graph Network (ST-DDGN), to solve
industry-scale DPDP. In our method, the delivery demands are first forecast
using spatial-temporal prediction method, which guides the neural network to
perceive spatial-temporal distribution of delivery demand when dispatching
vehicles. Besides, the relationships of individuals such as vehicles are
modelled by establishing a graph-based value function. ST-DDGN incorporates
attention-based graph embedding with Double DQN (DDQN). As such, it can make
the inference across vehicles more efficiently compared with traditional
methods. Our method is entirely data driven and thus adaptive, i.e., the
relational representation of adjacent vehicles can be learned and corrected by
ST-DDGN from data periodically. We have conducted extensive experiments over
real-world data to evaluate our solution. The results show that ST-DDGN reduces
11.27% number of the used vehicles and decreases 13.12% total transportation
cost on average over the strong baselines, including the heuristic algorithm
deployed in our UAT (User Acceptance Test) environment and a variety of vanilla
DRL methods. We are due to fully deploy our solution into our online logistics
system and it is estimated that millions of USD logistics cost can be saved per
year.


Abstract:
  We argue for a foundational epistemic claim and a hypothesis about the
production and uses of mathematical epidemiological models, exploring the
consequences for our political and socio-economic lives. First, in order to
make the best use of scientific models, we need to understand why models are
not truly representational of our world, but are already pitched towards
various uses. Second, we need to understand the implicit power relations in
numbers and models in public policy, and, thus, the implications for good
governance if numbers and models are used as the exclusive drivers of decision
making.


Abstract:
  Deep learning is found to be vulnerable to adversarial examples. However, its
adversarial susceptibility in image caption generation is under-explored. We
study adversarial examples for vision and language models, which typically
adopt an encoder-decoder framework consisting of two major components: a
Convolutional Neural Network (i.e., CNN) for image feature extraction and a
Recurrent Neural Network (RNN) for caption generation. In particular, we
investigate attacks on the visual encoder's hidden layer that is fed to the
subsequent recurrent network. The existing methods either attack the
classification layer of the visual encoder or they back-propagate the gradients
from the language model. In contrast, we propose a GAN-based algorithm for
crafting adversarial examples for neural image captioning that mimics the
internal representation of the CNN such that the resulting deep features of the
input image enable a controlled incorrect caption generation through the
recurrent network. Our contribution provides new insights for understanding
adversarial attacks on vision systems with language component. The proposed
method employs two strategies for a comprehensive evaluation. The first
examines if a neural image captioning system can be misled to output targeted
image captions. The second analyzes the possibility of keywords into the
predicted captions. Experiments show that our algorithm can craft effective
adversarial images based on the CNN hidden layers to fool captioning framework.
Moreover, we discover the proposed attack to be highly transferable. Our work
leads to new robustness implications for neural image captioning.


Abstract:
  This study investigates the convergence of two bibliometric approaches to the
measurement of interdisciplinary research: one based on analyzing disciplinary
diversity in the reference list of publications, the other based on the
disciplinary diversity of authors of publications. In particular we measure the
variety, balance, disparity and integrated diversity index of, respectively,
single-author, multi-author single-field, and multi-author multi-field
publications. We find that, in general, the diversity of the reference list
grows with the number of fields reflected in a paper's authors' list and, to a
lesser extent, with the number of authors being equal the number of fields.
Further, we find that when fields belonging to different disciplines are
reflected in the authors' list, the disparity in the reference list is higher
than in the case of fields belonging to the same discipline. However, this
general tendency varies across disciplines, and noticeable exceptions are found
at individual paper level.


Abstract:
  Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for
meta-learning, where a meta-model is learned with the ability of fast adapting
to new tasks. However, as a double-looped optimization problem, MAML needs to
differentiate through the whole inner-loop optimization path for every
outer-loop training step, which may lead to both computational inefficiency and
sub-optimal solutions. In this paper, we generalize MAML to allow meta-learning
to be defined in function spaces, and propose the first meta-learning paradigm
in the Reproducing Kernel Hilbert Space (RKHS) induced by the meta-model's
Neural Tangent Kernel (NTK). Within this paradigm, we introduce two
meta-learning algorithms in the RKHS, which no longer need a sub-optimal
iterative inner-loop adaptation as in the MAML framework. We achieve this goal
by 1) replacing the adaptation with a fast-adaptive regularizer in the RKHS;
and 2) solving the adaptation analytically based on the NTK theory. Extensive
experimental studies demonstrate advantages of our paradigm in both efficiency
and quality of solutions compared to related meta-learning algorithms. Another
interesting feature of our proposed methods is that they are demonstrated to be
more robust to adversarial attacks and out-of-distribution adaptation than
popular baselines, as demonstrated in our experiments.


Abstract:
  Visual appearance is an important aspect influencing the perception and
consequent acceptance of Embodied Conversational Agents (ECA). To this end, the
Uncanny Valley theory contradicts the common assumption that increased
humanization of characters leads to better acceptance. Rather, it shows that
anthropomorphic behavior may trigger feelings of eeriness and rejection in
people. The work presented in this paper explores whether four different
autonomous ECAs, specifically build for a European research project, are
affected by this effect, and how they compare to two slightly more
realistically looking human-controlled, i.e. face-tracked, ECAs with respect to
perceived humanness, eeriness, and attractiveness. Short videos of the ECAs in
combination with a validated questionnaire were used to investigate potential
differences. Results support existing theories highlighting that increased
perceived humanness correlates with increased perceived eeriness. Furthermore,
it was found, that neither the gender of survey participants, their age, nor
the sex of the ECA influences this effect, and that female ECAs are perceived
to be significantly more attractive than their male counterparts.


Abstract:
  This paper seeks a quantitative comparison between the curves in the KPZ line
ensemble [CH16] and a standard Brownian bridge under the $t^{1/3}$ vertical and
$t^{2/3}$ horizontal scaling. The estimate we obtained is parallel to the one
established in [Ham1], where the Airy line ensemble was studied. Our main tool
is the soft Brownian Gibbs property enjoyed by the KPZ line ensemble. In view
of the Gibbs property, the KPZ line ensemble differs from the Airy line
ensemble mainly due to the intersecting nature of its curves, which results in
the main technical difficulty in this paper. We develop a resampling framework,
the soft jump ensemble, to tackle this difficulty. Our method is highly
inspired by the jump ensemble technique developed in [Ham1].


Abstract:
  We show that using renormalization-group summation to generate the QCD
radiative corrections to the $\pi-\gamma$ transition form factor, calculated
with lightcone sum rules (LCSR), renders the strong coupling free of Landau
singularities while preserving the QCD form-factor asymptotics. This enables a
reliable applicability of the LCSR method to momenta well below 1 GeV$^2$. This
way, one can use the new preliminary BESIII data with unprecedented accuracy
below 1.5 GeV$^2$ to fine tune the prefactor of the twist-six contribution.
Using a combined fit to all available data below 3.1 GeV$^2$, we are able to
determine all nonperturbative scale parameters and a few Gegenbauer
coefficients entering the calculation of the form factor. Employing these
ingredients, we determine a pion distribution amplitude with conformal
coefficients $(b_2,b_4)$ that agree at the $1\sigma$ level with the data for
$Q^2 \leqslant 3.1$ GeV$^2$ and fulfill at the same time the lattice
constraints on $b_2$ at N$^3$LO together with the constraints from QCD sum
rules with nonlocal condensates.The form-factor prediction calculated herewith
reproduces the data below 1 GeV$^2$ significantly better than analogous
predictions based on a fixed-order power-series expansion in the strong
coupling constant.


Abstract:
  Promising results for subjective image quality prediction have been achieved
during the past few years by using convolutional neural networks (CNN).
However, the use of CNNs for high resolution image quality assessment remains a
challenge, since typical CNN architectures have been designed for small
resolution input images. In this study, we propose an image quality model that
attempts to mimic the attention mechanism of human visual system (HVS) by using
a recurrent neural network (RNN) for spatial pooling of the features extracted
from different spatial areas (patches) by a deep CNN-based feature extractor.
The experimental study, conducted by using images with different resolutions
from two recently published image quality datasets, indicates that the quality
prediction accuracy of the proposed method is competitive against benchmark
models representing the state-of-the-art, and the proposed method also performs
consistently on different resolution versions of the same dataset.


Abstract:
  Commonsense generation is a challenging task of generating a plausible
sentence describing an everyday scenario using provided concepts. Its
requirement of reasoning over commonsense knowledge and compositional
generalization ability even puzzles strong pre-trained language generation
models. We propose a novel framework using retrieval methods to enhance both
the pre-training and fine-tuning for commonsense generation. We retrieve
prototype sentence candidates by concept matching and use them as auxiliary
input. For fine-tuning, we further boost its performance with a trainable
sentence retriever. We demonstrate experimentally on the large-scale CommonGen
benchmark that our approach achieves new state-of-the-art results.


Abstract:
  In this paper, we investigate two methods that allow us to automatically
create profitable DeFi trades, one well-suited to arbitrage and the other
applicable to more complicated settings. We first adopt the Bellman-Ford-Moore
algorithm with DEFIPOSER-ARB and then create logical DeFi protocol models for a
theorem prover in DEFIPOSER-SMT. While DEFIPOSER-ARB focuses on DeFi
transactions that form a cycle and performs very well for arbitrage,
DEFIPOSER-SMT can detect more complicated profitable transactions. We estimate
that DEFIPOSER-ARB and DEFIPOSER-SMT can generate an average weekly revenue of
191.48ETH (76,592USD) and 72.44ETH (28,976USD) respectively, with the highest
transaction revenue being 81.31ETH(32,524USD) and22.40ETH (8,960USD)
respectively. We further show that DEFIPOSER-SMT finds the known economic bZx
attack from February 2020, which yields 0.48M USD. Our forensic investigations
show that this opportunity existed for 69 days and could have yielded more
revenue if exploited one day earlier. Our evaluation spans 150 days, given 96
DeFi protocol actions, and 25 assets.
  Looking beyond the financial gains mentioned above, forks deteriorate the
blockchain consensus security, as they increase the risks of double-spending
and selfish mining. We explore the implications of DEFIPOSER-ARB and
DEFIPOSER-SMT on blockchain consensus. Specifically, we show that the trades
identified by our tools exceed the Ethereum block reward by up to 874x. Given
optimal adversarial strategies provided by a Markov Decision Process (MDP), we
quantify the value threshold at which a profitable transaction qualifies as
Miner ExtractableValue (MEV) and would incentivize MEV-aware miners to fork the
blockchain. For instance, we find that on Ethereum, a miner with a hash rate of
10% would fork the blockchain if an MEV opportunity exceeds 4x the block
reward.


Abstract:
  The core for tackling the fine-grained visual categorization (FGVC) is to
learn subtle yet discriminative features. Most previous works achieve this by
explicitly selecting the discriminative parts or integrating the attention
mechanism via CNN-based approaches.However, these methods enhance the
computational complexity and make the modeldominated by the regions containing
the most of the objects. Recently, vision trans-former (ViT) has achieved SOTA
performance on general image recognition tasks. Theself-attention mechanism
aggregates and weights the information from all patches to the classification
token, making it perfectly suitable for FGVC. Nonetheless, the classifi-cation
token in the deep layer pays more attention to the global information, lacking
the local and low-level features that are essential for FGVC. In this work, we
proposea novel pure transformer-based framework Feature Fusion Vision
Transformer (FFVT)where we aggregate the important tokens from each transformer
layer to compensate thelocal, low-level and middle-level information. We design
a novel token selection mod-ule called mutual attention weight selection (MAWS)
to guide the network effectively and efficiently towards selecting
discriminative tokens without introducing extra param-eters. We verify the
effectiveness of FFVT on three benchmarks where FFVT achieves the
state-of-the-art performance.


Abstract:
  We examine Serrin's classical overdetermined problem under a perturbation of
the Neumann boundary condition. The solution of the problem for a constant
Neumann boundary condition exists provided that the underlying domain is a
ball. The question arises whether for a perturbation of the constant there
still are domains admitting solutions to the problem. Furthermore, one may ask
whether a domain that admits a solution for the perturbed problem is unique up
to translation and whether it is close to the ball. We develop a new implicit
function theorem for a pair of Banach triplets that is applicable to nonlinear
problems with loss of derivatives except at the point under consideration.
Combined with a detailed analysis of the linearized operator, we prove the
existence and local uniqueness of a domain admitting a solution to the
perturbed overdetermined problem. Moreover, an optimal linear stability
estimate for the shape of a domain is established.


Abstract:
  Finding codes given natural language query isb eneficial to the productivity
of software developers. Future progress towards better semantic matching
between query and code requires richer supervised training resources. To remedy
this, we introduce the CoSQA dataset.It includes 20,604 labels for pairs of
natural language queries and codes, each annotated by at least 3 human
annotators. We further introduce a contrastive learning method dubbed CoCLR to
enhance query-code matching, which works as a data augmenter to bring more
artificially generated training instances. We show that evaluated on CodeXGLUE
with the same CodeBERT model, training on CoSQA improves the accuracy of code
question answering by 5.1%, and incorporating CoCLR brings a further
improvement of 10.5%.


Abstract:
  Structured pruning is an effective compression technique to reduce the
computation of neural networks, which is usually achieved by adding
perturbations to reduce network parameters at the cost of slightly increasing
training loss. A more reasonable approach is to find a sparse minimizer along
the flat minimum valley found by optimizers, i.e. stochastic gradient descent,
which keeps the training loss constant. To achieve this goal, we propose the
structured directional pruning based on orthogonal projecting the perturbations
onto the flat minimum valley. We also propose a fast solver sDprun and further
prove that it achieves directional pruning asymptotically after sufficient
training. Experiments using VGG-Net and ResNet on CIFAR-10 and CIFAR-100
datasets show that our method obtains the state-of-the-art pruned accuracy
(i.e. 93.97% on VGG16, CIFAR-10 task) without retraining. Experiments using
DNN, VGG-Net and WRN28X10 on MNIST, CIFAR-10 and CIFAR-100 datasets demonstrate
our method performs structured directional pruning, reaching the same minimum
valley as the optimizer.


Abstract:
  Comprehending the nature of action potentials is fundamental to our
understanding of the functioning of nervous systems in general. Here we
consider their evolution and describe their functions of communication,
modulation and computation within nervous systems. The ionic mechanisms
underlying action potentials in the squid giant axon were first described by
Hodgkin and Huxley in 1952 and their findings have formed our orthodox view of
how the physiological action potential functions. However, substantial evidence
has now accumulated to show that the action potential is accompanied by a
synchronized coupled soliton pressure pulse in the cell membrane, the action
potential pulse (APPulse). Here we explore the interactions between the soliton
and the ionic mechanisms known to be associated with the action potential.
Computational models of the action potential usually describe it as a binary
event, but we suggest that it is quantum ternary event known as the
computational action potential (CAP), whose temporal fixed point is threshold,
rather than the rather plastic action potential peak used in other models. The
CAP accompanies the APPulse and the Physiological action potential. Therefore,
we conclude that nerve impulses appear to be an ensemble of three inseparable,
interdependent, concurrent states: the physiological action potential, the
APPulse and the CAP.


Abstract:
  HERMES-TP/SP (High Energy Rapid Modular Ensemble of Satellites Technologic
and Scientific Pathfinder) is a constellation of six 3U nano-satellites hosting
simple but innovative X-ray detectors, characterized by a large energy band and
excellent temporal resolution, and thus optimized for the monitoring of Cosmic
High Energy transients such as Gamma Ray Bursts and the electromagnetic
counterparts of Gravitational Wave Events, and for the determination of their
positions. The projects are funded by the Italian Ministry of University and
Research and by the Italian Space Agency, and by the European Union Horizon
2020 Research and Innovation Program under Grant Agreement No. 821896.
HERMES-TP/SP is an in-orbit demonstration, that should be tested starting from
2022. It is intrinsically a modular experiment that can be naturally expanded
to provide a global, sensitive all sky monitor for high-energy transients.


Abstract:
  Blockchain technologies can enable secure computing environments among
mistrusting parties. Permissioned blockchains are particularly enlightened by
companies, enterprises, and government agencies due to their efficiency,
customizability, and governance-friendly features. Obviously, seamlessly fusing
blockchain and cloud computing can significantly benefit permissioned
blockchains; nevertheless, most blockchains implemented on clouds are
originally designed for loosely-coupled networks where nodes communicate
asynchronously, failing to take advantages of the closely-coupled nature of
cloud servers. In this paper, we propose an innovative cloud-oriented
blockchain -- CloudChain, which is a modularized three-layer system composed of
the network layer, consensus layer, and blockchain layer. CloudChain is based
on a shared-memory model where nodes communicate synchronously by direct memory
accesses. We realize the shared-memory model with the Remote Direct Memory
Access technology, based on which we propose a shared-memory consensus
algorithm to ensure presistence and liveness, the two crucial blockchain
security properties countering Byzantine nodes. We also implement a CloudChain
prototype based on a RoCEv2-based testbed to experimentally validate our
design, and the results verify the feasibility and efficiency of CloudChain.


Abstract:
  In this work, we seek new insights into the underlying challenges of the
Scene Graph Generation (SGG) task. Quantitative and qualitative analysis of the
Visual Genome dataset implies -- 1) Ambiguity: even if inter-object
relationship contains the same object (or predicate), they may not be visually
or semantically similar, 2) Asymmetry: despite the nature of the relationship
that embodied the direction, it was not well addressed in previous studies, and
3) Higher-order contexts: leveraging the identities of certain graph elements
can help to generate accurate scene graphs. Motivated by the analysis, we
design a novel SGG framework, Local-to-Global Interaction Networks (LOGIN).
Locally, interactions extract the essence between three instances - subject,
object, and background - while baking direction awareness into the network by
constraining the input order. Globally, interactions encode the contexts
between every graph components -- nodes and edges. Also we introduce Attract &
Repel loss which finely adjusts predicate embeddings. Our framework enables
predicting the scene graph in a local-to-global manner by design, leveraging
the possible complementariness. To quantify how much LOGIN is aware of
relational direction, we propose a new diagnostic task called Bidirectional
Relationship Classification (BRC). We see that LOGIN can successfully
distinguish relational direction than existing methods (in BRC task) while
showing state-of-the-art results on the Visual Genome benchmark (in SGG task).


Abstract:
  Learning temporal patterns from multivariate longitudinal data is challenging
especially in cases when data is sporadic, as often seen in, e.g., healthcare
applications where the data can suffer from irregularity and asynchronicity as
the time between consecutive data points can vary across features and samples,
hindering the application of existing deep learning models that are constructed
for complete, evenly spaced data with fixed sequence lengths. In this paper, a
novel deep learning-based model is developed for modeling multiple temporal
features in sporadic data using an integrated deep learning architecture based
on a recurrent neural network (RNN) unit and a continuous-time autoregressive
(CAR) model. The proposed model, called CARRNN, uses a generalized
discrete-time autoregressive model that is trainable end-to-end using neural
networks modulated by time lags to describe the changes caused by the
irregularity and asynchronicity. It is applied to multivariate time-series
regression tasks using data provided for Alzheimer's disease progression
modeling and intensive care unit (ICU) mortality rate prediction, where the
proposed model based on a gated recurrent unit (GRU) achieves the lowest
prediction errors among the proposed RNN-based models and state-of-the-art
methods using GRUs and long short-term memory (LSTM) networks in their
architecture.


Abstract:
  The task of predicting smooth and edge-consistent depth maps is notoriously
difficult for single image depth estimation. This paper proposes a novel
Bilateral Grid based 3D convolutional neural network, dubbed as 3DBG-UNet, that
parameterizes high dimensional feature space by encoding compact 3D bilateral
grids with UNets and infers sharp geometric layout of the scene. Further,
another novel 3DBGES-UNet model is introduced that integrate 3DBG-UNet for
inferring an accurate depth map given a single color view. The 3DBGES-UNet
concatenates 3DBG-UNet geometry map with the inception network edge
accentuation map and a spatial object's boundary map obtained by leveraging
semantic segmentation and train the UNet model with ResNet backbone. Both
models are designed with a particular attention to explicitly account for edges
or minute details. Preserving sharp discontinuities at depth edges is critical
for many applications such as realistic integration of virtual objects in AR
video or occlusion-aware view synthesis for 3D display applications.The
proposed depth prediction network achieves state-of-the-art performance in both
qualitative and quantitative evaluations on the challenging NYUv2-Depth data.
The code and corresponding pre-trained weights will be made publicly available.


Abstract:
  The axion is a highly motivated elementary particle which could address two
fundamental questions in physics - the strong CP problem and the dark matter
mystery. Experimental searches for this hypothetical particle have started to
reach theoretically interesting sensitivity levels, particularly in the $\mu$eV
(GHz) region. They rely on large volume solenoid magnetic fields and microwave
resonators with signals read out by quantum noise limited amplifiers.
Concurrently, there have been intensive experimental efforts to widen the
search range by devising various techniques as well as to enhance sensitivities
by implementing advanced technologies. The developments and improvements in
these orthogonal approaches will enable us to explore most of the parameter
space of the axion and axion-like particles within the next couple of decades,
with the 1-25 GHz frequency range to be conquered well within the first decade.
We review the experimental aspects of axion physics and discuss the past,
present and future of the direct search programs.


Abstract:
  Data driven models of dynamical systems help planners and controllers to
provide more precise and accurate motions. Most model learning algorithms will
try to minimize a loss function between the observed data and the model's
predictions. This can be improved using prior knowledge about the task at hand,
which can be encoded in the form of constraints. This turns the unconstrained
model learning problem into a constrained one. These constraints allow models
with finite capacity to focus their expressive power on important aspects of
the system. This can lead to models that are better suited for certain tasks.
This paper introduces the constrained Sufficiently Accurate model learning
approach, provides examples of such problems, and presents a theorem on how
close some approximate solutions can be. The approximate solution quality will
depend on the function parameterization, loss and constraint function
smoothness, and the number of samples in model learning.


Abstract:
  Most prior approaches to offline reinforcement learning (RL) have taken an
iterative actor-critic approach involving off-policy evaluation. In this paper
we show that simply doing one step of constrained/regularized policy
improvement using an on-policy Q estimate of the behavior policy performs
surprisingly well. This one-step algorithm beats the previously reported
results of iterative algorithms on a large portion of the D4RL benchmark. The
one-step baseline achieves this strong performance while being notably simpler
and more robust to hyperparameters than previously proposed iterative
algorithms. We argue that the relatively poor performance of iterative
approaches is a result of the high variance inherent in doing off-policy
evaluation and magnified by the repeated optimization of policies against those
estimates. In addition, we hypothesize that the strong performance of the
one-step algorithm is due to a combination of favorable structure in the
environment and behavior policy.


Abstract:
  Thermally activated selective topography equilibration (TASTE) enables the
creation of 3D structures in resist using grayscale electron-beam lithography
followed by a thermal treatment to induce a selective polymer reflow. A blazed
grating topography can be created by reflowing repeating staircase patterns in
resist into wedge-like structures. Motivated by astronomical applications, such
patterns with periodicities 840 nm and 400 nm have been fabricated in 130
nm-thick PMMA using TASTE to provide a base for X-ray reflection gratings. A
path forward to integrate this alternative blazing technique into grating
fabrication recipes is discussed.