Abstract:
  Almost three decades ago, the problem of long term polar wander on a dynamic
planet was formulated and simplified within the framework of normal mode
theory. The underlying simplifications have been debated ever since, recently
in a series of papers by Hu et al. 2017a, 2017b, and 2019, who clarify the role
of neglecting short-term relaxation modes of the body. However, the authors
still do not solve the governing equations in full, because they make
approximations to the Liouville equation (LE). In this paper I use a time
domain approach and for previously studied test loads I solve both the
relaxation of the body and the LE in full. I also compute the energy balance of
true polar wander (TPW) in order to analyze the existing LE approximations. For
fast rotating bodies such as the Earth, I show that the rotation axis is always
aligned with the maximum principal axis of inertia (w||MMOI) once free
oscillations are damped. The w||MMOI assumption is also re-derived
theoretically. Contrary to previous beliefs, I demonstrate that it is not
necessarily linked to the quasi-fluid simplification of the body's viscoelastic
response to loading and rotation, but that it is an expression of neglecting
the Coriolis and Euler forces in the equation of motion. For slowly rotating
bodies such as Venus, the full LE together with energy analysis indicate that
previous estimates of TPW in the normal direction need to be revisited. The
numerical code LIOUSHELL is made freely available.


Abstract:
  End-to-end models for robust automatic speech recognition (ASR) have not been
sufficiently well-explored in prior work. With end-to-end models, one could
choose to preprocess the input speech using speech enhancement techniques and
train the model using enhanced speech. Another alternative is to pass the noisy
speech as input and modify the model architecture to adapt to noisy speech. A
systematic comparison of these two approaches for end-to-end robust ASR has not
been attempted before. We address this gap and present a detailed comparison of
speech enhancement-based techniques and three different model-based adaptation
techniques covering data augmentation, multi-task learning, and adversarial
learning for robust ASR. While adversarial learning is the best-performing
technique on certain noise types, it comes at the cost of degrading clean
speech WER. On other relatively stationary noise types, a new speech
enhancement technique outperformed all the model-based adaptation techniques.
This suggests that knowledge of the underlying noise type can meaningfully
inform the choice of adaptation technique.


Abstract:
  We investigated the development and breaking of the dineutron correlation in
$^{10}$Be by analyzing the elastic and inelastic scatterings with a framework
combing the microscopic structure and reaction models. For studying the
structure, the $^{10}$Be nucleus was constructed under the assumption of a
four-body ($\alpha + \alpha + n + n$) cluster model. In this work, we focused
on the change in the inner structure for the 0$_1^+$, 2$_1^+$, and 2$_2^+$
states when the strength of the spin-orbit interaction is varied. The inner
structure, including various physical quantities such as energy, radius, and
transition strength, is drastically influenced by the strength of the
spin-orbit interaction. In particular, the development and breaking of the
dineutron correlation is governed by the spin-orbit strength. The differences
in the inner structure can be manifested by applying the obtained wave
functions to elastic and inelastic scatterings with a proton target at $E/A =$
59.4 and 200 MeV. Although the 0$_1^+$ and 2$_1^+$ states are significantly
influenced by the spin-orbit strength of the nuclear structure calculation, the
elastic and inelastic cross sections are not much affected. On the other hand,
the inelastic cross section of the 2$_2^+$ state depends greatly on the
spin-orbit strength of the structure calculation. Thus, we discovered a way to
measure the degree of the development of dineutron cluster structure based on
its sensitivity to the inelastic cross section of the 2$_2^+$ state of
$^{10}$Be.


Abstract:
  In this work, we consider the following inhomogeneous nonlinear Schr\"odinger
equation \begin{align*}
  i\partial_t u+\Delta u +|x|^{-b}|u|^{p-1}u=0,\quad (x, t)\in
\mathbb{R}^N\times\mathbb{R} \end{align*} with $0<b<\mbox{min}\{2, N\}$ and
$1+\frac{4-2b}{N}<p<2^{\ast}$. We assume that $u_0 \in H^{1}(\mathbb{R}^N)$ and
satisfies \begin{align*}
  E(u_0)^{s_c}M(u_0)^{1-s_c}<E(Q)^{s_c}M(Q)^{1-s_c}, \end{align*} and
\begin{align*}
  \|\nabla u_0\|_{L^2}^{s_c}\|u_0\|_{L^2}^{1-s_c}>\|\nabla
Q\|_{L^2}^{s_c}\|Q\|_{L^2}^{1-s_c}, \end{align*} where $Q$ is the ground state
solution of the corresponding elliptic equation. We prove that when
$1+\frac{4-2b}{N}<p\leq1+\frac{4}{N}$, then the corresponding solution blows up
in finite time. Besides, when $1+\frac{4}{N}<p<2^{\ast}$, we prove the finite
or infinite time blow-up and obtain a precise lower bound of blow-up rate.


Abstract:
  The Super-Kamiokande detector can be used to search for neutrinos in time
coincidence with gravitational waves detected by the LIGO-Virgo Collaboration
(LVC). Both low-energy ($7-100$ MeV) and high-energy ($0.1-10^5$ GeV) samples
were analyzed in order to cover a very wide neutrino spectrum. Follow-ups of 36
(out of 39) gravitational waves reported in the GWTC-2 catalog were examined;
no significant excess above the background was observed, with 10 (24) observed
neutrinos compared with 4.8 (25.0) expected events in the high-energy
(low-energy) samples. A statistical approach was used to compute the
significance of potential coincidences. For each observation, p-values were
estimated using neutrino direction and LVC sky map ; the most significant event
(GW190602_175927) is associated with a post-trial p-value of $7.8\%$
($1.4\sigma$). Additionally, flux limits were computed independently for each
sample and by combining the samples. The energy emitted as neutrinos by the
identified gravitational wave sources was constrained, both for given flavors
and for all-flavors assuming equipartition between the different flavors,
independently for each trigger and by combining sources of the same nature.


Abstract:
  By means of in situ synchrotron X-ray diffraction and Raman spectroscopy
under hydrostatic pressure, we investigate the structural stability of the
quadruple perovskite LaMn7O12. At 34 GPa, the data unveil a first-order
structural phase transition from monoclinic I2/m to cubic Im-3 symmetry
characterized by a pronounced contraction of the unit cell and by a significant
modifications in the Raman phonon modes. The phase transition is also marked by
the suppression of Jahn-Teller distortion which is present in the ambient
monoclinic phase. In addition, above 20 GPa pressure, a sudden and simultaneous
broadening is observed in several Raman modes which suggests the onset of a
sizable electron-phonon interaction and incipient charge mobility. Considering
that LaMn7O12 is paramagnetic insulator at ambient, and Jahn-Teller distortion
is frozen in the high-pressure Im-3 phase, we argue that this phase could be a
potential candidate to host a purely electronic insulator-metal transition with
no participation of the lattice


Abstract:
  We present a simple proof of the minimum time for the quantum evolution
between two arbitrary states. This proof is performed in the absence of any
geometrical arguments. Then, being in the geometric framework of quantum
evolutions based upon the geometry of the projective Hilbert space, we discuss
the roles played by either minimum-time or maximum-energy uncertainty concepts
in defining a geometric efficiency measure of quantum evolutions between two
arbitrary quantum states. Finally, we provide a quantitative justification of
the validity of the efficiency inequality even when the system passes only
through nonorthogonal quantum states.


Abstract:
  This paper develops a grey-box approach to small-signal stability analysis of
complex power systems that facilitates root-cause tracing without requiring
disclosure of the full details of the internal control structure of apparatus
connected to the system. The grey-box enables participation analysis in
impedance models, which is popular in power electronics and increasingly
accepted in power systems for stability analysis. The Impedance participation
factor is proposed and defined in terms of the residue of the whole-system
admittance matrix. It is proved that, the so defined impedance participation
factor equals the sensitivity of the whole-system eigenvalue with respect to
apparatus impedance. The classic state participation factor is related to the
impedance participation factor via a chain-rule. Based on the chain-rule, a
three-layer grey-box approach, with three degrees of transparency, is proposed
for root-cause tracing to different depths, i.e. apparatus, states, and
parameters, according to the available information. The association of
impedance participation factor with eigenvalue sensitivity points to the
re-tuning that would stabilize the system. The impedance participation factor
can be measured in the field or calculated from the black-box impedance spectra
with little prior knowledge required.


Abstract:
  We solve a weakly supervised regression problem. Under "weakly" we understand
that for some training points the labels are known, for some unknown, and for
others uncertain due to the presence of random noise or other reasons such as
lack of resources. The solution process requires to optimize a certain
objective function (the loss function), which combines manifold regularization
and low-rank matrix decomposition techniques. These low-rank approximations
allow us to speed up all matrix calculations and reduce storage requirements.
This is especially crucial for large datasets. Ensemble clustering is used for
obtaining the co-association matrix, which we consider as the similarity
matrix. The utilization of these techniques allows us to increase the quality
and stability of the solution. In the numerical section, we applied the
suggested method to artificial and real datasets using Monte-Carlo modeling.


Abstract:
  We propose a low-cost reconfigurable metasurface transmitter to implement
generalized polarization-space modulation (GPSM) that has been proposed for
next-generation wireless communications. Unlike conventional multiple-input
multiple-output GPSM, our electromagnetically-compliant, reduced form-factor
design using Barium-Strontium-Titanate film yields more than two polarization
states. Full-wave simulations show our optimized design achieves 0.08-0.95
polarization conversion ratio with 5% bandwidth at 28 GHz and bit error rates
comparable or better than conventional systems.


Abstract:
  The Analog Ensemble (AnEn) technique has been shown effective on several
weather problems. Unlike previous weather analogs that are sought within a
large spatial domain and an extended temporal window, AnEn strictly confines
space and time, and independently generates results at each grid point within a
short time window. AnEn can find similar forecasts that lead to accurate and
calibrated ensemble forecasts. The central core of the AnEn technique is a
similarity metric that sorts historical forecasts with respect to a new target
prediction. A commonly used metric is Euclidean distance. However, a
significant difficulty using this metric is the definition of the weights for
all the parameters. Generally, feature selection and extensive weight search
are needed.
  This paper proposes a novel definition of weather analogs through a Machine
Learning (ML) based similarity metric. The similarity metric uses neural
networks that are trained and instantiated to search for weather analogs. This
new metric allows incorporating all variables without requiring a prior feature
selection and weight optimization. Experiments are presented on the application
of this new metric to forecast wind speed and solar irradiance. Results show
that the ML metric generally outperforms the original metric. The ML metric has
a better capability to correct for larger errors and to take advantage of a
larger search repository. Spatial predictions using a learned metric also show
the ability to define effective latent features that are transferable to other
locations.


Abstract:
  This article presents a new set of proton parton distribution functions,
ATLASepWZVjet20, produced in an analysis at next-to-next-to-leading order in
QCD. The new data sets considered are the measurements of $W^+$ and $W^-$ boson
and $Z$ boson production in association with jets in $pp$ collisions at
$\sqrt{s} = 8~\mathrm{TeV}$ performed by the ATLAS experiment at the LHC with
integrated luminosities of $20.2~\mathrm{fb}^{-1}$ and $19.9~\mathrm{fb}^{-1}$,
respectively. The analysis also considers the ATLAS measurements of
differential $W^{\pm}$ and $Z$ boson production at $\sqrt{s} = 7~\mathrm{TeV}$
with an integrated luminosity of $4.6~\mathrm{fb}^{-1}$ and
deep-inelastic-scattering data from $e^{\pm}p$ collisions at the HERA
accelerator. An improved determination of the sea-quark densities at high
Bjorken $x$ is shown, while confirming a strange-quark density similar in size
to the up- and down-sea-quark densities in the range $x \lesssim 0.02$ found by
previous ATLAS analyses.


Abstract:
  The radiometric force on a sphere due to its thermal polarization in a
rarefied gas flow being in equilibrium is investigated on the basis of a
kinetic model to the linearized Boltzmann equation. The scattering kernel
proposed by Cercignani and Lampis to model the gas-surface interaction using
two accommodation coefficients, namely the tangential momentum accommodation
coefficient and the normal energy accommodation coefficient, is employed as the
boundary condition. The radiometric force on the sphere, as well as the flow
field of the gas around it, are calculated in a wide range of the gas
rarefaction, defined as the ratio of the sphere radius to an equivalent free
path of gaseous particles, covering the free molecular, transition and
continuum regimes. The discrete velocity method is employed to solve the
kinetic equation numerically. The calculations are carried out for values of
accommodation coefficients considering most situations encountered in practice.
To confirm the reliability of the calculations, the reciprocity relation
between the cross phenomena is verified numerically within a numerical error of
0.1%. The temperature drop between two diametrically opposite points of the
spherical surface in the direction of the gas flow stream, which characterizes
the thermal polarization effect, is compared to experimental data for a
spherical particle of Pyrex glass immersed in helium and argon gases.


Abstract:
  Anomalies in images occur in various scales from a small hole on a carpet to
a large stain. However, anomaly detection based on sparse coding, one of the
widely used anomaly detection methods, has an issue in dealing with anomalies
that are out of the patch size employed to sparsely represent images. A large
anomaly can be considered normal if seen in a small scale, but it is not easy
to determine a single scale (patch size) that works well for all images. Then,
we propose to incorporate multi-scale features to sparse coding and improve the
performance of anomaly detection. The proposed method, multi-layer feature
sparse coding (MLF-SC), employs a neural network for feature extraction, and
feature maps from intermediate layers of the network are given to sparse
coding, whereas the standard sparse-coding-based anomaly detection method
directly works on given images. We show that MLF-SC outperforms
state-of-the-art anomaly detection methods including those employing deep
learning. Our target data are the texture categories of the MVTec Anomaly
Detection (MVTec AD) dataset, which is a modern benchmark dataset consisting of
images from the real world. Our idea can be a simple and practical option to
deal with practical data.


Abstract:
  Social networks have triumphed in communicating people online, but they have
also been exploited to launch influence operations for manipulating society.
The deployment of software-controlled accounts (e.g., social bots) has proven
to be one of the most effective enablers for that purpose, and tools for their
detection have been developed and widely adopted. However, the way to analyze
these accounts and measure their impact is heterogeneous in the literature,
where each case study performs unique measurements. To unify these efforts, we
propose a common framework to analyze the interference of social bots in
Twitter. The methodology compares the non-authentic actors with the rest of
users from different perspectives, thus building objective metrics to measure
their actual impact. We validate the framework by applying it to a dataset of
Twitter iterations dated in the weeks preceding the 2019 Spanish general
election. In this sense, we check that our framework facilitates the
quantitative evaluation of unauthentic groups, particularly discovering that
social bots changed the natural dynamics of the network in these days, but did
not have a significant impact. We also consider this methodology as a practical
tool for the qualitative interpretation of experimental results, particularly
suggesting within the aforementioned electoral context that semi-automated
accounts are potentially more threatening than fully automated ones.


Abstract:
  We study local spin polarization in the relativistic hydrodynamic model.
Generalizing the Wigner functions previously obtained from chiral kinetic
theory by Y. Hidaka et al. [Phys. Rev. D 97, 016004 (2018)] to the massive
case, we present the possible contributions up to the order of $\hbar$ from
thermal vorticity, shear viscous tensor, other terms associated with the
temperature and chemical-potential gradients, and electromagnetic fields to the
local spin polarization. We then implement the (3+1)-dimensional viscous
hydrodynamic model to study the spin polarizations from these sources with a
small chemical potential and ignorance of electromagnetic fields by adopting an
equation of state different from those in other recent studies. Although the
shear correction alone upon the local polarization results in a sign and
azimuthal-angle dependence more consistent with experimental observations, as
also discovered in other recent studies, it is mostly suppressed by the
contributions from thermal vorticity and other terms that yield an opposite
trend. It is found that the total local spin polarization can be very sensitive
to the equation of states, the ratio of shear viscosity to entropy density, and
the freeze-out temperature.


Abstract:
  We study the deformation theory of rational curves on primitive symplectic
varieties and show that if the rational curves cover a divisor, then, as in the
smooth case, they deform along their Hodge locus in the universal locally
trivial deformation. As applications, we extend Markman's deformation
invariance of prime exceptional divisors along their Hodge locus to this
singular framework and provide existence results for uniruled ample divisors on
primitive symplectic varieties which are locally trivial deformations of any
moduli space of semistable objects on a projective $K3$ or fibers of the
Albanese map of those on an abelian surface. We also present an application to
the existence of prime exceptional divisors.


Abstract:
  In this paper we present a class of potentials derived by no-scale
supergravity in order to explain the production of primordial black holes
(PBHs) in the Universe. By breaking the SU(2,1)/SU(2)$\times$U(1) symmetry we
fix one of the two chiral fields and we derive effective scalar potentials
which are capable of generating PBHs. Specifically, we modify well-known
superpotentials, which reduce to Starobinsky-like effective scalar potentials.
Thus, we derive scalar potentials which, on the one hand, explain the
production of PBHs and, on the other hand, they conserve the transformation
laws, which yield from the parametrization of the coset
SU(2,1)/SU(2)$\times$U(1). Moreover, we generate PBHs by modifying the kinetic
term of the Langrangian (or the K\"ahler potential) and we keep the
superpotentials unmodified. In all cases we evaluate the fractional abundances
of PBHs by comparing Press-Schechter approach and peak theory, while focusing
on explaining the dark matter in the Universe. All models are in complete
consistence with Planck constraints.


Abstract:
  Polyps in the colon are widely known as cancer precursors identified by
colonoscopy either related to diagnostic work-up for symptoms, colorectal
cancer screening or systematic surveillance of certain diseases. Whilst most
polyps are benign, the number, size and the surface structure of the polyp are
tightly linked to the risk of colon cancer. There exists a high missed
detection rate and incomplete removal of colon polyps due to the variable
nature, difficulties to delineate the abnormality, high recurrence rates and
the anatomical topography of the colon. In the past, several methods have been
built to automate polyp detection and segmentation. However, the key issue of
most methods is that they have not been tested rigorously on a large
multi-center purpose-built dataset. Thus, these methods may not generalise to
different population datasets as they overfit to a specific population and
endoscopic surveillance. To this extent, we have curated a dataset from 6
different centers incorporating more than 300 patients. The dataset includes
both single frame and sequence data with 3446 annotated polyp labels with
precise delineation of polyp boundaries verified by six senior
gastroenterologists. To our knowledge, this is the most comprehensive detection
and pixel-level segmentation dataset curated by a team of computational
scientists and expert gastroenterologists. This dataset has been originated as
the part of the Endocv2021 challenge aimed at addressing generalisability in
polyp detection and segmentation. In this paper, we provide comprehensive
insight into data construction and annotation strategies, annotation quality
assurance and technical validation for our extended EndoCV2021 dataset which we
refer to as PolypGen.


Abstract:
  In this work we demonstrate spectral-temporal correlation measurements of the
Hong-Ou-Mandel (HOM) interference effect with the use of a spectrometer based
on a photon-counting camera. This setup allows us to take, within seconds,
spectral temporal correlation measurements on entangled photon sources with
sub-nanometer spectral resolution and nanosecond timing resolution. Through
post processing, we can observe the HOM behaviour for any number of spectral
filters of any shape and width at any wavelength over the observable spectral
range. Our setup also offers great versatility in that it is capable of
operating at a wide spectral range from the visible to the near infrared and
does not require a pulsed pump laser for timing purposes. This work offers the
ability to gain large amounts of spectral and temporal information from a HOM
interferometer quickly and efficiently and will be a very useful tool for many
quantum technology applications and fundamental quantum optics research.


Abstract:
  Liu, Hong, Gu, and Lai proved if the second largest eigenvalue of the
adjacency matrix of graph $G$ with minimum degree $\delta \ge 2m+2 \ge 4$
satisfies $\lambda_2(G) < \delta - \frac{2m+1}{\delta+1}$, then $G$ contains at
least $m+1$ edge-disjoint spanning trees, which verified a generalization of a
conjecture by Cioab\u{a} and Wong. We show this bound is essentially the best
possible by constructing $d$-regular graphs $\mathcal{G}_{m,d}$ for all $d \ge
2m+2 \ge 4$ with at most $m$ edge-disjoint spanning trees and
$\lambda_2(\mathcal{G}_{m,d}) < d-\frac{2m+1}{d+3}$. As a corollary, we show
that a spectral inequality on graph rigidity by Cioab\u{a}, Dewar, and Gu is
essentially tight.


Abstract:
  The mainstay of canine rabies control is fixed point mass dog vaccination
campaigns (MDVC). However, in some regions, ideal vaccination coverage in dogs
is not obtained due to low participation in the MDVC. Travel distance to the
vaccination sites has been identified as an important barrier to participation.
We aim to increase MDVC participation by optimally placing fixed point
vaccination locations to minimize walking distance to the nearest vaccination
location. We quantified participation probability based on walking distance to
the nearest vaccination point using a Poisson regression model. The regression
was fit with survey data collected from 2016-2019. We then used a computational
recursive interchange technique to solve the facility location problem to find
a set of optimal placements of fixed point vaccination locations. Finally, we
compared predicted participation of optimally placed vaccination sites to
historical participation data from surveys collected from 2016-2019. We
identified the p-median algorithm to solve the facility location problem as
ideal for fixed point vaccination placement. We found a predicted increase in
MDVC participation if vaccination locations are placed optimally. We also found
a more even vaccination coverage with optimized vaccination sites; however, the
workload in some optimized locations increased significantly. We developed a
data-driven computational algorithm to combat an ongoing rabies epidemic by
optimally using limited resources to maximize vaccination coverage. The main
positive effects we expect if this algorithm is to be implemented would be
increased overall vaccination coverage and increased spatial evenness of
coverage. A potential negative effect could be the presence of long waiting
lines as participation increases.


Abstract:
  In this work we leverage recent advances in context-sensitive language models
to improve the task of query expansion. Contextualized word representation
models, such as ELMo and BERT, are rapidly replacing static embedding models.
We propose a new model, Contextualized Embeddings for Query Expansion (CEQE),
that utilizes query-focused contextualized embedding vectors. We study the
behavior of contextual representations generated for query expansion in ad-hoc
document retrieval. We conduct our experiments on probabilistic retrieval
models as well as in combination with neural ranking models. We evaluate CEQE
on two standard TREC collections: Robust and Deep Learning. We find that CEQE
outperforms static embedding-based expansion methods on multiple collections
(by up to 18% on Robust and 31% on Deep Learning on average precision) and also
improves over proven probabilistic pseudo-relevance feedback (PRF) models. We
further find that multiple passes of expansion and reranking result in
continued gains in effectiveness with CEQE-based approaches outperforming other
approaches. The final model incorporating neural and CEQE-based expansion score
achieves gains of up to 5% in P@20 and 2% in AP on Robust over the
state-of-the-art transformer-based re-ranking model, Birch.


Abstract:
  In news articles the lead bias is a common phenomenon that usually dominates
the learning signals for neural extractive summarizers, severely limiting their
performance on data with different or even no bias. In this paper, we introduce
a novel technique to demote lead bias and make the summarizer focus more on the
content semantics. Experiments on two news corpora with different degrees of
lead bias show that our method can effectively demote the model's learned lead
bias and improve its generality on out-of-distribution data, with little to no
performance loss on in-distribution data.


Abstract:
  This paper proposes an active attack detection scheme for constrained
cyber-physical systems. Despite passive approaches where the detection is based
on the analysis of the input-output data, active approaches interact with the
system by designing the control input so to improve detection. This paper
focuses on the prevented actuation attack, where the attacker prevents the
exchange of information between the controller and actuators. The proposed
scheme consists of two units: 1) detection, and 2) control. The detection unit
includes a set of parallel detectors, which are designed based on the
multiple-model adaptive estimation approach to detect the attack and to
identify the attacked actuator(s). For what regards the control unit, a
constrained optimization approach is developed to determine the control input
such that the control and detection aims are achieved. In the formulation of
the detection and control objective functions, a probabilistic approach is used
to reap the benefits of the \textit{a priori} information availability. The
effectiveness of the proposed scheme is demonstrated through a simulation study
on an irrigation channel.


Abstract:
  Weakly supervised learning has drawn considerable attention recently to
reduce the expensive time and labor consumption of labeling massive data. In
this paper, we investigate a novel weakly supervised learning problem of
learning from similarity-confidence (Sconf) data, where we aim to learn an
effective binary classifier from only unlabeled data pairs equipped with
confidence that illustrates their degree of similarity (two examples are
similar if they belong to the same class). To solve this problem, we propose an
unbiased estimator of the classification risk that can be calculated from only
Sconf data and show that the estimation error bound achieves the optimal
convergence rate. To alleviate potential overfitting when flexible models are
used, we further employ a risk correction scheme on the proposed risk
estimator. Experimental results demonstrate the effectiveness of the proposed
methods.


Abstract:
  This paper aims to mitigate straggler effects in synchronous distributed
learning for multi-agent reinforcement learning (MARL) problems. Stragglers
arise frequently in a distributed learning system, due to the existence of
various system disturbances such as slow-downs or failures of compute nodes and
communication bottlenecks. To resolve this issue, we propose a coded
distributed learning framework, which speeds up the training of MARL algorithms
in the presence of stragglers, while maintaining the same accuracy as the
centralized approach. As an illustration, a coded distributed version of the
multi-agent deep deterministic policy gradient(MADDPG) algorithm is developed
and evaluated. Different coding schemes, including maximum distance separable
(MDS)code, random sparse code, replication-based code, and regular low density
parity check (LDPC) code are also investigated. Simulations in several
multi-robot problems demonstrate the promising performance of the proposed
framework.


Abstract:
  In 1971, J. Lipman introduced the notion of strict closure of a ring in
another, and established the underlying theory in connection with a conjecture
of O. Zariski. In this paper, for further developments of the theory, we
investigate three different topics related to strict closure of rings. The
first one concerns construction of the closure, and the second one is the study
regarding the question of whether the strict closedness is inherited under flat
homomorphisms. We finally handle the question of when the Arf closure coincides
with the strict closure. Examples are explored to illustrate our theorems.


Abstract:
  We report on an asymmetric high energy dual optical parametric amplifier
(OPA) which is capable of having either the idlers, signals, or depleted pumps,
relatively phase locked at commensurate or incommensurate wavelengths. Idlers
and signals can be locked on the order of 200 mrad rms or better, corresponding
to a 212 as jitter at $\lambda$=2$\mu$m. The high energy arm of the OPA outputs
a combined 3.5 mJ of signal and idler, while the low energy arm outputs 1.5 mJ,
with the entire system being pumped with a 1 kHz, 18 mJ Ti:Sapphire laser. Both
arms are independently tunable from 1080 nm-2600 nm. The combination of
relative phase locking, high output power and peak intensity, and large
tunability makes our OPA an ideal tool for use in difference frequency
generation (DFG) in the strong pump regime, and for high peak field waveform
synthesis in the near-infrared. To demonstrate this ability we generate
terahertz radiation through two color waveform synthesis in air plasma and show
the influence of the relative phase on the generated terahertz intensity. The
ability to phase lock multiple incommensurate wavelengths at high energies
opens the door to a multitude of possibilities of strong pump DFG and waveform
synthesis.


Abstract:
  In bilayers consisting of a normal metal (N) with spin-orbit coupling and a
ferromagnet (F), the combination of the spin-Hall effect, the spin-transfer
torque, and the inverse spin-Hall effect gives a small correction to the
in-plane conductivity of N, which is referred to as spin-Hall magnetoresistance
(SMR). We here present a theory of the SMR and the associated off-diagonal
conductivity corrections for frequencies up to the terahertz regime. We show
that the SMR signal has pronounced singularities at the spin-wave frequencies
of F, which identifies it as a potential tool for all-electric spectroscopy of
magnon modes. A systematic change of the magnitude of the SMR at lower
frequencies is associated with the onset of a longitudinal magnonic
contribution to spin transport across the F-N interface.


Abstract:
  Inverse problems defined naturally on the sphere are becoming increasingly of
interest. In this article we provide a general framework for evaluation of
inverse problems on the sphere, with a strong emphasis on flexibility and
scalability. We consider flexibility with respect to the prior selection
(regularization), the problem definition - specifically the problem formulation
(constrained/unconstrained) and problem setting (analysis/synthesis) - and
optimization adopted to solve the problem. We discuss and quantify the
trade-offs between problem formulation and setting. Crucially, we consider the
Bayesian interpretation of the unconstrained problem which, combined with
recent developments in probability density theory, permits rapid, statistically
principled uncertainty quantification (UQ) in the spherical setting. Linearity
is exploited to significantly increase the computational efficiency of such UQ
techniques, which in some cases are shown to permit analytic solutions. We
showcase this reconstruction framework and UQ techniques on a variety of
spherical inverse problems. The code discussed throughout is provided under a
GNU general public license, in both C++ and Python.


Abstract:
  By using distribution dependent Zvonkin's transforms and Malliavin calculus,
the Bismut type formula is derived for the intrinisc/Lions derivatives of
distribution dependent SDEs with singular drifts, which generalizes the
corresponding results derived for classical SDEs and regular distribution
dependent SDEs.


Abstract:
  In this paper, we prove that the group $\mathrm{Aut}_\mathbb{Q}(X)$ of
numerically trivial automorphisms are uniformly bounded for smooth projective
threefolds $X$ of general type which either satisfy $q(X)\geq 3$ or have a
Gorenstein minimal model. If $X$ is furthermore of maximal Albanese dimension,
then $|\mathrm{Aut}_\mathbb{Q}(X)|\leq 4$, and equality can be achieved by an
unbounded family of threefolds previously constructed by the third author.
Along the way we prove a Noether type inequality for log canonical pairs of
general type with the coefficients of the boundary divisor from a given subset
$\mathcal{C}\subset (0,1]$ such that $\mathcal{C}\cup\{1\}$ attains the
minimum.


Abstract:
  Here we present stringent low-frequency 185MHz limits on coherent radio
emission associated with a short gamma-ray burst (SGRB). Our observations of
the short GRB 180805A were taken with the upgraded Murchison Widefield Array
(MWA) rapid-response system, which triggered within 20s of receiving the
transient alert from Swift, corresponding to 83.7s post-burst. The SGRB was
observed for 30m, resulting in a 3sigma persistent flux density upper-limit of
40.2mJy/beam. Transient searches were conducted at the Swift position of this
GRB on 0.5s, 5s, 30s, and 2m timescales, resulting in 3sigma limits of
570-1830, 270-630, 200-420, and 100-200mJy/beam, respectively. We also
performed a dedispersion search for prompt signals at the position of the SGRB
with a temporal and spectral resolution of 0.5s and 1.28MHz, resulting in a
6sigma fluence upper-limit range from 570Jyms at DM=3000pc/cm^3 (z~2.5) to
1750Jyms at DM=200pc/cm^3 (z~0.1). We compare the fluence prompt emission limit
and the persistent upper-limit to SGRB coherent emission models assuming the
merger resulted in a stable magnetar. Our observations were not sensitive
enough to detect prompt emission associated with the alignment of magnetic
fields of a binary neutron star just prior to the merger, from the interaction
between the relativistic jet and the interstellar medium or persistent
pulsar-like emission from the spin-down of the magnetar. However, in the case
of a more powerful SGRB (a gamma-ray fluence an order of magnitude higher than
GRB 180805A and/or a brighter X-ray counterpart), our MWA observations may be
sensitive enough to detect coherent radio emission from the jet-ISM interaction
and/or the magnetar remnant. Finally, we demonstrate that of all current
low-frequency radio telescopes, only the MWA has the sensitivity and response
times capable of probing prompt emission models associated with the initial
SGRB merger event.


Abstract:
  Using insight from numerical approximation of ODEs and the problem
formulation and solution methodology of TD learning through a Galerkin
relaxation, I propose a new class of TD learning algorithms. After applying the
improved numerical methods, the parameter being approximated has a guaranteed
order of magnitude reduction in the Taylor Series error of the solution to the
ODE for the parameter $\theta(t)$ that is used in constructing the linearly
parameterized value function. Predictor-Corrector Temporal Difference (PCTD) is
what I call the translated discrete time Reinforcement Learning(RL) algorithm
from the continuous time ODE using the theory of Stochastic Approximation(SA).
Both causal and non-causal implementations of the algorithm are provided, and
simulation results are listed for an infinite horizon task to compare the
original TD(0) algorithm against both versions of PCTD(0).


Abstract:
  This paper proposes an advanced method for contrast enhancement of capsule
endoscopic images, with the main objective to obtain sufficient information
about the vessels and structures in more distant (or darker) parts of capsule
endoscopic images. The proposed method (PM) combines two algorithms for the
enhancement of darker and brighter areas of capsule endoscopic images,
respectively. The half-unit weighted bilinear algorithm (HWB) proposed in our
previous work is used to enhance darker areas according to the darker map
content of its HSV's component V. Enhancement of brighter areas is achieved
thanks to the novel thresholded weighted-bilinear algorithm (TWB) developed to
avoid overexposure and enlargement of specular highlight spots while preserving
the hue, in such areas. The TWB performs enhancement operations following a
gradual increment of the brightness of the brighter map content of its HSV's
component V. In other words, the TWB decreases its averaged-weights as the
intensity content of the component V increases. Extensive experimental
demonstrations were conducted, and based on evaluation of the reference and PM
enhanced images, a gastroenterologist ({\O}H) concluded that the PM enhanced
images were the best ones based on the information about the vessels, contrast
in the images, and the view or visibility of the structures in more distant
parts of the capsule endoscopy images.


Abstract:
  This paper presents a new primal-dual method for computing an equilibrium of
generalized (continuous) Nash game (referred to as generalized Nash equilibrium
problem (GNEP)) where each player's feasible strategy set depends on the other
players' strategies. The method is based on a new form of Lagrangian function
with a quadratic approximation. First, we reformulate a GNEP as a saddle point
computation using the new Lagrangian and establish equivalence between a saddle
point of the Lagrangian and an equilibrium of the GNEP. We then propose a
simple algorithm that is convergent to the saddle point. Furthermore, we
establish global convergence by assuming that the Lagrangian function satisfies
the Kurdyka-{\L}ojasiewicz property. A distinctive feature of our analysis is
to make use of the new Lagrangian as a potential function to guide the iterate
convergence, which is based on the idea of turning a descent method into a
multiplier method. Our method has two novel features over existing approaches:
(i) it requires neither boundedness assumptions on the strategy set and the set
of multipliers of each player, nor any boundedness assumptions on the iterates
generated by the algorithm; (ii) it leads to a Jacobi-type decomposition
scheme, which, to the best of our knowledge, is the first development of a
distributed algorithm to solve a general class of GNEPs. Numerical experiments
are performed on benchmark test problems and the results demonstrate the
effectiveness of the proposed method.


Abstract:
  Context: Classical nova progenitors are cataclysmic variables and very old
novae are observed to match high mass transfer rate and (relatively) long
orbital period systems. However, the aftermath of a classical nova has never
been studied in detail. Aims: To probe the aftermath of a classical nova
explosion in cataclysmic variables and observe as the binary system relaxes to
quiescence. Methods: We used multi-wavelength time resolved optical and
near-infrared spectroscopy for a bright, well studied classical nova five years
after outburst. We were able to disentangle the contribution of the ejecta at
this late epoch using its previous characterization, separating the ejecta
emission from that of the binary system. Results: We determined the binary
orbital period (P=3.76 hr), the system separation and mass ratio (q>=0.17 for
an assumed white dwarf mass of 1.2 solar masses). We find evidence of an
irradiated secondary star and no unambiguous signature of an accretion disk,
although we identify a second emission line source tied to the white dwarf with
an impact point. The data are consistent with a bloated white dwarf envelope
and the presence of unsettled gas within the white dwarf Roche lobe.
Conclusions: At more than 5 years after eruption, it appears that this
classical nova has not yet relaxed.


Abstract:
  In this work, we propose a new signal routing method for solving routing
problems that occur in the design process of semiconductor package substrates.
Our work uses a topological transformation of the layers of the package
substrate in order to simplify the routing problem into a problem of connecting
points on a circle with non-intersecting straight line segments. The circle,
which we call the Circular Frame, is a polygonal schema, which is originally
used in topology to study the topological structure of 2-manifolds. We show
through experiments that our new routing method based on the Circular Frame
competes with certain grid-based routing algorithms.


Abstract:
  We study a frustrated antiferromagnetic Heisenberg model on a triangular
lattice with the Dzyaloshinskii-Moriya interaction (DMI) in the presence of an
external magnetic field and a single-ion anisotropy. Phase diagrams in the
temperature-field plane for both easy-axis and easy-plane anisotropy of a
varying strength are constructed in the regimes of a moderate and strong DMI by
parallel tempering Monte Carlo simulations. For the considered range of
parameters the phase diagrams featuring up to four ordered phases are
identified. A skyrmion lattice (SkX) phase is found to be stabilized within
some temperature and field window for sufficiently large DMI and not too strong
anisotropy. For the systems with moderate (larger) DMI, a small easy-plane
(easy-axis) anisotropy is concluded to be beneficial by extending (shifting)
the field window of the SkX appearance to lower values. The Metropolis dynamics
is employed to probe the persistence of SkX upon quenching the field to zero or
small finite values. The most favorable conditions for the skyrmions
persistence are confirmed at low temperatures, small DMI and small easy-plane
anisotropy values.


Abstract:
  Best match graphs (BMGs) are vertex-colored digraphs that naturally arise in
mathematical phylogenetics to formalize the notion of evolutionary closest
genes w.r.t. an a priori unknown phylogenetic tree. BMGs are explained by
unique least resolved trees. We prove that the property of a rooted,
leaf-colored tree to be least resolved for some BMG is preserved by the
contraction of inner edges. For the special case of two-colored BMGs, this
leads to a characterization of the least resolved trees (LRTs) of
binary-explainable trees and a simple, polynomial-time algorithm for the
minimum cardinality completion of the arc set of a BMG to reach a BMG that can
be explained by a binary tree.


Abstract:
  In this paper, we propose a novel computational implicit method, which we
call Isogeometric Residual Minimization (iGRM) with direction splitting. The
method mixes the benefits resulting from isogeometric analysis, implicit
dynamics, residual minimization, and alternating direction solver. We utilize
tensor product B-spline basis functions in space, implicit second order time
integration schemes, residual minimization in every time step, and we exploit
Kronecker product structure of the matrix to employ linear computational cost
alternating direction solver. We implement an implicit time integration scheme
and apply, for each space-direction, a stabilized mixed method based on
residual minimization. We show that the resulting system of linear equations
has a Kronecker product structure, which results in a linear computational cost
of the direct solver, even using implicit time integration schemes together
with the stabilized mixed formulation. We test our method on three
advection-diffusion computational examples, including model ``membrane''
problem, the circular wind problem, and the simulations modeling pollution
propagating from a chimney.


Abstract:
  In this paper Gabor system of certain type based on the unitary dual of the
Heisenberg group $\mathbb{H}^n$ is introduced and a sufficient condition is
obtained for the Gabor system to be a Bessel sequence for
$L^2(\mathbb{R}^*,\mathcal{B}_2;d\kappa)$ using the $Schr\"{o}dinger$
representation of $\mathbb{H}^n$, where $\mathcal{B}_2$ denotes the class of
Hilbert-Schmidt operators on $L^2(\mathbb{R}^n)$ and $d\kappa$ denotes the Haar
measure on $\mathbb{R}^*$. Further a necessary and sufficient condition is
provided for the Gabor system to be an orthonormal system, a Parseval frame
sequence, a frame sequence and a Riesz sequence.


Abstract:
  We present a high-dimensional Winfree model in this paper. The Winfree model
is the first mathematical model for synchronization on the circle. We
generalize this model to the high-dimensional sphere and we call it "the
Winfree sphere model." We restricted the support of the influence function in
the neighborhood of the attraction point with a small diameter to mimic the
influence function as the Dirac-delta distribution. Restricting the support of
the influence function allows several new conditions of the complete
phase-locking states for the identical Winfree sphere model compare to previous
results. We also provide the exponential $\ell^1$-stability and the existence
of the equilibrium solution to obtain the complete oscillator death state of
the Winfree sphere model.


Abstract:
  A numerical method to design nonlinear double- and multi-bend achromat (DBA
and MBA) lattices with approximate invariants of motion is investigated. The
search for such nonlinear lattices is motivated by Fermilab's Integrable Optics
Test Accelerator (IOTA), whose design is based on an integrable Hamiltonian
system with two invariants of motion. While it may not be possible to design an
achromatic lattice for a dedicated synchrotron light source storage ring with
one or more exact invariants of motion, it is possible to tune the sextupoles
and octupoles in existing DBA and MBA lattices to produce approximate
invariants. In our procedure, the lattice is tuned while minimizing the
turn-by-turn fluctuations of the Courant-Snyder actions $J_x$ and $J_y$ at
several distinct amplitudes, while simultaneously minimizing diffusion of the
on-energy betatron tunes. The resulting lattices share some important features
with integrable ones, such as a large dynamic aperture, trajectories confined
to invariant tori, robustness to resonances and errors, and a large
amplitude-dependent tune-spread. Compared to the nominal NSLS-II lattice, the
single- and multi-bunch instability thresholds are increased and the
bunch-by-bunch feedback gain can be reduced.


Abstract:
  In this work we analyze the quantum correlations in a spin-1/2 Ising-XXZ
diamond chain with one plaquette distorted impurity. We have shown that the
introduction of impurity into the chain can significantly increase entanglement
as well as quantum correlations compared to the original model, without
impurity. Due to the great flexibility in the choice of impurity parameters,
the model presented is very general and this fact can be very useful for future
experimental measurements. In addition to entanglement and quantum coherence,
we studied quantum teleportation through a quantum channel composed by a
coupled of Heisenberg dimers with distorted impurity in an Ising-XXZ diamond
chain, as well as fidelity in teleportation. Our analysis shows that the
appropriate choice of parameters can greatly increase all the measures
analyzed. For comparison purposes, we present all our results together with the
results of the measurements made for the original model, without impurity,
studied in previous works.


Abstract:
  We present the design of ATJ graphite rods developed for ablation experiments
under high heat flux (up to 50 MW/m2) in the lower divertor of the DIII-D
tokamak [1], a magnetic plasma confinement device. This work is motivated by
the need to test ablation models relevant to carbon-based thermal shields used
in high-speed spacecraft atmospheric entries, where the heat fluxes encountered
can be comparable to those achieved in the DIII-D divertor plasma. Several
different designs for the flow-facing side of the rod are analyzed, including
"sharp nose," "blunt," and "concave". The last shape is studied for its
potential to lower heat fluxes at the rod surface by increased radiation from
trapped neutrals and reduced parallel plasma pressure. We also analyze the
possibility of applying a thin (approximately 30 microns) layer of silicon
carbide (SiC) to the exposed part of several carbon ablation rods to benchmark
its erosion calculations and lifetime predictions. Such calculations are of
interest as SiC represents a promising material for both thermal protection
systems (TPS) and a fusion plasma-facing material (PFM). Preliminary results
from the DIII-D rod ablation experiments are also discussed.


Abstract:
  The discovery of topological order has revolutionized the understanding of
quantum matter in modern physics and provided the theoretical foundation for
many quantum error correcting codes. Realizing topologically ordered states has
proven to be extremely challenging in both condensed matter and synthetic
quantum systems. Here, we prepare the ground state of the toric code
Hamiltonian using an efficient quantum circuit on a superconducting quantum
processor. We measure a topological entanglement entropy near the expected
value of $\ln2$, and simulate anyon interferometry to extract the braiding
statistics of the emergent excitations. Furthermore, we investigate key aspects
of the surface code, including logical state injection and the decay of the
non-local order parameter. Our results demonstrate the potential for quantum
processors to provide key insights into topological quantum matter and quantum
error correction.


Abstract:
  We consider the 2D delta-Bose gas by a smooth mollification of the delta
potential, where the coupling constant is in the critical window. The main
result proves that for two particles, the approximate semigroups on $\mathscr
B_b(\Bbb R^2)$ for the Schr\"odinger operator with singular interaction at the
origin converge pointwise in the initial condition. This convergence extends
earlier functional analytic results for the convergence in the $L_2$-norm
resolvent sense. The central methods introduced here apply the excursion theory
of the 2D Bessel process and the ergodicity of the winding number of planar
Brownian motion. The limiting semigroup thus shows both the Kallianpur--Robbins
law for additive functionals of planar Brownian motion and Kasahara's
second-order law for the fluctuations. As an application, the mode of
convergence is extended to the $N$-particle delta-Bose gas for all $N\geq 3$.


Abstract:
  This paper presents a series of experiments in collective social robotics,
spanning more than 10 years, with the long-term aim of building embodied models
of (aspects) of cultural evolution. Initial experiments demonstrated the
emergence of behavioural traditions in a group of social robots programmed to
imitate each other's behaviours (we call these Copybots). These experiments
show that the noisy (i.e. less than perfect fidelity) imitation that comes for
free with real physical robots gives rise naturally to variation in social
learning. More recent experimental work extends the robots' cognitive
capabilities with simulation-based internal models, equipping them with a
simple artificial theory of mind. With this extended capability we explore, in
our current work, social learning not via imitation but robot-robot
storytelling, in an effort to model this very human mode of cultural
transmission. In this paper we give an account of the methods and inspiration
for these experiments, the experiments and their results, and an outline of
possible directions for this programme of research. It is our hope that this
paper stimulates not only discussion but suggestions for hypotheses to test
with the Storybots.


Abstract:
  Multiple reflections between transducer and imaged object can naturally occur
in ultrasound imaging and other acoustic sensing applications such as sonar.
The repeated interaction of the emitted wavefront with the imaged object is
traditionally regarded an undesired reverberation artifact, often
misinterpreted as fictitious acoustic boundaries. Here we introduce high order
reflection pulse-echo (HOPE)-ultrasound, a novel method that leverages high
order reflections to improve on several aspects of conventional ultrasound
imaging. HOPE is experimentally demonstrated to resolve sub-micrometer features
by breaking through the Nyquist resolution limit. The major contrast
enhancement of the high reflection orders allowed to reveal defects within
materials invisible to conventional scanning acoustic microscopy. The technique
is further shown to improve accuracy of frequency-dependent ultrasound
attenuation measurements from biological tissues. HOPE ultrasound requires no
additional hardware and is easy to implement, underscoring its potential to
boost imaging performance in a broad range of biomedical imaging,
nondestructive testing, and other acoustic sensing applications.


Abstract:
  Discrete event systems (DES) have been established and deeply developed in
the framework of probabilistic and fuzzy computing models due to the necessity
of practical applications in fuzzy and probabilistic systems. With the
development of quantum computing and quantum control, a natural problem is to
simulate DES by means of quantum computing models and to establish {\it quantum
DES} (QDES). The motivation is twofold: on the one hand, QDES have potential
applications when DES are simulated and processed by quantum computers, where
quantum systems are employed to simulate the evolution of states driven by
discrete events, and on the other hand, QDES may have essential advantages over
DES concerning state complexity for imitating some practical problems. The goal
of this paper is to establish a basic framework of QDES by using {\it quantum
finite automata} (QFA) as the modelling formalisms, and the supervisory control
theorems of QDES are established and proved. Then we present a polynomial-time
algorithm to decide whether or not the controllability condition holds. In
particular, we construct a number of new examples of QFA to illustrate the
supervisory control of QDES and to verify the essential advantages of QDES over
DES in state complexity.


Abstract:
  The search for neural architecture is producing many of the most exciting
results in artificial intelligence. It has increasingly become apparent that
task-specific neural architecture plays a crucial role for effectively solving
problems. This paper presents a simple method for learning neural architecture
through random mutation. This method demonstrates 1) neural architecture may be
learned during the agent's lifetime, 2) neural architecture may be constructed
over a single lifetime without any initial connections or neurons, and 3)
architectural modifications enable rapid adaptation to dynamic and novel task
scenarios. Starting without any neurons or connections, this method constructs
a neural architecture capable of high-performance on several tasks. The
lifelong learning capabilities of this method are demonstrated in an
environment without episodic resets, even learning with constantly changing
morphology, limb disablement, and changing task goals all without losing
locomotion capabilities.


Abstract:
  Spatially-coupled (SC) codes, known for their threshold saturation phenomenon
and low-latency windowed decoding algorithms, are ideal for streaming
applications. They also find application in various data storage systems
because of their excellent performance. SC codes are constructed by
partitioning an underlying block code, followed by rearranging and
concatenating the partitioned components in a "convolutional" manner. The
number of partitioned components determines the "memory" of SC codes. While
adopting higher memories results in improved SC code performance, obtaining
optimal SC codes with high memory is known to be hard. In this paper, we
investigate the relation between the performance of SC codes and the density
distribution of partitioning matrices. We propose a probabilistic framework
that obtains (locally) optimal density distributions via gradient descent.
Starting from random partitioning matrices abiding by the obtained
distribution, we perform low complexity optimization algorithms over the cycle
properties to construct high memory, high performance quasi-cyclic SC codes.
Simulation results show that codes obtained through our proposed method notably
outperform state-of-the-art SC codes with the same constraint length and codes
with uniform partitioning.


Abstract:
  We report three new FRBs discovered by the Five-hundred-meter Aperture
Spherical radio Telescope (FAST), namely FRB 181017.J0036+11, FRB 181118 and
FRB 181130, through the Commensal Radio Astronomy FAST Survey (CRAFTS).
Together with FRB 181123 that was reported earlier, all four FAST-discovered
FRBs share the same characteristics of low fluence ($\leq$0.2 Jy ms) and high
dispersion measure (DM, $>1000$ \dmu), consistent with the anti-correlation
between DM and fluence of the entire FRB population. FRB 181118 and FRB 181130
exhibit band-limited features. FRB 181130 is prominently scattered
($\tau_s\simeq8$ ms) at 1.25 GHz. FRB 181017.J0036+11 has full-bandwidth
emission with a fluence of 0.042 Jy ms, which is one of the faintest FRB
sources detected so far. CRAFTS starts to built a new sample of FRBs that fills
the region for more distant and fainter FRBs in the fluence-$\rm DM_E$ diagram,
previously out of reach of other surveys. The implied all sky event rate of
FRBs is $1.24^{+1.94}_{-0.90} \times 10^5$ sky$^{-1}$ day$^{-1}$ at the $95\%$
confidence interval above 0.0146 Jy ms. We also demonstrate here that the
probability density function of CRAFTS FRB detections is sensitive to the
assumed intrinsic FRB luminosity function and cosmological evolution, which may
be further constrained with more discoveries.


Abstract:
  Inspired by biological evolution, we explain the rationality of Vision
Transformer by analogy with the proven practical Evolutionary Algorithm (EA)
and derive that both of them have consistent mathematical representation.
Analogous to the dynamic local population in EA, we improve the existing
transformer structure and propose a more efficient EAT model, and design
task-related heads to deal with different tasks more flexibly. Moreover, we
introduce the spatial-filling curve into the current vision transformer to
sequence image data into a uniform sequential format. Thus we can design a
unified EAT framework to address multi-modal tasks, separating the network
architecture from the data format adaptation. Our approach achieves
state-of-the-art results on the ImageNet classification task compared with
recent vision transformer works while having smaller parameters and greater
throughput. We further conduct multi-modal tasks to demonstrate the superiority
of the unified EAT, e.g., Text-Based Image Retrieval, and our approach improves
the rank-1 by +3.7 points over the baseline on the CSS dataset.


Abstract:
  SARS-CoV-2, also known as COVID-19 or Coronavirus, is a viral contagious
disease that is infected by a novel coronavirus, and has been rapidly spreading
across the globe. It is very important to test and isolate people to reduce
spread, and from here comes the need to do this quickly and efficiently.
According to some studies, Chest-CT outperforms RT-PCR lab testing, which is
the current standard, when diagnosing COVID-19 patients. Due to this, computer
vision researchers have developed various deep learning systems that can
predict COVID-19 using a Chest-CT scan correctly to a certain degree. The
accuracy of these systems is limited since deep learning neural networks such
as CNNs (Convolutional Neural Networks) need a significantly large quantity of
data for training in order to produce good quality results. Since the disease
is relatively recent and more focus has been on CXR (Chest XRay) images, the
available chest CT Scan image dataset is much less. We propose a method, by
utilizing GANs, to generate synthetic chest CT images of both positive and
negative COVID-19 patients. Using a pre-built predictive model, we concluded
that around 40% of the generated images are correctly predicted as COVID-19
positive. The dataset thus generated can be used to train a CNN-based
classifier which can help determine COVID-19 in a patient with greater
accuracy.


Abstract:
  In the paper [On superspecial abelian surfaces over finite fields II. J.
Math. Soc. Japan, 72(1):303--331, 2020], Tse-Chung Yang and the first two
current authors computed explicitly the number $\lvert
\mathrm{SSp}_2(\mathbb{F}_q)\rvert$ of isomorphism classes of superspecial
abelian surfaces over an arbitrary finite field $\mathbb{F}_q$ of even degree
over the prime field $\mathbb{F}_p$. There it was assumed that certain
commutative $\mathbb{Z}_p$-orders satisfy an \'etale condition that excludes
the primes $p=2, 3, 5$. We treat these remaining primes in the present paper,
where the computations are more involved because of the ramifications. This
completes the calculation of $\lvert \mathrm{SSp}_2(\mathbb{F}_q)\rvert$ in the
even degree case. The odd degree case was previous treated by Tse-Chung Yang
and the first two current authors in [On superspecial abelian surfaces over
finite fields. Doc. Math., 21:1607--1643, 2016]. Along the proof of our main
theorem, we give the classification of lattices over local quaternion Bass
orders, which is a new input to our previous works.


Abstract:
  Simulated DAG models may exhibit properties that, perhaps inadvertently,
render their structure identifiable and unexpectedly affect structure learning
algorithms. Here, we show that marginal variance tends to increase along the
causal order for generically sampled additive noise models. We introduce
varsortability as a measure of the agreement between the order of increasing
marginal variance and the causal order. For commonly sampled graphs and model
parameters, we show that the remarkable performance of some continuous
structure learning algorithms can be explained by high varsortability and
matched by a simple baseline method. Yet, this performance may not transfer to
real-world data where varsortability may be moderate or dependent on the choice
of measurement scales. On standardized data, the same algorithms fail to
identify the ground-truth DAG or its Markov equivalence class. While
standardization removes the pattern in marginal variance, we show that data
generating processes that incur high varsortability also leave a distinct
covariance pattern that may be exploited even after standardization. Our
findings challenge the significance of generic benchmarks with independently
drawn parameters. The code is available at
https://github.com/Scriddie/Varsortability.


Abstract:
  The nonlinear dispersive wave equation inside the cylindrical elastic rod is
derived by applying the Navier-Bernoulli hypothesis and Love's relation in
\cite{5}. The elastic rod is assumed to be composed of the Murnaghan's
materials such as Lam$\acute{e}$'s coefficient, Poisson ratio and constitutive
constant which are compressible in nature. In this research paper we apply the
two integral architectures namely extended sine-Gordon method and modified
exponential function method to study the dispersive wave and solved for the
solitons and their classifications. The existence of the number of solutions
are proved with respect to the linear equation obtained by balancing principle.
The related two and three dimensional graphs are simulated and drawn to show
the complex structures.


Abstract:
  We study the minor relation for algebra homomorphims in finitely generated
quasivarieties that admit a logarithmic natural duality. We characterize the
minor homomorphism posets of finite algebras in terms of disjoint unions of
dual partition lattices and investigate reconstruction problems for
homomorphisms.


Abstract:
  We establish decidability for the infinitely many axiomatic extensions of the
commutative Full Lambek logic with weakening FLew (i.e. IMALLW) that have a
cut-free hypersequent proof calculus (specifically: every analytic structural
rule extension). Decidability for the corresponding extensions of its
contraction counterpart FLec was established recently but their computational
complexity was left unanswered. In the second part of this paper, we introduce
just enough on length functions for well-quasi-orderings and the fast-growing
complexity classes to obtain complexity upper bounds for both the weakening and
contraction extensions. A specific instance of this result yields the first
complexity bound for the prominent fuzzy logic MTL (monoidal t-norm based
logic) providing an answer to a long-standing open problem.


Abstract:
  Algorithmic risk assessments hold the promise of greatly advancing accurate
decision-making, but in practice, multiple real-world examples have been shown
to distribute errors disproportionately across demographic groups. In this
paper, we characterize why error disparities arise in the first place. We show
that predictive uncertainty often leads classifiers to systematically
disadvantage groups with lower-mean outcomes, assigning them smaller true and
false positive rates than their higher-mean counterparts. This can occur even
when prediction is group-blind. We prove that to avoid these error imbalances,
individuals in lower-mean groups must either be over-represented among positive
classifications or be assigned more accurate predictions than those in
higher-mean groups. We focus on the latter condition as a solution to bridge
error rate divides and show that data acquisition for low-mean groups can
increase access to opportunity. We call the strategy "affirmative information"
and compare it to traditional affirmative action in the classification task of
identifying creditworthy borrowers.


Abstract:
  6G will likely be the first generation of mobile communication that will
feature tight integration of localization and sensing with communication
functionalities. Among several worldwide initiatives, the Hexa-X flagship
project stands out as it brings together 25 key players from adjacent
industries and academia, and has among its explicit goals to research
fundamentally new radio access technologies and high-resolution localization
and sensing. Such features will not only enable novel use cases requiring
extreme localization performance, but also provide a means to support and
improve communication functionalities. This paper provides an overview of the
Hexa-X vision alongside the envisioned use cases. To close the required
performance gap of these use cases with respect to 5G, several technical
enablers will be discussed, together with the associated research challenges
for the coming years.


Abstract:
  We explore a field theoretical approach to quantum computing and control.
This book consists of three parts. The basics of systems theory and field
theory are reviewed in Part I. In Part II, a gauge theory is reinterpreted from
a systems theoretical perspective and applied to the formulation of quantum
gates. Then quantum systems are defined by introducing feedback to the gates.
In Part III, quantum gates and systems are reformulated from a quantum field
theoretical perspective using S-matrices. We also discuss how gauge fields are
related to feedback.


Abstract:
  The COVID19 pandemic has forced Indian engineering institutions (EIs) to
bring their previous half shut shades completely down. Fetching new admissions
to EI campuses during the pandemic has become a now or never situation for EIs.
During crisis situations, institutions have struggled to return to the normal
track. The pandemic has drastically changed students behavior and family
preferences due to mental stress and the emotional life attached to it.
Consequently, it becomes a prerequisite, and emergencies need to examine the
choice characteristics influencing the selection of EI during the COVID19
pandemic situation.
  The purpose of this study is to critically examine institutional influence
and pandemic influence due to COVID19 that affects students choice about an
engineering institution (EI) and consequently to explore relationships between
institutional and pandemic influence. The findings of this quantitative
research, conducted through a self-reported survey, have revealed that
institutional and pandemic influence have governed EI choice under the COVID19
pandemic. Second, pandemic influence is positively affected by institutional
influence. The study demonstrated that EIs will have to reposition themselves
to normalize pandemic influence by tuning institutional characteristics that
regulate situational influence and new enrollments. It can be yardstick for
policy makers to attract new enrollments under pandemic situations.


Abstract:
  A search for time-dependent violation of the charge-parity symmetry in $D^0
\to K^+ K^-$ and $D^0 \to \pi^+ \pi^-$ decays is performed at the LHCb
experiment using proton-proton collision data recorded from 2015 to 2018 at a
centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6
fb$^{-1}$. The $D^0$ meson is required to originate from a $D^*(2010)^+ \to D^0
\pi^+$ decay, such that its flavour at production is identified by the charge
of the accompanying pion. The slope of the time-dependent asymmetry of the
decay rates of $D^0$ and $\bar{D}^0$ mesons into the final states under
consideration is measured to be $\Delta Y_{K^+ K^-} = (-2.3 \pm 1.5 \pm 0.3)
\times 10^{-4}$, $\Delta Y_{\pi^+ \pi^-} = (-4.0 \pm 2.8 \pm 0.4)\times
10^{-4}$, where the first uncertainties are statistical and the second are
systematic. These results are compatible with the conservation of the
charge-parity symmetry at the level of 2 standard deviations and improve the
precision by nearly a factor of two.


Abstract:
  Understanding non-Landau Fermi liquids in dimensions higher than one, has
been a subject of great interest. Such phases may serve as parent states for
other unconventional phases of quantum matter, in a similar manner that
conventional broken symmetry states can be understood as instabilities of the
Landau Fermi liquid. In this work, we investigate the emergence of a novel
non-Landau Fermi liquid in two dimensions, where the fermions with quadratic
band-touching dispersion interact with a Bose metal. The bosonic excitations in
the Bose metal possess an extended nodal-line spectrum in momentum space, which
arises due to the subsystem symmetry or the restricted motion of bosons. Using
renormalization group analysis and direct computations, we show that the
extended infrared (IR) singularity of the Bose metal leads to a line of
interacting fixed points of novel non-Landau Fermi liquids, where the anomalous
dimension of the fermions varies continuously, akin to the Luttinger liquid in
one dimension. Further, the multi-patch generalization of the model is used to
explore other unusual features of the resulting ground state.


Abstract:
  The false data injection (FDI) attack is a crucial form of cyber-physical
security problems facing cyber-physical power systems. However, there is no
research revealing the problem of FDI attacks facing voltage source converter
based high voltage direct current transmission (VSC-HVDC) systems. Firstly, the
general form of the model of FDI attack strategies is proposed and the essence
of the problem of FDI attack strategies is further analyzed. Moreover, the
model of FDI attack strategies aiming at disrupting the operation security of
converter stations in VSC-HVDC systems is proposed and its solving algorithm is
then presented. And finally, the modified IEEE-14 bus system is utilized to
reveal the problem of FDI attacks facing VSC-HVDC systems, demonstrating that
attackers are capable of disrupting the operation security of converter
stations in VSC-HVDC systems by FDI attacks.


Abstract:
  Stalagmites are an extraordinarily powerful resource for the reconstruction
of climatological palaeoseasonality. Here, we provide a comprehensive review of
different types of seasonality preserved by stalagmites and methods for
extracting this information. A new drip classification scheme is introduced,
which facilitates the identification of stalagmites fed by seasonally
responsive drips and which highlights the wide variability in drip types
feeding stalagmites. This hydrological variability, combined with seasonality
in Earth atmospheric processes, meteoric precipitation, biological processes
within the soil, and cave atmosphere composition means that every stalagmite
retains a different and distinct (but correct) record of environmental
conditions. Replication of a record is extremely useful but should not be
expected unless comparing stalagmites affected by the same processes in the
same proportion. A short overview of common microanalytical techniques is
presented, and suggested best practice discussed. In addition to geochemical
methods, a new modelling technique for extracting meteoric precipitation and
temperature palaeoseasonality from stalagmite d18O data is discussed and tested
with both synthetic and real-world datasets. Finally, world maps of
temperature, meteoric precipitation amount, and meteoric precipitation oxygen
isotope ratio seasonality are presented and discussed, with an aim of helping
to identify regions most sensitive to shifts in seasonality.


Abstract:
  L\'{e}vy walks are a particular type of continuous-time random walks which
results in a super-diffusive spreading of an initially localized packet. The
original one-dimensional model has a simple schematization that is based on
starting a new unidirectional motion event either in the positive or in the
negative direction. We consider two-dimensional generalization of L\'{e}vy
walks in the form of the so-called XY-model. It describes a particle moving
with a constant velocity along one of the four basic directions and randomly
switching between them when starting a new motion event. We address the
ballistic regime and derive solutions for the asymptotic density profiles. The
solutions have a form of first-order integrals which can be evaluated
numerically. For specific values of parameters we derive an exact expression.
The analytic results are in perfect agreement with the results of finite-time
numerical samplings.


Abstract:
  The role of differential equations in the process of calculating Feynman
integrals is reviewed. An example of a diagram is given for which the method of
differential equations was introduced, the properties of the
inverse-mass-expansion coefficients are shown, and modern methods based on
differential equations are considered.


Abstract:
  This work presents a multilevel variant of Stein variational gradient descent
to more efficiently sample from target distributions. The key ingredient is a
sequence of distributions with growing fidelity and costs that converges to the
target distribution of interest. For example, such a sequence of distributions
is given by a hierarchy of ever finer discretization levels of the forward
model in Bayesian inverse problems. The proposed multilevel Stein variational
gradient descent moves most of the iterations to lower, cheaper levels with the
aim of requiring only a few iterations on the higher, more expensive levels
when compared to the traditional, single-level Stein variational gradient
descent variant that uses the highest-level distribution only. Under certain
assumptions, in the mean-field limit, the error of the proposed multilevel
Stein method decays by a log factor faster than the error of the single-level
counterpart with respect to computational costs. Numerical experiments with
Bayesian inverse problems show speedups of more than one order of magnitude of
the proposed multilevel Stein method compared to the single-level variant that
uses the highest level only.


Abstract:
  This paper provides a theoretical and numerical approach to show existence,
uniqueness, and the numerical determination of metalenses refracting radiation
with energy patterns. The theoretical part uses ideas from optimal transport
and for the numerical solution we study and implement a damped Newton algorithm
to solve the semi discrete problem. A detailed analysis is carried out to solve
the near field one source refraction problem and extensions to the far field
are also mentioned.


Abstract:
  The universal scaling law of cortical morphology describes cortical folding
as the covariance of average grey matter thickness, pial surface area, and
exposed surface area. It applies for mammalian species, humans, and across
lobes, however it remains to be shown that local cortical folding obeys the
same rules. Here, we develop a method to obtain morphological measures for
small regions across the cortex and correct surface areas by curvature to
account for differences in patch size, resulting in a map of local morphology.
It enables a near-pointwise analysis of morphological variables and their
regional changes due to processes such as healthy ageing. We confirm
empirically that the theorised covariance of morphological measures still holds
at this level of local partition sizes as predicted, justifying the use of
independent variables derived from the scaling law to identify regional
differences in folding, subject-specific abnormalities, and local effects of
ageing.


Abstract:
  For every $\mathcal{U} \subset \mathrm{Diff}^\infty_{vol}(\mathbb{T}^2)$
there is a measure of finite support contained in $\mathcal{U}$ which is
uniformly expanding.


Abstract:
  Motivated by the initial stages of high-energy heavy-ion collisions, we study
excitations of far-from-equilibrium 2+1 dimensional gauge theories using
classical-statistical lattice simulations. We evolve field perturbations over a
strongly overoccupied background undergoing self-similar evolution. While in
3+1D the excitations are described by hard-thermal loop theory, their structure
in 2+1D is nontrivial and nonperturbative. These nonperturbative interactions
lead to broad excitation peaks in spectral and statistical correlation
functions. Their width is comparable to the frequency of soft excitations,
demonstrating the absence of soft quasiparticles in these theories. Our results
also suggest that excitations at higher momenta are sufficiently long-lived,
such that an effective kinetic theory description for 2+1 dimensional
Glasma-like systems may exist, but its collision kernel must be
nonperturbatively determined.


Abstract:
  We propose a framework where generation and transmission capacities are
planned concurrently in market environments with a focus on the prosumers. This
paper is a continuation of Part I and presents numerical results from three
archetypal case studies. Following the proposed framework, optimal planning
decisions are shown in all the cases. Furthermore, in case study I, no-regret
planning decisions considering the uncertainties in future electricity market
designs are discussed. In case study II, we look at a situation where the
social resistance of wind energy prevails and the prosumers choose to not
invest in wind energy. This preference results in an increased system cost, and
thus may harm other prosumers. The framework is used as a negotiation simulator
to deal with this problem. Case study III presents numerical results for a
mixed bilateral/pool market. The case studies utilize realistic data from the
Dutch power system and the European power system to provide policy-relevant
results that aid their decarbonization in various market environments.


Abstract:
  We study the problem of matching a string in a labeled graph. Previous
research has shown that unless the Orthogonal Vectors Hypothesis (OVH) is
false, one cannot solve this problem in strongly sub-quadratic time, nor index
the graph in polynomial time to answer queries efficiently (Equi et al. ICALP
2019, SOFSEM 2021). These conditional lower-bounds cover even deterministic
graphs with binary alphabet, but there naturally exist also graph classes that
are easy to index: E.g. Wheeler graphs (Gagie et al. Theor. Comp. Sci. 2017)
cover graphs admitting a Burrows-Wheeler transform -based indexing scheme.
However, it is NP-complete to recognize if a graph is a Wheeler graph (Gibney,
Thankachan, ESA 2019).
  We propose an approach to alleviate the construction bottleneck of Wheeler
graphs. Rather than starting from an arbitrary graph, we study graphs induced
from multiple sequence alignments (MSAs). Elastic degenerate strings (Bernadini
et al. SPIRE 2017, ICALP 2019) can be seen as such graphs, and we introduce
here their generalization: elastic founder graphs. We first prove that even
such induced graphs are hard to index under OVH. Then we introduce two
subclasses, repeat-free and semi-repeat-free graphs, that are easy to index. We
give a linear time algorithm to construct a repeat-free non-elastic founder
graph from a gapless MSA, and (parameterized) near-linear time algorithms to
construct semi-repeat-free (repeat-free, respectively) elastic founder graphs
from general MSAs. Finally, we show that repeat-free elastic founder graphs
admit a reduction to Wheeler graphs in polynomial time.


Abstract:
  State of the art language models return a natural language text continuation
from any piece of input text. This ability to generate coherent text extensions
implies significant sophistication, including a knowledge of grammar and
semantics. In this paper, we propose a mathematical framework for passing from
probability distributions on extensions of given texts, such as the ones
learned by today's large language models, to an enriched category containing
semantic information. Roughly speaking, we model probability distributions on
texts as a category enriched over the unit interval. Objects of this category
are expressions in language, and hom objects are conditional probabilities that
one expression is an extension of another. This category is syntactical -- it
describes what goes with what. Then, via the Yoneda embedding, we pass to the
enriched category of unit interval-valued copresheaves on this syntactical
category. This category of enriched copresheaves is semantic -- it is where we
find meaning, logical operations such as entailment, and the building blocks
for more elaborate semantic concepts.


Abstract:
  Among several subpopulations of a given species with a normally-distributed
trait, such as height, blood pressure, or various test scores in humans, one
and only one of the subpopulations will always strongly dominate all the others
in the right tail, that is, in the high-end values of that trait. Examples are
given to show that this is not true in general for other common classes of
continuous centrally-symmetric unimodal distributions such as Laplace, nor even
for other bell-shaped distributions such as Cauchy.


Abstract:
  Can artificial agents benefit from human conventions? Human societies manage
to successfully self-organize and resolve the tragedy of the commons in
common-pool resources, in spite of the bleak prediction of non-cooperative game
theory. On top of that, real-world problems are inherently large-scale and of
low observability. One key concept that facilitates human coordination in such
settings is the use of conventions. Inspired by human behavior, we investigate
the learning dynamics and emergence of temporal conventions, focusing on
common-pool resources. Extra emphasis was given in designing a realistic
evaluation setting: (a) environment dynamics are modeled on real-world
fisheries, (b) we assume decentralized learning, where agents can observe only
their own history, and (c) we run large-scale simulations (up to 64 agents).
  Uncoupled policies and low observability make cooperation hard to achieve; as
the number of agents grow, the probability of taking a correct gradient
direction decreases exponentially. By introducing an arbitrary common signal
(e.g., date, time, or any periodic set of numbers) as a means to couple the
learning process, we show that temporal conventions can emerge and agents reach
sustainable harvesting strategies. The introduction of the signal consistently
improves the social welfare (by 258% on average, up to 3306%), the range of
environmental parameters where sustainability can be achieved (by 46% on
average, up to 300%), and the convergence speed in low abundance settings (by
13% on average, up to 53%).


Abstract:
  Scene flow in 3D point clouds plays an important role in understanding
dynamic environments. Although significant advances have been made by deep
neural networks, the performance is far from satisfactory as only per-point
translational motion is considered, neglecting the constraints of the rigid
motion in local regions. To address the issue, we propose to introduce the
motion consistency to force the smoothness among neighboring points. In
addition, constraints on the rigidity of the local transformation are also
added by sharing unique rigid motion parameters for all points within each
local region. To this end, a high-order CRFs based relation module (Con-HCRFs)
is deployed to explore both point-wise smoothness and region-wise rigidity. To
empower the CRFs to have a discriminative unary term, we also introduce a
position-aware flow estimation module to be incorporated into the Con-HCRFs.
Comprehensive experiments on FlyingThings3D and KITTI show that our proposed
framework (HCRF-Flow) achieves state-of-the-art performance and significantly
outperforms previous approaches substantially.


Abstract:
  Intelligence necessitates memory. Without memory, humans fail to perform
various nontrivial tasks such as reading novels, playing games or solving
maths. As the ultimate goal of machine learning is to derive intelligent
systems that learn and act automatically just like human, memory construction
for machine is inevitable. Artificial neural networks model neurons and
synapses in the brain by interconnecting computational units via weights, which
is a typical class of machine learning algorithms that resembles memory
structure. Their descendants with more complicated modeling techniques (a.k.a
deep learning) have been successfully applied to many practical problems and
demonstrated the importance of memory in the learning process of machinery
systems. Recent progresses on modeling memory in deep learning have revolved
around external memory constructions, which are highly inspired by
computational Turing models and biological neuronal systems. Attention
mechanisms are derived to support acquisition and retention operations on the
external memory. Despite the lack of theoretical foundations, these approaches
have shown promises to help machinery systems reach a higher level of
intelligence. The aim of this thesis is to advance the understanding on memory
and attention in deep learning. Its contributions include: (i) presenting a
collection of taxonomies for memory, (ii) constructing new memory-augmented
neural networks (MANNs) that support multiple control and memory units, (iii)
introducing variability via memory in sequential generative models, (iv)
searching for optimal writing operations to maximise the memorisation capacity
in slot-based memory networks, and (v) simulating the Universal Turing Machine
via Neural Stored-program Memory-a new kind of external memory for neural
networks.


Abstract:
  Current blockchain approaches for business contracts are based on smart
contracts, namely, software programs placed on a blockchain that are
automatically executed to realize a contract. However, smart contracts lack
flexibility and interfere with the autonomy of the parties concerned.
  We propose Hercule, an approach for declaratively specifying blockchain
applications in a manner that reflects business contracts. Hercule represents a
contract via regulatory norms that capture the involved parties' expectations
of one another. It computes the states of norms (hence, of contracts) from
events in the blockchain. Hercule's novelty and significance lie in that it
operationalizes declarative contracts over semistructured databases, the
underlying representation for practical blockchain such as Hyperledger Fabric
and Ethereum. Specifically, it exploits the map-reduce capabilities of such
stores to compute norm states.
  We demonstrate that our implementation over Hyperledger Fabric can process
thousands of events per second, sufficient for many applications.


Abstract:
  We study the problem of whether the curvature-dimension condition with
negative values of the generalized dimension parameter is stable under a
suitable notion of convergence. To this purpose, first of all we introduce an
appropriate setting to introduce the CD(K, N)-condition for $N < 0$, allowing
metric measure structures in which the reference measure is quasi-Radon. Then
in this class of spaces we introduce the distance $d_{\mathsf{iKRW}}$, which
extends the already existing notions of distance between metric measure spaces.
Finally, we prove that if a sequence of metric measure spaces satisfying the
CD(K, N)-condition with $N < 0$ is converging with respect to the distance
$d_{\mathsf{iKRW}}$ to some metric measure space, then this limit structure is
still a CD(K, N) space.


Abstract:
  In this paper, we establish the well-posedness for the third grade fluid
equation perturbed by a multiplicative white noise. This equation describes the
motion of a non-Newtonian fluid of differential type with relevant viscoelastic
properties. We are faced with a strongly nonlinear stochastic partial
differential equation supplemented with a Navier slip boundary condition.
Taking the initial condition in the Sobolev space H^2, we show the existence
and the uniqueness of the strong (in the probability sense) solution in a two
dimensional and non axisymmetric bounded domain.


Abstract:
  In this paper, we apply the long short-term memory (LSTM), an advanced
recurrent neural network based machine learning (ML) technique, to the problem
of transmitter selection (TS) for secrecy in an underlay small-cell cognitive
radio network with unreliable backhaul connections. The cognitive communication
scenario under consideration has a secondary small-cell network that shares the
same spectrum of the primary network with an agreement to always maintain a
desired outage probability constraint in the primary network. Due to the
interference from the secondary transmitter common to all primary
transmissions, the secrecy rates for the different transmitters are correlated.
LSTM exploits this correlation and matches the performance of the conventional
technique when the number of transmitters is small. As the number grows, the
performance degrades in the same manner as other ML techniques such as support
vector machine, $k$-nearest neighbors, naive Bayes, and deep neural network.
However, LSTM still significantly outperforms these techniques in
misclassification ratio and secrecy outage probability. It also reduces the
feedback overhead against conventional TS.


Abstract:
  In this note, we present constructive bijections from Dyck and Motzkin
meanders with catastrophes to Dyck paths avoiding some patterns. As a
byproduct, we deduce correspondences from Dyck and Motzkin excursions to
restricted Dyck paths.


Abstract:
  A number of stability criteria exist for dark energy theories, associated
with requiring the absence of ghost, gradient and tachyonic instabilities.
Tachyonic instabilities are the least well explored of these in the dark energy
context and we here discuss and derive criteria for their presence and size in
detail. Our findings suggest that, while the absence of ghost and gradient
instabilities is indeed essential for physically viable models and so priors
associated with the absence of such instabilities significantly increase the
efficiency of parameter estimations without introducing unphysical biases, this
is not the case for tachyonic instabilities. Even strong such instabilities can
be present without spoiling the cosmological validity of the underlying models.
Therefore, we caution against using exclusion priors based on requiring the
absence of (strong) tachyonic instabilities in deriving cosmological parameter
constraints. We illustrate this by explicitly computing such constraints within
the context of Horndeski theories, while quantifying the size and effect of
related tachyonic instabilities.


Abstract:
  Monte Carlo (MC) generators are crucial for analyzing data at hadron
colliders, however, even a small mismatch between the MC simulations and the
experimental data can undermine the interpretation of LHC searches in the SM
and beyond. The jet multiplicity distributions used in four-top searches, one
of the ultimate rare processes in the SM currently being explored at the LHC,
makes $pp\to t\bar tt \bar t$ an ideal testing ground to explore for new ways
to reduce the impact of MC mismodelling on such observables. In this Letter, we
propose a novel weakly-supervised method capable of disentangling the $t\bar t
t\bar t$ signal from the dominant background, while partially correcting for
possible MC imperfections. A mixture of multinomial distributions is used to
model the light-jet and $b$-jet multiplicities under the assumption that these
are conditionally independent given a categorical latent variable. The signal
and background distributions generated from a deliberately untuned MC simulator
are used as model priors. The posterior distributions, as well as the signal
fraction, are then learned from the data using Bayesian inference. We
demonstrate that our method can mitigate the effects of large MC mismodellings
using a realistic $t\bar tt\bar t$ search in the same-sign dilepton channel,
leading to corrected posterior distributions that better approximate the
underlying truth-level spectra.


Abstract:
  In this work, retrograde mean motion resonances (MMRs) are investigated by
means of analytical and numerical approaches. Initially, we define a new
resonant angle to describe the retrograde MMRs and then perform a series of
canonical transformations to formulate the resonant model, in which the phase
portrait, resonant centre and resonant width can be analytically determined. To
validate the analytical developments, the non-perturbative analysis is made by
taking advantage of Poincar\'e surfaces of section. Some modifications are
introduced in the production of Poincar\'e sections and, in particular, it
becomes possible to make direct comparisons between the analytical and
numerical results. It is found that there exists an excellent correspondence
between the phase portraits and the associated Poincar\'e sections, and the
analytical results agree well with the numerical results in terms of the
resonant width and the location of resonant centre. Finally, the numerical
approach is utilized to determine the resonant widths and resonant centres over
the full range of eccentricity. In particular, seven known examples of
retrograde asteroids including 2015 BZ509, 2008 SO218, 1999 LE31, 2000 DG8,
2014 AT28, 2016 LS and 2016 JK24 are found inside the libration zones of
retrograde MMRs with Jupiter. The results obtained in this work may be helpful
for understanding the dynamical evolution for asteroids inside retrograde MMRs.


Abstract:
  We formulate the path integral for Jackiw-Teitelboim gravity in the second
order formalism working directly with the metric and the dilaton. We consider
the theory both in Anti-de Sitter(AdS) and de Sitter space(dS) and analyze the
path integral for the disk topology and the "double trumpet" topology with two
boundaries. We also consider its behavior in the presence of conformal matter.
In the dS case the path integral evaluates the wavefunction of the universe
which arises in the no-boundary proposal. In the asymptotic AdS or dS limit
without matter we get agreement with the first order formalism. More generally,
away from this limit, the path integral is more complicated due to the presence
of modes from the gravity-dilaton sector and also matter sector with short
wavelengths along the boundary that are smaller than the AdS or dS scales. In
the double trumpet case, for both AdS and dS, we find that bosonic matter gives
rise to a diverging contribution in the moduli space integral rendering the
path integral ill-defined. The divergence occurs when the size of the wormhole
neck vanishes and is related to the Casimir effect. For fermions this
divergence can be avoided by imposing suitable boundary conditions. In this
case, in dS space the resulting path integral gives a finite contribution for
two disconnected universes to be produced by quantum tunneling.