Abstract:
  In this work we have performed a detailed study of chiral nuclear forces at
N4LO approximation applied to selected channels of the neutron-proton ($n p$)
scattering. The idea is to analyse the different contributions to the
nucleon-nucleon ($NN$) potential by separating the part coming from the
exchange of pions and the one coming from the contact interactions. We consider
two state-of-the-art chiral interactions at N4LO which are constructed using
different regularization procedures: the non-local Idaho-Salamanca force and
the semi-local interaction from the Bochum group. In order to compare the two
types of regularization we consider both interactions with a 500 MeV cutoff and
to analyse the cutoff dependence we select the Bochum potential with three
different cutoff values: 500, 450 and 400 MeV. Our results show that the
balance between pion exchanges and contact interactions depends strongly on the
regularization procedure. The non-local angle-independent regularization of
both components of the interaction implemented in the Idaho-Salamanca potential
make the contact terms to be present at large distances while the local
regularization of the pion exchanges in the Bochum potential restricts the
contact interactions to small distances. Also, the value of the cutoff affects
the strength of the potential but the interplay between pion exchanges and
contact terms remains qualitatively the same.


Abstract:
  In the clinical diagnosis and treatment of brain tumors, manual image reading
consumes a lot of energy and time. In recent years, the automatic tumor
classification technology based on deep learning has entered people's field of
vision. Brain tumors can be divided into primary and secondary intracranial
tumors according to their source. However, to our best knowledge, most existing
research on brain tumors are limited to primary intracranial tumor images and
cannot classify the source of the tumor. In order to solve the task of tumor
source type classification, we analyze the existing technology and propose an
attention guided deep convolution neural network (CNN) model. Meanwhile, the
method proposed in this paper also effectively improves the accuracy of
classifying the presence or absence of tumor. For the brain MR dataset, our
method can achieve the average accuracy of 99.18% under ten-fold
cross-validation for identifying the presence or absence of tumor, and 83.38%
for classifying the source of tumor. Experimental results show that our method
is consistent with the method of medical experts. It can assist doctors in
achieving efficient clinical diagnosis of brain tumors.


Abstract:
  Background: Positron Emission Tomography (PET) with 18F-fluorodeoxyglucose
(18F-FDG) reveals metabolic abnormalities in Parkinson's disease (PD) at a
systemic level. Previous metabolic connectome studies derived from groups of
patients have failed to identify the individual neurophysiological details. We
aim to establish an individual metabolic connectome method to characterize the
aberrant connectivity patterns and topological alterations of the
individual-level brain metabolic connectome and their diagnostic value in PD.
Methods: The 18F-FDG PET data of 49 PD patients and 49 healthy controls (HCs)
were recruited. Each individual's metabolic brain network was ascertained using
the proposed Jensen-Shannon Divergence Similarity Estimation (JSSE) method. The
intergroup difference of the individual's metabolic brain network and its
global and local graph metrics were analyzed to investigate the metabolic
connectome's alterations. The identification of the PD from HC individuals was
used by the multiple kernel support vector machine (MK-SVM) to combine the
information from connection and topological metrics. The validation was
conducted using the nest leave-one-out cross-validation strategy to confirm the
performance of the methods. Results: The proposed JSSE metabolic connectome
method showed the most involved metabolic motor networks were PUT-PCG, THA-PCG,
and SMA pathways in PD, which was similar to the typical group-level method,
and yielded another detailed individual pathological connectivity in ACG-PCL,
DCG-PHG and ACG pathways. These aberrant functional network measures exhibited
an ideal classification performance in the identifying of PD individuals from
HC individuals at an accuracy of up to 91.84%.


Abstract:
  Geothermal heat, as renewable energy, shows great advantage with respect to
its environmental impact due to its significantly lower CO2 emissions than
conventional fossil fuel. Open and closed-loop geothermal heat pumps, which
utilize shallow geothermal systems, are an efficient technology for cooling and
heating buildings, especially in urban areas. Integrated use of geothermal
energy technologies for district heating, cooling, and thermal energy storage
can be applied to optimize the subsurface for communities to provide them with
multiple sustainable energy and community resilience benefits. The utilization
of the subsurface resources may lead to a variation in the underground
environment, which might further impact local environmental conditions.
However, very few simulators can handle such a highly complex set of coupled
computations on a regional or city scale. We have developed high-performance
computing (HPC) based hydrothermal finite element (FE) simulator that can
simulate the subsurface and its hydrothermal conditions at a scale of tens of
km. The HPC simulator enables us to investigate the subsurface thermal and
hydrologic response to the built underground environment (such as basements and
subways) at the community scale. In this study, a coupled hydrothermal
simulator is developed based on the open-source finite element library deal.II.
The HPC simulator was validated by comparing the results of a benchmark case
study against COMSOL Multiphysics, in which Aquifer Thermal Energy Storage
(ATES) is modeled and a process of heat injection into ATES is simulated. The
use of an energy pile system at the Treasure Island redevelopment site (San
Francisco, CA, USA) was selected as a case study to demonstrate the HPC
capability of the developed simulator. The simulator is capable of modeling
multiple city-scale geothermal scenarios in a reasonable amount of time.


Abstract:
  We investigate the integrability of an anisotropic universe with matter and
cosmological constant formulated as Bianchi IX models. The presence of the
cosmological constant causes the existence of a critical point in the finite
part of the phase space. The separatrix associated to this Einstein's static
universe is entirely contained in an invariant isotropic plane forgetting the
singularity at the origin. This invariant plane of isotropy is an integrable
sub-space of the Taub type. In this paper we analyse the differential Galois
group of the second order variational equations to this plane in order to apply
the integrability theorem of the second author with Ramis and Sim\' o. The main
result is that the model is non-integrable by meromorphic functions.


Abstract:
  It is well-known that nonlinearity may lead to localization effects and
coupling of internally resonant modes. However, research focused primarily on
conservative systems commonly assumes that the near-resonant forced response
closely follows the autonomous dynamics. Our results for even a simple system
of two coupled oscillators with a cubic spring clearly contradict this common
belief. We demonstrate analytically and numerically global effects of a weak
local damping source in a harmonically forced nonlinear system under condition
of 1:3 internal resonance: The global motion becomes asynchronous, i.e., mode
complexity is introduced with a non-trivial phase difference between the modal
oscillations. In particular, we show that a maximum mode complexity with a
phase difference of $90^\circ$ is attained in a multi-harmonic sense. This
corresponds to a transition from generalized standing to traveling waves in the
system's modal space. We further demonstrate that the localization is crucially
affected by the system's damping. Finally, we propose an extension of the
definition of mode complexity and mode localization to nonlinear quasi-periodic
motions, and illustrate their application to a quasi-periodic regime in the
forced response.


Abstract:
  We consider the problem of regularization by noise for the three dimensional
magnetohydrodynamical (3D MHD) equations. It is shown that, in a suitable
scaling limit, multiplicative noise of transport type gives rise to bounds on
the vorticity fields of the fluid velocity and magnetic fields. As a result, if
the noise intensity is big enough, then the stochastic 3D MHD equations admit a
pathwise unique global solution for large initial data, with high probability.


Abstract:
  In this article, the goal is to find the shortest presentation of a matrix $A
\in PSL(2,\mathbb{Z})$ in terms of the so-called continuant matrices which are
most known for their role in continued fraction theory. In chapter 7 of
arXiv:1811.01229, Morier-G\'enoud and Ovsienko investigate this problem with
the restriction that all coefficients of the continuant matrices are positive.
Now, the goal is to determine the shortest presentation allowing all integer
coefficients. To determine this minimal presentation, a few characteristic
transformations will be introduced. It will also be investigated under which
conditions such a minimal presentation becomes unique. The results are also
generalized on conjugacy classes in $PSL(2,\mathbb{Z})$.
  If you wish to contact the author or you have some questions related to this
work, feel free to write an email to christian.streib@online.de.


Abstract:
  In this letter, we consider an underlay cognitive radio network assisted by
dual-hop decode-and-forward (DF) relaying. For a general multi-user network, we
adopt a max-min fairness relay selection scheme and analyse the outage
probability when the channels are subject to independent and non-identical
Nakagami-m fading. The relay network operates within the constraint imposed on
the peak interference power tolerable by the primary receiver. We then analyse
the asymptotic outage probability performance and illustrate the existence of
i) the full-diversity order when the interference level at the primary user
increases proportionally with the relay transmit power; and ii) an outage floor
when the transmit powers of the relays are restricted by the primary receiver.
We also analyse the outage probability with imperfect channel state information
(CSI) and the average throughput over Rayleigh fading channels. Illustrative
analytical results are accurately validated by numerical simulations.


Abstract:
  Beam-driven collinear wakefield accelerators (CWAs) that operate by using
slow-wave structures or plasmas hold great promise toward reducing the size of
contemporary accelerators. Sustainable acceleration of charged particles to
high energies in the CWA relies on using field-generating relativistic electron
bunches with a highly asymmetric peak current profile and a large energy chirp.
A new approach to obtaining such bunches has been proposed and illustrated with
the accelerator design supported by particle tracking simulations. It has been
shown that the required particle distribution in the longitudinal phase space
can be obtained without collimators, giving CWAs an opportunity for employment
in applications requiring a high repetition rate of operation.


Abstract:
  We study how Betti numbers of ideals in a local ring change under small
perturbations. Given $p\in\mathbb N$ and given an ideal $I$ of a Noetherian
local ring $(R,\mathfrak m)$, our main result states that there exists $N>0$
such that if $J$ is an ideal with $I\equiv J\bmod \mathfrak m^N$ and with the
same Hilbert function as $I$, then the Betti numbers $\beta_i^R(R/I)$ and
$\beta_i^R(R/J)$ coincide for $0\le i\le p$. Moreover, we present several cases
in which an ideal $J$ such that $I \equiv J \bmod \mathfrak m^N$ is forced to
have the same Hilbert function as $I$, and therefore the same Betti numbers.


Abstract:
  With there being many technical and ethical issues with artificial
intelligence (AI) that involve marginalized communities, there is a growing
interest for design methods used with marginalized people that may be
transferable to the design of AI technologies. Participatory design (PD) is a
design method that is often used with marginalized communities for the design
of social development, policy, IT and other matters and solutions. However,
there are issues with the current PD, raising concerns when it is applied to
the design of technologies, including AI technologies. This paper argues for
the use of PD for the design of AI technologies, and introduces and proposes a
new PD, which we call agile participatory design, that not only can could be
used for the design of AI and data-driven technologies, but also overcomes
issues surrounding current PD and its use in the design of such technologies.


Abstract:
  We present a unified description of extremal metrics for the Laplace and
Steklov eigenvalues on manifolds of arbitrary dimension using the notion of
$n$-harmonic maps. Our approach extends the well-known results linking extremal
metrics for eigenvalues on surfaces with minimal immersions and harmonic maps.
In the process, we uncover two previously unknown features of the Steklov
eigenvalues. First, we show that in higher dimensions there is a unique
normalization involving both the volume of the boundary and of the manifold
itself, which leads to meaningful extremal eigenvalue problems. Second, we
observe that the critical points of the eigenvalue functionals in a fixed
conformal class have a natural geometric interpretation provided one considers
the Steklov problem with a density. As an example, we construct a family of
free boundary harmonic annuli in the three-dimensional ball and conjecture that
they correspond to metrics maximizing the first Steklov eigenvalue in their
respective conformal classes.


Abstract:
  Topological quantum dots (TQDs) are 3D topological insulator nanoparticles
with radius below 100 nm, which display symmetry-protected surface states with
discretized energies. We propose a scheme which harnesses these energy levels
in a closed lasing scheme, in which a single TQD, when pumped with incoherent
THz light, lases from its surface states in the THz regime. The time scales
associated with the system are unusually slow, and lasing occurs with a very
low threshold. Due to the low threshold, we predict that blackbody radiation at
room-temperature provides enough photons to pump the system, providing a route
to room-temperature THz lasing with blackbody radiation as the pump.


Abstract:
  In this article we extend results of Kakutani, Adler--Flatto, Smilansky and
others on the classical $\alpha$-Kakutani equidistribution result for sequences
arising from finite partitions of the interval. In particular, we describe a
generalization of the equidistribution result to infinite partitions. In
addition, we give discrepancy estimates, extending results of Drmota--Infusino.


Abstract:
  Since the outbreak of COVID-19 policy makers have been relying upon
non-pharmacological interventions to control the outbreak. With air pollution
as a potential transmission vector there is need to include it in intervention
strategies. We propose a U-net driven quantile regression model to predict
$PM_{2.5}$ air pollution based on easily obtainable satellite imagery. We
demonstrate that our approach can reconstruct $PM_{2.5}$ concentrations on
ground-truth data and predict reasonable $PM_{2.5}$ values with their spatial
distribution, even for locations where pollution data is unavailable. Such
predictions of $PM_{2.5}$ characteristics could crucially advise public policy
strategies geared to reduce the transmission of and lethality of COVID-19.


Abstract:
  For flow-enhanced crystallization in fiber spinning, the viscoelastic
two-phase fiber models by Doufas et al. (J. Non-Newton. Fluid Mech., 2000) and
Shrikhande et al. (J. Appl. Polym. Sci., 2006) are state of the art. However,
the boundary conditions associated to the onset of crystallization are still
under discussion, as their choice might cause artificial boundary layers and
numerical difficulties. In this paper we show that the model class of ordinary
differential equations is singularly perturbed in a small parameter belonging
to the semi-crystalline relaxation time and derive asymptotically justified
boundary conditions. Their effect on the overall solution behavior is
restricted to a small region near the onset of crystallization. But their
impact on the performance of the numerical solvers is huge, since artificial
layering, ambiguities and parameter tunings are avoided. The numerics becomes
fast and robust and opens the field for simulation-based process design and
material optimization.


Abstract:
  Geosystems are geological formations altered by humans activities such as
fossil energy exploration, waste disposal, geologic carbon sequestration, and
renewable energy generation. Geosystems also represent a critical link in the
global water-energy nexus, providing both the source and buffering mechanisms
for enabling societal adaptation to climate variability and change. The
responsible use and exploration of geosystems are thus critical to the
geosystem governance, which in turn depends on the efficient monitoring, risk
assessment, and decision support tools for practical implementation. Fast
advances in machine learning (ML) algorithms and novel sensing technologies in
recent years have presented new opportunities for the subsurface research
community to improve the efficacy and transparency of geosystem governance.
Although recent studies have shown the great promise of scientific ML (SciML)
models, questions remain on how to best leverage ML in the management of
geosystems, which are typified by multiscality, high-dimensionality, and data
resolution inhomogeneity. This survey will provide a systematic review of the
recent development and applications of domain-aware SciML in geosystem
researches, with an emphasis on how the accuracy, interpretability,
scalability, defensibility, and generalization skill of ML approaches can be
improved to better serve the geoscientific community.


Abstract:
  A general approach for building a smart assistant that guides a user from a
forecast generated by a machine learning model through a sequence of
decision-making steps is presented. We develop a methodology to build such a
system. The system is demonstrated on a demand forecasting use case in
manufacturing. The methodology can be extended to several use cases in
manufacturing. The system provides means for knowledge acquisition, gathering
data from users. We envision active learning can be used to get data labels
where labeled data is scarce.


Abstract:
  The high computational cost of evaluating atomic interactions recently
motivated the development of computationally inexpensive kinetic models, which
can be parametrized from MD simulations of complex chemistry of thousands of
species or other processes and accelerate the prediction of the chemical
evolution by up to four order of magnitude. Such models go beyond the commonly
employed potential energy surface fitting methods in that they are aimed purely
at describing kinetic effects. So far, such kinetic models utilize molecular
descriptions of reactions and have been constrained to only reproduce molecules
previously observed in MD simulations. Therefore, these descriptions fail to
predict the reactivity of unobserved molecules, for example in the case of
large molecules or solids. Here we propose a new approach for the extraction of
reaction mechanisms and reaction rates from MD simulations, namely the use of
atomic-level features. Using the complex chemical network of hydrocarbon
pyrolysis as example, it is demonstrated that kinetic models built using atomic
features are able to explore chemical reaction pathways never observed in the
MD simulations used to parametrize them. Atomic-level features are shown to
construct reaction mechanisms and estimate reaction rates of unknown molecular
species from elementary atomic events. Through comparisons of the model ability
to extrapolate to longer simulation timescales and different chemical
compositions than the ones used for parameterization, it is demonstrated that
kinetic models employing atomic features retain the same level of accuracy and
transferability as the use of features based on molecular species, while being
more compact and parametrized with less data. We also find that atomic features
can better describe the formation of large molecules enabling the simultaneous
description of small molecules and condensed phases.


Abstract:
  In this paper, we study non sense-preserving harmonic mappings
$f=h+\overline{g}$ in $\mathbb{D}$ when its analytic part $h$ is convex and
injective in $\mathbb{D}$ and obtain radius of injectivety.


Abstract:
  Bulk superconductivity in a topological semimetal is a first step towards
realizing topological superconductors, which can host Majorana fermions
allowing us to achieve quantum computing. Here, we report superconductivity and
compensation of electrons and holes in single crystals of the nodal-line
semimetal CaSb$_2$. We characterize the superconducting state and find that
Cooper pairs have moderate-weak coupling, and the superconducting transition in
specific heat down to 0.22 K deviates from that of a BCS superconductor. The
non-saturating magnetoresistance and electron-hole compensation at low
temperature are consistent with density functional theory (DFT) calculations
showing nodal-line features. Furthermore, we observe de Haas-van Alphen (dHvA)
oscillations consistent with a small Fermi surface in the semimetallic state of
CaSb$_2$. Our DFT calculations show that the two electron bands crossing the
Fermi level are associated with Sb1 zig-zag chains, while the hole band is
associated with Sb2 zig-zag chains. The Sb1 zig-zag chains form a distorted
square net, which may relate the $M$Sb$_2$ family to the well known $M$SbTe
square net semimetals. Realization of superconductivity and a compensated
semimetal state in single crystals of CaSb$_2$ establishes the diantimonide
family as a candidate class of materials for achieving topological
superconductivity.


Abstract:
  The problem of finding an ancestral acyclic directed mixed graph (ADMG) that
represents the causal relationships between a set of variables is an important
area of research on causal inference. Most existing score-based structure
learning methods focus on learning directed acyclic graph (DAG) models without
latent variables. A number of score-based methods have recently been proposed
for the ADMG learning, yet they are heuristic in nature and do not guarantee an
optimal solution. We propose a novel exact score-based method that solves an
integer programming (IP) formulation and returns a score-maximizing ancestral
ADMG for a set of continuous variables that follow a multivariate Gaussian
distribution. We generalize the state-of-the-art IP model for DAG learning
problems and derive new classes of valid inequalities to formulate an IP model
for ADMG learning. Empirically, our model can be solved efficiently for
medium-sized problems and achieves better accuracy than state-of-the-art
score-based methods as well as benchmark constraint-based methods.


Abstract:
  Steerable needles are capable of accurately targeting difficult-to-reach
clinical sites in the body. By bending around sensitive anatomical structures,
steerable needles have the potential to reduce the invasiveness of many medical
procedures. However, inserting these needles with curved trajectories increases
the risk of tissue shearing due to large forces being exerted on the
surrounding tissue by the needle's shaft. Such shearing can cause significant
damage to surrounding tissue, potentially worsening patient outcomes. In this
work, we derive a tissue and needle force model based on a Cosserat string
formulation, which describes the normal forces and frictional forces along the
shaft as a function of the planned needle path, friction parameters, and tip
piercing force. We then incorporate this force model as a cost function in an
asymptotically near-optimal motion planner and demonstrate the ability to plan
motions that consider the tissue normal forces from the needle shaft during
planning in a simulated steering environment and a simulated lung tumor biopsy
scenario. By planning motions for the needle that aim to minimize the tissue
normal force explicitly, our method plans needle paths that reduce the risk of
tissue shearing while still reaching desired targets in the body.


Abstract:
  Contaminants such as dust, dirt and moisture adhering to the camera lens can
greatly affect the quality and clarity of the resulting image or video. In this
paper, we propose a video restoration method to automatically remove these
contaminants and produce a clean video. Our approach first seeks to detect
attention maps that indicate the regions that need to be restored. In order to
leverage the corresponding clean pixels from adjacent frames, we propose a flow
completion module to hallucinate the flow of the background scene to the
attention regions degraded by the contaminants. Guided by the attention maps
and completed flows, we propose a recurrent technique to restore the input
frame by fetching clean pixels from adjacent frames. Finally, a multi-frame
processing stage is used to further process the entire video sequence in order
to enforce temporal consistency. The entire network is trained on a synthetic
dataset that approximates the physical lighting properties of contaminant
artifacts. This new dataset and our novel framework lead to our method that is
able to address different contaminants and outperforms competitive restoration
approaches both qualitatively and quantitatively.


Abstract:
  Radio halos are diffuse synchrotron sources observed in dynamically unrelaxed
galaxy clusters. Current observations and models suggest that halos trace
turbulent regions in the intra-cluster medium where mildly relativistic
particles are re-accelerated during cluster mergers. Due to the higher
luminosities and detection rates with increasing cluster mass, radio halos have
been mainly observed in massive systems ($M_{500} \gtrsim 5 \times10^{14}$
M$_\odot$). Here, we report the discovery of a radio halo with a largest linear
scale of $\simeq$750 kpc in PSZ2G145.92-12.53 ($z=0.03$) using LOFAR
observations at 120$-$168 MHz. With a mass of $M_{500} = (1.9\pm0.2) \times
10^{14}$ M$_\odot$ and a radio power at 150 MHz of $P_{150} = (3.5 \pm 0.7)
\times 10^{23}$ W/Hz, this is the least powerful radio halo in the least
massive cluster discovered to date. Additionally, we discover a radio relic
with a mildly convex morphology at $\sim$1.7 Mpc from the cluster center. Our
results demonstrate that LOFAR has the potential to detect radio halos even in
low-mass clusters, where the expectation to form them is very low ($\sim$5%)
based on turbulent re-acceleration models. Together with the observation of
large samples of clusters, this opens the possibility to constrain the low end
of the power-mass relation of radio halos.


Abstract:
  The goal of this note is to generalize Isoperimetric Inequality for random
groups to the class of non-planar diagrams of bounded number of faces.


Abstract:
  We propose a $SU(5) \times U(1)_X \times U(1)_{PQ}$ model, where $U(1)_X$ is
the generalization of the $B-L$ (baryon minus lepton number) gauge symmetry and
$U(1)_{PQ}$ is the global Peccei-Quinn (PQ) symmetry. There are four fermions
families in $\bf{{\overline 5}} + \bf{10}$ representations of $SU(5)$, a mirror
family in $\bf{5}+\bf{{\overline {10}}}$ representations, and three $SU(5)$
singlet Majorana fermions. The $U(1)_X$ related anomalies all cancel in the
presence of the Majorana neutrinos. The $SU(5)$ symmetry is broken at $M_{GUT}
\simeq (6-9)\times 10^{15}$ GeV and the proton lifetime $\tau_p$ is estimated
to be well within the expected sensitivity of the future Hyper-Kamiokande
experiment, $\tau_p \lesssim 1.3 \times 10^{35}$ years. The $SU(5)$ breaking
also triggers the breaking of the PQ symmetry, resulting in axion dark matter
(DM), with the axion decay constant $f_a$ of order $M_{GUT}$ or somewhat
larger. The CASPEr experiment can search for such an axion DM candidate. The
Hubble parameter during inflation must be low, $H_{inf} \lesssim 10^9 $ GeV, in
order to successfully resolve the axion domain wall, axion DM isocurvature and
$SU(5)$ monopole problems. With the identification of the $U(1)_X$ breaking
Higgs field with the inflaton field, we implement inflection-point inflation,
which is capable of realizing the desired value for $H_{inf}$. The vectorlike
fermions in the model are essential for achieving successful unification of the
SM gauge couplings as well as the phenomenological viability of both axion DM
and inflation scenario.


Abstract:
  Coherent driving has established itself as a powerful tool for guiding a
many-body quantum system into a desirable, coherent non-equilibrium state. A
thermodynamically large system will, however, almost always saturate to a
featureless infinite temperature state under continuous driving and so the
optical manipulation of many-body systems is considered feasible only if a
transient, prethermal regime exists, where heating is suppressed. Here we show
that, counterintuitively, in a broad class of lattices Floquet heating can
actually be an advantageous effect. Specifically, we prove that the maximum
entropy steady states which form upon driving the ground state of the Hubbard
model on unbalanced bi-partite lattices possess uniform off-diagonal long-range
order which remains finite even in the thermodynamic limit. This creation of a
`hot' condensate can occur on \textit{any} driven unbalanced lattice and
provides an understanding of how heating can, at the macroscopic level, expose
and alter the order in a quantum system. We discuss implications for recent
experiments observing emergent superconductivity in photoexcited materials.


Abstract:
  The categorical models of the differential lambda-calculus are additive
categories because of the Leibniz rule which requires the summation of two
expressions. This means that, as far as the differential lambda-calculus and
differential linear logic are concerned, these models feature finite
nondeterminism and indeed these languages are essentially non-deterministic. We
introduce a categorical framework for differentiation which does not require
additivity and is compatible with deterministic models such as coherence spaces
and probabilistic models such as probabilistic coherence spaces. Based on this
semantics we sketch the syntax of a deterministic version of the differential
lambdacalculus.


Abstract:
  This paper is based on a three year project during which we studied
attackers' behavior, reading military planning literature, and thinking on how
would we do the same things they do, and what problems would we, as attackers,
face. This research is still ongoing, but while participating in applications
for other projects and talking to cyber security experts we constantly face the
same issues, namely attackers' behavior is not well understood, and
consequently, there are a number of misconceptions floating around that are
simply not true, or are only partially true. This is actually expected as
someone who casually follows news about incidents easily gets impression that
attackers and attacks are everywhere and every one is under attack. Our goal in
this paper is to debunk these myths, to show what attackers really can and can
not, what dilemmas they face, what we don't know about attackers and attacks,
etc. The conclusion is that, while attackers do have upper hand, they don't
have absolute advantage, i.e. they also operate in an uncertain environment.
Knowing this, means that defenses could be well established.


Abstract:
  During the development of NoSQL-backed software, the data model evolves
naturally alongside the application code. Especially in agile development, new
application releases are deployed frequently causing schema changes.
Eventually, decisions have to be made regarding the migration of versioned
legacy data which is persisted in the cloud-hosted production database. We
solve this schema evolution problem and present the results of near-exhaustive
calculations by means of which software project stakeholders can manage the
operative costs for data model evolution and adapt their software release
strategy accordingly in order to comply with service-level agreements regarding
the competing metrics of migration costs and latency. We clarify conclusively
how data model evolution in NoSQL databases impacts the metrics while taking
all relevant characteristics of migration scenarios into account. As
calculating all possible combinatorics in the search space of migration
scenarios would by far exceed computational means, we used a probabilistic
Monte Carlo method of repeated sampling, serving as a well-established means to
bring the complexity of data model evolution under control. Our experiments
show the qualitative and quantitative impact on the performance of migration
strategies with respect to intensity and distribution of data entity accesses,
the kinds of schema changes, and the characteristics of the underlying data
model.


Abstract:
  Model misspecification constitutes a major obstacle to reliable inference in
many inverse problems. Inverse problems in seismology, for example, are
particularly affected by misspecification of wave propagation velocities. In
this paper, we focus on a specific seismic inverse problem - full-waveform
moment tensor inversion - and develop a Bayesian framework that seeks
robustness to velocity misspecification. A novel element of our framework is
the use of transport-Lagrangian (TL) distances between observed and model
predicted waveforms to specify a loss function, and the use of this loss to
define a generalized belief update via a Gibbs posterior. The TL distance
naturally disregards certain features of the data that are more sensitive to
model misspecification, and therefore produces less biased or dispersed
posterior distributions in this setting. To make the latter notion precise, we
use several diagnostics to assess the quality of inference and uncertainty
quantification, i.e., continuous rank probability scores and rank histograms.
We interpret these diagnostics in the Bayesian setting and compare the results
to those obtained using more typical Gaussian noise models and squared-error
loss, under various scenarios of misspecification. Finally, we discuss
potential generalizability of the proposed framework to a broader class of
inverse problems affected by model misspecification.


Abstract:
  Double-pushout rewriting is an established categorical approach to the
rule-based transformation of graphs and graph-like objects. One of its standard
results is the construction of concurrent rules and the Concurrency Theorem
pertaining to it: The sequential application of two rules can equivalently be
replaced by the application of a concurrent rule and vice versa. We extend and
generalize this result by introducing generalized concurrent rules (GCRs).
Their distinguishing property is that they allow identifying and preserving
elements that are deleted by their first underlying rule and created by the
second one. We position this new kind of composition of rules among the
existing ones and obtain a Generalized Concurrency Theorem for it. We conduct
our work in the same generic framework in which the Concurrency Theorem has
been presented, namely double-pushout rewriting in M-adhesive categories via
rules equipped with application conditions.


Abstract:
  The modern field of quantum communication thrives on promise to deliver
efficient and unconditionally secure ways to exchange information by exploiting
quantum laws of physics. Here, quantum teleportation stands out as an exemplary
protocol allowing for the disembodied and safe transfer of unknown quantum
states using quantum entanglement and classical communication as resources. The
experimental feasibility of quantum teleportation with propagating waves,
relevant to communication scenarios, has been demonstrated in various physical
settings. However, an analogous implementation of quantum teleportation in the
microwave domain was missing so far. At the same time, recent breakthroughs in
quantum computation with superconducting circuits have triggered a demand for
quantum communication between spatially separated superconducting processors
operated at microwave frequencies. Here, we demonstrate a realization of
deterministic quantum teleportation of coherent microwave states by exploiting
two-mode squeezing and analog feedforward over macroscopic distances $d =
42\,$cm. We achieve teleportation fidelities $F = 0.689 \pm 0.004$ exceeding
the no-cloning $F_\mathrm{nc} = 2/3$ threshold for coherent states with an
average photon number of up to $n_\mathrm{d} = 1.1$. Our results provide a key
ingredient for the teleportation-based quantum gate for modular quantum
computing with superconducting circuits and establish a solid foundation for
future microwave quantum local area networks.


Abstract:
  Experimental demonstration of complex robotic behaviors relies heavily on
finding the correct controller gains. This painstaking process is often
completed by a domain expert, requiring deep knowledge of the relationship
between parameter values and the resulting behavior of the system. Even when
such knowledge is possessed, it can take significant effort to navigate the
nonintuitive landscape of possible parameter combinations. In this work, we
explore the extent to which preference-based learning can be used to optimize
controller gains online by repeatedly querying the user for their preferences.
This general methodology is applied to two variants of control Lyapunov
function based nonlinear controllers framed as quadratic programs, which have
nice theoretic properties but are challenging to realize in practice. These
controllers are successfully demonstrated both on the planar underactuated
biped, AMBER, and on the 3D underactuated biped, Cassie. We experimentally
evaluate the performance of the learned controllers and show that the proposed
method is repeatably able to learn gains that yield stable and robust
locomotion.


Abstract:
  In this paper we study the degeneration behavior of the norm of the Riemann
$\theta$-function in a family of principally polarized abelian varieties over
the punctured complex unit disc in terms of the associated polarized real
torus. As an application, we obtain the degeneration behavior of the
Zhang--Kawazumi invariant $\varphi(M_t)$ of a family of Riemann surfaces $M_t$
in terms of Zhang's invariant $\varphi(\Gamma)$ of the associated metrized
reduction graph $\Gamma$. This allows us to deduce a uniform lower bound for
the essential minimum of the N\'eron-Tate height on the tautological cycles of
any Jacobian variety over a number field.


Abstract:
  We introduce optimal energy shaping as an enhancement of classical
passivity-based control methods. A promising feature of passivity theory,
alongside stability, has traditionally been claimed to be intuitive performance
tuning along the execution of a given task. However, a systematic approach to
adjust performance within a passive control framework has yet to be developed,
as each method relies on few and problem-specific practical insights. Here, we
cast the classic energy-shaping control design process in an optimal control
framework; once a task-dependent performance metric is defined, an optimal
solution is systematically obtained through an iterative procedure relying on
neural networks and gradient-based optimization. The proposed method is
validated on state-regulation tasks.


Abstract:
  When a molecule dissociates, the exact Kohn-Sham (KS) and Pauli potentials
may form step structures. Reproducing these steps correctly is central for the
description of dissociation and charge-transfer processes in density functional
theory (DFT): The steps align the KS eigenvalues of the dissociating subsystems
relative to each other and determine where electrons localize. While the step
height can be calculated from the asymptotic behavior of the KS orbitals, this
provides limited insight into what causes the steps. We give an explanation of
the steps with an exact mapping of the many-electron problem to a one-electron
problem, the exact electron factorizaton (EEF). The potentials appearing in the
EEF have a clear physical meaning that translates to the DFT potentials by
replacing the interacting many-electron system with the KS system. With a
simple model of a diatomic, we illustrate that the steps are a consequence of
spatial electron entanglement and are the result of a charge transfer. From
this mechanism, the step height can immediately be deduced. Moreover, two
methods to approximately reproduce the potentials during dissociation suggest
themselves. One is based on the states of the dissociated system, while the
other one is based on an analogy to the Born-Oppenheimer treatment of a
molecule. The latter method also shows that the steps connect adiabatic
potential energy surfaces. The view of DFT from the EEF thus provides a better
understanding of how many-electron effects are encoded in a one-electron theory
and how they can be modeled.


Abstract:
  The ability to plan into the future while utilizing only raw high-dimensional
observations, such as images, can provide autonomous agents with broad
capabilities. Visual model-based reinforcement learning (RL) methods that plan
future actions directly have shown impressive results on tasks that require
only short-horizon reasoning, however, these methods struggle on temporally
extended tasks. We argue that it is easier to solve long-horizon tasks by
planning sequences of states rather than just actions, as the effects of
actions greatly compound over time and are harder to optimize. To achieve this,
we draw on the idea of collocation, which has shown good results on
long-horizon tasks in optimal control literature, and adapt it to the
image-based setting by utilizing learned latent state space models. The
resulting latent collocation method (LatCo) optimizes trajectories of latent
states, which improves over previously proposed shooting methods for visual
model-based RL on tasks with sparse rewards and long-term goals. Videos and
code at https://orybkin.github.io/latco/.


Abstract:
  Arxiv is acting weird and throwing error: "Bad character(s) in field
Abstract." for no reason. Please refer to the manuscript.


Abstract:
  Controllable synthesis of defect-free graphene is crucial for applications
since the properties of graphene are highly sensitive to any deviations from
the crystalline lattice. We focus here on the emerging use of liquid Cu
catalysts, which has high potential for fast and efficient industrial-scale
production of high-quality graphene. The interface between graphene and liquid
Cu is studied using force field and ab initio molecular dynamics, revealing a
complete or partial embedding of finite-sized flakes. By analyzing flakes of
different sizes we find that the size-dependence of the embedding can be
rationalized based on the energy cost of embedding versus bending the graphene
flake. The embedding itself is driven by the formation of covalent bonds
between the under-coordinated edge C atoms and the liquid Cu surface, which is
accompanied by a significant charge transfer. In contrast, the central flake
atoms are located around or slightly above 3 {\AA} from the liquid Cu surface
and exhibit weak vdW-bonding and much lower charge transfer. The structural and
electronic properties of the embedded state revealed in our work provides the
atomic-scale information needed to develop effective models to explain the
special growth observed in experiments where various interesting phenomena such
as flake self-assembly and rotational alignment, high growth speeds and low
defect densities in the final graphene product have been observed.


Abstract:
  Sparse regression algorithms have been proposed as the appropriate framework
to model the governing equations of a system from data, without needing prior
knowledge of the underlying physics. In this work, we use sparse regression to
build an accurate and explainable model of the stellar mass of central galaxies
given properties of their host dark matter (DM) halo. Our data set comprises
9,521 central galaxies from the EAGLE hydrodynamic simulation. By matching the
host halos to a DM-only simulation, we collect the halo mass and specific
angular momentum at present time and for their main progenitors in 10 redshift
bins from $z=0$ to $z=4$. The principal component of our governing equation is
a third-order polynomial of the host halo mass, which models the stellar-mass
halo-mass relation. The scatter about this relation is driven by the halo mass
evolution and is captured by second and third-order correlations of the halo
mass evolution with the present halo mass. An advantage of sparse regression
approaches is that unnecessary terms are removed. Although we include
information on halo specific angular momentum, these parameters are discarded
by our methodology. This suggests that halo angular momentum has little
connection to galaxy formation efficiency. Our model has a root mean square
error (RMSE) of $0.167 \log_{10}(M^*/M_\odot)$, and accurately reproduces both
the stellar mass function and central galaxy correlation function of EAGLE. The
methodology appears to be an encouraging approach for populating the halos of
DM-only simulations with galaxies, and we discuss the next steps that are
required.


Abstract:
  Imaging individual vacancies in solids and revealing their interactions with
solute atoms remains one of the frontiers in microscopy and microanalysis. Here
we study a creep-deformed binary Ni-2 at.% Ta alloy. Atom probe tomography
reveals a random distribution of Ta. Field ion microscopy, with contrast
interpretation supported by density-functional theory and time-of-flight mass
spectrometry, evidences a positive correlation of tantalum with vacancies. Our
results support solute-vacancy binding, which explains improvement in creep
resistance of Ta-containing Ni-based superalloys and helps guide future
material design strategies.


Abstract:
  The vulnerability of deep neural networks to adversarial examples, which are
crafted maliciously by modifying the inputs with imperceptible perturbations to
misled the network produce incorrect outputs, reveals the lack of robustness
and poses security concerns. Previous works study the adversarial robustness of
image classifiers on image level and use all the pixel information in an image
indiscriminately, lacking of exploration of regions with different semantic
meanings in the pixel space of an image. In this work, we fill this gap and
explore the pixel space of the adversarial image by proposing an algorithm to
looking for possible perturbations pixel by pixel in different regions of the
segmented image. The extensive experimental results on CIFAR-10 and ImageNet
verify that searching for the modified pixel in only some pixels of an image
can successfully launch the one-pixel adversarial attacks without requiring all
the pixels of the entire image, and there exist multiple vulnerable points
scattered in different regions of an image. We also demonstrate that the
adversarial robustness of different regions on the image varies with the amount
of semantic information contained.


Abstract:
  In author name disambiguation, author forenames are used to decide which name
instances are disambiguated together and how much they are likely to refer to
the same author. Despite such a crucial role of forenames, their effect on the
performances of heuristic (string matching) and algorithmic disambiguation is
not well understood. This study assesses the contributions of forenames in
author name disambiguation using multiple labeled datasets under varying ratios
and lengths of full forenames, reflecting real-world scenarios in which an
author is represented by forename variants (synonym) and some authors share the
same forenames (homonym). Results show that increasing the ratios of full
forenames improves substantially the performances of both heuristic and
machine-learning-based disambiguation. Performance gains by algorithmic
disambiguation are pronounced when many forenames are initialized or homonym is
prevalent. As the ratios of full forenames increase, however, they become
marginal compared to the performances by string matching. Using a small portion
of forename strings does not reduce much the performances of both heuristic and
algorithmic disambiguation compared to using full-length strings. These
findings provide practical suggestions such as restoring initialized forenames
into a full-string format via record linkage for improved disambiguation
performances.


Abstract:
  Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for
regional myocardium deformation and cardiac strain estimation. However, this
technique has not been widely used in clinical diagnosis, as a result of the
difficulty of motion tracking encountered with t-MRI images. In this paper, we
propose a novel deep learning-based fully unsupervised method for in vivo
motion tracking on t-MRI images. We first estimate the motion field (INF)
between any two consecutive t-MRI frames by a bi-directional generative
diffeomorphic registration neural network. Using this result, we then estimate
the Lagrangian motion field between the reference frame and any other frame
through a differentiable composition layer. By utilizing temporal information
to perform reasonable estimations on spatio-temporal motion fields, this novel
method provides a useful solution for motion tracking and image registration in
dynamic medical imaging. Our method has been validated on a representative
clinical t-MRI dataset; the experimental results show that our method is
superior to conventional motion tracking methods in terms of landmark tracking
accuracy and inference efficiency.


Abstract:
  Ices are an important constituent of protoplanetary disks. New observational
facilities, notably the James Webb Space Telescope (JWST), will greatly enhance
our view of disk ices by measuring their infrared spectral features. We present
a suite of models to complement these upcoming observations. Our models use a
kinetics-based gas-grain chemical evolution code to simulate the distribution
of ices in a disk, followed by a radiative transfer code using a subset of key
ice species to simulate the observations. We present models reflecting both
molecular inheritance and chemical reset initial conditions. We find that
near-to-mid-IR absorption features of H$_2$O, CO$_2$, and CH$_3$OH are readily
observable in disk-integrated spectra of highly inclined disks while CO,
NH$_3$, and CH$_4$ ice do not show prominent features. CH$_3$OH ice has low
abundance and is not observable in the reset model, making this species an
excellent diagnostic of initial chemical conditions. CO$_2$ ice features
exhibit the greatest change over disk lifetime, decreasing and increasing for
the inheritance and reset models, respectively. Spatially resolved spectra of
edge-on disks, possible with JWST's integral field unit observing modes, are
ideal for constraining the vertical distribution of ices and may be able to
isolate features from ices closer to the midplane (e.g., CO) given sufficient
sensitivity. Spatially-resolved spectra of face-on disks can trace
scattered-light features from H$_2$O, CO$_2$, and CH$_3$OH, plus CO and CH$_4$
from the outermost regions. We additionally simulate far-IR H$_2$O ice emission
features and find they are strongest for disks viewed face-on.


Abstract:
  We study the multi-agent safe control problem where agents should avoid
collisions to static obstacles and collisions with each other while reaching
their goals. Our core idea is to learn the multi-agent control policy jointly
with learning the control barrier functions as safety certificates. We propose
a novel joint-learning framework that can be implemented in a decentralized
fashion, with generalization guarantees for certain function classes. Such a
decentralized framework can adapt to an arbitrarily large number of agents.
Building upon this framework, we further improve the scalability by
incorporating neural network architectures that are invariant to the quantity
and permutation of neighboring agents. In addition, we propose a new
spontaneous policy refinement method to further enforce the certificate
condition during testing. We provide extensive experiments to demonstrate that
our method significantly outperforms other leading multi-agent control
approaches in terms of maintaining safety and completing original tasks. Our
approach also shows exceptional generalization capability in that the control
policy can be trained with 8 agents in one scenario, while being used on other
scenarios with up to 1024 agents in complex multi-agent environments and
dynamics.


Abstract:
  We study, in the framework of the entanglement harvesting protocol, the
entanglement harvesting of both a pair of inertial and uniformly accelerated
detectors locally interacting with vacuum massless scalar fields subjected to a
perfectly reflecting plane boundary. We find that the presence of the boundary
generally degrades the harvested entanglement when two detectors are very close
to the boundary. However, when the distance between detectors and the boundary
becomes comparable to the interaction duration parameter, the amount of the
harvested entanglement approaches a peak, which even goes beyond that without a
boundary. Remarkably, the parameter space of the detectors' separation and the
magnitude of acceleration that allows
  entanglement harvesting to occur is enlarged due to the presence of the
boundary. In this sense, the boundary plays a double-edged role on entanglement
harvesting,
  degrading in general the harvested entanglement while enlarging the
entanglement harvesting-achievable parameter space.
  A comparison of three different acceleration scenarios of the detectors with
respect to the boundary, i.e., parallel, anti-parallel and mutually
perpendicular acceleration, shows that the phenomenon of entanglement
harvesting crucially depends on the acceleration, the separation between two
detectors and the detectors' distance from the boundary.


Abstract:
  We propose a change-point detection method for large scale multiple testing
problems with data having clustered signals. Unlike the classic change-point
setup, the signals can vary in size within a cluster. The clustering structure
on the signals enables us to effectively delineate the boundaries between
signal and non-signal segments. New test statistics are proposed for
observations from one and/or multiple realizations. Their asymptotic
distributions are derived. We also study the associated variance estimation
problem. We allow the variances to be heteroscedastic in the multiple
realization case, which substantially expands the applicability of the proposed
method. Simulation studies demonstrate that the proposed approach has a
favorable performance. Our procedure is applied to {an array based Comparative
Genomic Hybridization (aCGH)} dataset.


Abstract:
  This letter explores covariance matching-based adaptive robust cubature
Kalman filter (CMRACKF). In this method, the innovation sequence is used to
determine the covariance matrix of measurement noise that can overcome the
limitation of conventional CKF. In the proposed algorithm, weights are
adaptively adjusted and used for updating the measurement noise covariance
matrices online. It can also enhance the adaptive capability of the ACKF. The
simulation results are illustrated to evaluate the performance of the proposed
algorithm.


Abstract:
  Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of
transition between seabed types. Due to these regions, it is difficult to label
and segment the imagery and, furthermore, challenging to score the image
segmentations appropriately. While there are many approaches to quantify
performance in standard crisp segmentation schemes, drawing hard boundaries in
remote sensing imagery where gradients and regions of uncertainty exist is
inappropriate. These cases warrant weak labels and an associated appropriate
scoring approach. In this paper, a labeling approach and associated modified
version of the Rand index for weakly-labeled data is introduced to address
these issues. Results are evaluated with the new index and compared to
traditional segmentation evaluation methods. Experimental results on a SAS data
set containing must-link and cannot-link labels show that our Weakly-Labeled
Rand index scores segmentations appropriately in reference to qualitative
performance and is more suitable than traditional quantitative metrics for
scoring weakly-labeled data.


Abstract:
  A recent article (Khair and Kabarowski; Phys. Rev. Fluids 5, 033702) has
studied the cross-streamline migration of electrophoretic particles in
unbounded shear flows with weak inertia or viscoelasticity. That work compares
their results with those reported in two of our previous studies (Choudhary et
al.: J. Fluid Mech. 874; J. Fluid Mech. 898) and reports a disagreement in the
derived analytical expressions. In this comment, we resolve this discrepancy.
For viscoelastic flows, we show that Khair and Kabarowski have not accounted
for a leading order surface integral of polymeric stress in their calculation
of first-order viscoelastic lift. When this integral is included, the resulting
migration velocity matches exactly with that reported in our work (J. Fluid
Mech. 898). This qualitatively changes the migration direction that is reported
by Khair and Kabarowski for viscoelastic flows. For inertial flows, we clarify
that Khair and Kabarowski find the coefficient of lift to be 1.75$\pi$,
compared to 2.35$ \pi $ in our previous work (J. Fluid Mech. 874). We show that
this difference occurs because Khair and Kabarowski accurately include the
effect of rapidly decaying $ \sim O(1/r^{4}) $ velocity field (a correction to
the stresslet field $ \sim 1/r^{2} $), which was neglected in our previous work
(J. Fluid Mech. 874).


Abstract:
  Let $\Lambda$ be a row-finite and source-free higher rank graph with finitely
many vertices. In this paper, we define the Higman-Thompson like group $\Lht$
of the graph C*-algebra $\mathcal{O}_\Lambda$ to be a special subgroup of the
unitary group in $\O_\Lambda$. It is shown that $\Lht$ is closely related to
the topological full groups of the groupoid associated with $\Lambda$. Some
properties of $\Lht$ are also investigated. We show that its commutator group
$\DLht$ is simple and that $\DLht$ has only one nontrivial uniformly recurrent
subgroup if $\Lambda$ is aperiodic and strongly connected. Furthermore, if
$\Lambda$ is single-vertex, then we prove that $\Lht$ is C*-simple and also
provide an explicit description on the stabilizer uniformly recurrent subgroup
of $\Lht$ under a natural action on the infinite path space of $\Lambda$.


Abstract:
  In this note, we present some ideas for describing the distributions of the
running maximum/minimum, first passage times and telegraphic meanders. Explicit
formulae for joint distribution of the extrema, the number of velocity switches
and the terminal position are derived using coupled integral equations
technique.


Abstract:
  Computational models of managerial search often build on backward-looking
search based on hill-climbing algorithms. Regardless of its prevalence, there
is some evidence that this family of algorithms does not universally represent
managers' search behavior. Against this background, the paper proposes an
alternative algorithm that captures key elements of Simon's concept of
satisficing which received considerable support in behavioral experiments. The
paper contrasts the satisficing-based algorithm to two variants of
hill-climbing search in an agent-based model of a simple decision-making
organization. The model builds on the framework of NK fitness landscapes which
allows controlling for the complexity of the decision problem to be solved. The
results suggest that the model's behavior may remarkably differ depending on
whether satisficing or hill-climbing serves as an algorithmic representation
for decision-makers' search. Moreover, with the satisficing algorithm, results
indicate oscillating aspiration levels, even to the negative, and intense - and
potentially destabilizing - search activities when intra-organizational
complexity increases. Findings may shed some new light on prior computational
models of decision-making in organizations and point to avenues for future
research.


Abstract:
  In this paper, a lifelong learning problem is studied for an Internet of
Things (IoT) system. In the considered model, each IoT device aims to balance
its information freshness and energy consumption tradeoff by controlling its
computational resource allocation at each time slot under dynamic environments.
An unmanned aerial vehicle (UAV) is deployed as a flying base station so as to
enable the IoT devices to adapt to novel environments. To this end, a new
lifelong reinforcement learning algorithm, used by the UAV, is proposed in
order to adapt the operation of the devices at each visit by the UAV. By using
the experience from previously visited devices and environments, the UAV can
help devices adapt faster to future states of their environment. To do so, a
knowledge base shared by all devices is maintained at the UAV. Simulation
results show that the proposed algorithm can converge $25\%$ to $50\%$ faster
than a policy gradient baseline algorithm that optimizes each device's decision
making problem in isolation.


Abstract:
  Large-scale atomistic computer simulations of materials rely on interatomic
potentials providing computationally efficient predictions of energy and
Newtonian forces. Traditional potentials have served in this capacity for over
three decades. Recently, a new class of potentials has emerged, which is based
on a radically different philosophy. The new potentials are constructed using
machine-learning (ML) methods and a massive reference database generated by
quantum-mechanical calculations. While the traditional potentials are derived
from physical insights into the nature of chemical bonding, the ML potentials
utilize a high-dimensional mathematical regression to interpolate between the
reference energies. We review the current status of the interatomic potential
field, comparing the strengths and weaknesses of the traditional and ML
potentials. A third class of potentials is introduced, in which an ML model is
coupled with a physics-based potential to improve the transferability to
unknown atomic environments. The discussion is focused on potentials intended
for materials science applications. Possible future directions in this field
are outlined.


Abstract:
  The contribution of this paper includes two aspects. First, we study the
lower bound complexity for the minimax optimization problem whose objective
function is the average of $n$ individual smooth component functions. We
consider Proximal Incremental First-order (PIFO) algorithms which have access
to gradient and proximal oracle for each individual component. We develop a
novel approach for constructing adversarial problems, which partitions the
tridiagonal matrix of classical examples into $n$ groups. This construction is
friendly to the analysis of incremental gradient and proximal oracle. With this
approach, we demonstrate the lower bounds of first-order algorithms for finding
an $\varepsilon$-suboptimal point and an $\varepsilon$-stationary point in
different settings. Second, we also derive the lower bounds of minimization
optimization with PIFO algorithms from our approach, which can cover the
results in \citep{woodworth2016tight} and improve the results in
\citep{zhou2019lower}.


Abstract:
  We consider Steklov eigenvalues of three-dimensional, nearly-spherical
domains. In previous work, we have shown that the Steklov eigenvalues are
analytic functions of the domain perturbation parameter. Here, we compute the
first-order term of the asymptotic expansion, which can explicitly be written
in terms of the Wigner 3-jsymbols. We analyze the asymptotic expansion and
prove the isoperimetric result that, if l is a square integer, the
volume-normalized l-th Steklov eigenvalue is stationary for a ball.


Abstract:
  We consider Schr\"odinger operators with periodic potentials on periodic
discrete graphs. Their spectrum consists of a finite number of bands. We obtain
two-sided estimates of the total bandwidth for the Schr\"odinger operators in
terms of geometric parameters of the graph and the potentials. In particular,
we show that these estimates are sharp. It means that these estimates become
identities for specific graphs and potentials. The proof is based on the
Floquet theory and trace formulas for fiber operators. The traces are expressed
as finite Fourier series of the quasimomentum with coefficients depending on
the potentials and cycles of the quotient graph from some specific cycle sets.
In order to obtain our results we estimate these Fourier coefficients in terms
of geometric parameters of the graph and the potentials.


Abstract:
  Learning from imprecise labels such as "animal" or "bird", but making precise
predictions like "snow bunting" at test time is an important capability when
expertly labeled training data is scarce. Contributions by volunteers or
results of web crawling lack precision in this manner, but are still valuable.
And crucially, these weakly labeled examples are available in larger quantities
for lower cost than high-quality bespoke training data.
  CHILLAX, a recently proposed method to tackle this task, leverages a
hierarchical classifier to learn from imprecise labels. However, it has two
major limitations. First, it is not capable of learning from effectively
unlabeled examples at the root of the hierarchy, e.g. "object". Second, an
extrapolation of annotations to precise labels is only performed at test time,
where confident extrapolations could be already used as training data.
  In this work, we extend CHILLAX with a self-supervised scheme using
constrained extrapolation to generate pseudo-labels. This addresses the second
concern, which in turn solves the first problem, enabling an even weaker
supervision requirement than CHILLAX. We evaluate our approach empirically and
show that our method allows for a consistent accuracy improvement of 0.84 to
1.19 percent points over CHILLAX and is suitable as a drop-in replacement
without any negative consequences such as longer training times.


Abstract:
  Integrability of the XXZ model induces an extensive number of conserved
quantities. In this paper we give a closed form expression for the series of
local conserved charges of the XXZ model on a closed chain with or without a
twist. We prove that each element of the series commutes with the Hamiltonian.


Abstract:
  The success of deep reinforcement learning (DRL) is due to the power of
learning a representation that is suitable for the underlying exploration and
exploitation task. However, existing provable reinforcement learning algorithms
with linear function approximation often assume the feature representation is
known and fixed. In order to understand how representation learning can improve
the efficiency of RL, we study representation learning for a class of low-rank
Markov Decision Processes (MDPs) where the transition kernel can be represented
in a bilinear form. We propose a provably efficient algorithm called ReLEX that
can simultaneously learn the representation and perform exploration. We show
that ReLEX always performs no worse than a state-of-the-art algorithm without
representation learning, and will be strictly better in terms of sample
efficiency if the function class of representations enjoys a certain mild
"coverage'' property over the whole state-action space.


Abstract:
  Binary polynomial optimization is equivalent to the problem of minimizing a
linear function over the intersection of the multilinear set with a polyhedron.
Many families of valid inequalities for the multilinear set are available in
the literature, though giving a polyhedral characterization of the convex hull
is not tractable in general as binary polynomial optimization is NP-hard. In
this paper, we study the cardinality constrained multilinear set in the special
case when the number of monomials is exactly two. We give an extended
formulation, with two more auxiliary variables and exponentially many
inequalities, of the convex hull of solutions of the standard linearization of
this problem. We also show that the separation problem can be solved
efficiently.


Abstract:
  We study upward planar straight-line drawings that use only a constant number
of slopes. In particular, we are interested in whether a given directed graph
with maximum in- and outdegree at most $k$ admits such a drawing with $k$
slopes. We show that this is in general NP-hard to decide for outerplanar
graphs ($k = 3$) and planar graphs ($k \ge 3$). On the positive side, for
cactus graphs deciding and constructing a drawing can be done in polynomial
time. Furthermore, we can determine the minimum number of slopes required for a
given tree in linear time and compute the corresponding drawing efficiently.


Abstract:
  This paper studies classical weight modules over the $\imath$quantum group
$\mathbf{U}^{\imath}$ of type AI. We introduce the notion of based
$\mathbf{U}^{\imath}$-modules by generalizing the notion of based modules over
the quantum groups. We prove that each finite-dimensional irreducible classical
weight $\mathbf{U}^{\imath}$-module with integer highest weight is a based
$\mathbf{U}^{\imath}$-module. As a byproduct, a new combinatorial formula for
the branching rule from $\mathfrak{sl}_n$ to $\mathfrak{so}_n$ is obtained.


Abstract:
  The Simons Observatory (SO) will be a cosmic microwave background (CMB)
survey experiment with three small-aperture telescopes and one large-aperture
telescope, which will observe from the Atacama Desert in Chile. In total, SO
will field over 60,000 transition-edge sensor (TES) bolometers in six spectral
bands centered between 27 and 280 GHz in order to achieve the sensitivity
necessary to measure or constrain numerous cosmological quantities, as outlined
in The Simons Observatory Collaboration et al. (2019). These telescopes require
33 highly transparent, large aperture, refracting optics. To this end, we
developed mechanically robust, highly efficient, metamaterial anti-reflection
(AR) coatings with octave bandwidth coverage for silicon optics up to 46 cm in
diameter for the 22-55, 75-165, and 190-310 GHz bands. We detail the design,
the manufacturing approach to fabricate the SO lenses, their performance, and
possible extensions of metamaterial AR coatings to optical elements made of
harder materials such as alumina.


Abstract:
  Starlight subtraction algorithms based on the method of Karhunen-Lo\`eve
eigenimages have proved invaluable to exoplanet direct imaging. However, they
scale poorly in runtime when paired with differential imaging techniques. In
such observations, reference frames and frames to be starlight-subtracted are
drawn from the same set of data, requiring a new subset of references (and
eigenimages) for each frame processed to avoid self-subtraction of the signal
of interest. The data rates of extreme adaptive optics instruments are such
that the only way to make this computationally feasible has been to downsample
the data. We develop a technique that updates a pre-computed singular value
decomposition of the full dataset to remove frames (i.e. a "downdate") without
a full recomputation, yielding the modified eigenimages. This not only enables
analysis of much larger data volumes in the same amount of time, but also
exhibits near-linear scaling in runtime as the number of observations
increases. We apply this technique to archival data and investigate its scaling
behavior for very large numbers of frames $N$. The resulting algorithm provides
speed improvements of $2.6\times$ (for 200 eigenimages at $N = 300$) to $140
\times$ (at $N = 10^4$) with the advantage only increasing as $N$ grows. This
algorithm has allowed us to substantially accelerate KLIP even for modest $N$,
and will let us quickly explore how KLIP parameters affect exoplanet
characterization in large $N$ datasets.


Abstract:
  We suggest a possible origin of noncollinear magnetic textures in
ferromagnets (FMs) with the $D_{3h}$ point group symmetry. Suggested mechanism
is different from the Dzyaloshinskii-Moriya interaction (DMI). Considered
symmetry class is important because a large fraction of all single-layer
intrinsic FMs should belong to it. In particular, so does a monolayer
Fe$_3$GeTe$_2$. At the same time, DMI vanishes identically in materials
described by this point group, in the continuous limit. We use symmetry
analysis to derive all possible contributions to the free energy density that
are of the fourth order with respect to the unit vector $\boldsymbol n$ of the
local magnetization direction and linear with respect to spatial derivatives of
$\boldsymbol n$. There are precisely seven such contributions. However, up to
boundary terms, only two of them can be considered as independent. Moreover,
for a two-dimensional system, one of these two necessarily vanishes! We
investigate whether the remaining contribution can serve as a source of spin
spirals and skyrmions recently observed in Fe$_3$GeTe$_2$. We also address a
possible stabilization of bimerons by the same contribution.


Abstract:
  We study certain toric Gorenstein varieties with isolated singularities which
are the quotient spaces of generic unimodular representation spaces by the
one-dimensional torus, or the product of the one-dimensional torus with a
finite abelian group. Based on the works of \v{S}penko and Van den Bergh
[Invent. Math. 210 (2017), no. 1, 3-67] and Mori and Ueyama [Adv. Math. 297
(2016), 54-92], we show that the singularity categories of these varieties
admit tilting objects, and hence are triangle equivalent to the derived
categories of some finite dimensional algebras.


Abstract:
  Uncertainty and confidence have been shown to be useful metrics in a wide
variety of techniques proposed for deep learning testing, including test data
selection and system supervision.We present uncertainty-wizard, a tool that
allows to quantify such uncertainty and confidence in artificial neural
networks. It is built on top of the industry-leading tf.keras deep learning API
and it provides a near-transparent and easy to understand interface. At the
same time, it includes major performance optimizations that we benchmarked on
two different machines and different configurations.


Abstract:
  Quantized neural networks (NN) are the common standard to efficiently deploy
deep learning models on tiny hardware platforms. However, we notice that
quantized NNs are as vulnerable to adversarial attacks as the full-precision
models. With the proliferation of neural networks on small devices that we
carry or surround us, there is a need for efficient models without sacrificing
trust in the prediction in presence of malign perturbations. Current mitigation
approaches often need adversarial training or are bypassed when the strength of
adversarial examples is increased.
  In this work, we investigate how a probabilistic framework would assist in
overcoming the aforementioned limitations for quantized deep learning models.
We explore Stochastic-Shield: a flexible defense mechanism that leverages input
filtering and a probabilistic deep learning approach materialized via Monte
Carlo Dropout. We show that it is possible to jointly achieve efficiency and
robustness by accurately enabling each module without the burden of
re-retraining or ad hoc fine-tuning.


Abstract:
  Modern vehicles can be thought of as complex distributed embedded systems
that run a variety of automotive applications with real-time constraints.
Recent advances in the automotive industry towards greater autonomy are driving
vehicles to be increasingly connected with various external systems (e.g.,
roadside beacons, other vehicles), which makes emerging vehicles highly
vulnerable to cyber-attacks. Additionally, the increased complexity of
automotive applications and the in-vehicle networks results in poor attack
visibility, which makes detecting such attacks particularly challenging in
automotive systems. In this work, we present a novel anomaly detection
framework called LATTE to detect cyber-attacks in Controller Area Network (CAN)
based networks within automotive platforms. Our proposed LATTE framework uses a
stacked Long Short Term Memory (LSTM) predictor network with novel attention
mechanisms to learn the normal operating behavior at design time. Subsequently,
a novel detection scheme (also trained at design time) is used to detect
various cyber-attacks (as anomalies) at runtime. We evaluate our proposed LATTE
framework under different automotive attack scenarios and present a detailed
comparison with the best-known prior works in this area, to demonstrate the
potential of our approach.


Abstract:
  In this paper, we prove precise late-time asymptotics for solutions to the
wave equation supported on angular frequencies greater or equal to $\ell$ on
the domain of outer communications of subextremal Reissner-Nordstr\"om
spacetimes up to and including the event horizon. Our asymptotics yield, in
particular, sharp upper and lower decay rates which are consistent with Price's
law on such backgrounds. We present a theory for inverting the time operator
and derive an explicit representation of the leading-order asymptotic
coefficient in terms of the Newman-Penrose charges at null infinity associated
with the time integrals. Our method is based on purely physical space
techniques. For each angular frequency $\ell$ we establish a sharp hierarchy of
$r$-weighted radially commuted estimates with length $2\ell+5$. We complement
this hierarchy with a novel hierarchy of weighted elliptic estimates of length
$\ell+1$.


Abstract:
  Various attempts have been made in the literature at describing the origin
and the physical mechanisms behind flaring events in blazars with radiative
emission models, but detailed properties of multi-wavelength (MWL) light curves
still remain difficult to reproduce. We have developed a versatile radiative
code, based on a time-dependent treatment of particle acceleration, escape and
radiative cooling, allowing us to test different scenarios to connect the
continuous low-state emission self-consistently with that during flaring
states. We consider flares as weak perturbations of the quiescent state and
apply this description to the February 2010 MWL flare of Mrk 421, the brightest
Very High Energy (VHE) flare ever detected from this archetypal blazar,
focusing on interpretations with a minimum number of free parameters. A general
criterion is obtained, which disfavours a one-zone model connecting low and
high state under our assumptions. A two-zone model combining physically
connected acceleration and emission regions yields a satisfactory
interpretation of the available time-dependent MWL light curves and spectra of
Mrk 421, although certain details remain difficult to reproduce. The two-zone
scenario finally proposed for the complex quiescent and flaring VHE emitting
region involves both Fermi-I and Fermi-II acceleration mechanisms, respectively
at the origin of the quiescent and flaring emission.


Abstract:
  We present self-energy corrected tight-binging(TB) parameters in the basis of
the directed hybridised atomic orbitals constructed from first principles, for
nano-diamonds as well as bulk diamond and zinc blende structures made of
elements of group 13, 14 and 15 in the 2p, 3p and 4p blocks. With increasing
principal quantum number of frontier orbitals, the lowering of self-energy
corrections(SEC) to the band-gap and consequently to the dominant inter-atomic
TB parameters, is much faster in bulk than in nano-diamonds and hence not
transferable from bulks to nano-structures. However, TB parameters transfered
from smaller nano-diamonds to much larger ones exclusively through mapping of
neighbourhoods of atoms not limited to nearest neighbours, are found to render
HOMO-LUMO gaps of the larger nano-diamonds with few hundreds of atoms in good
agreement with their explicitly computed values at the DFT as well as DFT+G0W0
levels. TB parameters and their SEC are found to vary significantly from 2p to
3p block but negligibly from 3p to 4p, while varying rather slowly within each
block, implying the possibility of transfer of SEC across block with increasing
principal quantum number. The demonstrated easy transferability of self-energy
corrected TB parameters in the hybrid orbital basis thus promises
computationally inexpensive estimation of quasi-particle electronic structure
of large finite systems with thousands of atoms.


Abstract:
  Clusters and nanodroplets hold the promise of enhancing high-order nonlinear
optical effects due to their high local density. However, only moderate
enhancement has been demonstrated to date. Here, we report the observation of
energetic electrons generated by above-threshold ionization (ATI) of helium
(He) nanodroplets which are resonantly excited by ultrashort extreme
ultraviolet (XUV) free-electron laser pulses and subsequently ionized by
near-infrared (NIR) or near-ultraviolet (UV) pulses. The electron emission due
to high-order ATI is enhanced by several orders of magnitude compared to He
atoms. The crucial dependence of the ATI intensities with the number of
excitations in the droplets suggests a local collective enhancement effect.


Abstract:
  The inhomogeneous Muskat problem models the dynamics of an interface between
two fluids of differing characteristics inside a non-uniform porous medium. We
consider the case of a porous media with a permeability jump across a
horizontal boundary away from an interface between two fluids of different
viscosities and densities. For initial data of explicit medium size, depending
on the characteristics of the fluids and porous media, we will prove the global
existence and uniqueness of a solution which is instantly analytic and decays
in time to the flat interface.


Abstract:
  As the major target of many vaccines and neutralizing antibodies against
SARS-CoV-2, the spike (S) protein is observed to mutate over time. In this
paper, we present statistical approaches to tackle some challenges associated
with the analysis of S-protein data. We build a Bayesian hierarchical model to
study the temporal and spatial evolution of S-protein sequences, after grouping
the sequences into representative clusters. We then apply sampling methods to
investigate possible changes to the S-protein's 3-D structure as a result of
commonly observed mutations. While the increasing spread of D614G variants has
been noted in other research, our results also show that the co-occurring
mutations of D614G together with S477N or A222V may spread even more rapidly,
as quantified by our model estimates.


Abstract:
  A temporal constraint language is a set of relations that are first-order
definable over (Q;<). We show that several temporal constraint languages whose
constraint satisfaction problem is maximally tractable are also maximally
tractable for the more expressive quantified constraint satisfaction problem.
These constraint languages are defined in terms of preservation under certain
binary polymorphisms. We also present syntactic characterizations of the
relations in these languages.


Abstract:
  In recent times, as a result of COVID-19 pandemic, higher institutions in
Nigeria have been shutdown and the leadership of Academic Staff Union of
University (ASUU) said that Nigerian universities cannot afford to mount Online
learning platforms let alone conduct such learning system in Nigeria due to
lack of infrastructure, capacity and skill sets in the face of COVID-19
pandemic. In the light of this, this research undertook an online survey using
University of Nigeria, Nsukka (UNN) as a case study to know which type of
online learning system ASUU leadership is talking about - Asynchronous or
Synchronous? How did ASUU come about their facts? Did ASUU base their assertion
on facts, if YES, what are the benchmarks? Therefore, this research project is
focused on providing benchmarks to assess if a Nigerian University has what it
takes to run a synchronous Online Learning. It includes Infrastructure needed
(Hardware, Software, Network connectivity), Skill sets from staff (Computer
literacy level). In a bid to do this, an online survey was administered to the
staff of Centre for Distance and E-learning of UNN and out of the 40 members of
that section of the University, we had 32 respondents. The survey seeks to find
whether UNN has the requisite infrastructure and the skill sets to mount
synchronous online learning. The available results of the study reveal that UNN
is deficit in both the requisite infrastructure and Skills sets to mount
synchronous online learning.


Abstract:
  Acquisition and analysis of time-tagged events is a ubiquitous tool in
scientific and industrial applications. With increasing time resolution, number
of input channels, and acquired events, the amount of data can be overwhelming
for standard processing techniques. We developed the Extensible Time-tag
Analyzer (ETA), a powerful and versatile, yet easy to use software to
efficiently analyze and display time-tagged data. Our tool allows for flexible
extraction of correlation from time-tagged data beyond start-stop measurements
that were traditionally used. A combination of state diagrams and simple code
snippets allows for analysis of arbitrary complexity while keeping
computational efficiency high.


Abstract:
  In this paper, a novel model-free wide-area damping control (WADC) method is
proposed, which can achieve full decoupling of modes and damp multiple critical
inter-area oscillations simultaneously using grid-connected voltage source
converters (VSCs). The proposed method is purely measurement based and requires
no knowledge of the network topology and the dynamic model parameters. Hence,
the designed controller using VSCs can update the control signals online as the
system operating condition varies. Numerical studies in the modified IEEE
68-bus system with grid-connected VSCs show that the proposed method can
estimate the system dynamic model accurately and can damp inter-area
oscillations effectively under different working conditions and network
topologies.


Abstract:
  Closed quantum systems exhibit different dynamical regimes, like Many-Body
Localization or thermalization, which determine the mechanisms of spread and
processing of information. Here we address the impact of these dynamical phases
in quantum reservoir computing, an unconventional computing paradigm recently
extended into the quantum regime that exploits dynamical systems to solve
nonlinear and temporal tasks. We establish that the thermal phase is naturally
adapted to the requirements of quantum reservoir computing and report an
increased performance at the thermalization transition for the studied tasks.
Uncovering the underlying physical mechanisms behind optimal information
processing capabilities of spin networks is essential for future experimental
implementations and provides a new perspective on dynamical phases.


Abstract:
  The red giant branch (RGB) of globular clusters (GCs) is home to some exotic
stars, which may provide clues on the formation of multiple stellar populations
in GCs. It is well known that binary interactions are responsible for many
exotic stars. Thus, it is important to understand what fraction of stars on the
RGB of GCs is the result of binary interactions. In this paper, we performed a
binary population synthesis study to track the number of
post-binary-interaction (post-BI) stars that appear on the RGB, with particular
emphasis on the evolved blue straggler stars (E-BSSs). Assuming an initial
binary fraction of nearly 50%, we find that about half of the objects on the
RGB (called giants) underwent the binary interactions, and that E-BSSs account
for around 10% of the giants in our standard simulation. We also compare the
properties of post-BI giants that evolved from different channels. We find that
the initial orbital period and mass ratio distributions significantly affect
the fraction of post-BI giants. Our results imply that the non-standard stars
from binary interactions provide a non-negligible contribution to the RGB stars
in GCs, which should be considered in future investigations of the origin of
multiple stellar populations.


Abstract:
  The accuracy of photometric redshifts (photo-zs) particularly affects the
results of the analyses of galaxy clustering with photometrically-selected
galaxies (GCph) and weak lensing. In the next decade, space missions like
Euclid will collect photometric measurements for millions of galaxies. These
data should be complemented with upcoming ground-based observations to derive
precise and accurate photo-zs. In this paper, we explore how the tomographic
redshift binning and depth of ground-based observations will affect the
cosmological constraints expected from Euclid. We focus on GCph and extend the
study to include galaxy-galaxy lensing (GGL). We add a layer of complexity to
the analysis by simulating several realistic photo-z distributions based on the
Euclid Consortium Flagship simulation and using a machine learning photo-z
algorithm. We use the Fisher matrix formalism and these galaxy samples to study
the cosmological constraining power as a function of redshift binning, survey
depth, and photo-z accuracy. We find that bins with equal width in redshift
provide a higher Figure of Merit (FoM) than equipopulated bins and that
increasing the number of redshift bins from 10 to 13 improves the FoM by 35%
and 15% for GCph and its combination with GGL, respectively. For GCph, an
increase of the survey depth provides a higher FoM. But the addition of faint
galaxies beyond the limit of the spectroscopic training data decreases the FoM
due to the spurious photo-zs. When combining both probes, the number density of
the sample, which is set by the survey depth, is the main factor driving the
variations in the FoM. We conclude that there is more information that can be
extracted beyond the nominal 10 tomographic redshift bins of Euclid and that we
should be cautious when adding faint galaxies into our sample, since they can
degrade the cosmological constraints.


Abstract:
  The heterogeneous edge-cloud computing paradigm can provide a more optimal
direction to deploy scientific workflows than traditional distributed computing
or cloud computing environments. Due to the different sizes of scientific
datasets and some of these datasets must keep private, it is still a difficult
problem to finding an data placement strategy that can minimize data
transmission as well as placement cost. To address this issue, this paper
combines advantages of both edge and cloud computing to construct a data
placement model, which can balance data transfer time and data placement cost
using intelligent computation. The most difficult research challenge the model
solved is to consider many constrain in this hybrid computing environments,
which including shared datasets within individual and among multiple workflows
across various geographical regions. According to the constructed model, the
study propose a new data placement strategy named DE-DPSO-DPS, which using a
discrete particle swarm optimization algorithm with differential evolution
(DE-DPSO-DPA) to distribute these scientific datasets. The strategy also not
only consider the characteristics such as the number and storage capacity of
edge micro-datacenters, the bandwidth between different datacenters and the
proportion of private datasets, but also analysis the performance of algorithm
during the workflows execution. Comprehensive experiments are designed in
simulated heterogeneous edge-cloud computing environments demonstrate that the
data placement strategy can effectively reduce the data transmission time and
placement cost as compared to traditional strategies for data-sharing
scientific workflows.


Abstract:
  This paper investigates requirements on a networked dynamic system (NDS) such
that its subsystem interactions can be solely determined from experiment data
or reconstructed from its overall model. The NDS is constituted from several
subsystems whose dynamics are described through a descriptor form. Except
regularity on each subsystem and the whole NDS, no other restrictions are put
on either subsystem dynamics or subsystem interactions. A matrix rank based
necessary and sufficient condition is derived for the global identifiability of
subsystem interactions, which leads to several conclusions about NDS structure
identifiability when there is some a priori information. This matrix also gives
an explicit description for the set of subsystem interactions that can not be
distinguished from experiment data only. In addition, under a well-posedness
assumption, a necessary and sufficient condition is obtained for the
reconstructibility of subsystem interactions from an NDS descriptor form model.
This condition can be verified with each subsystem separately and is therefore
attractive in the analysis and synthesis of a large-scale NDS. Simulation
results show that rather than increases monotonically with the distance of
subsystem interactions to the undifferentiable set, the magnitude of the
external output differences between two NDSs with distinct subsystem
interactions increases much more rapidly when one of them is close to be
unstable. In addition, directions of probing signals are also very important in
distinguishing external outputs of distinctive NDSs.These findings are expected
to be helpful in identification experiment designs, etc.


Abstract:
  This article deals with the investigation of perfect fluid $GRW$ spacetimes.
It is shown that in a $GRW$ perfect fluid spacetime, the Weyl tensor is
divergence free and in dimension 4, a perfect fluid $GRW$ spacetime is a $RW$
spacetime. Moreover, we prove that if a $GRW$ perfect fluid spacetime admits a
second order symmetric parallel tensor, then either the state equation of the
perfect fluid $GRW$ spacetime is characterized by $p=\frac{3-n}{n-1}\mu$ , or
the tensor is a constant multiple of the metric tensor. We also characterize
the perfect fluid $GRW$ spacetimes whose Lorentzian metrics are Ricci soliton,
gradient Ricci soliton, gradient Yamabe solitons and gradient $m$-quasi
Einstein solitons, respectively.


Abstract:
  We present a new method for using the observed starlight polarization and
polarized submm emission to constrain the shapes and porosities of interstellar
grains. We present the modified picket fence approximation (MPFA), and verify
that it is sufficiently accurate for modeling starlight polarization. We
introduce the starlight polarization integral $\Pi_{\rm obs}$ as a measure of
overall strength of the observed polarization of starlight, and the starlight
polarization efficiency integral $\Phi$ to characterize the effectiveness of
different grain types for producing polarization of starlight. The starlight
polarization integral $\Pi_{\rm obs}$ determines the mass-weighted alignment
$\langle f_{\rm align}\rangle$ of the grains. Approximating the aligned grains
in the interstellar medium as spheroids, we use $\Pi_{\rm obs}/\Phi$ to show
that the observed starlight polarization constrains the grains to have a
minimum degree of asphericity. For porosity ${\cal P}=0$, the minimum axial
ratio is $\sim$1.4 for oblate spheroids, or $\sim$1.8 for prolate spheroids. If
the grains are porous, more extreme axial ratios are required. The same grains
that produce the starlight polarization are able to provide the observed
polarized emission at submm wavelengths, but with further limits on shape and
porosity. Porosities ${\cal P}>0.75$ are ruled out. If interstellar grains can
be approximated by astrodust spheroids, we predict the ratio of 10$\mu{\rm m}$
polarization to starlight polarization $p_V$: $p(10\mu{\rm
m})/p_V=0.222\pm0.026$. For Cyg OB2-12 we predict $p(10\mu{\rm
m})=(2.1\pm0.3)\%$, which should be observable.


Abstract:
  These notes introduce a handful of core ideas from quantum information
science that figure prominently in modern research on quantum gravity. The
central concept that forms the base of these notes is that of a quantum
channel; that is, the most general physically-reasonable map between quantum
states and between operators on Hilbert space. After reviewing some
fundamentals, we will study channels and their properties, and then go on to
formulate quantum error correction in terms of quantum channels. Along the way,
we will see how a handful of problems in high energy physics, such as the black
hole information problem and bulk reconstruction in AdS/CFT, can be cast in the
information-theoretic language being set up.


Abstract:
  We present fully general relativistic simulations of the quasi-circular
inspiral and merger of charged, non-spinning, binary black holes with
charge-to-mass ratio $\lambda \le 0.3$. We discuss the key features that
enabled long term and stable evolutions of these binaries. We also present a
formalism for computing the angular momentum carried away by electromagnetic
waves, and the electromagnetic contribution to black-hole horizon properties.
We implement our formalism and present the results for the first time in
numerical-relativity simulations. In addition, we compare our full non-linear
solutions with existing approximate models for the inspiral and ringdown
phases. We show that Newtonian models based on the quadrupole approximation
have errors of 20 % - 100 % in key gauge-invariant quantities. On the other
hand, for the systems considered, we find that estimates of the remnant black
hole spin based on the motion of test particles in Kerr-Newman spacetimes agree
with our non-linear calculations to within a few percent. Finally, we discuss
the prospects for detecting black hole charge by future gravitational-wave
detectors using either the inspiral-merger-ringdown signal or the ringdown
signal alone.