Abstract:
  A schizophrenia relapse has severe consequences for a patient's health, work,
and sometimes even life safety. If an oncoming relapse can be predicted on
time, for example by detecting early behavioral changes in patients, then
interventions could be provided to prevent the relapse. In this work, we
investigated a machine learning based schizophrenia relapse prediction model
using mobile sensing data to characterize behavioral features. A
patient-independent model providing sequential predictions, closely
representing the clinical deployment scenario for relapse prediction, was
evaluated. The model uses the mobile sensing data from the recent four weeks to
predict an oncoming relapse in the next week. We used the behavioral rhythm
features extracted from daily templates of mobile sensing data, self-reported
symptoms collected via EMA (Ecological Momentary Assessment), and demographics
to compare different classifiers for the relapse prediction. Naive Bayes based
model gave the best results with an F2 score of 0.083 when evaluated in a
dataset consisting of 63 schizophrenia patients, each monitored for up to a
year. The obtained F2 score, though low, is better than the baseline
performance of random classification (F2 score of 0.02 $\pm$ 0.024). Thus,
mobile sensing has predictive value for detecting an oncoming relapse and needs
further investigation to improve the current performance. Towards that end,
further feature engineering and model personalization based on the behavioral
idiosyncrasies of a patient could be helpful.


Abstract:
  We propose a vector dark matter model with an exotic dark SU(2) gauge group.
Two Higgs triplets are introduced to spontaneously break the symmetry. All of
the dark gauge bosons become massive, and the lightest one is a viable vector
DM candidate. Its stability is guaranteed by a remaining Z_2 symmetry. We study
the parameter space constrained by the Higgs measurement data, the dark matter
relic density, and direct and indirect detection experiments. We find numerous
parameter points satisfying all the constraints, and they could be further
tested in future experiments. Similar methodology can be used to construct
vector dark matter models from an arbitrary SO(N) gauge group.


Abstract:
  Following E. Wigner's original vision, we prove that sampling the eigenvalue
gaps within the bulk spectrum of a .fixed (deformed) Wigner matrix $H$ yields
the celebrated Wigner-Dyson-Mehta universal statistics with high probability.
Similarly, we prove universality for a monoparametric family of deformed Wigner
matrices $H+xA$ with a deterministic Hermitian matrix $A$ and a fixed Wigner
matrix $H$, just using the randomness of a single scalar real random variable
$x$. Both results constitute quenched versions of bulk universality that has so
far only been proven in annealed sense with respect to the probability space of
the matrix ensemble.


Abstract:
  The demand for streaming media and live video conferencing is at peak and
expected to grow further, thereby the need for low-cost streaming services with
better quality and lower latency is essential. Therefore, in this paper, we
propose a novel peer-to-peer (P2P) live streaming platform, called fybrrStream,
where a logical mesh and physical tree i.e., hybrid topology-based approach is
leveraged for low latency streaming. fybrrStream distributes the load on
participating peers in a hierarchical manner by considering their network
bandwidth, network latency, and node stability. fybrrStream costs as low as the
cost of just hosting a light-weight website and the performance is comparable
to the existing state-of-the-art media streaming services. We evaluated and
tested the proposed fybrrStream platform with real-field experiments using 50+
users spread across India and results obtained show significant improvements in
the live streaming performance over other schemes.


Abstract:
  We conjecture the existence of hidden Onsager algebra symmetries in two
interacting quantum integrable lattice models, i.e. spin-1/2 XXZ model and
spin-1 Zamolodchikov-Fateev model at arbitrary root of unity values of the
anisotropy. The conjectures relate the Onsager generators to the conserved
charges obtained from semi-cyclic transfer matrices. The conjectures are
motivated by two examples which are spin-1/2 XX model and spin-1 U(1)-invariant
clock model. A novel construction of the semi-cyclic transfer matrices of
spin-1 Zamolodchikov-Fateev model at arbitrary root of unity value of the
anisotropy is carried out via transfer matrix fusion procedure.


Abstract:
  Robust multi-agent trajectory prediction is essential for the safe control of
robots and vehicles that interact with humans. Many existing methods treat
social and temporal information separately and therefore fall short of
modelling the joint future trajectories of all agents in a socially consistent
way. To address this, we propose a new class of Latent Variable Sequential Set
Transformers which autoregressively model multi-agent trajectories. We refer to
these architectures as "AutoBots". AutoBots model the contents of sets (e.g.
representing the properties of agents in a scene) over time and employ
multi-head self-attention blocks over these sequences of sets to encode the
sociotemporal relationships between the different actors of a scene. This
produces either the trajectory of one ego-agent or a distribution over the
future trajectories for all agents under consideration. Our approach works for
general sequences of sets and we provide illustrative experiments modelling the
sequential structure of the multiple strokes that make up symbols in the
Omniglot data. For the single-agent prediction case, we validate our model on
the NuScenes motion prediction task and achieve competitive results on the
global leaderboard. In the multi-agent forecasting setting, we validate our
model on TrajNet. We find that our method outperforms physical extrapolation
and recurrent network baselines and generates scene-consistent trajectories.


Abstract:
  We present the results of radio observations from the eMERLIN telescope
combined with X-ray data from Swift for the short-duration Gamma-ray burst
(GRB) 200826A, located at a redshift of 0.71. The radio light curve shows
evidence of a sharp rise, a peak around 4-5 days post-burst, followed by a
relatively steep decline. We provide two possible interpretations based on the
time at which the light curve reached its peak. (1) If the light curve peaks
earlier, the peak is produced by the synchrotron self-absorption frequency
moving through the radio band, resulting from the forward shock propagating
into a wind medium and (2) if the light curve peaks later, the turn over in the
light curve is caused by a jet break. In the former case, we find a minimum
equipartition energy of ~3x10^47 erg and bulk Lorentz factor of ~5, while in
the latter case we estimate the jet opening angle of ~9-16 degrees. Due to the
lack of data, it is impossible to determine which is the correct
interpretation, however, due to its relative simplicity and consistency with
other multi-wavelength observations which hint at the possibility that GRB
200826A is in fact a long GRB, we prefer scenario one over scenario two.


Abstract:
  In this paper, we present a positivity-preserving limiter for nodal
Discontinuous Galerkin disctretizations of the compressible Euler equations. We
use a Legendre-Gauss-Lobatto (LGL) Discontinuous Galerkin Spectral Element
Method (DGSEM) and blend it locally with a consistent LGL-subcell Finite Volume
(FV) discretization using a hybrid FV/DGSEM scheme that was recently proposed
for entropy stable shock capturing. We show that our strategy is able to ensure
robust simulations with positive density and pressure when using the standard
and the split-form DGSEM. Furthermore, we show the applicability of our FV
positivity limiter in extremely under-resolved vortex dominated simulations and
in problems with shocks.


Abstract:
  We present covariant symmetry operators for the conformal wave equation in
the (off-shell) Kerr-NUT-AdS spacetimes. These operators, that are constructed
from the principal Killing-Yano tensor, its `symmetry descendants', and the
curvature tensor, guarantee separability of the conformal wave equation in
these spacetimes. We next discuss how these operators give rise to a full set
of conformally invariant mutually commuting operators for the conformally
rescaled spacetimes and underlie the $R$-separability of the conformal wave
equation therein. Finally, by employing the WKB approximation we derive the
associated Hamilton-Jacobi equation with a scalar curvature potential term and
show its separability in the Kerr-NUT-AdS spacetimes.


Abstract:
  We study the problem of repeatedly auctioning off an item to one of $k$
bidders where: a) bidders have a per-round individual rationality constraint,
b) bidders may leave the mechanism at any point, and c) the bidders' valuations
are adversarially chosen (the prior-free setting). Without these constraints,
the auctioneer can run a second-price auction to "sell the business" and
receive the second highest total value for the entire stream of items. We show
that under these constraints, the auctioneer can attain a constant fraction of
the "sell the business" benchmark, but no more than $2/e$ of this benchmark.
  In the course of doing so, we design mechanisms for a single bidder problem
of independent interest: how should you repeatedly sell an item to a (per-round
IR) buyer with adversarial valuations if you know their total value over all
rounds is $V$ but not how their value changes over time? We demonstrate a
mechanism that achieves revenue $V/e$ and show that this is tight.


Abstract:
  Chemical surfactants are omnipresent in consumers' products but they suffer
from environmental concerns. For this reason, complete replacement of
petrochemical surfactants by biosurfactants constitute a holy grail but this is
far from occurring any soon. If the "biosurfactants revolution" has not
occurred, yet, mainly due to the higher cost and lower availability of
biosurfactants, another reason explains this fact: the poor knowledge of their
properties in solution. This tutorial review aims at reviewing the
self-assembly properties and phase behavior, experimental (sections 2.3 and
2.4) and from molecular modelling (section 5), in water of the most important
microbial biosurfactants (sophorolipids, rhamnolipids, surfacting,
cellobioselipids, glucolipids) as well as their major derivatives. A critical
discussion of such properties in light of the well-known packing parameter of
surfactants is also provided (section 2.5). The relationship between the
nanoscale self-assembly and macroscopic materials properties, including
hydrogelling, solid foaming, templating or encapsulation is specifically
discussed (section 2.7). We also present their self-assembly and adsorption at
flat and complex air/liquid (e.g., foams), air/solid (adhesion), liquid/solid
(nanoparticles) and liquid/liquid (e.g., emulsions) interfaces (section 3). A
critical discussion on the use of biosurfactants as capping agents for the
development of stable nanoparticles is specifically provided (section 3.2.4).
Finally, we discuss the major findings involving biosurfactants and
macromolecules, including proteins, enzymes, polymers and polyelectrolytes.


Abstract:
  We have deduced the structure of the \ce{bromobenzene}--\ce{I2} heterodimer
and the \ce{(bromobenzene)2} homodimer inside helium droplets using a
combination of laser-induced alignment, Coulomb explosion imaging, and
three-dimensional ion imaging. The complexes were fixed in a variety of
orientations in the laboratory frame, then in each case multiply ionized by an
intense laser pulse. A three dimensional ion imaging detector, including a
Timepix3 detector allowed us to measure the correlations between velocity
vectors of different fragments and, in conjunction with classical simulations,
work backward to the initial structure of the complex prior to explosion. For
the heterodimer, we find that the \ce{I2} molecular axis intersects the phenyl
ring of the bromobenzene approximately perpendicularly. The homodimer has a
stacked parallel structure, with the two bromine atoms pointing in opposite
directions. These results illustrate the ability of Coulomb explosion imaging
to determine the structure of large complexes, and point the way toward
real-time measurements of bimolecular reactions inside helium droplets.


Abstract:
  We present a theoretical investigation of anisotropic superconducting spin
transport at a magnetic interface between a p-wave superconductor and a
ferromagnetic insulator. Our formulation describes the ferromagnetic resonance
modulations due to spin-triplet current generation, including the frequency
shift and enhanced Gilbert damping, in a unified manner. We find that the
Cooper pair symmetry is detectable from the qualitative behavior of the
ferromagnetic resonance modulation. Our theory paves the way toward anisotropic
superconducting spintronics.


Abstract:
  In this paper, we develop a new free-stream preserving (FP) method for
high-order upwind conservative finite-difference (FD) schemes on the
curvilinear grids. This FP method is constrcuted by subtracting a reference
cell-face flow state from each cell-center value in the local stencil of the
original upwind conservative FD schemes, which effectively leads to a
reformulated dissipation. It is convenient to implement this method, as it does
not require to modify the original forms of the upwind schemes. In addition,
the proposed method removes the constraint in the traditional FP conservative
FD schemes that require a consistent discretization of the mesh metrics and the
fluxes. With this, the proposed method is more flexible in simulating the
engineering problems which usually require a low-order scheme for their
low-quality mesh, while the high-order schemes can be applied to approximate
the flow states to improve the resolution. After demonstrating the strict FP
property and the order of accuracy by two simple test cases, we consider
various validation cases, including the supersonic flow around the cylinder,
the subsonic flow past the three-element airfoil, and the transonic flow around
the ONERA M6 wing, etc., to show that the method is suitable for a wide range
of fluid dynamic problems containing complex geometries. Moreover, these test
cases also indicate that the discretization order of the metrics have no
significant influences on the numerical results if the mesh resolution is not
sufficiently large.


Abstract:
  The traditional setup of link prediction in networks assumes that a test set
of node pairs, which is usually balanced, is available over which to predict
the presence of links. However, in practice, there is no test set: the
ground-truth is not known, so the number of possible pairs to predict over is
quadratic in the number of nodes in the graph. Moreover, because graphs are
sparse, most of these possible pairs will not be links. Thus, link prediction
methods, which often rely on proximity-preserving embeddings or heuristic
notions of node similarity, face a vast search space, with many pairs that are
in close proximity, but that should not be linked. To mitigate this issue, we
introduce LinkWaldo, a framework for choosing from this quadratic,
massively-skewed search space of node pairs, a concise set of candidate pairs
that, in addition to being in close proximity, also structurally resemble the
observed edges. This allows it to ignore some high-proximity but
low-resemblance pairs, and also identify high-resemblance, lower-proximity
pairs. Our framework is built on a model that theoretically combines Stochastic
Block Models (SBMs) with node proximity models. The block structure of the SBM
maps out where in the search space new links are expected to fall, and the
proximity identifies the most plausible links within these blocks, using
locality sensitive hashing to avoid expensive exhaustive search. LinkWaldo can
use any node representation learning or heuristic definition of proximity, and
can generate candidate pairs for any link prediction method, allowing the
representation power of current and future methods to be realized for link
prediction in practice. We evaluate LinkWaldo on 13 networks across multiple
domains, and show that on average it returns candidate sets containing 7-33%
more missing and future links than both embedding-based and heuristic
baselines' sets.


Abstract:
  We report in this paper the analysis for the linear and nonlinear version of
the flux corrected transport (FEM-FCT) scheme in combination with the backward
Euler time-stepping scheme applied to time-dependent
convection-diffusion-reaction problems. We present the stability and error
estimates for the linear and nonlinear FEM-FCT scheme. Numerical results
confirm the theoretical predictions.


Abstract:
  This paper proposes a new reinforcement learning with hyperbolic discounting.
Combining a new temporal difference error with the hyperbolic discounting in
recursive manner and reward-punishment framework, a new scheme to learn the
optimal policy is derived. In simulations, it is found that the proposal
outperforms the standard reinforcement learning, although the performance
depends on the design of reward and punishment. In addition, the averages of
discount factors w.r.t. reward and punishment are different from each other,
like a sign effect in animal behaviors.


Abstract:
  The discrepancy between theory and experiment severely limits the development
of quantum key distribution (QKD). Reference-frame-independent (RFI) protocol
has been proposed to avoid alignment of the reference frame. However, multiple
optical modes caused by Trojan horse attacks and equipment loopholes lead to
the imperfect emitted signal unavoidably. In this paper, we analyzed the
security of the RFI-QKD protocol with non-qubit sources based on generalizing
loss-tolerant techniques. The simulation results show that our work can
effectively defend against non-qubit sources including a misaligned reference
frame, state preparation flaws, multiple optical modes, and Trojan horse
attacks. Moreover, it only requires the preparation of four quantum states,
which reduces the complexity of the experiment in the future.


Abstract:
  Humans are arguably one of the most important subjects in video streams, many
real-world applications such as video summarization or video editing workflows
often require the automatic search and retrieval of a person of interest.
Despite tremendous efforts in the person reidentification and retrieval
domains, few works have developed audiovisual search strategies. In this paper,
we present the Audiovisual Person Search dataset (APES), a new dataset composed
of untrimmed videos whose audio (voices) and visual (faces) streams are densely
annotated. APES contains over 1.9K identities labeled along 36 hours of video,
making it the largest dataset available for untrimmed audiovisual person
search. A key property of APES is that it includes dense temporal annotations
that link faces to speech segments of the same identity. To showcase the
potential of our new dataset, we propose an audiovisual baseline and benchmark
for person retrieval. Our study shows that modeling audiovisual cues benefits
the recognition of people's identities. To enable reproducibility and promote
future research, the dataset annotations and baseline code are available at:
https://github.com/fuankarion/audiovisual-person-search


Abstract:
  The COVID-19 pandemic has influenced the lives of people globally. In the
past year many researchers have proposed different models and approaches to
explore in what ways the spread of the disease could be mitigated. One of the
models that have been used a great deal is the
Susceptible-Exposed-Infectious-Recovered (SEIR) model. Some researchers have
modified the traditional SEIR model, and proposed new versions of it. However,
to the best of our knowledge, the state-of-the-art papers have not considered
the effect of different vaccine types, meaning single shot and double shot
vaccines, in their SEIR model. In this paper, we propose a modified version of
the SEIR model which takes into account the effect of different vaccine types.
We compare how different policies for the administration of the vaccine can
influence the rate at which people are exposed to the disease, get infected,
recover, and pass away. Our results suggest that taking the double shot vaccine
such as Pfizer-BioNTech and Moderna does a better job at mitigating the spread
and fatality rate of the disease compared to the single shot vaccine, due to
its higher efficacy.


Abstract:
  We study the potential of gravitational wave astronomy to observe the quantum
aspects of black holes. According to Bekenstein's quantization, we find that
black hole area discretization can have observable imprints on the
gravitational wave signal from an inspiraling binary black hole. We study the
impact of quantization on tidal heating. We model the absorption lines and
compute gravitational wave flux due to tidal heating in such a case. By
including the quantization we find the dephasing of the gravitational wave, to
our knowledge it has never been done before. We discuss the observability of
the phenomena in different parameter ranges of the binary. We show that in the
inspiral, it leads to vanishing tidal heating for the high spin values.
Therefore measuring non-zero tidal heating can rule out area quantization. We
also argue that if area quantization is present in nature then our current
modeling with reflectivity can possibly probe the Hawking radiation which may
bring important information regarding the quantum nature of gravity.


Abstract:
  Summarization evaluation remains an open research problem: current metrics
such as ROUGE are known to be limited and to correlate poorly with human
judgments. To alleviate this issue, recent work has proposed evaluation metrics
which rely on question answering models to assess whether a summary contains
all the relevant information in its source document. Though promising, the
proposed approaches have so far failed to correlate better than ROUGE with
human judgments.
  In this paper, we extend previous approaches and propose a unified framework,
named QuestEval. In contrast to established metrics such as ROUGE or BERTScore,
QuestEval does not require any ground-truth reference. Nonetheless, QuestEval
substantially improves the correlation with human judgments over four
evaluation dimensions (consistency, coherence, fluency, and relevance), as
shown in the extensive experiments we report.


Abstract:
  One of the best ways to understand the gravitation of a massive object is by
studying the photon's motion around it. We study the null geodesic of a regular
black hole in anti-de Sitter spacetime, including a Gaussian matter
distribution. Obtaining the effective potential and possible motions of the
photon are discussed for different energy levels. The nature of the effective
potential implies that the photon is prevented from reaching the black hole's
center. Different types of possible orbits are considered. A photon with
negative energy is trapped in a potential hole and has a back and forth motion
between two horizons of the metric. However, for specific values of positive
energy, the trapped photon still has a back and forth motion; however, it
crosses the horizons in every direction. The effective potential has an
unstable point outside the horizons, which indicates the possible circular
motion of the photon. The closest approach of the photon and the bending angle
are also investigated.


Abstract:
  We discuss excitation of string oscillation modes by an initial singularity
of inflation. The initial singularity of inflation is known to occur with a
finite Hubble parameter, which is generally lower than the string scale, and
hence it is not clear that stringy effects become significant around it. With
the help of Penrose limit, we find that infinitely heavy oscillation modes get
excited when a singularity is strong in the sense of Krolak's classification.
We demonstrate that the initial singularities of Starobinsky and hill top
inflation, assuming the slow roll inflation to the past infinity, are strong.
Hence stringy corrections are inevitable in the very early stage of these
inflation models. We also find that the initial singularity of the hill top
inflation could be weak for non-slow roll case.


Abstract:
  The leading-order approximation to a Filippov system $f$ about a generic
boundary equilibrium $x^*$ is a system $F$ that is affine one side of the
boundary and constant on the other side. We prove $x^*$ is exponentially stable
for $f$ if and only if it is exponentially stable for $F$ when the constant
component of $F$ is not tangent to the boundary. We then show exponential
stability and asymptotic stability are in fact equivalent for $F$. We also show
exponential stability is preserved under small perturbations to the pieces of
$F$. Such results are well known for homogeneous systems. To prove the results
here additional techniques are required because the two components of $F$ have
different degrees of homogeneity. The primary function of the results is to
reduce the problem of the stability of $x^*$ from the general Filippov system
$f$ to the simpler system $F$. Yet in general this problem remains difficult.
We provide a four-dimensional example of $F$ for which orbits appear to
converge to $x^*$ in a chaotic fashion. By utilising the presence of both
homogeneity and sliding motion the dynamics of $F$ can in this case be reduced
to the combination of a one-dimensional return map and a scalar function.


Abstract:
  Network design, a cornerstone of mathematical optimization, is about defining
the main characteristics of a network satisfying requirements on connectivity,
capacity, and level-of-service. It finds applications in logistics and
transportation, telecommunications, data sharing, energy distribution, and
distributed computing. In multi-commodity network design, one is required to
design a network minimizing the installation cost of its arcs and the
operational cost to serve a set of point-to-point connections. The definition
of this prototypical problem was recently enriched by additional constraints
imposing that each origin-destination of a connection is served by a single
path satisfying one or more level-of-service requirements, thus defining the
Network Design with Service Requirements [Balakrishnan, Li, and Mirchandani.
Operations Research, 2017]. These constraints are crucial, e.g., in
telecommunications and computer networks, in order to ensure reliable and
low-latency communication. In this paper we provide a new formulation for the
problem, where variables are associated with paths satisfying the end-to-end
service requirements. We present a fast algorithm for enumerating all the
exponentially-many feasible paths and, when this is not viable, we provide a
column generation scheme that is embedded into a branch-and-cut-and-price
algorithm. Extensive computational experiments on a large set of instances show
that our approach is able to move a step further in the solution of the Network
Design with Service Requirements, compared with the current state-of-the-art.


Abstract:
  To ensure protection of the intellectual property rights of DNN models,
watermarking techniques have been investigated to insert side-information into
the models without seriously degrading the performance of original task. One of
the threats for the DNN watermarking is the pruning attack such that less
important neurons in the model are pruned to make it faster and more compact as
well as to remove the watermark. In this study, we investigate a channel coding
approach to resist the pruning attack. As the channel model is completely
different from conventional models like digital images, it has been an open
problem what kind of encoding method is suitable for DNN watermarking. A novel
encoding approach by using constant weight codes to immunize the effects of
pruning attacks is presented. To the best of our knowledge, this is the first
study that introduces an encoding technique for DNN watermarking to make it
robust against pruning attacks.


Abstract:
  Three-dimensional line-nodal superconductors exhibit nontrivial topology,
which is protected by the time-reversal symmetry. Here we investigate four
types of short-range interaction between the gapless line-nodal fermionic
quasiparticles by carrying renormalization group analysis. We find that such
interactions can induce the dynamical breaking of time-reversal symmetry, which
alters the topology and might lead to six possible distinct superconducting
states, distinguished by the group representations. After computing the
susceptibilities for all the possible phase-transition instabilities, we
establish that the superconducting pairing characterized by $id_{xz}$-wave gap
symmetry is the leading instability in noncentrosymmetric superconductors.
Appropriate extension of this approach is promising to pick out the most
favorable superconducting pairing during similar topology-changing transition
in the polar phase of $^3$He.


Abstract:
  Malnutrition is a major public health concern in low-and-middle-income
countries (LMICs). Understanding food and nutrient intake across communities,
households and individuals is critical to the development of health policies
and interventions. To ease the procedure in conducting large-scale dietary
assessments, we propose to implement an intelligent passive food intake
assessment system via egocentric cameras particular for households in Ghana and
Uganda. Algorithms are first designed to remove redundant images for minimising
the storage memory. At run time, deep learning-based semantic segmentation is
applied to recognise multi-food types and newly-designed handcrafted features
are extracted for further consumed food weight monitoring. Comprehensive
experiments are conducted to validate our methods on an in-the-wild dataset
captured under the settings which simulate the unique LMIC conditions with
participants of Ghanaian and Kenyan origin eating common Ghanaian/Kenyan
dishes. To demonstrate the efficacy, experienced dietitians are involved in
this research to perform the visual portion size estimation, and their
predictions are compared to our proposed method. The promising results have
shown that our method is able to reliably monitor food intake and give feedback
on users' eating behaviour which provides guidance for dietitians in regular
dietary assessment.


Abstract:
  Increased connectivity has made us all more vulnerable. Cyberspace, besides
all its benefits, spawned more devices to hack and more opportunities to commit
cybercrime. Criminals have found it lucrative to target both individuals and
businesses, by holding or stealing their assets via different types of cyber
attacks. The cyber-enabled theft of Intellectual Property (IP), as one of the
most important and critical intangible assets of nations, organizations and
individuals, by foreign countries has been a devastating challenge of the
United States (U.S.) in the past decades. In this study, we conduct a
socio-technical root cause analysis to investigate one of the recent cases of
IP theft by employing a holistic approach. It concludes with a list of root
causes and some corrective actions to stop the impact and prevent the
recurrence of the problem in the future. Building upon the findings of this
study, the U.S. requires a detailed revision of IP strategies bringing the
whole socio-technical regulatory system into focus and strengthen IP rights
protection considering China's indigenous innovation policies. It is critical
that businesses and other organizations take steps to reduce their exposure to
cyber attacks. It is particularly important to train employees on how to spot
potential threats, and to institute policies that encourage workers to report
potential security failures so that action can be taken quickly. Finally, we
discuss how cyber ranges can provide an efficient and safe platform for dealing
with such challenges. The results of this study can be expanded to other
countries in order to protect their IP rights and deter or prevent and respond
to future incidents.


Abstract:
  Braiding Majorana zero modes (MZMs) is the key procedure toward topological
quantum computation. However, the complexity of the braiding manipulation
hinders its experimental realization. Here we propose an experimental setup
composing of MZMs and a quantum dot state which can substantially simplify the
braiding protocol of MZMs. Such braiding scheme, which corresponds to a
specific closed loop in the parameter space, is quite universal and can be
realized in various platforms. Moreover, the braiding results can be directly
measured and manifested through electric current, which provides a simple and
novel way to detect the non-Abelian statistics of MZMs.


Abstract:
  Dense optical flow estimation is challenging when there are large
displacements in a scene with heterogeneous motion dynamics, occlusion, and
scene homogeneity. Traditional approaches to handle these challenges include
hierarchical and multiresolution processing methods. Learning-based optical
flow methods typically use a multiresolution approach with image warping when a
broad range of flow velocities and heterogeneous motion is present. Accuracy of
such coarse-to-fine methods is affected by the ghosting artifacts when images
are warped across multiple resolutions and by the vanishing problem in smaller
scene extents with higher motion contrast. Previously, we devised strategies
for building compact dense prediction networks guided by the effective
receptive field (ERF) characteristics of the network (DDCNet). The DDCNet
design was intentionally simple and compact allowing it to be used as a
building block for designing more complex yet compact networks. In this work,
we extend the DDCNet strategies to handle heterogeneous motion dynamics by
cascading DDCNet based sub-nets with decreasing extents of their ERF. Our
DDCNet with multiresolution capability (DDCNet-Multires) is compact without any
specialized network layers. We evaluate the performance of the DDCNet-Multires
network using standard optical flow benchmark datasets. Our experiments
demonstrate that DDCNet-Multires improves over the DDCNet-B0 and -B1 and
provides optical flow estimates with accuracy comparable to similar lightweight
learning-based methods.


Abstract:
  Convergence to equilibrium of underdamped Langevin dynamics is studied under
general assumptions on the potential $U$ allowing for singularities. By
modifying the direct approach to convergence in $L^2$ pioneered by F. H\'erau
and developped by Dolbeault, Mouhot and Schmeiser, we show that the dynamics
converges exponentially fast to equilibrium in the topologies $L^2(d\mu)$ and
$L^2(W^* d\mu)$, where $\mu$ denotes the invariant probability measure and
$W^*$ is a suitable Lyapunov weight. In both norms, we make precise how the
exponential convergence rate depends on the friction parameter $\gamma$ in
Langevin dynamics, by providing a lower bound scaling as $\min(\gamma,
\gamma^{-1})$. The results hold for usual polynomial-type potentials as well as
potentials with singularities such as those arising from pairwise Lennard-Jones
interactions between particles.


Abstract:
  Mobile communication networks were designed to mainly support ubiquitous
wireless communications, yet they are expected to also achieve radio sensing
capabilities in the near future. Most prior studies on radar sensing focus on
distant targets, which usually rely on far-field assumption with uniform plane
wave (UPW) models. However, with ever-increasing antenna size, together with
the growing need to also sense nearby targets, the far-field assumption may
become invalid. This paper studies radar sensing with extremely large-scale
(XL) antenna arrays, where a generic model that takes into account both
spherical wavefront and amplitude variations across array elements is
developed. Furthermore, new closed-form expressions of the sensing
signal-to-noise ratios (SNRs) are derived for both XL-MIMO radar and
XL-phased-array radar modes. Our results reveal that different from the
conventional UPW model where the SNR scales linearly and unboundedly with N for
MIMO radar and with MN for phased-array radar, with M and N being the transmit
and receive antenna numbers, respectively, more practical SNR scaling laws are
obtained. For XL-phased-array radar with optimal power allocation, the SNR
increases with M and N with diminishing returns, governed by new parameters
called the transmit and receive angular spans. On the other hand, for XL-MIMO
radar, while the same SNR scaling as XL-phased-array radar is obeyed for N, the
SNR first increases and then decreases with M.


Abstract:
  In this paper LQG control over unreliable communication links is derived.
That is to say, the communication channels between the controller and the
actuators and between the sensors and the controller are unreliable. Previous
solutions to finite horizon discrete time hold-input LQG control for this case
do not fully utilize the available information. Here a new solution is
presented which resolves this limitation. The focus is to derive and present a
full mathematical proof to derive the optimal control sequence.


Abstract:
  We add non-linear and state-dependent terms to quantum field theory. We show
that the resulting low-energy theory, non-linear quantum mechanics, is causal,
preserves probability and permits a consistent description of the process of
measurement. We explore the consequences of such terms and show that non-linear
quantum effects can be observed in macroscopic systems even in the presence of
de-coherence. We find that current experimental bounds on these non-linearities
are weak and propose several experimental methods to significantly probe these
effects. The locally exploitable effects of these non-linearities have enormous
technological implications. For example, they would allow large scale
parallelization of computing (in fact, any other effort) and enable quantum
sensing beyond the standard quantum limit. We also expose a fundamental
vulnerability of any non-linear modification of quantum mechanics - these
modifications are highly sensitive to cosmic history and their locally
exploitable effects can dynamically disappear if the observed universe has a
tiny overlap with the overall quantum state of the universe, as is predicted in
conventional inflationary cosmology. We identify observables that persist in
this case and discuss opportunities to detect them in cosmic ray experiments,
tests of strong field general relativity and current probes of the equation of
state of the universe. Non-linear quantum mechanics also enables novel
gravitational phenomena and may open new directions to solve the black hole
information problem and uncover the theory underlying quantum field theory and
gravitation.


Abstract:
  In 2021, Dzhunusov and Zaitseva classified two-dimensional normal affine
commutative algebraic monoids. In this work, we extend this classification to
noncommutative monoid structures on normal affine surfaces. We prove that
two-dimensional algebraic monoids are toric. We also show how to find all
monoid structures on a normal toric surface. Every such structure is induced by
a comultiplication formula involving Demazure roots. We also give descriptions
of opposite monoids, quotient monoids, and boundary divisors.


Abstract:
  We study nonlinear pantograph-type reaction-diffusion PDEs, which, in
addition to the unknown $u=u(x,t)$, also contain the same functions with
dilated or contracted arguments of the form $w=u(px,t)$, $w=u(x,qt)$, and
$w=u(px,qt)$, where $p$ and $q$ are the free scaling parameters (for equations
with proportional delay we have $0<p<1$, $0<q<1$). A brief review of
publications on pantograph-type ODEs and PDEs and their applications is given.
Exact solutions and reductions of various types of such nonlinear partial
functional differential equations are described for the first time. We present
examples of nonlinear pantograph-type PDEs with proportional delay, which admit
traveling-wave and self-similar solutions (note that PDEs with constant delay
do not have self-similar solutions). Additive, multiplicative and functional
separable solutions, as well as some other exact solutions are also obtained.
Special attention is paid to nonlinear pantograph-type PDEs of a rather general
form, which contain one or two arbitrary functions. In total, more than forty
nonlinear pantograph-type reaction-diffusion PDEs with dilated or contracted
arguments, admitting exact solutions, have been considered. Multi-pantograph
nonlinear PDEs are also discussed. The principle of analogy is formulated,
which makes it possible to efficiently construct exact solutions of nonlinear
pantograph-type PDEs. A number of exact solutions of more complex nonlinear
functional differential equations with varying delay, which arbitrarily depends
on time or spatial coordinate, are also described. The presented equations and
their exact solutions can be used to formulate test problems designed to
evaluate the accuracy of numerical and approximate analytical methods for
solving the corresponding nonlinear initial-boundary value problems for PDEs
with varying delay.


Abstract:
  The regulatory framework of cryptocurrencies (and, in general, blockchain
tokens) is of paramount importance. This framework drives nearly all key
decisions in the respective business areas. In this work, a computational model
is proposed for quantitatively estimating the regulatory stance of countries
with respect to cryptocurrencies. This is conducted via web mining utilizing
web search engines. The proposed model is experimentally validated. In
addition, unsupervised learning (clustering) is applied for better analyzing
the automatically derived estimations. Overall, very good performance is
achieved by the proposed algorithmic approach.


Abstract:
  Automatic speech recognition systems have been largely improved in the past
few decades and current systems are mainly hybrid-based and end-to-end-based.
The recently proposed CTC-CRF framework inherits the data-efficiency of the
hybrid approach and the simplicity of the end-to-end approach. In this paper,
we further advance CTC-CRF based ASR technique with explorations on modeling
units and neural architectures. Specifically, we investigate techniques to
enable the recently developed wordpiece modeling units and Conformer neural
networks to be succesfully applied in CTC-CRFs. Experiments are conducted on
two English datasets (Switchboard, Librispeech) and a German dataset from
CommonVoice. Experimental results suggest that (i) Conformer can improve the
recognition performance significantly; (ii) Wordpiece-based systems perform
slightly worse compared with phone-based systems for the target language with a
low degree of grapheme-phoneme correspondence (e.g. English), while the two
systems can perform equally strong when such degree of correspondence is high
for the target language (e.g. German).


Abstract:
  We state and prove in modern terms a Splitting Principle first claimed by
Beniamino Segre in 1938, which should be regarded as a strong form of the
classical Principle of Connectedness.


Abstract:
  The flex locus parameterizes plane cubics with three collinear cocritical
points under a projection, and the gothic locus arises from quadratic
differentials with zeros at a fiber of the projection and with poles at the
cocritical points. The flex and gothic loci provide the first example of a
primitive, totally geodesic subvariety of moduli space and new ${\rm
SL}_2(\mathbb{R})$-invariant varieties in Teichm\"uller dynamics, as discovered
by McMullen-Mukamel-Wright. In this paper we determine the divisor class of the
flex locus as well as various tautological intersection numbers on the gothic
locus. For the case of the gothic locus our result confirms numerically a
conjecture of Chen-M\"oller-Sauvaget about computing sums of Lyapunov exponents
for ${\rm SL}_2(\mathbb{R})$-invariant varieties via intersection theory.


Abstract:
  The concept of E-learning in Universities has grown rapidly over the years to
include not just only a learning management system but also tools initially not
designed for learning such as Facebook and advanced learning tools, for example
games, simulations and virtualization. As a result, Cloud-based LMS is being
touted as the next evolution of the traditional LMS. It is hoped that Cloud
based LMS will resolve some of the challenges associated with the traditional
LMS implementation process. In a previous study, we reported that lack of
involvement of faculty and students in the LMS implementation process results
in the limited use of the LMS by faculty and students. The question then is,
Will the cloud-based LMS resolve these issues? We conducted a review of
literature and presented an overview of the traditional LMS, cloud computing
and the cloudbased LMS and we described how the cloud computing LMS resolve
issues raised by faculty and students. we find that even though, cloud-based
LMS resolve most of the technical issues associated with the traditional LMS,
some of the human issues were not resolved. We hope that this study draws
attention to non-technical issues associated with the LMS implementation
process.


Abstract:
  Deep learning is vulnerable to adversarial examples. Many defenses based on
randomized neural networks have been proposed to solve the problem, but fail to
achieve robustness against attacks using proxy gradients such as the
Expectation over Transformation (EOT) attack. We investigate the effect of the
adversarial attacks using proxy gradients on randomized neural networks and
demonstrate that it highly relies on the directional distribution of the loss
gradients of the randomized neural network. We show in particular that proxy
gradients are less effective when the gradients are more scattered. To this
end, we propose Gradient Diversity (GradDiv) regularizations that minimize the
concentration of the gradients to build a robust randomized neural network. Our
experiments on MNIST, CIFAR10, and STL10 show that our proposed GradDiv
regularizations improve the adversarial robustness of randomized neural
networks against a variety of state-of-the-art attack methods. Moreover, our
method efficiently reduces the transferability among sample models of
randomized neural networks.


Abstract:
  This paper is devoted to show that the last quarter of the past century can
be considered as the golden age of the Mathematical Finance. In this period the
collaboration of great economists and the best generation of probabilists, most
of them from the Strasbourg's School led by Paul Andr\'e Meyer, gave rise to
the foundations of this discipline. They established the two fundamentals
theorems of arbitrage theory, close formulas for options, the main modelling a


Abstract:
  Two dimensional SrTiO3-based interfaces stand out among non-centrosymmetric
superconductors due to their intricate interplay of gate tunable Rashba
spin-orbit coupling and multi-orbital electronic occupations, whose combination
theoretically prefigures various forms of non-standard superconductivity.
However, a convincing demonstration by phase sensitive measurements has been
elusive so far. Here, by employing superconducting transport measurements in
nano-devices we present clear-cut experimental evidences of unconventional
superconductivity in the LaAlO3/SrTiO3 interface. The central observations are
the substantial anomalous enhancement of the critical current by small magnetic
fields applied perpendicularly to the plane of electron motion, and the
asymmetric response with respect to the magnetic field direction. These
features have a unique trend in intensity and sign upon electrostatic gating
that, together with their dependence on temperature and nanowire dimensions,
cannot be accommodated within a scenario of canonical spin-singlet
superconductivity. We theoretically demonstrate that the hall-marks of the
experimental observations unambiguously indicate a coexistence of Josephson
channels with sign difference and intrinsic phase shift. The character of these
findings establishes the occurrence of independent components of unconventional
pairing in the superconducting state due to inversion symmetry breaking. The
outcomes open new venues for the investigation of multi-orbital
non-centrosymmetric superconductivity and Josephson-based devices for quantum
technologies.


Abstract:
  We clarify the undecided case $c_2 = 3$ of a theorem of Ein, Hartshorne and
Vogelaar [Math. Ann. 259 (1982), 541--569] about the restriction of a stable
rank 3 vector bundle with $c_1 = 0$ on the projective 3-space to a general
plane. It turns out that there are more exceptions to the stable restriction
property than those conjectured by the three authors. One of them is a
Schwarzenberger bundle (twisted by $-1$); it has $c_3 = 6$. There are also some
exceptions with $c_3 = 2$ (plus, of course, their duals). We also prove, for
completeness, the basic properties of the corresponding moduli spaces; they are
all nonsingular and connected, of dimension 28.


Abstract:
  The Variational Quantum Eigensolver (VQE) is a promising algorithm for Noisy
Intermediate Scale Quantum (NISQ) computation. Verification and validation of
NISQ algorithms' performance on NISQ devices is an important task. We consider
the exactly-diagonalizable Lipkin-Meshkov-Glick (LMG) model as a candidate for
benchmarking NISQ computers. We use the Bethe ansatz to construct eigenstates
of the trigonometric LMG model using quantum circuits inspired by the LMG's
underlying algebraic structure. We construct circuits with depth
$\mathcal{O}(N)$ and $\mathcal{O}(\log_2N)$ that can prepare any trigonometric
LMG eigenstate of $N$ particles. The number of gates required for both circuits
is $\mathcal{O}(N)$. The energies of the eigenstates can then be measured and
compared to the exactly-known answers.


Abstract:
  In this article, we deal with the order of growth of solutions of
non-homogeneous linear differential-difference equation \begin{equation*}
\sum_{i=0}^{n}\sum_{j=0}^{m}A_{ij}f^{(j)}(z+c_{i})=F(z), \end{equation*} where
$A_{ij},$ $F\left( z\right) $ are entire or meromorphic functions and $c_{i}$
$\left( 0,1,...,n\right) $ are non-zero distinct complex numbers. Under the
sufficient condition that there exists one coefficient having the maximal lower
order or having the maximal lower type strictly greater than the order or the
type of other coefficients, we obtain estimates of the lower bound of the order
of meromorphic solutions of the above equation.


Abstract:
  Labeling objects at a subordinate level typically requires expert knowledge,
which is not always available when using random annotators. As such, learning
directly from web images for fine-grained recognition has attracted broad
attention. However, the presence of label noise and hard examples in web images
are two obstacles for training robust fine-grained recognition models.
Therefore, in this paper, we propose a novel approach for removing irrelevant
samples from real-world web images during training, while employing useful hard
examples to update the network. Thus, our approach can alleviate the harmful
effects of irrelevant noisy web images and hard examples to achieve better
performance. Extensive experiments on three commonly used fine-grained datasets
demonstrate that our approach is far superior to current state-of-the-art
web-supervised methods.


Abstract:
  In this paper, we investigate joint information-theoretic security and covert
communication on a network in the presence of a single transmitter (Alice), a
friendly jammer, a single untrusted user, two legitimate users, and a single
warden of the channel (Willie). In the considered network, one of the
authorized users, Bob, needs a secure and covert communication, and therefore
his message must be sent securely, and at the same time, the existence of his
communication with the transmitter should not be detected by the channel's
warden, Willie, Meanwhile, another authorized user, Carol, needs covert
communication. The purpose of secure communication is to prevent the message
being decoded by the untrusted user who is present on the network, which leads
us to use one of the physical layer security methods, named the secure
transmission of information theory. In some cases, in addition to protecting
the content of the message, it is important for the user that the existence of
the transmission not being detected by an adversary, which leads us to covert
communication. In the proposed network model, it is assumed that for covert
communication requirements, Alice will not send any messages to legitimate
users in one time slot and in another time slot will send to them both (Bob and
Carol). One of the main challenges in covert communication is low transmission
rate, because we have to reduce the transmission power such that the main
message get hide in background noise.


Abstract:
  Permutation Mastermind is a version of the classical mastermind game in which
the number of positions $n$ is equal to the number of colors $k$, and
repetition of colors is not allowed, neither in the codeword nor in the
queries. In this paper we solve the main open question from Glazik, J\"ager,
Schiemann and Srivastav (2021), who asked whether their bound of $O(n^{1.525})$
for the static version can be improved to $O(n \log n)$, which would be best
possible. By using a simple probabilistic argument we show that this is indeed
the case.


Abstract:
  This is the second in a sequence of three papers investigating the question
for which positive integers $m$ there exists a maximal antichain of size $m$ in
the Boolean lattice $B_n$ (the power set of $[n]:=\{1,2,\dots,n\}$, ordered by
inclusion). In the previous paper we characterized those $m$ between
$\binom{n}{\lceil n/2\rceil}-\lceil n/2\rceil^2$ and the maximum size
$\binom{n}{\lceil n/2 \rceil}$ that are not sizes of maximal antichains. In
this paper we show that all smaller $m$ are sizes of maximal antichains.


Abstract:
  The Planetary Instrument for X-ray Lithochemistry (PIXL) is a micro-focus
X-ray fluorescence spectrometer mounted on the robotic arm of NASA's
Perseverance rover. PIXL will acquire high spatial resolution observations of
rock and soil chemistry, rapidly analyzing the elemental chemistry of a target
surface. In 10 seconds, PIXL can use its powerful 120 micrometer diameter X-ray
beam to analyze a single, sand-sized grain with enough sensitivity to detect
major and minor rock-forming elements, as well as many trace elements. Over a
period of several hours, PIXL can autonomously scan an area of the rock surface
and acquire a hyperspectral map comprised of several thousand individual
measured points.


Abstract:
  We review some recent results concerning the Hartle--Hawking wavefunction of
the universe. We focus on pure Einstein theory of gravity in the presence of a
positive cosmological constant. We carefully implement the gauge-fixing
procedure for the minisuperspace path integral, by identifying the single
modulus and by using diffeomorphism-invariant measures for the ghosts and the
scale factor. Field redefinitions of the scale factor yield different
prescriptions for computing the no-boundary ground-state wavefunction. They
give rise to an infinite set of ground-state wavefunctions, each satisfying a
different Wheeler--DeWitt equation, at the semi-classical level. The
differences in the form of the Wheeler--DeWitt equations can be traced to
ordering ambiguities in constructing the Hamiltonian upon canonical
quantization. However, the inner products of the corresponding Hilbert spaces
turn out to be equivalent, at least semi-classically. Thus, the model yields
universal quantum predictions.


Abstract:
  The virtual try-on task is so attractive that it has drawn considerable
attention in the field of computer vision. However, presenting the
three-dimensional (3D) physical characteristic (e.g., pleat and shadow) based
on a 2D image is very challenging. Although there have been several previous
studies on 2D-based virtual try-on work, most 1) required user-specified target
poses that are not user-friendly and may not be the best for the target
clothing, and 2) failed to address some problematic cases, including facial
details, clothing wrinkles and body occlusions. To address these two
challenges, in this paper, we propose an innovative template-free try-on image
synthesis (TF-TIS) network. The TF-TIS first synthesizes the target pose
according to the user-specified in-shop clothing. Afterward, given an in-shop
clothing image, a user image, and a synthesized pose, we propose a novel model
for synthesizing a human try-on image with the target clothing in the best
fitting pose. The qualitative and quantitative experiments both indicate that
the proposed TF-TIS outperforms the state-of-the-art methods, especially for
difficult cases.


Abstract:
  We study piece-wise constant signals corrupted by additive Gaussian noise
over a $d$-dimensional lattice. Data of this form naturally arise in a host of
applications, and the tasks of signal detection or testing, de-noising and
estimation have been studied extensively in the statistical and signal
processing literature. In this paper we consider instead the problem of
partition recovery, i.e.~of estimating the partition of the lattice induced by
the constancy regions of the unknown signal, using the
computationally-efficient dyadic classification and regression tree (DCART)
methodology proposed by \citep{donoho1997cart}. We prove that, under
appropriate regularity conditions on the shape of the partition elements, a
DCART-based procedure consistently estimates the underlying partition at a rate
of order $\sigma^2 k^* \log (N)/\kappa^2$, where $k^*$ is the minimal number of
rectangular sub-graphs obtained using recursive dyadic partitions supporting
the signal partition, $\sigma^2$ is the noise variance, $\kappa$ is the minimal
magnitude of the signal difference among contiguous elements of the partition
and $N$ is the size of the lattice. Furthermore, under stronger assumptions,
our method attains a sharper estimation error of order
$\sigma^2\log(N)/\kappa^2$, independent of $k^*$, which we show to be minimax
rate optimal. Our theoretical guarantees further extend to the partition
estimator based on the optimal regression tree estimator (ORT) of
\cite{chatterjee2019adaptive} and to the one obtained through an NP-hard
exhaustive search method. We corroborate our theoretical findings and the
effectiveness of DCART for partition recovery in simulations.


Abstract:
  This paper investigates the problem of correcting multiple criss-cross
insertions and deletions in arrays. More precisely, we study the unique
recovery of $n \times n$ arrays affected by $t$-criss-cross deletions defined
as any combination of $t_r$ row and $t_c$ column deletions such that $t_r + t_c
= t$ for a given $t$. We show an equivalence between correcting $t$-criss-cross
deletions and $t$-criss-cross insertions and show that a code correcting
$t$-criss-cross insertions/deletions has redundancy at least $tn + t \log n -
\log(t!)$. Then, we present an existential construction of $t$-criss-cross
insertion/deletion correcting code with redundancy bounded from above by $tn +
\mathcal{O}(t^2 \log^2 n)$. The main ingredients of the presented code
construction are systematic binary $t$-deletion correcting codes and Gabidulin
codes. The first ingredient helps locating the indices of the inserted/deleted
rows and columns, thus transforming the insertion/deletion-correction problem
into a row/column erasure-correction problem which is then solved using the
second ingredient.


Abstract:
  The concept of mean inactivity time plays a crucial role in reliability, risk
theory and life testing. In this regard, we introduce a weighted mean
inactivity time function by considering a non-negative weight function. Based
on this function, we provide expressions for the variance of transformed random
variable and the weighted generalized cumulative entropy. The latter concept is
an important measure of uncertainty which is shift-dependent and is of interest
in certain applied contexts, such as reliability or mathematical neurobiology.
Moreover, based on the comparison of mean inactivity times of a certain
function of two lifetime random variables, we introduce and study a new
stochastic order in terms of the weighted mean inactivity time function.
Several characterizations and preservation properties of the new order under
shock models, random maxima and renewal theory are discussed.


Abstract:
  Prepositional supersense annotation is time-consuming and requires expert
training. Here, we present two sensible methods for obtaining prepositional
supersense annotations by eliciting surface substitution and similarity
judgments. Four pilot studies suggest that both methods have potential for
producing prepositional supersense annotations that are comparable in quality
to expert annotations.


Abstract:
  Deep learning methods have reached state-of-the-art performance in cardiac
image segmentation. Currently, the main bottleneck towards their effective
translation into clinics requires assuring continuous high model performance
and segmentation results. In this work, we present a novel learning framework
to monitor the performance of heart segmentation models in the absence of
ground truth. Formulated as an anomaly detection problem, the monitoring
framework allows deriving surrogate quality measures for a segmentation and
allows flagging suspicious results. We propose two different types of quality
measures, a global score and a pixel-wise map. We demonstrate their use by
reproducing the final rankings of a cardiac segmentation challenge in the
absence of ground truth. Results show that our framework is accurate, fast, and
scalable, confirming it is a viable option for quality control monitoring in
clinical practice and large population studies.


Abstract:
  We introduce a non-standard model for percolation on the integer lattice
$\mathbb Z^2$. Randomly assign to each vertex $a \in \mathbb Z^2$ a potential,
denoted $\phi_a$, chosen independently and uniformly from the interval $[0,
1]$. For fixed $\epsilon \in [0,1]$, draw a directed edge from vertex $a$ to a
nearest-neighbor vertex $b$ if $\phi_b < \phi_a + \epsilon$, yielding a
directed subgraph of the infinite directed graph $\overrightarrow{G}$ whose
vertex set is $\mathbb Z^2$, with nearest-neighbor edge set. We define notions
of weak and strong percolation for our model, and observe that when $\epsilon =
0$ the model fails to percolate weakly, while for $\epsilon = 1$ it percolates
strongly. We show that there is a positive $\epsilon_0$ so that for $0 \le
\epsilon \le \epsilon_0$, the model fails to percolate weakly, and that when
$\epsilon > p_\text{site}$, the critical probability for standard site
percolation in $\mathbb Z^2$, the model percolates strongly. We study the
number of infinite strongly connected clusters occurring in a typical
configuration. We show that for these models of percolation on directed graphs,
there are some subtle issues that do not arise for undirected percolation.
Although our model does not have the finite energy property, we are able to
show that, as in the standard model, the number of infinite strongly connected
clusters is almost surely 0, 1 or $\infty$.


Abstract:
  This paper describes a method for using Transformer-based Language Models
(TLMs) to understand public opinion from social media posts. In this approach,
we train a set of GPT models on several COVID-19 tweet corpora that reflect
populations of users with distinctive views. We then use prompt-based queries
to probe these models to reveal insights into the biases and opinions of the
users. We demonstrate how this approach can be used to produce results which
resemble polling the public on diverse social, political and public health
issues. The results on the COVID-19 tweet data show that transformer language
models are promising tools that can help us understand public opinions on
social media at scale.


Abstract:
  In 2017 April, the Event Horizon Telescope (EHT) observed the near-horizon
region around the supermassive black hole at the core of the M87 galaxy. These
1.3 mm wavelength observations revealed a compact asymmetric ring-like source
morphology. This structure originates from synchrotron emission produced by
relativistic plasma located in the immediate vicinity of the black hole. Here
we present the corresponding linear-polarimetric EHT images of the center of
M87. We find that only a part of the ring is significantly polarized. The
resolved fractional linear polarization has a maximum located in the southwest
part of the ring, where it rises to the level of about 15%. The polarization
position angles are arranged in a nearly azimuthal pattern. We perform
quantitative measurements of relevant polarimetric properties of the compact
emission and find evidence for the temporal evolution of the polarized source
structure over one week of EHT observations. The details of the polarimetric
data reduction and calibration methodology are provided. We carry out the data
analysis using multiple independent imaging and modeling techniques, each of
which is validated against a suite of synthetic data sets. The gross
polarimetric structure and its apparent evolution with time are insensitive to
the method used to reconstruct the image. These polarimetric images carry
information about the structure of the magnetic fields responsible for the
synchrotron emission. Their physical interpretation is discussed in an
accompanying publication.


Abstract:
  The dense plasma focus is a plasma discharge powered by a capacitor bank.
Standard diagnostics include measurement of the time derivative of the current
through and the voltage across its connections with the capacitor bank.
Interpretation of this diagnostic data often involves some assumptions
regarding the representation of the dense plasma focus as a time varying
inductance. One of the characteristic features of the current derivative
waveform is a relatively sharp dip and an associated sharp voltage spike. This
has often been interpreted as a result of a rapid rise in the time varying
inductance of the plasma. Sometimes, an anomalous plasma impedance is invoked.
This Letter discusses instances where such interpretation creates conceptual
difficulties. A first principles approach to the representation of the dense
plasma focus as a circuit element reveals some fundamental problems with the
traditional representation of plasma focus as a time varying inductance. The
anomalous impedance is shown to be necessary to account for the difference in
the motional impedance implied by a time-varying inductance in the circuit
element representation and a first principles description based on Poynting's
Theorem. Dynamo effects that convert post-stagnation local motion of plasma
into 3-dimensional magnetic fields are shown to contribute to the effective
inductance of the plasma focus and resolve the observed conceptual difficulties


Abstract:
  We discuss the solvability of a fairly general class of systems of perturbed
Hammerstein integral equations with functional terms that depend on several
parameters. The nonlinearities and the functionals are allowed to depend on the
components of the system and their derivatives. The results are applicable to
systems of nonlocal second order ordinary differential equations subject to
functional boundary conditions, this is illustrated in an example. Our approach
is based on the classical fixed point index.


Abstract:
  We deal with the as yet unresolved exponential stability problem for a
stretched Euler-Bernoulli beam on a star-shaped geometric graph with three
identical edges. The edges are hinged with respect to the boundary vertices.
The inner vertex is capable of both translation and rotation, the latter of
which is subject to a combination of elastic and frictional effects. We present
detailed results on the asymptotic location and structure of the spectrum of
the linear operator associated with the spectral problem in Hilbert space.
Within this framework it is shown that the eigenvectors have the property of
forming an unconditional or Riesz basis, which makes it possible to directly
deduce the exponential stability of the corresponding $C_0$-semigroup. As an
aside it is shown that the particular choice of connectivity conditions ensures
the exponential stability even when the elasticity acting on the slopes of the
edges is absent.


Abstract:
  In classical set theory, there are many equivalent ways to introduce
ordinals. In a constructive setting, however, the different notions split
apart, with different advantages and disadvantages for each. We consider three
different notions of ordinals in homotopy type theory, and show how they relate
to each other: A notation system based on Cantor normal forms, a refined notion
of Brouwer trees (inductively generated by zero, successor and countable
limits), and wellfounded extensional orders. For Cantor normal forms, most
properties are decidable, whereas for wellfounded extensional transitive
orders, most are undecidable. Formulations for Brouwer trees are usually
partially decidable. We demonstrate that all three notions have properties
expected of ordinals: their order relations, although defined differently in
each case, are all extensional and wellfounded, and the usual arithmetic
operations can be defined in each case. We connect these notions by
constructing structure preserving embeddings of Cantor normal forms into
Brouwer trees, and of these in turn into wellfounded extensional orders. We
have formalised most of our results in cubical Agda.


Abstract:
  Given a closed connected spin manifold M with non-negative and somewhere
positive scalar curvature, we show that the Dirac operator twisted with any
flat Hilbert module bundle is invertible.


Abstract:
  In a recent Letter [Phys. Rev. Lett. 125, 180604 (2020)], we introduced a
closed-form analytic expression for the average bipartite von Neumann
entanglement entropy of many-body eigenstates of random quadratic Hamiltonians.
Namely, of Hamiltonians whose single-particle eigenstates have random
coefficients in the position basis. A paradigmatic Hamiltonian for which the
expression is valid is the quadratic Sachdev-Ye-Kitaev (SYK2) model in its
Dirac fermion formulation. Here we show that the applicability of our result is
much broader. Most prominently, it is also relevant for local Hamiltonians such
as the three-dimensional (3D) Anderson model at weak disorder. Moreover, it
describes the average entanglement entropy in Hamiltonians without
particle-number conservation, such as the SYK2 model in the Majorana fermion
formulation and the 3D Anderson model with additional terms that break
particle-number conservation. We extend our analysis to the average bipartite
second R\'enyi entanglement entropy of eigenstates of the same quadratic
Hamiltonians, which is derived analytically and tested numerically. We
conjecture that our results for the entanglement entropies of many-body
eigenstates apply to quadratic Hamiltonians whose single-particle eigenstates
exhibit quantum chaos, to which we refer as quantum-chaotic quadratic
Hamiltonians.


Abstract:
  The paper proposes an optimal management strategy for a system composed by a
battery and a photovoltaic power plant. This integrated system is called to
deliver the photovoltaic power and to simultaneously provide droop-based
primary frequency regulation to the main grid. The battery state-of-energy is
controlled by power offset signals, which are determined using photovoltaic
energy generation forecasts and predictions of the energy required to operate
frequency regulation. A two level control architecture is developed. A
day-ahead planning algorithm schedules the energy profile which is traded at
the day-ahead market and defines the primary control reserve that the
integrated system is able to provide in the considered day. During the day
operations, a second level algorithm corrects the dispatched plan using updated
information, in order to guarantee a continuous and reliable service. Both
control algorithms take into account the uncertainties of the photovoltaic
generation and of the frequency dynamics using stochastic optimization.


Abstract:
  One of the most widely used methods for solving large-scale stochastic
optimization problems is distributed asynchronous stochastic gradient descent
(DASGD), a family of algorithms that result from parallelizing stochastic
gradient descent on distributed computing architectures (possibly)
asychronously. However, a key obstacle in the efficient implementation of DASGD
is the issue of delays: when a computing node contributes a gradient update,
the global model parameter may have already been updated by other nodes several
times over, thereby rendering this gradient information stale. These delays can
quickly add up if the computational throughput of a node is saturated, so the
convergence of DASGD may be compromised in the presence of large delays. Our
first contribution is that, by carefully tuning the algorithm's step-size,
convergence to the critical set is still achieved in mean square, even if the
delays grow unbounded at a polynomial rate. We also establish finer results in
a broad class of structured optimization problems (called variationally
coherent), where we show that DASGD converges to a global optimum with
probability $1$ under the same delay assumptions. Together, these results
contribute to the broad landscape of large-scale non-convex stochastic
optimization by offering state-of-the-art theoretical guarantees and providing
insights for algorithm design.


Abstract:
  Anatomical motion and deformation pose challenges to the understanding of the
delivered dose distribution during radiotherapy treatments. Hence, deformable
image registration (DIR) algorithms are increasingly used to map contours and
dose distributions from one image set to another. However, the lack of
validation tools slows their clinical adoption, despite their commercial
availability. This work presents a novel water-equivalent deformable dosimeter
that simultaneously measures the dose distribution and tracks deformation
vector fields (DVF). The dosimeter in made of an array of 19 scintillating
fiber detectors embedded in a cylindrical elastomer matrix. It is imaged by two
pairs of stereoscopic cameras tracking the position and angulation of the
scintillators, while measuring the dose. The resulting system provides a
precision of 0.3 mm on DVF measurements. The dosimeter was irradiated with
5$\times$3, 4$\times$3 and 3$\times$3 cm$^2$ 6 MV photon beams in both fixed
and deformed conditions. The measured DVF was compared to the one computed with
a DIR algorithm (Plastimatch). The deviations between the computed and measured
DVFs was below 1.5 mm. As for dose measurements, the dosimeter acquired the
dose distribution in fixed and deformed conditions within 1\% of the treatment
planning system calculation and complementary dose validation using the
Hyperscint dosimetry system. Using the demonstrated qualities of scintillating
detectors, we developed a real-time, water-equivalent deformable dosimeter.
Given it's sensor tracking position precision and dose measurements accuracy,
the developed detector is a promising tools for the validation of DIR
algorithms as well as dose distribution measurements under fixed and deformed
conditions.


Abstract:
  Sparse Principal Component Analysis (SPCA) is widely used in data processing
and dimension reduction; it uses the lasso to produce modified principal
components with sparse loadings for better interpretability. However, sparse
PCA never considers an additional grouping structure where the loadings share
similar coefficients (i.e., feature grouping), besides a special group with all
coefficients being zero (i.e., feature selection). In this paper, we propose a
novel method called Feature Grouping and Sparse Principal Component Analysis
(FGSPCA) which allows the loadings to belong to disjoint homogeneous groups,
with sparsity as a special case. The proposed FGSPCA is a subspace learning
method designed to simultaneously perform grouping pursuit and feature
selection, by imposing a non-convex regularization with naturally adjustable
sparsity and grouping effect. To solve the resulting non-convex optimization
problem, we propose an alternating algorithm that incorporates the
difference-of-convex programming, augmented Lagrange and coordinate descent
methods. Additionally, the experimental results on real data sets show that the
proposed FGSPCA benefits from the grouping effect compared with methods without
grouping effect.


Abstract:
  The resource constraints and accuracy requirements for Internet of Things
(IoT) memory chips need three-dimensional (3D) monolithic integrated circuits,
of which the increasing stack layers (currently more than 176) also cause
excessive energy consumption and increasing wire length. In this paper, a novel
3D wireless network on chips (3DWiNoCs) model transmitting signal directly to
the destination in arbitrary layer is proposed and characterized. However, due
to the the reflection and refraction characteristics in each layer, the complex
and diverse wireless paths in 3DWiNoC add great difficulty to the channel
characterization. To facilitate the modeling in massive layer NoC situation,
both boundary-less model boundary-constrained 3DWiNoC model are proposed, of
which the channel gain can be obtained by a computational efficient approximate
algorithm. These 3DWiNoC models with approximation algorithm can well
characterize the 3DWiNoC channel in aspect of complete reflection and
refraction characteristics, and avoid massive wired connections, high power
consumption of cross-layer communication and high-complexity of 3DWiNoC channel
characterization. Numerical results show that: 1) The difference rate between
the two models is lower than 0.001% (signal transmit through 20 layers); 2) the
channel gain decreases sharply if refract time increases; and 3) the
approximate algorithm can achieve an acceptable accuracy (error rate lower than
0.1%).


Abstract:
  We present Atacama Large Millimeter/submillimeter Array (ALMA) observations
of $\mathrm{^{13}CO(J=1-0)}$ line and 104 GHz continuum emission from NGC 604,
a giant HII region (GHR) in the nearby spiral galaxy M33. Our high spatial
resolution images ( 3.2"$\times$ 2.4", corresponding to $13 \times 10$ pc
physical scale) allow us to detect fifteen molecular clouds. We find spatial
offsets between the $^{13}CO$ and 104 GHz continuum emission and also detect
continuum emission near the centre of the GHR. The identified molecular clouds
have sizes ranging from 5-21 pc, linewidths of 0.3-3.0 $\mathrm{kms^{-1}}$ and
luminosity-derived masses of (0.4-80.5) $\times 10^3$ M$_{\bigodot}$. These
molecular clouds are in near virial equilibrium, with a spearman correlation
coefficient of 0.98. The linewidth-size relationship for these clouds is offset
from the corresponding relations for the Milky Way and for NGC 300, although
this may be an artefact of the dendrogram process.


Abstract:
  Despite that deep neural networks (DNNs) have achieved enormous success in
many domains like natural language processing (NLP), they have also been proven
to be vulnerable to maliciously generated adversarial examples. Such inherent
vulnerability has threatened various real-world deployed DNNs-based
applications. To strength the model robustness, several countermeasures have
been proposed in the English NLP domain and obtained satisfactory performance.
However, due to the unique language properties of Chinese, it is not trivial to
extend existing defenses to the Chinese domain. Therefore, we propose AdvGraph,
a novel defense which enhances the robustness of Chinese-based NLP models by
incorporating adversarial knowledge into the semantic representation of the
input. Extensive experiments on two real-world tasks show that AdvGraph
exhibits better performance compared with previous work: (i) effective - it
significantly strengthens the model robustness even under the adaptive attacks
setting without negative impact on model performance over legitimate input;
(ii) generic - its key component, i.e., the representation of connotative
adversarial knowledge is task-agnostic, which can be reused in any
Chinese-based NLP models without retraining; and (iii) efficient - it is a
light-weight defense with sub-linear computational complexity, which can
guarantee the efficiency required in practical scenarios.


Abstract:
  CoSi single crystal is a known realization of a chiral topological semimetal
with simultaneously broken mirror and inversion symmetries. In addition to the
symmetry-induced spin-orbit coupling, surface ferromagnetism is known in
nominally diamagnetic CoSi structures, which appears due to the distorted bonds
and ordered vacancies near the surface. We experimentally investigate electron
transport through a thin CoSi flake at high current density. Surprisingly, we
demonstrate $dV/dI(I)$ curves which are qualitatively similar to ones for
ferromagnetic multilayers with characteristic $dV/dI$ magnon peaks and
unconventional magnetic field evolution of the peaks' positions. We understand
these observations as a result of current-induced spin polarization due to the
significant spin-orbit coupling in CoSi. Scattering of non-equilibrium
spin-polarized carriers within the surface ferromagnetic layer is responsible
for the precessing spin-wave excitations, so the observed magnon modes are the
joint effect of surface ferromagnetism and spin-orbit coupling in a CoSi chiral
topological semimetal. Thus, thin CoSi flakes behave as magnetic conductors
with broken inversion symmetry, which is important for different spintronic
phenomena.


Abstract:
  The communication technology revolution in this era has increased the use of
smartphones in the world of transportation. In this paper, we propose to
leverage IoT device data, capturing passengers' smartphones' Wi-Fi data in
conjunction with weather conditions to predict the expected number of
passengers waiting at a bus stop at a specific time using deep learning models.
Our study collected data from the transit bus system at James Madison
University (JMU) in Virginia, USA. This paper studies the correlation between
the number of passengers waiting at bus stops and weather conditions.
Empirically, an experiment with several bus stops in JMU, was utilized to
confirm a high precision level. We compared our Deep Neural Network (DNN) model
against two baseline models: Linear Regression (LR) and a Wide Neural Network
(WNN). The gap between the baseline models and DNN was 35% and 14% better Mean
Squared Error (MSE) scores for predictions in favor of the DNN compared to LR
and WNN, respectively.


Abstract:
  Despite rapid progress, current deep learning methods face a number of
critical challenges. These include high energy consumption, catastrophic
forgetting, dependance on global losses, and an inability to reason
symbolically. By combining concepts from information bottleneck theory and
vector-symbolic architectures, we propose and implement a novel information
processing architecture, the 'Bridge network.' We show this architecture
provides unique advantages which can address the problem of global losses and
catastrophic forgetting. Furthermore, we argue that it provides a further basis
for increasing energy efficiency of execution and the ability to reason
symbolically.


Abstract:
  Federated Learning (FL) is a promising framework that has great potentials in
privacy preservation and in lowering the computation load at the cloud. FedAvg
and FedProx are two widely adopted algorithms. However, recent work raised
concerns on these two methods: (1) their fixed points do not correspond to the
stationary points of the original optimization problem, and (2) the common
model found might not generalize well locally.
  In this paper, we alleviate these concerns. Towards this, we adopt the
statistical learning perspective yet allow the distributions to be
heterogeneous and the local data to be unbalanced. We show, in the general
kernel regression setting, that both FedAvg and FedProx converge to the
minimax-optimal error rates. Moreover, when the kernel function has a finite
rank, the convergence is exponentially fast. Our results further analytically
quantify the impact of the model heterogeneity and characterize the federation
gain - the reduction of the estimation error for a worker to join the federated
learning compared to the best local estimator. To the best of our knowledge, we
are the first to show the achievability of minimax error rates under FedAvg and
FedProx, and the first to characterize the gains in joining FL. Numerical
experiments further corroborate our theoretical findings on the statistical
optimality of FedAvg and FedProx and the federation gains.


Abstract:
  Conventional indirect dark matter (DM) searches look for an excess in the
electromagnetic emission from the sky that cannot be attributed to known
astrophysical sources. Here, we argue that the photon polarisation is an
important feature to understand new physics interactions and can be exploited
to improve our sensitivity to DM. In particular, circular polarisation can be
generated from Beyond the Standard Model interactions if they violate parity
and there is an asymmetry in the number of particles which participate in the
interaction. In this work, we consider a simplified model for fermionic
(Majorana) DM and study the circularly polarised gamma rays below 10 GeV from
the scattering of cosmic ray electrons on DM. We calculate the differential
flux of positive and negative polarised photons from the Galactic Center and
show that the degree of circular polarization can reach up to 90%. Finally,
once collider and DM constraints have been taken into account, we estimate the
required sensitivity from future experiments to detect this signal finding
that, although a distinctive peak will be present in the photon flux spectrum,
a near future observation is unlikely. However, different sources or models not
considered in this work could provide higher intensity fluxes, leading to a
possible detection by e-ASTROGAM. In the event of a discovery, we argue that
the polarisation fraction is a valuable characterisation feature of the new
sector.


Abstract:
  Inspired by the studies on the influence of transition metal impurities in
high Tc superconductors and what is already known about nonmagnetic suppression
of Tc in unconventional superconductors, we set out to investigate the behavior
of the nonmagnetic disordered elastic scattering for a realistic 2D anisotropic
high Tc superconductor with line nodes and a Fermi surface in the tight-binding
approximation. For this purpose, we performed a detailed self-consistent 2D
numerical study of the disordered averaged scattering matrix with nonmagnetic
impurities and a singlet line nodes order parameter, varying the concentration
and the strength of the impurities potential in the Born, intermediate and
unitary limits. In a high Tc anisotropic superconductor with a tight binding
dispersion law averaging over the Fermi surface, including hopping parameters
and an order parameter in agreement with experimental data, the tight-binding
approximation reflects the anisotropic effects. In this study, we also included
a detailed visualization of the behavior of the scattering matrix with
different sets of physical parameters involved in the nonmagnetic disorder,
which allowed us to model the dressed scattering behavior in different regimes
for very low and high energies. With this study, we demonstrate that the
scattering elastic matrix is affected by the non-magnetic disorder, as well as
the importance of an order parameter and a Fermi surface in agreement with
experiments when studying this effect in unconventional superconductors.


Abstract:
  Let $X$ be a complex space and $M$ a pure Hodge module with strict support
$X$. We introduce a kind of coherent subsheaf $S(M,\varphi)$ of M. Saito's
$S(M)$ which is a combination of $S(M)$ and the multiplier ideal sheaf
$\mathscr{I}(\varphi)$. An $L^2$-resolution of $S(M,\varphi)$ is constructed.
This generalizes MacPherson's conjecture on the $L^2$-representation of the
Grauert-Riemenschneider sheaf. Various vanishing theorems for $S(M)$ (Saito's
vanishing, Kawamata-Viehweg vanishing and some new ones like Nadel vanishing,
partial vanishing) are proved via standard differential geometric arguments.
Some applications on the relative version of Fujita's conjecture are presented.


Abstract:
  Exposing a solution to a temperature gradient can lead to the accumulation of
particles on either the cold or warm side. This phenomenon, known as
thermophoresis, has been discovered more than a century ago, and yet its
microscopic origin is still debated. Here, we show that thermophoresis can be
observed in any system such that the transitions between different internal
states are modulated by temperature and such that different internal states
have different transport properties. We establish thermophoresis as a genuine
non-equilibrium effect, whereby a system of currents in real and internal space
that is consistent with the thermodynamic necessity of transporting heat from
warm to cold regions. Our approach also provides an expression for the Soret
coefficient, which decides whether particles accumulate on the cold or on the
warm side, that is associated with the correlation between the energies of the
internal states and their transport properties, that instead remain
system-specific quantities. Finally, we connect our results to previous
approaches based on close-to-equilibrium energetics. Our thermodynamically
consistent approach thus encompasses and generalizes previous findings.


Abstract:
  The assessment of program functionality can generally be accomplished with
straight-forward unit tests. However, assessing the design quality of a program
is a much more difficult and nuanced problem. Design quality is an important
consideration since it affects the readability and maintainability of programs.
Assessing design quality and giving personalized feedback is very time
consuming task for instructors and teaching assistants. This limits the scale
of giving personalized feedback to small class settings. Further, design
quality is nuanced and is difficult to concisely express as a set of rules. For
these reasons, we propose a neural network model to both automatically assess
the design of a program and provide personalized feedback to guide students on
how to make corrections. The model's effectiveness is evaluated on a corpus of
student programs written in Python. The model has an accuracy rate from 83.67%
to 94.27%, depending on the dataset, when predicting design scores as compared
to historical instructor assessment. Finally, we present a study where students
tried to improve the design of their programs based on the personalized
feedback produced by the model. Students who participated in the study improved
their program design scores by 19.58%.


Abstract:
  A growing area of research in epidemiology is the identification of
health-related sibling spillover effects, or the effect of one individual's
exposure on their sibling's outcome. The health and health care of family
members may be inextricably confounded by unobserved factors, rendering
identification of spillover effects within families particularly challenging.
We demonstrate a gain-score regression method for identifying
exposure-to-outcome spillover effects within sibling pairs in a linear fixed
effects framework. The method can identify the exposure-to-outcome spillover
effect if only one sibling's exposure affects the other's outcome; and it
identifies the difference between the spillover effects if both siblings'
exposures affect the others' outcomes. The method fails in the presence of
outcome-to-exposure spillover and outcome-to-outcome spillover. Analytic
results and Monte Carlo simulations demonstrate the method and its limitations.
To exercise this method, we estimate the spillover effect of a child's preterm
birth on an older sibling's literacy skills, measured by the Phonological
Awarenesses Literacy Screening-Kindergarten test. We analyze 20,010 sibling
pairs from a population-wide, Wisconsin-based (United States) birth cohort.
Without covariate adjustment, we estimate that preterm birth modestly decreases
an older sibling's test score (-2.11 points; 95% confidence interval: -3.82,
-0.40 points). In conclusion, gain-scores are a promising strategy for
identifying exposure-to-outcome spillovers in sibling pairs while controlling
for sibling-invariant unobserved confounding in linear settings.


Abstract:
  We propose a supervised machine learning algorithm, decision trees, to
analyze molecular dynamics output. The approach aims to identify the
predominant geometric features which correlate with trajectories that
transition between two arbitrarily defined states. The data-based algorithm
aims to identify such features in an approach which is unbiased by human
"chemical intuition". We demonstrate the method by analyzing proton exchange
reactions in formic acid (FA) solvated in small water clusters. The simulations
were performed with ab initio molecular dynamics combined with a method for
generating rare events, specifically path sampling. Our machine learning
analysis identified mechanistic descriptions of the proton transfer reaction
for the different water clusters.


Abstract:
  In a recent Letter, Dornheim et al. [PRL 125, 085001 (2020)] have
investigated the nonlinear density response of the uniform electron gas in the
warm dense matter regime. More specifically, they have studied the cubic
response function at the first harmonic, which cannot be neglected in many
situations of experimental relevance. In this work, we go one step further and
study the full spectrum of excitations at the higher harmonics of the original
perturbation based on extensive new ab initio path integral Monte Carlo (PIMC)
simulations. We find that the dominant contribution to the density response
beyond linear response theory is given by the quadratic response function at
the second harmonic in the moderately nonlinear regime. Furthermore, we show
that the nonlinear density response is highly sensitive to exchange-correlation
effects, which makes it a potentially valuable new tool of diagnostics. To this
end, we present a new theoretical description of the nonlinear electronic
density response based on the recent effective static approximation to the
local field correction [PRL 125, 235001 (2020)], which accurately reproduces
our PIMC data with negligible computational cost.


Abstract:
  Message passing Graph Neural Networks (GNNs) provide a powerful modeling
framework for relational data. However, the expressive power of existing GNNs
is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test,
which means GNNs that are not able to predict node clustering coefficients and
shortest path distances, and cannot differentiate between different d-regular
graphs. Here we develop a class of message passing GNNs, named Identity-aware
Graph Neural Networks (ID-GNNs), with greater expressive power than the 1-WL
test. ID-GNN offers a minimal but powerful solution to limitations of existing
GNNs. ID-GNN extends existing GNN architectures by inductively considering
nodes' identities during message passing. To embed a given node, ID-GNN first
extracts the ego network centered at the node, then conducts rounds of
heterogeneous message passing, where different sets of parameters are applied
to the center node than to other surrounding nodes in the ego network. We
further propose a simplified but faster version of ID-GNN that injects node
identity information as augmented node features. Altogether, both versions of
ID-GNN represent general extensions of message passing GNNs, where experiments
show that transforming existing GNNs to ID-GNNs yields on average 40% accuracy
improvement on challenging node, edge, and graph property prediction tasks; 3%
accuracy improvement on node and graph classification benchmarks; and 15% ROC
AUC improvement on real-world link prediction tasks. Additionally, ID-GNNs
demonstrate improved or comparable performance over other task-specific graph
networks.


Abstract:
  Lithium niobate (LN), an outstanding and versatile material, has influenced
our daily life for decades: from enabling high-speed optical communications
that form the backbone of the Internet to realizing radio-frequency filtering
used in our cell phones. This half-century-old material is currently embracing
a revolution in thin-film LN integrated photonics. The success of manufacturing
wafer-scale, high-quality, thin films of LN on insulator (LNOI), accompanied
with breakthroughs in nanofabrication techniques, have made high-performance
integrated nanophotonic components possible. With rapid development in the past
few years, some of these thin-film LN devices, such as optical modulators and
nonlinear wavelength converters, have already outperformed their legacy
counterparts realized in bulk LN crystals. Furthermore, the nanophotonic
integration enabled ultra-low-loss resonators in LN, which unlocked many novel
applications such as optical frequency combs and quantum transducers. In this
Review, we cover -- from basic principles to the state of the art -- the
diverse aspects of integrated thin-film LN photonics, including the materials,
basic passive components, and various active devices based on electro-optics,
all-optical nonlinearities, and acousto-optics. We also identify challenges
that this platform is currently facing and point out future opportunities. The
field of integrated LNOI photonics is advancing rapidly and poised to make
critical impacts on a broad range of applications in communication, signal
processing, and quantum information.


Abstract:
  Infrared divergences in perturbative gravitational scattering amplitudes have
been recently argued to be governed by the two-point function of the
supertranslation Goldstone mode on the celestial sphere. We show that the form
of this celestial two-point function simply derives from an effective action
that also controls infrared divergences in the symplectic structure of General
Relativity with asymptotically flat boundary conditions. This effective action
finds its natural place in a path integral formulation of a celestial conformal
field theory, as we illustrate by re-deriving the infrared soft factors in
terms of celestial correlators. Our analysis relies on a well-posed action
principle close to spatial infinity introduced by Comp\`ere and Dehouck.