Abstract:
  Increasing the batch size of a deep learning model is a challenging task.
Although it might help in utilizing full available system memory during
training phase of a model, it results in significant loss of test accuracy most
often. LARS solved this issue by introducing an adaptive learning rate for each
layer of a deep learning model. However, there are doubts on how popular
distributed machine learning systems such as SystemML or MLlib will perform
with this optimizer. In this work, we apply LARS optimizer to a deep learning
model implemented using SystemML.We perform experiments with various batch
sizes and compare the performance of LARS optimizer with \textit{Stochastic
Gradient Descent}. Our experimental results show that LARS optimizer performs
significantly better than Stochastic Gradient Descent for large batch sizes
even with the distributed machine learning framework, SystemML.


Abstract:
  Multiview representation learning of data can help construct coherent and
contextualized users' representations on social media. This paper suggests a
joint embedding model, incorporating users' social and textual information to
learn contextualized user representations used for understanding their
lifestyle choices. We apply our model to tweets related to two lifestyle
activities, `Yoga' and `Keto diet' and use it to analyze users' activity type
and motivation. We explain the data collection and annotation process in detail
and provide an in-depth analysis of users from different classes based on their
Twitter content. Our experiments show that our model results in performance
improvements in both domains.


Abstract:
  Analyzing structural properties of social networks, such as identifying their
clusters or finding their most central nodes, has many applications. However,
these applications are not supported by federated social networks that allow
users to store their social links locally on their end devices. In the
federated regime, users want access to personalized services while also keeping
their social links private. In this paper, we take a step towards enabling
analytics on federated networks with differential privacy guarantees about
protecting the user links or contacts in the network. Specifically, we present
the first work to compute hierarchical cluster trees using local differential
privacy. Our algorithms for computing them are novel and come with theoretical
bounds on the quality of the trees learned. The private hierarchical cluster
trees enable a service provider to query the community structure around a user
at various granularities without the users having to share their raw contacts
with the provider. We demonstrate the utility of such queries by redesigning
the state-of-the-art social recommendation algorithms for the federated setup.
Our recommendation algorithms significantly outperform the baselines which do
not use social contacts and are on par with the non-private algorithms that use
contacts.


Abstract:
  We present a photometric and kinematical analysis of two and poorly studied
open clusters; Koposov 12 (FSR 802) and Koposov 43 (FSR 848) by using
cross-matched data from PPMXL and Gaia DR2 catalog. We use astrometric
parameters to identify 285 and 310 cluster members for Koposov 12 and Koposov
43, respectively. Using the extracted member candidates and isochrone fitting
to near-infrared (J, H, Ks) and Gaia DR2 bands (G, GBP, GRP), and Color
Magnitude Diagrams (CMDs), we have estimated ages: log (age/yr) = 9.00 +/- 0.20
and 9.50 +/- 0.20, and distances d = 1850 +/- 43 pc and 2500 +/- 50 pc for
Koposov 12 and Koposov 43, respectively, assuming Solar metallicity (Z=0.019).
The estimated masses of the cluster derived using initial mass function and
synthetic CMD are 364 +/- 19 M_sun and 352 +/- 19 M_sun. We have also computed
their velocity ellipsoid parameters based on (3x3) matrix elements (mu_ij).


Abstract:
  We present here a novel cantilever based apparatus to perform translational
stress or strain controlled rheology in very soft solids, and obtain
simultaneous confocal imaging of the 3 dimensional microstructure. The stress
is measured using eddy based sensors. Both the stress and strain are controlled
by applying PID control loops on measured quantities and changing position
using a micromanipulator. To get rid of surface tension forces, the sample and
cantilever are immersed. This enables stress measurement and control down to
3mPa. With this apparatus, we can independently apply shear and normal stress,
or strain, with same precision. We demonstrate the technical capability of the
setup with steady shear strain or stress experiments on a soft protein gel
system. The simultaneous confocal imaging offers insight into the macroscopic
breaking observed in an increasing shear strain experiment.


Abstract:
  The massive machine-type communications (mMTC) paradigm based on media
modulation in conjunction with massive MIMO base stations (BSs) is emerging as
a viable solution to support the massive connectivity for the future
Internet-of-Things, in which the inherent massive access at the BSs poses
significant challenges for device activity and data detection (DADD). This
paper considers the DADD problem for both uncoded and coded media modulation
based mMTC with a slotted access frame structure, where the device activity
remains unchanged within one frame. Specifically, due to the slotted access
frame structure and the adopted media modulated symbols, the access signals
exhibit a doubly structured sparsity in both the time domain and the modulation
domain. Inspired by this, a doubly structured approximate message passing
(DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we
derive the state evolution of the DS-AMP algorithm to theoretically
characterize its performance. As for the coded case, we develop a
bit-interleaved coded media modulation scheme and propose an iterative DS-AMP
(IDS-AMP) algorithm based on successive inference cancellation (SIC), where the
signal components associated with the detected active devices are successively
subtracted to improve the data decoding performance. In addition, the channel
estimation problem for media modulation based mMTC is discussed and an
efficient data-aided channel state information (CSI) update strategy is
developed to reduce the training overhead in block fading channels. Finally,
simulation results and computational complexity analysis verify the superiority
of the proposed algorithms in both uncoded and coded cases. Also, our results
verify the validity of the proposed data-aided CSI update strategy.


Abstract:
  In this work, we approach the minimization of a continuously differentiable
convex function under linear equality constraints by a second-order dynamical
system with asymptotically vanishing damping term. The system is formulated in
terms of the augmented Lagrangian associated to the minimization problem. We
show fast convergence of the primal-dual gap, the feasibility measure, and the
objective function value along the generated trajectories. In case the
objective function has Lipschitz continuous gradient, we show that the
primal-dual trajectory asymptotically weakly converges to a primal-dual optimal
solution of the underlying minimization problem. To the best of our knowledge,
this is the first result which guarantees the convergence of the trajectory
generated by a primal-dual dynamical system with asymptotic vanishing damping.
Moreover, we will rediscover in case of the unconstrained minimization of a
convex differentiable function with Lipschitz continuous gradient all
convergence statements obtained in the literature for Nesterov's accelerated
gradient method.


Abstract:
  Vector-boson scattering (VBS) processes probe the innermost structure of
electroweak interactions in the Standard Model, and provide a unique
sensitivity for new physics phenomena affecting the gauge sector. In this
review, we report on the salient aspects of this class of processes, both from
the theory and experimental point of view. We start by discussing recent
achievements relevant for their theoretical description, some of which have set
important milestones in improving the precision and accuracy of the
corresponding simulations. We continue by covering the development of
experimental techniques aimed at detecting these rare processes and improving
the signal sensitivity over large backgrounds. We then summarise the details of
the most relevant VBS signatures and review the related measurements available
to date, along with their comparison with Standard-Model predictions. We
conclude by discussing the perspective at the upcoming Large Hadron Collider
runs and at future hadron facilities.


Abstract:
  We develop a self-consistent approach to study the spectral properties of a
class of quantum mechanical operators by using the knowledge about monodromies
of $2\times 2$ linear systems (Riemann-Hilbert correspondence). Our technique
applies to a variety of problems, though in this paper we only analyse in
detail two examples. First we review the case of the (modified) Mathieu
operator, which corresponds to a certain linear system on the sphere and makes
contact with the Painlev\'e $\mathrm{III}_3$ equation. Then we extend the
analysis to the 2-particle elliptic Calogero-Moser operator, which corresponds
to a linear system on the torus. By using the Kiev formula for the
isomonodromic tau functions, we obtain the spectrum of such operators in terms
of self-dual Nekrasov functions ($\epsilon_1+\epsilon_2=0$). Through blowup
relations, we also find Nekrasov-Shatashvili type of quantizations
($\epsilon_2=0$).
  In the case of the torus with one regular singularity we obtain certain
results which are interesting by themselves. Namely, we derive blowup equations
(filling some gaps in the literature) and we relate them to the bilinear form
of the isomonodromic deformation equations. In addition, we extract the
$\epsilon_2\to 0$ limit of the blowup relations from the regularized action
functional and CFT arguments.


Abstract:
  Ultra-luminous infrared galaxies (ULIRGs) have infrared luminosities
$L_{\mathrm{IR}} \geq 10^{12} L_{\odot}$, making them the most luminous objects
in the infrared sky. These dusty objects are generally powered by starbursts
with star-formation rates that exceed $100~ M_{\odot}~ \mathrm{yr}^{-1}$,
possibly combined with a contribution from an active galactic nucleus. Such
environments make ULIRGs plausible sources of astrophysical high-energy
neutrinos, which can be observed by the IceCube Neutrino Observatory at the
South Pole. We present a stacking search for high-energy neutrinos from a
representative sample of 75 ULIRGs with redshift $z \leq 0.13$ using 7.5 years
of IceCube data. The results are consistent with a background-only observation,
yielding upper limits on the neutrino flux from these 75 ULIRGs. For an
unbroken $E^{-2.5}$ power-law spectrum, we report an upper limit on the stacked
flux $\Phi_{\nu_\mu + \bar{\nu}_\mu}^{90\%} = 3.24 \times 10^{-14}~
\mathrm{TeV^{-1}~ cm^{-2}~ s^{-1}}~ (E/10~ \mathrm{TeV})^{-2.5}$ at 90%
confidence level. In addition, we constrain the contribution of the ULIRG
source population to the observed diffuse astrophysical neutrino flux as well
as model predictions.


Abstract:
  We have studied the Co layer thickness dependences of magnetocrystalline
anisotropy (MCA), Dzyaloshinskii-Moriya interaction (DMI), and orbital moment
anisotropy (OMA) in W/Co/Pt trilayers. We find the MCA favors magnetization
along the film normal and monotonically increases with decreasing effective
magnetic layer thickness ($t_\mathrm{eff}$). The magnitude of the
Dzyaloshinskii-Moriya exchange constant ($|D|$) increases with decreasing
$t_\mathrm{eff}$ until $t_\mathrm{eff} \sim$1 nm, below which $|D|$ decreases.
MCA and $|D|$ scale with $1/t_\mathrm{eff}$ for $t_\mathrm{eff}$ larger than
$\sim$1.0 nm, indicating an interfacial origin. To clarify the cause of the
$t_\mathrm{eff}$ dependences of MCA and DMI, the OMA of Co in W/Co/Pt trilayers
is studied using x-ray magnetic circular dichroism (XMCD). We find non-zero OMA
when $t_\mathrm{eff}$ is smaller than $\sim$0.8 nm. The OMA increases with
decreasing $t_\mathrm{eff}$ at a rate that is larger than what is expected from
the MCA and Bruno's formula, indicating that other factors contribute to the
MCA at small $t_\mathrm{eff}$ to break the $1/t_\mathrm{eff}$ scaling. The
$t_\mathrm{eff}$ dependence of the OMA also suggests that $|D|$ at
$t_\mathrm{eff}$ smaller than $\sim$1 nm is independent of the OMA at the
interface. We consider the growth of Co on W results in a strain that reduces
the interfacial MCA and DMI at small $t_\mathrm{eff}$, indicating the
importance of lattice structure to control their properties.


Abstract:
  Curvature in form of the Hessian or its generalized Gauss-Newton (GGN)
approximation is valuable for algorithms that rely on a local model for the
loss to train, compress, or explain deep networks. Existing methods based on
implicit multiplication via automatic differentiation or Kronecker-factored
block diagonal approximations do not consider noise in the mini-batch. We
present ViViT, a curvature model that leverages the GGN's low-rank structure
without further approximations. It allows for efficient computation of
eigenvalues, eigenvectors, as well as per-sample first- and second-order
directional derivatives. The representation is computed in parallel with
gradients in one backward pass and offers a fine-grained cost-accuracy
trade-off, which allows it to scale. As examples for ViViT's usefulness, we
investigate the directional gradients and curvatures during training, and how
noise information can be used to improve the stability of second-order methods.


Abstract:
  This paper is interested in independent sets (or equivalently, cliques) in
uniform random cographs. We also study their permutation analogs, namely,
increasing subsequences in uniform random separable permutations.
  First, we prove that, with high probability as $n$ gets large, the largest
independent set in a uniform random cograph with $n$ vertices has size $o(n)$.
This answers a question of Kang, McDiarmid, Reed and Scott. Using the
connection between graphs and permutations via inversion graphs, we also give a
similar result for the longest increasing subsequence in separable
permutations. These results are proved using the self-similarity of the
Brownian limits of random cographs and random separable permutations, and
actually apply more generally to all families of graphs and permutations with
the same limit.
  Second, and unexpectedly given the above results, we show that for $\beta >0$
sufficiently small, the expected number of independent sets of size $\beta n$
in a uniform random cograph with $n$ vertices grows exponentially fast with
$n$. We also prove a permutation analog of this result. This time the proofs
rely on singularity analysis of the associated bivariate generating functions.


Abstract:
  We investigate the formation of Free Trade Agreement (FTA) in a competing
importers framework with $n$ countries. We show that (i) FTA formation causes a
negative externality to non-participants, (ii) a non-participant is willing to
join an FTA, and (iii) new participation may decrease the welfare of incumbent
participants. A unique subgame perfect equilibrium of a sequential FTA
formation game does not achieve global free trade under an open-access rule
where a new applicant needs consent of members for accession, currently
employed by many open regionalism agreements including APEC. We further show
that global FTA is a unique subgame perfect equilibrium under an open-access
rule without consent.


Abstract:
  This project discusses the control schemes for distribution grids with a
large amount of wind penetration. Microgrids are constantly gaining popularity,
especially in the countries, where there is energy crisis. Various systems,
including synchronous generators, grid and loads, have been investigated in
this project. Major focus is placed on active and reactive power sharing. Droop
control for multiple synchronous generators is explored. The phenomenon of load
transients has also been reviewed and associated simulations have been carried
out on SimPower Systems. Constant wind power has been introduced and behaviour
of the electrical system is observed. Behaviour of the system under variable
wind has also been analysed. Moreover, recent development projects and previous
works, regarding microgrids and distributed generation, have been discussed.


Abstract:
  Deep learning models can enable accurate and efficient disease diagnosis, but
have thus far been hampered by the data scarcity present in the medical world.
Automated diagnosis studies have been constrained by underpowered single-center
datasets, and although some results have shown promise, their generalizability
to other institutions remains questionable as the data heterogeneity between
institutions is not taken into account. By allowing models to be trained in a
distributed manner that preserves patients' privacy, federated learning
promises to alleviate these issues, by enabling diligent multi-center studies.
We present the first federated learning study on the modality of cardiovascular
magnetic resonance (CMR) and use four centers derived from subsets of the M\&M
and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy
(HCM). We adapt a 3D-CNN network pretrained on action recognition and explore
two different ways of incorporating shape prior information to the model, and
four different data augmentation set-ups, systematically analyzing their impact
on the different collaborative learning choices. We show that despite the small
size of data (180 subjects derived from four centers), the privacy preserving
federated learning achieves promising results that are competitive with
traditional centralized learning. We further find that federatively trained
models exhibit increased robustness and are more sensitive to domain shift
effects.


Abstract:
  Recently, we have determined the spectrum and the wave functions of the
Hamiltonian of a Landau particle with time-dependent mass and frequency
undergoing the influence of a uniform time-dependent electric field[J. Math.
Phys. 56, 072104 (2018)]. In the present paper we extend the study of this
model that we name the time-dependent Landau problem into the context of
coherent states. By means of the traditional factorization method of the
eigenfunctions of this system expressed in terms of the generalized Laguerre
polynomials, we derive the generators of the su(1,1) Lie algebra and we
construct the coherent states a la Barut-Girardello. These states are shown to
satisfy the Klauder's mathematical requirement to build coherent states and
some of their statistical properties are calculated and analyzed. We find that
these states are sub-Poissonian in nature. We show that, addition of photons
from these coherent states, increases the statistical properties and changes
the mathematical properties of these states.


Abstract:
  Many types of distributional word embeddings (weakly) encode linguistic
regularities as directions (the difference between "jump" and "jumped" will be
in a similar direction to that of "walk" and "walked," and so on). Several
attempts have been made to explain this fact. We respond to Allen and
Hospedales' recent (ICML, 2019) theoretical explanation, which claims that
word2vec and GloVe will encode linguistic regularities whenever a specific
relation of paraphrase holds between the four words involved in the regularity.
We demonstrate that the explanation does not go through: the paraphrase
relations needed under this explanation do not hold empirically.


Abstract:
  High-fidelity large-eddy simulations (LES) are performed to investigate
aeroacoustic characteristics of jets issuing from twin rectangular nozzles with
an aspect ratio of 2:1 at two over-expanded conditions and the design
condition. For all three jet conditions simulated, LES predicts qualitatively
similar near-field flow statistics to those measured at the University of
Cincinnati. Using the Ffowcs Williams-Hawkings method, LES captures the
fundamental screech tone and its harmonics fairly well at multiple observer
locations in the far-field. Intense jet flapping motions in the near-field
along the minor axis, which are enhanced by jet-to-jet interactions, are found
to correspond to those frequencies. Moreover, the overall sound pressure levels
are within 1-2 dB difference from the experimental measurements. However, the
screech tones appear to be intermittent, as the twin-jet interaction pattern
varies irregularly. To extract dominant flow structures at the screech
frequencies and identify the twin-jet coupling modes, spectral proper
orthogonal decomposition (SPOD) analysis is used. SPOD analysis recovers
energetic peaks at the screech frequencies, and the corresponding leading modes
indicate strong upstream radiation originating from the fifth/sixth
shock-cells. For the two over-expanded conditions, the leading modes show
anti-symmetric coupling in the minor axis at the fundamental screech
frequencies. In contrast, the two jets behave symmetrically with respect to
each other in the major axis, in line with the absence of jet flapping in this
direction. Furthermore, the leading SPOD eigenvalues turn out to be, at least,
two orders of magnitude larger than higher-order eigenvalues, suggesting
potential of reduced-order models for the twin-jet screech.


Abstract:
  We research relations between optimal transport theory (OTT) and approximate
Bayesian computation (ABC) possibly connected to relevant metrics defined on
probability measures. Those of ABC are computational methods based on Bayesian
statistics and applicable to a given generative model to estimate its a
posteriori distribution in case the likelihood function is intractable. The
idea is therefore to simulate sets of synthetic data from the model with
respect to assigned parameters and, rather than comparing prospects of these
data with the corresponding observed values as typically ABC requires, to
employ just a distance between a chosen distribution associated to the
synthetic data and another of the observed values. Our focus lies in
theoretical and methodological aspects, although there would exist a remarkable
part of algorithmic implementation, and more precisely issues regarding
mathematical foundation and asymptotic properties are carefully analysed,
inspired by an in-depth study of what is then our main bibliographic reference,
that is Bernton et al. (2019), carrying out what follows: a rigorous
formulation of the set-up for the ABC rejection algorithm, also to regain a
transparent and general result of convergence as the ABC threshold goes to zero
whereas the number n of samples from the prior stays fixed; general technical
proposals about distances leaning on OTT; weak assumptions which lead to lower
bounds for small values of threshold and as n goes to infinity, ultimately
showing a reasonable possibility of lack of concentration which is contrary to
what is proposed in Bernton et al. (2019) itself.


Abstract:
  The objective of this paper is to explore how financial big data and machine
learning methods can be applied to model and understand complex financial
products. We focus on residential mortgage backed securities, resMBS, that were
at the heart of the 2008 US financial crisis. The securities are contained
within a prospectus and have a complex payoff structure. Multiple financial
institutions form a supply chain to create the prospectuses. We provide insight
into the performance of the resMBS securities through a series of increasingly
complex models. First, models at the security level directly identify salient
features of resMBS securities that impact their performance. Second, we extend
the model to include prospectus level features. We are the first to demonstrate
that the composition of the prospectus is associated with the performance of
securities. Finally, to develop a deeper understanding of the role of the
supply chain, we use unsupervised probabilistic methods, in particular, dynamic
topics models (DTM), to understand community formation and temporal evolution
along the chain. A comprehensive model provides insight into the impact of DTM
communities on the issuance and evolution of prospectuses, and eventually the
performance of resMBS securities.


Abstract:
  Phase-locked loops (PLLs) are now widely used in communication systems and
have been a classic system for more than 60 years. Well-known mathematical
models of such systems are constructed in a number of approximations, so
questions about how they describe the experimental dynamics qualitatively and
quantitatively, and how the accuracy of the model description depends on the
behavior mode, remain open. One of the most direct approaches to the
verification of any model is its reconstruction from the time series obtained
in experiment. If it is possible to fit the model to experimental data and the
resulting parameter values are close to the expected values (calculated from
the first principles), the quantitative correspondence between the model and
the physical object is nearly proved. In this paper, for the first time, the
equations of the PLL model with a bandpass filter are reconstructed from the
experimental signals of the generator in various modes. The reconstruction
showed that the model known in the literature generally describes the
experimental dynamics in regular and chaotic regimes. The relative error of
parameter estimation is between 2% and 50% for different regimes and
parameters. The reconstructed nonlinear function of phase is not harmonic and
highly asymmetric in contrary to the model one.


Abstract:
  We report polarization properties for eight narrowband bursts from FRB 121102
that have been re-detected in a high-frequency (4-8 GHz) Breakthrough Listen
observation with the Green Bank Telescope, originally taken on 2017 August 26.
The bursts were found to exhibit nearly 100% linear polarization, Faraday
rotation measures (RM) bordering 9.3$\times$10$^4$ rad-m$^{-2}$, and stable
polarization position angles (PA), all of which agree with burst properties
previously reported for FRB 121102 at the same epoch. We confirm that these
detections are indeed physical bursts with limited spectral occupancies and
further support the use of sub-banded search techniques in FRB detection.


Abstract:
  Rockfall detection is a crucial procedure in the field of geology, which
helps to reduce the associated risks. Currently, geologists identify rockfall
events almost manually utilizing point cloud and imagery data obtained from
different caption devices such as Terrestrial Laser Scanner or digital cameras.
Multi-temporal comparison of the point clouds obtained with these techniques
requires a tedious visual inspection to identify rockfall events which implies
inaccuracies that depend on several factors such as human expertise and the
sensibility of the sensors. This paper addresses this issue and provides an
intelligent framework for rockfall event detection for any individual working
in the intersection of the geology domain and decision support systems. The
development of such an analysis framework poses significant research challenges
and justifies intensive experimental analysis. In particular, we propose an
intelligent system that utilizes multiple machine learning algorithms to detect
rockfall clusters of point cloud data. Due to the extremely imbalanced nature
of the problem, a plethora of state-of-the-art resampling techniques
accompanied by multiple models and feature selection procedures are being
investigated. Various machine learning pipeline combinations have been
benchmarked and compared applying well-known metrics to be incorporated into
our system. Specifically, we developed statistical and machine learning
techniques and applied them to analyze point cloud data extracted from
Terrestrial Laser Scanner in two distinct case studies, involving different
geological contexts: the basaltic cliff of Castellfollit de la Roca and the
conglomerate Montserrat Massif, both located in Spain. Our experimental data
suggest that some of the above-mentioned machine learning pipelines can be
utilized to detect rockfall incidents on mountain walls, with experimentally
proven accuracy.


Abstract:
  Given a periodic quotient of a torsion-free hyperbolic group, we provide a
fine lower estimate of the growth function of any sub-semi-group. This
generalizes results of Razborov and Safin for free groups.


Abstract:
  We prove an existence and uniqueness theorem for exact WKB solutions of
general singularly perturbed linear second-order ODEs in the complex domain.
These include the one-dimensional time-independent complex Schr\"odinger
equation. Notably, our results are valid both in the case of generic WKB
trajectories as well as closed WKB trajectories. We also explain in what sense
exact and formal WKB solutions form a basis. As a corollary of the proof, we
establish the Borel summability of formal WKB solutions for a large class of
problems, and derive an explicit formula for the Borel transform.


Abstract:
  We give upper bounds for the bottom of the essential spectrum of properly
immersed minimal submanifolds of $\mathbb{R}^{n}$ in terms of their volume
growth. Our result improves the extrinsic version of Brook's essential spectrum
estimate given by Ilias-Nelli-Soret in \cite[Cor.3]{ins}.


Abstract:
  Structured light are custom light fields where the phase, polarization, and
intensity vary with position. It has been used for nanotweezers, nanoscale
imaging, and quantum information technology, but its role in exciting optical
transitions in materials has been little examined so far. Here we use group
theory to derive the optical selection rules for nanosystems that get excited
by structured light. If the size of the nanostructure is comparable to the
light wavelength, it will sample the full beam profile during excitation with
profound consequences on optical excitations. Using nanooligomers as model
nanosystems, we show that structured light excites optical transitions that are
forbidden for linearly polarized or unpolarized light. Such dipole forbidden
modes have longer lifetimes and narrower resonances than dipole allowed
transitions. We derive symmetry-adapted eigenmodes for nanooligomers containing
up to six monomers. Our study includes tables with selection rules for
cylindrical vector beams, for beams with orbital angular momentum, and for
field retardation along the propagation direction. We discuss multi-photon
processes of nonlinear optics in addition to one-photon absorption. Structured
light will unlock a broad range of excitations in nanooligomers and other
nanostructures that are currently inaccessible to optical studies.


Abstract:
  In a recent work [B. Koc et al., arXiv:2010.03750, SIAM J. Numer. Anal., to
appear], the authors showed that including difference quotients (DQs) is
necessary in order to prove optimal pointwise in time error bounds for proper
orthogonal decomposition (POD) reduced order models of the heat equation. In
this work, we introduce a new approach to including DQs in the POD procedure.
Instead of computing the POD modes using all of the snapshot data and DQs, we
only use the first snapshot along with all of the DQs and special POD weights.
We show that this approach retains all of the numerical analysis benefits of
the standard POD DQ approach, while using a POD data set that has half the
number of snapshots as the standard POD DQ approach, i.e., the new approach is
more computationally efficient. We illustrate our theoretical results with
numerical experiments.


Abstract:
  In this paper we construct short time classical solutions to a class of
master equations in the presence of non-degenerate individual noise arising in
the theory of mean field games. The considered Hamiltonians are non-separable
and $local$ functions of the measure variable, therefore the equation is
restricted to absolutely continuous measures whose densities lie in suitable
Sobolev spaces. Our results hold for smooth enough Hamiltonians, without any
additional structural conditions as convexity or monotonicity.


Abstract:
  Inspection of ageing, reinforced concrete structures is a world-wide
challenge. Existing non-destructive evaluation techniques in civil and
structural engineering have limited penetration depth and don't allow to
precisely ascertain the configuration of reinforcement within large concrete
objects. The big challenge for critical infrastructure (bridges, dams, dry
docks, nuclear bioshields etc.) is understanding the internal condition of the
concrete and steel, not just the location of the reinforcement. In most new
constructions the location should be known and recorded in the as-built
drawings, where these might not exist due to poor record keeping for older
structures. Muon scattering tomography is a non-destructive and non-invasive
technique which shows great promise for high-depth 3D concrete imaging.
Previously, we have demonstrated that individual bars with a diameter of 33.7
+- 7.3 mm at 50 cm depth can be located using muon scattering tomography. Here
we present an improved method that exploits the periodicity of bar structures.
With this new method, reinforcement with bars down to 6 mm thickness can be
detected and imaged.


Abstract:
  In this article, we deal with the global regularity of weak solutions to a
class of problems involving the fractional $(p,q)$-Laplacian, denoted by
$(-\Delta)^{s_1}_{p}+(-\Delta)^{s_2}_{q}$, for $s_2, s_1\in (0,1)$ and
$1<p,q<\infty$. We establish completely new H\"older continuity results, up to
the boundary, for the weak solutions to fractional $(p,q)$-problems involving
singular as well as regular nonlinearities. Moreover, as applications to
boundary estimates, we establish new Hopf type maximum principle and strong
comparison principle in both situations.


Abstract:
  Viewing optimization methods as numerical integrators for ordinary
differential equations (ODEs) provides a thought-provoking modern framework for
studying accelerated first-order optimizers. In this literature, acceleration
is often supposed to be linked to the quality of the integrator (accuracy,
energy preservation, symplecticity). In this work, we propose a novel ordinary
differential equation that questions this connection: both the explicit and the
semi-implicit (a.k.a symplectic) Euler discretizations on this ODE lead to an
accelerated algorithm for convex programming. Although semi-implicit methods
are well-known in numerical analysis to enjoy many desirable features for the
integration of physical systems, our findings show that these properties do not
necessarily relate to acceleration.


Abstract:
  Our work focuses on extra gradient learning algorithms for finding Nash
equilibria in bilinear zero-sum games. The proposed method, which can be
formally considered as a variant of Optimistic Mirror Descent
\cite{DBLP:conf/iclr/MertikopoulosLZ19}, uses a large learning rate for the
intermediate gradient step which essentially leads to computing (approximate)
best response strategies against the profile of the previous iteration.
Although counter-intuitive at first sight due to the irrationally large, for an
iterative algorithm, intermediate learning step, we prove that the method
guarantees last-iterate convergence to an equilibrium. Particularly, we show
that the algorithm reaches first an $\eta^{1/\rho}$-approximate Nash
equilibrium, with $\rho > 1$, by decreasing the Kullback-Leibler divergence of
each iterate by at least $\Omega(\eta^{1+\frac{1}{\rho}})$, for sufficiently
small learning rate, $\eta$, until the method becomes a contracting map, and
converges to the exact equilibrium. Furthermore, we perform experimental
comparisons with the optimistic variant of the multiplicative weights update
method, by \cite{Daskalakis2019LastIterateCZ} and show that our algorithm has
significant practical potential since it offers substantial gains in terms of
accelerated convergence.


Abstract:
  Latency-insensitive design mitigates increasing interconnect delay and
enables productive component reuse in complex digital systems. This design
style has been adopted in high-level design flows because untimed functional
blocks connected through latency-insensitive interfaces provide a natural
communication abstraction. However, latency-insensitive design with high-level
languages also introduces a unique set of verification challenges that
jeopardize functional correctness. In particular, bugs due to invalid
consumption of inputs and deadlocks can be difficult to detect and debug with
dynamic simulation methods. To tackle these two classes of bugs, we propose
formal model checking methods to guarantee that a high-level
latency-insensitive design is unaffected by invalid input data and is free of
deadlock. We develop a well-structured verification wrapper for each property
to automatically construct the corresponding formal model for checking. Our
experiments demonstrate that the formal checks are effective in realistic bug
scenarios from high-level designs.


Abstract:
  In this short note we present several infinite dimensional theorems which
generalize corresponding facts from the finite dimensional differential
inclusions theory.


Abstract:
  Curvilinear, multiblock summation-by-parts finite difference methods with the
simultaneous approximation term method provide a stable and accurate method for
solving the wave equation in second order form. That said, the standard method
can become arbitrarily stiff when characteristic boundary conditions and
nonlinear interface conditions are used. Here we propose a new technique that
avoids this stiffness by using characteristic variables to "upwind" the
boundary and interface treatment. This is done through the introduction of an
additional block boundary displacement variable. Using a unified energy, which
expresses both the standard as well as characteristic boundary and interface
treatment, we show that the resulting scheme has semidiscrete energy stability
for the anistropic wave equation. The theoretical stability results are
confirmed with numerical experiments that also demonstrate the accuracy and
robustness of the proposed scheme. The numerical results also show that the
characteristic scheme has a time step restriction based on standard wave
propagation considerations and not the boundary closure.


Abstract:
  Life pattern clustering is essential for abstracting the groups'
characteristics of daily mobility patterns and activity regularity. Based on
millions of GPS records, this paper proposed a framework on the life pattern
clustering which can efficiently identify the groups have similar life pattern.
The proposed method can retain original features of individual life pattern
data without aggregation. Metagraph-based data structure is proposed for
presenting the diverse life pattern. Spatial-temporal similarity includes
significant places semantics, time sequential properties and frequency are
integrated into this data structure, which captures the uncertainty of an
individual and the diversities between individuals.
Non-negative-factorization-based method was utilized for reducing the
dimension. The results show that our proposed method can effectively identify
the groups have similar life pattern and takes advantages in computation
efficiency and robustness comparing with the traditional method. We revealed
the representative life pattern groups and analyzed the group characteristics
of human life patterns during different periods and different regions. We
believe our work will help in future infrastructure planning, services
improvement and policies making related to urban and transportation, thus
promoting a humanized and sustainable city.


Abstract:
  Fanaroff-Riley (FR) 0 radio galaxies compose a new class of radio galaxies,
which are usually weaker but much more numerous than the well-established class
of FR 1 and FR 2 galaxies. The latter classes have been proposed as sources of
the ultra-high-energy cosmic rays (UHECRs) with energies reaching up to
${\sim}10^{20}$ eV. Based on this conjecture, the possibility of UHECR
acceleration and survival in an FR 0 source environment is examined in this
work.
  In doing so, an average spectral energy distribution (SED) based on data from
the FR 0 catalog (FR0CAT) is compiled. The resulting photon fields are used as
targets for UHECRs, which suffer from electromagnetic pair production,
photo-disintegration, photo-meson production losses, and synchrotron radiation.
Multiple mechanisms are discussed to assess the UHECR acceleration probability,
including Fermi-I order and gradual shear accelerations, and particle escape
from the source region.
  This work shows that in a hybrid scenario, combining Fermi and shear
accelerations, FR 0 galaxies can contribute to the observed UHECR flux, as long
as $\Gamma_\mathrm{j}\gtrsim 1.6$, where shear acceleration starts to dominate
over escape. Even in less optimistic scenarios, FR 0s can be expected to
contribute to the cosmic-ray flux between the knee and the ankle. Our results
are relatively robust with respect to the realized magnetic turbulence model
and the speed of the accelerating shocks.


Abstract:
  In this analysis, we consider the effects of non-quiescent initial conditions
driven by pre-impact air-water interactions on the classical Wagner model of
impact theory. We consider the problem of a rigid, solid impactor moving
vertically towards a liquid pool. Prior to impact, viscous forces in the air
act to deform the liquid free surface, inducing a flow in the pool. These
interactions are then incorporated as initial conditions in the post-impact
analysis. We derive expressions for the size of the effective contact set, the
leading-order pressure and force on the impactor, and the speed and thickness
of the jet at its base. In all cases, we show that the effect of the pre-impact
behaviour is to cushion the impactor, reducing the size of the effective
contact set and, hence, the force on the impactor. Small- and large-time
asymptotic solutions are derived for general power-law impactors, and we show
that the effects of the air die away as the impact progresses, so that we
approach the classical Wagner solution.


Abstract:
  In this paper, we establish a local regularity result for
$W^{2,p}_{\mathrm{loc}}$ solutions to complex degenerate nonlinear elliptic
equations $F(D^2_{\mathbb{C}} u)=f$ when they are comparable to the
Monge-Amp\`ere equation. Notably, we apply our result to the so-called
$k$-Monge-Amp\`ere equation.


Abstract:
  In this paper, we develop a drift homotopy implicit particle filter method.
The methodology of our approach is to adopt the concept of drift homotopy in
the resampling procedure of the particle filter method for solving the
nonlinear filtering problem, and we introduce an implicit particle filter
method to improve the efficiency of the drift homotopy resampling procedure.
Numerical experiments are carried out to demonstrate the effectiveness and
efficiency of our drift homotopy implicit particle filter.


Abstract:
  We present IMRPhenomTPHM, a phenomenological model for the gravitational wave
signals emitted by the coalescence of quasi-circular precessing binary black
holes systems. The model is based on the "twisting up" approximation, which
maps non-precessing signals to precessing ones in terms of a time dependent
rotation described by three Euler angles, and which has been utilized in
several frequency domain waveform models that have become standard tools in
gravitational wave data analysis. Our model is however constructed in the time
domain, which allows several improvements over the frequency domain models: we
do not use the stationary phase approximation, we employ a simple approximation
for the precessing Euler angles for the ringdown signal, and we implement a new
method for computing the Euler angles through the evolution of the spin
dynamics of the system, which is more accurate and also computationally
efficient.


Abstract:
  Semantic hashing represents documents as compact binary vectors (hash codes)
and allows both efficient and effective similarity search in large-scale
information retrieval. The state of the art has primarily focused on learning
hash codes that improve similarity search effectiveness, while assuming a
brute-force linear scan strategy for searching over all the hash codes, even
though much faster alternatives exist. One such alternative is multi-index
hashing, an approach that constructs a smaller candidate set to search over,
which depending on the distribution of the hash codes can lead to sub-linear
search time. In this work, we propose Multi-Index Semantic Hashing (MISH), an
unsupervised hashing model that learns hash codes that are both effective and
highly efficient by being optimized for multi-index hashing. We derive novel
training objectives, which enable to learn hash codes that reduce the candidate
sets produced by multi-index hashing, while being end-to-end trainable. In
fact, our proposed training objectives are model agnostic, i.e., not tied to
how the hash codes are generated specifically in MISH, and are straight-forward
to include in existing and future semantic hashing models. We experimentally
compare MISH to state-of-the-art semantic hashing baselines in the task of
document similarity search. We find that even though multi-index hashing also
improves the efficiency of the baselines compared to a linear scan, they are
still upwards of 33% slower than MISH, while MISH is still able to obtain
state-of-the-art effectiveness.


Abstract:
  Does the Heisenberg uncertainty principle (HUP) apply along the time
dimension in the same way it applies along the three space dimensions?
Relativity says it should; current practice says no. With recent advances in
measurement at the attosecond scale it is now possible to decide this question
experimentally.
  The most direct test is to measure the time-of-arrival of a quantum particle:
if the HUP applies in time, then the dispersion in the time-of-arrival will be
measurably increased.
  We develop an appropriate metric of time-of-arrival in the standard case;
extend this to include the case where there is uncertainty in time; then
compare. There is -- as expected -- increased uncertainty in the
time-of-arrival if the HUP applies along the time axis. The results are fully
constrained by Lorentz covariance, therefore uniquely defined, therefore
falsifiable.
  So we have an experimental question on our hands. Any definite resolution
would have significant implications with respect to the role of time in quantum
mechanics and relativity. A positive result would also have significant
practical applications in the areas of quantum communication, attosecond
physics (e.g. protein folding), and quantum computing.


Abstract:
  We present a novel artificial cognitive mapping system using generative deep
neural networks, called variational autoencoder/generative adversarial network
(VAE/GAN), which can map input images to latent vectors and generate temporal
sequences internally. The results show that the distance of the predicted image
is reflected in the distance of the corresponding latent vector after training.
This indicates that the latent space is self-organized to reflect the proximity
structure of the dataset and may provide a mechanism through which many aspects
of cognition are spatially represented. The present study allows the network to
internally generate temporal sequences that are analogous to the hippocampal
replay/pre-play ability, where VAE produces only near-accurate replays of past
experiences, but by introducing GANs, the generated sequences are coupled with
instability and novelty.


Abstract:
  Bilevel optimization has recently attracted growing interests due to its wide
applications in modern machine learning problems. Although recent studies have
characterized the convergence rate for several such popular algorithms, it is
still unclear how much further these convergence rates can be improved. In this
paper, we address this fundamental question from two perspectives. First, we
provide the first-known lower complexity bounds of
$\widetilde{\Omega}(\frac{1}{\sqrt{\mu_x}\mu_y})$ and $\widetilde
\Omega\big(\frac{1}{\sqrt{\epsilon}}\min\{\frac{1}{\mu_y},\frac{1}{\sqrt{\epsilon^{3}}}\}\big)$
respectively for strongly-convex-strongly-convex and convex-strongly-convex
bilevel optimizations. Second, we propose an accelerated bilevel optimizer
named AccBiO, for which we provide the first-known complexity bounds without
the gradient boundedness assumption (which was made in existing analyses) under
the two aforementioned geometries. We also provide significantly tighter upper
bounds than the existing complexity when the bounded gradient assumption does
hold. We show that AccBiO achieves the optimal results (i.e., the upper and
lower bounds match up to logarithmic factors) when the inner-level problem
takes a quadratic form with a constant-level condition number. Interestingly,
our lower bounds under both geometries are larger than the corresponding
optimal complexities of minimax optimization, establishing that bilevel
optimization is provably more challenging than minimax optimization.


Abstract:
  We present a novel analogue for finite exchangeable sequences of the de
Finetti, Hewitt and Savage theorem and investigate its implications for
multi-marginal optimal transport (MMOT) and Bayesian statistics. If
$(Z_1,...,Z_N)$ is a finitely exchangeable sequence of $N$ random variables
taking values in some Polish space $X$, we show that the law $\mu_k$ of the
first $k$ components has a representation of the form
  $\mu_k=\int_{{\mathcal P}_{\frac{1}{N}}(X)} F_{N,k}(\lambda) \, \mbox{d}
\alpha(\lambda)$
  for some probability measure $\alpha$ on the set of $1/N$-quantized
probability measures on $X$ and certain universal polynomials $F_{N,k}$. The
latter consist of a leading term $N^{k-1}\! /{\small \prod_{j=1}^{k-1}(N\! -\!
j)\, \lambda^{\otimes k}}$ and a finite, exponentially decaying series of
correlated corrections of order $N^{-j}$ ($j=1,...,k$). The $F_{N,k}(\lambda)$
are precisely the extremal such laws, expressed via an explicit polynomial
formula in terms of their one-point marginals $\lambda$. Applications include
novel approximations of MMOT via polynomial convexification and the
identification of the remainder which is estimated in the celebrated error
bound of Diaconis-Freedman between finite and infinite exchangeable laws.


Abstract:
  The Aria project consists of a plant, hosting a 350 m cryogenic isotopic
distillation column, the tallest ever built, which is currently in the
installation phase in a mine shaft at Carbosulcis S.p.A., Nuraxi-Figus (SU),
Italy. Aria is one of the pillars of the argon dark-matter search experimental
program, lead by the Global Argon Dark Matter Collaboration. Aria was designed
to reduce the isotopic abundance of $^{39}$Ar, a $\beta$-emitter of cosmogenic
origin, whose activity poses background and pile-up concerns in the detectors,
in the argon used for the dark-matter searches, the so-called Underground Argon
(UAr). In this paper, we discuss the requirements, design, construction, tests,
and projected performance of the plant for the isotopic cryogenic distillation
of argon. We also present the successful results of isotopic cryogenic
distillation of nitrogen with a prototype plant, operating the column at total
reflux.


Abstract:
  The sharpest known high probability generalization bounds for uniformly
stable algorithms (Feldman, Vondr\'{a}k, 2018, 2019), (Bousquet, Klochkov,
Zhivotovskiy, 2020) contain a generally inevitable sampling error term of order
$\Theta(1/\sqrt{n})$. When applied to excess risk bounds, this leads to
suboptimal results in several standard stochastic convex optimization problems.
We show that if the so-called Bernstein condition is satisfied, the term
$\Theta(1/\sqrt{n})$ can be avoided, and high probability excess risk bounds of
order up to $O(1/n)$ are possible via uniform stability. Using this result, we
show a high probability excess risk bound with the rate $O(\log n/n)$ for
strongly convex and Lipschitz losses valid for \emph{any} empirical risk
minimization method. This resolves a question of Shalev-Shwartz, Shamir,
Srebro, and Sridharan (2009). We discuss how $O(\log n/n)$ high probability
excess risk bounds are possible for projected gradient descent in the case of
strongly convex and Lipschitz losses without the usual smoothness assumption.


Abstract:
  This paper revisits two classical distributed problems in anonymous networks,
namely spanning tree construction and topology recognition, from the point of
view of graph covering theory. For both problems, we characterize necessary and
sufficient conditions on the communication graph in terms of directed symmetric
coverings. These characterizations answer along-standing open question posed by
Yamashita and Kameda [YK96], and shed new light on the connection between
coverings and the concepts of views and quotient graphs developed by the same
authors. Characterizing conditions in terms of coverings is significant because
it connects the field with a vast body of classical literature in graph theory
and algebraic topology. In particular, it gives access to powerful tools such
as Reidemeister's theorem and Mazurkiewicz's algorithm. Combined together,
these tools allow us to present elegant proofs of otherwise intricate results,
and their constructive nature makes them effectively usable in the algorithms.
This paper also gives us the opportunity to present the field of covering
theory in a pedagogical way, with a focus on the two aforementioned tools,
whose potential impact goes beyond the specific problems considered in this
work.


Abstract:
  With the unprecedented shift towards automated urban environments in recent
years, a new paradigm is required to study pedestrian behaviour. Studying
pedestrian behaviour in futuristic scenarios requires modern data sources that
consider both the Automated Vehicle (AV) and pedestrian perspectives. Current
open datasets on AVs predominantly fail to account for the latter, as they do
not include an adequate number of events and associated details that involve
pedestrian and vehicle interactions. To address this issue, we propose using
Virtual Reality (VR) data as a complementary resource to current datasets,
which can be designed to measure pedestrian behaviour under specific
conditions. In this research, we focus on the context-aware pedestrian
trajectory prediction framework for automated vehicles at mid-block
unsignalized crossings. For this purpose, we develop a novel multi-input
network of Long Short-Term Memory (LSTM) and fully connected dense layers. In
addition to past trajectories, the proposed framework incorporates pedestrian
head orientations and distance to the upcoming vehicles as sequential input
data. By merging the sequential data with contextual information of the
environment, we train a model to predict the future pedestrian trajectory. Our
results show that the prediction error and overfitting to the training data are
reduced by considering contextual information in the model. To analyze the
application of the methods to real AV data, the proposed framework is trained
and applied to pedestrian trajectory extracted from an open-access video
dataset. Finally, by implementing a game theory-based model interpretability
method, we provide detailed insights and propose recommendations to improve the
current automated vehicle sensing systems from a pedestrian-oriented point of
view.


Abstract:
  The present paper deals with the generalization of Midpoint Ellipse Drawing
Algorithm (MPEDA) to minimize the error in the existing MPEDA in cartesian
form. In this method, we consider three different values of h, i.e., 1, 0.5 and
0.1. For h = 1, all the results of MPEDA have been verified. For other values
of h it is observed that as the value of h decreases, the number of iteration
increases but the error between the points generated and the original ellipse
points decreases and vice-versa.


Abstract:
  The solidification behavior of a eutectic AlCu specimen is investigated via
in situ scanning transmission electron microscope (STEM) experiments.
Solidification conditions are varied by imposing various cooling rates via a
micro-electro-mechanical system (MEMS) based membrane. The methodology allows
the use of material processed by a melting and casting route close to
industrial metallurgically fabricated material for in situ STEM solidification
studies. Several rapid solidification morphologies could be obtained solely on
a single specimen by the demonstrated strategy. A change from nanometer scaled
lamellar to dendritic morphology is observed by differing cooling from medium
to the highest tested rates. Cooling rates of 100, 10000 and 30000 K/s are
used. For the comparatively slow cooling rate of 100 K/s, a coarser
three-dimensional morphology is gained. Additional post-solidification heat
treatments are investigated in terms of observation of spheroidization of
lamellas during annealing at elevated temperatures.


Abstract:
  Schizophrenia (SZ) is a mental disorder that typically emerges in late
adolescence or early adulthood. It reduces the life expectancy of patients by
15 years. Abnormal behavior, perception of emotions, social relationships, and
reality perception are among its most significant symptoms. Past studies have
revealed the temporal and anterior lobes of hippocampus regions of brain get
affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and
decreased volume of white and gray matter can be observed due to this disease.
The magnetic resonance imaging (MRI) is the popular neuroimaging technique used
to explore structural/functional brain abnormalities in SZ disorder owing to
its high spatial resolution. Various artificial intelligence (AI) techniques
have been employed with advanced image/signal processing methods to obtain
accurate diagnosis of SZ. This paper presents a comprehensive overview of
studies conducted on automated diagnosis of SZ using MRI modalities. Main
findings, various challenges, and future works in developing the automated SZ
detection are described in this paper.


Abstract:
  Nielsen's theorem states that any triangle can be inscribed in a planar
Jordan curve. We prove a generalisation of this theorem, extending to any
Jordan curve $J$ embedded in $\mathbb{R}^{n}$, for a restricted set of
triangles. We then conclude by investigating a condition under which a given
point of $J$ inscribes an equilateral triangle in particular.


Abstract:
  One-shot learning can be achieved by algorithms and animals, but how the
latter do it is poorly understood as most of the algorithms are not
biologically plausible. Experiments studying one-shot learning in rodents have
shown that after initial gradual learning of associations between cues and
locations, new associations can be learned with just a single exposure to each
new cue-location pair. Foster, Morris and Dayan (2000) developed a hybrid
temporal difference - symbolic model that exhibited one-shot learning for dead
reckoning to displaced single locations. While the temporal difference rule for
learning the agent's actual coordinates was biologically plausible, the model's
symbolic mechanism for learning target coordinates was not, and one-shot
learning for multiple target locations was not addressed. Here we extend the
model by replacing the symbolic mechanism with a reservoir of recurrently
connected neurons resembling cortical microcircuitry. Biologically plausible
learning of target coordinates was achieved by subjecting the reservoir's
output weights to synaptic plasticity governed by a novel 4-factor variant of
the exploratory Hebbian (EH) rule. As with rodents, the reservoir model
exhibited one-shot learning for multiple paired associations.


Abstract:
  We formulate a Boolean algebra in the set of idempotents of Clifford algebra
Cl($R^{n,n}$) and within this frame we examine different formulations of the
Boolean Satisfiability Problem in Clifford algebra. Exploiting the isomorphism
between null subspaces of $R^{n,n}$ associated to simple spinors and the
orthogonal group O(n) we ultimately give a continuous formulation of the
Boolean Satisfiability Problem within this group that opens unexplored
perspectives.


Abstract:
  We consider the edge-triangle model, a two-parameter family of exponential
random graphs in which dependence between edges is introduced through
triangles. In the so-called replica symmetric regime, the limiting free energy
exists together with a complete characterization of the phase diagram of the
model. We borrow tools from statistical mechanics to obtain limit theorems for
the edge density. First, we determine the asymptotic distribution of this
quantity, as the graph size tends to infinity, in the various phases. Then we
study the fluctuations of the edge density around its average value off the
critical curve and formulate conjectures about the behavior at criticality
based on the analysis of a mean-field approximation of the model. Some of our
results can be extended with no substantial changes to more general classes of
exponential random graphs.


Abstract:
  Objective: To compare non-contact acoustic micro-tapping optical coherence
elastography (AuT-OCE) with destructive mechanical tests to confirm corneal
elastic anisotropy.
  Design: Ex vivo, laboratory study with non-contact AuT-OCE followed by
mechanical rheometry and extensometry.
  Subjects: Inflated cornea of whole-globe porcine eyes.
  Methods: A non-contact transducer was used to launch mechanical waves in the
cornea that were imaged with phase-sensitive OCT at physiologically relevant
pressures. Reconstruction of both Young's modulus (E) and out-of-plane shear
modulus (G) in the cornea from experimental data was performed using a model of
a nearly incompressible transversally isotropic (NITI) medium. Samples were
then excised and parallel plate rheometry was performed to measure the shear
modulus G. Corneal samples were then subjected to strip extensomety to measure
the Young's modulus.
  Main Outcome Measures: Strong corneal anisotropy was confirmed with both
AuT-OCE and mechanical tests, with the Young's and shear moduli differing by
over an order of magnitude. These results show that AuT-OCE can quantify both
moduli with a non-contact, non-invasive, clinically translatable technique.


Abstract:
  We present a novel mixed reality (MR) telepresence system enabling a local
user to interact with a remote user through full-body avatars in their own
rooms. If the remote rooms have different sizes and furniture arrangements,
directly applying a user's motion to an avatar leads to a mismatch of placement
and deictic gesture. To overcome this problem, we retarget the placement, arm
gesture, and head movement of a local user to an avatar in a remote room to
preserve a local user's environment and interaction context. This allows
avatars to utilize real furniture and interact with a local user and shared
objects as if they were in the same room. This paper describes our system's
design and implementation in detail and a set of example scenarios in the
living room and office room. A qualitative user study delves into a user
experience, challenges, and possible extensions of the proposed system.


Abstract:
  New particles $\phi$ in the MeV-GeV range produced at colliders and escaping
detection can be searched for at operating $b-$ and $\tau-$factories such as
Belle II. A typical search topology involves pair-produced $\tau$s (or mesons),
one of which decaying to visibles plus the $\phi$, and the other providing a
tag. One crucial impediment of these searches is the limited ability to
reconstruct the parents' separate boosts. This is the case in the 'typical'
topology where both decay branches include escaping particles. We observe that
such topology lends itself to the use of kinematic variables such as $M_2$,
designed for pairwise decays to visibles plus escaping particles, and endowed
with a built-in ('MAOS') way to efficiently guess the parents' separate boosts.
Starting from this observation, we construct several kinematic quantities able
to discriminate signal from background, and apply them to a benchmark search,
$\tau \to e + \phi$, where $\phi$ can be either an axion-like particle or a
hidden photon. Our considered variables can be applied to a wider range of
topologies than the current reference technique, based on the event thrust,
with which they are nearly uncorrelated. Application of our strategy leads to
an improvement by a factor close to 3 in the branching-ratio upper limit for
$\tau \to e \phi$, with respect to the currently expected limit, assuming
$m_\phi \lesssim 1$ MeV. For example, we anticipate a sensitivity of $1.7
\times 10^{-5}$ with the data collected before the 2022 shutdown.


Abstract:
  We calculate the strength of the effective onsite Coulomb interaction
(Hubbard $U$) in two-dimensional (2D) transition-metal (TM) dihalides MX$_2$
and trihalides MX$_3$ (M=Ti, V, Cr, Mn, Fe, Co, Ni; X=Cl, Br, I) from first
principles using the constrained random-phase approximation. The correlated
subspaces are formed from $t_{2g}$ or $e_g$ bands at the Fermi energy.
Elimination of the efficient screening taking place in these narrow bands gives
rise to sizable interaction parameters U between the localized $t_{2g}$ ($e_g$)
electrons. Due to this large Coulomb interaction, we find $U/W >1$ (with the
band width $W$) in most TM halides, making them strongly correlated materials.
Among the metallic TM halides in paramagnetic state, the correlation strength
$U/W$ reaches a maximum in NiX$_2$ and CrX$_3$ with values much larger than the
corresponding values in elementary TMs and other TM compounds. Based on the
Stoner model and the calculated $U$ and $J$ values, we discuss the tendency of
the electron spins to order ferromagnetically.


Abstract:
  The cold dark matter paradigm has been posited as the standard explanation
for the non-Keplerian behavior of galaxy rotation curves, where for galaxies
satisfying the Tully-Fisher relation, the mass of the dark matter halo from a
large class of universal dark matter profiles ought to roughly increase
linearly with radial distance at large distances, $m(r) \sim r/nG$ ($G$ is the
gravitational constant and $n$ is a dimensionless parameter which depends on
the amount of baryonic matter $M$ within the galaxy). Despite numerous advances
in modeling galaxy formation and evolution, a scientific consensus on the
origin of the observed dependence of the dimensionless parameter $n =
(GMa_{0})^{-1/2}$ on the mass of baryonic matter $M$ within the galaxy (the
Tully-Fisher relation), and the connection of the cosmological constant
$\Lambda$ to the parameter $a_{0} \sim (\Lambda/3)^{1/2}$ remains elusive.
Here, we show that Einstein Field Equations can be remolded into
$\nabla_{\nu}\mathcal{K}^{\nu}_{\,\,\mu} = 8\pi
GM\Psi^{*}\mathcal{D}_{\mu}\Psi$, where $\mathcal{K}_{\mu\nu}$ is a complex
Hermitian tensor, $\mathcal{D}_{\mu}$ is a covariant derivative and $\Psi$ is a
complex-valued function. This avails a novel constraint,
$\nabla_{\mu}\nabla_{\nu}\mathcal{K}^{\mu\nu} = 0$ not necessarily available in
Einstein's General Relativity. In the weak-field regime, we can readily
reproduce the Tully-Fisher relation using the usual charge-less pressure-less
fluid. Moreover, our approach is equivalent to a Ginzburg-Landau theory of $n$
bosons, where the order parameter is normalized as $\int_{0}^{1/a_{0}} dr\,4\pi
r^2\Psi^*\Psi = n$ and $1/a_{0} \sim (\Lambda/3)^{-1/2}$ is the cut-off length
scale comparable to the size of the de Sitter universe. Our investigations
provide a framework that reproduces the mass-asymptotic speed relation in
galaxies within the cold dark matter paradigm.


Abstract:
  Deep learning is a rapidly-evolving technology with possibility to
significantly improve physics reach of collider experiments. In this study we
developed a novel algorithm of vertex finding for future lepton colliders such
as the International Linear Collider. We deploy two networks; one is simple
fully-connected layers to look for vertex seeds from track pairs, and the other
is a customized Recurrent Neural Network with an attention mechanism and an
encoder-decoder structure to associate tracks to the vertex seeds. The
performance of the vertex finder is compared with the standard ILC
reconstruction algorithm.


Abstract:
  The Extragalactic Distance Database (EDD) was created as a repository for
high quality, redshift-independent distances. A key component of EDD is the
Color Magnitude Diagrams/Tip of the Red Giant Branch (CMDs/TRGB) catalog, which
provides information on the stellar content of nearby galaxies observed with
the Hubble Space Telescope (HST). Here we provide a decadal update to this
catalog, which has now doubled in size to over 500 galaxies. We highlight the
additions to our data reduction and analysis techniques, and provide examples
of the science that has been made possible with this large data set. We find
the TRGB to be a reliable measure for distance, and we aim to extend its
distance coverage with HST to every galaxy within 10 Mpc. In the near-future,
the combination of the James Webb Space Telescope and the Nancy Grace Roman
Space Telescope will dramatically increase the number of targets within our
grasp.


Abstract:
  The peculiar morphology of Head-Tail (HT) radio galaxies indicates strong
interactions between the radio jets and their intra-cluster medium. We
systematically search for HT radio galaxies from LOFAR Two-metre Sky Survey
first data release (LoTSS DR1) at 144 MHz frequency. We present here a
catalogue of fifty new HT radio sources, among them, five are Narrow-Angle
Tailed sources (NATs) and forty-five are Wide Angle Tailed sources (WATs). NATs
are characterized by tails bent in a narrow V like shape with less than a
ninety-degree opening angle. For WAT radio galaxies, the opening angle between
jets is more than ninety degrees which exhibit wide C like morphologies. We
found that thirty-one out of fifty HT sources are associated with known galaxy
clusters. The various physical properties and statistical studies of these HT
sources are also presented in this paper.


Abstract:
  In this paper, we propose an end-to-end lifelong learning mixture of experts.
Each expert is implemented by a Variational Autoencoder (VAE). The experts in
the mixture system are jointly trained by maximizing a mixture of individual
component evidence lower bounds (MELBO) on the log-likelihood of the given
training samples. The mixing coefficients in the mixture, control the
contributions of each expert in the goal representation. These are sampled from
a Dirichlet distribution whose parameters are determined through non-parametric
estimation during lifelong learning. The model can learn new tasks fast when
these are similar to those previously learnt. The proposed Lifelong mixture of
VAE (L-MVAE) expands its architecture with new components when learning a
completely new task. After the training, our model can automatically determine
the relevant expert to be used when fed with new data samples. This mechanism
benefits both the memory efficiency and the required computational cost as only
one expert is used during the inference. The L-MVAE inference model is able to
perform interpolation in the joint latent space across the data domains
associated with different tasks and is shown to be efficient for disentangled
learning representation.


Abstract:
  Let $A$ be a Noetherian flat $K[t]$-algebra, $h$ an integer and let $N$ be a
graded $K[t]$-module, we introduce and study "$N$-fiber-full up to $h$"
$A$-modules. We prove that an $A$-module $M$ is $N$-fiber-full up to $h$ if and
only if $\mathrm{Ext}^i_A(M, N)$ is flat over $K[t]$ for all $i\le h-1$. And we
show some applications of this result extending the recent result on squarefree
Gr\"obner degenerations by Conca and Varbaro.


Abstract:
  Considered in this paper is the generalized Camassa-Holm-Novikov equation
with high order nonlinearity, which unifies the Camassa-Holm and Novikov
equations as special cases. We show that the solution map of generalized
Camassa-Holm-Novikov equation is not uniformly continuous on the initial data
in Besov spaces $B_{p, r}^s(\mathbb{R})$ with $s>\max\{1+\frac{1}{p},
\frac{3}{2}\}$, $1\leq p, r< \infty$ as well as in critical space $B_{2,
1}^{\frac{3}{2}}(\mathbb{R}).$ Our result covers and improves the previous work
given by Li et al. \cite{Li 2020, 1Li 2020, Li 2021}(J. Differ. Equ. 269 (2020)
8686-8700; J. Math. Fluid Mech. 22 (2020) 4:50; J. Math. Fluid Mech., (2021)
23:36).


Abstract:
  We study phase-imprinted solitons of ultracold bosons in an optical lattice
with a harmonic trap, which shows the superfluid (SF) and Mott-insulator (MI)
shell structures. The earlier study [Konstantin V. Krutitsky, J. Larson, and M.
Lewenstein, Phys. Rev. A 82, 033618 (2010).] reported three types of
phase-imprinted solitons in the Bose-Hubbard model: in-phase soliton,
out-of-phase soliton, and wavelet. In this paper, we uncover the dynamical
phase diagram of these phase-imprinted solitons, and find another type of the
phase-imprinted soliton namely, the hybrid soliton. In the harmonically trapped
system, the solitonic excitations created at the SF core cannot penetrate into
the outer SF shell. This repulsion at the surface of the outer SF shell can be
cured by inposing a repulsive potential at the center of the trap. These
results can be interpreted as a kind of the impedance matching of excitations
in BECs in terms of the effective chemical potentials or the local particle
numbers in the shell, and the analogous results can be observed also in the
sound wave created by the local single-shot pulse potential.


Abstract:
  We introduce a novel class of quantum circuits that are unitary along three
distinct "arrows of time". These dynamics share some of the analytical
tractability of "dual-unitary" circuits, while exhibiting distinctive and
richer phenomenology. We find that two-point correlations in these dynamics are
strictly confined to three directions in $(1+1)$-dimensional spacetime -- the
two light cone edges, $\delta x=\pm v\delta t$, and the static worldline
$\delta x=0$. Along these directions, correlation functions are obtained
exactly in terms of quantum channels built from the individual gates that make
up the circuit. We prove that, for a class of initial states, entanglement
grows at the maximum allowed speed up to an entropy density of at least one
half of the thermal value, at which point it becomes model-dependent. Finally,
we extend our circuit construction to $2+1$ dimensions, where two-point
correlation functions are confined to the one-dimensional edges of a
tetrahedral light cone -- a subdimensional propagation of information
reminiscent of "fractonic" physics.


Abstract:
  Early prediction of cerebral palsy is essential as it leads to early
treatment and monitoring. Deep learning has shown promising results in
biomedical engineering thanks to its capacity of modelling complicated data
with its non-linear architecture. However, due to their complex structure, deep
learning models are generally not interpretable by humans, making it difficult
for clinicians to rely on the findings. In this paper, we propose a channel
attention module for deep learning models to predict cerebral palsy from
infants' body movements, which highlights the key features (i.e. body joints)
the model identifies as important, thereby indicating why certain diagnostic
results are found. To highlight the capacity of the deep network in modelling
input features, we utilize raw joint positions instead of hand-crafted
features. We validate our system with a real-world infant movement dataset. Our
proposed channel attention module enables the visualization of the vital joints
to this disease that the network considers. Our system achieves 91.67%
accuracy, suppressing other state-of-the-art deep learning methods.


Abstract:
  Spiking neural networks (SNNs) have emerged as energy-efficient neural
networks with temporal information. SNNs have shown a superior efficiency on
neuromorphic devices, but the devices are susceptible to noise, which hinders
them from being applied in real-world applications. Several studies have
increased noise robustness, but most of them considered neither deep SNNs nor
temporal information. In this paper, we investigate the effect of noise on deep
SNNs with various neural coding methods and present a noise-robust deep SNN
with temporal information. With the proposed methods, we have achieved a deep
SNN that is efficient and robust to spike deletion and jitter.


Abstract:
  We propose and study the single-frame anisoplanatic deconvolution problem
associated with image classification using machine learning algorithms, named
the nonuniform defocus removal (NDR) problem. Mathematical analysis of the NDR
problem is done and the so-called defocus removal (DR) algorithm for solving it
is proposed. Global convergence of the DR algorithm is established without
imposing any unverifiable assumption. Numerical results on simulation data show
significant features of DR including solvability, noise robustness,
convergence, model insensitivity and computational efficiency. Physical
relevance of the NDR problem and practicability of the DR algorithm are tested
on experimental data. Back to the application that originally motivated the
investigation of the NDR problem, we show that the DR algorithm can improve the
accuracy of classifying distorted images using convolutional neural networks.
The key difference of this paper compared to most existing works on
single-frame anisoplanatic deconvolution is that the new method does not
require the data image to be decomposable into isoplanatic subregions.
Therefore, solution approaches partitioning the image into isoplanatic zones
are not applicable to the NDR problem and those handling the entire image such
as the DR algorithm need to be developed and analyzed.


Abstract:
  The study investigates the relationship between bank profitability and a
comprehensive list of bank specific, industry specific and macroeconomic
variables using unique panel data from 23 Bangladeshi banks with large market
shares from 2005 to 2019 employing the Pooled Ordinary Least Square (POLS)
Method for regression estimation. The random Effect model has been used to
check for robustness. Three variables, namely, Return on Asset (ROA), Return on
Equity (ROE), and Net Interest Margin (NIM), have been used as profitability
proxies. Non-interest income, capital ratio, and GDP growth have been found to
have a significant relationship with ROA. In addition to non-interest income,
market share, bank size, and real exchange rates are significant explaining
variables if profitability is measured as NIM. The only significant determinant
of profitability measured by ROE is market share. The primary contribution of
this study to the existing knowledge base is an extensive empirical analysis by
covering the entire gamut of independent variables (bank specific, industry
related, and macroeconomic) to explain the profitability of the banks in
Bangladesh. It also covers an extensive and recent data set. Banking sector
stakeholders may find great value from the outputs of this paper. Regulators
and policymakers may find this useful in undertaking analyses in setting policy
rates, banking industry stability, and impact assessment of critical policy
measures before and after the enactment, etc. Investors and the bank management
are to use the findings of this paper in analyzing the real drivers of
profitability of the banks they are contemplating to invest and managing on a
daily basis.


Abstract:
  Proper identification of grade levels of children's reading materials is an
important step towards effective learning. Recent studies in readability
assessment for the English domain applied modern approaches in natural language
processing (NLP) such as machine learning (ML) techniques to automate the
process. There is also a need to extract the correct linguistic features when
modeling readability formulas. In the context of the Filipino language, limited
work has been done [1, 2], especially in considering the language's lexical
complexity as main features. In this paper, we explore the use of lexical
features towards improving the development of readability identification of
children's books written in Filipino. Results show that combining lexical
features (LEX) consisting of type-token ratio, lexical density, lexical
variation, foreign word count with traditional features (TRAD) used by previous
works such as sentence length, average syllable length, polysyllabic words,
word, sentence, and phrase counts increased the performance of readability
models by almost a 5% margin (from 42% to 47.2%). Further analysis and ranking
of the most important features were shown to identify which features contribute
the most in terms of reading complexity.


Abstract:
  In this paper we tackle the problem of pose guided person image generation,
which aims to transfer a person image from the source pose to a novel target
pose while maintaining the source appearance. Given the inefficiency of
standard CNNs in handling large spatial transformation, we propose a
structure-aware flow based method for high-quality person image generation.
Specifically, instead of learning the complex overall pose changes of human
body, we decompose the human body into different semantic parts (e.g., head,
torso, and legs) and apply different networks to predict the flow fields for
these parts separately. Moreover, we carefully design the network modules to
effectively capture the local and global semantic correlations of features
within and among the human parts respectively. Extensive experimental results
show that our method can generate high-quality results under large pose
discrepancy and outperforms state-of-the-art methods in both qualitative and
quantitative comparisons.


Abstract:
  On a closed manifold, consider the space of all Riemannian metrics for which
-\Delta + kR is positive (nonnegative) definite, where k > 0 and R is the
scalar curvature. This spectral generalization of positive (nonnegative) scalar
curvature arises naturally for different values of k in the study of scalar
curvature in dimension via minimal hypersurfaces, the Yamabe problem, and
Perelman's Ricci flow with surgery. When k=1/2, the space models apparent
horizons in time-symmetric initial data to the Einstein equations. We study
these spaces in unison and generalize Coda Marques's path-connectedness
theorem. Applying this with k=1/2, we compute the Bartnik mass of 3-dimensional
apparent horizons and the Bartnik--Bray mass of their outer-minimizing
generalizations in all dimensions. Our methods also yield efficient
constructions for the scalar-nonnegative fill-in problem.


Abstract:
  Accelerated particles are ubiquitous in the Cosmos and play a fundamental
role in many processes governing the evolution of the Universe at all scales,
from the sub-AU ones relevant for the formation and evolution of stars and
planets to the Mpc ones involved in Galaxy assembly. We reveal the presence of
energetic particles in many classes of astrophysical sources thanks to their
production of non-thermal radiation, and we detect them directly at Earth as
Cosmic Rays. In the last two decades both direct and indirect observations have
provided us a wealth of new, high quality data about Cosmic Rays and their
interactions both in sources and during propagation, in the Galaxy and in the
solar system. Some of the new data have confirmed existing theories about
particle acceleration and propagation and their interplay with the environment
in which they occur. Some others have brought about interesting surprises,
whose interpretation is not straightforward within the standard framework and
may require a change of paradigm in terms of our ideas about the origin of
cosmic rays of different species or in different energy ranges. In this
article, we will focus on Cosmic Rays of galactic origin, namely with energies
below a few PeV, where a steepening is observed in the spectrum of energetic
particles detected at the Earth. We review the recent observational findings
and the current status of the theory about the origin and propagation of
Galactic Cosmic Rays.


Abstract:
  Rare earth intermetallic compounds have been fascinating scientists due to
rich phenomena induced by the interplay between localized $f$-orbitals and
conduction electrons. However, since the energy scale of the
crystal-electric-field (CEF) splitting, which defines $f$-orbitals, is very
small only in a few meV, the nature of mobile electrons accompanied by
CEF-excitations has not been unveiled so far. It thus leaves these systems as
frontiers for discovering exotic quasiparticles not yet captured in condensed
matter physics. Here, we examined very low-energy electronic structures of CeSb
going through the anomalous magnetostructural transitions below the N{\'{e}}el
temperature ($T_{\rm{N}}$) $\sim$17 K, called devil's staircase, by a
combination of laser angle-resolved photoemission, Raman and neutron scattering
spectroscopies. We found a new type of electron-boson coupling between the
mobile electrons and quadrupole CEF-excitations of the 4$f$-orbitals, which
renormalizes the Sb 5$p$ band prominently, yielding a remarkable kink at very
low-energy ($\sim$7 meV). This coupling strength is exceedingly strong and
exhibits anomalous step-like enhancement during the devil's staircase
transition, unveiling a new type of quasiparticle, named multipole polaron,
that is a mobile electron largely dressed with a cloud of the quadrupole
CEF-polarization.


Abstract:
  As algorithms are increasingly applied to screen applicants for high-stakes
decisions in employment, lending, and other domains, concerns have been raised
about the effects of algorithmic monoculture, in which many decision-makers all
rely on the same algorithm. This concern invokes analogies to agriculture,
where a monocultural system runs the risk of severe harm from unexpected
shocks. Here we show that the dangers of algorithmic monoculture run much
deeper, in that monocultural convergence on a single algorithm by a group of
decision-making agents, even when the algorithm is more accurate for any one
agent in isolation, can reduce the overall quality of the decisions being made
by the full collection of agents. Unexpected shocks are therefore not needed to
expose the risks of monoculture; it can hurt accuracy even under "normal"
operations, and even for algorithms that are more accurate when used by only a
single decision-maker. Our results rely on minimal assumptions, and involve the
development of a probabilistic framework for analyzing systems that use
multiple noisy estimates of a set of alternatives.


Abstract:
  Reverb plays a critical role in music production, where it provides listeners
with spatial realization, timbre, and texture of the music. Yet, it is
challenging to reproduce the musical reverb of a reference music track even by
skilled engineers. In response, we propose an end-to-end system capable of
switching the musical reverb factor of two different mixed vocal tracks. This
method enables us to apply the reverb of the reference track to the source
track to which the effect is desired. Further, our model can perform
de-reverberation when the reference track is used as a dry vocal source. The
proposed model is trained in combination with an adversarial objective, which
makes it possible to handle high-resolution audio samples. The perceptual
evaluation confirmed that the proposed model can convert the reverb factor with
the preferred rate of 64.8%. To the best of our knowledge, this is the first
attempt to apply deep neural networks to converting music reverb of vocal
tracks.


Abstract:
  An MRI-only adaptive radiotherapy (ART) workflow is desirable for managing
interfractional changes in anatomy, but producing synthetic CT (sCT) data
through paired data-driven deep learning (DL) for abdominal dose calculations
remains a challenge due to the highly variable presence of intestinal gas. We
present the preliminary dosimetric evaluation of our novel approach to sCT
reconstruction that is well suited to handling intestinal gas in abdominal
MRI-only ART.
  We utilize a paired data DL approach enabled by the intensity projection
prior, in which well-matching training pairs are created by propagating air
from MRI to corresponding CT scans. Evaluations focus on two classes: patients
with (1) little involvement of intestinal gas, and (2) notable differences in
intestinal gas presence between corresponding scans. Comparisons between
sCT-based plans and CT-based clinical plans for both classes are made at the
first treatment fraction to highlight the dosimetric impact of the variable
presence of intestinal gas.
  Class 1 patients ($n=13$) demonstrate differences in prescribed dose coverage
of the PTV of $1.3 \pm 2.1\%$ between clinical plans and sCT-based plans. Mean
DVH differences in all structures for Class 1 patients are found to be
statistically insignificant. In Class 2 ($n=20$), target coverage is $13.3 \pm
11.0\%$ higher in the clinical plans and mean DVH differences are found to be
statistically significant.
  Significant deviations in calculated doses arising from the variable presence
of intestinal gas in corresponding CT and MRI scans may limit the effectiveness
of adaptive dose escalation efforts. We have proposed a paired data-driven DL
approach to sCT reconstruction for accurate dose calculations in abdominal ART
enabled by the creation of a clinically unavailable training data set with
well-matching representations of intestinal gas.


Abstract:
  This paper computes the Fadell-Husseini index of Stiefel manifolds in the
case where the group acts via permutations of the orthogonal vectors. The
computations are carried out in the case of elementary Abelian $p$-groups. The
results are shown to imply certain generalizations of the
Kakutani-Yamabe-Yujobo theorem.


Abstract:
  We are interested in the dynamics of a population structured by a phenotypic
trait. Individuals reproduce sexually, which is represented by a non-linear
integral operator. This operator is combined to a multiplicative operator
representing selection. When the strength of selection is small, we show that
the dynamics of the population is governed by a simple macroscopic differential
equation, and that solutions converge exponentially to steady-states that are
locally unique. The analysis is based on Wasserstein distance inequalities
using a uniform lower bound on distributions. These inequalities are coupled to
tail estimates to show the stability of the steady-states.


Abstract:
  Context: Many if not most planetary nebulae (PNe) are now thought to be the
outcome of binary evolutionary scenarios. However only a few percent of PNe in
the Milky Way are known to host binary systems. The high precision repeated
observing and long time baseline of Gaia make it well suited to detect new
close binaries through photometric variability.
  Aims: We aim to find new close binary central stars of PNe (CSPNe) using data
from the Gaia mission, building towards a statistically significant sample of
post common envelope, close binary CSPNe.
  Methods: As the vast majority of Gaia sources do not have published epoch
photometry, we use the uncertainty in the mean photometry as a proxy for
determining the variability of our CSPN sample in the second Gaia data release.
We derive a quantity that expresses the significance of the variability, and
consider what is necessary to build a clean sample of genuine variable sources.
  Results: Our selection recovers a large fraction of the known close binary
CSPN population, while other CSPNe lying in the same region of the parameter
space likely represent low-hanging fruit for ground-based confirmatory followup
observations. Gaia epoch photometry for four of the newly identified variable
sources confirms that the variability is genuine and consistent with binarity


Abstract:
  Since the past few decades, several designs and control methods have been
developed for the Spherical Robots (SRs) with thoroughly analyzed mechanics on
generalized 3D terrains. But the vertical motion and the steep inclination
maneuver has been an unsolved problem with the existing SR's driving
mechanisms. Also, the possibilities of wind-powered or air-propelled SRs have
not been fully explored. This paper introduces the new Omnidirectional
Spherical Robot mechanism named SPIROS: The Six Propeller and Intermeshed Rotor
based Omnidirectional Spherical Robot. The SPIROS is driven by a novel
octahedral arrangement of six intermeshed rotary air thrusters placed in the
Goldberg Polyhedral shaped spherical gridshell. The advantage of the proposed
design lies in its air-powered propulsion, which improves on the obstacle
avoidance and the slope climbing abilities. The robot's dynamic models are
derived using the existing kinematical models of the Continuous Rolling
Spherical Robots (CR-SR), with subcategories, triple axes rolling (3R-SR), dual
axes rolling (2R-SR) and rolling and turning (RT-SR) spherical robots. The path
tracking control scheme based on the pure pursuit algorithm is presented.
Simulations are carried out in MATLAB and Simulink to validate the developed
models and the effectiveness of the proposed control schemes.


Abstract:
  ECH capacities are rich obstructions to symplectic embeddings in 4-dimensions
that have also been seen to arise in the context of algebraic positivity for
(possibly singular) projective surfaces. We extend this connection to relate
general convex toric domains on the symplectic side with towers of polarised
toric surfaces on the algebraic side, and then use this perspective to show
that the sub-leading asymptotics of ECH capacities for all convex and concave
toric domains are $O(1)$. We obtain sufficient criteria for when the
sub-leading asymptotics converge in this context, generalising results of
Hutchings and of the author, and derive new obstructions to embeddings between
toric domains of the same volume. We also propose two invariants to more
precisely describe when convergence occurs in the toric case. Our methods are
largely non-toric in nature, and apply more widely to towers of polarised
Looijenga pairs.


Abstract:
  Multi-modal MRIs are widely used in neuroimaging applications since different
MR sequences provide complementary information about brain structures. Recent
works have suggested that multi-modal deep learning analysis can benefit from
explicitly disentangling anatomical (shape) and modality (appearance)
information into separate image presentations. In this work, we challenge
mainstream strategies by showing that they do not naturally lead to
representation disentanglement both in theory and in practice. To address this
issue, we propose a margin loss that regularizes the similarity in
relationships of the representations across subjects and modalities. To enable
robust training, we further use a conditional convolution to design a single
model for encoding images of all modalities. Lastly, we propose a fusion
function to combine the disentangled anatomical representations as a set of
modality-invariant features for downstream tasks. We evaluate the proposed
method on three multi-modal neuroimaging datasets. Experiments show that our
proposed method can achieve superior disentangled representations compared to
existing disentanglement strategies. Results also indicate that the fused
anatomical representation has potential in the downstream task of zero-dose PET
reconstruction and brain tumor segmentation. The code is available at
\url{https://github.com/ouyangjiahong/representation-disentanglement}.


Abstract:
  Recently, word enhancement has become very popular for Chinese Named Entity
Recognition (NER), reducing segmentation errors and increasing the semantic and
boundary information of Chinese words. However, these methods tend to ignore
the information of the Chinese character structure after integrating the
lexical information. Chinese characters have evolved from pictographs since
ancient times, and their structure often reflects more information about the
characters. This paper presents a novel Multi-metadata Embedding based
Cross-Transformer (MECT) to improve the performance of Chinese NER by fusing
the structural information of Chinese characters. Specifically, we use
multi-metadata embedding in a two-stream Transformer to integrate Chinese
character features with the radical-level embedding. With the structural
characteristics of Chinese characters, MECT can better capture the semantic
information of Chinese characters for NER. The experimental results obtained on
several well-known benchmarking datasets demonstrate the merits and superiority
of the proposed MECT method.\footnote{The source code of the proposed method is
publicly available at https://github.com/CoderMusou/MECT4CNER.


Abstract:
  Let $\mathcal{I}_{d,g,R}$ be the union of irreducible components of the
Hilbert scheme whose general points parametrize smooth, irreducible, curves of
degree $d$, genus $g$, which are non--degenerate in the projective space
$\mathbb{P}^R$. Under some numerical assumptions on $d$, $g$ and $R$, we
construct irreducible components of $\mathcal{I}_{d,g,R}$ other than the
so-called {\em distinguished component}, dominating the moduli space
$\mathcal{M}_g$ of smooth genus--$g$ curves, which are generically smooth and
turn out to be of dimension higher than the expected one. The general point of
any such a component corresponds to a curve $X \subset \mathbb{P}^R$ which is a
suitable ramified $m$--cover of an irrational curve $Y \subset
\mathbb{P}^{R-1}$, $m \geqslant 2$, lying in a surface cone over $Y$. The paper
extends some of the results in previous papers of Y. Choi, H. Iliev, S. Kim
(cf. [12,13] in Bibliography).


Abstract:
  For $k\ge1$, a $k$-almost prime is a positive integer with exactly $k$ prime
factors, counted with multiplicity. In this article we give elementary proofs
of precise asymptotics for the reciprocal sum of $k$-almost primes. Our results
match the strength of those of classical analytic methods. We also study the
limiting behavior of the constants appearing in these estimates, which may be
viewed as higher analogues of the Mertens constant $\beta=0.2614..$. Further,
in the case $k=2$ of semiprimes we give yet finer-scale and explicit estimates,
as well as a conjecture.