Abstract:
  Written language contains stylistic cues that can be exploited to
automatically infer a variety of potentially sensitive author information.
Adversarial stylometry intends to attack such models by rewriting an author's
text. Our research proposes several components to facilitate deployment of
these adversarial attacks in the wild, where neither data nor target models are
accessible. We introduce a transformer-based extension of a lexical replacement
attack, and show it achieves high transferability when trained on a weakly
labeled corpus -- decreasing target model performance below chance. While not
completely inconspicuous, our more successful attacks also prove notably less
detectable by humans. Our framework therefore provides a promising direction
for future privacy-preserving adversarial attacks.


Abstract:
  This paper details the Leibniz generalization of Lie-theoretic results from
Peggy Batten's 1993 dissertation. We first show that the multiplier of a
Leibniz algebra is characterized by its second cohomology group with
coefficients in the field. We then establish criteria for when the center of a
cover maps onto the center of the algebra. Along the way, we obtain a
collection of exact sequences and a brief theory of unicentral Leibniz
algebras.


Abstract:
  Generalized correlation analysis (GCA) is concerned with uncovering linear
relationships across multiple datasets. It generalizes canonical correlation
analysis that is designed for two datasets. We study sparse GCA when there are
potentially multiple generalized correlation tuples in data and the loading
matrix has a small number of nonzero rows. It includes sparse CCA and sparse
PCA of correlation matrices as special cases. We first formulate sparse GCA as
generalized eigenvalue problems at both population and sample levels via a
careful choice of normalization constraints. Based on a Lagrangian form of the
sample optimization problem, we propose a thresholded gradient descent
algorithm for estimating GCA loading vectors and matrices in high dimensions.
We derive tight estimation error bounds for estimators generated by the
algorithm with proper initialization. We also demonstrate the prowess of the
algorithm on a number of synthetic datasets.


Abstract:
  We present a detailed study of the Bose-Hubbard model in a $p$-band
triangular lattice by focusing on the evolution of orbital order across the
superfluid-Mott insulator transition. Two distinct phases are found in the
superfluid regime. One of these phases adiabatically connects the weak
interacting limit. This phase is characterized by the intertwining of axial
$p_\pm=p_x \pm ip_y$ and in-plane $p_\theta=\cos\theta p_x+\sin\theta p_y$
orbital orders, which break the time-reversal symmetry and lattice symmetries
simultaneously. In addition, the calculated Bogoliubov excitation spectrum gaps
the original Dirac points in the single-particle spectrum but exhibits emergent
Dirac points. The other superfluid phase in close proximity to the Mott
insulator with unit boson filling shows a detwined in-plane ferro-orbital
order. Finally, an orbital exchange model is constructed for the Mott insulator
phase. Its classical ground state has an emergent SO$(2)$ rotational symmetry
in the in-plane orbital space and therefore enjoys an infinite degeneracy,
which is ultimately lifted by the orbital fluctuation via the order by disorder
mechanism. Our systematic analysis suggests that the in-plane ferro-orbital
order in the Mott insulator phase agrees with and likely evolves from the
latter superfluid phase.


Abstract:
  Recent studies have shown that the majority of published computational models
in systems biology and physiology are not repeatable or reproducible. There are
a variety of reasons for this. One of the most likely reasons is that given how
busy modern researchers are and the fact that no credit is given to authors for
publishing repeatable work, it is inevitable that this will be the case. The
situation can only be rectified when government agencies, universities and
other research institutions change policies and that journals begin to insist
that published work is in fact at least repeatable if not reproducible. In this
chapter guidelines are described that can be used by researchers to help make
sure their work is repeatable. A scoring system is suggested that authors can
use to determine how well they are doing.


Abstract:
  We study the effect of torsional deformations on the electronic properties of
single-walled transition metal dichalcogenide (TMD) nanotubes. In particular,
considering forty-five select armchair and zigzag TMD nanotubes, we perform
symmetry-adapted Kohn-Sham density functional theory calculations to determine
the variation in bandgap and effective mass of charge carriers with twist. We
find that metallic nanotubes remain so even after deformation, whereas
semiconducting nanotubes experience a decrease in bandgap with twist --
originally direct bandgaps become indirect -- resulting in semiconductor to
metal transitions. In addition, the effective mass of holes and electrons
continuously decrease and increase with twist, respectively, resulting in
n-type to p-type semiconductor transitions. We find that this behavior is
likely due to rehybridization of orbitals in the metal and chalcogen atoms,
rather than charge transfer between them. Overall, torsional deformations
represent a powerful avenue to engineer the electronic properties of
semiconducting TMD nanotubes, with applications to devices like sensors and
semiconductor switches.


Abstract:
  Our all electron (DFBG) calculations show differences between relativistic
and non-relativistic calculations for the structure of the isomers of Og(CO)6


Abstract:
  We discover the formation of a temporal boundary soliton (TBS) in the close
proximity of a temporal boundary, moving in a nonlinear optical medium, upon
high-intensity pulse collision with the boundary. We show that the TBS
excitation causes giant intensity fluctuations in reflection (transmission)
from (through) the temporal boundary even for very modest input pulse intensity
fluctuations. We advance a statistical theory of the phenomenon and show that
the TBS emerges as an extremely rare event in a nonintegrable nonlinear system,
heralded by colossal intensity fluctuations with unprecedented magnitudes of
the normalized intensity autocorrelation function of the reflected/transmitted
pulse ensemble.


Abstract:
  We introduce Automorphism-based graph neural networks (Autobahn), a new
family of graph neural networks. In an Autobahn, we decompose the graph into a
collection of subgraphs and apply local convolutions that are equivariant to
each subgraph's automorphism group. Specific choices of local neighborhoods and
subgraphs recover existing architectures such as message passing neural
networks. Our formalism also encompasses novel architectures: as an example, we
introduce a graph neural network that decomposes the graph into paths and
cycles. The resulting convolutions reflect the natural way that parts of the
graph can transform, preserving the intuitive meaning of convolution without
sacrificing global permutation equivariance. We validate our approach by
applying Autobahn to molecular graphs, where it achieves state-of-the-art
results.


Abstract:
  This paper discusses prime numbers that are (resp. are not) congruent
numbers. Particularly the only case not fully covered by earlier results,
namely primes of the form $p=8k+1$, receives attention.


Abstract:
  We describe methods for simulating general second-quantized Hamiltonians
using the compact encoding, in which qubit states encode only the occupied
modes in physical occupation number basis states. These methods apply to
second-quantized Hamiltonians composed of a constant number of interactions,
i.e., linear combinations of ladder operator monomials of fixed form. Compact
encoding leads to qubit requirements that are optimal up to logarithmic
factors. We show how to use sparse Hamiltonian simulation methods for
second-quantized Hamiltonians in compact encoding, give explicit
implementations for the required oracles, and analyze the methods. We also
describe several example applications including the free boson and fermion
theories, the $\phi^4$-theory, and the massive Yukawa model, all in both
equal-time and light-front quantization. Our methods provide a general-purpose
tool for simulating second-quantized Hamiltonians, with optimal or near-optimal
scaling with error and model parameters.


Abstract:
  The neutron drop is firstly investigated in an axially symmetric harmonic
oscillator (ASHO) field, whose potential strengths of different directions can
be controlled artificially. The shape of the neutron drop will change from
spherical to oblate or prolate according to the anisotropy of the external
field. With the potential strength increasing in the axial direction, the
neutron prefers to occupy the orbital perpendicular to the symmetry axis. On
the contrary, the neutron likes to stay in the orbital parallel to the symmetry
axis when the potential strength increases in the radial direction. Meanwhile,
when the potential strength of one direction disappears, the neutron drop
cannot bind together. These investigations are not only helpful to simulate the
properties of neutrons in finite nuclei but also provide the theoretical
predictions to the future artificial operations on the nuclei like the
ultracold atom system, for a deeper realization of quantum many-body systems.


Abstract:
  Derived from a biophysical model for the motion of a crawling cell, the
system \[(*)~\begin{cases}u_t=\Delta u-\nabla\cdot(u\nabla v)\\0=\Delta
v-kv+u\end{cases}\] is investigated in a finite domain
$\Omega\subset\mathbb{R}^n$, $n\geq2$, with $k\geq0$. While a comprehensive
literature is available for cases with $(*)$ describing chemotaxis systems and
hence being accompanied by homogeneous Neumann-type boundary conditions, the
presently considered modeling context, besides yet requiring the flux
$\partial_\nu u-u\partial_\nu v$ to vanish on $\partial\Omega$, inherently
involves homogeneous Dirichlet conditions for the attractant $v$, which in the
current setting corresponds to the cell's cytoskeleton being free of pressure
at the boundary.
  This modification in the boundary setting is shown to go along with a
substantial change with respect to the potential to support the emergence of
singular structures: It is, inter alia, revealed that in contexts of radial
solutions in balls there exist two critical mass levels, distinct from each
other whenever $k>0$ or $n\geq3$, that separate ranges within which (i) all
solutions are global in time and remain bounded, (ii) both global bounded and
exploding solutions exist, or (iii) all nontrivial solutions blow up in finite
time. While critical mass phenomena distinguishing between regimes of type (i)
and (ii) belong to the well-understood characteristics of $(*)$ when posed
under classical no-flux boundary conditions in planar domains, the discovery of
a distinct secondary critical mass level related to the occurrence of (iii)
seems to have no nearby precedent.
  In the planar case with the domain being a disk, the analytical results are
supplemented with some numerical illustrations, and it is discussed how the
findings can be interpreted biophysically for the situation of a cell on a flat
substrate.


Abstract:
  Our knowledge of white dwarf planetary systems predominately arises from the
region within a few Solar radii of the white dwarfs, where minor planets break
up, form rings and discs, and accrete onto the star. The entry location, angle
and speed into this Roche sphere has rarely been explored but crucially
determines the initial geometry of the debris, accretion rates onto the
photosphere, and ultimately the composition of the minor planet. Here we evolve
a total of over 10^5 asteroids with single-planet N-body simulations across the
giant branch and white dwarf stellar evolution phases to quantify the geometry
of asteroid injection into the white dwarf Roche sphere as a function of
planetary mass and eccentricity. We find that lower planetary masses increase
the extent of anisotropic injection and decrease the probability of head-on
(normal to the Roche sphere) encounters. Our results suggest that one can use
dynamical activity within the Roche sphere to make inferences about the hidden
architectures of these planetary systems.


Abstract:
  We report the observation of discrete vortex bound states with the energy
levels deviating from the widely believed ratio of 1:3:5 in the vortices of an
iron based superconductor KCa2Fe4As4F2 through scanning tunneling microcopy
(STM). Meanwhile Friedel oscillations of vortex bound states are also observed
for the first time in related vortices. By doing self-consistent calculations
of Bogoliubov-de Gennes equations, we find that at extreme quantum limit, the
superconducting order parameter exhibits a Friedel-like oscillation, which
modifies the energy levels of the vortex bound states and explains why it
deviates from the ratio of 1:3:5. The observed Friedel oscillations of the
bound states can also be roughly interpreted by the theoretical calculations,
however some features at high energies could not be explained. We attribute
this discrepancy to the high energy bound states with the influence of nearby
impurities. Our combined STM measurement and the self-consistent calculations
illustrate a generalized feature of the vortex bound states in type-II
superconductors.


Abstract:
  The removal of organic micropollutants (OMPs) has been investigated in
constructed wetlands (CWs) operated as bioelectrochemical systems (BES). The
operation of CWs as BES (CW-BES), either in the form of microbial fuel cells
(MFC) or microbial electrolysis cells (MEC), has only been investigated in
recent years. The presented experiment used CW meso-scale systems applying a
realistic horizontal flow regime and continuous feeding of real urban
wastewater spiked with four OMPs (pharmaceuticals), namely carbamazepine (CBZ),
diclofenac (DCF), ibuprofen (IBU) and naproxen (NPX). The study evaluated the
removal efficiency of conventional CW systems (CW-control) as well as CW
systems operated as closed-circuit MFCs (CW-MFCs) and MECs (CW-MECs). Although
a few positive trends were identified for the CW-BES compared to the CW-control
(higher average CBZ, DCF and NPX removal by 10-17% in CW-MEC and 5% in CW-MFC),
these proved to be not statistically significantly different. Mesoscale
experiments with real wastewater could thus not confirm earlier positive
effects of CW-BES found under strictly controlled laboratory conditions with
synthetic wastewaters.


Abstract:
  We have studied the effect of nitriding on the humidity sensing properties of
hydrogenated amorphous carbon (a-C:H) films. The films were prepared in two
stages combining the techniques of physical deposition in vapor phase
evaporation (PAPVD) and plasma pulsed nitriding. By deconvolution of the Raman
spectrum we identified two peaks corresponding to the D and G modes
characteristic of a-C:H. After the N$_2$-H$_2$ plasma treating, the peaks
narrowed and shifted to the right, which we associated with the incorporation
of N into the structure. We compared the sensitivity to the relative humidity
(RH) of the films before and after the N$_2$-H$_2$ plasma treatment. The
nitriding improved the humidity sensitivity measured as the low frequency
resistance.
  By impedance spectroscopy we studied the frequency dependence of the AC
conductivity $\sigma$ at different RH conditions. Before nitriding
$\sigma(\omega)\sim A \omega^s$, it seemed to have the universal behaviour seen
in other amorphous systems. The humidity changed the overall scale $A$. After
nitriding, the exponent $s$ increased, and became RH dependent. We associated
this behaviour to the change of the interaction mechanism between the water
molecule and the substrate when the samples were nitriding.


Abstract:
  Stealing attack against controlled information, along with the increasing
number of information leakage incidents, has become an emerging cyber security
threat in recent years. Due to the booming development and deployment of
advanced analytics solutions, novel stealing attacks utilize machine learning
(ML) algorithms to achieve high success rate and cause a lot of damage.
Detecting and defending against such attacks is challenging and urgent so that
governments, organizations, and individuals should attach great importance to
the ML-based stealing attacks. This survey presents the recent advances in this
new type of attack and corresponding countermeasures. The ML-based stealing
attack is reviewed in perspectives of three categories of targeted controlled
information, including controlled user activities, controlled ML model-related
information, and controlled authentication information. Recent publications are
summarized to generalize an overarching attack methodology and to derive the
limitations and future directions of ML-based stealing attacks. Furthermore,
countermeasures are proposed towards developing effective protections from
three aspects -- detection, disruption, and isolation.


Abstract:
  Small-scale Mixed-Integer Quadratic Programming (MIQP) problems often arise
in embedded control and estimation applications. Driven by the need for
algorithmic simplicity to target computing platforms with limited memory and
computing resources, this paper proposes a few approaches to solving MIQPs,
either to optimality or suboptimally. We specialize an existing Accelerated
Dual Gradient Projection (GPAD) algorithm to effectively solve the Quadratic
Programming (QP) relaxation that arise during Branch and Bound (B&B) and
propose a generic framework to warm-start the binary variables which reduces
the number of QP relaxations. Moreover, in order to find an integer feasible
combination of the binary variables upfront, two heuristic approaches are
presented: ($i$) without using B&B, and ($ii$) using B&B with a significantly
reduced number of QP relaxations. Both heuristic approaches return an integer
feasible solution that may be suboptimal but involve a much reduced computation
effort. Such a feasible solution can be either implemented directly or used to
set an initial upper bound on the optimal cost in B&B. Through different hybrid
control and estimation examples involving binary decision variables, we show
that the performance of the proposed methods, although very simple to code, is
comparable to that of state-of-the-art MIQP solvers.


Abstract:
  We propose a new model for networks of time series that influence each other.
Graph structures among time series are found in diverse domains, such as web
traffic influenced by hyperlinks, product sales influenced by recommendation,
or urban transport volume influenced by road networks and weather. There has
been recent progress in graph modeling and in time series forecasting,
respectively, but an expressive and scalable approach for a network of series
does not yet exist. We introduce Radflow, a novel model that embodies three key
ideas: a recurrent neural network to obtain node embeddings that depend on
time, the aggregation of the flow of influence from neighboring nodes with
multi-head attention, and the multi-layer decomposition of time series. Radflow
naturally takes into account dynamic networks where nodes and edges change over
time, and it can be used for prediction and data imputation tasks. On
real-world datasets ranging from a few hundred to a few hundred thousand nodes,
we observe that Radflow variants are the best performing model across a wide
range of settings. The recurrent component in Radflow also outperforms N-BEATS,
the state-of-the-art time series model. We show that Radflow can learn
different trends and seasonal patterns, that it is robust to missing nodes and
edges, and that correlated temporal patterns among network neighbors reflect
influence strength. We curate WikiTraffic, the largest dynamic network of time
series with 366K nodes and 22M time-dependent links spanning five years. This
dataset provides an open benchmark for developing models in this area, with
applications that include optimizing resources for the web. More broadly,
Radflow has the potential to improve forecasts in correlated time series
networks such as the stock market, and impute missing measurements in
geographically dispersed networks of natural phenomena.


Abstract:
  We need intelligent robots for mobile construction, the process of navigating
in an environment and modifying its structure according to a geometric design.
In this task, a major robot vision and learning challenge is how to exactly
achieve the design without GPS, due to the difficulty caused by the
bi-directional coupling of accurate robot localization and navigation together
with strategic environment manipulation. However, many existing robot vision
and learning tasks such as visual navigation and robot manipulation address
only one of these two coupled aspects. To stimulate the pursuit of a generic
and adaptive solution, we reasonably simplify mobile construction as a
partially observable Markov decision process (POMDP) in 1/2/3D grid worlds and
benchmark the performance of a handcrafted policy with basic localization and
planning, and state-of-the-art deep reinforcement learning (RL) methods. Our
extensive experiments show that the coupling makes this problem very
challenging for those methods, and emphasize the need for novel task-specific
solutions.


Abstract:
  Analogous to the case of the binomial random graph $G(d+1,p)$, it is known
that the behaviour of a random subgraph of a $d$-dimensional hypercube, where
we include each edge independently with probability $p$, which we denote by
$Q^d_p$, undergoes a phase transition around the critical value of
$p=\frac{1}{d}$. More precisely, standard arguments show that significantly
below this value of $p$, with probability tending to one as $d \to \infty$ (whp
for short) all components of this graph have order $O(d)$, whereas Ajtai,
Koml\'{o}s and Szemer\'{e}di showed that significantly above this value, in the
\emph{supercritical regime}, whp there is a unique `giant' component of order
$\Theta\left(2^d\right)$. In $G(d+1,p)$ much more is known about the complex
structure of the random graph which emerges in this supercritical regime. For
example, it is known that in this regime whp $G(d+1,p)$ contains paths and
cycles of length $\Omega(d)$, as well as complete minors of order
$\Omega\left(\sqrt{d}\right)$. In this paper we obtain analogous results in
$Q^d_p$. In particular, we show that for supercritical $p$, i.e., when
$p=\frac{1+\epsilon}{d}$ for a positive constant $\epsilon$, whp $Q^d_p$
contains a cycle of length $\Omega\left(\frac{2^d}{d^3(\log d)^3} \right)$ and
a complete minor of order $\Omega\left(\frac{2^{\frac{d}{2}}}{d^3(\log d)^3
}\right)$. In order to prove these results, we show that whp the largest
component of $Q^d_p$ has good edge-expansion properties, a result of
independent interest. We also consider the genus of $Q^d_p$ and show that, in
this regime of $p$, whp the genus is $\Omega\left(2^d\right)$.


Abstract:
  We develop the concept of quasi-phasematching (QPM) by implementing it in the
recently proposed Josephson traveling-wave parametric amplifier (JTWPA) with
three-wave mixing (3WM). The amplifier is based on a ladder transmission line
consisting of flux-biased radio-frequency SQUIDs whose nonlinearity is of
$\chi^{(2)}$-type. QPM is achieved in the 3WM process,
$\omega_p=\omega_s+\omega_i$ (where $\omega_p$, $\omega_s$, and $\omega_i$ are
the pump, signal, and idler frequencies, respectively) due to designing the
JTWPA to include periodically inverted groups of these SQUIDs that reverse the
sign of the nonlinearity. Modeling shows that the JTWPA bandwidth is relatively
large (ca. $0.4\omega_p$) and flat, while unwanted modes, including
$\omega_{2p}=2\omega_p$, $\omega_+=\omega_p +\omega_s$, $\omega_- = 2\omega_p -
\omega_s$, etc., are strongly suppressed with the help of engineered
dispersion.


Abstract:
  A sequence of point configurations on a compact complex manifold is
asymptotically Fekete if it is close to maximizing a sequence of Vandermonde
determinants. These Vandermonde determinants are defined by tensor powers of a
Hermitian ample line bundle and the point configurations in the sequence
possess good sampling properties with respect to sections of the line bundle.
In this paper, given a collection of toric Hermitian ample line bundles, we
give necessary and sufficient condition for existence of a sequence of point
configurations which is asymptotically Fekete (and hence possess good sampling
properties) with respect to each one of the line bundles. When they exist, we
also present a way of constructing such sequences. As a byproduct we get a new
equidistribution property for maximizers of products of Vandermonde
determinants.


Abstract:
  Recently, the study of graph neural network (GNN) has attracted much
attention and achieved promising performance in molecular property prediction.
Most GNNs for molecular property prediction are proposed based on the idea of
learning the representations for the nodes by aggregating the information of
their neighbor nodes (e.g. atoms). Then, the representations can be passed to
subsequent layers to deal with individual downstream tasks. Therefore, the
architectures of GNNs can be considered as being composed of two core parts:
graph-related layers and task-specific layers. Facing real-world molecular
problems, the hyperparameter optimization for those layers are vital.
Hyperparameter optimization (HPO) becomes expensive in this situation because
evaluating candidate solutions requires massive computational resources to
train and validate models. Furthermore, a larger search space often makes the
HPO problems more challenging. In this research, we focus on the impact of
selecting two types of GNN hyperparameters, those belonging to graph-related
layers and those of task-specific layers, on the performance of GNN for
molecular property prediction. In our experiments. we employed a
state-of-the-art evolutionary algorithm (i.e., CMA-ES) for HPO. The results
reveal that optimizing the two types of hyperparameters separately can gain the
improvements on GNNs' performance, but optimising both types of hyperparameters
simultaneously will lead to predominant improvements. Meanwhile, our study also
further confirms the importance of HPO for GNNs in molecular property
prediction problems.


Abstract:
  In a complete metric space equipped with a doubling measure supporting a
$p$-Poincar\'e inequality, we prove sharp growth and integrability results for
$p$-harmonic Green functions and their minimal $p$-weak upper gradients. We
show that these properties are determined by the growth of the underlying
measure near the singularity. Corresponding results are obtained also for more
general $p$-harmonic functions with poles, as well as for singular solutions of
elliptic differential equations in divergence form on weighted $\mathbf{R}^n$
and on manifolds.
  The proofs are based on a new general capacity estimate for annuli, which
implies precise pointwise estimates for $p$-harmonic Green functions. The
capacity estimate is valid under considerably milder assumptions than above. We
also use it, under these milder assumptions, to characterize singletons of zero
capacity and the $p$-parabolicity of the space. This generalizes and improves
earlier results that have been important especially in the context of
Riemannian manifolds.


Abstract:
  A number of recent approaches have been proposed for pruning neural network
parameters at initialization with the goal of reducing the size and
computational burden of models while minimally affecting their training
dynamics and generalization performance. While each of these approaches have
some amount of well-founded motivation, a rigorous analysis of the effect of
these pruning methods on network training dynamics and their formal
relationship to each other has thus far received little attention. Leveraging
recent theoretical approximations provided by the Neural Tangent Kernel, we
unify a number of popular approaches for pruning at initialization under a
single path-centric framework. We introduce the Path Kernel as the
data-independent factor in a decomposition of the Neural Tangent Kernel and
show the global structure of the Path Kernel can be computed efficiently. This
Path Kernel decomposition separates the architectural effects from the
data-dependent effects within the Neural Tangent Kernel, providing a means to
predict the convergence dynamics of a network from its architecture alone. We
analyze the use of this structure in approximating training and generalization
performance of networks in the absence of data across a number of
initialization pruning approaches. Observing the relationship between input
data and paths and the relationship between the Path Kernel and its natural
norm, we additionally propose two augmentations of the SynFlow algorithm for
pruning at initialization.


Abstract:
  Common modal decomposition techniques for flowfield analysis, data-driven
modeling and flow control, such as proper orthogonal decomposition (POD) and
dynamic mode decomposition (DMD) are usually performed in an Eulerian (fixed)
frame of reference with snapshots from measurements or evolution equations. The
Eulerian description poses some difficulties, however, when the domain or the
mesh deforms with time as, for example, in fluid-structure interactions. For
such cases, we first formulate a Lagrangian modal analysis (LMA) ansatz by a
posteriori transforming the Eulerian flow fields into Lagrangian flow maps
through an orientation and measure-preserving domain diffeomorphism. The
development is then verified for Lagrangian variants of POD and DMD using
direct numerical simulations (DNS) of two canonical flow configurations at Mach
0.5, the lid-driven cavity and flow past a cylinder, representing internal and
external flows, respectively, at pre- and post-bifurcation Reynolds numbers.
The LMA is demonstrated for several situations encompassing unsteady flow
without and with boundary and mesh deformation as well as non-uniform base
flows that are steady in Eulerian but not in Lagrangian frames. We show that
LMA application to steady nonuniform base flow yields insights into flow
stability and post-bifurcation dynamics. LMA naturally leads to Lagrangian
coherent flow structures and connections with finite-time Lyapunov exponents
(FTLE). We examine the mathematical link between FTLE and LMA by considering a
double-gyre flow pattern. Dynamically important flow features in the Lagrangian
sense are recovered by performing LMA with forward and backward (adjoint) time
procedures.


Abstract:
  Network models provide an efficient way to represent many real life problems
mathematically. In the last few decades, the field of network optimization has
witnessed an upsurge of interest among researchers and practitioners. The
network models considered in this thesis are broadly classified into four types
including transportation problem, shortest path problem, minimum spanning tree
problem and maximum flow problem. Quite often, we come across situations, when
the decision parameters of network optimization problems are not precise and
characterized by various forms of uncertainties arising from the factors, like
insufficient or incomplete data, lack of evidence, inappropriate judgements and
randomness. Considering the deterministic environment, there exist several
studies on network optimization problems. However, in the literature, not many
investigations on single and multi objective network optimization problems are
observed under diverse uncertain frameworks. This thesis proposes seven
different network models under different uncertain paradigms. Here, the
uncertain programming techniques used to formulate the uncertain network models
are (i) expected value model, (ii) chance constrained model and (iii) dependent
chance constrained model. Subsequently, the corresponding crisp equivalents of
the uncertain network models are solved using different solution methodologies.
The solution methodologies used in this thesis can be broadly categorized as
classical methods and evolutionary algorithms. The classical methods, used in
this thesis, are Dijkstra and Kruskal algorithms, modified rough Dijkstra
algorithm, global criterion method, epsilon constraint method and fuzzy
programming method. Whereas, among the evolutionary algorithms, we have
proposed the varying population genetic algorithm with indeterminate crossover
and considered two multi objective evolutionary algorithms.


Abstract:
  The wavelet analysis technique is a powerful tool and is widely used in broad
disciplines of engineering, technology, and sciences. In this work, we present
a novel scheme of constructing continuous wavelet functions, in which the
wavelet functions are obtained by taking the first derivative of smoothing
functions with respect to the scale parameter. Due to this wavelet constructing
scheme, the inverse transforms are only one-dimensional integrations with
respect to the scale parameter, and hence the continuous wavelet transforms
constructed in this way are more ready to use than the usual scheme. We then
apply the Gaussian-derived wavelet constructed by our scheme to computations of
the density power spectrum for dark matter, the velocity power spectrum and the
kinetic energy spectrum for baryonic fluid. These computations exhibit the
convenience and strength of the continuous wavelet transforms. The transforms
are very easy to perform, and we believe that the simplicity of our wavelet
scheme will make continuous wavelet transforms very useful in practice.


Abstract:
  We prove the irreducibility of integer polynomials $f(X)$ whose roots lie
inside an Apollonius circle associated to two points on the real axis with
integer abscisae $a$ and $b$, with ratio of the distances to these points
depending on the canonical decomposition of $f(a)$ and $f(b)$. In particular,
we obtain irreducibility criteria for the case where $f(a)$ and $f(b)$ have few
prime factors, and $f$ is either an Enestr\"om-Kakeya polynomial, or has a
large leading coefficient. Analogous results are also provided for multivariate
polynomials over arbitrary fields, in a non-Archimedean setting.


Abstract:
  We construct the complete set of boundary states of two-dimensional fermionic
CFTs using that of the bosonic counterpart. We see that there are two groups of
boundary conditions, which contributes to the open-string partition function by
characters with integer coefficients, or with $\sqrt{2}$ times integer
coefficients. We argue that, using the argument of [JHEP 09 (2020) 018], this
$\sqrt{2}$ indicates a single unpaired Majorana zero mode, and that these two
groups of boundary conditions are mutually incompatible. We end the paper by
mentioning a possible interpretation of the result in terms of the entanglement
entropy.


Abstract:
  We develop a dynamical density functional theory based model for the drying
of colloidal films on planar surfaces. We consider mixtures of two different
sizes of hard-sphere colloids. Depending on the solvent evaporation rate and
the initial concentrations of the two species, we observe varying degrees of
stratification in the final dried films. Our model predicts the various
structures described in the literature previously from experiments and computer
simulations, in particular the small-on-top stratified films. Our model also
includes the influence of adsorption of particles to the interfaces.


Abstract:
  A perennial objection against Bayes factor point-null hypothesis tests is
that the point-null hypothesis is known to be false from the outset. Following
Morey and Rouder (2011) we examine the consequences of approximating the sharp
point-null hypothesis by a hazy `peri-null' hypothesis instantiated as a narrow
prior distribution centered on the point of interest. The peri-null Bayes
factor then equals the point-null Bayes factor multiplied by a correction term
which is itself a Bayes factor. For moderate sample sizes, the correction term
is relatively inconsequential; however, for large sample sizes the correction
term becomes influential and causes the peri-null Bayes factor to be
inconsistent and approach a limit that depends on the ratio of prior ordinates
evaluated at the maximum likelihood estimate. We characterize the asymptotic
behavior of the peri-null Bayes factor and discuss how to construct peri-null
Bayes factor hypothesis tests that are also consistent.


Abstract:
  NonlinearSchrodinger.jl is a Julia package with a simple interface for
studying solutions of nonlinear Schr\"odinger equations (NLSEs). In
approximately ten lines of code, one can perform a simulation of the cubic NLSE
using one of 32 algorithms, including symplectic and Runge-Kutta-Nystr\"om
integrators up to eighth order. Furthermore, it is possible to compute
analytical solutions via a numerical implementation of the Darboux
transformation for extended NLSEs up to fifth order, with an equally simple
interface. In what follows, we review the fundamentals of solving this class of
equations numerically and analytically, discuss the implementation, and provide
several examples.


Abstract:
  In a 2004 paper by V. M. Buchstaber and D. V. Leykin, published in
"Functional Analysis and Its Applications," for each $g > 0$, a system of $2g$
multidimensional heat equations in a nonholonomic frame was constructed. The
sigma function of the universal hyperelliptic curve of genus $g$ is a solution
of this system. In the work arXiv:2007.08966 explicit expressions for the
Schr\"odinger operators that define the equations of the system considered were
obtained in the hyperelliptic case.
  In this work we use these results to show that if the initial condition of
the system considered is polynomial, then the solution of the system is
uniquely determined up to a constant factor. This has important applications in
the well-known problem of series expansion for the hyperelliptic sigma
function. We give an explicit description of the connection of such solutions
to well-known Burchnall-Chaundy polynomials and Adler-Moser polynomials. We
find a system of linear second-order differential equations that determines the
corresponding Adler-Moser polynomial.


Abstract:
  Photon-mediated interactions between atomic systems are the cornerstone of
quantum information transfer. They can arise via coupling to a common
electromagnetic mode or by quantum interference. This can manifest in
cooperative light-matter coupling, yielding collective rate enhancements such
as those at the heart of superradiance, or remote entanglement via
measurement-induced path erasure. Here, we report coherent control of
cooperative emission arising from two distant but indistinguishable solid-state
emitters due to path erasure. The primary signature of cooperative emission,
the emergence of "bunching" at zero-delay in an intensity correlation
experiment, is used to characterise the indistinguishability of the emitters,
their dephasing, and the degree of correlation in the joint system which can be
coherently controlled. In a stark departure from a pair of uncorrelated
emitters, we observe photon statistics resembling that of a weak coherent state
in Hong-Ou-Mandel type interference measurements. Our experiments establish new
techniques to control and characterize cooperative behaviour between matter
qubits using the full quantum optics toolbox, a key stepping stone on the route
to realising large-scale quantum photonic networks.


Abstract:
  Galaxy clusters are considered to be gigantic reservoirs of cosmic rays
(CRs). Some of the clusters are found with extended radio emission, which
provides evidence for the existence of magnetic fields and CR electrons in the
intra-cluster medium (ICM). The mechanism of radio halo (RH) emission is still
under debate, and it has been believed that turbulent reacceleration plays an
important role. In this paper, we study the reacceleration of CR protons and
electrons in detail by numerically solving the Fokker-Planck equation, and show
how radio and gamma-ray observations can be used to constrain CR distributions
and resulting high-energy emission for the Coma cluster. We take into account
the radial diffusion of CRs and follow the time evolution of their
one-dimensional distribution, by which we investigate the radial profile of the
CR injection that is consistent with the observed RH surface brightness. We
find that the required injection profile is non-trivial, depending on whether
CR electrons have the primary or secondary origin. Although the secondary CR
electron scenario predicts larger gamma-ray and neutrino fluxes, it is in
tension with the observed RH spectrum. In either scenario, we find that galaxy
clusters can make a sizable contribution to the all-sky neutrino intensity if
the CR energy spectrum is nearly flat.


Abstract:
  This letter presents an energy- and memory-efficient pattern-matching engine
for a network intrusion detection system (NIDS) in the Internet of Things.
Tightly coupled architecture and circuit co-designs are proposed to fully
exploit the statistical behaviors of NIDS pattern matching. The proposed engine
performs pattern matching in three phases, where the phase-1 prefix matching
employs reconfigurable pipelined automata processing to minimize memory
footprint without loss of throughput and efficiency. The processing elements
utilize 8-T content-addressable memory (CAM) cells for dual-port search by
leveraging proposed fixed-1s encoding. A 65-nm prototype demonstrates
best-in-class 1.54-fJ energy per search per pattern byte and 0.9-byte memory
usage per pattern byte.


Abstract:
  As part of a programme to develop parton showers with controlled logarithmic
accuracy, we consider the question of collinear spin correlations within the
PanScales family of parton showers. We adapt the well-known Collins-Knowles
spin-correlation algorithm to PanScales antenna and dipole showers, using an
approach with similarities to that taken by Richardson and Webster. To study
the impact of spin correlations, we develop Lund-declustering based observables
that are sensitive to spin-correlation effects both within and between jets and
extend the MicroJets collinear single-logarithmic resummation code to include
spin correlations. Together with a 3-point energy correlation observable
proposed recently by Chen, Moult and Zhu, this provides a powerful set of
constraints for validating the logarithmic accuracy of our shower results. The
new observables and their resummation further open the pathway to
phenomenological studies of these important quantum mechanical effects.


Abstract:
  In this paper, we demonstrate how Hyperledger Fabric, one of the most popular
permissioned blockchains, can benefit from network-attached acceleration. The
scalability and peak performance of Fabric is primarily limited by the
bottlenecks present in its block validation/commit phase. We propose Blockchain
Machine, a hardware accelerator coupled with a hardware-friendly communication
protocol, to act as the validator peer. It can be adapted to applications and
their smart contracts, and is targeted for a server with network-attached FPGA
acceleration card. The Blockchain Machine retrieves blocks and their
transactions in hardware directly from the network interface, which are then
validated through a configurable and efficient block-level and
transaction-level pipeline. The validation results are then transferred to the
host CPU where non-bottleneck operations are executed. From our implementation
integrated with Fabric v1.4 LTS, we observed up to 12x speedup in block
validation when compared to software-only validator peer, with commit
throughput of up to 68,900 tps. Our work provides an acceleration platform that
will foster further research on hardware acceleration of permissioned
blockchains.


Abstract:
  We propose multirate training of neural networks: partitioning neural network
parameters into "fast" and "slow" parts which are trained simultaneously using
different learning rates. By choosing appropriate partitionings we can obtain
large computational speed-ups for transfer learning tasks. We show that for
various transfer learning applications in vision and NLP we can fine-tune deep
neural networks in almost half the time, without reducing the generalization
performance of the resulting model. We also discuss other splitting choices for
the neural network parameters which are beneficial in enhancing generalization
performance in settings where neural networks are trained from scratch.
Finally, we propose an additional multirate technique which can learn different
features present in the data by training the full network on different time
scales simultaneously. The benefits of using this approach are illustrated for
ResNet architectures on image data. Our paper unlocks the potential of using
multirate techniques for neural network training and provides many starting
points for future work in this area.


Abstract:
  Investigations of magnetically ordered phases on the femtosecond timescale
have provided significant insights into the influence of charge and lattice
degrees of freedom on the magnetic sub-system. However, short-range magnetic
correlations occurring in the absence of long-range order, for example in
spin-frustrated systems, are inaccessible to many ultrafast techniques. Here,
we show how time-resolved resonant inelastic X-ray scattering (trRIXS) is
capable of probing such short-ranged magnetic dynamics in a charge-transfer
insulator through the detection of a Zhang-Rice singlet exciton. Utilizing
trRIXS measurements at the O K-edge, and in combination with model
calculations, we probe the short-range spin-correlations in the frustrated spin
chain material CuGeO3 following photo-excitation, revealing a strong coupling
between the local lattice and spin sub-systems.


Abstract:
  Many modern workloads, such as neural networks, databases, and graph
processing, are fundamentally memory-bound. For such workloads, the data
movement between main memory and CPU cores imposes a significant overhead in
terms of both latency and energy. A major reason is that this communication
happens through a narrow bus with high latency and limited bandwidth, and the
low data reuse in memory-bound workloads is insufficient to amortize the cost
of main memory access. Fundamentally addressing this data movement bottleneck
requires a paradigm where the memory system assumes an active role in computing
by integrating processing capabilities. This paradigm is known as
processing-in-memory (PIM).
  Recent research explores different forms of PIM architectures, motivated by
the emergence of new 3D-stacked memory technologies that integrate memory with
a logic layer where processing elements can be easily placed. Past works
evaluate these architectures in simulation or, at best, with simplified
hardware prototypes. In contrast, the UPMEM company has designed and
manufactured the first publicly-available real-world PIM architecture.
  This paper provides the first comprehensive analysis of the first
publicly-available real-world PIM architecture. We make two key contributions.
First, we conduct an experimental characterization of the UPMEM-based PIM
system using microbenchmarks to assess various architecture limits such as
compute throughput and memory bandwidth, yielding new insights. Second, we
present PrIM, a benchmark suite of 16 workloads from different application
domains (e.g., linear algebra, databases, graph processing, neural networks,
bioinformatics).


Abstract:
  Let $(\Omega, \mathcal{A}, \mu)$ be a probability space. The classical
Borel-Cantelli Lemma states that for any sequence of $\mu$-measurable sets
$E_i$ ($i=1,2,3,\dots$), if the sum of their measures converges then the
corresponding $\limsup$ set $E_\infty$ is of measure zero. In general the
converse statement is false. However, it is well known that the divergence
counterpart is true under various additional 'independence' hypotheses. In this
paper we revisit these hypotheses and establish both sufficient and necessary
conditions for $E_\infty$ to have either positive or full measure.


Abstract:
  Automatic speech recognition (ASR) in Sanskrit is interesting, owing to the
various linguistic peculiarities present in the language. The Sanskrit language
is lexically productive, undergoes euphonic assimilation of phones at the word
boundaries and exhibits variations in spelling conventions and in
pronunciations. In this work, we propose the first large scale study of
automatic speech recognition (ASR) in Sanskrit, with an emphasis on the impact
of unit selection in Sanskrit ASR. In this work, we release a 78 hour ASR
dataset for Sanskrit, which faithfully captures several of the linguistic
characteristics expressed by the language. We investigate the role of different
acoustic model and language model units in ASR systems for Sanskrit. We also
propose a new modelling unit, inspired by the syllable level unit selection,
that captures character sequences from one vowel in the word to the next vowel.
We also highlight the importance of choosing graphemic representations for
Sanskrit and show the impact of this choice on word error rates (WER). Finally,
we extend these insights from Sanskrit ASR for building ASR systems in two
other Indic languages, Gujarati and Telugu. For both these languages, our
experimental results show that the use of phonetic based graphemic
representations in ASR results in performance improvements as compared to ASR
systems that use native scripts.


Abstract:
  We classify connected graphs $G$ whose binomial edge ideal is Gorenstein. The
proof uses methods in prime characteristic.


Abstract:
  We provide a comprehensive study of the energy transfer phenomenon --
populating a given energy level -- in 3- and 4-level quantum systems coupled to
two thermal baths. In particular, we examine the effects of an external
periodic driving and the coherence induced by the baths on the efficiency of
the energy transfer. We consider the Floquet-Lindblad and the Floquet-Redfield
scenarios, which both are in the Born-Markov, weak-coupling regime but differ
in the treatment of the secular approximation, and for the latter, we develop
an appropriate Floquet-type master equation by employing a partial secular
approximation. Throughout the whole analysis we keep Lamb-shift corrections in
the master equations. We observe that, especially in the Floquet-Redfield
scenario, the driving field can enhance the energy transfer efficiency compared
to the nondriven scenario. In addition, unlike degenerate systems where
Lamb-shift corrections do not contribute significantly on the energy transfer,
in the Redfield and the Floquet-Redfield scenarios these corrections have
nonnegligible effects.


Abstract:
  For years, the extragalactic community has divided galaxies in two distinct
populations. One of them, featuring blue colours, is actively forming stars,
while the other is made up of "red-and-dead" objects with negligible star
formation. Yet, are these galaxies really dead? Here we would like to highlight
that, as previously reported by several independent groups, state-of-the-art
cosmological numerical simulations predict the existence of a large number of
quenched galaxies that have not formed any star over the last few Gyr. In
contrast, observational measurements of large galaxy samples in the nearby
Universe suggest that even the most passive systems still form stars at some
residual level close to $sSFR\sim10^{-12}~\text{yr}^{-1}$. Unfortunately,
extremely low star formation poses a challenge for both approaches. We conclude
that, at present, the fraction of truly dead galaxies is still an important
open question that must be addressed in order to understand galaxy formation
and evolution.


Abstract:
  Magneto-optical traps (MOTs) are widely used for laser cooling of atoms. We
have developed a high-flux compact cold-atom source based on a pyramid MOT with
a unique adjustable aperture that is highly suitable for portable quantum
technology devices, including space-based experiments. The adjustability
enabled an investigation into the previously unexplored impact of aperture size
on the atomic flux, and optimisation of the aperture size allowed us to
demonstrate a higher flux than any reported cold-atom sources that use a
pyramid, LVIS, 3D-MOT or grating MOT. We achieved 2.0(1)x10^10 atoms/s of 87-Rb
with a mean velocity of 32(1)m/s, FWHM of 27.6(9)m/s and divergence of
58(3)mrad. Halving the total optical power to 195mW caused only a 26% reduction
of the flux, and a 33% decrease in mean velocity. Methods to further decrease
the velocity as required have been identified. The low power consumption and
small size make this design suitable for a wide range of cold-atom
technologies.


Abstract:
  The third version of the Hypertext Transfer Protocol (HTTP) is currently in
its final standardization phase by the IETF. Besides better security and
increased flexibility, it promises benefits in terms of performance. HTTP/3
adopts a more efficient header compression schema and replaces TCP with QUIC, a
transport protocol carried over UDP, originally proposed by Google and
currently under standardization too. Although HTTP/3 early implementations
already exist and some websites announce its support, it has been subject to
few studies. In this work, we provide a first measurement study on HTTP/3. We
testify how, during 2020, it has been adopted by some of the leading Internet
companies such as Google, Facebook and Cloudflare. We run a large-scale
measurement campaign toward thousands of websites adopting HTTP/3, aiming at
understanding to what extent it achieves better performance than HTTP/2. We
find that adopting websites often host most web page objects on third-party
servers, which support only HTTP/2 or even HTTP/1.1. Our experiments show that
HTTP/3 provides sizable benefits only in scenarios with high latency or very
poor bandwidth. Despite the adoption of QUIC, we do not find benefits in case
of high packet loss, but we observe large diversity across website providers'
infrastructures.


Abstract:
  We present a novel method of performing spelling correction on short input
strings, such as search queries or individual words. At its core lies a
procedure for generating artificial typos which closely follow the error
patterns manifested by humans. This procedure is used to train the production
spelling correction model based on a transformer architecture. This model is
currently served in the HubSpot product search. We show that our approach to
typo generation is superior to the widespread practice of adding noise, which
ignores human patterns. We also demonstrate how our approach may be extended to
resource-scarce settings and train spelling correction models for Arabic,
Greek, Russian, and Setswana languages, without using any labeled data.


Abstract:
  Fair clustering is the process of grouping similar entities together, while
satisfying a mathematically well-defined fairness metric as a constraint. Due
to the practical challenges in precise model specification, the prescribed
fairness constraints are often incomplete and act as proxies to the intended
fairness requirement, leading to biased outcomes when the system is deployed.
We examine how to identify the intended fairness constraint for a problem based
on limited demonstrations from an expert. Each demonstration is a clustering
over a subset of the data.
  We present an algorithm to identify the fairness metric from demonstrations
and generate clusters using existing off-the-shelf clustering techniques, and
analyze its theoretical properties. To extend our approach to novel fairness
metrics for which clustering algorithms do not currently exist, we present a
greedy method for clustering. Additionally, we investigate how to generate
interpretable solutions using our approach. Empirical evaluation on three
real-world datasets demonstrates the effectiveness of our approach in quickly
identifying the underlying fairness and interpretability constraints, which are
then used to generate fair and interpretable clusters.


Abstract:
  The median webpage has increased in size by more than 80% in the last 4
years. This extra complexity allows for a rich browsing experience, but it
hurts the majority of mobile users which still pay for their traffic. This has
motivated several data-saving solutions, which aim at reducing the complexity
of webpages by transforming their content. Despite each method being unique,
they either reduce user privacy by further centralizing web traffic through
data-saving middleboxes or introduce web compatibility (Webcompat) issues by
removing content that breaks pages in unpredictable ways. In this paper, we
argue that data-saving is still possible without impacting either users privacy
or Webcompat. Our main observation is that Web images make up a large portion
of Web traffic and have negligible impact on Webcompat. To this end we make two
main contributions. First, we quantify the potential savings that image
manipulation, such as dimension resizing, quality compression, and transcoding,
enables at large scale: 300 landing and 880 internal pages. Next, we design and
build Browselite, an entirely client-side tool that achieves such data savings
through opportunistically instrumenting existing server-side tooling to perform
image compression, while simultaneously reducing the total amount of image data
fetched. The effect of Browselite on the user experience is quantified using
standard page load metrics and a real user study of over 200 users across 50
optimized web pages. Browselite allows for similar savings to middlebox
approaches, while offering additional security, privacy, and Webcompat
guarantees.


Abstract:
  Neural volumetric representations such as Neural Radiance Fields (NeRF) have
emerged as a compelling technique for learning to represent 3D scenes from
images with the goal of rendering photorealistic images of the scene from
unobserved viewpoints. However, NeRF's computational requirements are
prohibitive for real-time applications: rendering views from a trained NeRF
requires querying a multilayer perceptron (MLP) hundreds of times per ray. We
present a method to train a NeRF, then precompute and store (i.e. "bake") it as
a novel representation called a Sparse Neural Radiance Grid (SNeRG) that
enables real-time rendering on commodity hardware. To achieve this, we
introduce 1) a reformulation of NeRF's architecture, and 2) a sparse voxel grid
representation with learned feature vectors. The resulting scene representation
retains NeRF's ability to render fine geometric details and view-dependent
appearance, is compact (averaging less than 90 MB per scene), and can be
rendered in real-time (higher than 30 frames per second on a laptop GPU).
Actual screen captures are shown in our video.


Abstract:
  The recent advancements in multicore machines highlight the need to simplify
concurrent programming in order to leverage their computational power. One way
to achieve this is by designing efficient concurrent data structures (e.g.
stacks, queues, hash-tables, etc.) and synchronization techniques (e.g. locks,
combining techniques, etc.) that perform well in machines with large amounts of
cores. In contrast to ordinary, sequential data-structures, the concurrent
data-structures allow multiple threads to simultaneously access and/or modify
them.
  Synch is an open-source framework that not only provides some common
high-performant concurrent data-structures, but it also provides researchers
with the tools for designing and benchmarking high performant concurrent
data-structures. The Synch framework contains a substantial set of concurrent
data-structures such as queues, stacks, combining-objects, hash-tables, locks,
etc. and it provides a user-friendly runtime for developing and benchmarking
concurrent data-structures. Among other features, the provided runtime provides
functionality for creating threads easily (both POSIX and user-level threads),
tools for measuring performance, etc. Moreover, the provided concurrent
data-structures and the runtime are highly optimized for contemporary NUMA
multiprocessors such as AMD Epyc and Intel Xeon.


Abstract:
  The exponential functional link network (EFLN) has been recently investigated
and applied to nonlinear filtering. This brief proposes an adaptive EFLN
filtering algorithm based on a novel inverse square root (ISR) cost function,
called the EFLN-ISR algorithm, whose learning capability is robust under
impulsive interference. The steady-state performance of EFLN-ISR is rigorously
derived and then confirmed by numerical simulations. Moreover, the validity of
the proposed EFLN-ISR algorithm is justified by the actually experimental
results with the application to hysteretic nonlinear system identification.


Abstract:
  Cerebral hematoma grows rapidly in 6-24 hours and misprediction of the growth
can be fatal if it is not operated by a brain surgeon. There are two types of
cerebral hematomas: one that grows rapidly and the other that does not grow
rapidly. We are developing the technique of artificial intelligence to
determine whether the CT image includes the cerebral hematoma which leads to
the rapid growth. This problem has various difficulties: the few positive cases
in this classification problem of cerebral hematoma and the targeted hematoma
has deformable object. Other difficulties include the imbalance classification,
the covariate shift, the small data, and the spurious correlation problems. It
is difficult with the plain CNN classification such as VGG. This paper proposes
the joint learning of semantic segmentation and classification and evaluate the
performance of this.


Abstract:
  We have followed up two ultra-diffuse galaxies (UDGs), detected adjacent to
stellar streams, with Hubble Space Telescope (HST) imaging and HI mapping with
the Jansky Very Large Array (VLA) in order to investigate the possibility that
they might have a tidal origin. With the HST F814W and F555W images we measure
the globular cluster (GC) counts for NGC 2708-Dw1 and NGC 5631-Dw1 as
$2^{+1}_{-1}$ and $5^{+1}_{-2}$, respectively. NGC 2708-Dw1 is undetected in HI
down to a 3$\sigma$ limit of $\log (M_\mathrm{HI}/\mathrm{M_\odot}) = 7.3$, and
there is no apparent HI associated with the nearby stellar stream. There is a
2$\sigma$ HI feature coincident with NGC 5631-Dw1. However, this emission is
blended with a large gaseous tail emanating from NGC 5631 and is not
necessarily associated with the UDG. The presence of any GCs and the lack of
clear HI connections between the UDGs and their parent galaxies strongly
disfavor a tidal dwarf galaxy origin, but cannot entirely rule it out. The GC
counts are consistent with those of normal dwarf galaxies, and the most
probable formation mechanism is one where these UDGs were born as normal dwarfs
and were later tidally stripped and heated. We also identify an over-luminous
($M_\mathrm{V} = -11.1$) GC candidate in NGC 2708-Dw1, which may be a nuclear
star cluster transitioning to an ultra-compact dwarf as the surrounding dwarf
galaxy gets stripped of stars.


Abstract:
  Hierarchical classification is significant for complex tasks by providing
multi-granular predictions and encouraging better mistakes. As the label
structure decides its performance, many existing approaches attempt to
construct an excellent label structure for promoting the classification
results. In this paper, we consider that different label structures provide a
variety of prior knowledge for category recognition, thus fusing them is
helpful to achieve better hierarchical classification results. Furthermore, we
propose a multi-task multi-structure fusion model to integrate different label
structures. It contains two kinds of branches: one is the traditional
classification branch to classify the common subclasses, the other is
responsible for identifying the heterogeneous superclasses defined by different
label structures. Besides the effect of multiple label structures, we also
explore the architecture of the deep model for better hierachical
classification and adjust the hierarchical evaluation metrics for multiple
label structures. Experimental results on CIFAR100 and Car196 show that our
method obtains significantly better results than using a flat classifier or a
hierarchical classifier with any single label structure.


Abstract:
  Classical machine learning approaches are sensitive to non-stationarity.
Transfer learning can address non-stationarity by sharing knowledge from one
system to another, however, in areas like machine prognostics and defense, data
is fundamentally limited. Therefore, transfer learning algorithms have little,
if any, examples from which to learn. Herein, we suggest that these constraints
on algorithmic learning can be addressed by systems engineering. We formally
define transfer distance in general terms and demonstrate its use in
empirically quantifying the transferability of models. We consider the use of
transfer distance in the design of machine rebuild procedures to allow for
transferable prognostic models. We also consider the use of transfer distance
in predicting operational performance in computer vision. Practitioners can use
the presented methodology to design and operate systems with consideration for
the learning theoretic challenges faced by component learning systems.


Abstract:
  This paper considers the inverse problem of recovering both the unknown,
spatially-dependent conductivity $a(x)$ and the nonlinear reaction term $f(u)$
in a reaction-diffusion equation from overposed data. These measurements can
consist of: the value of two different solution measurements taken at a later
time $T$; time-trace profiles from two solutions; or both final time and
time-trace measurements from a single forwards solve data run. We prove both
uniqueness results and the convergence of iteration schemes designed to recover
these coefficients. The last section of the paper shows numerical
reconstructions based on these algorithms.


Abstract:
  Einstein equivalence principle (EEP), as one of the foundations of general
relativity, is a fundamental test of gravity theories. In this paper, we
propose a new method to test the EEP of electromagnetic interactions through
observations of black hole photon rings, which naturally extends the scale of
Newtonian and post-Newtoian gravity where the EEP violation through a variable
fine structure constant has been well constrained to that of stronger gravity.
We start from a general form of Lagrangian that violates EEP, where a specific
EEP violation model could be regarded as one of the cases of this Lagrangian.
Within the geometrical optical approximation, we find that the dispersion
relation of photons is modified: for photons moving in circular orbit, the
dispersion relation simplifies, and behaves such that photons with different
linear polarizations perceive different gravitational potentials. This makes
the size of black hole photon ring depend on polarization. Further assuming
that the EEP violation is small, we derive an approximate analytic expression
for spherical black holes showing that the change in size of the photon ring is
proportional to the violation parameters. We also discuss several cases of this
analytic expression for specific models. Finally, we explore the effects of
black hole rotation and derive a modified proportionality relation between the
change in size of photon ring and the violation parameters. The numerical and
analytic results show that the influence of black hole rotation on the
constraints of EEP violation is relatively weak for small magnitude of EEP
violation and small rotation speed of black holes.


Abstract:
  One of the important and widely used classes of models for non-Gaussian time
series is the generalized autoregressive model average models (GARMA), which
specifies an ARMA structure for the conditional mean process of the underlying
time series. However, in many applications one often encounters conditional
heteroskedasticity. In this paper we propose a new class of models, referred to
as GARMA-GARCH models, that jointly specify both the conditional mean and
conditional variance processes of a general non-Gaussian time series. Under the
general modeling framework, we propose three specific models, as examples, for
proportional time series, nonnegative time series, and skewed and heavy-tailed
financial time series. Maximum likelihood estimator (MLE) and quasi Gaussian
MLE (GMLE) are used to estimate the parameters. Simulation studies and three
applications are used to demonstrate the properties of the models and the
estimation procedures.


Abstract:
  In this paper we consider the second eigenfunction of the Laplacian with
Dirichlet boundary conditions in convex domains. If the domain has \emph{large
eccentricity} then the eigenfunction has \emph{exactly} two nondegenerate
critical points (of course they are one maximum and one minimum). The proof
uses some estimates proved by Jerison ([Jer95a]) and Grieser-Jerison ([GJ96])
jointly with a topological degree argument. Analogous results for higher order
eigenfunctions are proved in rectangular-like domains considered in [GJ09].


Abstract:
  Transient field-resolved spectroscopy enables studies of ultrafast dynamics
in molecules, nanostructures, or solids with sub-cycle resolution, but previous
work has so far concentrated on extracting the dielectric response at
frequencies below 50\,THz. Here, we implemented transient field-resolved
reflectometry at 50-100\,THz (3-6\,$\mu$m) with MHz repetition rate employing
800\,nm few-cycle excitation pulses that provide sub-10\,fs temporal
resolution. The capabilities of the technique are demonstrated in studies of
ultrafast photorefractive changes in the semiconductors Ge and GaAs, where the
high frequency range permitted to explore the resonance-free Drude response.
The extended frequency range in transient field-resolved spectroscopy can
further enable studies with so far inaccessible transitions, including
intramolecular vibrations in a large range of systems.


Abstract:
  The decoupling of heavy fields as required by the Appelquist-Carazzone
theorem plays a fundamental role in the construction of any effective field
theory. However, it is not a trivial task to implement a renormalization
prescription that produces the expected decoupling of massive fields, and it is
even more difficult in curved spacetime. Focused on this idea, we consider the
renormalization of the one-loop effective action for the Yukawa interaction
with a background scalar field in curved space. We compute the beta functions
within a generalized DeWitt-Schwinger subtraction procedure and discuss the
decoupling in the running of the coupling constants. For the case of a
quantized scalar field, all the beta function exhibit decoupling, including
also the gravitational ones. For a quantized Dirac field, decoupling appears
almost for all the beta functions. We obtain the anomalous result that the mass
of the background scalar field does not decouple.


Abstract:
  Baryon-to-meson and baryon-to-photon transition distribution amplitudes
(TDAs) arise in the collinear factorized description of a class of hard
exclusive reactions characterized by the exchange of a non-zero baryon number
in the cross channel. These TDAs extend the concepts of generalized parton
distributions (GPDs) and baryon distribution amplitudes (DAs). In this review
we discuss the general properties and physical interpretation of
baryon-to-meson and baryon-to-photon TDAs. We argue that these non-perturbative
objects are a convenient complementary tool to explore the structure of baryons
at the partonic level. We present an overview of hard exclusive reactions
admitting a description in terms of TDAs. We discuss the first signals from
hard exclusive backward meson electroproduction at JLab with the 6 GeV electron
beam and explore further experimental opportunities to access TDAs at JLab@12
GeV, PANDA and J-PARC.


Abstract:
  We study homogeneous nucleation in the two-dimensional $q-$state Potts model
for $q=3,5,10,20$ and ferromagnetic couplings $J_{ij} \propto \Theta (R -
|i-j|)$, by means of Monte Carlo simulations employing heat bath dynamics.
Metastability is induced in the low temperature phase through an instantaneous
quench of the magnetic field coupled to one of the $q$ spin states. The quench
depth is adjusted, depending on the value of temperature $T$, interaction range
$R$, and number of states $q$, in such a way that a constant nucleation time is
always obtained. In this setup we analyze the crossover between the classical
compact droplet regime occurring in presence of short range interactions $R
\sim 1$, and the long-range regime $R\gg 1$ where the properties of nucleation
are influenced by the presence of a mean-field spinodal singularity. We
evaluate the metastable susceptibility of the order parameter as well as
various critical droplet properties, which along with the evolution of the
quench depth as a function of $q,T$ and $R$, are then compared with the field
theoretical predictions valid in the large $R$ limit in order to find the onset
of spinodal-assisted nucleation. We find that, with a mild dependence on the
values of $q$ and $T$ considered, spinodal scaling holds for interaction ranges
$R\gtrsim 8-10$, and that signatures of the presence of a pseudo-spinodal are
already visible for remarkably small interaction ranges $R\sim 4-5$. The
influence of spinodal singularities on the occurrence of multi-step nucleation
is also discussed.


Abstract:
  Consider any network of $n$ identical Kuramoto oscillators in which each
oscillator is coupled bidirectionally with unit strength to at least $\mu
(n-1)$ other oscillators. There is a critical value of the connectivity,
$\mu_c$, such that whenever $\mu>\mu_c$, the system is guaranteed to converge
to the all-in-phase synchronous state for almost all initial conditions, but
when $\mu<\mu_c$, there are networks with other stable states. The precise
value of the critical connectivity remains unknown, but it has been conjectured
to be $\mu_c=0.75$. In 2020, Lu and Steinerberger proved that $\mu_c\leq
0.7889$, and Yoneda, Tatsukawa, and Teramae proved in 2021 that $\mu_c >
0.6838$. In this paper, we prove that $\mu_c\leq 0.75$ and explain why this is
the best upper bound that one can obtain by a purely linear stability analysis.


Abstract:
  Integrated circuits (ICs) that can operate at high temperature have a wide
variety of applications in the fields of automotive, aerospace, space
exploration, and deep-well drilling. Conventional silicon-based complementary
metal-oxide-semiconductor (CMOS) circuits cannot work at higher than 200
$^\circ$C, leading to the use of wide bandgap semiconductor, especially silicon
carbide (SiC). However, high-density defects at an oxide-SiC interface make it
impossible to predict electrical characteristics of SiC CMOS logic gates in a
wide temperature range and high supply voltage (typically ${\geqq 15}$ V) is
required to compensate their large logic threshold voltage shift. Here, we show
that SiC complementary logic gates composed of p- and n-channel junction
field-effect transistors (JFETs) operate at 300 $^\circ$C with a supply voltage
as low as 1.4 V. The logic threshold voltage shift of the complementary JFET
(CJFET) inverter is 0.2 V from room temperature to 300 $^\circ$C. Furthermore,
temperature dependencies of the static and dynamic characteristics of the CJFET
inverter are well explained by a simple analytical model of SiC JFETs. This
allows us to perform electronic circuit simulation, leading to superior
designability of complex circuits or memories based on SiC CJFET technology,
which operate within a wide temperature range.


Abstract:
  The Istanbul options were first introduced by Michel Jacques in 1997. These
derivatives are considered as an extension of the Asian options. In this paper,
we propose an analytical approximation formula for a geometric Istanbul call
option (GIC) under the Black-Scholes model. Our approximate pricing formula is
obtained in closed-form using a second-order Taylor expansion. We compare our
theoretical results with those of Monte-Carlo simulations using the control
variates method. Finally, we study the effects of changes in the price of the
underlying asset on the value of GIC.


Abstract:
  We study three-terminal thermoelectric transport in a two-dimensional Quantum
Point Contact (QPC) connected to left and right electronic reservoirs, as well
as a third one represented by a scanning probe tip. The latter acts as a
voltage probe exchanging heat with the system but no charges on average. The
thermoelectric coefficients are calculated numerically within the
Landauer-B\"uttiker formalism in the low-temperature and linear response
regimes. We find tip-induced oscillations of the local and non-local
thermopowers and study their dependence on the QPC opening. If the latter is
tuned on a conductance plateau, the system behaves as a perfect thermoelectric
diode: for some tip positions the charge current through the QPC, driven by a
local Seebeck effect, can flow in one direction only.


Abstract:
  Identifying harmful instances, whose absence in a training dataset improves
model performance, is important for building better machine learning models.
Although previous studies have succeeded in estimating harmful instances under
supervised settings, they cannot be trivially extended to generative
adversarial networks (GANs). This is because previous approaches require that
(1) the absence of a training instance directly affects the loss value and that
(2) the change in the loss directly measures the harmfulness of the instance
for the performance of a model. In GAN training, however, neither of the
requirements is satisfied. This is because, (1) the generator's loss is not
directly affected by the training instances as they are not part of the
generator's training steps, and (2) the values of GAN's losses normally do not
capture the generative performance of a model. To this end, (1) we propose an
influence estimation method that uses the Jacobian of the gradient of the
generator's loss with respect to the discriminator's parameters (and vice
versa) to trace how the absence of an instance in the discriminator's training
affects the generator's parameters, and (2) we propose a novel evaluation
scheme, in which we assess harmfulness of each training instance on the basis
of how GAN evaluation metric (e.g., inception score) is expect to change due to
the removal of the instance. We experimentally verified that our influence
estimation method correctly inferred the changes in GAN evaluation metrics.
Further, we demonstrated that the removal of the identified harmful instances
effectively improved the model's generative performance with respect to various
GAN evaluation metrics.


Abstract:
  We present a post-training weight pruning method for deep neural networks
that achieves accuracy levels tolerable for the production setting and that is
sufficiently fast to be run on commodity hardware such as desktop CPUs or edge
devices. We propose a data-free extension of the approach for computer vision
models based on automatically-generated synthetic fractal images. We obtain
state-of-the-art results for data-free neural network pruning, with ~1.5% top@1
accuracy drop for a ResNet50 on ImageNet at 50% sparsity rate. When using real
data, we are able to get a ResNet50 model on ImageNet with 65% sparsity rate in
8-bit precision in a post-training setting with a ~1% top@1 accuracy drop. We
release the code as a part of the OpenVINO(TM) Post-Training Optimization tool.


Abstract:
  We show that, in a weakly regular $p$-adic Lie group $G$, the subgroup $G_u$
spanned by the one-parameter subgroups of $G$ admits a Levi decomposition. As a
consequence, there exists a regular open subgroup of $G$ which contains $G_u$.


Abstract:
  A motion-blurred image is the temporal average of multiple sharp frames over
the exposure time. Recovering these sharp video frames from a single blurred
image is nontrivial, due to not only its strong ill-posedness, but also various
types of complex motion in reality such as rotation and motion in depth. In
this work, we report a generalized video extraction method using the affine
motion modeling, enabling to tackle multiple types of complex motion and their
mixing. In its workflow, the moving objects are first segemented in the alpha
channel. This allows separate recovery of different objects with different
motion. Then, we reduce the variable space by modeling each video clip as a
series of affine transformations of a reference frame, and introduce the
$l0$-norm total variation regularization to attenuate the ringing artifact. The
differentiable affine operators are employed to realize gradient-descent
optimization of the affine model, which follows a novel coarse-to-fine strategy
to further reduce artifacts. As a result, both the affine parameters and sharp
reference image are retrieved. They are finally input into stepwise affine
transformation to recover the sharp video frames. The stepwise retrieval
maintains the nature to bypass the frame order ambiguity. Experiments on both
public datasets and real captured data validate the state-of-the-art
performance of the reported technique.


Abstract:
  We study multicomponent coagulation via the Smoluchowski coagulation equation
under non-equilibrium stationary conditions induced by a source of small
clusters. The coagulation kernel can be very general, merely satisfying certain
power law asymptotic bounds in terms of the total number of monomers in a
cluster. The bounds are characterized by two parameters and we extend previous
results for one-component systems to classify the parameter values for which
the above stationary solutions do or do not exist. Moreover, we also obtain
criteria for the existence or non-existence of solutions which yield a constant
flux of mass towards large clusters.


Abstract:
  Ultraviolet (UV) exposure significantly contributes to non-melanoma skin
cancer. In the context of health, UV exposure is the product of time and the UV
Index (UVI), a weighted sum of the irradiance I(lambda) over all wavelengths
from lambda = 250 to 400nm. In our analysis of the United States Environmental
Protection Agency's UV-Net database of over four-hundred thousand spectral
irradiance measurements taken over several years, we found that the UVI is well
estimated by UVI = 77 I(310nm). To better understand this result, we applied an
optical atmospheric model of the terrestrial irradiance spectra and found that
it applies across a wide range of conditions.


Abstract:
  The use of the full potential of stellar seismology is made difficult by the
improper modeling of the upper-most layers of solar-like stars and their
influence on the modeled frequencies. Our knowledge on these \emph{surface
effects} has improved thanks to the use of 3D hydrodynamical simulations but
the calculation of eigenfrequencies relies on empirical models for the
description of the Lagrangian perturbation of turbulent pressure: the
reduced-$\Gamma_1$ model (RGM) and the gas-$\Gamma_1$ model (GGM). Starting
from the fully compressible turbulence equations, we derive both the GGM and
RGM models using a closure to model the flux of turbulent kinetic energy. It is
found that both models originate from two terms: the source of turbulent
pressure due to compression produced by the oscillations and the divergence of
the flux of turbulent pressure. It is also demonstrated that they are both
compatible with the adiabatic approximation but also imply a number of
questionable assumptions mainly regarding mode physics. Among others
hypothesis, one has to neglect the Lagrangian perturbation of the dissipation
of turbulent kinetic energy into heat and the Lagrangian perturbation of
buoyancy work.


Abstract:
  We give a link criterion for normal embeddings of definable sets in o-minimal
structures. Namely, we prove that given a definable germ $(X, 0)\subset
(\mathbb{R}^n,0)$ with $(X\setminus\{0\},0)$ connected and a continuous
definable function $\rho: (X,0) \to \mathbb{R}_{\geq 0}$ such that $\rho(x)
\sim \|x\|$, then $(X,0)$ is Lipschitz normally embedded (LNE) if and only if
$(X,0)$ is link Lipschitz normally embedded (LLNE) with respect to $\rho$
(i.e., for $r>0$ small enough, $X\cap \rho^{-1}(r)$ is Lipschitz normally
embedded and its LNE constant is bounded by a constant $C$ independent of $r$).
This is a generalization of Mendes--Sampaio's result for the subanalytic case.
As an application, we give a counterexample to a question on the relation
between Lipschitz normal embedding and MD Homology asked by Bobadilla et al in
their paper about Moderately Discontinuous Homology.


Abstract:
  We say that a random integer variable $X$ is monotone if the modulus of the
characteristic function of $X$ is decreasing on $[0,\pi]$. This is the case for
many commonly encountered variables, e.g., Bernoulli, Poisson and geometric
random variables. In this note, we provide estimates for the probability that
the sum of independent monotone integer variables attains precisely a specific
value. We do not assume that the variables are identically distributed. Our
estimates are sharp when the specific value is close to the mean, but they are
not useful further out in the tail. By combining with the trick of
\emph{exponential tilting}, we obtain sharp estimates for the point
probabilities in the tail under a slightly stronger assumption on the random
integer variables which we call strong monotonicity.


Abstract:
  The lack of stability guarantee restricts the practical use of learning-based
methods in core control problems in robotics. We develop new methods for
learning neural control policies and neural Lyapunov critic functions in the
model-free reinforcement learning (RL) setting. We use sample-based approaches
and the Almost Lyapunov function conditions to estimate the region of
attraction and invariance properties through the learned Lyapunov critic
functions. The methods enhance stability of neural controllers for various
nonlinear systems including automobile and quadrotor control.


Abstract:
  Let $T$ be a linear operator on an $\mathbb{F}_q$-vector space $V$ of
dimension $n$. For any divisor $m$ of $n$, an $m$-dimensional subspace $W$ of
$V$ is $T$-splitting if
  $$ V =W\oplus TW\oplus \cdots \oplus T^{d-1}W, $$ where $d=n/m$. Let
$\sigma(m,d;T)$ denote the number of $m$-dimensional $T$-splitting subspaces.
Determining $\sigma(m,d;T)$ for an arbitrary operator $T$ is an open problem.
This problem is closely related to another open problem on Krylov spaces. We
discuss this connection and give explicit formulae for $\sigma(m,d;T)$ in the
case where the invariant factors of $T$ satisfy certain degree conditions. A
connection with another enumeration problem on polynomial matrices is also
discussed.


Abstract:
  We study the probability distribution of the number of particle and
antiparticle pairs produced via the Schwinger effect when a uniform but
time-dependent electric field is applied to noninteracting scalars or spinors
initially at a thermodynamic equilibrium. We derive the formula for the
characteristic function by employing techniques in mesoscopic physics,
reflecting a close analogy between the Schwinger effect and mesoscopic
tunneling transports. In particular, we find that the pair production in a
medium is enhanced (suppressed) for scalars (spinors) due to the Bose
stimulation (Pauli blocking). Furthermore, in addition to the production of
accelerated pairs by the electric field, the annihilation of decelerated pairs
is found to take place in a medium. Our formula allows us to extract the
probability distributions in various situations, such as those obeying the
generalized trinomial statistics for spin-momentum resolved counting and the
bidirectional Poisson statistics for spin-momentum unresolved counting.


Abstract:
  The Fornax dwarf spheroidal galaxy has an anomalous number of globular
clusters, five, for its stellar mass. There is a longstanding debate about a
potential sixth globular cluster (Fornax~6) that has recently been
`rediscovered' in DECam imaging. We present new Magellan/M2FS spectroscopy of
the Fornax~6 cluster and Fornax dSph. Combined with literature data we identify
$\sim15-17$ members of the Fornax~6 cluster that this overdensity is indeed a
star cluster and associated with the Fornax dSph. The cluster is significantly
more metal-rich (mean metallicity of $\overline{\rm [Fe/H]}=-0.71\pm0.05$) than
the other five Fornax globular clusters ($-2.5<[Fe/H]<-1.4$) and more
metal-rich than the bulk of Fornax. We measure a velocity dispersion of
$5.6_{-1.6}^{+2.0}\,{\rm km \, s^{-1}}$ corresponding to anomalously high
mass-to-light of 15$<$M/L$<$258 at 90\% confidence when calculated assuming
equilibrium. Two stars inflate this dispersion and may be either Fornax field
stars or as yet unresolved binary stars. Alternatively the Fornax~6 cluster may
be undergoing tidal disruption. Based on its metal-rich nature, the Fornax 6
cluster is likely younger than the other Fornax clusters, with an estimated age
of $\sim2$ Gyr when compared to stellar isochrones. The chemodynamics and star
formation history of Fornax shows imprints of major events such as infall into
the Milky Way, multiple pericenter passages, star formation bursts, and/or
potential mergers or interactions. Any of these events may have triggered the
formation of the Fornax~6 cluster.


Abstract:
  Two-dimensional (2D) van der Waals (vdW) magnets provide an ideal platform
for exploring, on the fundamental side, new microscopic mechanisms and for
developing, on the technological side, ultra-compact spintronic applications.
So far, bilinear spin Hamiltonians have been commonly adopted to investigate
the magnetic properties of 2D magnets, neglecting higher order magnetic
interactions. However, we here provide quantitative evidence of giant
biquadratic exchange interactions in monolayer NiX2 (X=Cl, Br and I), by
combining first-principles calculations and the newly developed machine
learning method for constructing Hamiltonian. Interestingly, we show that the
ferromagnetic ground state within NiCl2 single layers cannot be explained by
means of bilinear Heisenberg Hamiltonian; rather, the nearest-neighbor
biquadratic interaction is found to be crucial. Furthermore, using a
three-orbitals Hubbard model, we propose that the giant biquadratic exchange
interaction originates from large hopping between unoccupied and occupied
orbitals on neighboring magnetic ions. On a general framework, our work
suggests biquadratic exchange interactions to be important in 2D magnets with
edge-shared octahedra.


Abstract:
  The Abstraction and Reasoning Corpus (ARC) is a challenging program induction
dataset that was recently proposed by Chollet (2019). Here, we report the first
set of results collected from a behavioral study of humans solving a subset of
tasks from ARC (40 out of 1000). Although this subset of tasks contains
considerable variation, our results showed that humans were able to infer the
underlying program and generate the correct test output for a novel test input
example, with an average of 80% of tasks solved per participant, and with 65%
of tasks being solved by more than 80% of participants. Additionally, we find
interesting patterns of behavioral consistency and variability within the
action sequences during the generation process, the natural language
descriptions to describe the transformations for each task, and the errors
people made. Our findings suggest that people can quickly and reliably
determine the relevant features and properties of a task to compose a correct
solution. Future modeling work could incorporate these findings, potentially by
connecting the natural language descriptions we collected here to the
underlying semantics of ARC.


Abstract:
  This paper proposes a comparison of four popular interface capturing methods
: the volume of fluid (VOF), the standard level set (SLS), the accurate
conservative level set (ACLS) and the coupled level set and volume of fluid
(CLSVOF). All methods are embedded into a unified low-Mach framework based on a
Cartesian-grid finite-volume discretization. This framework includes a sharp
transport of the interface, a wellbalanced surface tension discretization and a
consistent mass and momentum transport which allows capillary-driven
simulations with high density ratio. The comparison relies on shared metrics
for geometrical accuracy, mass and momentum conservation which exposes the
weakness and strengths of each method. Finally, the versatility and
capabilities of the proposed solver are demonstrated on the simulation of a 3D
head-on collision of two water droplets. Overall, all methods manage to
retrieve reasonable results for all test cases presented. VOF, CLSVOF and ACLS
tend to artificially create little structures while SLS suffers from
conservation issues in the mesh resolution limit. This study leads us to the
conclusion that CLSVOF is the most promising method for two-phase flow
simulations in our specific framework because of its inherent conservation
properties and topology accuracy.


Abstract:
  In a few years, space telescopes will investigate our Galaxy to detect
evidence of life, mainly by observing rocky planets. In the last decade, the
observation of exoplanet atmospheres and the theoretical works on biosignature
gasses have experienced a considerable acceleration. The~most attractive
feature of the realm of exoplanets is that 40\% of M dwarfs host super-Earths
with a minimum mass between 1 and 30 Earth masses, orbital periods shorter than
50 days, and radii between those of the Earth and Neptune (1--3.8 R$_\oplus$).
Moreover, the recent finding of cyanobacteria able to use far-red (FR) light
for oxygenic photosynthesis due to the synthesis of chlorophylls $d$ and $f$,
extending in vivo light absorption up to 750\ nm, suggests the possibility of
exotic photosynthesis in planets around M dwarfs. Using innovative laboratory
instrumentation, we exposed different cyanobacteria to an M dwarf star
simulated irradiation, comparing their responses to those under solar and FR
simulated lights.~As expected, in FR light, only the cyanobacteria able to
synthesize chlorophyll $d$ and $f$ could grow. Surprisingly, all strains, both
able or unable to use FR light, grew and photosynthesized under the M dwarf
generated spectrum in a similar way to the solar light and much more
efficiently than under the FR one. Our findings highlight the importance of
simulating both the visible and FR light components of an M dwarf spectrum to
correctly evaluate the photosynthetic performances of oxygenic organisms
exposed under such an exotic light~condition.


Abstract:
  Virtual Reality (VR) has become more and more popular with dropping prices
for systems and a growing number of users. However, the issue of accessibility
in VR has been hardly addressed so far and no uniform approach or standard
exists at this time. In this position paper, we propose a customisable toolkit
implemented at the system-level and discuss the potential benefits of this
approach and challenges that will need to be overcome for a successful
implementation.


Abstract:
  The task of image-based virtual try-on aims to transfer a target clothing
item onto the corresponding region of a person, which is commonly tackled by
fitting the item to the desired body part and fusing the warped item with the
person. While an increasing number of studies have been conducted, the
resolution of synthesized images is still limited to low (e.g., 256x192), which
acts as the critical limitation against satisfying online consumers. We argue
that the limitation stems from several challenges: as the resolution increases,
the artifacts in the misaligned areas between the warped clothes and the
desired clothing regions become noticeable in the final results; the
architectures used in existing methods have low performance in generating
high-quality body parts and maintaining the texture sharpness of the clothes.
To address the challenges, we propose a novel virtual try-on method called
VITON-HD that successfully synthesizes 1024x768 virtual try-on images.
Specifically, we first prepare the segmentation map to guide our virtual try-on
synthesis, and then roughly fit the target clothing item to a given person's
body. Next, we propose ALIgnment-Aware Segment (ALIAS) normalization and ALIAS
generator to handle the misaligned areas and preserve the details of 1024x768
inputs. Through rigorous comparison with existing methods, we demonstrate that
VITON-HD highly surpasses the baselines in terms of synthesized image quality
both qualitatively and quantitatively. Code is available at
https://github.com/shadow2496/VITON-HD.


Abstract:
  Symmetries naturally occur in real-world networks and can significantly
influence the observed dynamics. For instance, many synchronization patterns
result from the underlying network symmetries, and high symmetries are known to
increase the stability of synchronization. Yet, here we find that general
macroscopic features of network solutions such as regularity can be induced by
breaking their symmetry of interactions. We demonstrate this effect in an
ecological multilayer network where the topological asymmetries occur
naturally. These asymmetries rescue the system from chaotic oscillations by
establishing stable periodic orbits and equilibria. We call this phenomenon
asymmetry-induced order and uncover its mechanism by analyzing both
analytically and numerically the suppression of dynamics on the system's
synchronization manifold. Moreover, the bifurcation scenario describing the
route from chaos to order is also disclosed. We demonstrate that this result
also holds for generic node dynamics by analyzing coupled paradigmatic
R\"ossler and Lorenz systems.


Abstract:
  This paper describes the submission to the IWSLT 2021 offline speech
translation task by the UPC Machine Translation group. The task consists of
building a system capable of translating English audio recordings extracted
from TED talks into German text. Submitted systems can be either cascade or
end-to-end and use a custom or given segmentation. Our submission is an
end-to-end speech translation system, which combines pre-trained models
(Wav2Vec 2.0 and mBART) with coupling modules between the encoder and decoder,
and uses an efficient fine-tuning technique, which trains only 20% of its total
parameters. We show that adding an Adapter to the system and pre-training it,
can increase the convergence speed and the final result, with which we achieve
a BLEU score of 27.3 on the MuST-C test set. Our final model is an ensemble
that obtains 28.22 BLEU score on the same set. Our submission also uses a
custom segmentation algorithm that employs pre-trained Wav2Vec 2.0 for
identifying periods of untranscribable text and can bring improvements of 2.5
to 3 BLEU score on the IWSLT 2019 test set, as compared to the result with the
given segmentation.