Abstract:
  Neural Radiance Fields (NeRF) have recently gained a surge of interest within
the computer vision community for its power to synthesize photorealistic novel
views of real-world scenes. One limitation of NeRF, however, is its requirement
of accurate camera poses to learn the scene representations. In this paper, we
propose Bundle-Adjusting Neural Radiance Fields (BARF) for training NeRF from
imperfect (or even unknown) camera poses -- the joint problem of learning
neural 3D representations and registering camera frames. We establish a
theoretical connection to classical image alignment and show that
coarse-to-fine registration is also applicable to NeRF. Furthermore, we show
that na\"ively applying positional encoding in NeRF has a negative impact on
registration with a synthesis-based objective. Experiments on synthetic and
real-world data show that BARF can effectively optimize the neural scene
representations and resolve large camera pose misalignment at the same time.
This enables view synthesis and localization of video sequences from unknown
camera poses, opening up new avenues for visual localization systems (e.g.
SLAM) and potential applications for dense 3D mapping and reconstruction.


Abstract:
  We show that in cubic crystals with anisotropic impurity centers the sum of
squares of the magnetic resonance (EPR) frequencies is invariant with respect
to the magnetic field direction. The connection between such an invariant and
the g-tensor components of the impurity is derived for different types of
centers. The established regularity is confirmed experimentally for the
spin-noise spectra of a CaF2-Nd3+ crystal. We show how this property of the EPR
spectra can be efficiently used for the assignment of paramagnetic centers in
cubic crystals.


Abstract:
  Graph transformer networks (GTN) are a variant of graph convolutional
networks (GCN) that are targeted to heterogeneous graphs in which nodes and
edges have associated type information that can be exploited to improve
inference accuracy. GTNs learn important metapaths in the graph, create
weighted edges for these metapaths, and use the resulting graph in a GCN.
Currently, the only available implementation of GTNs uses dense matrix
multiplication to find metapaths. Unfortunately, the space overhead of this
approach can be large, so in practice it is used only for small graphs. In
addition, the matrix-based implementation is not fine-grained enough to use
random-walk based methods to optimize metapath finding. In this paper, we
present a graph-based formulation and implementation of the GTN metapath
finding problem. This graph-based formulation has two advantages over the
matrix-based approach. First, it is more space efficient than the original GTN
implementation and more compute-efficient for metapath sizes of practical
interest. Second, it permits us to implement a sampling method that reduces the
number of metapaths that must be enumerated, allowing the implementation to be
used for larger graphs and larger metapath sizes. Experimental results show
that our implementation is $6.5\times$ faster than the original GTN
implementation on average for a metapath length of 4, and our sampling
implementation is $155\times$ faster on average than this implementation
without compromising on the accuracy of the GTN.


Abstract:
  We analyze the consistency of electroweak breaking, neutrino and dark matter
phenomenology within the simplest scoto-seesaw model. By adding the minimal
dark sector to the simplest "missing partner" type-I seesaw one has a physical
picture for the neutrino oscillation lengths: the "atmospheric" mass scale
arises from the tree-level seesaw, while the "solar" scale is induced
radiatively, mediated by the dark sector. We identify parameter regions
consistent with theoretical constraints, as well as dark matter relic abundance
and direct detection searches. Using two-loop renormalization group equations
we explore the stability of the vacuum and the consistency of the underlying
dark parity symmetry. One also has a lower bound for the neutrinoless double
beta decay amplitude.


Abstract:
  In this paper, we present AISHELL-4, a sizable real-recorded Mandarin speech
dataset collected by 8-channel circular microphone array for speech processing
in conference scenario. The dataset consists of 211 recorded meeting sessions,
each containing 4 to 8 speakers, with a total length of 120 hours. This dataset
aims to bridge the advanced research on multi-speaker processing and the
practical application scenario in three aspects. With real recorded meetings,
AISHELL-4 provides realistic acoustics and rich natural speech characteristics
in conversation such as short pause, speech overlap, quick speaker turn, noise,
etc. Meanwhile, accurate transcription and speaker voice activity are provided
for each meeting in AISHELL-4. This allows the researchers to explore different
aspects in meeting processing, ranging from individual tasks such as speech
front-end processing, speech recognition and speaker diarization, to
multi-modality modeling and joint optimization of relevant tasks. Given most
open source dataset for multi-speaker tasks are in English, AISHELL-4 is the
only Mandarin dataset for conversation speech, providing additional value for
data diversity in speech community. We also release a PyTorch-based training
and evaluation framework as baseline system to promote reproducible research in
this field.


Abstract:
  We study the Fundamental Plane (FP) for a volume- and luminosity-limited
sample of 560 early-type galaxies from the SAMI survey. Using r-band sizes and
luminosities from new Multi-Gaussian Expansion (MGE) photometric measurements,
and treating luminosity as the dependent variable, the FP has coefficients
a=1.294$\pm$0.039, b= 0.912$\pm$0.025, and zero-point c= 7.067$\pm$0.078. We
leverage the high signal-to-noise of SAMI integral field spectroscopy, to
determine how structural and stellar-population observables affect the scatter
about the FP. The FP residuals correlate most strongly (8$\sigma$ significance)
with luminosity-weighted simple-stellar-population (SSP) age. In contrast, the
structural observables surface mass density, rotation-to-dispersion ratio,
S\'ersic index and projected shape all show little or no significant
correlation. We connect the FP residuals to the empirical relation between age
(or stellar mass-to-light ratio $\Upsilon_\star$) and surface mass density, the
best predictor of SSP age amongst parameters based on FP observables. We show
that the FP residuals (anti-)correlate with the residuals of the relation
between surface density and $\Upsilon_\star$. This correlation implies that
part of the FP scatter is due to the broad age and $\Upsilon_\star$
distribution at any given surface mass density. Using virial mass and
$\Upsilon_\star$ we construct a simulated FP and compare it to the observed FP.
We find that, while the empirical relations between observed stellar population
relations and FP observables are responsible for most (75%) of the FP scatter,
on their own they do not explain the observed tilt of the FP away from the
virial plane.


Abstract:
  We describe in details the interplay between binary symplectic geometry and
quantum computation, with the ultimate goal of constructing highly structured
codebooks. The Binary Chirps (BCs) are Complex Grassmannian Lines in $N = 2^m$
dimensions used in deterministic compressed sensing and random/unsourced
multiple access in wireless networks. Their entries are fourth roots of unity
and can be described in terms of second order Reed-Muller codes. The Binary
Subspace Chirps (BSSCs) are a unique collection of BCs of $\textit{ranks}$
ranging from $r=0$ to $r = m$, embedded in $N$ dimensions according to an
on-off pattern determined by a rank $r$ binary subspace. This yields a codebook
that is asymptotically 2.38 times larger than the codebook of BCs, has the same
minimum chordal distance as the codebook of BCs, and the alphabet is minimally
extended from $\{\pm 1,\pm i\}$ to $\{\pm 1,\pm i, 0\}$. Equivalently, we show
that BSSCs are stabilizer states, and we characterize them as columns of a
well-controlled collection of Clifford matrices. By construction, the BSSCs
inherit all the properties of BCs, which in turn makes them good candidates for
a variety of applications. For applications in wireless communication, we use
the rich algebraic structure of BSSCs to construct a low complexity decoding
algorithm that is reliable against Gaussian noise. In simulations, BSSCs
exhibit an error probability comparable or slightly lower than BCs, both for
single-user and multi-user transmissions.


Abstract:
  Line-graph (LG) lattices are known for having topological flat bands (FBs)
from the destructive interference of Bloch wavefunctions encoded in lattice
symmetry. Here, we develop an atomic/molecular orbital design principle for the
existence of FBs in non-LG lattices. Using a generic tight-binding model, we
demonstrate that the underlying wavefunction symmetry of FBs in a LG lattice
can be transformed into the atomic/molecular orbital symmetry in a non-LG
lattice. We show such orbital-designed topological FBs in three common 2D
non-LG, square, trigonal, and hexagonal lattices, where the chosen orbitals
faithfully reproduce the corresponding localized FB plaquette states of
checkerboard, Kagome, and diatomic-Kagome lattices, respectively. Fundamentally
our theory enriches the FB physics; practically the proposed orbital design
principle is expected to significantly expand the scope of FB materials, since
most materials have multiple atomic/molecular orbitals at each lattice site,
rather than a single s orbital mandated in graph theory.


Abstract:
  3D video avatars can empower virtual communications by providing compression,
privacy, entertainment, and a sense of presence in AR/VR. Best 3D
photo-realistic AR/VR avatars driven by video, that can minimize uncanny
effects, rely on person-specific models. However, existing person-specific
photo-realistic 3D models are not robust to lighting, hence their results
typically miss subtle facial behaviors and cause artifacts in the avatar. This
is a major drawback for the scalability of these models in communication
systems (e.g., Messenger, Skype, FaceTime) and AR/VR. This paper addresses
previous limitations by learning a deep learning lighting model, that in
combination with a high-quality 3D face tracking algorithm, provides a method
for subtle and robust facial motion transfer from a regular video to a 3D
photo-realistic avatar. Extensive experimental validation and comparisons to
other state-of-the-art methods demonstrate the effectiveness of the proposed
framework in real-world scenarios with variability in pose, expression, and
illumination. Please visit https://www.youtube.com/watch?v=dtz1LgZR8cc for more
results. Our project page can be found at
https://www.cs.rochester.edu/u/lchen63.


Abstract:
  A deep learning-based monocular depth estimation (MDE) technique is proposed
for selection of most informative frames (key frames) of an endoscopic video.
In most of the cases, ground truth depth maps of polyps are not readily
available and that is why the transfer learning approach is adopted in our
method. An endoscopic modalities generally capture thousands of frames. In this
scenario, it is quite important to discard low-quality and clinically
irrelevant frames of an endoscopic video while the most informative frames
should be retained for clinical diagnosis. In this view, a key-frame selection
strategy is proposed by utilizing the depth information of polyps. In our
method, image moment, edge magnitude, and key-points are considered for
adaptively selecting the key frames. One important application of our proposed
method could be the 3D reconstruction of polyps with the help of extracted key
frames. Also, polyps are localized with the help of extracted depth maps.


Abstract:
  All robots create consequential sound -- sound produced as a result of the
robot's mechanisms -- yet little work has explored how sound impacts
human-robot interaction. Recent work shows that the sound of different robot
mechanisms affects perceived competence, trust, human-likeness, and discomfort.
However, the physical sound characteristics responsible for these perceptions
have not been clearly identified. In this paper, we aim to explore key
characteristics of robot sound that might influence perceptions. A pilot study
from our past work showed that quieter and higher-pitched robots may be
perceived as more competent and less discomforting. To better understand how
variance in these attributes affects perception, we performed audio
manipulations on two sets of industrial robot arm videos within a series of
four new studies presented in this paper. Results confirmed that quieter robots
were perceived as less discomforting. In addition, higher-pitched robots were
perceived as more energetic, happy, warm, and competent. Despite the robot's
industrial purpose and appearance, participants seemed to prefer more "cute"
(or "kawaii") sound profiles, which could have implications for the design of
more acceptable and fulfilling sound profiles for human-robot interactions with
practical collaborative robots.


Abstract:
  Course selection is a crucial activity for students as it directly impacts
their workload and performance. It is also time-consuming, prone to
subjectivity, and often carried out based on incomplete information. This task
can, nevertheless, be assisted with computational tools, for instance, by
predicting performance based on historical data. We investigate the effects of
showing grade predictions to students through an interactive visualization
tool. A qualitative study suggests that in the presence of predictions,
students may focus too much on maximizing their performance, to the detriment
of other factors such as the workload. A follow-up quantitative study explored
whether these effects are mitigated by changing how predictions are conveyed.
Our observations suggest the presence of a framing effect that induces students
to put more effort into course selection when faced with more specific
predictions. We discuss these and other findings and outline considerations for
designing better data-driven course selection tools.


Abstract:
  Understanding the basic operational logics of the nervous system is essential
to advancing neuroscientific research. However, theoretical efforts to tackle
this fundamental problem are lacking, despite the abundant empirical data about
the brain that has been collected in the past few decades. To address this
shortcoming, this document introduces a hypothetical framework for the
functional nature of primitive neural networks. It analyzes the idea that the
activity of neurons and synapses can symbolically reenact the dynamic changes
in the world and thus enable an adaptive system of behavior. More
significantly, the network achieves this without participating in an
algorithmic structure. When a neuron's activation represents some symbolic
element in the environment, each of its synapses can indicate a potential
change to the element and its future state. The efficacy of a synaptic
connection further specifies the element's particular probability for, or
contribution to, such a change. As it fires, a neuron's activation is
transformed to its postsynaptic targets, resulting in a chronological shift of
the represented elements. As the inherent function of summation in a neuron
integrates the various presynaptic contributions, the neural network mimics the
collective causal relationship of events in the observed environment.


Abstract:
  In recent years, the use of machine learning-based surrogate models for
computational fluid dynamics (CFD) simulations has emerged as a promising
technique for reducing the computational cost associated with engine design
optimization. However, such methods still suffer from drawbacks. One main
disadvantage of is that the default machine learning (ML) hyperparameters are
often severely suboptimal for a given problem. This has often been addressed by
manually trying out different hyperparameter settings, but this solution is
ineffective in a high-dimensional hyperparameter space. Besides this problem,
the amount of data needed for training is also not known a priori. In response
to these issues that need to be addressed, the present work describes and
validates an automated active learning approach, AutoML-GA, for surrogate-based
optimization of internal combustion engines. In this approach, a Bayesian
optimization technique is used to find the best machine learning
hyperparameters based on an initial dataset obtained from a small number of CFD
simulations. Subsequently, a genetic algorithm is employed to locate the design
optimum on the ML surrogate surface. In the vicinity of the design optimum, the
solution is refined by repeatedly running CFD simulations at the projected
optimum and adding the newly obtained data to the training dataset. It is
demonstrated that AutoML-GA leads to a better optimum with a lower number of
CFD simulations, compared to the use of default hyperparameters. The proposed
framework offers the advantage of being a more hands-off approach that can be
readily utilized by researchers and engineers in industry who do not have
extensive machine learning expertise.


Abstract:
  Open set domain recognition has got the attention in recent years. The task
aims to specifically classify each sample in the practical unlabeled target
domain, which consists of all known classes in the manually labeled source
domain and target-specific unknown categories. The absence of annotated
training data or auxiliary attribute information for unknown categories makes
this task especially difficult. Moreover, exiting domain discrepancy in label
space and data distribution further distracts the knowledge transferred from
known classes to unknown classes. To address these issues, this work presents
an end-to-end model based on attention-based GCN and semantic matching
optimization, which first employs the attention mechanism to enable the central
node to learn more discriminating representations from its neighbors in the
knowledge graph. Moreover, a coarse-to-fine semantic matching optimization
approach is proposed to progressively bridge the domain gap. Experimental
results validate that the proposed model not only has superiority on
recognizing the images of known and unknown classes, but also can adapt to
various openness of the target domain.


Abstract:
  We demonstrate, both analytically and experimentally, free-space pin-like
optical vortex beams (POVBs).Such angular-momentum-carrying beams feature
tunable peak intensity and undergo robust anti-diffracting propagation,
realized by judiciously modulating both the amplitude and the phase profile of
a standard laser beam. Specifically, they are generated by superimposing a
radially-symmetric power-law phase on a helical phase structure, which allows
the inclusion of an orbital angular momentum term to the POVBs. During
propagation in free-space, these POVBs initially exhibit autofocusing dynamics,
and subsequently, their amplitude patterns morph into a high-order Bessel-like
profile characterized by a hollow-core and an annular main-lobe with a constant
or tunable width during propagation. In contrast with numerous previous
endeavors on Bessel beams, our work represents the first demonstration of
long-distance free-space generation of optical vortex 'pins', with their peak
intensity evolution controlled by the impressed amplitude structure. Both the
Poynting vectors and the optical radiation forces associated with these beams
are also numerically analyzed, revealing novel properties that may be useful
for a wide range of applications.


Abstract:
  For an associative ring $R$ with identity, we study the absence of
$k$-torsion in NK_1GQ(R); Bass nil-groups for the general quadratic or Bak's
unitary groups. By using a graded version of Quillen--Suslin theory we deduce
an analog for the graded rings.


Abstract:
  Generative Adversarial Networks (GANs) are powerful generative models that
achieved strong results, mainly in the image domain. However, the training of
GANs is not trivial, presenting some challenges tackled by different
strategies. Evolutionary algorithms, such as COEGAN, were recently proposed as
a solution to improve the GAN training, overcoming common problems that affect
the model, such as vanishing gradient and mode collapse. In this work, we
propose an evaluation method based on t-distributed Stochastic Neighbour
Embedding (t-SNE) to assess the progress of GANs and visualize the distribution
learned by generators in training. We propose the use of the feature space
extracted from trained discriminators to evaluate samples produced by
generators and from the input dataset. A metric based on the resulting t-SNE
maps and the Jaccard index is proposed to represent the model quality.
Experiments were conducted to assess the progress of GANs when trained using
COEGAN. The results show both by visual inspection and metrics that the
Evolutionary Algorithm gradually improves discriminators and generators through
generations, avoiding problems such as mode collapse.


Abstract:
  This is a companion paper to our previous work, where we proved the
finiteness of the Tate-Shafarevich group for an arbitrary torus $T$ over a
finitely generated field $K$ with respect to any divisorial set $V$ of places
of $K$. Here, we extend this result to any $K$-group $D$ whose connected
component is a torus (for the same $V$), and as a consequence obtain a
finiteness result for the local-to-global conjugacy of maximal tori in
reductive groups over finitely generated fields. Moreover, we prove the
finiteness of the Tate-Shafarevich group for tori over function fields $K$ of
normal varieties defined over base fields of characteristic zero and satisfying
Serre's condition (F), in which case $V$ consists of the discrete valuations
associated with the prime divisors on the variety (geometric places). In this
situation, we also establish the finiteness of the number of $K$-isomorphism
classes of algebraic $K$-tori of a given dimension having good reduction at all
$v \in V$, and then discuss ways of extending this result to positive
characteristic. Finally, we prove the finiteness of the number of isomorphism
classes of forms of an absolutely almost simple group defined over the function
field of a complex surface that have good reduction at all geometric places.


Abstract:
  The high penetration of volatile renewable energy sources such as solar make
methods for coping with the uncertainty associated with them of paramount
importance. Probabilistic forecasts are an example of these methods, as they
assist energy planners in their decision-making process by providing them with
information about the uncertainty of future power generation. Currently, there
is a trend towards the use of deep learning probabilistic forecasting methods.
However, the point at which the more complex deep learning methods should be
preferred over more simple approaches is not yet clear. Therefore, the current
article presents a simple comparison between a long short-term memory neural
network and other more simple approaches. The comparison consists of training
and comparing models able to provide one-day-ahead probabilistic forecasts for
a solar power system. Moreover, the current paper makes use of an open-source
dataset provided during the Global Energy Forecasting Competition of 2014
(GEFCom14).


Abstract:
  We generalize Zwanziger's pairwise little group to include a boost subgroup.
We do so by working in the celestial sphere representation of scattering
amplitudes. We propose that due to late time soft photon and graviton
exchanges, matter particles in the asymptotic states in massless QED and
gravity transform under the Poincare group with an additional pair of pairwise
celestial representations for each pair of matter particles. We demonstrate
that the massless abelian and gravitational exponentiation theorems are
consistent with the proposed pairwise Poincare transformation properties. For
massless QED we demonstrate that our results are consistent with the effects of
the Faddeev-Kulish dressing and the abelian exponentiation theorem for
celestial amplitudes found in [2012.04208]. We discuss electric and magnetic
charges simultaneously as it is especially natural to do so in this formalism.


Abstract:
  We present a spherical version of the theorem of Blaschke that every body of
constant width $w < \frac{\pi}{2}$ can be approximated as well as we wish in
the sense of the Hausdorff distance by a body of constant width $w$ whose
boundary consists only of pieces of circles of radius $w$. This is a special
case of our theorem about approximation of spherical reduced bodies.


Abstract:
  We report on the status of a users' end-station, MAC: a Multipurpose station
for Atomic, molecular and optical sciences and Coherent diffractive imaging,
designed for studies of structure and dynamics of matter in the femtosecond
time-domain. MAC is located in the E1 experimental hall on the high harmonic
generation (HHG) beamline of the ELI Beamlines facility. The extreme
ultraviolet beam from the HHG beamline can be used at the MAC end-station
together with a synchronized pump beam (which will cover the NIR/Vis/UV or THz
range) for time-resolved experiments on different samples. Sample delivery
systems at the MAC end-station include a molecular beam, a source for pure or
doped clusters, ultrathin cylindrical or flat liquid jets, and focused beams of
substrate-free nanoparticles produced by an electrospray or a gas dynamic
virtual nozzle combined with an aerodynamic lens stack. We further present the
available detectors: electron/ion time-of-flight and velocity map imaging
spectrometers and an X-ray camera, and discuss future upgrades: a magnetic
bottle electron spectrometer, production of doped nanodroplets and the planned
developments of beam capabilities at the MAC end-station.


Abstract:
  It has recently been demonstrated that various topological states, including
Dirac, Weyl, nodal-line, and triple-point semimetal phases, can emerge in
antiferromagnetic (AFM) half-Heusler compounds. However, how to determine the
AFM structure and to distinguish different topological phases from transport
behaviors remains unknown. We show that, due to the presence of combined
time-reversal and fractional translation symmetry, the recently proposed
second-order nonlinear Hall effect can be used to characterize different
topological phases with various AFM configurations. Guided by the symmetry
analysis, we obtain the expressions of the Berry curvature dipole for different
AFM configurations. Based on the effective model, we explicitly calculate the
Berry curvature dipole, which is found to be vanishingly small for the
triple-point semimetal phase, and large in the Weyl semimetal phase. Our
results not only put forward an effective method for the identification of
magnetic orders and topological phases in AFM half-Heusler materials, but also
suggest these materials as a versatile platform for engineering the non-linear
Hall effect.


Abstract:
  We consider the discretized Bachelier model where hedging is done on an
equidistant set of times. Exponential utility indifference prices are studied
for path-dependent European options and we compute their non-trivial scaling
limit for a large number of trading times $n$ and when risk aversion is scaled
like $n\ell$ for some constant $\ell>0$. Our analysis is purely probabilistic.
We first use a duality argument to transform the problem into an optimal drift
control problem with a penalty term. We further use martingale techniques and
strong invariance principles and get that the limiting problem takes the form
of a volatility control problem.


Abstract:
  This is an opinion paper. We hope to deliver a key message that current
visual recognition systems are far from complete, i.e., recognizing everything
that human can recognize, yet it is very unlikely that the gap can be bridged
by continuously increasing human annotations. Based on the observation, we
advocate for a new type of pre-training task named learning-by-compression. The
computational models (e.g., a deep network) are optimized to represent the
visual data using compact features, and the features preserve the ability to
recover the original data. Semantic annotations, when available, play the role
of weak supervision. An important yet challenging issue is the evaluation of
image recovery, where we suggest some design principles and future research
directions. We hope our proposal can inspire the community to pursue the
compression-recovery tradeoff rather than the accuracy-complexity tradeoff.


Abstract:
  Video inpainting aims to fill the given spatiotemporal holes with realistic
appearance but is still a challenging task even with prosperous deep learning
approaches. Recent works introduce the promising Transformer architecture into
deep video inpainting and achieve better performance. However, it still suffers
from synthesizing blurry texture as well as huge computational cost. Towards
this end, we propose a novel Decoupled Spatial-Temporal Transformer (DSTT) for
improving video inpainting with exceptional efficiency. Our proposed DSTT
disentangles the task of learning spatial-temporal attention into 2 sub-tasks:
one is for attending temporal object movements on different frames at same
spatial locations, which is achieved by temporally-decoupled Transformer block,
and the other is for attending similar background textures on same frame of all
spatial positions, which is achieved by spatially-decoupled Transformer block.
The interweaving stack of such two blocks makes our proposed model attend
background textures and moving objects more precisely, and thus the attended
plausible and temporally-coherent appearance can be propagated to fill the
holes. In addition, a hierarchical encoder is adopted before the stack of
Transformer blocks, for learning robust and hierarchical features that maintain
multi-level local spatial structure, resulting in the more representative token
vectors. Seamless combination of these two novel designs forms a better
spatial-temporal attention scheme and our proposed model achieves better
performance than state-of-the-art video inpainting approaches with significant
boosted efficiency.


Abstract:
  This paper resolves the problem of comparing the skein modules defined using
the skein relations discovered by R. Kirby and P. Melvin that underlie the
Reshetikhin-Turaev model for $SU(2)$ Chern-Simons theory to the Kauffman
bracket skein modules. Several applications and examples are presented.


Abstract:
  In this work we study static black holes in the regularized 4D
Einstein-Gauss-Bonnet theory of gravity; a shift-symmetric scalar-tensor theory
that belongs to the Horndeski class. This theory features a simple black hole
solution that can be written in closed form, and which we show is the unique
static, spherically-symmetric and asymptotically-flat black hole vacuum
solution of the theory. We further show that no asymptotically-flat,
time-dependent, spherically-symmetric perturbations to this geometry are
allowed, which suggests that it may be the only spherically-symmetric vacuum
solution that this theory admits (a result analogous to Birkhoff's theorem).
Finally, we consider the thermodynamic properties of these black holes, and
find that their final state after evaporation is a remnant with a size
determined by the coupling constant of the theory. We speculate that remnants
of this kind from primordial black holes could act as dark matter, and we
constrain the parameter space for their formation mass, as well as the coupling
constant of the theory.


Abstract:
  We present a new approach for redirected walking in static and dynamic scenes
that uses techniques from robot motion planning to compute the redirection
gains that steer the user on collision-free paths in the physical space. Our
first contribution is a mathematical framework for redirected walking using
concepts from motion planning and configuration spaces. This framework
highlights various geometric and perceptual constraints that tend to make
collision-free redirected walking difficult. We use our framework to propose an
efficient solution to the redirection problem that uses the notion of
visibility polygons to compute the free spaces in the physical environment and
the virtual environment. The visibility polygon provides a concise
representation of the entire space that is visible, and therefore walkable, to
the user from their position within an environment. Using this representation
of walkable space, we apply redirected walking to steer the user to regions of
the visibility polygon in the physical environment that closely match the
region that the user occupies in the visibility polygon in the virtual
environment. We show that our algorithm is able to steer the user along paths
that result in significantly fewer resets than existing state-of-the-art
algorithms in both static and dynamic scenes. Our project website is available
at https://gamma.umd.edu/vis_poly/.


Abstract:
  We use the $SU(5)$ model to show the presence in grand unified theories of an
electroweak monopole and a magnetic dumbbell ("meson") made up of a
monopole-antimonopole pair connected by a $Z$-magnetic flux tube. The monopole
is associated with the spontaneous breaking of the weak $SU(2)_L$ gauge
symmetry by the induced vacuum expectation value of a heavy scalar $SU(2)_L$
triplet with zero weak hypercharge contained in the adjoint Higgs 24-plet. This
monopole carries a Coulomb magnetic charge of $(3/4) (2\pi/e)$ as well as
$Z$-magnetic charge, where $2\pi/e$ denotes the unit Dirac magnetic charge. Its
total magnetic charge is $\sqrt{3/8}(4\pi/e)$, which is in agreement with the
Dirac quantization condition. The monopole weighs about 700 GeV, but because of
the attached $Z$-magnetic tube it exists, together with the antimonopole, in a
magnetic dumbbell configuration whose mass is expected to lie in the TeV range.
The presence of these topological structures in $SU(5)$ and $SO(10)$ and in
their supersymmetric extensions provides an exciting new avenue for testing
these theories in high-energy colliders.


Abstract:
  We study a sequence of single server queues with customer abandonment
(GI/GI/1+GI) under heavy traffic. The patience time distributions vary with the
sequence, which allows for a wider scope of applications. It is known ([20,
18]) that the sequence of scaled offered waiting time processes converges
weakly to a reflecting diffusion process with non-linear drift, as the traffic
intensity approaches one. In this paper, we further show that the sequence of
stationary distributions and moments of the offered waiting times, with
diffusion scaling, converge to those of the limit diffusion process. This
justifies the stationary performance of the diffusion limit as a valid
approximation for the stationary performance of the GI/GI/1+GI queue.
Consequently, we also derive the approximation for the abandonment probability
for the GI/GI/1+GI queue in the stationary state.


Abstract:
  We investigate the quantum cosmologies of the Bianchi IX and VIII models when
a cosmological constant, aligned electromagnetic field and free scalar field
are present. The conserved quantity $p_{\phi}$ associated with our free scalar
field results in $\phi$ classically being a quantity which monotonically
increases with respect to time, thus allowing it to fulfill the role of an
'emergent' internal clock for our constrained quantum systems. We embark on
this investigation to better understand how matter sources can affect general
anisotropic quantum cosmologies. To aid us we use the Euclidean-signature semi
classical method to obtain our wave functions and analyze them. In addition we
study briefly the quantum Taub models when an $e^{ \phi}$ potential and aligned
electromagnetic field are present. One of the interesting things we found was
that our aligned electromagnetic field, depending on how strong it is, can
create or destroy geometric states in our 'excited' state wave functions that
quantum Bianchi IX universes can tunnel in and out of. This creation of a state
is somewhat similar to how non-commutativity in the minisuperspace variables
can cause new quantum states to emerge in the quantum Kantowski-Sachs and
Bianchi I models. Our results further show the utility of the
Euclidean-signature semi classical method towards tackling Lorentzian signature
problems without having to invoke a Wick rotation. This feature of not needing
to apply a Wick rotation makes this method potentially very useful for tackling
a variety of problems in bosonic relativistic field theory and quantum gravity.


Abstract:
  A generalized Wigner-Moyal statistical theory of radiation is used to obtain
a general dispersion relation for Stimulated Brillouin Scattering (SBS) driven
by a broadband radiation field with arbitrary statistics. The monochromatic
limit is recovered from our general result, reproducing the classic
monochromatic dispersion relation. The behavior of the growth rate of the
instability as a simultaneous function of the bandwidth of the pump wave, the
intensity of the incident field and the wave number of the scattered wave is
further explored by numerically solving the dispersion relation. Our results
show that the growth rate of SBS can be reduced by 1/3 for a bandwidth of 0.3
nm, for typical experimental parameters.


Abstract:
  Despite the achievements of large-scale multimodal pre-training approaches,
cross-modal retrieval, e.g., image-text retrieval, remains a challenging task.
To bridge the semantic gap between the two modalities, previous studies mainly
focus on word-region alignment at the object level, lacking the matching
between the linguistic relation among the words and the visual relation among
the regions. The neglect of such relation consistency impairs the
contextualized representation of image-text pairs and hinders the model
performance and the interpretability. In this paper, we first propose a novel
metric, Intra-modal Self-attention Distance (ISD), to quantify the relation
consistency by measuring the semantic distance between linguistic and visual
relations. In response, we present Inter-modal Alignment on Intra-modal
Self-attentions (IAIS), a regularized training method to optimize the ISD and
calibrate intra-modal self-attentions from the two modalities mutually via
inter-modal alignment. The IAIS regularizer boosts the performance of
prevailing models on Flickr30k and MS COCO datasets by a considerable margin,
which demonstrates the superiority of our approach.


Abstract:
  Enabling autonomous driving (AD) can be considered one of the biggest
challenges in today's technology. AD is a complex task accomplished by several
functionalities, with environment perception being one of its core functions.
Environment perception is usually performed by combining the semantic
information captured by several sensors, i.e., lidar or camera. The semantic
information from the respective sensor can be extracted by using convolutional
neural networks (CNNs) for dense prediction. In the past, CNNs constantly
showed state-of-the-art performance on several vision-related tasks, such as
semantic segmentation of traffic scenes using nothing but the red-green-blue
(RGB) images provided by a camera. Although CNNs obtain state-of-the-art
performance on clean images, almost imperceptible changes to the input,
referred to as adversarial perturbations, may lead to fatal deception. The goal
of this article is to illuminate the vulnerability aspects of CNNs used for
semantic segmentation with respect to adversarial attacks, and share insights
into some of the existing known adversarial defense strategies. We aim to
clarify the advantages and disadvantages associated with applying CNNs for
environment perception in AD to serve as a motivation for future research in
this field.


Abstract:
  In this paper, we develop the theory of convolutional codes over finite
commutative chain rings. In particular, we focus on maximum distance profile
(MDP) convolutional codes and we provide a characterization of these codes,
generalizing the one known for fields. Moreover, we relate MDP convolutional
codes over a finite chain ring with MDP convolutional codes over its residue
field. Finally, we provide a construction of MDP convolutional codes over
finite chain rings generalizing the notion of superregular matrices.


Abstract:
  Hypergeometric functions over finite fields were introduced by Greene in the
1980s as a finite field analogue of classical hypergeometric series. These
functions, and their generalizations, naturally lend themselves to, and have
been widely used in, character sum evaluations and counting points on algebraic
varieties. More interestingly, perhaps, are their links to Fourier coefficients
of modular forms. In this paper, we outline the main results in this area and
also conjecture 13 new relations.


Abstract:
  We study a strongly Non-Markovian variant of random walk in which the
probability of visiting a given site $i$ is a function $f$ of number of
previous visits $v(i)$ to the site. If the probability is inversely
proportional to number of visits to the site, say
$f(i)=\frac{1}{(v(i)+1)^{\alpha}}$ the probability distribution of visited
sites tends to be flat for ${\alpha}>0$ compared to simple random walk. For
$f(i)=e^{-v(i)}$, we observe a distribution with two peaks. The origin is no
longer the most probable site. The probability is maximum at site k(t) which
increases in time. For $f(i)=e^{-v(i)}$ and for ${\alpha}>0$ the properties do
not change as the walk ages. However, for ${\alpha}<0$, the properties are
similar to simple random walk asymptotically. We study lattice covering time
for these functions. The lattice covering time scales as $N^{z}$, with $z=2$,
for ${\alpha} \le 0$, $z>2$ for ${\alpha} >0$ and $z<2$ for $f(i)=e^{-v(i)}$.


Abstract:
  Expanded Blaum-Roth (EBR) codes consist of $n\times n$ arrays such that lines
of slopes $i$, $0\leq i\leq r-1$ for $2\leq r<n$, as well as vertical lines,
have even parity. The codes are MDS with respect to columns, i.e., they can
recover any $r$ erased columns, if and only if $n$ is a prime number. Recently
a generalization of EBR codes, called generalized expanded Blaum-Roth (GEBR)
codes, was presented. GEBR codes consist of $p\tau\times (k+r)$ arrays, where
$p$ is prime and $\tau\geq 1$, such that lines of slopes $i$, $0\leq i\leq
r-1$, have even parity and every column in the array, when regarded as a
polynomial, is a multiple of $1+x^{\tau}$. In particular, it was shown that
when $p$ is an odd prime number, 2 is primitive in $GF(p)$ and $\tau = p^j$,
$j\geq 0$, the GEBR code consisting of $p\tau\times (p-1)\tau$ arrays is MDS.
We extend this result further by proving that GEBR codes consisting of
$p\tau\times p\tau$ arrays are MDS if and only if $\tau = p^j$, where $0\leq j$
and $p$ is any odd prime.


Abstract:
  Teaching industry staff on cybersecurity issues is a fundamental activity
that must be undertaken in order to guarantee the delivery of successful and
robust products to market. Much research attention has been devoted to this
topic over the last years. However, the research which has been done has not
focused on developing secure code in industrial environments. In this paper we
take a look at the constraints and requirements for delivering a training, by
means of cybersecurity challenges, that covers secure coding topics from an
industry perspective. Using requirements engineering, we aim at understanding
the design requirements for such challenges. Along the way, we give details on
our experience of delivering cybersecurity challenges in an industrial setting
and show the outcome and lessons learned. The proposed requirements for
cybersecurity challenges geared towards software developers in an industrial
environment are based on systematic literature review, interviews with security
experts from the industry and semi-structured evaluation of participant
feedback.


Abstract:
  Ensembles of independently trained neural networks are a state-of-the-art
approach to estimate predictive uncertainty in Deep Learning, and can be
interpreted as an approximation of the posterior distribution via a mixture of
delta functions. The training of ensembles relies on non-convexity of the loss
landscape and random initialization of their individual members, making the
resulting posterior approximation uncontrolled. This paper proposes a novel and
principled method to tackle this limitation, minimizing an $f$-divergence
between the true posterior and a kernel density estimator in a function space.
We analyze this objective from a combinatorial point of view, and show that it
is submodular with respect to mixture components for any $f$. Subsequently, we
consider the problem of ensemble construction, and from the marginal gain of
the total objective, we derive a novel diversity term for training ensembles
greedily. The performance of our approach is demonstrated on computer vision
out-of-distribution detection benchmarks in a range of architectures trained on
multiple datasets. The source code of our method is publicly available at
https://github.com/MIPT-Oulu/greedy_ensembles_training.


Abstract:
  In the last decade, autonomous vertical take-off and landing (VTOL) vehicles
have become increasingly important as they lower mission costs thanks to their
re-usability. However, their development is complex, rendering even the basic
experimental validation of the required advanced guidance and control (G & C)
algorithms prohibitively time-consuming and costly. In this paper, we present
the design of an inexpensive small-scale VTOL platform that can be built from
off-the-shelf components for less than 1000 USD. The vehicle design mimics the
first stage of a reusable launcher, making it a perfect test-bed for G & C
algorithms. To control the vehicle during ascent and descent, we propose a
real-time optimization-based G & C algorithm. The key features are a real-time
minimum fuel and free-final-time optimal guidance combined with an offset-free
tracking model predictive position controller. The vehicle hardware design and
the G & C algorithm are experimentally validated both indoors and outdoor,
showing reliable operation in a fully autonomous fashion with all computations
done on-board and in real-time.


Abstract:
  We describe a simple and fast technique to perform ultrasound differential
phase contrast (DPC) imaging in arbitrarily thick scattering media. Though
configured in a reflection geometry, DPC is based on transmission imaging and
is a direct analogue of optical differential interference contrast (DIC). DPC
exploits the memory effect and works in combination with standard pulse-echo
imaging, with no additional hardware or data requirements, enabling
complementary phase contrast (in the transverse direction) without any need for
intensive numerical computation. We experimentally demonstrate the principle of
DPC using tissue phantoms with calibrated speed-of-sound inclusions.


Abstract:
  One-side head-tail (OHT) galaxies are radio galaxies with a peculiar shape.
They usually appear in galaxy clusters, but they have never been cataloged
systematically. We design an automatic procedure to search for them in the
Faint Images of the Radio Sky at Twenty-Centimeters source catalog and compile
a sample with 115 HT candidates. After cross-checking with the Sloan Digital
Sky Survey photometric data and catalogs of galaxy clusters, we find that 69 of
them are possible OHT galaxies. Most of them are close to the center of galaxy
clusters. The lengths of their tails do not correlate with the projection
distance to the center of the nearest galaxy clusters, but show weak
anticorrelation with the cluster richness, and are inversely proportional to
the radial velocity differences between clusters and host galaxies. Our catalog
provides a unique sample to study this special type of radio galaxies.


Abstract:
  Offset Rademacher complexities have been shown to provide tight upper bounds
for the square loss in a broad class of problems including improper statistical
learning and online learning. We show that the offset complexity can be
generalized to any loss that satisfies a certain general convexity condition.
Further, we show that this condition is closely related to both exponential
concavity and self-concordance, unifying apparently disparate results. By a
novel geometric argument, many of our bounds translate to improper learning in
a non-convex class with Audibert's star algorithm. Thus, the offset complexity
provides a versatile analytic tool that covers both convex empirical risk
minimization and improper learning under entropy conditions. Applying the
method, we recover the optimal rates for proper and improper learning with the
$p$-loss for $1 < p < \infty$, and show that improper variants of empirical
risk minimization can attain fast rates for logistic regression and other
generalized linear models.


Abstract:
  Linear recurrent sequences are those whose elements are defined as linear
combinations of preceding elements, and finding recurrence relations is a
fundamental problem in computer algebra. In this paper, we focus on sequences
whose elements are vectors over the ring $\mathbb{A} = \mathbb{K}[x]/(x^d)$ of
truncated polynomials. Finding the ideal of their recurrence relations has
applications such as the computation of minimal polynomials and determinants of
sparse matrices over $\mathbb{A}$. We present three methods for finding this
ideal: a Berlekamp-Massey-like approach due to Kurakin, one which computes the
kernel of some block-Hankel matrix over $\mathbb{A}$ via a minimal approximant
basis, and one based on bivariate Pad\'e approximation. We propose complexity
improvements for the first two methods, respectively by avoiding the
computation of redundant relations and by exploiting the Hankel structure to
compress the approximation problem. Then we confirm these improvements
empirically through a C++ implementation, and we discuss the above-mentioned
applications.


Abstract:
  Simple dynamical models can produce intricate behaviors in large networks.
These behaviors can often be observed in a wide variety of physical systems
captured by the network of interactions. Here we describe a phenomenon where
the increase of dimensions self-consistently generates a force field due to
dynamical instabilities. This can be understood as an unstable ("rumbling")
tunneling mechanism between minima in an effective potential. We dub this
collective and nonperturbative effect a "Lyapunov force" which steers the
system towards the global minimum of the potential function, even if the full
system has a constellation of equilibrium points growing exponentially with the
system size. The system we study has a simple mapping to a flow network,
equivalent to current-driven memristors. The mechanism is appealing for its
physical relevance in nanoscale physics, and to possible applications in
optimization, novel Monte Carlo schemes and machine learning.


Abstract:
  We use the method of quadratic Chabauty on the quotients $X_0^+(N)$ of
modular curves $X_0(N)$ by their Fricke involutions to provably compute all the
rational points of these curves for prime levels $N$ of genus four, five, and
six. We find that the only such curves with exceptional rational points are of
levels $137$ and $311$. In particular there are no exceptional rational points
on those curves of genus five and six. More precisely, we determine the
rational points on the curves $X_0^+(N)$ for
$N=137,173,199,251,311,157,181,227,263,163,197,211,223,269,271,359$.


Abstract:
  Recently the observation of a new pentaquark state, the hidden-charmed
strange $P_{cs}(4459)^0$, was reported by the LHCb Collaboration. The
spin-parity quantum numbers of this state were not determined as a result of
insufficient statistics. To shed light on its quantum numbers, we investigate
its decay, $P_{cs}(4459)^0 \rightarrow J/\psi \Lambda $, the mode that this
state has been observed, within the QCD sum rule framework. We obtain the width
of this decay assigning the spin-parity quantum numbers of $P_{cs}(4459)^0$
state as $J^P=\frac{1}{2}^-$ and its substructure as diquark-diquark-antiquark.
To this end, we first calculate the strong coupling constants defining the
considered decay and then use them in the width calculations. The obtained
width is consistent with the experimental observation, confirming the quantum
numbers $J^P=\frac{1}{2}^-$ and compact pentaquark nature for $P_{cs}(4459)^0$
state.


Abstract:
  Domain adaptation deals with training models using large scale labeled data
from a specific source domain and then adapting the knowledge to certain target
domains that have few or no labels. Many prior works learn domain agnostic
feature representations for this purpose using a global distribution alignment
objective which does not take into account the finer class specific structure
in the source and target domains. We address this issue in our work and propose
an instance affinity based criterion for source to target transfer during
adaptation, called ILA-DA. We first propose a reliable and efficient method to
extract similar and dissimilar samples across source and target, and utilize a
multi-sample contrastive loss to drive the domain alignment process. ILA-DA
simultaneously accounts for intra-class clustering as well as inter-class
separation among the categories, resulting in less noisy classifier boundaries,
improved transferability and increased accuracy. We verify the effectiveness of
ILA-DA by observing consistent improvements in accuracy over popular domain
adaptation approaches on a variety of benchmark datasets and provide insights
into the proposed alignment approach. Code will be made publicly available at
https://github.com/astuti/ILA-DA.


Abstract:
  Quantum process tomography might be the most important paradigm shift which
has yet to be translated fully into theoretical chemistry. Its fundamental
strength, long established in quantum information science, offers a wealth of
information about quantum dynamic processes which lie at the heart of many (if
not all) chemical processes. However, due to its complexity its application to
real chemical systems is currently beyond experimental reach. Furthermore, it
is susceptible to errors due to experimental and theoretical inaccuracies and
disorder has long been thought to be an obstacle in its applicability. Here, I
present the first results of a study into the use of quantum light for quantum
process tomography. By using a toy model and comparing numerical simulations to
theoretical predictions the possible enhancement of using non-conventional
light is studied. It is found, however, that disorder is necessary make the use
of quantum light suitable for process tomography and that, in contrast to
conventional wisdom, disorder can make the results more accurate than in an
ordered system.


Abstract:
  Neural Machine Translation models are sensitive to noise in the input texts,
such as misspelled words and ungrammatical constructions. Existing robustness
techniques generally fail when faced with unseen types of noise and their
performance degrades on clean texts. In this paper, we focus on three types of
realistic noise that are commonly generated by humans and introduce the idea of
visual context to improve translation robustness for noisy texts. In addition,
we describe a novel error correction training regime that can be used as an
auxiliary task to further improve translation robustness. Experiments on
English-French and English-German translation show that both multimodal and
error correction components improve model robustness to noisy texts, while
still retaining translation quality on clean texts.


Abstract:
  We study Weyl fermions in a linear class of topologically trivial
G\"odel-type geometries in Einstein's general relativity. We solve the Weyl
equation and evaluate in detail the energy eigenvalues and the corresponding
wave functions.


Abstract:
  A saturated fusion system over a finite $p$-group $S$ is a category whose
objects are the subgroups of $S$ and whose morphisms are injective
homomorphisms between the subgroups satisfying certain axioms. A fusion system
over $S$ is realized by a finite group $G$ if $S$ is a Sylow $p$-subgroup of
$G$ and morphisms in the category are those induced by conjugation in $G$. One
recurrent question in this subject is to find criteria as to whether a given
saturated fusion system is realizable or not.
  One main result in this paper is that a saturated fusion system is realizable
if all of its components (in the sense of Aschbacher) are realizable. Another
result is that all realizable fusion systems are tame: a finer condition on
realizable fusion systems that involves describing automorphisms of a fusion
system in terms of those of some group that realizes it. Stated in this way,
these results depend on the classification of finite simple groups, but we also
give more precise formulations whose proof is independent of the
classification.


Abstract:
  We investigate imbibition of a monodisperse emulsion into a low-aspect ratio
microfluidic channel with the height h comparable to the droplet diameter d.
For confinement ratio d/h = 1.2, the tightly confined disk-like droplets in the
channel move more slowly compared to the average suspension velocity. Behind
the meniscus that drives the imbibition, there is a droplet-free region,
separated from the suspension region by a sharp concentration front. The
suspension exhibits strong droplet density and velocity fluctuations, but on
average, the suspension domain remains uniform. For weaker confinement, d/h =
0.65, the spherical droplets move faster than the average suspension flow,
resulting in the formation of a dynamically unstable high-concentration region
near the meniscus. We describe the macroscopic suspension dynamics using linear
transport equations for the particle-phase flux and suspension flux that are
driven by the local pressure gradient. A dipolar particle interaction model
explains the observed large density and velocity fluctuations in terms of the
dynamics of elongated particle clusters with different orientations.


Abstract:
  Laplace's equation appears frequently in physical applications involving
conservable quantities. Among these applications, miniaturized devices have
been of interest, in particular those using interdigitated arrays. Therefore,
we solved the two-dimensional Laplace's equation for a shallow or finite domain
consisting of interdigitated boundaries. We achieved this by using Jacobian
elliptic functions to conformally transform the interdigitated domain into a
parallel plates domain. The obtained expressions for potential distribution,
flux density and flux allow for arbitrary domain height, different band widths
and asymmetric potentials at the interdigitated array, besides considering
fringing effects at both ends of the array. All these expressions depend only
on relative dimensions, instead of absolute ones. With these results we showed
that the behavior in shallow or finite domains approaches that of a
semi-infinite domain, when its height is greater than the separation between
the centers of consecutive bands. We also found that, for any desired but fixed
flux, bands of equal width minimize the total surface of the interdigitated
array. Finally, we present approximate expressions for the flux, based on
elementary functions, which can be applied to ease the calculation of currents
(faradaic or non-faradaic), capacitances and resistances among other possible
applications.


Abstract:
  Cross-modal retrieval is an important functionality in modern search engines,
as it increases the user experience by allowing queries and retrieved objects
to pertain to different modalities. In this paper, we focus on the
image-sentence retrieval task, where the objective is to efficiently find
relevant images for a given sentence (image-retrieval) or the relevant
sentences for a given image (sentence-retrieval). Computer vision literature
reports the best results on the image-sentence matching task using deep neural
networks equipped with attention and self-attention mechanisms. They evaluate
the matching performance on the retrieval task by performing sequential scans
of the whole dataset. This method does not scale well with an increasing amount
of images or captions. In this work, we explore different preprocessing
techniques to produce sparsified deep multi-modal features extracting them from
state-of-the-art deep-learning architectures for image-text matching. Our main
objective is to lay down the paths for efficient indexing of complex
multi-modal descriptions. We use the recently introduced TERN architecture as
an image-sentence features extractor. It is designed for producing fixed-size
1024-d vectors describing whole images and sentences, as well as
variable-length sets of 1024-d vectors describing the various building
components of the two modalities (image regions and sentence words
respectively). All these vectors are enforced by the TERN design to lie into
the same common space. Our experiments show interesting preliminary results on
the explored methods and suggest further experimentation in this important
research direction.


Abstract:
  In $\rm\Lambda$CDM cosmology, Dark Matter (DM) and neutrinos are assumed to
be non-interacting. However, it is possible to have scenarios, where
DM-neutrino interaction may be present, leading to scattering of DM with
neutrinos and annihilation of DM into neutrinos. We investigate the viability
of such scenarios in the light of cosmological data by making use of the Planck
2018 dataset (high-l TT+TE+EE, low-l TT, low-l EE) and constrain these
processes in the light of the same. We also discuss a viable particle DM model
where DM-neutrino interaction is present, and map the constraints obtained to
the parameter space of the model.


Abstract:
  In this paper, we propose a deep evolutionary learning (DEL) process that
integrates fragment-based deep generative model and multi-objective
evolutionary computation for molecular design. Our approach enables (1)
evolutionary operations in the latent space of the generative model, rather
than the structural space, to generate novel promising molecular structures for
the next evolutionary generation, and (2) generative model fine-tuning using
newly generated high-quality samples. Thus, DEL implements a data-model
co-evolution concept which improves both sample population and generative model
learning. Experiments on two public datasets indicate that sample population
obtained by DEL exhibits improved property distributions, and dominates samples
generated by multi-objective Bayesian optimization algorithms.


Abstract:
  Antibubbles, which consist of a shell of a low-density fluid inside a
high-density fluid, have several promising applications. We show, via extensive
direct numerical simulations (DNSs), in both two and three dimensions (2D and
3D), that the spatiotemporal evolution of antibubbles can be described
naturally by the coupled Cahn-Hilliard-Navier-Stokes (CHNS) equations for a
binary fluid. Our DNSs capture elegantly the gravity-induced thinning and
breakup of an antibubble via the time evolution of the Cahn-Hilliard scalar
order parameter field $\phi$, which varies continuously across interfaces, so
we do not have to enforce complicated boundary conditions at the moving
antibubble interfaces. To ensure that our results are robust, we supplement our
CHNS simulations with sharp-interface Volume-of-Fluid (VoF) DNSs. We track the
thickness of the antibubble and calculate the dependence of the lifetime of an
antibubble on several parameters; we show that our DNS results agree with
various experimental results; in particular, the velocity with which the arms
of the antibubble retract after breakup scales as $\sigma^{1/2}$, where
$\sigma$ is the surface tension.


Abstract:
  Transfer learning can boost the performance on the targettask by leveraging
the knowledge of the source domain. Recent worksin neural architecture search
(NAS), especially one-shot NAS, can aidtransfer learning by establishing
sufficient network search space. How-ever, existing NAS methods tend to
approximate huge search spaces byexplicitly building giant super-networks with
multiple sub-paths, anddiscard super-network weights after a child structure is
found. Both thecharacteristics of existing approaches causes repetitive network
trainingon source tasks in transfer learning. To remedy the above issues, we
re-duce the super-network size by randomly dropping connection betweennetwork
blocks while embedding a larger search space. Moreover, wereuse super-network
weights to avoid redundant training by proposinga novel framework consisting of
two modules, the neural architecturesearch module for architecture transfer and
the neural weight searchmodule for weight transfer. These two modules conduct
search on thetarget task based on a reduced super-networks, so we only need to
trainonce on the source task. We experiment our framework on both MS-COCO and
CUB-200 for the object detection and fine-grained imageclassification tasks,
and show promising improvements with onlyO(CN)super-network complexity.


Abstract:
  In the paper, we introduce and address the problem of structural averaged
controllability for linear ensemble systems. We provide examples highlighting
the differences between this problem and others. In particular, we show that
structural averaged controllability is strictly weaker than structural
controllability for single (or ensembles of) linear systems. We establish a set
of necessary or sufficient conditions for sparsity patterns to be structurally
averaged controllable.


Abstract:
  The critical quantum metrology, which exploits the quantum phase transition
for high precision measurement, has gained increasing attention recently. The
critical quantum metrology with the continuous quantum phase transition,
however, is experimentally very challenging since the continuous quantum phase
transition only exists at the thermal dynamical limit. Here, we propose an
adiabatic scheme on a perturbed Ising spin model with the first order quantum
phase transition. By employing the Landau-Zener anticrossing, we can not only
encode the unknown parameter in the ground state but also tune the energy gap
to control the evolution time of the adiabatic passage. We experimentally
implement the adiabatic scheme on the nuclear magnetic resonance and show that
the achieved precision attains the Heisenberg scaling. The advantages of the
scheme-easy implementation, robust against the decay, tunable energy gap-are
critical for practical applications of quantum metrology.


Abstract:
  We propose a new iterative optimization method for the {\bf Data-Fitting}
(DF) problem in Machine Learning, e.g. Neural Network (NN) training. The
approach relies on {\bf Graphical Model} (GM) representation of the DF problem,
where variables are fitting parameters and factors are associated with the
Input-Output (IO) data. The GM results in the {\bf Belief Propagation}
Equations considered in the {\bf Large Deviation Limit} corresponding to the
practically important case when the number of the IO samples is much larger
than the number of the fitting parameters. We suggest the {\bf Message Passage
Descent} algorithm which relies on the piece-wise-polynomial representation of
the model DF function. In contrast with the popular gradient descent and
related algorithms our MPD algorithm rely on analytic (not automatic)
differentiation, while also (and most importantly) it descents through the
rugged DF landscape by \emph{making non local updates of the parameters} at
each iteration. The non-locality guarantees that the MPD is not trapped in the
local-minima, therefore resulting in better performance than locally-updated
algorithms of the gradient-descent type. We illustrate superior performance of
the algorithm on a Feed-Forward NN with a single hidden layer and a
piece-wise-linear activation function.


Abstract:
  Medical image segmentation is routinely performed to isolate regions of
interest, such as organs and lesions. Currently, deep learning is the state of
the art for automatic segmentation, but is usually limited by the need for
supervised training with large datasets that have been manually segmented by
trained clinicians. The goal of semi-superised and unsupervised image
segmentation is to greatly reduce, or even eliminate, the need for training
data and therefore to minimze the burden on clinicians when training
segmentation models. To this end we introduce a novel network architecture for
capable of unsupervised and semi-supervised image segmentation called
TricycleGAN. This approach uses three generative models to learn translations
between medical images and segmentation maps using edge maps as an intermediate
step. Distinct from other approaches based on generative networks, TricycleGAN
relies on shape priors rather than colour and texture priors. As such, it is
particularly well-suited for several domains of medical imaging, such as
ultrasound imaging, where commonly used visual cues may be absent. We present
experiments with TricycleGAN on a clinical dataset of kidney ultrasound images
and the benchmark ISIC 2018 skin lesion dataset.


Abstract:
  Measurements of response inhibition components of reactive inhibition and
proactive inhibition within the stop signal paradigm have been of special
interest for researchers since the 1980s. While frequentist nonparametric and
Bayesian parametric methods have been proposed to precisely estimate the entire
distribution of reactive inhibition, quantified by stop signal reaction
times(SSRT), there is no method yet in the stop-signal task literature to
precisely estimate the entire distribution of proactive inhibition. We
introduce an Asymmetric Laplace Gaussian (ALG) model to describe the
distribution of proactive inhibition. The proposed method is based on two
assumptions of independent trial type(go/stop) reaction times, and Ex-Gaussian
(ExG) models for them. Results indicated that the four parametric, ALG model
uniquely describes the proactive inhibition distribution and its key shape
features; and, its hazard function is monotonically increasing as are its three
parametric ExG components. In conclusion, both response inhibition components
can be uniquely modeled via variations of the four parametric ALG model
described with their associated similar distributional features.


Abstract:
  The purpose of this paper is to introduce and investigate the notion of
derivation for quandle algebras. More precisely, we describe the symmetries on
structure constants providing a characterization for a linear map to be a
derivation. We obtain a complete characterization of derivations in the case of
quandle algebras of \emph{dihedral quandles} over fields of characteristic
zero, and provide the dimensionality of the Lie algebra of derivations. Many
explicit examples and computations are given over both zero and positive
characteristic.
  Furthermore, we investigate inner derivations, in the sense of Schafer for
non-associative structures. We obtain necessary conditions for the Lie
transformation algebra of quandle algebras of Alexander quandles, with explicit
computations in low dimensions.


Abstract:
  The directional mean shift (DMS) algorithm is a nonparametric method for
pursuing local modes of densities defined by kernel density estimators on the
unit hypersphere. In this paper, we show that any DMS iteration can be viewed
as a generalized Expectation-Maximization (EM) algorithm; in particular, when
the von Mises kernel is applied, it becomes an exact EM algorithm. Under the
(generalized) EM framework, we provide a new proof for the ascending property
of density estimates and demonstrate the global convergence of directional mean
shift sequences. Finally, we give a new insight into the linear convergence of
the DMS algorithm.


Abstract:
  Streaming processing of speech audio is required for many contemporary
practical speech recognition tasks. Even with the large corpora of manually
transcribed speech data available today, it is impossible for such corpora to
cover adequately the long tail of linguistic content that's important for tasks
such as open-ended dictation and voice search. We seek to address both the
streaming and the tail recognition challenges by using a language model (LM)
trained on unpaired text data to enhance the end-to-end (E2E) model. We extend
shallow fusion and cold fusion approaches to streaming Recurrent Neural Network
Transducer (RNNT), and also propose two new competitive fusion approaches that
further enhance the RNNT architecture. Our results on multiple languages with
varying training set sizes show that these fusion methods improve streaming
RNNT performance through introducing extra linguistic features. Cold fusion
works consistently better on streaming RNNT with up to a 8.5% WER improvement.


Abstract:
  Eddy current stimulated thermography is an emerging technique for
non-destructive testing and evaluation of conductive materials. However,
quantitative estimation of the depth of subsurface defects in metallic
materials by thermography techniques remains challenging due to significant
lateral thermal diffusion. This work presents the application of eddy current
pulse compression thermography to detect surface and subsurface defects with
various depths in an aluminum sample. Kernel Principal Component analysis and
Low Rank Sparse modelling were used to enhance the defective area, and cross
point feature was exploited to quantitatively evaluate the defects depth. Based
on experimental results, it is shown that the crossing point feature has a
monotonic relationship with surface and subsurface defects depth, and it can
also indicate whether the defect is within or beyond the eddy current skin
depth. In addition, the comparison study between aluminum and composites in
terms of impulse response and proposed features are also presented.


Abstract:
  Cooperative ad hoc unmanned aerial vehicle (UAV) networks need essential
security services to ensure their communication security. Cryptography, as the
inseparable tool for providing security services, requires a robust key
management system. Alas, the absence of infrastructure in cooperative networks
leads to the infeasibility of providing conventional key management systems.
Key pre-distribution schemes have shown promising performance in different
cooperative networks due to their lightweight nature. However, intermediate
decryption-encryption (DE) steps and the lack of key updates are the most
concerning issues they suffer from. In this paper, we propose a simple and
effective key management algorithm inspired by the idea of key
pre-distribution, where it utilizes the highly dynamic UAV node movement in 3D
space to provide the key update feature and optimizes the number of
intermediate DE steps. Although it is a general model for any mobile ad hoc
network, we have selected UAV network as an example domain to show the
efficiency of the model given the high mobility. We define the communication
density parameter to analytically show that using any highly dynamic random
movement pattern leads our algorithm to work effectively. To show the proposed
algorithm's effectiveness, we exhaustively analyze its security and performance
in the UAV network using the ns-3 network simulator. Results validate our
analytical findings and show how the highly dynamic UAV network movement helps
our algorithm to provide the key update feature and to optimize the number of
DE steps.


Abstract:
  There has been emerging interest to use transductive learning for adversarial
robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020). Compared to
traditional "test-time" defenses, these defense mechanisms "dynamically
retrain" the model based on test time input via transductive learning; and
theoretically, attacking these defenses boils down to bilevel optimization,
which seems to raise the difficulty for adaptive attacks. In this paper, we
first formalize and analyze modeling aspects of transductive robustness. Then,
we propose the principle of attacking model space for solving bilevel attack
objectives, and present an instantiation of the principle which breaks previous
transductive defenses. These attacks thus point to significant difficulties in
the use of transductive learning to improve adversarial robustness. To this
end, we present new theoretical and empirical evidence in support of the
utility of transductive learning.


Abstract:
  We treat the lattice sine-Gordon equation and two of its generalised
symmetries as a compatible system. Elimination of shifts from the two
symmetries of the lattice sine-Gordon equation yields an integrable NLS-type
system. An auto-B\"acklund transformation and a superposition formula for the
NLS-type system is obtained by elimination of shifts from the lattice
sine-Gordon equation and its down-shifted version. We use the obtained formulae
to calculate a superposition of two and three elementary solutions.


Abstract:
  We present a possible correlation between the properties of scattered and
thermal radiation from dust and the principal dust characteristics responsible
for this relationship. To this end, we use the NASA/PDS archival polarimetric
data on cometary dust in the Red (0.62--0.73 $\mu$m) and K (2.00--2.39 $\mu$m)
domains to leverage the relative excess of the polarisation degree of a comet
to the average trend at the given phase angle ($P_{\rm excess}$) as a metric of
the dust's scattered light characteristics. The flux excess of silicate
emissions to the continuum around 10 $\mu$m ($F_{\rm Si}/F_{\rm cont}$) is
adopted from previous studies as a metric of the dust's MIR feature. The two
metrics show a positive correlation when $P_{\rm excess}$ is measured in the K
domain. No significant correlation was identified in the Red domain. The
gas-rich comets have systematically weaker $F_{\rm Si}/F_{\rm cont}$ than the
dust-rich ones, yet both groups retain the same overall tendency with different
slope values. The observed positive correlation between the two metrics
indicates that composition is a peripheral factor in characterising the dust's
polarimetric and silicate emission properties. The systematic difference in
$F_{\rm Si}/F_{\rm cont}$ for gas-rich versus dust-rich comets would rather
correspond with the difference in their dust size distribution. Hence, our
results suggest that the current MIR spectral models of cometary dust should
prioritise the dust size and porosity over the composition. With light
scattering being sensitive to different size scales in two wavebands, we expect
the K-domain polarimetry to be sensitive to the properties of dust aggregates,
such as size and porosity, which might have been influenced by evolutionary
processes. On the other hand, the Red-domain polarimetry reflects the
characteristics of sub-$\mu$m constituents in the aggregate.


Abstract:
  It has been shown in a recent work by Yakir-Zeitouni that the minimum modulus
of random trigonometric polynomials with Gaussian coefficients has a limiting
exponential distribution. We show this is a universal phenomenon. Our approach
relates the joint distribution of small values of the polynomial at a fixed
number $m$ of points on the circle to the distribution of a certain random walk
in a $4m$-dimensional phase space. Under Diophantine approximation conditions
on the angles, we obtain strong small ball estimates and a local central limit
theorem for the distribution of the walk.


Abstract:
  We reformulate the twistor construction for hyper- and quaternion-K\"ahler
manifolds, introducing new sigma models that compute scalar potentials for the
geometry. These sigma models have the twistor space of the quaternionic
manifold as their target and encode finite non-linear perturbations of the flat
structures. In the hyperk\"ahler case our twistor sigma models compute both
Plebanski fundamental forms (including the K\"ahler potential), while in the
quaternion-K\"ahler setting the twistor sigma model computes the K\"ahler
potential for the hyperk\"ahler structure on non-projective twistor space. In
four-dimensions, one of the models provides the generating functional of
tree-level MHV graviton scattering amplitudes; perturbations of the
hyperk\"ahler structure corresponding to positive helicity gravitons. The sigma
model's perturbation theory gives rise to a sum of tree diagrams observed
previously in the literature, and their summation via a matrix tree theorem
gives a first-principles derivation of Hodges' formula for MHV graviton
amplitudes directly from general relativity. We generalise the twistor sigma
model to higher-degree (defined in the first case with a cosmological
constant), giving a new generating principle for the full tree-level graviton
S-matrix.


Abstract:
  A challenge in designing self-assembling building blocks is to ensure the
target state is both thermodynamically stable and kinetically accessible. These
two objectives are known to be typically in competition, but it is not known
how to simultaneously optimize them. We consider this problem through the lens
of multi-objective optimization theory: we develop a genetic algorithm to
compute the Pareto fronts characterizing the tradeoff between equilibrium
probability and folding rate, for a model system of small polymers of colloids
with tunable short-ranged interaction energies. We use a coarse-grained model
for the particles' dynamics that allows us to efficiently search over
parameters, for systems small enough to be enumerated. For most target states
there is a tradeoff when the number of types of particles is small, with
medium-weak bonds favouring fast folding, and strong bonds favouring high
equilibrium probability. The tradeoff disappears when the number of particle
types reaches a value $m_*$, that is usually much less than the total number of
particles. This general approach of computing Pareto fronts allows one to
identify the minimum number of design parameters to avoid a
thermodynamic-kinetic tradeoff. However, we argue, by contrasting our
coarse-grained model's predictions with those of Brownian dynamics simulations,
that particles with short-ranged isotropic interactions should generically have
a tradeoff, and avoiding it in larger systems will require
orientation-dependent interactions.


Abstract:
  We propose a two-population lattice Boltzmann model on standard lattices for
the simulation of compressible flows. The model is fully on-lattice and uses
the single relaxation time Bhatnagar-Gross-Krook kinetic equations along with
appropriate correction terms to recover the Navier-Stokes-Fourier equations.
The accuracy and performance of the model are analyzed through simulations of
compressible benchmark cases including Sod shock tube, sound generation in
shock-vortex interaction and compressible decaying turbulence in a box with
eddy shocklets. It is demonstrated that the present model provides an accurate
representation of compressible flows, even in the presence of turbulence and
shock waves.


Abstract:
  We present the first Fermi - Large Area Telescope (LAT) solar flare catalog
covering the 24 th solar cycle. This catalog contains 45 Fermi -LAT solar
flares (FLSFs) with emission in the gamma-ray energy band (30 MeV - 10 GeV)
detected with a significance greater than 5 sigma over the years 2010-2018. A
subsample containing 37 of these flares exhibit delayed emission beyond the
prompt-impulsive hard X-ray phase with 21 flares showing delayed emission
lasting more than two hours. No prompt-impulsive emission is detected in four
of these flares. We also present in this catalog the observations of GeV
emission from 3 flares originating from Active Regions located behind the limb
(BTL) of the visible solar disk. We report the light curves, spectra, best
proton index and localization (when possible) for all the FLSFs. The gamma-ray
spectra is consistent with the decay of pions produced by >300 MeV protons.
This work contains the largest sample of high-energy gamma-ray flares ever
reported and provides the unique opportunity to perform population studies on
the different phases of the flare and thus allowing to open a new window in
solar physics.


Abstract:
  Molecular dynamics simulations are used to show that strong magnetization
significantly increases the space and time scales associated with interparticle
correlations. The physical mechanism responsible is a channeling effect whereby
particles are confined to move along narrow cylinders with a width
characterized by the gyroradius and a length characterized by the collision
mean free path. The predominant interaction is $180^\circ$ collisions at the
ends of the collision cylinders, resulting in a long-range correlation parallel
to the magnetic field. Its influence is demonstrated via the dependence of the
velocity autocorrelation functions and self-diffusion coefficients on the
domain size and run time in simulations of the one-component plasma. A very
large number of particles, and therefore domain size, must be used to resolve
the long-range correlations, suggesting that the number of charged particles in
the collection must increase in order to constitute a plasma. Correspondingly,
this effect significantly delays the time it takes to reach a diffusive regime,
in which the mean square displacement of particles increases linearly in time.
This result presents challenges for connecting measurements in non-neutral and
ultracold neutral plasma experiments, as well as molecular dynamics
simulations, with fluid transport properties due to their finite size.


Abstract:
  Although the share of platform work is very small compared to conventional
and traditional employment system, research shows that the use of platform work
is increasingly growing all over the world. Trade unions have also paid special
attention to the platform work because they know that the transfer of a
percentage of human resources to the platforms is undeniable. To this end, the
trade unions prepare themselves for the challenges and dynamics of this
emerging phenomenon in the field of human resources. Using a qualitative
research method and a case study of Hungary, the present study aimed to
identify the strategies adopted by Trade Unions to manage the transition to
platform works and provide suggestions for both practitioners and future
research.


Abstract:
  Differential buoyancy surface sources in the ocean may induce a
density-driven flow that joins faster flow components to create a multi-scale,
3D flow. Potential temperature and salinity are active tracers that determine
the ocean's potential density: their distribution strongly affects the
density-driven component while the overall flow affects their distribution. We
present a robust framework that allows one to study the effects of a general 3D
flow on a density-driven velocity component, by constructing a modular
observation-based 3D model of intermediate complexity. The model contains an
incompressible velocity that couples two advection-diffusion equations, for
temperature and salinity. Instead of solving the Navier-Stokes equations for
the velocity, we consider a flow composed of several temporally separated,
spatially predetermined modes. One of these modes models the density-driven
flow: its spatial form describes the density-driven flow structure and its
strength is determined dynamically by average density differences. The other
modes are completely predetermined, consisting of any incompressible, possibly
unsteady, 3D flow, e.g. as determined by kinematic models, observations, or
simulations. The model is a non-linear, weakly coupled system of two non-local
PDEs. We prove its well-posedness in the sense of Hadamard, and obtain rigorous
bounds regarding analytical solutions. The model's relevance to oceanic systems
is demonstrated by tuning the model to mimic the North Atlantic ocean's
dynamics. In one limit the model recovers a simplified oceanic box model and in
another limit a kinematic model of oceanic chaotic advection, suggesting it can
be utilized to study spatially dependent feedback processes in the ocean.


Abstract:
  Memory encoding by cyclic shear is a reliable process to store information in
jammed solids, yet its underlying mechanism and its connection to the amorphous
structure are not fully understood. When a jammed sphere packing is repeatedly
sheared with cycles of the same strain amplitude, it optimizes its mechanical
response to the cyclic driving and stores a memory of it. We study memory by
cyclic shear training as a function of the underlying stability of the
amorphous structure in marginally stable and highly stable packings, the latter
produced by minimizing the potential energy using both positional and radial
degrees of freedom. We find that jammed solids need to be marginally stable in
order to store a memory by cyclic shear. In particular, highly stable packings
store memories only after overcoming brittle yielding and the cyclic shear
training takes place in the shear band, a region which we show to be marginally
stable.


Abstract:
  In this paper, an autonomous aerial manipulation task of pulling a plug out
of an electric socket is conducted, where maintaining the stability and
robustness is challenging due to sudden disappearance of a large interaction
force. The abrupt change in the dynamical model before and after the separation
of the plug can cause destabilization or mission failure. To accomplish aerial
plug-pulling, we employ the concept of hybrid automata to divide the task into
three operative modes, i.e, wire-pulling, stabilizing, and free-flight. Also, a
strategy for trajectory generation and a design of disturbance-observer-based
controllers for each operative mode are presented. Furthermore, the theory of
hybrid automata is used to prove the stability and robustness during the mode
transition. We validate the proposed trajectory generation and control method
by an actual wire-pulling experiment with a multirotor-based aerial
manipulator.


Abstract:
  Few-shot semantic segmentation aims at learning to segment a target object
from a query image using only a few annotated support images of the target
class. This challenging task requires to understand diverse levels of visual
cues and analyze fine-grained correspondence relations between the query and
the support images. To address the problem, we propose Hypercorrelation Squeeze
Networks (HSNet) that leverages multi-level feature correlation and efficient
4D convolutions. It extracts diverse features from different levels of
intermediate convolutional layers and constructs a collection of 4D correlation
tensors, i.e., hypercorrelations. Using efficient center-pivot 4D convolutions
in a pyramidal architecture, the method gradually squeezes high-level semantic
and low-level geometric cues of the hypercorrelation into precise segmentation
masks in coarse-to-fine manner. The significant performance improvements on
standard few-shot segmentation benchmarks of PASCAL-5i, COCO-20i, and FSS-1000
verify the efficacy of the proposed method.


Abstract:
  Although the real shapes and trajectories of erupting solar prominences in
three dimensions have been intensively studied, the three-dimensional (3D)
shapes of stable prominences before eruptions have not been measured accurately
so far. We intend to make such a measurement to constrain 3D prominence models
and to extend our knowledge of prominences. Using multiperspective observations
from the Atmospheric Imaging Assembly on board SDO and the Extreme Ultraviolet
Imager on board STEREO, we reconstructed 3D coordinates of three stable
prominences: a quiescent, an intermediate, and a mixed type. Based on the 3D
coordinates, we measured the height, length, and inclination angle of the legs
of these prominences. To study the spatial relationship between the footpoints
of prominences and photospheric magnetic structures, we also used the Global
Oscillation Network Group H alpha images and magnetograms from the HMI on board
the SDO. In three stable prominences, we find that the axes of the prominence
legs are inclined by 68 degrees on average to the solar surface. Legs at
different locations along a prominence axis have different heights with a two-
to threefold difference. Our investigation suggests that over 96% of prominence
footpoints in a sample of 70 footpoints are located at supergranular
boundaries. The widths of two legs have similar values measured in two
orthogonal lines of sight. We also find that a prominence leg above the solar
limb showed horizontal oscillations with larger amplitudes at higher locations.
With a limited image resolution and number of cases, our measurement suggests
that the legs of prominences may have various orientations and do not always
stand vertically on the surface of the sun. Moreover, the locations of
prominence legs are closely related to supergranules.


Abstract:
  Respiratory rate (RR) is a clinical sign representing ventilation. An
abnormal change in RR is often the first sign of health deterioration as the
body attempts to maintain oxygen delivery to its tissues. There has been a
growing interest in remotely monitoring of RR in everyday settings which has
made photoplethysmography (PPG) monitoring wearable devices an attractive
choice. PPG signals are useful sources for RR extraction due to the presence of
respiration-induced modulations in them. The existing PPG-based RR estimation
methods mainly rely on hand-crafted rules and manual parameters tuning. An
end-to-end deep learning approach was recently proposed, however, despite its
automatic nature, the performance of this method is not ideal using the real
world data. In this paper, we present an end-to-end and accurate pipeline for
RR estimation using Cycle Generative Adversarial Networks (CycleGAN) to
reconstruct respiratory signals from raw PPG signals. Our results demonstrate a
higher RR estimation accuracy of up to 2$\times$ (mean absolute error of
1.9$\pm$0.3 using five fold cross validation) compared to the state-of-th-art
using a identical publicly available dataset. Our results suggest that CycleGAN
can be a valuable method for RR estimation from raw PPG signals.


Abstract:
  Remaining useful life (RUL) estimation is a crucial component in the
implementation of intelligent predictive maintenance and health management.
Deep neural network (DNN) approaches have been proven effective in RUL
estimation due to their capacity in handling high-dimensional non-linear
degradation features. However, the applications of DNN in practice face two
challenges: (a) online update of lifetime information is often unavailable, and
(b) uncertainties in predicted values may not be analytically quantified. This
paper addresses these issues by developing a hybrid DNN-based prognostic
approach, where a Wiener-based-degradation model is enhanced with adaptive
drift to characterize the system degradation. An LSTM-CNN encoder-decoder is
developed to predict future degradation trajectories by jointly learning noise
coefficients as well as drift coefficients, and adaptive drift is updated via
Bayesian inference. A computationally efficient algorithm is proposed for the
calculation of RUL distributions. Numerical experiments are presented using
turbofan engines degradation data to demonstrate the superior accuracy of RUL
prediction of our proposed approach.


Abstract:
  Though stellar-mass black holes (BHs) are likely abundant in the Milky Way
(N=10^8-10^9), only ~20 have been detected to date, all in accreting binary
systems (Casares 2006). Gravitational microlensing is a proposed technique to
search for isolated BHs, which to date have not been detected. Two microlensing
events, MACHO-1996-BLG-5 (M96-B5) and MACHO-1998-BLG-6 (M98-B6), initially
observed near the lens-source minimum angular separation in 1996 and 1998,
respectively, have long Einstein crossing times (>300 days), identifying the
lenses as candidate black holes. Twenty years have elapsed since the time of
lens-source closest approach for each of these events, indicating that if the
lens and source are both luminous, and if their relative proper motion is
sufficiently large, the two components should be spatially resolvable. We
attempt to eliminate the possibility of a stellar lens for these events by: (1)
using Keck near-infrared adaptive optics images to search for a potentially
now-resolved, luminous lens; and (2) examining multi-band photometry of the
source to search for flux contributions from a potentially unresolved, luminous
lens. We combine detection limits from NIRC2 images with light curve data to
eliminate all non-BH lenses for relative lens-source proper motions above 0.81
mas/yr for M96-B5 and 2.48 mas/yr for M98-B6. Further, we use WFPC2 broadband
images to eliminate the possibility of stellar lenses at any proper motion. We
present the narrow range of non-BH possibilities allowed by our varied
analyses. Finally, we suggest future observations that would constrain the
remaining parameter space with the methods developed in this work.


Abstract:
  Several recent studies have indicated that artificial subhalo disruption (the
spontaneous, non-physical disintegration of a subhalo) remains prevalent in
state-of-the-art dark matter-only cosmological simulations. In order to
quantify the impact of disruption on the inferred subhalo demographics, we
augment the semi-analytical SatGen dynamical subhalo evolution model with an
improved treatment of tidal stripping that is calibrated using the DASH
database of idealized high-resolution simulations of subhalo evolution, which
are free from artificial disruption. We also develop a model of artificial
disruption that reproduces the statistical properties of disruption in the
Bolshoi simulation. Using this framework, we predict subhalo mass functions
(SHMFs), number density profiles, and substructure mass fractions and study how
these quantities are impacted by artificial disruption and mass resolution
limits. We find that artificial disruption affects these quantities at the
$10-20\%$ level, ameliorating previous concerns that it may suppress the SHMF
by as much as a factor of two. We demonstrate that semi-analytical substructure
modeling must include orbit integration in order to properly account for
splashback haloes, which make up roughly half of the subhalo population. We
show that the resolution limit of $N$-body simulations, rather than artificial
disruption, is the primary cause of the radial bias in subhalo number density
found in dark matter-only simulations. Hence, we conclude that the mass
resolution remains the primary limitation of using such simulations to study
subhaloes. Our model provides a fast, flexible, and accurate alternative to
studying substructure statistics in the absence of both numerical resolution
limits and artificial disruption.