Abstract:
  This work presents a non-intrusive surrogate modeling scheme based on machine
learning technology for predictive modeling of complex systems, described by
parametrized time-dependent PDEs. For these problems, typical finite element
approaches involve the spatiotemporal discretization of the PDE and the
solution of the corresponding linear system of equations at each time step.
Instead, the proposed method utilizes a convolutional autoencoder in
conjunction with a feed forward neural network to establish a low-cost and
accurate mapping from the problem's parametric space to its solution space. For
this purpose, time history response data are collected by solving the
high-fidelity model via FEM for a reduced set of parameter values. Then, by
applying the convolutional autoencoder to this data set, a low-dimensional
representation of the high-dimensional solution matrices is provided by the
encoder, while the reconstruction map is obtained by the decoder. Using the
latent representation given by the encoder, a feed-forward neural network is
efficiently trained to map points from the problem's parametric space to the
compressed version of the respective solution matrices. This way, the encoded
response of the system at new parameter values is given by the neural network,
while the entire response is delivered by the decoder. This approach
effectively bypasses the need to serially formulate and solve the system's
governing equations at each time increment, thus resulting in a significant
cost reduction and rendering the method ideal for problems requiring repeated
model evaluations or 'real-time' computations. The elaborated methodology is
demonstrated on the stochastic analysis of time-dependent PDEs solved with the
Monte Carlo method, however, it can be straightforwardly applied to other
similar-type problems, such as sensitivity analysis, design optimization, etc.


Abstract:
  With the success of pre-trained language models in recent years, more and
more researchers focus on opening the "black box" of these models. Following
this interest, we carry out a qualitative and quantitative analysis of
constituency grammar in attention heads of BERT and RoBERTa. We employ the
syntactic distance method to extract implicit constituency grammar from the
attention weights of each head. Our results show that there exist heads that
can induce some grammar types much better than baselines, suggesting that some
heads act as a proxy for constituency grammar. We also analyze how attention
heads' constituency grammar inducing (CGI) ability changes after fine-tuning
with two kinds of tasks, including sentence meaning similarity (SMS) tasks and
natural language inference (NLI) tasks. Our results suggest that SMS tasks
decrease the average CGI ability of upper layers, while NLI tasks increase it.
Lastly, we investigate the connections between CGI ability and natural language
understanding ability on QQP and MNLI tasks.


Abstract:
  Image recovery from compressive measurements requires a signal prior for the
images being reconstructed. Recent work has explored the use of deep generative
models with low latent dimension as signal priors for such problems. However,
their recovery performance is limited by high representation error. We
introduce a method for achieving low representation error using generators as
signal priors. Using a pre-trained generator, we remove one or more initial
blocks at test time and optimize over the new, higher-dimensional latent space
to recover a target image. Experiments demonstrate significantly improved
reconstruction quality for a variety of network architectures. This approach
also works well for out-of-training-distribution images and is competitive with
other state-of-the-art methods. Our experiments show that test-time
architectural modifications can greatly improve the recovery quality of
generator signal priors for compressed sensing.


Abstract:
  Consider a nonlinear wave equation for a massless scalar field with
self-interaction in the spatially flat Friedmann-Lema\^{i}tre-Robertson-Walker
spacetimes. We treat the so-called heatlike case where the critical exponent is
affected by the Fujita exponent. We show upper bounds of the lifespan of
blow-up solutions by distinguishing subcritical and critical cases.


Abstract:
  In certain instances an electric network transforms in natural ways by the
addition or removal of an edge. This can have interesting consequences for
random walks, in light of the known relationships between electric resistance
and random walks. We exhibit several instances in which this can be used to
prove facts or simplify calculations. In particular, a new proof is given for
the formula for the expected return time of a random walk on a graph. We also
show how hitting times can be calculated in certain instances when a network
differs from a highly symmetric one by one edge.


Abstract:
  It has been shown that placing an obstacle in front of an exit door has
proven to be a successful method to improve pedestrian evacuations. In this
work, we will focus on the space limited by the exit and the obstacles (i.e.
the vestibule structure). We analyzed two different types of vestibules: the
two-entry vestibule (which consists of a single panel-like obstacle) and the
three-entry vestibule (which consists of two panel-like obstacles). In the
former, we studied the effects of varying the walls' friction coefficient
$\kappa_w$ and the distance from the obstacle to the exit door $d$. In the
latter, we varied the space between the two panels (gap). We found that the
three above mentioned parameters control the vestibule's density, which
subsequently affects the evacuation flow (fundamental diagram). We have also
found that reducing the distance $d$ or increasing the friction facilitates the
formation of blocking clusters at the vestibule entries, and hence, diminishes
the density. If the density is too large or too low, the evacuation flow is
suboptimal, whereas if the density is around $2 \pm 2$, the flow is maximized.
Our most important result is that the density (and therefore the evacuation
flow) can be precisely controlled by $\kappa_w$, $d$, and the gap. Moreover,
the three-entry vestibule produced the highest evacuation flow for specific
configurations of the gap and the distance from the panels to the door.


Abstract:
  We study 10 C/C++ projects that have been using a static analysis security
testing tool. We analyze the historical scan reports generated by the tool and
study how frequently memory-related alerts appeared. We also studied the
subsequent developer action on those alerts. We also look at the CVEs published
for these projects within the study timeline and investigate how many of them
are memory related. Moreover, for one of this project, Linux, we investigate if
the involved flaws in the CVE were identified by the studied security tool when
they were first introduced in the code. We found memory related alerts to be
frequently detected during static analysis security testing. However, based on
how actively the project developers are monitoring the tool alerts, these
errors can take years to get fixed. For the ten studied projects, we found a
median lifespan of 77 days before memory alerts get fixed. We also find that
around 40% of the published CVEs for the studied C/C++ projects are related to
memory. These memory CVEs have higher CVSS severity ratings and likelihood of
having an exploit script public than non-memory CVEs. We also found only 2.5%
Linux CVEs were possibly detected during static analysis security testing.


Abstract:
  I analyse the extension of the Ryu Takayanagi formula from the perspective of
the universal coefficient theorem and cohomology with different coefficients.
Looking at the axioms of cohomology, the so called Eilenberg-Steenrod axioms,
the third axiom states that the cohomology of a point is trivial in all orders.
If this axiom is abandoned the point may have additional structure. This
phenomenon is not unusual in physics, a simple example for that being the spin
structure or even the point particle to string transition. I studied the
transition between different coefficients in cohomology and hence between
different structures of the geometrical point by means of the universal
coefficient theorem. The results may have impact on recent island calculations
of the black hole radiation entropy and the Page curve as well as on a
potential mapping of higher genus diagrams to genus one diagrams in the
topological expansion of string theory.


Abstract:
  In this work we study the issue of geodesic extendibility on complete and
locally compact metric length spaces. We focus on the geometric structure of
the space $(\Sigma (X),d_H)$ of compact balls endowed with the Hausdorff
distance and give an explicit isometry between $(\Sigma (X),d_H)$ and the
closed half-space $ X\times \mathbb{R}_{\ge 0}$ endowed with a taxicab metric.
Among the applications we establish a group isometry between $\mbox{Iso} (X,d)$
and $\mbox{Iso} (\Sigma (X),d_H)$ when $(X,d)$ is a Hadamard space.


Abstract:
  In this paper, we characterize the extremal digraphs with the maximal or
minimal $\alpha$-spectral radius among some digraph classes such as rose
digraphs, generalized theta digraphs and tri-ring digraphs with given size $m$.
These digraph classes are denoted by $\mathcal{R}_{m}^k$,
$\widetilde{\boldsymbol{\Theta}}_k(m)$ and $\INF(m)$ respectively. The main
results about spectral extremal digraph by Guo and Liu in \cite{MR2954483} and
Li and Wang in \cite{MR3777498} are generalized to $\alpha$-spectral graph
theory. As a by-product of our main results, an open problem in
\cite{MR3777498} is answered.
  Furthermore, we determine the digraphs with the first three minimal
$\alpha$-spectral radius among all strongly connected digraphs. Meanwhile, we
determine the unique digraph with the fourth minimal $\alpha$-spectral radius
among all strongly connected digraphs for $0\le \alpha \le \frac{1}{2}$.


Abstract:
  Physics-Informed Neural Networks (PINNs) have recently shown great promise as
a way of incorporating physics-based domain knowledge, including fundamental
governing equations, into neural network models for many complex engineering
systems. They have been particularly effective in the area of inverse problems,
where boundary conditions may be ill-defined, and data-absent scenarios, where
typical supervised learning approaches will fail. Here, we further explore the
use of this modeling methodology to surrogate modeling of a fluid dynamical
system, and demonstrate additional undiscussed and interesting advantages of
such a modeling methodology over conventional data-driven approaches: 1)
improving the model's predictive performance even with incomplete description
of the underlying physics; 2) improving the robustness of the model to noise in
the dataset; 3) reduced effort to convergence during optimization for a new,
previously unseen scenario by transfer optimization of a pre-existing model.
Hence, we noticed the inclusion of a physics-based regularization term can
substantially improve the equivalent data-driven surrogate model in many
substantive ways, including an order of magnitude improvement in test error
when the dataset is very noisy, and a 2-3x improvement when only partial
physics is included. In addition, we propose a novel transfer optimization
scheme for use in such surrogate modeling scenarios and demonstrate an
approximately 3x improvement in speed to convergence and an order of magnitude
improvement in predictive performance over conventional Xavier initialization
for training of new scenarios.


Abstract:
  An equation is derived for the energy of a small disturbance in a system that
is generated by a distribution function (DF) of the form $f(\vJ)$ -- most
galaxies and star clusters can be closely approximated by such a DF. The theory
of van Kampen modes is extended to such general systems. An inner product on
the space of DFs is defined such that the energy of a disturbance is its norm
under this product. It is shown that van Kampen modes that differ in frequency
are then orthogonal, with the consequence that the energies of van Kampen modes
are additive. Consequently, most of the insight into the dynamics of ergodic
systems that was gained in a recent paper on the van Kampen modes of ergodic
systems applies to real clusters and galaxies.


Abstract:
  We present self-supervised geometric perception (SGP), the first general
framework to learn a feature descriptor for correspondence matching without any
ground-truth geometric model labels (e.g., camera poses, rigid
transformations). Our first contribution is to formulate geometric perception
as an optimization problem that jointly optimizes the feature descriptor and
the geometric models given a large corpus of visual measurements (e.g., images,
point clouds). Under this optimization formulation, we show that two important
streams of research in vision, namely robust model fitting and deep feature
learning, correspond to optimizing one block of the unknown variables while
fixing the other block. This analysis naturally leads to our second
contribution -- the SGP algorithm that performs alternating minimization to
solve the joint optimization. SGP iteratively executes two meta-algorithms: a
teacher that performs robust model fitting given learned features to generate
geometric pseudo-labels, and a student that performs deep feature learning
under noisy supervision of the pseudo-labels. As a third contribution, we apply
SGP to two perception problems on large-scale real datasets, namely relative
camera pose estimation on MegaDepth and point cloud registration on 3DMatch. We
demonstrate that SGP achieves state-of-the-art performance that is on-par or
superior to the supervised oracles trained using ground-truth labels.


Abstract:
  While large-scale pretrained language models have significantly improved
writing assistance functionalities such as autocomplete, more complex and
controllable writing assistants have yet to be explored. We leverage advances
in language modeling to build an interactive writing assistant that generates
and rephrases text according to fine-grained author specifications. Users
provide input to our Intent-Guided Assistant (IGA) in the form of text
interspersed with tags that correspond to specific rhetorical directives (e.g.,
adding description or contrast, or rephrasing a particular sentence). We
fine-tune a language model on a dataset heuristically-labeled with author
intent, which allows IGA to fill in these tags with generated text that users
can subsequently edit to their liking. A series of automatic and crowdsourced
evaluations confirm the quality of IGA's generated outputs, while a small-scale
user study demonstrates author preference for IGA over baseline methods in a
creative writing task. We release our dataset, code, and demo to spur further
research into AI-assisted writing.


Abstract:
  This thesis work aims to study the possibility of energy production on
Martian soil and, in particular, to establish what might be an optimal
configuration for an energy system. This goal has been contextualized in the
will to feed a scientific base, based the concept of "Mars Direct" (Robert
Zubrin, 1990). This habitat has been recreated in its thermal features, in
order to perform an analysis of the heat loss over a Martian year (1,88
terrestrial years). As part of this analysis, two possible scenarios have been
studied: clear sky with medium solar radiation ("sun season") and sand storm
season ("storm season"). Subsequently, a basic life support system have been
simulated thanks to Aspen PLUS. Using the results of the thermal analysis, it
has been possible to obtain a thermal and electrical demand profile for the
Hab. After identifying every possible energy source (solar, wind, nuclear, fuel
cells, rtg), a calculation on Excel has been set with the purpose of finding
one of the configurations with the lowest possible mass and pave the way for a
further, more rigorous, optimization. It is indeed clear that shipping 1
kilogram to Mars has a cost of hundreds of thousand of dollars.


Abstract:
  We report the systematic analysis of knots, hotspots, and lobes in 57 active
galactic nuclei (AGNs) to investigate the variation of the magnetic field along
with the jet from the sub-pc base to the terminus in kpc-to-Mpc scales.
Expanding the number of radio/X-ray samples in Kataoka & Stawarz (2005), we
analyzed the data in 12 FR I and 30 FR II radio galaxies, 12 quasars, and 3 BL
Lacs that contained 76 knots, 42 hotspots, and 29 radio lobes. We first derived
the equipartition magnetic fields in the cores and then estimated those in
various jet components by assuming $B_{\rm est}$ $\propto$ $d^{-1}$, where $d$
is the distance from the jet base. On the other hand, the magnetic field in
large-scale jets (knots, hotspots, and lobes), $B_{\rm eq}$, can be estimated
from the observed flux and spatial extent under the equipartition hypothesis.
We show that the magnetic field decreases as the distance along the jet
increases, but generally gentler than $\propto d^{-1}$. The increase in $B_{\rm
eq}/B_{\rm est}$ at a larger $d$ may suggest the deceleration of the jet around
the downstream, but there is no difference between FR I and FR II jets.
Moreover, the magnetic fields in the hotspots are systematically larger than
those of knots and lobes. Finally, we applied the same analysis to knots and
lobes in Centaurus A to check whether the above discussion will hold even in a
single jet source.


Abstract:
  Federated learning (FL) is a promising approach to distributed compute, as
well as distributed data, and provides a level of privacy and compliance to
legal frameworks. This makes FL attractive for both consumer and healthcare
applications. While the area is actively being explored, few studies have
examined FL in the context of larger language models and there is a lack of
comprehensive reviews of robustness across tasks, architectures, numbers of
clients, and other relevant factors. In this paper, we explore the fine-tuning
of Transformer-based language models in a federated learning setting. We
evaluate three popular BERT-variants of different sizes (BERT, ALBERT, and
DistilBERT) on a number of text classification tasks such as sentiment analysis
and author identification. We perform an extensive sweep over the number of
clients, ranging up to 32, to evaluate the impact of distributed compute on
task performance in the federated averaging setting. While our findings suggest
that the large sizes of the evaluated models are not generally prohibitive to
federated training, we found that the different models handle federated
averaging to a varying degree. Most notably, DistilBERT converges significantly
slower with larger numbers of clients, and under some circumstances, even
collapses to chance level performance. Investigating this issue presents an
interesting perspective for future research.


Abstract:
  We investigate theoretically the Bose-Hubbard version of the celebrated
Su-Schrieffer-Heeger topological model, which essentially describes a
one-dimensional dimerized array of coupled oscillators with on-site
interactions. We study the physics arising from the whole gamut of possible
dimerizations of the chain, including both the weakly and the strongly
dimerized limiting cases. Focusing on two-excitation subspace, we
systematically uncover and characterize the different types of states which may
emerge due to the competition between the inter-oscillator couplings, the
intrinsic topology of the lattice, and the strength of the on-site
interactions. In particular, we discuss the formation of scattering bands full
of extended states, bound bands full of two-particle pairs (including so-called
`doublons', when the pair occupies the same lattice site), and different
flavors of topological edge states. The features we describe may be realized in
a plethora of systems, including nanoscale architectures such as photonic
cavities and optical lattices, and provide perspectives for topological
many-body physics.


Abstract:
  Periodic quasars have been suggested to host supermassive binary black holes
(BBHs) in their centers, and their optical/UV periodicities are interpreted as
caused by either the Doppler-boosting (DB) effect of continuum emission from
the disk around the secondary black hole (BH) or intrinsic accretion rate
variation. However, no other definitive evidence has been found to confirm such
a BBH interpretation(s). In this paper, we investigate the responses of broad
emission lines (BELs) to the continuum variations for these quasars under two
BBH scenarios, and check whether they can be distinguished from each other and
from that of a single BH system. We assume a simple circumbinary broad-line
region (BLR) model, compatible with BLR size estimates, with a standard
$\Gamma$ distribution of BLR clouds. We find that BELs may change significantly
and periodically under the BBH scenarios due to (1) the position variation of
the secondary BH and (2) the DB effect, if significant, and/or intrinsic
variation, which is significantly different from the case of a single BH
system. For the two BBH scenarios, the responses of BELs to (apparent)
continuum variations, caused by the DB effect or intrinsic rate variation, are
also significantly different from each other, mainly because the DB effect has
a preferred direction along the direction of motion of the secondary BH, while
that due to intrinsic variation does not. Such differences in the responses of
BELs from different scenarios may offer a robust way to distinguish different
interpretations of periodic quasars and to identify BBHs, if any, in these
systems.


Abstract:
  Due to the resource consumption for transmitting massive data and the concern
for exposing sensitive data, it is impossible or undesirable to upload clients'
local databases to a central server. Thus, federated learning has become a hot
research area in enabling the collaborative training of machine learning models
among multiple clients that hold sensitive local data. Nevertheless,
unconstrained federated optimization has been studied mainly using stochastic
gradient descent (SGD), which may converge slowly, and constrained federated
optimization, which is more challenging, has not been investigated so far. This
paper investigates sample-based and feature-based federated optimization,
respectively, and considers both the unconstrained problem and the constrained
problem for each of them. We propose federated learning algorithms using
stochastic successive convex approximation (SSCA) and mini-batch techniques. We
show that the proposed algorithms can preserve data privacy through the model
aggregation mechanism, and their security can be enhanced via additional
privacy mechanisms. We also show that the proposed algorithms converge to
Karush-Kuhn-Tucker (KKT) points of the respective federated optimization
problems. Besides, we customize the proposed algorithms to application examples
and show that all updates have closed-form expressions. Finally, numerical
experiments demonstrate the inherent advantages of the proposed algorithms in
convergence speeds, communication costs, and model specifications.


Abstract:
  We propose the first approach to automatically and jointly synthesize both
the synchronous 3D conversational body and hand gestures, as well as 3D face
and head animations, of a virtual character from speech input. Our algorithm
uses a CNN architecture that leverages the inherent correlation between facial
expression and hand gestures. Synthesis of conversational body gestures is a
multi-modal problem since many similar gestures can plausibly accompany the
same input speech. To synthesize plausible body gestures in this setting, we
train a Generative Adversarial Network (GAN) based model that measures the
plausibility of the generated sequences of 3D body motion when paired with the
input audio features. We also contribute a new way to create a large corpus of
more than 33 hours of annotated body, hand, and face data from in-the-wild
videos of talking people. To this end, we apply state-of-the-art monocular
approaches for 3D body and hand pose estimation as well as dense 3D face
performance capture to the video corpus. In this way, we can train on orders of
magnitude more data than previous algorithms that resort to complex in-studio
motion capture solutions, and thereby train more expressive synthesis
algorithms. Our experiments and user study show the state-of-the-art quality of
our speech-synthesized full 3D character animations.


Abstract:
  Er3+:Y2SiO5 is a material of particular interest due to its compatibility in
realizing telecom-band optical quantum memories and in the implementation of
quantum transducers interfacing optical communication with quantum computers
working in the microwave regime. Extending the coherence lifetimes of the
electron spins and the nuclear spins is the essential prerequisite for
implementing efficient quantum information processing. The electron spin
coherence time of this material is so far limited to several microseconds, and
there are significant challenges in optimizing coherence lifetimes
simultaneously for both the electron and nuclear spins. Here we perform to our
knowledge the first pulsed-ENDOR (Electron Nuclear DOuble Resonance)
investigation for an Er3+-doped material at sub-Kelvin temperatures, based on a
home-built sub-Kelvin pulsed ENDOR spectrometer. At the lowest working
temperature, the electron spin coherence time reaches 273 us, which is enhanced
by more than 40 times compared with the previous results. In the sub-Kelvin
regime, a rapid increase in the nuclear spin coherence time is observed, and
the longest coherence time of 738 us is obtained. These results are obtained
with the compatibility of fast and efficient operations, which establish the
foundation for quantum storage and quantum transduction from microwave to
optical frequencies at telecom C-band.


Abstract:
  The Gini index is a popular inequality measure with many applications in
social and economic studies. This paper studies semiparametric inference on the
Gini indices of two semicontinuous populations. We characterize the
distribution of each semicontinuous population by a mixture of a discrete point
mass at zero and a continuous skewed positive component. A semiparametric
density ratio model is then employed to link the positive components of the two
distributions. We propose the maximum empirical likelihood estimators of the
two Gini indices and their difference, and further investigate the asymptotic
properties of the proposed estimators. The asymptotic results enable us to
construct confidence intervals and perform hypothesis tests for the two Gini
indices and their difference. We show that the proposed estimators are more
efficient than the existing fully nonparametric estimators. The proposed
estimators and the asymptotic results are also applicable to cases without
excessive zero values. Simulation studies show the superiority of our proposed
method over existing methods. Two real-data applications are presented using
the proposed methods.


Abstract:
  The advent of AI and ML algorithms has led to opportunities as well as
challenges. In this paper, we provide an overview of bias and fairness issues
that arise with the use of ML algorithms. We describe the types and sources of
data bias, and discuss the nature of algorithmic unfairness. This is followed
by a review of fairness metrics in the literature, discussion of their
limitations, and a description of de-biasing (or mitigation) techniques in the
model life cycle.


Abstract:
  Aims. To explore the chemical pattern of early-type stars with planets,
searching for a possible signature of planet formation. In particular, we study
a likely relation between the lambda Bootis chemical pattern and the presence
of giant planets. Methods. We performed a detailed abundance determination in a
sample of early-type stars with and without planets via spectral synthesis.
Results. We compared the chemical pattern of the stars in our sample (13 stars
with planets and 24 stars without detected planets) with those of lambda Bootis
and other chemically peculiar stars. We have found four lambda Bootis stars in
our sample, two of which present planets and circumstellar disks (HR 8799 and
HD 169142) and one without planets detected (HD 110058). We have also
identified the first lambda Bootis star orbited by a brown dwarf (zeta Del).
This interesting pair lambda Bootis star + brown dwarf could help to test
stellar formation scenarios. We found no unique chemical pattern for the group
of early-type stars bearing giant planets. However, our results support, in
principle, a suggested scenario in which giant planets orbiting
pre-main-sequence stars possibly block the dust of the disk and result in a
lambda Bootis-like pattern. On the other hand, we do not find a lambda Bootis
pattern in different hot-Jupiter planet host stars, which do not support the
idea of possible accretion from the winds of hot-Jupiters, recently proposed in
the literature. Then, other mechanisms should account for the presence of the
lambda Bootis pattern between main-sequence stars. Finally, we suggest that the
formation of planets around lambda Bootis stars such as HR 8799 and HD 169142
is also possible through the core accretion process and not only gravitational
instability [abridged]


Abstract:
  The quest for Kitaev quantum spin liquids has led to great interest in
honeycomb quantum magnets with strong spin-orbit coupling. It has been recently
proposed that even Mott insulators with $3d$ transition metal ions, having
nominally weak spin-orbit coupling, can realize such exotic physics. Motivated
by this, we study the rhombohedral honeycomb cobaltates CoTiO$_3$,
BaCo$_2$(PO$_4$)$_2$, and BaCo$_2$(AsO$_4$)$_2$, using $\textit{ab initio}$
density functional theory, which takes into account realistic crystal field
distortions and chemical information, in conjunction with exact diagonalization
numerics. We show that these Co$^{2+}$ magnets host $j_\text{eff}=1/2$ local
moments with highly anisotropic $g$-factors, and we extract their full spin
Hamiltonians including longer-range and anisotropic exchange couplings. For
CoTiO$_3$, we find a nearest-neighbor easy-plane ferromagnetic $XXZ$ model with
additional bond-dependent anisotropies and interlayer exchange, which supports
three-dimensional (3D) Dirac nodal line magnons. In contrast, for
BaCo$_2$(PO$_4$)$_2$ and BaCo$_2$(AsO$_4$)$_2$, we find a strongly suppressed
interlayer coupling, and significant frustration from additional third-neighbor
antiferromagnetic exchange mediated by P/As. Such bond-anisotropic $J_1$-$J_3$
spin models can support collinear zig-zag or coplanar spiral ground states; we
discuss their dynamical spin correlations which reveal a gapped Goldstone mode,
and argue that the effective parameters of these pseudospin-$1/2$ models may be
strongly renormalized by coupling to a low energy spin-exciton. Our results
call for re-examining proposals for realizing Kitaev spin liquids in the
honeycomb cobaltates.


Abstract:
  Recently a number of empirical "universal" scaling law papers have been
published, most notably by OpenAI. `Scaling laws' refers to power-law decreases
of training or test error w.r.t. more data, larger neural networks, and/or more
compute. In this work we focus on scaling w.r.t. data size $n$. Theoretical
understanding of this phenomenon is largely lacking, except in
finite-dimensional models for which error typically decreases with $n^{-1/2}$
or $n^{-1}$, where $n$ is the sample size. We develop and theoretically analyse
the simplest possible (toy) model that can exhibit $n^{-\beta}$ learning curves
for arbitrary power $\beta>0$, and determine whether power laws are universal
or depend on the data distribution.


Abstract:
  Inland waterway transportation network significantly supports the overall
freight transportation of the nation. In order to ensure efficient and timely
commodity transportation through this network, this study aims at developing a
reliable inland waterway transportation network considering the interactions
between different transportation entities along with considering the uncertain
commodity supply and unpredictable waterway conditions over time. A
capacitated, multi-commodity, multi-period, stochastic, two-stage mixed-integer
linear programming (MILP) model is proposed to capture this stochastic, time
variant behavior of the water-depth along any link of the inland waterway under
consideration. Additionally, we proposed a parallelized hybrid decomposition
algorithm to solve the real-life test instances of this complex NP-hard
problem. The proposed algorithm is capable of producing high quality solutions
within a reasonable amount of time. Further, a case study is demonstrated for
the Southeast region of the United States and a number of managerial insights
are drawn that magnifies the impact of different key input parameters on the
overall inland waterway transportation network under consideration.


Abstract:
  We prove that a generic holomorphic foliation on a weighted projective plane
has no algebraic solutions when the degree is big enough. We also prove an
analogous result for foliations on Hirzebruch surfaces.


Abstract:
  We demonstrate theoretically that an off-resonant circularly polarized
electromagnetic field can induce a persistent current in carbon nanotubes,
which corresponds to electron rotation about the nanotube axis. As a
consequence, the nanotubes acquire magnetic moment along the axis, which
depends on their crystal structure and can be detected in state-of-the-art
measurements. This effect and related phenomena are analyzed within the
developed Floquet theory describing the electronic properties of the nanotubes
irradiated by the field.


Abstract:
  In the present paper, we study the inflationary phenomenology of a
$k$-inflation corrected Einstein-Gauss-Bonnet theory. Non-canonical kinetic
terms are known for producing Jean instabilities or superluminal sound wave
velocities in the aforementioned era, but we demonstrate in this work that by
adding Gauss-Bonnet string corrections and assuming that the non-canonical
kinetic term $\omega X^\gamma$ is in quadratic, one can obtain a ghost free
description. Demanding compatibility with the recent GW170817 event forces one
to accept that the relation $\ddot\xi=H\dot\xi$ for the scalar coupling
function $\xi (\phi)$. As a result, the scalar functions of the theory are
revealed to be interconnected and by assuming a specific form for one of them,
specifies immediately the other. Here, we shall assume that the scalar
potential is directly derivable from the equations of motion, once the
Gauss-Bonnet coupling is appropriately chosen, but obviously the opposite is
feasible as well. As a result, each term entering the equations of motion, can
be written in terms of the scalar field and a relatively tractable
phenomenology is produced. For quadratic kinetic terms, the resulting scalar
potential is quite elegant functionally. Different exponents, which lead to
either a more perplexed solution for the scalar potential, are still a
possibility which was not further studied. We also discuss in brief the
non-Gaussianities issue under the slow-roll and constant-roll conditions
holding true, and we demonstrate that the predicted amount of non-Gaussianities
is significantly enhanced in comparison to the $k$-inflation free
Einstein-Gauss-Bonnet theory.


Abstract:
  In the hydrodynamic model description of heavy ion collisions, the
final-state anisotropic flow $v_n$ are linearly related to the strength of the
multi-pole shape of the distribution of nucleons in the transverse plane
$\varepsilon_n$, $v_n\propto \varepsilon_n$. The $\varepsilon_n$, for
$n=1,2,3,4$, are sensitive to the shape of the colliding ions, characterized by
the quadrupole $\beta_2$, octupole $\beta_3$ and hexadecapole $\beta_4$
deformations. This sensitivity is investigated analytically and also in a Monte
Carlo Glauber model. One observes a robust linear relation,
$\langle\varepsilon_n^2\rangle = a_n'+b_n'\beta_n^2$, for events in a fixed
centrality. The $\langle\varepsilon_1^2\rangle$ has a contribution from
$\beta_3$ and $\beta_4$, and $\langle\varepsilon_3^2\rangle$ from $\beta_4$. In
the ultra-central collisions, there are little cross contributions between
$\beta_2$ and $\varepsilon_3$ and between $\beta_3$ and $\varepsilon_2$, but
clear cross contributions are present in non-central collisions. Additionally,
$\langle\varepsilon_n^2\rangle$ are insensitive to non-axial shape parameters
such as the triaxiality. This is good news because the measurements of $v_2$,
$v_3$ and $v_4$ can be used to constrain simultaneously the $\beta_2$,
$\beta_3$, and $\beta_4$ values. This is best done by comparing two colliding
ions with similar mass numbers and therefore nearly identical $a_n'$, to obtain
simple equation that relates the $\beta_n$ of the two species. This opens up
the possibility to map the shape of the atomic nuclei at a timescale
($<10^{-24}$s) much shorter than probed by low-energy nuclear structure physics
($<10^{-21}$s), which ultimately may provide information complementary to those
obtained in the nuclear structure experiments.


Abstract:
  The possibility of creating a UCN source on a periodic pulsed reactor is
considered. It is shown that the implementation of the time focusing principle
based on non-stationary neutron diffraction and the idea of the UCN trap pulse
filling allow creating a sufficiently intense UCN source in a moderate-power
pulsed reactor.


Abstract:
  Because loops execute their body many times, compiler developers place much
emphasis on their optimization. Nevertheless, in view of highly diverse source
code and hardware, compilers still struggle to produce optimal target code. The
sheer number of possible loop optimizations, including their combinations,
exacerbates the problem further. Today's compilers use hard-coded heuristics to
decide when, whether, and which of a limited set of optimizations to apply.
Often, this leads to highly unstable behavior, making the success of compiler
optimizations dependent on the precise way a loop has been written. This paper
presents LoopLearner, which addresses the problem of compiler instability by
predicting which way of writing a loop will lead to efficient compiled code. To
this end, we train a neural network to find semantically invariant source-level
transformations for loops that help the compiler generate more efficient code.
Our model learns to extract useful features from the raw source code and
predicts the speedup that a given transformation is likely to yield. We
evaluate LoopLearner with 1,895 loops from various performance-relevant
benchmarks. Applying the transformations that our model deems most favorable
prior to compilation yields an average speedup of 1.14x. When trying the top-3
suggested transformations, the average speedup even increases to 1.29x.
Comparing the approach with an exhaustive search through all available code
transformations shows that LoopLearner helps to identify the most beneficial
transformations in several orders of magnitude less time.


Abstract:
  Stateful and stateless web tracking gathered much attention in the last
decade, however they were always measured separately. To the best of our
knowledge, our study is the first to detect and measure cookie respawning with
browser and machine fingerprinting. We develop a detection methodology that
allows us to detect cookies dependency on browser and machine features. Our
results show that 1,150 out of the top 30, 000 Alexa websites deploy this
tracking mechanism. We further uncover how domains collaborate to respawn
cookies through fingerprinting. We find out that this technique can be used to
track users across websites even when third-party cookies are deprecated.
Together with a legal scholar, we conclude that cookie respawning with browser
fingerprinting lacks legal interpretation under the GDPR and the ePrivacy
directive, but its use in practice may breach them, thus subjecting it to fines
up to 20 million euro.


Abstract:
  We address the problem of performing backpropagation for computation graphs
involving 3D transformation groups SO(3), SE(3), and Sim(3). 3D transformation
groups are widely used in 3D vision and robotics, but they do not form vector
spaces and instead lie on smooth manifolds. The standard backpropagation
approach, which embeds 3D transformations in Euclidean spaces, suffers from
numerical difficulties. We introduce a new library, which exploits the group
structure of 3D transformations and performs backpropagation in the tangent
spaces of manifolds. We show that our approach is numerically more stable,
easier to implement, and beneficial to a diverse set of tasks. Our
plug-and-play PyTorch library is available at
https://github.com/princeton-vl/lietorch.


Abstract:
  Supersingular isogeny graphs are known to have very few loops and
multi-edges. We formalize this idea by studying and finding bounds for the
number of loops and multi-edges in such graphs. We also find conditions under
which the supersingular isogeny graph $\Lambda_p(\ell)$ is simple. The methods
presented in this paper can be used to study many kinds of collisions in
supersingular isogeny graphs. As an application, we introduce the notion of
bi-route number for two graphs $\Lambda_p(\ell_1),\Lambda_p(\ell_2)$ and
compute bounds for it. We also study the number of edges in common between the
graphs $\Lambda_p(\ell_1),\Lambda_p(\ell_2)$.


Abstract:
  Unlike the brain, artificial neural networks, including state-of-the-art deep
neural networks for computer vision, are subject to "catastrophic forgetting":
they rapidly forget the previous task when trained on a new one. Neuroscience
suggests that biological synapses avoid this issue through the process of
synaptic consolidation and metaplasticity: the plasticity itself changes upon
repeated synaptic events. In this work, we show that this concept of
metaplasticity can be transferred to a particular type of deep neural networks,
binarized neural networks, to reduce catastrophic forgetting.


Abstract:
  The small silicon chip of Schottky diode (0.8x0.8x0.4 mm3) with planar
arrangement of electrodes (chip PSD) as temperature sensor, which functions
under the operating conditions of pressure sensor, was developed. The forward
I-V characteristic of chip PSD is determined by potential barrier between Mo
and n-Si (ND = 3x1015 cm-3). Forward voltage UF = 208 mV and temperature
coefficient TC = -1.635 mV/C (with linearity kT <0.4% for temperature range of
-65 to +85 C) at supply current IF = 1 mA is achieved. The reverse I-V
characteristic has high breakdown voltage UBR > 85 V and low leakage current IL
< 5 {\mu}A at 25 C and IL < 130 {\mu}A at 85 C (UR = 20 V) because chip PSD
contains the structure of two p-type guard rings along the anode perimeter. The
application of PSD chip for wider temperature range from -65 to +115 C is
proved. The separate chip PSD of temperature sensor located at a distance of
less than 1.5 mm from the pressure sensor chip. The PSD chip transmits input
data for temperature compensation of pressure sensor errors by ASIC and for
direct temperature measurement.


Abstract:
  Building up on classical linear formulations, we posit that a broad class of
problems in signal synthesis and in signal recovery are reducible to the basic
task of finding a point in a closed convex subset of a Hilbert space that
satisfies a number of nonlinear equations involving firmly nonexpansive
operators. We investigate this formalism in the case when, due to inaccurate
modeling or perturbations, the nonlinear equations are inconsistent. A relaxed
formulation of the original problem is proposed in the form of a variational
inequality. The properties of the relaxed problem are investigated and a
provenly convergent block-iterative algorithm, whereby only blocks of the
underlying firmly nonexpansive operators are activated at a given iteration, is
devised to solve it. Numerical experiments illustrate robust recoveries in
several signal and image processing applications.


Abstract:
  We consider the novel task of learning disentangled representations of object
shape and appearance across multiple domains (e.g., dogs and cars). The goal is
to learn a generative model that learns an intermediate distribution, which
borrows a subset of properties from each domain, enabling the generation of
images that did not exist in any domain exclusively. This challenging problem
requires an accurate disentanglement of object shape, appearance, and
background from each domain, so that the appearance and shape factors from the
two domains can be interchanged. We augment an existing approach that can
disentangle factors within a single domain but struggles to do so across
domains. Our key technical contribution is to represent object appearance with
a differentiable histogram of visual features, and to optimize the generator so
that two images with the same latent appearance factor but different latent
shape factors produce similar histograms. On multiple multi-domain datasets, we
demonstrate our method leads to accurate and consistent appearance and shape
transfer across domains.


Abstract:
  We propose PHASER, a correspondence-free global registration of
sensor-centric pointclouds that is robust to noise, sparsity, and partial
overlaps. Our method can seamlessly handle multimodal information and does not
rely on keypoint nor descriptor preprocessing modules. By exploiting properties
of Fourier analysis, PHASER operates directly on the sensor's signal, fusing
the spectra of multiple channels and computing the 6-DoF transformation based
on correlation. Our registration pipeline starts by finding the most likely
rotation followed by computing the most likely translation. Both estimates are
distributed according to a probability distribution that takes the underlying
manifold into account, i.e., a Bingham and Gaussian distribution, respectively.
This further allows our approach to consider the periodic-nature of rotations
and naturally represent its uncertainty. We extensively compare PHASER against
several well-known registration algorithms on both simulated datasets, and
real-world data acquired using different sensor configurations. Our results
show that PHASER can globally align pointclouds in less than 100ms with an
average accuracy of 2cm and 0.5deg, is resilient against noise, and can handle
partial overlap.


Abstract:
  We develop a fully automatic Bayesian Lasso via variational inference. This
is a scalable procedure for approximating the posterior distribution. Special
attention is driven to the knot selection in regression spline. In order to
carry through our proposal, a full automatic variational Bayesian Lasso, a
Jefferey's prior is proposed for the hyperparameters and a decision theoretical
approach is introduced to decide if a knot is selected or not. Extensive
simulation studies were developed to ensure the effectiveness of the proposed
algorithms. The performance of the algorithms were also tested in some real
data sets, including data from the world pandemic Covid-19. Again, the
algorithms showed a very good performance in capturing the data structure.


Abstract:
  In this work we revisit the steady state, spherically symmetric gas accretion
problem from the non-relativistic regime to the ultra-relativistic one. We
first perform a detailed comparison between the Bondi and Michel models, and
show how the mass accretion rate in the Michel solution approaches a constant
value as the fluid temperature increases, whereas the corresponding Bondi value
continually decreases, the difference between these two predicted values
becoming arbitrarily large at ultra-relativistic temperatures. Additionally, we
extend the Michel solution to the case of a fluid with an equation of state
corresponding to a monoatomic, relativistic gas. Finally, using general
relativistic hydrodynamic simulations, we study spherical accretion onto a
rotating black hole, exploring the influence of the black hole spin on the mass
accretion rate, the flow morphology and characteristics, and the sonic surface.
The effect of the black hole spin becomes more significant as the gas
temperature increases and as the adiabatic index $\gamma$ stiffens. For an
ideal gas in the ultra-relativistic limit ($\gamma=4/3$), we find a reduction
of 10 per cent in the mass accretion rate for a maximally rotating black hole
as compared to a non-rotating one, while this reduction is of up to 50 per cent
for a stiff fluid ($\gamma=2$).


Abstract:
  K-Means++ and its distributed variant K-Means$\|$ have become de facto tools
for selecting the initial seeds of K-means. While alternatives have been
developed, the effectiveness, ease of implementation, and theoretical grounding
of the K-means++ and $\|$ methods have made them difficult to "best" from a
holistic perspective. By considering the limited opportunities within seed
selection to perform pruning, we develop specialized triangle inequality
pruning strategies and a dynamic priority queue to show the first acceleration
of K-Means++ and K-Means$\|$ that is faster in run-time while being
algorithmicly equivalent. For both algorithms we are able to reduce distance
computations by over $500\times$. For K-means++ this results in up to a
17$\times$ speedup in run-time and a $551\times$ speedup for K-means$\|$. We
achieve this with simple, but carefully chosen, modifications to known
techniques which makes it easy to integrate our approach into existing
implementations of these algorithms.


Abstract:
  We investigate the stellar populations for a sample of 161 massive, mainly
quiescent galaxies at $\langle z_{\rm obs} \rangle=0.8$ with deep Keck/DEIMOS
rest-frame optical spectroscopy (HALO7D survey). With the fully Bayesian
framework Prospector, we simultaneously fit the spectroscopic and photometric
data with an advanced physical model (including non-parametric star-formation
histories, emission lines, variable dust attenuation law, and dust and AGN
emission) together with an uncertainty and outlier model. We show that both
spectroscopy and photometry are needed to break the dust-age-metallicity
degeneracy. We find a large diversity of star-formation histories: although the
most massive ($M_{\star}>2\times10^{11}~M_{\odot}$) galaxies formed the
earliest (formation redshift of $z_{\rm f}\approx5-10$ with a short
star-formation timescale of $\tau_{\rm SF}\lesssim1~\mathrm{Gyr}$), lower-mass
galaxies have a wide range of formation redshifts, leading to only a weak trend
of $z_{\rm f}$ with $M_{\star}$. Interestingly, several low-mass galaxies with
have formation redshifts of $z_{\rm f}\approx5-8$. Star-forming galaxies evolve
about the star-forming main sequence, crossing the ridgeline several times in
their past. Quiescent galaxies show a wide range and continuous distribution of
quenching timescales ($\tau_{\rm quench}\approx0-5~\mathrm{Gyr}$) with a median
of $\langle\tau_{\rm quench}\rangle=1.0_{-0.9}^{+0.8}~\mathrm{Gyr}$ and of
quenching epochs of $z_{\rm quench}\approx0.8-5.0$ ($\langle z_{\rm
quench}\rangle=1.3_{-0.4}^{+0.7}$). This large diversity of quenching
timescales and epochs points toward a combination of internal and external
quenching mechanisms. In our sample, rejuvenation and "late bloomers" are
uncommon. In summary, our analysis supports the "grow & quench" framework and
is consistent with a wide and continuously-populated diversity of quenching
timescales.


Abstract:
  We study the quantum phase transitions of frustrated antiferromagnetic
Heisenberg spin-1 systems on the 3/4 and 3/5 skewed two leg ladder geometries.
These systems can be viewed as arising by periodically removing rung bonds from
a zigzag ladder. We find that in large systems, the ground state (gs) of the
3/4 ladder switches from a singlet to a magnetic state for $J_1 \ge 1.82$; the
gs spin corresponds to ferromagnetic alignment of effective $S = 2$ objects on
each unit cell. The gs of antiferromagnetic exchange Heisenberg spin-1 system
on a 3/5 skewed ladder is highly frustrated and has spiral spin arrangements.
The amplitude of the spin density wave in the 3/5 ladder is significantly
larger compared to that in the magnetic state of the 3/4 ladder. The gs of the
system switches between singlet state and low spin magnetic states multiple
times on tuning $J_1$ in a finite size system. The switching pattern is
nonmonotonic as a function of $J_1$, and depends on the system size. It appears
to be the consequence of higher $J_1$ favoring higher spin magnetic state and
the finite system favoring a standing spin wave. For some specific parameter
values, the magnetic gs in the 3/5 system is doubly degenerate in two different
mirror symmetry subspaces. This degeneracy leads to spontaneous spin parity and
mirror symmetry breaking giving rise to spin current in the gs of the system.


Abstract:
  A new ternary intermetallic compound $\mathrm{Pr_2Rh_2Ga}$ was synthesized by
arc-melting and was characterized by powder X-ray diffraction (PXRD),
magnetization, heat capacity $\mathrm{C}_p(\textit{T})$, and electrical
resistivity $\rho(T)$ measurements. PXRD patterns revealed that
$\mathrm{Pr_2Rh_2Ga}$ crystallizes in the $\rm{La_2Ni_3}$-type of orthorhombic
structure with the space group $Cmca$. The temperature variation of magnetic
susceptibility, $\mathrm {C}_p(\textit{T})$ and $\rho(T)$ confirmed that
$\mathrm{Pr_2Rh_2Ga}$ exhibits a ferromagnetic behavior with the transition
temperature of 18 K. The estimated Sommerfeld coefficient $\gamma$ = 640
mJ/($\mathrm{Pr.mole.K^2}$) from the $\mathrm {C}_p(\textit{T})$ results in the
paramagnetic region just above $T_{C}$~was large in comparison to ordinary
metals. In the paramagnetic region $\rho(T)$ data showed a metallic behavior
characteristic of electron - phonon scattering. The maximum negative
magneto-resistance at high field occurs in the region near the magnetic phase
transition temperature. The maximum value of magnetic entropy change ($\rm
-\Delta \textit{S}_{M}$) and adiabatic temperature change ($\rm \Delta
\textit{T}_{ad}$) are $\rm8.2~J/kg.K$ and $\rm3.6~K$, respectively, around the
transition temperature for the change of magnetic field 0-9 T. The calculated
refrigerant capacity is $\rm70~J/kg$, and $\rm135~J/kg$ for a change of
magnetic field 0-5 T and 0-9 T, respectively. Arrott plot derived from
isothermal magnetization and the universal scaling plot by normalizing $\rm
-\Delta \textit{S}_{M}$ confirm that the compound undergoes a second order
ferromagnetic to paramagnetic phase transition.


Abstract:
  We remark on the use of regularized Stokeslets in the slender body theory
(SBT) approximation of Stokes flow about a thin fiber of radius $\epsilon>0$.
Denoting the regularization parameter by $\delta$, we consider regularized SBT
based on the most common regularized Stokeslet plus a regularized doublet
correction. Given sufficiently smooth force data along the filament, we derive
$L^\infty$ bounds for the difference between regularized SBT and its classical
counterpart in terms of $\delta$, $\epsilon$, and the force data. We show that
the regularized and classical expressions for the velocity of the filament
itself differ by a term proportional to $\log(\delta/\epsilon)$ -- in
particular, $\delta=\epsilon$ is necessary to avoid an $O(1)$ discrepancy
between the theories. However, the flow at the surface of the fiber differs by
an expression proportional to $\log(1+\delta^2/\epsilon^2)$, and any choice of
$\delta\propto\epsilon$ will result in an $O(1)$ discrepancy as $\epsilon\to
0$. Consequently, the flow around a slender fiber due to regularized SBT does
not converge to the solution of the well-posed \emph{slender body PDE} which
classical SBT is known to approximate. Numerics verify this $O(1)$ discrepancy
but also indicate that the difference may have little impact in practice.


Abstract:
  In the multicentric calculus one takes a polynomial with simple roots as a
new global variable and replaces scalar functions {\varphi} by functions f
taking values in C^d with d the degree of the polynomial leading to an
efficient holomorphic functional calculus for bounded operators. This calculus
was extended for non-holomorphic functions, so that if A is not diagonalizable
one can find a p such that p(A) is diagonalizable and apply the calculus to all
matrices. This was done in [7] by creating a Banach algebra for C^d-valued
continuous functions in such a way that the original functions {\varphi} appear
as Gelfand transforms of f. In this paper we consider constructing a Banach
algebra for functions from C^2 into C^(d_1 x d_2) which then likewise leads to
a functional calculus for commuting pairs of matrices.


Abstract:
  This paper addresses the general problem of designing ordinal classification
methods based on comparing actions with limiting boundaries of ordered classes
(categories). The fundamental requirement of the method consists of setting a
relational system (D,S), where S and D are reflexive and transitive relations,
respectively, S should be compatible with the order of the set of classes, and
D is a subset of S. An asymmetric preference relation P is defined from S.
Other requirements are imposed on the actions which compose the limiting
boundaries between adjacent classes, in such a way that each class is closed
below and above. The paper proposes S-based and P-based assignment procedures.
Each of them is composed of two complementary assignment procedures, which
correspond through the transposition operation and should be used conjointly.
The methods work under several basic conditions on the set of limiting
boundaries. Under other more demanding separability requirements, each
procedure fulfills the set of structural properties established for other
outranking-based ordinal classification methods. Our proposal avoids the
conflict between the required correspondence through the transposition
operation and the assignment of limiting actions to the classes to which they
belong. We thus propose very diverse S and P-based ordinal classification
approaches with desirable properties, which can be designed by using decision
models with the capacity to build preference relations fulfilling the basic
requirements to S and D.


Abstract:
  Lane formation in bidirectional pedestrian streams is based on a
stimulus-response mechanism and strategies of navigation in a fast-changing
environment. Although microscopic models that only guarantee volume exclusion
can qualitatively reproduce this phenomenon, they are not sufficient for a
quantitative description. To quantitatively describe this phenomenon, a minimal
anticipatory collision-free velocity model is introduced. Compared to the
original velocity model, the new model reduces the occurrence of gridlocks and
reproduces the movement of pedestrians more realistically. For a quantitative
description of the phenomenon, the definition of an order parameter is used to
describe the formation of lanes at transient states and to show that the
proposed model compares relatively well with experimental data. Furthermore,
the model is validated by the experimental fundamental diagrams of
bidirectional flows.


Abstract:
  We carried out a search for unknown dwarf novae in a public data release of
the Zwicky Transient Facility survey and suspected that the object ZTF18abdlzhd
is a SU UMa-type star. Performed multicolor CCD observations permit us to
follow its fading from an outburst in August and an entire superoutburst in
October 2020. The duration of the superoutburst is 13 days. We detected
superhumps with period P = 0.06918(3) d that are characteristic of UGSU type
stars.


Abstract:
  We study the strong constraining problem in Lagrangian dynamics in the
degenerate codimension one case. This is the first time that degenerate
potentials at the constraint are considered for this problem. These new results
cover several real analytic potentials that the previous do not. Some
counterintuitive effects of the degenerate constraining problem are discussed.


Abstract:
  To any Frobenius superalgebra $A$ we associate an oriented Frobenius Brauer
category and an affine oriented Frobenius Brauer categeory. We define natural
actions of these categories on categories of supermodules for general linear
Lie superalgebras $\mathfrak{gl}_{m|n}(A)$ with entries in $A$. These actions
generalize those on module categories for general linear Lie superalgebras and
queer Lie superalgebras, which correspond to the cases where $A$ is the ground
field and the two-dimensional Clifford algebra, respectively.


Abstract:
  We show that it is possible to realize a cosmological bouncing solution in an
anisotropic but homogeneous Bianchi-I background in a class of non-local,
infinite derivative theories of gravity. We show that the anisotropic shear
grows slower than in general relativity during the contraction phase, peaks to
a finite value at the bounce point, and then decreases as the universe
asymptotes towards isotropy and homogeneity, and ultimately to de Sitter. Along
with a cosmological constant, the matter sector required to drive such a bounce
is found to consist of three components - radiation, stiff matter and
$k$-matter (whose energy density decays like the inverse square of the average
scale factor). Generically, $k$-matter exerts anisotropic pressures. We will
test the bouncing solution in local and non-local gravity and show that in the
latter case it is possible to simultaneously satisfy positivity of energy
density and, at least in the late time de Sitter phase, avoid the introduction
of propagating ghost/tachyonic modes.


Abstract:
  Objective: Neural network de-identification studies have focused on
individual datasets. These studies assume the availability of a sufficient
amount of human-annotated data to train models that can generalize to
corresponding test data. In real-world situations, however, researchers often
have limited or no in-house training data. Existing systems and external data
can help jump-start de-identification on in-house data; however, the most
efficient way of utilizing existing systems and external data is unclear. This
article investigates the transferability of a state-of-the-art neural clinical
de-identification system, NeuroNER, across a variety of datasets, when it is
modified architecturally for domain generalization and when it is trained
strategically for domain transfer. Methods and Materials: We conducted a
comparative study of the transferability of NeuroNER using four clinical note
corpora with multiple note types from two institutions. We modified NeuroNER
architecturally to integrate two types of domain generalization approaches. We
evaluated each architecture using three training strategies. We measured:
transferability from external sources; transferability across note types; the
contribution of external source data when in-domain training data are
available; and transferability across institutions. Results and Conclusions:
Transferability from a single external source gave inconsistent results. Using
additional external sources consistently yielded an F1-score of approximately
80%. Fine-tuning emerged as a dominant transfer strategy, with or without
domain generalization. We also found that external sources were useful even in
cases where in-domain training data were available. Transferability across
institutions differed by note type and annotation label but resulted in
improved performance.


Abstract:
  Parametric 3D models have enabled a wide variety of tasks in computer
graphics and vision, such as modeling human bodies, faces, and hands. However,
the construction of these parametric models is often tedious, as it requires
heavy manual tweaking, and they struggle to represent additional complexity and
details such as wrinkles or clothing. To this end, we propose Neural Parametric
Models (NPMs), a novel, learned alternative to traditional, parametric 3D
models, which does not require hand-crafted, object-specific constraints. In
particular, we learn to disentangle 4D dynamics into latent-space
representations of shape and pose, leveraging the flexibility of recent
developments in learned implicit functions. Crucially, once learned, our neural
parametric models of shape and pose enable optimization over the learned spaces
to fit to new observations, similar to the fitting of a traditional parametric
model, e.g., SMPL. This enables NPMs to achieve a significantly more accurate
and detailed representation of observed deformable sequences. We show that NPMs
improve notably over both parametric and non-parametric state of the art in
reconstruction and tracking of monocular depth sequences of clothed humans and
hands. Latent-space interpolation as well as shape/pose transfer experiments
further demonstrate the usefulness of NPMs. Code is publicly available at
https://pablopalafox.github.io/npms.


Abstract:
  Multi-agent imitation learning aims to train multiple agents to perform tasks
from demonstrations by learning a mapping between observations and actions,
which is essential for understanding physical, social, and team-play systems.
However, most existing works on modeling multi-agent interactions typically
assume that agents make independent decisions based on their observations,
ignoring the complex dependence among agents. In this paper, we propose to use
copula, a powerful statistical tool for capturing dependence among random
variables, to explicitly model the correlation and coordination in multi-agent
systems. Our proposed model is able to separately learn marginals that capture
the local behavioral patterns of each individual agent, as well as a copula
function that solely and fully captures the dependence structure among agents.
Extensive experiments on synthetic and real-world datasets show that our model
outperforms state-of-the-art baselines across various scenarios in the action
prediction task, and is able to generate new trajectories close to expert
demonstrations.


Abstract:
  Reed-Muller (RM) codes are conjectured to achieve the capacity of any
binary-input memoryless symmetric (BMS) channel, and are observed to have a
comparable performance to that of random codes in terms of scaling laws. On the
negative side, RM codes lack efficient decoders with performance close to that
of a maximum likelihood decoder for general parameters. Also, they only admit
certain discrete sets of rates. In this paper, we focus on subcodes of RM codes
with flexible rates that can take any code dimension from 1 to n, where n is
the blocklength. We first extend the recursive projection-aggregation (RPA)
algorithm proposed recently by Ye and Abbe for decoding RM codes. To lower the
complexity of our decoding algorithm, referred to as subRPA in this paper, we
investigate different ways for pruning the projections. We then derive the
soft-decision based version of our algorithm, called soft-subRPA, that is shown
to improve upon the performance of subRPA. Furthermore, it enables training a
machine learning (ML) model to search for \textit{good} sets of projections in
the sense of minimizing the decoding error rate. Training our ML model enables
achieving very close to the performance of full-projection decoding with a
significantly reduced number of projections. For instance, our simulation
results on a (64,14) RM subcode show almost identical performance for
full-projection decoding and pruned-projection decoding with 15 projections
picked via training our ML model. This is equivalent to lowering the complexity
by a factor of more than 4 without sacrificing the decoding performance.


Abstract:
  In 1999 Khabibullin established the best estimate in Paley problem for a
plurisubharmonic function $u$ of finite lower order $0\leq\rho\leq 1$. For
$\rho>1$ obtaining a sharp estimate has remained an open question. In this work
we solve this problem. We also provide some estimates for the types of the
characteristic functions $T(r,u)$ and $M(r,u)$.


Abstract:
  Depth maps obtained by commercial depth sensors are always in low-resolution,
making it difficult to be used in various computer vision tasks. Thus, depth
map super-resolution (SR) is a practical and valuable task, which upscales the
depth map into high-resolution (HR) space. However, limited by the lack of
real-world paired low-resolution (LR) and HR depth maps, most existing methods
use downsampling to obtain paired training samples. To this end, we first
construct a large-scale dataset named "RGB-D-D", which can greatly promote the
study of depth map SR and even more depth-related real-world tasks. The "D-D"
in our dataset represents the paired LR and HR depth maps captured from mobile
phone and Lucid Helios respectively ranging from indoor scenes to challenging
outdoor scenes. Besides, we provide a fast depth map super-resolution (FDSR)
baseline, in which the high-frequency component adaptively decomposed from RGB
image to guide the depth map SR. Extensive experiments on existing public
datasets demonstrate the effectiveness and efficiency of our network compared
with the state-of-the-art methods. Moreover, for the real-world LR depth maps,
our algorithm can produce more accurate HR depth maps with clearer boundaries
and to some extent correct the depth value errors.


Abstract:
  In this work, new finite difference schemes are presented for dealing with
the upper-convected time derivative in the context of the generalized Lie
derivative. The upper-convected time derivative, which is usually encountered
in the constitutive equation of the popular viscoelastic models, is
reformulated in order to obtain approximations of second-order in time for
solving a simplified constitutive equation in one and two dimensions. The
theoretical analysis of the truncation errors of the methods takes into account
the linear and quadratic interpolation operators based on a Lagrangian
framework. Numerical experiments illustrating the theoretical results for the
model equation defined in one and two dimensions are included. Finally, the
finite difference approximations of second-order in time are also applied for
solving a two-dimensional Oldroyd-B constitutive equation subjected to a
prescribed velocity field at different Weissenberg numbers.


Abstract:
  A cycle $C$ in a graph $G$ is called a Tutte cycle if, after deleting $C$
from $G$, each component has at most three neighbors on $C$. Tutte cycles play
an important role in the study of Hamiltonicity of planar graphs. Thomas and Yu
and independently Sanders proved the existence of Tutte cycles containining
three specified edges of a facial cycle in a 2-connected plane graph. We prove
a quantitative version of this result, bounding the number of components of the
graph obtained by deleting a Tutte cycle. As a corollary, we can find long
cycles in essentially 4-connected plane graphs that also contain three
prescribed edges of a facial cycle.


Abstract:
  Dynamic and steady state aspects of wave propagation are deeply connected in
lossless open systems in which the scattering matrix is unitary. There is then
an equivalence among the energy excited within the medium, the Wigner time
delay, and the density of states. But these equivalences fall away in the
presence of material loss or gain. In this paper, we use microwave
measurements, numerical simulations, and theoretical analysis to discover the
changing relationships among the internal field, transmission, transmission
time, dwell time, total excited energy, and the density of states in with loss
and gain, and their dependence upon dimensionality and spectral overlap. The
total energy excited is equal to the dwell time. In 1D, the average
transmission time is independent of loss, gain, and scattering strength. In
higher dimensions, however, the average transmission time falls with loss,
scattering strength and channel number, and increases with gain. It is the sum
over Lorentzian functions associated with the zeros as well as the poles of the
transmission matrix. This endows transmission zeros with robust topological
characteristics: in unitary media, zeros occur either singly on the real axis
or as conjugate pairs in the complex frequency plane. In weakly absorbing
media, the spectrum of the transmission time of the lowest transmission
eigenchannel is the sum of Lorentzians due to transmission zeros plus a
background due to far-off-resonance poles. The average density of transmission
zeros in the complex plane is found from the fall of the average transmission
time with absorption as zeros are swept into the lower half of the complex
frequency plane. We show that the disposition of poles and zeros in the complex
frequency plane provides a framework for understanding and controlling wave
propagation in non-Hermitian systems.


Abstract:
  The relaxation dynamics of hot carriers in silicon (100) is studied via a
novel holistic approach based on phase-resolved transient absorption
spectroscopy with few-cycle optical pulses. After excitation by a sub-5 fs
light pulse, strong electron-phonon coupling leads to an ultrafast momentum
relaxation with time constant of 10 fs. The thermalization of the hot carriers
occurs on a time constant of 150 fs, visible in the temporal evolution of the
collision time as extracted from the Drude model. We find an increase of the
collision time from 3 fs for the shortest timescales with a saturation at
approximately 18 fs. Moreover, the optical effective mass of the hot carrier
ensemble evolves on ultrafast timescales as well, with a bi-exponential
decrease from 0.7 $m_e$ to about 0.125 $m_e$ and time constants of 4 fs and 58
fs. The presented information on the electron mass dynamics as well as the
momentum-, energy-, and collision-scattering times with unprecedented time
resolution is important for all hot carrier optoelectronic devices.


Abstract:
  The Semi Symmetric Nonnegative Factorization is a factorization of an $n
\times n$ nonnegative symmetric matrix $A$ of the form $BCB^T$, where $C$ is a
$k \times k$ symmetric matrix, and both $B$ and $C$ are required to be
nonnegative. The minimal $k$ for which such factorization exists is called the
SSNF-rank of $A$. In this work, basic properties of the SSNF-rank are given,
SSNF-rank of low rank matrices is explored, and selected constructions of
matrices that control the SSNF-rank are presented.


Abstract:
  We study the effect of reaction times on the kinetics of relaxation to
stationary states and on congestion transitions in heterogeneous traffic.
Heterogeneity is modeled as quenched disorders in the parameters of the car
following model and in the reaction times of the drivers. We observed that at
low densities, the relaxation to stationary state from a homogeneous initial
state is governed by the same power laws as derived by E. Ben-Naim et al.,
Kinetics of clustering in traffic flow, Phys. Rev. E 50, 822 (1994). The
stationary state, at low densities, is a single giant platoon of vehicles with
the slowest vehicle being the leader. We observed formation of spontaneous jams
inside the giant platoon which move upstream as stop-go waves and dissipate at
its tail. The transition happens when the head of the giant platoon interacts
with its tail, stable stop-go waves form, which circulate in the ring without
dissipating. We observed that the system behaves differently when the
transition density is approached from above that it does when approached from
below. When the transition density is approached from below, the gap
distribution behind the leader has a double peak and is fat-tailed but has a
bounded support and thus the maximum gap in the system and the variance of the
gap distribution tend to size-independent values. When the transition density
is approached from above, the gap distribution becomes a power law and,
consequently, the maximum gap in the system and the variance in the gaps
diverge as a power law, thereby creating a discontinuity at the transition.
Thus, we observe a phase transition of unusual kind in which both a
discontinuity and a power law are observed at the transition density. These
unusual features vanish in the absence of reaction time (e.g., automated
driving).


Abstract:
  We study acceleration phenomena in monostable integro-differential equations
with ignition nonlinearity. Our results cover fractional Laplace operators and
standard convolutions in a unified way, which is also a contribution of this
paper. To achieve this, we construct a sub-solution that captures the expected
dynamics of the accelerating solution, and this is here the main difficulty.
This study involves the flattening effect occurring in accelerated propagation
phenomena.


Abstract:
  We show that the difference between the topological 4-genus of a knot and the
minimal genus of a surface bounded by that knot that can be decomposed into a
smooth concordance followed by an algebraically simple locally flat surface can
be arbitrarily large. This extends work of Hedden-Livingston-Ruberman showing
that there are topologically slice knots which are not smoothly concordant to
any knot with trivial Alexander polynomial.


Abstract:
  During daily activities, humans use their hands to grasp surrounding objects
and perceive sensory information which are also employed for perceptual and
motor goals. Multiple cortical brain regions are known to be responsible for
sensory recognition, perception and motor execution during sensorimotor
processing. While various research studies particularly focus on the domain of
human sensorimotor control, the relation and processing between motor execution
and sensory processing is not yet fully understood. Main goal of our work is to
discriminate textured surfaces varying in their roughness levels during active
tactile exploration using simultaneously recorded electroencephalogram (EEG)
data, while minimizing the variance of distinct motor exploration movement
patterns. We perform an experimental study with eight healthy participants who
were instructed to use the tip of their dominant hand index finger while
rubbing or tapping three different textured surfaces with varying levels of
roughness. We use an adversarial invariant representation learning neural
network architecture that performs EEG-based classification of different
textured surfaces, while simultaneously minimizing the discriminability of
motor movement conditions (i.e., rub or tap). Results show that the proposed
approach can discriminate between three different textured surfaces with
accuracies up to 70%, while suppressing movement related variability from
learned representations.


Abstract:
  Many-body localization (MBL) is well characterized in Fock space. To quantify
the degree of this Fock space localization, the multifractal dimension $D_q$ is
employed; it has been claimed that $D_q$ shows a jump from the delocalized
value $D_q=1$ in the ETH phase (ETH: eigenstate thermalization hypothesis) to a
smaller value $0<D_q<1$ at the ETH-MBL transition, yet exhibiting a conspicuous
discrepancy from the fully localized value $D_q=0$, which indicate that
multifractality remains inside the MBL phase. Here, to better quantify the
situation we employ, instead of the commonly used computational basis, the
one-particle density matrix (OPDM) and use its eigenstates (natural orbitals)
as a Fock state basis for representing many-body eigenstates $|\psi\rangle$ of
the system. Using this basis, we compute $D_q$ and other indices quantifying
the Fock space localization, such as the local purity $S$, which is derived
from the occupation spectrum $\{n_\alpha\}$ (eigenvalues of the OPDM). We
highlight the statistical distribution of Hamming distance $x_{\mu\nu}$
occurring in the pair-wise coefficients $|a_\mu|^2|a_\nu|^2$ in $S$, and
compare this with a related quantity considered in the literature.


Abstract:
  We investigate the nonexistence and existence of nontrivial positive
solutions to $\Delta_m u+u^p|\nabla u|^q\leq0$ on noncompact geodesically
complete Riemannian manifolds, where $m>1$, and $(p,q)\in \mathbb{R}^2$.
According to classification of $(p, q)$, we establish different volume growth
conditions to obtain Liouville's theorems for the above quasilinear
differential inequalities, and we also show these volume growth conditions are
sharp in most cases. Moreover, the results are completely new for $(p, q)$ of
negative pair, even in the Euclidean space.


Abstract:
  Recent architectural approaches that address speculative side-channel attacks
aim to prevent software from exposing the microarchitectural state changes of
transient execution. The Delay-on-Miss technique is one such approach, which
simply delays loads that miss in the L1 cache until they become
non-speculative, resulting in no transient changes in the memory hierarchy.
However, this costs performance, prompting the use of value prediction (VP) to
regain some of the delay. However, the problem cannot be solved by simply
introducing a new kind of speculation (value prediction). Value-predicted loads
have to be validated, which cannot be commenced until the load becomes
non-speculative. Thus, value-predicted loads occupy the same amount of precious
core resources (e.g., reorder buffer entries) as Delay-on-Miss. The end result
is that VP only yields marginal benefits over Delay-on-Miss. In this paper, our
insight is that we can achieve the same goal as VP (increasing performance by
providing the value of loads that miss) without incurring its negative
side-effect (delaying the release of precious resources), if we can safely,
non-speculatively, recompute a value in isolation (without being seen from the
outside), so that we do not expose any information by transferring such a value
via the memory hierarchy. Value Recomputation, which trades computation for
data transfer was previously proposed in an entirely different context: to
reduce energy-expensive data transfers in the memory hierarchy. In this paper,
we demonstrate the potential of value recomputation in relation to the
Delay-on-Miss approach of hiding speculation, discuss the trade-offs, and show
that we can achieve the same level of security, reaching 93% of the unsecured
baseline performance (5% higher than Delay-on-miss), and exceeding (by 3%) what
even an oracular (100% accuracy and coverage) value predictor could do.


Abstract:
  Giant exoplanets on wide orbits have been directly imaged around young stars.
If the thermal background in the mid-infrared can be mitigated, then exoplanets
with lower masses can also be imaged. Here we present a ground-based
mid-infrared observing approach that enables imaging low-mass temperate
exoplanets around nearby stars, and in particular within the closest stellar
system, Alpha Centauri. Based on 75-80% of the best quality images from 100
hours of cumulative observations, we demonstrate sensitivity to warm
sub-Neptune-sized planets throughout much of the habitable zone of Alpha
Centauri A. This is an order of magnitude more sensitive than state-of-the-art
exoplanet imaging mass detection limits. We also discuss a possible exoplanet
or exozodiacal disk detection around Alpha Centauri A. However, an instrumental
artifact of unknown origin cannot be ruled out. These results demonstrate the
feasibility of imaging rocky habitable-zone exoplanets with current and
upcoming telescopes.


Abstract:
  We describe a quantum-assisted machine learning (QAML) method in which
multivariate data is encoded into quantum states in a Hilbert space whose
dimension is exponentially large in the length of the data vector. Learning in
this space occurs through applying a low-depth quantum circuit with a tree
tensor network (TTN) topology, which acts as an unsupervised feature extractor
to identify the most relevant quantum states in a data-driven fashion. This
unsupervised feature extractor then feeds a supervised linear classifier and
encodes the output in a small-dimensional quantum register. In contrast to
previous work on \emph{quantum-inspired} TTN classifiers, in which the
embedding map and class decision weights did not map the data to well-defined
quantum states, we present an approach that can be implemented on gate-based
quantum computing devices. In particular, we identify an embedding map with
accuracy similar to exponential machines (Novikov \emph{et al.},
arXiv:1605.03795), but which produces valid quantum states from classical data
vectors, and utilize manifold-based gradient optimization schemes to produce
isometric operations mapping quantum states to a register of qubits defining a
class decision. We detail methods for efficiently obtaining one- and two-point
correlation functions of the decision boundary vectors of the quantum model,
which can be used for model interpretability, as well as methods for obtaining
classifications from partial data vectors. Further, we show that the use of
isometric tensors can significantly aid in the human interpretability of the
correlation functions extracted from the decision weights, and may produce
models that are less susceptible to adversarial perturbations. We demonstrate
our methodologies in applications utilizing the MNIST handwritten digit dataset
and a multivariate timeseries dataset of human activity recognition.


Abstract:
  Linear carbon-based materials such as polyyne and cumulene oligomers provide
a versatile platform for nano-physics and engineering. Direct gap quasi-1D
polyyne structures are promising for the observation of strong and unusual
excitonic eects arising due to the two-dimensional quantum connement. Recently,
we reported on the observation of sharp exciton peaks in low temperature
photoluminescence spectra of polyyne chains. Here, we analyse the time-resolved
optical response of this system. We extend the non-local dielectric response
theory to predict the exciton radiative lifetime dependence on the band-gap
value and on the length of the chain. A good agreement between the experiment
and the theory is achieved.


Abstract:
  The ability of near-term quantum computers to represent
classically-intractable quantum states has brought much interest in using such
devices for estimating the ground and excited state energies of fermionic
Hamiltonians. The usefulness of such near-term techniques, generally based on
the Variational Quantum Eigensolver (VQE), however, is limited by device noise
and the need to perform many circuit repetitions. This paper addresses these
challenges by generalizing VQE to consider wavefunctions in a subspace spanned
by classically tractable states and states that can be prepared on a quantum
computer. The manuscript shows how the ground and excited state energies can be
estimated using such "classical-boosting" and how this approach can be combined
with VQE Hamiltonian decomposition techniques. Unlike existing VQE approaches,
the sensitivity to sampling error and device noise approaches zero in the limit
where the classically tractable states are able to describe an eigenstate. A
detailed analysis of the measurement requirements in the simplest case, where a
single computational basis state is used to boost conventional VQE, shows that
the ground-state energy estimation of several closed-shell homonuclear diatomic
molecules can be accelerated by a factor of approximately 10-1000. The analysis
also shows that the measurement reduction of such single basis state boosting,
relative to conventional VQE, can be estimated using only the overlap between
the ground state and the computational basis state used for boosting.


Abstract:
  We have adapted the use of exponentially averaged momentum in PSO to
multi-objective optimization problems. The algorithm was built on top of SMPSO,
a state-of-the-art MOO solver, and we present a novel mathematical analysis of
constriction fairness. We extend this analysis to the use of momentum and
propose rich alternatives of parameter sets which are theoretically sound. We
call our proposed algorithm "Fairly Constricted PSO with Exponentially-Averaged
Momentum", FCPSO-em.


Abstract:
  Capturing scene dynamics and predicting the future scene state is challenging
but essential for robotic manipulation tasks, especially when the scene
contains both rigid and deformable objects. In this work, we contribute a
simulation environment and generate a novel dataset for task-specific
manipulation, involving interactions between rigid objects and a deformable
bag. The dataset incorporates a rich variety of scenarios including different
object sizes, object numbers and manipulation actions. We approach dynamics
learning by proposing an object-centric graph representation and two modules
which are Active Prediction Module (APM) and Position Prediction Module (PPM)
based on graph neural networks with an encode-process-decode architecture. At
the inference stage, we build a two-stage model based on the learned modules
for single time step prediction. We combine modules with different prediction
horizons into a mixed-horizon model which addresses long-term prediction. In an
ablation study, we show the benefits of the two-stage model for single time
step prediction and the effectiveness of the mixed-horizon model for long-term
prediction tasks. Supplementary material is available at
https://github.com/wengzehang/deformable_rigid_interaction_prediction


Abstract:
  We perform an analysis in harmonic space of the Dark Energy Survey Year 1
Data (DES-Y1) galaxy clustering data using products obtained for the real-space
analysis. We test our pipeline with a suite of lognormal simulations, which are
used to validate scale cuts in harmonic space as well as to provide a
covariance matrix that takes into account the DES-Y1 mask. We then apply this
pipeline to DES-Y1 data taking into account survey property maps derived for
the real-space analysis. We compare with real-space DES-Y1 results obtained
from a similar pipeline. We show that the harmonic space analysis we develop
yields results that are compatible with the real-space analysis for the bias
parameters. This verification paves the way to performing a harmonic space
analysis for the upcoming DES-Y3 data.


Abstract:
  Online Learning to Rank (OL2R) eliminates the need of explicit relevance
annotation by directly optimizing the rankers from their interactions with
users. However, the required exploration drives it away from successful
practices in offline learning to rank, which limits OL2R's empirical
performance and practical applicability. In this work, we propose to estimate a
pairwise learning to rank model online. In each round, candidate documents are
partitioned and ranked according to the model's confidence on the estimated
pairwise rank order, and exploration is only performed on the uncertain pairs
of documents, i.e., \emph{divide-and-conquer}. Regret directly defined on the
number of mis-ordered pairs is proven, which connects the online solution's
theoretical convergence with its expected ranking performance. Comparisons
against an extensive list of OL2R baselines on two public learning to rank
benchmark datasets demonstrate the effectiveness of the proposed solution.


Abstract:
  As the market for commercially available unmanned aerial vehicles (UAVs)
booms, there is an increasing number of small, teleoperated or autonomous
aircraft found in protected or sensitive airspace. Existing solutions for
removal of these aircraft are either military-grade and too disruptive for
domestic use, or compose of cumbersomely teleoperated counter-UAV vehicles that
have proven ineffective in high-profile domestic cases. In this work, we
examine the use of a quadrotor for autonomously targeting semi-stationary and
moving aerial objects with little or no prior knowledge of the target's flight
characteristics. Guidance and control commands are generated with information
just from an onboard monocular camera. We draw inspiration from literature in
missile guidance, and demonstrate an optimal guidance method implemented on a
quadrotor but not usable by missiles. Results are presented for first-pass hit
success and pursuit duration with various methods. Finally, we cover the CMU
Team Tartan entry in the MBZIRC 2020 Challenge 1 competition, demonstrating the
effectiveness of simple line-of-sight guidance methods in a structured
competition setting.


Abstract:
  Phosphorus-doped diamond is relevant for applications in sensing,
optoelectronics and quantum photonics, since the unique optical properties of
color centers in diamond can be combined with the n-type conductivity attained
by the inclusion of phosphorus. Here, we investigate the photoluminescence
signal of the nitrogen-vacancy and silicon-vacancy color centers in
phosphorus-doped diamond as a function of temperature starting from ambient
conditions up to about 100$^\circ$ Celsius, focusing on the zero-phonon line
(ZPL). We find that the wavelength and width of the ZPL of the two color
centers exhibit a comparable dependence on temperature, despite the strong
difference in the photoluminescence spectra. Moreover, the temperature
sensitivity of the ZPL of the silicon-vacancy center is not significantly
affected by phosphorus-doping, as we infer by comparison with silicon-vacancy
centers in electronic-grade single-crystal diamond.


Abstract:
  Context. The eROSITA X-ray telescope onboard the Spectrum-Roentgen-Gamma
(SRG) observatory combines a large field of view and collecting area in the
energy range $\sim$0.2 to $\sim$8.0 keV with the capability to perform uniform
scanning observations of large sky areas.
  Aims. SRG/eROSITA performed scanning observations of the $\sim$140 square
degrees eROSITA Final Equatorial Depth Survey (eFEDS) field as part of its
performance verification phase. The observing time was chosen to slightly
exceed the depth of equatorial fields after the completion of the eROSITA
all-sky survey. We present a catalog of detected X-ray sources in the eFEDS
field providing source positions and extent information, as well as fluxes in
multiple energy bands and document a suite of tools and procedures developed
for eROSITA data processing and analysis, validated and optimized by the eFEDS
work.
  Methods. A multi-stage source detection procedure was optimized and
calibrated by performing realistic simulations of the eROSITA eFEDS
observations. We cross-matched the eROSITA eFEDS source catalog with previous
XMM-ATLAS observations, confirming excellent agreement of the eROSITA and
XMM-ATLAS source fluxes.
  Result. We present a primary catalog of 27910 X-ray sources, including 542
with significant spatial extent, detected in the 0.2-2.3 keV energy range with
detection likelihoods $\ge 6$, corresponding to a point source flux limit of
$\approx 7 \times 10^{-15}$ erg/cm$^2$/s in the 0.5-2.0 keV energy band. A
supplementary catalog contains 4774 low-significance source candidates with
detection likelihoods between 5 and 6. In addition, a hard band sample of 246
sources detected in the energy range 2.3-5.0 keV above a detection likelihood
of 10 is provided. A description of the dedicated data analysis software,
calibration database and standard calibrated data products is provided in
appendix.


Abstract:
  Seven million people suffer complications after surgery each year. With
sufficient surgical training and feedback, half of these complications could be
prevented. Automatic surgical video analysis, especially for minimally invasive
surgery, plays a key role in training and review, with increasing interests
from recent studies on tool and workflow detection. In this research, a novel
machine learning architecture, RPM-CNN, is created to perform real-time
surgical video analysis. This architecture, for the first time, integrates
visual simultaneous localization and mapping (vSLAM) into Mask R-CNN.
Spatio-temporal information, in addition to the visual features, is utilized to
increase the accuracy to 96.8 mAP for tool detection and 97.5 mean Jaccard for
workflow detection, surpassing all previous works via the same benchmark
dataset. As a real-time prediction, the RPM-CNN model reaches a 50 FPS runtime
performance speed, 10x faster than region based CNN, by modeling the
spatio-temporal information directly from surgical videos during the vSLAM 3D
mapping. Additionally, this novel Region Proposal Module (RPM) replaces the
region proposal network (RPN) in Mask R-CNN, accurately placing bounding-boxes
and lessening the annotation requirement. In principle, this architecture
integrates the best of both worlds, inclusive of (1) vSLAM on object detection,
through focusing on geometric information for region proposals and (2) CNN on
object recognition, through focusing on semantic information for image
classification; the integration of these two technologies into one joint
training process opens a new door in computer vision. Furthermore, to apply
RPM-CNN's real-time top performance to the real world, a Microsoft HoloLens 2
application is developed to provide an augmented reality (AR) based solution
for both surgical training and assistance.


Abstract:
  Dynamical systems are ubiquitous in science and engineering as models of
phenomena that evolve over time. Although complex dynamical systems tend to
have important modular structure, conventional modeling approaches suppress
this structure. Building on recent work in applied category theory, we show how
deterministic dynamical systems, discrete and continuous, can be composed in a
hierarchical style. In mathematical terms, we reformulate some existing operads
of wiring diagrams and introduce new ones, using the general formalism of
$\mathcal{C}$-sets (copresheaves). We then establish dynamical systems as
algebras of these operads. In a computational vein, we show that Euler's method
is functorial for undirected systems, extending a previous result for directed
systems. All the ideas in this paper are implemented as practical software
using Catlab and the AlgebraicJulia ecosystem, written in the Julia programming
language for scientific computing.


Abstract:
  The goal of this article is two-fold: in a first part, we prove
Azuma-Hoeffding type concentration inequalities around the drift for the
displacement of non-elementary random walks on hyperbolic spaces. For a proper
hyperbolic space $M$, we obtain explicit bounds that depend only on $M$, the
size of support of the measure as in the classical case of sums of independent
random variables, and on the norm of the driving probability measure in the
left regular representation of the group of isometries. We obtain uniform
bounds in the case of hyperbolic groups and effective bounds for simple linear
groups of rank-one. In a second part, using our concentration inequalities, we
give quantitative finite-time estimates on the probability that two independent
random walks on the isometry group of a hyperbolic space generate a free
non-abelian subgroup. Our concentration results follow from a more general, but
less explicit statement that we prove for cocycles which satisfy a certain
cohomological equation. For example, this also allows us to obtain subgaussian
concentration bounds around the top Lyapunov exponent of random matrix products
in arbitrary dimension.