Abstract:
  We learn an interactive vision-based driving policy from pre-recorded driving
logs via a model-based approach. A forward model of the world supervises a
driving policy that predicts the outcome of any potential driving trajectory.
To support learning from pre-recorded logs, we assume that the world is on
rails, meaning neither the agent nor its actions influence the environment.
This assumption greatly simplifies the learning problem, factorizing the
dynamics into a nonreactive world model and a low-dimensional and compact
forward model of the ego-vehicle. Our approach computes action-values for each
training trajectory using a tabular dynamic-programming evaluation of the
Bellman equations; these action-values in turn supervise the final vision-based
driving policy. Despite the world-on-rails assumption, the final driving policy
acts well in a dynamic and reactive world. At the time of writing, our method
ranks first on the CARLA leaderboard, attaining a 25% higher driving score
while using 40 times less data. Our method is also an order of magnitude more
sample-efficient than state-of-the-art model-free reinforcement learning
techniques on navigational tasks in the ProcGen benchmark.


Abstract:
  We present new radio observations of the binary neutron star merger GW170817
carried out with the Karl G. Jansky Very large Array (VLA) more than 3\,yrs
after the merger. Our combined dataset is derived by co-adding more than
$\approx32$\,hours of VLA time on-source, and as such provides the deepest
combined observation (rms sensitivity $\approx 0.99\,\mu$Jy) of the GW170817
field obtained to date at 3\,GHz. We find no evidence for a late-time radio
re-brightening at a mean epoch of $t\approx 1200$\,d since merger, in contrast
to a $\approx 2.1\,\sigma$ excess observed at X-ray wavelengths at the same
mean epoch. Our measurements agree with expectations from the post-peak decay
of the radio afterglow of the GW170817 structured jet. Using these results, we
constrain the parameter space of models that predict a late-time radio
re-brightening possibly arising from the high-velocity tail of the GW170817
kilonova ejecta, which would dominate the radio and X-ray emission years after
the merger (once the structured jet afterglow fades below detection level). Our
results point to a steep energy-speed distribution of the kilonova ejecta (with
energy-velocity power law index $\alpha \gtrsim 5$). We suggest possible
implications of our radio analysis, when combined with the recent tentative
evidence for a late-time re-brightening in the X-rays, and highlight the need
for continued radio-to-X-ray monitoring to test different scenarios.


Abstract:
  We have obtained new observations of the absorption system at
$z_\mathrm{abs}=0.48$ toward QSO Q0454-220, which we use to constrain its
chemical and physical conditions. The system features metal-enriched gas and
previously unknown low-metallicity gas detected $\sim 200 \, \mathrm{km \,
s^{-1}}$ blueward of the metal-enriched gas. The low-metallicity gas is
detected in multiple Lyman series lines but is not detected in any metal lines.
Our analysis includes low-ionization (e.g., Fe II, Mg II) metal lines,
high-ionization (e.g., C IV, O VI, N V) metal lines, and several Lyman series
lines. We use new UV spectra taken with HST/COS along with data taken from
HST/STIS, Keck/HIRES, and VLT/UVES. We find that the absorption system can be
explained with a photoionized low-ionization phase with $\mathrm{[Fe/H]} \sim
-0.5$ and $n_\mathrm{H} \sim 10^{-2.3} \, \mathrm{cm}^{-3}$, a photoionized
high-ionization phase with a conservative lower limit of $-3.3 <
\mathrm{[Fe/H]}$ and $n_\mathrm{H} \sim 10^{-3.8} \, \mathrm{cm}^{-3}$, and a
low-metallicity component with a conservative upper limit of $\mathrm{[Fe/H]} <
-2.5$ that may be photoionized or collisionally ionized. We suggest that the
low-ionization phase may be due to cold-flow accretion via large-scale
filamentary structure or due to recycled accretion while the high-ionization
phase is the result of ancient outflowing material from a nearby galaxy. The
low-metallicity component may come from pristine accretion. The velocity spread
and disparate conditions among the absorption system's components suggest a
combination of gas arising near galaxies along with gas arising from intergroup
material.


Abstract:
  Minimally invasive surgery mainly consists of a series of sub-tasks, which
can be decomposed into basic gestures or contexts. As a prerequisite of
autonomic operation, surgical gesture recognition can assist motion planning
and decision-making, and build up context-aware knowledge to improve the
surgical robot control quality. In this work, we aim to develop an effective
surgical gesture recognition approach with an explainable feature extraction
process. A Bidirectional Multi-Layer independently RNN (BML-indRNN) model is
proposed in this paper, while spatial feature extraction is implemented via
fine-tuning of a Deep Convolutional Neural Network(DCNN) model constructed
based on the VGG architecture. To eliminate the black-box effects of DCNN,
Gradient-weighted Class Activation Mapping (Grad-CAM) is employed. It can
provide explainable results by showing the regions of the surgical images that
have a strong relationship with the surgical gesture classification results.
The proposed method was evaluated based on the suturing task with data obtained
from the public available JIGSAWS database. Comparative studies were conducted
to verify the proposed framework. Results indicated that the testing accuracy
for the suturing task based on our proposed method is 87.13%, which outperforms
most of the state-of-the-art algorithms.


Abstract:
  R Coronae Borealis (RCB) stars are rare hydrogen-deficient carbon-rich
variable supergiants thought to be the result of dynamically unstable white
dwarf mergers. We attempt to model RCBs through all the relevant timescales by
simulating a merger event in Octo-tiger, a 3D adaptive mesh refinement (AMR)
hydrodynamics code and mapping the post-merger object into MESA, a 1D stellar
evolution code. We then post-process the nucleosynthesis on a much larger
nuclear reaction network to study the enhancement of s-process elements. We
present models that match observations or previous studies in most surface
abundances, isotopic ratios, early evolution and lifetimes. We also observe
similar mixing behavior as previous modeling attempts which result in the
partial He-burning products visible on the surface in observations. However, we
do note that our sub-solar models lack any enhancement in s-process elements,
which we attribute to a lack of hydrogen in the envelope. We also find that the
Oxygen-16/Oxygen-18 isotopic ratio is very sensitive to initial hydrogen
abundance and increases outside of the acceptable range with a hydrogen mass
fraction greater than $10^{-4}$.


Abstract:
  We report a detailed theoretical study of a coherent macroscopic
quantum-mechanical phenomenon - quantum beats of a single magnetic fluxon
trapped in a two-cell SQUID of high kinetic inductance. We calculate
numerically and analytically the low-lying energy levels of the fluxon, and
explore their dependence on externally applied magnetic fields. The quantum
dynamics of the fluxon shows quantum beats originating from its coherent
quantum tunneling between the SQUID cells. We analyze the experimental setup
based on a three-cell SQUID, allowing for time-resolved measurements of quantum
beats of the fluxon.


Abstract:
  We present an approach for continual learning (CL) that is based on fully
probabilistic (or generative) models of machine learning. In contrast to, e.g.,
GANs that are "generative" in the sense that they can generate samples, fully
probabilistic models aim at modeling the data distribution directly.
Consequently, they provide functionalities that are highly relevant for
continual learning, such as density estimation (outlier detection) and sample
generation. As a concrete realization of generative continual learning, we
propose Gaussian Mixture Replay (GMR). GMR is a pseudo-rehearsal approach using
a Gaussian Mixture Model (GMM) instance for both generator and classifier
functionalities. Relying on the MNIST, FashionMNIST and Devanagari benchmarks,
we first demonstrate unsupervised task boundary detection by GMM density
estimation, which we also use to reject untypical generated samples. In
addition, we show that GMR is capable of class-conditional sampling in the way
of a cGAN. Lastly, we verify that GMR, despite its simple structure, achieves
state-of-the-art performance on common class-incremental learning problems at
very competitive time and memory complexity.


Abstract:
  We report measurements of the parity-conserving beam-normal single-spin
elastic scattering asymmetries $B_n$ on $^{12}$C and $^{27}$Al, obtained with
an electron beam polarized transverse to its momentum direction. These
measurements add an additional kinematic point to a series of previous
measurements of $B_n$ on $^{12}$C and provide a first measurement on $^{27}$Al.
The experiment utilized the Qweak apparatus at Jefferson Lab with a beam energy
of 1.158 GeV. The average lab scattering angle for both targets was 7.7
degrees, and the average $Q^2$ for both targets was 0.02437 GeV$^2$ (Q=0.1561
GeV). The asymmetries are $B_n$ = -10.68 $\pm$ 0.90 stat) $\pm$ 0.57 (syst) ppm
for $^{12}$C and $B_n$ = -12.16 $\pm$ 0.58 (stat) $\pm$ 0.62 (syst) ppm for
$^{27}$Al. The results are consistent with theoretical predictions, and are
compared to existing data. When scaled by Z/A, the Q-dependence of all the
far-forward angle (theta < 10 degrees) data from $^{1}$H to $^{27}$Al can be
described by the same slope out to $Q \approx 0.35$ GeV. Larger-angle data from
other experiments in the same Q range are consistent with a slope about twice
as steep.


Abstract:
  Multi Layer Capacitors MLCs are considered as the most promising refrigerant
elements to design and develop electrocaloric cooling devices. Recently, the
heat transfer of these MLCs has been considered. However, the heat exchange
with the surrounding environment has been poorly, if not, addressed. In this
work, we measure by infrared thermography the temperature change versus time in
four different heat exchange configurations. Depending on the configurations,
Newtonian and non-Newtonian regimes with their corresponding Biot number are
determined allowing to provide useful thermal characteristics. Indeed, in case
of large area thermal pad contacts, heat transfer coefficients up to 3400 W m-2
K-1 are obtained showing that the standard MLCs already reach the needs for
designing efficient prototypes. We also determine the ideal Brayton cooling
power in case of thick wires contact which varies between 3.4 mW and 9.8 mW for
operating frequencies varying from 0.25 Hz to 1 Hz. While only heat conduction
is considered here, our work provides some design rules for improving heat
exchanges in future devices.


Abstract:
  Complex plasmas consist of microparticles embedded in a low-temperature
plasma and allow investigating various effects by tracing the motion of these
microparticles. Dust density waves appear in complex plasmas as self-excited
acoustic waves in the microparticle fluid at low neutral gas pressures. Here we
show that various properties of these waves depend on the position of the
microparticle cloud with respect to the plasma sheath and explain this finding
in terms of the underlying ion-drift instability. These results may be helpful
in better understanding the propagation of dust density waves in complex
plasmas and beyond, for instance, in astrophysical dusty plasmas.


Abstract:
  In the second of two papers on the peculiar interacting transient AT 2016jbu,
we present the bolometric lightcurve, identification and analysis of the
progenitor candidate, as well as preliminary modelling to help elucidate the
nature of this event. We identify the progenitor candidate for AT 2016jbu in
quiescence, and find it to be consistent with a $\sim$20 M$_{\odot}$ yellow
hypergiant surrounded by a dusty circumstellar shell. We see evidence for
significant photometric variability in the progenitor, as well as strong
H$\alpha$ emission consistent with pre-existing circumstellar material. The age
of the resolved stellar population surrounding AT 2016jbu, as well as
integral-field unit spectra of the region support a progenitor age of >16 Myr,
again consistent with a progenitor mass of $\sim$20 M$_{\odot}$. Through a
joint analysis of the velocity evolution of AT 2016jbu, and the photospheric
radius inferred from the bolometric lightcurve, we find that the transient is
consistent with two successive outbursts or explosions. The first outburst
ejected a shell of material with velocity 650 km $s^{-1}$, while the second
more energetic event ejected material at 4500 km $s^{-1}$. Whether the latter
is the core-collapse of the progenitor remains uncertain, as the required
ejecta mass is relatively low (few tenths of M$_{\odot}$). We also place a
restrictive upper limit on the ejected $^{56}$Ni mass of <0.016 M$_{\odot}$.
Using the BPASS code, we explore a wide range of possible progenitor systems,
and find that the majority of these are in binaries, some of which are
undergoing mass transfer or common envelope evolution immediately prior to
explosion. Finally, we use the SNEC code to demonstrate that the low-energy
explosion of some of these systems together with sufficient CSM can reproduce
the overall morphology of the lightcurve of AT 2016jbu.


Abstract:
  In the present paper, we introduce a concept of Ricci curvature on
hypergraphs for a nonlinear Laplacian. We prove that our definition of the
Ricci curvature is a generalization of Lin-Lu-Yau coarse Ricci curvature for
graphs to hypergraphs. We also show a lower bound of nonzero eigenvalues of
Laplacian, gradient estimate of heat flow, and diameter bound of Bonnet-Myers
type for our curvature notion. This research leads to understanding how
nonlinearity of Laplacian causes complexity of curvatures.


Abstract:
  The Jahn-Teller theorem constitutes one of the most popular and stringent
concepts, applicable to all fields of chemistry. In open shell transition
elements chemistry and physics, 3d4, 3d9, and 3d7(low-spin) configurations in
octahedral complexes serve as particular illustrative and firm examples, where
a striking change (distortion) in local geometry is associated to a substantial
reduction of electronic energy. However, there has been a lasting debate, about
the fact that the octahedra are found to exclusively elongate, (at least for eg
electrons). Against this background, the title compound displays two marked
features, (1) the octahedron of oxygen atoms around Os6+ (d2) is drastically
compressed, in contrast to the standard JT expectations, and (2) the splitting
of the t2g set induced by this compression is extreme, such that a diamagnetic
ground state results. What we see is obviously a Jahn-Teller distortion
resulting in a compression of the respective octahedron and acting on the t2g
set of orbitals. Both these issues are unprecedented. Noteworthy, the splitting
into a lower dxy (hosting two d electrons with opposite spin) and two higher
dxz and dyz orbitals is so large that for the first time ever the Hund's
coupling for t2g electrons is overcome. We show that these effects are not
forced by structural frustration, the structure offers sufficient space for Os
to shift the apical oxygen atoms to a standard distance. Local electronic
effects appear to be responsible, instead. The relevance of these findings is
far reaching, since they provide insights in the hierarchy of perturbations
defining ground states of open shell electronic systems. The system studied
here, offers substantially more structural and compositional degrees of
freedom, such that a configuration could form that enables Os6+ to adopt its
apparently genuine diamagnetic ground state.


Abstract:
  There are several methods for model selection in cosmology which have at
least two major goals, that of finding the correct model or predicting well. In
this work we discuss through a study of well-known model selection methods like
Akaike information criterion (AIC), Bayesian information criterion (BIC),
deviance information criterion (DIC) and Bayesian evidence, how these different
goals are pursued in each paradigm. We also apply another method for model
selection which less seen in cosmological literature, the Cross-validation
method. Using these methods we will compare two different scenarios in
cosmology, $\Lambda$CDM model and dynamical dark energy. We show that each of
the methods tends to different results in model selection. While BIC and
Bayesian evidence overrule the dynamical dark energy scenarios with 2 or 3
extra degree of freedom, the DIC and cross-validation method prefer these
dynamical models to $\Lambda$CDM model. Assuming the numerical results of
different analysis and combining cosmological and statistical aspects of the
subject, we propose cross-validation as an interesting method for model
selection in cosmology that can lead to different results in comparison with
usual methods of model selection.


Abstract:
  Sound event detection is a core module for acoustic environmental analysis.
Semi-supervised learning technique allows to largely scale up the dataset
without increasing the annotation budget, and recently attracts lots of
research attention. In this work, we study on two advanced semi-supervised
learning techniques for sound event detection. Data augmentation is important
for the success of recent deep learning systems. This work studies the
audio-signal random augmentation method, which provides an augmentation
strategy that can handle a large number of different audio transformations. In
addition, consistency regularization is widely adopted in recent
state-of-the-art semi-supervised learning methods, which exploits the
unlabelled data by constraining the prediction of different transformations of
one sample to be identical to the prediction of this sample. This work finds
that, for semi-supervised sound event detection, consistency regularization is
an effective strategy, especially the best performance is achieved when it is
combined with the MeanTeacher model.


Abstract:
  We derive the non-relativistic limit of a massive vector field. We show that
the Cartesian spatial components of the vector behave as three identical,
non-interacting scalar fields. We find classes of spherical, cylindrical, and
planar self-gravitating vector solitons in the Newtonian limit. The
gravitational properties of the lowest-energy vector solitons$\mathrm{-}$the
gravitational potential and density field$\mathrm{-}$depend only on the net
mass of the soliton and the vector particle mass. In particular, these
self-gravitating, ground-state vector solitons are independent of the
distribution of energy across the vector field components, and are
indistinguishable from their scalar-field counterparts. Fuzzy Vector Dark
Matter models can therefore give rise to halo cores with identical
observational properties to the ones in scalar Fuzzy Dark Matter models. We
also provide novel hedgehog vector soliton solutions, which cannot be observed
in scalar-field theories. The gravitational binding of the lowest-energy
hedgehog halo is about three times weaker than the ground-state vector soliton.
Finally, we show that no spherically symmetric solitons exist with a
divergence-free vector field.


Abstract:
  Given two algebraic groups $G$, $H$ over a field $k$, we investigate the
representability of the functor of morphisms (of schemes) $\mathbf{Hom}(G,H)$
and the subfunctor of homomorphisms (of algebraic groups) $\mathbf{Hom}_{\rm
gp}(G,H)$. We show that $\mathbf{Hom}(G,H)$ is represented by a group scheme,
locally of finite type, if the $k$-vector space $\mathcal{O}(G)$ is
finite-dimensional; the converse holds if $H$ is not \'etale. When $G$ is
linearly reductive and $H$ is smooth, we show that $\mathbf{Hom}_{\rm gp}(G,H)$
is represented by a smooth scheme $M$; moreover, every orbit of $H$ acting by
conjugation on $M$ is open.


Abstract:
  Modern power systems face a grand challenge in grid management due to
increased electricity demand, imminent disturbances, and uncertainties
associated with renewable generation, which can compromise grid security. The
security assessment is directly connected to the robustness of the operating
condition and is evaluated by analyzing proximity to the power flow solution
space's boundary. Calculating location of such a boundary is a computationally
challenging task, linked to the power flow equations' non-linear nature,
presence of technological constraints, and complicated network topology. In
this paper we introduce a general framework to characterize points on the power
flow solution space boundary in terms of auxiliary variables subject to
algebraic constraints. Then we develop an adaptive continuation algorithm to
trace 1-dimensional sections of boundary curves which exhibits robust
performance and computational tractability. Implementation of the algorithm is
described in detail, and its performance is validated on different test
networks.


Abstract:
  We investigate optimal states of photon pairs to excite a target transition
in a multilevel quantum system. With the help of coherent control theory for
two-photon absorption with quantum light, we infer the maximal population
achievable by optimal entangled vs. separable states of light. Interference
between excitation pathways, as well as the presence of nearby states, may
hamper the selective excitation of a particular target state, but we show that
quantum correlations can help to overcome this problem, and enhance the
achievable "selectivity" between two energy levels, i.e. the relative
difference in population transferred into each of them. We find that the added
value of optimal entangled states of light increases with broadening linewidths
of the target states.


Abstract:
  In non-centrosymmetric metals, spin-orbit coupling (SOC) induces
momentum-dependent spin polarization at the Fermi surfaces. This is exemplified
by the valley-contrasting spin polarization in monolayer transition metal
dichalcogenides (TMDCs) with in-plane inversion asymmetry. However, the valley
configuration of massive Dirac fermions in TMDCs is fixed by the graphene-like
structure, which limits the variety of spin-valley coupling. Here, we show that
the layered polar metal BaMn$X_2$ ($X =$Bi, Sb) hosts tunable
spin-valley-coupled Dirac fermions, which originate from the distorted $X$
square net with in-plane lattice polarization. We found that in spite of the
larger SOC, BaMnBi$_2$ has approximately one-tenth the lattice distortion of
BaMnSb$_2$, from which a different configuration of spin-polarized Dirac
valleys is theoretically predicted. This was experimentally observed as a clear
difference in the Shubnikov-de Haas oscillation at high fields between the two
materials. The chemically tunable spin-valley coupling in BaMn$X_2$ makes it a
promising material for various spin-valleytronic devices.


Abstract:
  Traditionally, for most machine learning settings, gaining some degree of
explainability that tries to give users more insights into how and why the
network arrives at its predictions, restricts the underlying model and hinders
performance to a certain degree. For example, decision trees are thought of as
being more explainable than deep neural networks but they lack performance on
visual tasks. In this work, we empirically demonstrate that applying methods
and architectures from the explainability literature can, in fact, achieve
state-of-the-art performance for the challenging task of domain generalization
while offering a framework for more insights into the prediction and training
process. For that, we develop a set of novel algorithms including DivCAM, an
approach where the network receives guidance during training via gradient based
class activation maps to focus on a diverse set of discriminative features, as
well as ProDrop and D-Transformers which apply prototypical networks to the
domain generalization task, either with self-challenging or attention
alignment. Since these methods offer competitive performance on top of
explainability, we argue that the proposed methods can be used as a tool to
improve the robustness of deep neural network architectures.


Abstract:
  Static analysis tools typically address the problem of excessive false
positives by requiring programmers to explicitly annotate their code. However,
when faced with incomplete annotations, many analysis tools are either too
conservative, yielding false positives, or too optimistic, resulting in unsound
analysis results. In order to flexibly and soundly deal with
partially-annotated programs, we propose to build upon and adapt the gradual
typing approach to abstract-interpretation-based program analyses.
Specifically, we focus on null-pointer analysis and demonstrate that a gradual
null-pointer analysis hits a sweet spot, by gracefully applying static analysis
where possible and relying on dynamic checks where necessary for soundness. In
addition to formalizing a gradual null-pointer analysis for a core imperative
language, we build a prototype using the Infer static analysis framework, and
present preliminary evidence that the gradual null-pointer analysis reduces
false positives compared to two existing null-pointer checkers for Infer.
Further, we discuss ways in which the gradualization approach used to derive
the gradual analysis from its static counterpart can be extended to support
more domains. This work thus provides a basis for future analysis tools that
can smoothly navigate the tradeoff between human effort and run-time overhead
to reduce the number of reported false positives.


Abstract:
  In the absence of dissipation a non-rotating magnetic nanoparticle can be
stably levitated in a static magnetic field as a consequence of the spin origin
of its magnetization. Here, we study the effects of dissipation on the
stability of the system, considering the interaction with the background gas
and the intrinsic Gilbert damping of magnetization dynamics. We find that
dissipation limits the time over which a particle can be stably levitated. At
large applied magnetic fields we identify magnetization switching induced by
Gilbert damping as the key limiting factor for stable levitation. At low
applied magnetic fields and for small particle dimensions magnetization
switching is prevented due to the strong coupling of rotation and magnetization
dynamics, and the stability is mainly limited by the gas-induced dissipation.
In this latter case, high vacuum should be sufficient to extend stable
levitation over experimentally relevant timescales. Our results demonstrate the
possibility to experimentally observe the phenomenon of quantum spin stabilized
magnetic levitation.


Abstract:
  Cryptocurrencies are increasingly popular. Even people who are not experts
have started to invest in these securities, and nowadays, cryptocurrency
exchanges process transactions for over 100 billion US dollars per month. In
spite of this, many cryptocurrencies have low liquidity, and therefore, they
are highly prone to market manipulation. This paper performs an in-depth
analysis of two market manipulations organized by communities over the
Internet: The pump and dump and the crowd pump. The pump and dump scheme is a
fraud as old as the stock market. Now, it got new vitality in the loosely
regulated market of cryptocurrencies. Groups of highly coordinated people
arrange this scam, usually on Telegram and Discord. We monitored these groups
for more than 3 years detecting around 900 individual events. We analyze how
these communities are organized and how they carry out the fraud. We report on
three case studies of pump and dump. Then, we leverage our unique dataset of
the verified pump and dumps to build a machine learning model able to detect a
pump and dump in 25 seconds from the moment it starts, achieving the results of
94.5% of F1-score. Then, we move on to the crowd pump, a new phenomenon that
hit the news in the first months of 2021, when a Reddit community inflates the
price of the GameStop stocks (GME) of over 1,900% on Wall Street, the world's
largest stock exchange. Later, other Reddit communities replicate the operation
on the cryptocurrency markets. The targets were Dogecoin (DOGE) and Ripple
(XRP). We reconstruct how these operations developed, and we discuss
differences and analogies with the standard pump and dump. Lastly, we
illustrate how it is possible to leverage our classifier to detect this kind of
operation too.


Abstract:
  Two-dimensional (2D) magnetic materials with strong magnetostriction are
interesting systems for strain-tuning the magnetization, enabling potential for
realizing spintronic and nanomagnetic devices. Realizing this potential
requires understanding of the magneto-mechanical coupling in the 2D limit. In
this work, we suspend thin Cr$_2$Ge$_2$Te$_6$ layers, creating nanomechanical
membrane resonators. We probe its mechanical and magnetic properties as a
function of temperature and strain. Pronounced signatures of magneto-elastic
coupling are observed in the temperature-dependent resonance frequency of these
membranes near $T_{\rm C}$. We further utilize Cr$_2$Ge$_2$Te$_6$ in
heterostructures with thin layers of WSe$_2$ and FePS$_3$, which have positive
thermal expansion coefficients, to compensate the negative thermal expansion
coefficient of Cr$_2$Ge$_2$Te$_6$ and quantitatively probe the corresponding
$T_{\rm C}$. Finally, we induce a strain of $0.016\%$ in a suspended
heterostructure via electrostatic force and demonstrate a resulting enhancement
of $T_{\rm C}$ by $2.5 \pm 0.6$ K in the absence of an external magnetic field.


Abstract:
  Progress on the study of synchronisation in quantum systems has been largely
driven by specific examples which resulted in several examples of frequency
entrainment as well as mutual synchronisation. Here we study quantum
synchronisation by utilising Liouville space perturbation theory. We begin by
clarifying the role of centers, symmetries and oscillating coherences in the
context of quantum synchronisation. We then analyse the eigenspectrum of the
Liouville superoperator generating the dynamics of the quantum system and
determine the conditions under which synchronisation arises. We apply our
framework to derive a powerful relationship between energy conservation,
degeneracies and synchronisation in quantum systems. Finally, we demonstrate
our approach by analysing two mutually coupled thermal machines and the close
relationship between synchronisation and thermodynamic quantities.


Abstract:
  Structural optimization (topology, shapes, sizing) is an important tool for
facilitating the emergence of new concepts in structural design. Normally,
topology optimization is carried out at the early stage of design and then
shape and sizing design are performed sequentially. Unlike traditional topology
optimization method, explicit methodologies have attracted a great deal of
attention because of the advantages of shortcuting the costly CAD/CAE processes
while dealing with low order number of design variables compared to implicit
method (such as SIMP). This paper aims at presenting an adaptation of a
flow-inspired approach so-called Moving Node Approach (MNA) in topology
optimization. In this approach, the discretization is decoupled from the
material distribution and the final objective is to recognize the best beam
assembly while minimizing compliance. The paradigm has here changed and new
design variables are used such as nodes location, elements length/orientation
and size providing a lower number of design variables than pixels-based. The
methodology is validated using 2 classical testcases in the field of Topology
Optimization: the Cantilever beam and the L-Shape problem.


Abstract:
  The increase in the sensitivity of gravitational wave interferometers will
bring additional detections of binary black hole and double neutron star
mergers. It will also very likely add many merger events of black hole -
neutron star binaries. Distinguishing mixed binaries from binary black holes
mergers for high mass ratios could be challenging because in this situation the
neutron star coalesces with the black hole without experiencing significant
disruption. To investigate the transition of mixed binary mergers into those
behaving more like binary black hole coalescences, we present results from
merger simulations for different mass ratios. We show how the degree of
deformation and disruption of the neutron star impacts the inspiral and merger
dynamics, the properties of the final black hole, the accretion disk formed
from the circularization of the tidal debris, the gravitational waves, and the
strain spectrum and mismatches. The results also show the effectiveness of the
initial data method that generalizes the Bowen-York initial data for black hole
punctures to the case of binaries with neutron star companions.


Abstract:
  Pre-trained contextualized language models (PrLMs) have led to strong
performance gains in downstream natural language understanding tasks. However,
PrLMs can still be easily fooled by adversarial word substitution, which is one
of the most challenging textual adversarial attack methods. Existing defence
approaches suffer from notable performance loss and complexities. Thus, this
paper presents a compact and performance-preserved framework, Anomaly Detection
with Frequency-Aware Randomization (ADFAR). In detail, we design an auxiliary
anomaly detection classifier and adopt a multi-task learning procedure, by
which PrLMs are able to distinguish adversarial input samples. Then, in order
to defend adversarial word substitution, a frequency-aware randomization
process is applied to those recognized adversarial input samples. Empirical
results show that ADFAR significantly outperforms those newly proposed defense
methods over various tasks with much higher inference speed. Remarkably, ADFAR
does not impair the overall performance of PrLMs. The code is available at
https://github.com/LilyNLP/ADFAR


Abstract:
  We consider the renormalization-group evolution of the matrix element of
$\langle 0| \bar{q}(z)_\beta [z,0]b(0)_\alpha| \bar{B}\rangle$, which can be
used to define the distribution amplitudes for $B$ meson and widely applied in
studies of $B$ meson decays. The contribution to the renormalization constant
of the non-local operator $\bar{q}(z)_\beta [z,0]b(0)_\alpha$ is considered up
to one-loop order in QCD. Since the quark fields in this operator are not
directly coupled fields, momentum can not flow freely through this non-local
operator. Momentum involved in this operator can be treated stringently in
coordinate space. We find that the ultraviolet divergences regulated by
dimensional parameter $\epsilon$ cancel with each other, and the evolution
effect vanishes. The matrix element $\langle 0| \bar{q}(z)_\beta
[z,0]b(0)_\alpha| \bar{B}\rangle$ escapes from the renormalization-group
evolution. We then apply the matrix element in calculating $B\to\pi$ transition
form factor, where the matrix element is obtained by using the $B$ meson wave
function calculated in QCD-inspired potential model. By comparing with
experimental data for the semileptonic decay of $B\to \pi \ell\nu$ and
light-cone sum rule calculation, we analyse the perturbative and
non-perturbative contributions to $B\to\pi$ transition form factor in the frame
work of perturbative QCD approach. We find that the effectiveness of the
suppression of Sudakov factor to soft contribution depends on the end-point
behavior of $B$ meson wave function, and with the $B$-meson wave function used
in this work, soft contribution can not be well suppressed. The hard
contribution to the $B\to\pi$ transition form factor is about 59\%, and soft
contribution can be as large as 41\% in the naive calculation. To make the
perturbative calculation reliable, a soft momentum cutoff in the calculation
and soft form factor have to be introduced.


Abstract:
  It has been shown that temperature cycles on airless bodies of our Solar
System can cause damaging of surface materials. Nevertheless, propagation
mechanisms in the case of space objects are still poorly understood. Present
work combines a thermoelasticity model together with linear elastic fracture
mechanics theory to predict fracture propagation in the presence of thermal
gradients generated by diurnal temperature cycling and under conditions similar
to those existing on the asteroid Bennu. The crack direction is computed using
the maximal strain energy release rate criterion, which is implemented using
finite elements and the so-called G$\theta$ method (Uribe-Su\'arez et al. 2020.
Eng. Fracture Mech. 227:106918). Using the implemented methodology, crack
propagation direction for an initial crack tip in different positions and for
different orientations is computed. It is found that cracks preferentially
propagate in the North to South (N-S), in the North-East to South-West (NE-SW)
and in the North-West to South-East (NW-SE) directions. Finally, thermal
fatigue analysis was performed in order to estimate the crack growth rate.
Computed value is in good agreement with available experimental evidence.


Abstract:
  We establish sharp dynamical implications of convexity on symmetric spheres
that do not follow from dynamical convexity. It allows us to show the existence
of elliptic and non-hyperbolic periodic orbits and to furnish new examples of
dynamically convex contact forms, in any dimension, that are not equivalent to
convex ones via contactomorphisms that preserve the symmetry. Moreover, these
examples are $C^1$-stable in the sense that they are actually not equivalent to
convex ones via contactomorphisms that are $C^1$-close to those preserving the
symmetry. We also show the multiplicity of symmetric non-hyperbolic and
symmetric (not necessarily non-hyperbolic) closed Reeb orbits under suitable
pinching conditions.


Abstract:
  We introduce a geometric operation, which we call the relative Whitney trick,
that removes a single double point between properly immersed surfaces in a
$4$-manifold with boundary. Using the relative Whitney trick we prove that
every link in a homology sphere is homotopic to a link that is topologically
slice in a contractible topological $4$-manifold. We further prove that any
link in a homology sphere is order $k$ Whitney tower concordant to a link in
$S^3$ for all $k$. Finally, we explore the minimum Gordian distance from a link
in $S^3$ to a homotopically trivial link. Extending this notion to links in
homology spheres, we use the relative Whitney trick to make explicit
computations for 3-component links and establish bounds in general.


Abstract:
  We use relative hyperbolicity of mapping tori and Dehn fillings of relatively
hyperbolic groups to solve the conjugacy problem between certain outer
automorphisms. We reduce this problem to algorithmic problems entirely
expressed in terms of the parabolic subgroups of the mapping tori. As an
immediate application, we solve the conjugacy problem for outer automorphisms
of free groups, whose polynomial part is piecewise inner. This proposes a path
toward a full solution to the conjugacy problem for $\mathrm{Out}(F_n)$.


Abstract:
  We investigate how the choice of equation of state (EOS) and resolution
conspire to affect the outcomes of giant impact (GI) simulations. We focus on
the simple case of equal mass collisions of two Earth-like $0.5\,M_\oplus$
proto-planets showing that the choice of EOS has a profound impact on the
outcome of such collisions as well as on the numerical convergence with
resolution. In simulations where the Tillotson EOS is used, impacts generate an
excess amount of vapour due to the lack of a thermodynamically consistent
treatment of phase transitions and mixtures. In oblique collisions this
enhances the artificial angular momentum (AM) transport from the planet to the
circum-planetary disc reducing the planet's rotation period over time. Even at
a resolution of $1.3 \times 10^6$ particles the result is not converged. In
head-on collisions the lack of a proper treatment of the solid/liquid-vapour
phase transition allows the bound material to expand to very low densities
which in turn results in very slow numerical convergence of the critical
specific impact energy for catastrophic disruption $Q_{RD}^*$ with increasing
resolution as reported in prior work. The simulations where ANEOS is used for
oblique impacts are already converged at a modest resolution of $10^5$
particles, while head-on collisions converge when they evidence the post-shock
formation of a dense iron-rich ring, which promotes gravitational
re-accumulation of material. Once sufficient resolution is reached to resolve
the liquid-vapour phase transition of iron in the ANEOS case, and this ring is
resolved, the value of $Q_{RD}^*$ has then converged.


Abstract:
  We studied theoretically the effect of a low concentration of adsorbed polar
molecules on the optical conductivity of graphene, within the Kubo linear
response approximation. Our analysis is based on a continuum model
approximation that includes up to next to nearest neighbors in the pristine
graphene effective Hamiltonian, thus extending the field-theoretical analysis
developed in Refs.[1,2]. Our results show that the conductivity can be
expressed in terms of renormalized quasiparticle parameters $\tilde{v}_F$,
$\tilde{M}$ and $\tilde{\mu}$ that include the effect of the molecular surface
concentration $n_{dip}$ and dipolar moment $\boldsymbol{\mathcal{P}}$, thus
providing an analytical model for a graphene-based chemical sensor.


Abstract:
  Learning an empirically effective model with generalization using limited
data is a challenging task for deep neural networks. In this paper, we propose
a novel learning framework called PurifiedLearning to exploit task-irrelevant
features extracted from task-irrelevant labels when training models on
small-scale datasets. Particularly, we purify feature representations by using
the expression of task-irrelevant information, thus facilitating the learning
process of classification. Our work is built on solid theoretical analysis and
extensive experiments, which demonstrate the effectiveness of PurifiedLearning.
According to the theory we proved, PurifiedLearning is model-agnostic and
doesn't have any restrictions on the model needed, so it can be combined with
any existing deep neural networks with ease to achieve better performance. The
source code of this paper will be available in the future for reproducibility.


Abstract:
  Virtual-reality (VR) and augmented-reality (AR) technology is increasingly
combined with eye-tracking. This combination broadens both fields and opens up
new areas of application, in which visual perception and related cognitive
processes can be studied in interactive but still well controlled settings.
However, performing a semantic gaze analysis of eye-tracking data from
interactive three-dimensional scenes is a resource-intense task, which so far
has been an obstacle to economic use. In this paper we present a novel approach
which minimizes time and information necessary to annotate volumes of interest
(VOIs) by using techniques from object recognition. To do so, we train
convolutional neural networks (CNNs) on synthetic data sets derived from
virtual models using image augmentation techniques. We evaluate our method in
real and virtual environments, showing that the method can compete with
state-of-the-art approaches, while not relying on additional markers or
preexisting databases but instead offering cross-platform use.


Abstract:
  On a compact K\"ahler manifold $X$, Toeplitz operators determine a
deformation quantization $(\operatorname{C}^\infty(X, \mathbb{C})[[\hbar]],
\star)$ with separation of variables [10] with respect to transversal complex
polarizations $T^{1, 0}X, T^{0, 1}X$ as $\hbar \to 0^+$ [15]. The analogous
statement is proved for compact symplectic manifolds with transversal
non-singular real polarizations [13].
  In this paper, we establish the analogous result for transversal singular
real polarizations on compact toric symplectic manifolds $X$. Due to toric
singularities, half-form correction and localization of our Toeplitz operators
are essential. Via norm estimations, we show that these Toeplitz operators
determine a star product on $X$ as $\hbar \to 0^+$.


Abstract:
  We construct analogues of the Hecke operators for the moduli space of
G-bundles on a curve X over a local field F with parabolic structures at
finitely many points. We conjecture that they define commuting compact normal
operators on the Hilbert space of half-densities on this moduli space. In the
case F=C, we also conjecture that their joint spectrum is in a natural
bijection with the set of opers on X for the Langlands dual group with real
monodromy. This may be viewed as an analytic version of the Langlands
correspondence for complex curves. Furthermore, we conjecture an explicit
formula relating the eigenvalues of the Hecke operators and the global
differential operators studied in our previous paper arXiv:1908.09677. Assuming
the compactness conjecture, this formula follows from a certain system of
differential equations satisfied by the Hecke operators, which we prove in this
paper for G=PGL(n).


Abstract:
  The potential of using millimeter-wave (mmWave) to encounter the current
bandwidth shortage has motivated packing more antenna elements in the same
physical size which permits the advent of massive
multiple-input-multiple-output (MIMO) for mmWave communication. However, with
increasing number of antenna elements, the ability of allocating a single
RF-chain per antenna becomes infeasible and unaffordable. As a cost-effective
alternative, the design of hybrid precoding has been considered where the
limited-scattering signals are captured by a high-dimensional RF precoder
realized by an analog phase-shifter network followed by a low-dimensional
digital precoder at baseband. In this paper, the max-min fair problem is
considered to design a low-complexity hybrid precoder for multi-group
multicasting systems in mmWave channels. The problem is non-trivial due to two
main reasons: the original max-min problem for multi-group multicasting for a
fully-digital precoder is non-convex, and the analog precoder places constant
modules constraint which restricts the feasible set of the precoders in the
design problem. Therefore, we consider a low complexity hybrid precoder design
to tackle and benefit from the mmWave channel structure. Each analog beamformer
was designed to maximize the minimum matching component for users within a
given group. Once obtained, the digital precoder was attained by solving the
max-min problem of the equivalent channel.


Abstract:
  Background: To prevent future outbreaks of COVID-19, Australia is pursuing a
mass-vaccination approach in which a targeted group of the population
comprising healthcare workers, aged-care residents and other individuals at
increased risk of exposure will receive a highly effective priority vaccine.
The rest of the population will instead have access to a less effective
vaccine.
  Methods: We apply a large-scale agent-based model of COVID-19 in Australia to
investigate the possible implications of this hybrid approach to
mass-vaccination. The model is calibrated to recent epidemiological and
demographic data available in Australia, and accounts for several components of
vaccine efficacy.
  Findings: Within a feasible range of vaccine efficacy values, our model
supports the assertion that complete herd immunity due to vaccination is not
likely in the Australian context. For realistic scenarios in which herd
immunity is not achieved, we simulate the effects of mass-vaccination on
epidemic growth rate, and investigate the requirements of lockdown measures
applied to curb subsequent outbreaks. In our simulations, Australia's
vaccination strategy can feasibly reduce required lockdown intensity and
initial epidemic growth rate by 43% and 52%, respectively. The severity of
epidemics, as measured by the peak number of daily new cases, decreases by up
to two orders of magnitude under plausible mass-vaccination and lockdown
strategies.
  Interpretation: The study presents a strong argument for a large-scale
vaccination campaign in Australia, which would substantially reduce both the
intensity of future outbreaks and the stringency of non-pharmaceutical
interventions required for their suppression.


Abstract:
  The study of network structural controllability focuses on the minimum number
of driver nodes needed to control a whole network. Despite intensive studies on
this topic, most of them consider static networks only. It is well-known,
however, that real networks are growing, with new nodes and links added to the
system. Here, we analyze controllability of evolving networks and propose a
general rule for the change of driver nodes. We further apply the rule to solve
the problem of network augmentation subject to the controllability constraint.
The findings fill a gap in our understanding of network controllability and
shed light on controllability of real systems.


Abstract:
  This article presents the results of investigations using topic modeling of
the Voynich Manuscript (Beinecke MS408). Topic modeling is a set of
computational methods which are used to identify clusters of subjects within
text. We use latent dirichlet allocation, latent semantic analysis, and
nonnegative matrix factorization to cluster Voynich pages into `topics'. We
then compare the topics derived from the computational models to clusters
derived from the Voynich illustrations and from paleographic analysis. We find
that computationally derived clusters match closely to a conjunction of scribe
and subject matter (as per the illustrations), providing further evidence that
the Voynich Manuscript contains meaningful text.


Abstract:
  Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated excellent
scalability in Natural Language Processing. In Computer Vision, however, almost
all performant networks are "dense", that is, every input is processed by every
parameter. We present a Vision MoE (V-MoE), a sparse version of the Vision
Transformer, that is scalable and competitive with the largest dense networks.
When applied to image recognition, V-MoE matches the performance of
state-of-the-art networks, while requiring as little as half of the compute at
inference time. Further, we propose an extension to the routing algorithm that
can prioritize subsets of each input across the entire batch, leading to
adaptive per-image compute. This allows V-MoE to trade-off performance and
compute smoothly at test-time. Finally, we demonstrate the potential of V-MoE
to scale vision models, and train a 15B parameter model that attains 90.35% on
ImageNet.


Abstract:
  A recent proposal for a superdeterministic account of quantum mechanics,
named Invariant-set theory, appears to bring ideas from several diverse fields
like chaos theory, number theory and dynamical systems to quantum foundations.
However, a clear cut hidden-variable model has not been developed, which makes
it difficult to assess the proposal from a quantum foundational perspective. In
this article, we first build a hidden-variable model based on the proposal, and
then critically analyse several aspects of the proposal using the model. We
show that several arguments related to counter-factual measurements,
nonlocality, non-commutativity of quantum observables, measurement independence
etcetera that appear to work in the proposal fail when considered in our model.
We further show that our model is not only superdeterministic but also
nonlocal, with an ontic quantum state. We argue that the bit string defined in
the model is a hidden variable and that it contains redundant information.
Lastly, we apply the analysis developed in a previous work (Proc. R. Soc. A,
476(2243):20200214, 2020) to illustrate the issue of superdeterministic
conspiracy in the model. Our results lend further support to the view that
superdeterminism is unlikely to solve the puzzle posed by the Bell
correlations.


Abstract:
  We consider statistical models arising from the common set of solutions to a
sparse polynomial system with general coefficients. The maximum likelihood
degree counts the number of critical points of the likelihood function
restricted to the model.
  We prove the maximum likelihood degree of a sparse polynomial system is
determined by its Newton polytopes and equals the mixed volume of a related
Lagrange system of equations. As a corollary, we find that the algebraic degree
of several optimization problems is equal to a similar mixed volume.


Abstract:
  Single crystals of SrMn2P2 and CaMn2P2 were grown using Sn flux and
characterized by single-crystal x-ray diffraction, electrical resistivity rho,
heat capacity Cp, and magnetic susceptibility chi = M/H measurements versus
temperature T and magnetization M versus applied magnetic field H isotherm
measurements. The x-ray diffraction results show that both compounds adopt the
trigonal CaAl2Si2-type structure. The rho(T) measurements demonstrate
insulating ground states for both compounds. The chi(T) and Cp(T) data reveal a
weak first-order antiferromagnetic (AFM) transition at the Neel temperature TN
= 53(1) K for SrMn2P2 and a strong first-order AFM transition at TN = 69.8(3) K
for CaMn2P2. Both compounds show an isotropic and nearly T-independent chi(T <
TN). {31}P NMR measurements confirm the strong first-order transition in
CaMn2P2 but show critical slowing down near TN for SrMn2P2 thus evidencing
second-order character. The NMR measurements also indicate that the AFM
structure of CaMn2P2 is commensurate with the lattice whereas that of SrMn2P2
is incommensurate. These first-order AFM transitions are unique among the class
of trigonal (Ca, Sr, Ba)Mn2(P, As, Sb, Bi)2 compounds which otherwise exhibit
second-order AFM transitions. This result presents a challenge to understand
the systematics of magnetic ordering in this class of materials in which
magnetically-frustrated antiferromagnetism is quasi-two-dimensional.


Abstract:
  We used a convolutional neural network to infer stellar rotation periods from
a set of synthetic light curves simulated with realistic spot evolution
patterns. We convolved these simulated light curves with real TESS light curves
containing minimal intrinsic astrophysical variability to allow the network to
learn TESS systematics and estimate rotation periods despite them. In addition
to periods, we predict uncertainties via heteroskedastic regression to estimate
the credibility of the period predictions. In the most credible half of the
test data, we recover 10%-accurate periods for 46% of the targets, and
20%-accurate periods for 69% of the targets. Using our trained network, we
successfully recover periods of real stars with literature rotation
measurements, even past the 13.7-day limit generally encountered by TESS
rotation searches using conventional period-finding techniques. Our method also
demonstrates resistance to half-period aliases. We present the neural network
and simulated training data, and introduce the software butterpy used to
synthesize the light curves using realistic star spot evolution.


Abstract:
  We pursue a novel strategy towards a first detection of continuous
gravitational waves from rapidly-rotating deformed neutron stars. Computational
power is focused on a narrow region of signal parameter space selected by a
strategically-chosen benchmark. We search data from the 2nd observing run of
the LIGO Observatory with an optimised analysis run on graphics processing
units. While no continuous waves are detected, the search achieves a
sensitivity to gravitational wave strain of $h_0 = 1.01{\times}10^{-25}$ at 90%
confidence, 24% to 69% better than past searches of the same parameter space.
Constraints on neutron star deformity are within theoretical maxima, thus a
detection by this search was not inconceivable.


Abstract:
  A high imbalance exists between technical debt and non-technical debt source
code comments. Such imbalance affects Self-Admitted Technical Debt (SATD)
detection performance, and existing literature lacks empirical evidence on the
choice of balancing technique. In this work, we evaluate the impact of multiple
balancing techniques, including Data level, Classifier level, and Hybrid, for
SATD detection in Within-Project and Cross-Project setup. Our results show that
the Data level balancing technique SMOTE or Classifier level Ensemble
approaches Random Forest or XGBoost are reasonable choices depending on whether
the goal is to maximize Precision, Recall, F1, or AUC-ROC. We compared our
best-performing model with the previous SATD detection benchmark
(cost-sensitive Convolution Neural Network). Interestingly the top-performing
XGBoost with SMOTE sampling improved the Within-project F1 score by 10% but
fell short in Cross-Project set up by 9%. This supports the higher
generalization capability of deep learning in Cross-Project SATD detection, yet
while working within individual projects, classical machine learning algorithms
can deliver better performance. We also evaluate and quantify the impact of
duplicate source code comments in SATD detection performance. Finally, we
employ SHAP and discuss the interpreted SATD features. We have included the
replication package and shared a web-based SATD prediction tool with the
balancing techniques in this study.


Abstract:
  Quantum dots are arguably one of the best platforms for optically accessible
spin based qubits. The paramount demand of extended qubit storage time can be
met by using quantum-dot-confined dark exciton: a longlived electron-hole pair
with parallel spins. Despite its name the dark exciton reveals weak
luminescence that can be directly measured. The origins of this optical
activity remain largely unexplored. In this work, using the atomistic
tight-binding method combined with configuration-interaction approach, we
demonstrate that atomic-scale randomness strongly affects oscillator strength
of dark excitons confined in self-assembled cylindrical InGaAs quantum dots
with no need for faceting or shape-elongation. We show that this process is
mediated by two mechanisms: mixing dark and bright configurations by exchange
interaction, and equally important appearance of non-vanishing optical
transition matrix elements that otherwise correspond to nominally forbidden
transitions in a non-alloyed case. The alloy randomness has essential impact on
both bright and dark exciton states, including their energy, emission
intensity, and polarization angle. We conclude that, due to the atomic-scale
alloy randomness, finding dots with desired dark exciton properties may require
exploration of a large ensemble, similarly to how dots with low bright exciton
splitting are selected for entanglement generation.


Abstract:
  Superpixel is generated by automatically clustering pixels in an image into
hundreds of compact partitions, which is widely used to perceive the object
contours for its excellent contour adherence. Although some works use the
Convolution Neural Network (CNN) to generate high-quality superpixel, we
challenge the design principles of these networks, specifically for their
dependence on manual labels and excess computation resources, which limits
their flexibility compared with the traditional unsupervised segmentation
methods. We target at redefining the CNN-based superpixel segmentation as a
lifelong clustering task and propose an unsupervised CNN-based method called
LNS-Net. The LNS-Net can learn superpixel in a non-iterative and lifelong
manner without any manual labels. Specifically, a lightweight feature embedder
is proposed for LNS-Net to efficiently generate the cluster-friendly features.
With those features, seed nodes can be automatically assigned to cluster pixels
in a non-iterative way. Additionally, our LNS-Net can adapt the sequentially
lifelong learning by rescaling the gradient of weight based on both channel and
spatial context to avoid overfitting. Experiments show that the proposed
LNS-Net achieves significantly better performance on three benchmarks with
nearly ten times lower complexity compared with other state-of-the-art methods.


Abstract:
  In the May-Leonard model of three cyclically competing species, we analyze
the statistics of rare events in which all three species go extinct due to
strong but rare fluctuations. These fluctuations are from the tails of the
probability distribution of species concentrations. They render a coexistence
of three populations unstable even if the coexistence is stable in the
deterministic limit. We determine the mean time to extinction (MTE) by using a
WKB-ansatz in the master equation that represents the stochastic description of
this model. This way, the calculation is reduced to a problem of classical
mechanics and amounts to solving a Hamilton-Jacobi equation with zero-energy
Hamiltonian. We solve the corresponding Hamilton's equations of motion in
six-dimensional phase space numerically by using the Iterative Action
Minimization Method. This allows to project on the optimal path to extinction,
starting from a parameter choice where the three-species coexistence-fixed
point undergoes a Hopf bifurcation and becomes stable. Specifically for our
system of three species, extinction events can be triggered along various paths
to extinction, differing in their intermediate steps. We compare our analytical
predictions with results from Gillespie simulations for two-species
extinctions, complemented by an analytical calculation of the MTE in which the
remaining third species goes extinct. From Gillespie simulations we also
analyze how the distributions of times to extinction change upon varying the
bifurcation parameter. Even within the same model and the same dynamical
regime, the MTE depends on the distance from the bifurcation point in a way
that contains the system size dependence in the exponent. It is challenging and
worthwhile to quantify how rare the rare events of extinction are.


Abstract:
  Extracting critical behavior in the wake of quantum quenches has recently
been at the forefront of theoretical and experimental investigations in
condensed matter physics and quantum synthetic matter, with particular emphasis
on experimental feasibility. Here, we investigate the potential of single-site
observables in probing equilibrium phase transitions and dynamical criticality
in short-range transverse-field Ising chains. For integrable and
near-integrable models, our exact and mean-field-theory analyses reveal a truly
out-of-equilibrium universal scaling exponent in the vicinity of the transition
that is independent of the initial state and the location of the probe site so
long as the latter is sufficiently close to the edge of the chain. Signature of
a dynamical crossover survives when integrability is strongly broken. Our work
provides a robust scheme for the experimental detection of quantum critical
points and dynamical scaling laws in short-range interacting models using
modern ultracold-atom setups.


Abstract:
  A common concern in observational studies focuses on properly evaluating the
causal effect, which usually refers to the average treatment effect or the
average treatment effect on the treated. In this paper, we propose a data
preprocessing method, the Kernel-distance-based covariate balancing, for
observational studies with binary treatments. This proposed method yields a set
of unit weights for the treatment and control groups, respectively, such that
the reweighted covariate distributions can satisfy a set of pre-specified
balance conditions. This preprocessing methodology can effectively reduce
confounding bias of subsequent estimation of causal effects. We demonstrate the
implementation and performance of Kernel-distance-based covariate balancing
with Monte Carlo simulation experiments and a real data analysis.


Abstract:
  Apart from the role the clustering coefficient plays in the definition of the
small-world phenomena, it also has great relevance for practical problems
involving networked dynamical systems. To study the impact of the clustering
coefficient on dynamical processes taking place on networks, some authors have
focused on the construction of graphs with tunable clustering coefficients.
These constructions are usually realized through a stochastic process, either
by growing a network through the preferential attachment procedure, or by
applying a random rewiring process. In contrast, we consider here several
families of static graphs whose clustering coefficients can be determined
explicitly. The basis for these families is formed by the $k$-regular graphs on
$N$ nodes, that belong to the family of so-called circulant graphs denoted by
$C_{N,k}$. We show that the expression for the clustering coefficient of
$C_{N,k}$ reported in literature, only holds for sufficiently large $N$. Next,
we consider three generalizations of the circulant graphs, either by adding
some pendant links to $C_{N,k}$, or by connecting, in two different ways, an
additional node to some nodes of $C_{N,k}$. For all three generalizations, we
derive explicit expressions for the clustering coefficient. Finally, we
construct a family of pairs of generalized circulant graphs, with the same
number of nodes and links, but with different clustering coefficients.


Abstract:
  In this paper we study lumpy black holes with AdS${}_p \times S^q$
asymptotics, where the isometry group coming from the sphere factor is broken
down to SO($q$). Depending on the values of $p$ and $q$, these are solutions to
a certain Supergravity theory with a particular gauge field. We have considered
the values $(p,q) = (5,5)$ and $(p,q) = (4,7)$, corresponding to type IIB
supergravity in ten dimensions and eleven-dimensional supergravity
respectively. These theories presumably contain an infinite spectrum of
families of lumpy black holes, labeled by a harmonic number $\ell$, whose
endpoints in solution space merge with another type of black holes with
different horizon topology. We have numerically constructed the first four
families of lumpy solutions, corresponding to $\ell = 1, 2^+, 2^-$ and $3$. We
show that the geometry of the horizon near the merger is well-described by a
cone over a triple product of spheres, thus extending Kol's local model to the
present asymptotics. Interestingly, the presence of non-trivial fluxes in the
internal sphere implies that the cone is no longer Ricci flat. This conical
manifold accounts for the geometry and the behavior of the physical quantities
of the solutions sufficiently close to the critical point. Additionally, we
show that the vacuum expectation values of the dual scalar operators approach
their critical values with a power law whose exponents are dictated by the
local cone geometry in the bulk.


Abstract:
  We propose an efficient framework of reversible data hiding to preserve
compatibility between normal printing and printing with a special color ink by
using a single common image. The special color layer is converted to a binary
image by digital halftoning and losslessly compressed using JBIG2. Then, the
compressed information of the binarized special color layer is reversibly
embedded into the general color layer without significant distortion. Our
experimental results show the availability of the proposed method in terms of
the marked image quality.


Abstract:
  Two-dimensional (2D) palladium ditelluride (PdTe2) and platinum ditelluride
(PtTe2) are two Dirac semimetals which demonstrate fascinating quantum
properties such as superconductivity, magnetism and topological order,
illustrating promising applications in future nanoelectronics and
optoelectronics. However, the synthesis of their monolayers is dramatically
hindered by strong interlayer coupling and orbital hybridization. In this
study, an efficient synthesis method for monolayer PdTe2 and PtTe2 is
demonstrated. Taking advantages of the surface reaction, epitaxial growth of
large-area and high quality monolayers of PdTe2 and patterned PtTe2 is achieved
by direct tellurization of Pd(111) and Pt(111). A well-ordered PtTe2 pattern
with Kagome lattice formed by Te vacancy arrays is successfully grown.
Moreover, multilayer PtTe2 can be also obtained and potential excitation of
Dirac plasmons is observed. The simple and reliable growth procedure of
monolayer PdTe2 and patterned PtTe2 gives unprecedented opportunities for
investigating new quantum phenomena and facilitating practical applications in
optoelectronics.


Abstract:
  We present Sandwich Batch Normalization (SaBN), a frustratingly easy
improvement of Batch Normalization (BN) with only a few lines of code changes.
SaBN is motivated by addressing the inherent feature distribution heterogeneity
that one can be identified in many tasks, which can arise from data
heterogeneity (multiple input domains) or model heterogeneity (dynamic
architectures, model conditioning, etc.). Our SaBN factorizes the BN affine
layer into one shared sandwich affine layer, cascaded by several parallel
independent affine layers. Concrete analysis reveals that, during optimization,
SaBN promotes balanced gradient norms while still preserving diverse gradient
directions -- a property that many application tasks seem to favor. We
demonstrate the prevailing effectiveness of SaBN as a drop-in replacement in
four tasks: conditional image generation, neural architecture search (NAS),
adversarial training, and arbitrary style transfer. Leveraging SaBN immediately
achieves better Inception Score and FID on CIFAR-10 and ImageNet conditional
image generation with three state-of-the-art GANs; boosts the performance of a
state-of-the-art weight-sharing NAS algorithm significantly on NAS-Bench-201;
substantially improves the robust and standard accuracies for adversarial
defense; and produces superior arbitrary stylized results. We also provide
visualizations and analysis to help understand why SaBN works. Codes are
available at: https://github.com/VITA-Group/Sandwich-Batch-Normalization.


Abstract:
  Flat band moir\'e superlattices have recently emerged as unique platforms for
investigating the interplay between strong electronic correlations, nontrivial
band topology, and multiple isospin 'flavor' symmetries. Twisted
monolayer-bilayer graphene (tMBG) is an especially rich system owing to its low
crystal symmetry and the tunability of its bandwidth and topology with an
external electric field. Here, we find that orbital magnetism is abundant
within the correlated phase diagram of tMBG, giving rise to the anomalous Hall
effect (AHE) in correlated metallic states nearby most odd integer fillings of
the flat conduction band, as well as correlated Chern insulator states
stabilized in an external magnetic field. The behavior of the states at zero
field appears to be inconsistent with simple spin and valley polarization for
the specific range of twist angles we investigate, and instead may plausibly
result from an intervalley coherent (IVC) state with an order parameter that
breaks time reversal symmetry. The application of a magnetic field further
tunes the competition between correlated states, in some cases driving
first-order topological phase transitions. Our results underscore the rich
interplay between closely competing correlated ground states in tMBG, with
possible implications for probing exotic IVC ordering.


Abstract:
  We consider a magnetic skyrmion crystal formed at the surface of a
topological insulator. Incorporating the exchange interaction between the
helical Dirac surface states and the periodic N\'eel or Bloch skyrmion texture,
we obtain the resulting electronic band structures. We discuss the properties
of the reconstructed skyrmion bands, namely the impact of symmetries on the
energies and Berry curvature. We find substantive qualitative differences
between the N\'eel and Bloch cases, with the latter generically permitting a
low-energy tight-binding representation whose parameters are tightly
constrained by symmetries. We explicitly construct the associated Wannier
orbitals, which resemble the ring-like chiral bound states of helical Dirac
fermions coupled to a single skyrmion in a ferromagnetic background. We
construct a two-band tight-binding model with complex nearest-neighbor hoppings
which captures the salient topological features of the low-energy bands. Our
results are relevant to magnetic topological insulators (TIs), as well as to
TI-magnetic thin film heterostructures, in which skyrmion crystals may be
stabilized.


Abstract:
  This paper underlines a subtle property of batch-normalization (BN):
Successive batch normalizations with random linear transformations make hidden
representations increasingly orthogonal across layers of a deep neural network.
We establish a non-asymptotic characterization of the interplay between depth,
width, and the orthogonality of deep representations. More precisely, under a
mild assumption, we prove that the deviation of the representations from
orthogonality rapidly decays with depth up to a term inversely proportional to
the network width. This result has two main implications: 1) Theoretically, as
the depth grows, the distribution of the representation -- after the linear
layers -- contracts to a Wasserstein-2 ball around an isotropic Gaussian
distribution. Furthermore, the radius of this Wasserstein ball shrinks with the
width of the network. 2) In practice, the orthogonality of the representations
directly influences the performance of stochastic gradient descent (SGD). When
representations are initially aligned, we observe SGD wastes many iterations to
orthogonalize representations before the classification. Nevertheless, we
experimentally show that starting optimization from orthogonal representations
is sufficient to accelerate SGD, with no need for BN.


Abstract:
  Planet formation via core accretion requires the production of km-sized
planetesimals from cosmic dust. This process must overcome barriers to simple
collisional growth, for which the Streaming Instability (SI) is often invoked.
Dust evolution is still required to create particles large enough to undergo
vigorous instability. The SI has been studied primarily with single size dust,
and the role of the full evolved dust distribution is largely unexplored. We
survey the Polydispserse Streaming Instability (PSI) with physical parameters
corresponding to plausible conditions in protoplanetary discs. We consider a
full range of particle stopping times, generalized dust size distributions, and
the effect of turbulence. We find that, while the PSI grows in many cases more
slowly with a interstellar power-law dust distribution than with a single size,
reasonable collisional dust evolution, producing an enhancement of the largest
dust sizes, produces instability behaviour similar to the monodisperse case.
Considering turbulent diffusion the trend is similar. We conclude that if fast
linear growth of PSI is required for planet formation, then dust evolution
producing a distribution with peak stopping times on the order of 0.1 orbits
and an enhancement of the largest dust significantly above the single power-law
distribution produced by a fragmentation cascade is sufficient, along with
local enhancement of the dust to gas volume mass density ratio to order unity.


Abstract:
  In today's networked society, many real-world problems can be formalized as
predicting links in networks, such as Facebook friendship suggestions,
e-commerce recommendations, and the prediction of scientific collaborations in
citation networks. Increasingly often, link prediction problem is tackled by
means of network embedding methods, owing to their state-of-the-art
performance. However, these methods lack transparency when compared to simpler
baselines, and as a result their robustness against adversarial attacks is a
possible point of concern: could one or a few small adversarial modifications
to the network have a large impact on the link prediction performance when
using a network embedding model? Prior research has already investigated
adversarial robustness for network embedding models, focused on classification
at the node and graph level. Robustness with respect to the link prediction
downstream task, on the other hand, has been explored much less.
  This paper contributes to filling this gap, by studying adversarial
robustness of Conditional Network Embedding (CNE), a state-of-the-art
probabilistic network embedding model, for link prediction. More specifically,
given CNE and a network, we measure the sensitivity of the link predictions of
the model to small adversarial perturbations of the network, namely changes of
the link status of a node pair. Thus, our approach allows one to identify the
links and non-links in the network that are most vulnerable to such
perturbations, for further investigation by an analyst. We analyze the
characteristics of the most and least sensitive perturbations, and empirically
confirm that our approach not only succeeds in identifying the most vulnerable
links and non-links, but also that it does so in a time-efficient manner thanks
to an effective approximation.


Abstract:
  We calculate Galactic Chemical Evolution (GCE) of Mo and Ru by taking into
account the contribution from $\nu p$-process nucleosynthesis. We estimate
yields of $p$-nuclei such as $^{92,94}\mathrm{Mo}$ and $^{96,98}\mathrm{Ru}$
through the $\nu p$-process in various supernova (SN) progenitors based upon
recent models. In particular, the $\nu p$-process in energetic hypernovae
produces a large amount of $p$-nuclei compared to the yield in ordinary
core-collapse SNe. Because of this the abundances of $^{92,94}\mathrm{Mo}$ and
$^{96,98}\mathrm{Ru}$ in the Galaxy are significantly enhanced at [Fe/H]=0 by
the $\nu p$-process. We find that the $\nu p$-process in hypernovae is the main
contributor to the elemental abundance of $^{92}$Mo at low metallicity
[Fe/H$]<-2$. Our theoretical prediction of the elemental abundances in
metal-poor stars becomes more consistent with observational data when the $\nu
p$-process in hypernovae is taken into account.


Abstract:
  Photo-Induced Enhanced Raman Spectroscopy (PIERS) is a new surface enhanced
Raman spectroscopy (SERS) modality with an order-of-magnitude Raman signal
enhancement of adsorbed analytes over that of typical SERS substrates. Despite
the impressive PIERS enhancement factors and explosion in recent demonstrations
of its utility, the detailed enhancement mechanism remains undetermined. Using
a range of optical and X-ray spectroscopies, supported by density functional
theory calculations, we elucidate the chemical and atomic-scale mechanism
behind the PIERS enhancement. Stable PIERS substrates with enhancement factors
of 10^6 were fabricated using self-organized hexagonal arrays of TiO2 nanotubes
that were defect-engineered via annealing in inert atmospheres, and silver
nanoparticles were deposited by magnetron sputtering and subsequent thermal
dewetting. We identified the key source of the enhancement of PIERS vs. SERS in
these structures as an increase in the Raman polarizability of the adsorbed
probe molecule upon photo-induced charge transfer. A balance between
crystallinity, which enhances charge transfer due to higher electron mobility
in anatase-rutile heterostructures but decreases visible light absorption, and
oxygen vacancy defects, which increase visible light absorption and
photo-induced electron transfers, was critical to achieve high PIERS
enhancements.


Abstract:
  The optical phase shifter that constantly rotates the local oscillator phase
is a necessity in continuous-variable quantum key distribution systems with
heterodyne detection. In previous experimental implementations, the optical
phase shifter is generally regarded as an ideal passive optical device that
perfectly rotates the phase of the electromagnetic wave of $90^\circ$. However,
the optical phase shifter in practice introduces imperfections, mainly the
measurement angular error, which inevitably deteriorates the security of the
practical systems. Here, we will give a concrete interpretation of measurement
angular error in practical systems and the corresponding entanglement-based
description. Subsequently, from the parameter estimation, we deduce the
overestimated excess noise and the underestimated transmittance, which lead to
a reduction in the final secret key rate. Simultaneously, we propose an
estimation method of the measurement angular error. Next, the practical
security analysis is provided in detail, and the effect of the measurement
angular error and its corresponding compensation scheme are demonstrated. We
conclude that measurement angular error severely degrades the security, but the
proposed calibration and compensation method can significantly help improve the
performance of the practical CV-QKD systems.


Abstract:
  Deep energy renovation of building stock came more into focus in the European
Union due to energy efficiency related directives. Many buildings that must
undergo deep energy renovation are old and may lack design/renovation
documentation, or possible degradation of materials might have occurred in
building elements over time. Thermal transmittance (i.e. U-value) is one of the
most important parameters for determining the transmission heat losses through
building envelope elements. It depends on the thickness and thermal properties
of all the materials that form a building element. In-situ U-value can be
determined by ISO 9869-1 standard (Heat Flux Method - HFM). Still, measurement
duration is one of the reasons why HFM is not widely used in field testing
before the renovation design process commences. This paper analyzes the
possibility of reducing the measurement time by conducting parallel
measurements with one heat-flux sensor. This parallelization could be achieved
by applying a specific class of the Artificial Neural Network (ANN) on HFM
results to predict unknown heat flux based on collected interior and exterior
air temperatures. After the satisfying prediction is achieved, HFM sensor can
be relocated to another measuring location. Paper shows a comparison of four
ANN cases applied to HFM results for a measurement held on one multi-layer wall
- multilayer perceptron with three neurons in one hidden layer, long short-term
memory with 100 units, gated recurrent unit with 100 units and combination of
50 long short-term memory units and 50 gated recurrent units. The analysis gave
promising results in term of predicting the heat flux rate based on the two
input temperatures. Additional analysis on another wall showed possible
limitations of the method that serves as a direction for further research on
this topic.


Abstract:
  HR 8799 hosts four directly imaged giant planets, but none has a mass
measured from first principles. We present the first dynamical mass measurement
in this planetary system, finding that the innermost planet HR~8799~e has a
mass of $9.6^{+1.9}_{-1.8} \, M_{\rm Jup}$. This mass results from combining
the well-characterized orbits of all four planets with a new astrometric
acceleration detection (5$\sigma$) from the Gaia EDR3 version of the
Hipparcos-Gaia Catalog of Accelerations. We find with 95\% confidence that
HR~8799~e is below $13\, M_{\rm Jup}$, the deuterium-fusing mass limit. We
derive a hot-start cooling age of $42^{+24}_{-16}$\,Myr for HR~8799~e that
agrees well with its hypothesized membership in the Columba association but is
also consistent with an alternative suggested membership in the
$\beta$~Pictoris moving group. We exclude the presence of any additional
$\gtrsim$5-$M_{\rm Jup}$ planets interior to HR~8799~e with semi-major axes
between $\approx$3-16\,au. We provide proper motion anomalies and a matrix
equation to solve for the mass of any of the planets of HR~8799 using only mass
ratios between the planets.


Abstract:
  In this paper, we explore methods for computing wall-normal derivatives used
for calculating wall skin friction and heat transfer over a solid wall in
unstructured simplex-element (triangular/tetrahedral) grids generated by
anisotropic grid adaptation. Simplex-element grids are considered as efficient
and suitable for automatic grid generation and adaptation, but present a
challenge to accurately predict wall-normal derivatives. For example,
wall-normal derivatives computed by a simple finite-difference approximation,
as typically done in practical fluid-dynamics simulation codes, are often
contaminated with numerical noise. To address this issue, we propose an
improved method based on a common step-length for the finite-difference
approximation, which is otherwise random due to grid irregularity and thus
expected to smooth the wall-normal derivative distribution over a boundary.
Also, we consider using least-squares gradients to compute the wall-normal
derivatives and discuss their possible improvements. Numerical results show
that the improved methods greatly reduce the noise in the wall-normal
derivatives for irregular simplex-element grids.


Abstract:
  In this study we extract the deep features and investigate the compression of
the Mg II k spectral line profiles observed in quiet Sun regions by NASA's IRIS
satellite. The data set of line profiles used for the analysis was obtained on
April 20th, 2020, at the center of the solar disc, and contains almost 300,000
individual Mg II k line profiles after data cleaning. The data are separated
into train and test subsets. The train subset was used to train the autoencoder
of the varying embedding layer size. The early stopping criterion was
implemented on the test subset to prevent the model from overfitting. Our
results indicate that it is possible to compress the spectral line profiles
more than 27 times (which corresponds to the reduction of the data
dimensionality from 110 to 4) while having a 4 DN average reconstruction error,
which is comparable to the variations in the line continuum. The mean squared
error and the reconstruction error of even statistical moments sharply decrease
when the dimensionality of the embedding layer increases from 1 to 4 and almost
stop decreasing for higher numbers. The observed occasional improvements in
training for values higher than 4 indicate that a better compact embedding may
potentially be obtained if other training strategies and longer training times
are used. The features learned for the critical four-dimensional case can be
interpreted. In particular, three of these four features mainly control the
line width, line asymmetry, and line dip formation respectively. The presented
results are the first attempt to obtain a compact embedding for spectroscopic
line profiles and confirm the value of this approach, in particular for feature
extraction, data compression, and denoising.


Abstract:
  The J-PARC muon $g - 2$/EDM experiment aims to measure the muon magnetic
moment anomaly $a_{\mu} = (g -2) / 2 ~ $ and the muon electric dipole moment
(EDM) $d_{\mu}$. The target sensitivity for $a_{\mu}$ is a statistical
uncertainty of $450 \times 10^{-9}$, and for $d_{\mu}$ it is $1.5 \times
10^{-21}$ $e \cdot \rm{cm}$. The readout electronics require a DC to DC
converter to provide 1.5 V and 3.3 V electric voltage within a specific
environment. To capture the positron decay from the muon, the detector
components are placed in the muon storage area where a 3 T magnetic field is
applied in a vacuum. The extra magnetic field from the converter should be less
than 30 $\mu$T to reach the aforementioned sensitivity on $a_{\mu}$. In
addition, the electric field produced by the converter has to be as small as 1
V/m to also reach the target sensitivity on $d_{\mu}$. For this purpose, we
develop a step-down converter. We discuss the development process and the
performance of our DC to DC converter for the J-PARC muon $g - 2$/EDM
experiment.


Abstract:
  Few-shot Named Entity Recognition (NER) exploits only a handful of
annotations to identify and classify named entity mentions. Prototypical
network shows superior performance on few-shot NER. However, existing
prototypical methods fail to differentiate rich semantics in other-class words,
which will aggravate overfitting under few shot scenario. To address the issue,
we propose a novel model, Mining Undefined Classes from Other-class (MUCO),
that can automatically induce different undefined classes from the other class
to improve few-shot NER. With these extra-labeled undefined classes, our method
will improve the discriminative ability of NER classifier and enhance the
understanding of predefined classes with stand-by semantic knowledge.
Experimental results demonstrate that our model outperforms five
state-of-the-art models in both 1-shot and 5-shots settings on four NER
benchmarks. We will release the code upon acceptance. The source code is
released on https: //github.com/shuaiwa16/OtherClassNER.git.


Abstract:
  We present a new trainable system for physically plausible markerless 3D
human motion capture, which achieves state-of-the-art results in a broad range
of challenging scenarios. Unlike most neural methods for human motion capture,
our approach, which we dub physionical, is aware of physical and environmental
constraints. It combines in a fully differentiable way several key innovations,
i.e., 1. a proportional-derivative controller, with gains predicted by a neural
network, that reduces delays even in the presence of fast motions, 2. an
explicit rigid body dynamics model and 3. a novel optimisation layer that
prevents physically implausible foot-floor penetration as a hard constraint.
The inputs to our system are 2D joint keypoints, which are canonicalised in a
novel way so as to reduce the dependency on intrinsic camera parameters -- both
at train and test time. This enables more accurate global translation
estimation without generalisability loss. Our model can be finetuned only with
2D annotations when the 3D annotations are not available. It produces smooth
and physically principled 3D motions in an interactive frame rate in a wide
variety of challenging scenes, including newly recorded ones. Its advantages
are especially noticeable on in-the-wild sequences that significantly differ
from common 3D pose estimation benchmarks such as Human 3.6M and MPI-INF-3DHP.
Qualitative results are available at
http://gvv.mpi-inf.mpg.de/projects/PhysAware/


Abstract:
  It is known that if $f\colon {\mathbb R}^2 \to {\mathbb R}$ is a polynomial
in each variable, then $f$ is a polynomial. We present generalizations of this
fact, when ${\mathbb R}^2$ is replaced by $G\times H$, where $G$ and $H$ are
topological Abelian groups. We show, e.g., that the conclusion holds (with
generalized polynomials in place of polynomials) if $G$ is a connected Baire
space and $H$ has a dense subgroup of finite rank or, for continuous functions,
if $G$ and $H$ are connected Baire spaces. The condition of continuity can be
omitted if $G$ and $H$ are locally compact or complete metric spaces. We
present several examples showing that the results are not far from being
optimal.


Abstract:
  We introduce a new sequential methodology to calibrate the fixed parameters
and track the stochastic dynamical variables of a state-space system. The
proposed method is based on the nested hybrid filtering (NHF) framework of [1],
that combines two layers of filters, one inside the other, to compute the joint
posterior probability distribution of the static parameters and the state
variables. In particular, we explore the use of deterministic sampling
techniques for Gaussian approximation in the first layer of the algorithm,
instead of the Monte Carlo methods employed in the original procedure. The
resulting scheme reduces the computational cost and so makes the algorithms
potentially better-suited for high-dimensional state and parameter spaces. We
describe a specific instance of the new method and then study its performance
and efficiency of the resulting algorithms for a stochastic Lorenz 63 model
with uncertain parameters.


Abstract:
  We present an Extended Kalman Filter framework for system identification and
control of a stochastic high-dimensional epidemic model. The scale and severity
of the COVID-19 emergency have highlighted the need for accurate forecasts of
the state of the pandemic at a high resolution. Mechanistic compartmental
models are widely used to produce such forecasts and assist in the design of
control and relief policies. Unfortunately, the scale and stochastic nature of
many of these models often makes the estimation of their parameters difficult.
With the goal of calibrating a high dimensional COVID-19 model using low-level
mobility data, we introduce a method for tractable maximum likelihood
estimation that combines tools from Bayesian inference with scalable
optimization techniques from machine learning. The proposed approach uses
automatic backward-differentiation to directly compute the gradient of the
likelihood of COVID-19 incidence and death data. The likelihood of the
observations is estimated recursively using an Extended Kalman Filter and can
be easily optimized using gradient-based methods to compute maximum likelihood
estimators. Our compartmental model is trained using GPS mobility data that
measures the mobility patterns of millions of mobile phones across the United
States. We show that, after calibrating against incidence and deaths data from
the city of Philadelphia, our model is able to produce an accurate 30-day
forecast of the evolution of the pandemic.


Abstract:
  We propose a minimal model based on Lepton number symmetry (and violation),
to address a common origin of baryon asymmetry, dark matter (DM) and neutrino
mass generation, having one-to-one correspondence. The {\em unnatural}
largeness and smallness of the parameters required to describe correct
experimental limits are attributed to lepton number violation. Allowed
parameter space of the model is illustrated via numerical scan.


Abstract:
  In 2019, The Centers for Medicare and Medicaid Services (CMS) launched an
Artificial Intelligence (AI) Health Outcomes Challenge seeking solutions to
predict risk in value-based care for incorporation into CMS Innovation Center
payment and service delivery models. Recently, modern language models have
played key roles in a number of health related tasks. This paper presents, to
the best of our knowledge, the first application of these models to patient
readmission prediction. To facilitate this, we create a dataset of 1.2 million
medical history samples derived from the Limited Dataset (LDS) issued by CMS.
Moreover, we propose a comprehensive modeling solution centered on a deep
learning framework for this data. To demonstrate the framework, we train an
attention-based Transformer to learn Medicare semantics in support of
performing downstream prediction tasks thereby achieving 0.91 AUC and 0.91
recall on readmission classification. We also introduce a novel data
pre-processing pipeline and discuss pertinent deployment considerations
surrounding model explainability and bias.


Abstract:
  Reconstructing the shape and appearance of real-world objects using measured
2D images has been a long-standing problem in computer vision. In this paper,
we introduce a new analysis-by-synthesis technique capable of producing
high-quality reconstructions through robust coarse-to-fine optimization and
physics-based differentiable rendering.
  Unlike most previous methods that handle geometry and reflectance largely
separately, our method unifies the optimization of both by leveraging image
gradients with respect to both object reflectance and geometry. To obtain
physically accurate gradient estimates, we develop a new GPU-based Monte Carlo
differentiable renderer leveraging recent advances in differentiable rendering
theory to offer unbiased gradients while enjoying better performance than
existing tools like PyTorch3D and redner. To further improve robustness, we
utilize several shape and material priors as well as a coarse-to-fine
optimization strategy to reconstruct geometry. We demonstrate that our
technique can produce reconstructions with higher quality than previous methods
such as COLMAP and Kinect Fusion.


Abstract:
  This paper derives the CS decomposition for orthogonal tensors (T-CSD) and
the generalized singular value decomposition for two tensors (T-GSVD) via the
T-product. The structures of the two decompositions are analyzed in detail and
are consistent with those for matrix cases. Then the corresponding algorithms
are proposed respectively. Finally, T-GSVD can be used to give the explicit
expression for the solution of tensor Tikhonov regularization. Numerical
examples demonstrate the effectiveness of T-GSVD in solving image restoration
problems.


Abstract:
  While there have been many results on lower bounds for Max Cut in unweighted
graphs, the only lower bound for non-integer weights is that by Poljak and
Turzik (1986). In this paper, we launch an extensive study of lower bounds for
Max Cut in weighted graphs. We introduce a new approach for obtaining lower
bounds for Weighted Max Cut. Using it, Probabilistic Method, Vizing's chromatic
index theorem, and other tools, we obtain several lower bounds for arbitrary
weighted graphs, weighted graphs of bounded girth and triangle-free weighted
graphs. We pose conjectures and open questions.


Abstract:
  This paper addresses the problem of creating abstract transformers
automatically. The method we present provides the basis for creating a tool to
automate the construction of program analyzers in a fashion similar to the way
yacc automates the construction of parsers. Our method treats the problem as a
program-synthesis problem. The user provides specifications of (i) the concrete
semantics of a given operation O, (ii) the abstract domain A to be used by the
analyzer, and (iii) the semantics of a domain-specific language L in which the
abstract transformer is to be expressed. As output, our method creates an
abstract transformer for O for abstract domain A, expressed in DSL L. We
implemented our method, and used it to create a set of replacement abstract
transformers for those used in an existing analyzer, and obtained essentially
identical performance. However, when we compared the existing transformers with
the generated transformers, we discovered that two of the existing transformers
were unsound, which demonstrates the risk of using manually created
transformers.