Abstract:
  A graph $G$ is semilinear of complexity $t$ if the vertices of $G$ are
elements of $\mathbb{R}^{d}$ for some $d\in\mathbb{Z}^{+}$, and the edges of
$G$ are defined by the sign patterns of $t$ linear functions
$f_1,\dots,f_t:\mathbb{R}^{d}\times \mathbb{R}^{d}\rightarrow\mathbb{R}$. We
show that semilinear graphs of constant complexity have very tame Ramsey
properties. More precisely, we prove that if $G$ is a semilinear graph of
complexity $t$ which contains no clique of size $s$ and no independent set of
size $n$, then $G$ has at most $O_{s,t}(n)\cdot(\log n)^{O_t(1)}$ vertices. We
also show that the logarithmic term cannot be omitted. In particular, this
implies that if $G$ is a semilinear graph of constant complexity on $n$
vertices, and $G$ contains no clique of size $s$, then $G$ can be properly
colored with $\mbox{polylog}(n)$ colors. In the past 60 years, this coloring
question was extensively studied for several special instances of semilinear
graphs, e.g. shift graphs, intersection and disjointness graphs of certain
geometric objects, and overlap graphs. Our main result provides a general upper
bound on the chromatic number of all such, seemingly unrelated, graphs.
  Furthermore, we consider the symmetric Ramsey problem for semilinear graphs
as well. It is known that if there exists an intersection graph of $N$ boxes in
$\mathbb{R}^{d}$ (such graphs are semilinear of complexity $2d$) that contains
no clique or independent set of size $n$, then $N=O_d(n^2(\log n)^{d-1})$. That
is, the exponent of $n$ does not grow with the dimension. We prove a result
about the symmetric Ramsey properties of semilinear graphs, which puts this
phenomenon in a more general context.


Abstract:
  This paper proves Lee-Lee's conjecture that establishes a coincidence between
the set of associated roots of non-self-intersecting curves in a $n$-punctured
disc and the set of real Schur roots of acyclic (valued) quivers with $n$
vertices.


Abstract:
  We explore a simple extension to the Standard Model containing two gauge
singlets: a Dirac fermion and a real pseudoscalar. In some regions of the
parameter space both singlets are stable without the necessity of additional
symmetries, then becoming a possible two-component dark matter model. We study
the relic abundance production via freeze-out, with the latter determined by
annihilations, conversions and semi-annihilations. Experimental constraints
from invisible Higgs decay, dark matter relic abundance and direct/indirect
detection are studied. We found three viable regions of the parameter space,
and the model is sensitive to indirect searches.


Abstract:
  We give the first examples of collapsing Ricci limit spaces on which the
Hausdorff dimension of the singular set exceeds that of the regular set;
moreover, the Hausdorff dimension of these spaces can be non-integers. This
answers a question of Cheeger-Colding about collapsing Ricci limit spaces.


Abstract:
  Proper function of a wastewater treatment plant (WWTP) relies on maintaining
a delicate balance between a multitude of competing microorganisms. Gaining a
detailed understanding of the complex network of interactions therein is
essential to maximising not only current operational efficiencies, but also for
the effective design of new treatment technologies. Metagenomics offers an
insight into these dynamic systems through the analysis of the microbial DNA
sequences present. Unique taxa are inferred through sequence clustering to form
operational taxonomic units (OTUs), with per-taxa abundance estimates obtained
from corresponding sequence counts. The data in this study comprise weekly OTU
counts from an activated sludge (AS) tank of a WWTP. To model the OTU dynamics,
we develop a Bayesian hierarchical vector autoregressive model, which is a
linear approximation to the commonly used generalised Lotka-Volterra (gLV)
model. To tackle the high dimensionality and sparsity of the data, they are
first clustered into 12 "bins" using a seasonal phase-based approach. The
autoregressive coefficient matrix is assumed to be sparse, so we explore
different shrinkage priors by analysing simulated data sets before selecting
the regularised horseshoe prior for the biological application. We find that
ammonia and chemical oxygen demand have a positive relationship with several
bins and pH has a positive relationship with one bin. These results are
supported by findings in the biological literature. We identify several
negative interactions, which suggests OTUs in different bins may be competing
for resources and that these relationships are complex. We also identify two
positive interactions. Although simpler than a gLV model, our vector
autoregression offers valuable insight into the microbial dynamics of the WWTP.


Abstract:
  We investigate the potential added value of running three limited-area
ensemble systems (with the WRF, Meso-NH and MOLOCH models and a grid spacing of
approximately 2.5 km) for two heavy-precipitation events in Italy. Such
high-resolution ensembles include an explicit treatment of convective processes
and dynamically downscale the ECMWF global data, which have a grid spacing of
approximately 18 km. The predictions are verified against rain-gauge data and
their accuracy is evaluated over that of the driving coarser-resolution
ensemble system. Furthermore, we compare the simulation speed (defined as the
ratio of simulation length to wall-clock time) of the three limited-area models
to estimate the computational effort for operational convection-permitting
ensemble forecasting. We also study how the simulation wall-clock time scales
with increasing numbers of computing elements (from 36 to 1152 cores).
Objective verification methods generally show that convection-permitting
forecasts outperform global forecasts for both events, although precipitation
peaks remain largely underestimated for one of the two events. Comparing
simulation speeds, the MOLOCH model is the fastest and the Meso-NH is the
slowest one. The WRF model attains efficient scalability, whereas it is limited
for the Meso-NH and MOLOCH models when using more than 288 cores. We finally
demonstrate how the model simulation speed has the largest impact on a joint
evaluation with the model performance because the accuracy of the three
limited-area ensembles, amplifying the forecasting capability of the global
predictions, does not differ substantially.


Abstract:
  We consider a fluid model including viscoelastic and viscoplastic effects.
The state is given by the fluid velocity and an internal stress tensor that is
transported along the flow with the Zaremba-Jaumann derivative. Moreover, the
stress tensor obeys a nonlinear and nonsmooth dissipation law as well as stress
diffusion. We prove the existence of global-in-time weak solutions satisfying
an energy inequality under general Dirichlet conditions for the velocity field
and Neumann conditions for the stress tensor.


Abstract:
  Food recommendation has become an important means to help guide users to
adopt healthy dietary habits. Previous works on food recommendation either i)
fail to consider users' explicit requirements, ii) ignore crucial health
factors (e.g., allergies and nutrition needs), or iii) do not utilize the rich
food knowledge for recommending healthy recipes. To address these limitations,
we propose a novel problem formulation for food recommendation, modeling this
task as constrained question answering over a large-scale food knowledge
base/graph (KBQA). Besides the requirements from the user query, personalized
requirements from the user's dietary preferences and health guidelines are
handled in a unified way as additional constraints to the QA system. To
validate this idea, we create a QA style dataset for personalized food
recommendation based on a large-scale food knowledge graph and health
guidelines. Furthermore, we propose a KBQA-based personalized food
recommendation framework which is equipped with novel techniques for handling
negations and numerical comparisons in the queries. Experimental results on the
benchmark show that our approach significantly outperforms non-personalized
counterparts (average 59.7% absolute improvement across various evaluation
metrics), and is able to recommend more relevant and healthier recipes.


Abstract:
  One of the most distinctive hallmarks of many-body systems far from
equilibrium is the spontaneous emergence of order under conditions where
disorder would be plausibly expected. Here, we report on the self-transition
between ordered and disordered emulsions in divergent microfluidic channels,
i.e. from monodisperse assemblies to heterogeneous polydisperse foam-like
structures, and back again to ordered ones. The transition is driven by the
nonlinear competition between viscous dissipation and surface tension forces as
controlled by the device geometry, particularly the aperture angle of the
divergent microfluidic channel. An unexpected route back to order is observed
in the regime of large opening angles, where a trend towards increasing
disorder would be intuitively expected.


Abstract:
  Dynamics of jammed packings of soft athermal disks under finite-rate shear
are studied by means of molecular dynamics simulations. Particularly, we
investigate the spatial structures of stress drop events, which are expected to
provide information about plasticity. Investigating the displacement fields
during stress drop events, we show that there are qualitatively different two
types of events in the low rate limit: localized ones and extended ones. We
further investigate the time evolution of events and clarify that both types of
events are due to oscillatory motion of the stress, which is unique for systems
under finite-rate shear. The difference between two types of events is the
regime that events reside in: while localized events take place during plastic
events, extended ones occur in the elastic branch.


Abstract:
  The Boltzmann distribution of an ideal gas is determined by the Hamiltonian
function generating single particle dynamics. Systems with higher complexity
often exhibit topological constraints, which are independent of the Hamiltonian
and may affect the shape of the distribution function as well. Here, we study a
further source of heterogeneity, the curvature of spacetime arising from the
general theory of relativity. The present construction relies on three
assumptions: first, the statistical ensemble is made of particles obeying
geodesic equations, which define the phase space of the system. Next, the
metric coefficients are time-symmetric, implying that, if thermodynamic
equilibrium is achieved, all physical observables are independent of coordinate
time. Finally, ergodicity is enforced with respect to proper time, so that
ambiguity in the choice of a time variable for the statistical ensemble is
removed. Under these hypothesis, we derive the distribution function of
thermodynamic equilibrium, and verify that it reduces to the Boltzmann
distribution in the classical limit. We further show that spacetime curvature
affects physical observables, even far from the source of the metric. Two
examples are analyzed: an ideal gas in Schwarzschild spacetime and a charged
gas in Kerr-Newman spacetime. In the Schwarzschild case, conservation of
macroscopic constraints, such as angular momentum, combined with relativistic
distortion of the distribution function can produce configurations with
decreasing density and growing azimuthal rotation velocity far from the event
horizon of the central mass. In the Kerr-Newman case, it is found that kinetic
energy associated with azimuthal rotations is an increasing function of the
radial coordinate, and it eventually approaches a constant value corresponding
to classical equipartition, even though spatial particle density decreases.


Abstract:
  There are many deterministic mathematical operations (e.g. compression,
clipping, downsampling) that degrade speech quality considerably. In this paper
we introduce a neural network architecture, based on a modification of the
DiffWave model, that aims to restore the original speech signal. DiffWave, a
recently published diffusion-based vocoder, has shown state-of-the-art
synthesized speech quality and relatively shorter waveform generation times,
with only a small set of parameters. We replace the mel-spectrum upsampler in
DiffWave with a deep CNN upsampler, which is trained to alter the degraded
speech mel-spectrum to match that of the original speech. The model is trained
using the original speech waveform, but conditioned on the degraded speech
mel-spectrum. Post-training, only the degraded mel-spectrum is used as input
and the model generates an estimate of the original speech. Our model results
in improved speech quality (original DiffWave model as baseline) on several
different experiments. These include improving the quality of speech degraded
by LPC-10 compression, AMR-NB compression, and signal clipping. Compared to the
original DiffWave architecture, our scheme achieves better performance on
several objective perceptual metrics and in subjective comparisons.
Improvements over baseline are further amplified in a out-of-corpus evaluation
setting.


Abstract:
  Ganymede's atmosphere is produced by charged particle sputtering and
sublimation of its icy surface. Previous far-ultraviolet observations of the
O{\small I\,}1356-{\AA} and O{\small I\,}1304-{\AA} oxygen emissions were used
to infer sputtered molecular oxygen (O$_2$) as an atmospheric constituent, but
an expected sublimated water (H$_2$O) component remained undetected. Here we
present an analysis of high-sensitivity spectra and spectral images acquired by
the Hubble Space Telescope revealing H$_2$O in Ganymede's atmosphere. The
relative intensity of the oxygen emissions requires contributions from
dissociative excitation of water vapor, indicating that H$_2$O is more abundant
than O$_2$ around the sub-solar point. Away from the sub-solar region, the
emissions are consistent with a pure O$_2$ atmosphere. Eclipse observations
constrain atomic oxygen to be at least two orders of magnitude less abundant
than these other species. The higher H$_2$O/O$_2$ ratio above the warmer
trailing hemisphere compared to the colder leading hemisphere, the spatial
concentration to the sub-solar region, and the estimated abundance of
$\sim$10$^{15}$ H$_2$O/cm$^{2}$ are consistent with sublimation of the icy
surface as source.


Abstract:
  Conventional works that learn grasping affordance from demonstrations need to
explicitly predict grasping configurations, such as gripper approaching angles
or grasping preshapes. Classic motion planners could then sample trajectories
by using such predicted configurations. In this work, our goal is instead to
fill the gap between affordance discovery and affordance-based policy learning
by integrating the two objectives in an end-to-end imitation learning framework
based on deep neural networks. From a psychological perspective, there is a
close association between attention and affordance. Therefore, with an
end-to-end neural network, we propose to learn affordance cues as visual
attention that serves as a useful indicating signal of how a demonstrator
accomplishes tasks, instead of explicitly modeling affordances. To achieve
this, we propose a contrastive learning framework that consists of a Siamese
encoder and a trajectory decoder. We further introduce a coupled triplet loss
to encourage the discovered affordance cues to be more affordance-relevant. Our
experimental results demonstrate that our model with the coupled triplet loss
achieves the highest grasping success rate in a simulated robot environment.
Our project website can be accessed at
https://sites.google.com/asu.edu/affordance-aware-imitation/project.


Abstract:
  The friction f is the property of wall-bounded flows that sets the pumping
cost of a pipeline, the draining capacity of a river, and other variables of
practical relevance. For highly turbulent rough-walled pipe flows, f depends
solely on the roughness length scale r, and the f -- r relation may be
expressed by the Strickler empirical scaling f $\propto$ r$^{\frac{1}{3}}$.
Here, we show experimentally that for soap film flows that are the
two-dimensional (2D) equivalent of highly turbulent rough-walled pipe flows, f
$\propto$ r and the f -- r relation is not the same in 2D as in 3D. Our
findings are beyond the purview of the standard theory of friction but
consistent with a competing theory in which f is linked to the turbulent
spectrum via the spectral exponent $\alpha$: In 3D, $\alpha$ = 5/3 and the
theory yields f $\propto$ r$^{\frac{1}{3}}$; in 2D, $\alpha$ = 3 and the theory
yields f $\propto$ r.


Abstract:
  In the last few years, the notion of optimal polynomial approximant has
appeared in the mathematics literature in connection with Hilbert spaces of
analytic functions of one or more variables. In the 70s, researchers in
engineering and applied mathematics introduced least-squares inverses in the
context of digital filters in signal processing. It turns out that in the Hardy
space $H^2$ these objects are identical. This paper is a survey of known
results about optimal polynomial approximants. In particular, we will examine
their connections with orthogonal polynomials and reproducing kernels in
weighted spaces and digital filter design. We will also describe what is known
about the zeros of optimal polynomial approximants, their rates of decay, and
convergence results. Throughout the paper, we state many open questions that
may be of interest.


Abstract:
  To address the issue of inaccurate distributions in practical stochastic
systems, a minimax linear-quadratic control method is proposed using the
Wasserstein metric. Our method aims to construct a control policy that is
robust against errors in an empirical distribution of underlying uncertainty,
by adopting an adversary that selects the worst-case distribution. The opponent
receives a Wasserstein penalty proportional to the amount of deviation from the
empirical distribution. A closed-form expression of the finite-horizon optimal
policy pair is derived using a Riccati equation. The result is then extended to
the infinite-horizon average cost setting by identifying conditions under which
the Riccati recursion converges to the unique positive semi-definite solution
to an algebraic Riccati equation. Our method is shown to possess several
salient features including closed-loop stability, and an out-of-sample
performance guarantee. We also discuss how to optimize the penalty parameter
for enhancing the distributional robustness of our control policy. Last but not
least, a theoretical connection to the classical $H_\infty$-method is
identified from the perspective of distributional robustness.


Abstract:
  We consider the differential entropy of probability measures absolutely
continuous with respect to a given $\sigma$-finite reference measure on an
arbitrary measurable space. We state the asymptotic equipartition property in
this general case; the result is part of the folklore but our presentation is
to some extent novel. Then we study a general framework under which such
entropies satisfy a chain rule: disintegrations of measures. We give an
asymptotic interpretation for conditional entropies in this case. Finally, we
apply our result to Haar measures in canonical relation.


Abstract:
  We consider parabolic equations on bounded smooth open sets $\Om\subset \R^N$
($N\ge 1$) with mixed Dirichlet type boundary-exterior conditions associated
with the elliptic operator $\mathscr{L} \coloneqq - \Delta + (-\Delta)^{s}$
($0<s<1$). Firstly, we prove several well-posedness and regularity results of
the associated elliptic and parabolic problems with smooth, and then with
singular boundary-exterior data. Secondly, we show the existence of optimal
solutions of associated optimal control problems, and we characterize the
optimality conditions. This is the first time that such topics have been
presented and studied in a unified fashion for mixed local-nonlocal PDEs with
singular data.


Abstract:
  We present the timing results of out-of-eclipse observations of Centaurus X-3
spanning half a binary orbit, performed on 12-13 December, 2016 with the Large
Area X-ray Proportional Counter (LAXPC) on-board AstroSat. The pulse profile
was confirmed to exhibit a prominent pulse peak with a secondary inter-pulse.
The systemic spin period of the pulsar was found to be $4.80188 \pm 0.000085$ s
in agreement with its spin up trend. The spin up timescale seems to have
increased to $7709 \pm 58$ yr that points to negative torque effects in the
inner accretion disk. We also report the derived values of projected semi-major
axis and orbital velocity of the neutron star.


Abstract:
  Fully homomorphic encryption (FHE) is an encryption scheme which enables
computation on encrypted data without revealing the underlying data. While
there have been many advances in the field of FHE, developing programs using
FHE still requires expertise in cryptography. In this white paper, we present a
fully homomorphic encryption transpiler that allows developers to convert
high-level code (e.g., C++) that works on unencrypted data into high-level code
that operates on encrypted data. Thus, our transpiler makes transformations
possible on encrypted data.
  Our transpiler builds on Google's open-source XLS SDK
(https://github.com/google/xls) and uses an off-the-shelf FHE library, TFHE
(https://tfhe.github.io/tfhe/), to perform low-level FHE operations. The
transpiler design is modular, which means the underlying FHE library as well as
the high-level input and output languages can vary. This modularity will help
accelerate FHE research by providing an easy way to compare arbitrary programs
in different FHE schemes side-by-side. We hope this lays the groundwork for
eventual easy adoption of FHE by software developers. As a proof-of-concept, we
are releasing an experimental transpiler
(https://github.com/google/fully-homomorphic-encryption/tree/main/transpiler)
as open-source software.


Abstract:
  We investigate macroscopic and microscopic quantum correlations in a heavy
quark-gluon medium holographically. In order to represent heavy quarks, we
consider the string cloud geometry which involves uniformly distributed open
strings. In this system, the macroscopic quantum correlation is described by
the entanglement entropy and increases when the gluon's excitation and the
quark's density increase. We also investigate a microscopic two-point
correlation of local operators in the quark-gluon medium. Our calculation shows
that the two-point function of local operators decreases as the background
entanglement entropy increases. This is because the strong quantum correlation
of the background medium gives rise to a strong screening effect. Furthermore,
we survey the effect of the quark-gluon medium on the quark-antiquark pair by
employing the holographic Wilson loop.


Abstract:
  Deep generative models are becoming widely used across science and industry
for a variety of purposes. A common challenge is achieving a precise implicit
or explicit representation of the data probability density. Recent proposals
have suggested using classifier weights to refine the learned density of deep
generative models. We extend this idea to all types of generative models and
show how latent space refinement via iterated generative modeling can
circumvent topological obstructions and improve precision. This methodology
also applies to cases were the target model is non-differentiable and has many
internal latent dimensions which must be marginalized over before refinement.
We demonstrate our Latent Space Refinement (LaSeR) protocol on a variety of
examples, focusing on the combinations of Normalizing Flows and Generative
Adversarial Networks.


Abstract:
  The hot nucleus $^{171}$Yb is investigated by the covariant density
functional theory with the PC-PK1 effective interaction. The thermodynamic
quantities are evaluated with the canonical ensemble theory. The pairing
correlations is treated by the shell-model-like approach, in which the particle
numbers are conserved strictly and in which the blocking effect is handled
exactly. An S-shaped heat capacity versus temperature of $^{171}$Yb appears. It
has been studied in terms of the blocking effect, the single-particle levels,
the pairing gap, and defined seniority components, and compared to the heat
capacity of $^{172}$Yb. The pairing transition from the superfluid state to the
normal state can result in the S-shaped heat capacity of $^{172}$Yb where the
one-pair-broken and two-pair-broken states dominate, while the single-particle
level structure near the Fermi surface is associated with the S-shaped heat
capacity of $^{171}$Yb. For odd-A nuclei, although the one-pair-broken and
two-pair-broken states still contribute, the pairing gap and the pairing
transition is relatively weak. The S-shaped heat capacity could be affected due
to the blocking of the single-particle level near the Fermi surface.


Abstract:
  We consider extrapolation of the Arnoldi algorithm to accelerate computation
of the dominant eigenvalue/eigenvector pair. The basic algorithm uses sequences
of Krylov vectors to form a small eigenproblem which is solved exactly. The two
dominant eigenvectors output from consecutive Arnoldi steps are then recombined
to form an extrapolated iterate, and this accelerated iterate is used to
restart the next Arnoldi process. We present numerical results testing the
algorithm on a variety of cases and find on most examples it substantially
improves the performance of restarted Arnoldi. The extrapolation is a simple
post-processing step which has minimal computational cost.


Abstract:
  We define and study the existence of very stable Higgs bundles on Riemann
surfaces, how it implies a precise formula for the multiplicity of the very
stable components of the global nilpotent cone and its relationship to mirror
symmetry. The main ingredients are the Bialynicki-Birula theory of ${\mathbb
C}^*$-actions on semiprojective varieties, ${\mathbb C}^*$ characters of
indices of ${\mathbb C}^*$-equivariant coherent sheaves, Hecke transformation
for Higgs bundles, relative Fourier-Mukai transform along the Hitchin
fibration, hyperholomorphic structures on universal bundles and cominuscule
Higgs bundles.


Abstract:
  In this paper we carry out numerical analysis for a family of simplified gas
transport models with hydrate formation and dissociation in subsurface, in
equilibrium and non-equilibrium conditions. These models are adequate for
simulation of hydrate phase change at basin and at shorter time scales, but the
analysis does not account directly for the related effects of evolving
hydraulic properties.
  To our knowledge this is the first analysis of such a model. It is carried
out for the transport steps while keeping the pressure solution fixed. We frame
the transport model as conservation law with a non-smooth space-dependent flux
function; the kinetic model approximates this equilibrium. We prove weak
stability of the upwind scheme applied to the regularized conservation law. We
illustrate the model, confirm convergence with numerical simulations, and
illustrate its use for some relevant equilibrium and non-equilibrium scenarios.


Abstract:
  Emotions are usually evoked in humans by images. Recently, extensive research
efforts have been dedicated to understanding the emotions of images. In this
chapter, we aim to introduce image emotion analysis (IEA) from a computational
perspective with the focus on summarizing recent advances and suggesting future
directions. We begin with commonly used emotion representation models from
psychology. We then define the key computational problems that the researchers
have been trying to solve and provide supervised frameworks that are generally
used for different IEA tasks. After the introduction of major challenges in
IEA, we present some representative methods on emotion feature extraction,
supervised classifier learning, and domain adaptation. Furthermore, we
introduce available datasets for evaluation and summarize some main results.
Finally, we discuss some open questions and future directions that researchers
can pursue.


Abstract:
  Many multi-agent systems in nature are comprised of agents that interact
with, and respond to, the dynamics of their environment. In this paper, we
approach the study of such agent-environment interactions through the study of
passively compliant vehicles coupled to their environment via simple
nonholonomic constraints. We first consider a single passively compliant
Chaplygin beanie atop a platform having translational compliance, introduce the
reduced equations for the system using the notion of nonholonomic momentum, and
provide proof for its stability under arbitrary deformations of the elastic
element modeling its compliance. We then direct our focus to results concerning
the frequency response and control of passive Chaplygin beanies under actuation
of the platform, discuss rich dynamical features arising from periodic
actuation, and develop rules by which control can be exerted to collect and
disperse multiple passive vehicles. We then discuss how the latter of these
results clarifies the extent to which stable behavior can be excited in the
system through exogenous control.


Abstract:
  Many experiments have shown large flow enhancement ratios (up to 10^5) in
carbon nanotubes (CNT) with diameters larger than 5nm. However, molecular
dynamics simulations have never replicated these results maintaining a
three-order-of-magnitude gap with measurements. Our study provides a generic
model of nanofluidics for continuum slip flow (diameter>3nm) that fills this
significant gap and sheds light on its origin. Compared to 140 literature
cases, the model explains the entire range of experimental flow enhancements by
changes of nanotube diameters and finite variations of interfacial energies.
Despite large variations of flow enhancement ratios spanning 5 orders of
magnitude in experimental results, the ratio between these data and
corresponding model predictions approaches unity for the majority of
experiments. The role of viscous entrance effects is discussed. The model
provides insight into puzzling observations such as differences of CNTs and
boron nitride nanotubes, the slip on low-contact-angle surfaces and massive
functionalization effects. This study could advance our understanding of
nano-scale transport mechanisms and aid the design of tailored nanomembranes.


Abstract:
  Using the short-cadence data (1-min interval) of the Kepler space telescope,
we conducted a statistical analysis for the characteristic time of stellar
flares on Sun-like stars (SLS). Akin to solar flares, stellar flares show rise
and decay light-curve profiles, which reflect the two distinct phases (rise
phase and decay phase) of the flare process. We derived characteristic times of
the two phases for the stellar flares of SLS, resulting in a median rise time
of about 5.9 min and a median decay time of 22.6 min. It is found that both the
rise time and the decay time of the stellar flares follow a lognormal
distribution. The peak positions of the lognormal distributions for flare rise
time and decay time are 3.5 min and 14.8 min, respectively. These time values
for stellar flares are similar to the time-scale of solar flares, which
supports the idea that stellar flares and solar flares have the same physical
mechanism. The statistical results obtained in this work for SLS can be a
benchmark of flare characteristic times when comparing with other types of
stars.


Abstract:
  Optical imaging of genetically encoded calcium indicators is a powerful tool
to record the activity of a large number of neurons simultaneously over a long
period of time from freely behaving animals. However, determining the exact
time at which a neuron spikes and estimating the underlying firing rate from
calcium fluorescence data remains challenging, especially for calcium imaging
data obtained from a longitudinal study. We propose a multi-trial time-varying
$\ell_0$ penalized method to jointly detect spikes and estimate firing rates by
robustly integrating evolving neural dynamics across trials. Our simulation
study shows that the proposed method performs well in both spike detection and
firing rate estimation. We demonstrate the usefulness of our method on calcium
fluorescence trace data from two studies, with the first study showing
differential firing rate functions between two behaviors and the second study
showing evolving firing rate function across trials due to learning.


Abstract:
  The performance of a binary classifier ("predictor") depends heavily upon the
context ("workflow") in which it operates. Classic measures of predictor
performance do not reflect the realized utility of predictors unless certain
implied workflow assumptions are met. Failure to meet these implied assumptions
results in suboptimal classifier implementations and a mismatch between
predicted or assessed performance and the actual performance obtained in
real-world deployments. The mismatch commonly arises when multiple predictions
can be made for the same event, the event is relatively rare, and redundant
true positive predictions for the same event add little value, e.g., a system
that makes a prediction each minute, repeatedly issuing interruptive alarms for
a predicted event that may never occur.
  We explain why classic metrics do not correctly represent the performance of
predictors in such contexts, and introduce an improved performance assessment
technique ("u-metrics") using utility functions to score each prediction.
U-metrics explicitly account for variability in prediction utility arising from
temporal relationships. Compared to traditional performance measures, u-metrics
more accurately reflect the real-world benefits and costs of a predictor
operating in a workflow context. The difference can be significant.
  We also describe the use of "snoozing," a method whereby predictions are
suppressed for a period of time, commonly improving predictor performance by
reducing false positives while retaining the capture of events. Snoozing is
especially useful when predictors generate interruptive alerts, as so often
happens in clinical practice. Utility-based performance metrics correctly
predict and track the performance benefits of snoozing, whereas traditional
performance metrics do not.


Abstract:
  Collecting a large number of reliable training images annotated by multiple
land-cover class labels in the framework of multi-label classification is
time-consuming and costly in remote sensing (RS). To address this problem,
publicly available thematic products are often used for annotating RS images
with zero-labeling-cost. However, such an approach may result in constructing a
training set with noisy multi-labels, distorting the learning process. To
address this problem, we propose a Consensual Collaborative Multi-Label
Learning (CCML) method. The proposed CCML identifies, ranks and corrects
training images with noisy multi-labels through four main modules: 1)
discrepancy module; 2) group lasso module; 3) flipping module; and 4) swap
module. The discrepancy module ensures that the two networks learn diverse
features, while obtaining the same predictions. The group lasso module detects
the potentially noisy labels by estimating the label uncertainty based on the
aggregation of two collaborative networks. The flipping module corrects the
identified noisy labels, whereas the swap module exchanges the ranking
information between the two networks. The experimental results confirm the
success of the proposed CCML under high (synthetically added) multi-label noise
rates. The code of the proposed method is publicly available at
https://noisy-labels-in-rs.org


Abstract:
  Automatic sequences are not suitable sequences for cryptographic applications
since both their subword complexity and their expansion complexity are small,
and their correlation measure of order 2 is large. These sequences are highly
predictable despite having a large maximum order complexity. However, recent
results show that polynomial subsequences of automatic sequences, such as the
Thue--Morse sequence, are better candidates for pseudorandom sequences. A
natural generalization of automatic sequences are morphic sequences, given by a
fixed point of a prolongeable morphism that is not necessarily uniform. In this
paper we prove a lower bound for the maximum order complexity of the sum of
digits function in Zeckendorf base which is an example of a morphic sequence.
We also prove that the polynomial subsequences of this sequence keep large
maximum order complexity, such as the Thue--Morse sequence.


Abstract:
  We revisit recent methods that employ graph neural networks for decoding
error correcting codes and employ messages that are computed in an
autoregressive manner. The outgoing messages of the variable nodes are
conditioned not only on the incoming messages, but also on an estimation of the
SNR and on the inferred codeword and on two downstream computations: (i) an
extended vector of parity check outcomes, (ii) the mismatch between the
inferred codeword and the re-encoding of the information bits of this codeword.
Unlike most learned methods in the field, our method violates the symmetry
conditions that enable the other methods to train exclusively with the
zero-word. Despite not having the luxury of training on a single word, and the
inability to train on more than a small fraction of the relevant sample space,
we demonstrate effective training. The new method obtains a bit error rate that
outperforms the latest methods by a sizable margin.


Abstract:
  We introduce Activity Graph Transformer, an end-to-end learnable model for
temporal action localization, that receives a video as input and directly
predicts a set of action instances that appear in the video. Detecting and
localizing action instances in untrimmed videos requires reasoning over
multiple action instances in a video. The dominant paradigms in the literature
process videos temporally to either propose action regions or directly produce
frame-level detections. However, sequential processing of videos is problematic
when the action instances have non-sequential dependencies and/or non-linear
temporal ordering, such as overlapping action instances or re-occurrence of
action instances over the course of the video. In this work, we capture this
non-linear temporal structure by reasoning over the videos as non-sequential
entities in the form of graphs. We evaluate our model on challenging datasets:
THUMOS14, Charades, and EPIC-Kitchens-100. Our results show that our proposed
model outperforms the state-of-the-art by a considerable margin.


Abstract:
  Suspended microparticles subjected to AC electrical fields collectively
organize into band patterns perpendicular to the field direction. The bands
further develop into zigzag shaped patterns, in which the particles are
observed to circulate. We demonstrate that this phenomenon can be observed
quite generically by generating such patterns with a wide range of particles:
silica spheres, fatty acid, oil, and coacervate droplets, bacteria, and ground
coffee. We show that the phenomenon can be well understood in terms of second
order electrokinetic flow, which correctly predicts the hydrodynamic
interactions required for the pattern formation process.Brownian particle
simulations based on these interactions accurately recapitulate all of the
observed pattern formation and symmetry-breaking events, starting from a
homogeneous particle suspension. The emergence of the formed patterns can be
predicted quantitatively within a parameter-free theory.


Abstract:
  For $\mathcal{O}$ a reduced operad, a generalized divergence from the
derivations of a free $\mathcal{O}$-algebra to a suitable trace space is
constructed. In the case of the Lie operad, this corresponds to Satoh's trace
map and, for the associative operad, to the double divergence of Alekseev,
Kawazumi, Kuno and Naef. The generalized divergence is shown to be a
$1$-cocycle for the usual Lie algebra structure on derivations. These results
place the previous constructions into a unified framework; moreover, they are
natural with respect to the operad.
  An important new ingredient is the use of naturality with respect to the
category of finite-rank free modules and split monomorphisms over a commutative
ring $R$. This allows the notion of torsion for such functors to be exploited.
  Supposing that the ring $R$ is a PID and that the operad $\mathcal{O}$ is
binary, the main result relates the kernel of the generalized divergence to the
sub Lie algebra of the Lie algebra of derivations that is generated by the
elements of degree one with respect to the grading induced by arity.


Abstract:
  We establish a version of Kashiwara's theorem for twisted sheaves of
Berthelot's arithmetic differential operators for a closed immersion between
smooth p-adic formal schemes. As an application, we construct simple modules
for crystalline distribution algebras of reductive groups using the twisted
version of the crystalline localisation theorem.


Abstract:
  We derive exact analytic results for several four-point correlation functions
for statistical models exhibiting phase separation in two-dimensions. Our
theoretical results are then specialized to the Ising model on the
two-dimensional strip and found to be in excellent agreement with
high-precision Monte Carlo simulations.


Abstract:
  Serverless computing, or Function-as-a-Service (FaaS), enables a new way of
building and scaling applications by allowing users to deploy fine-grained
functions while providing fully-managed resource provisioning and auto-scaling.
Custom FaaS container support is gaining traction as it enables better control
over OSes, versioning, and tooling for modernizing FaaS applications. However,
providing rapid container provisioning introduces non-trivial challenges for
FaaS providers, since container provisioning is costly, and real-world FaaS
workloads exhibit highly dynamic patterns. In this paper, we design FaaSNet, a
highly-scalable middleware system for accelerating FaaS container provisioning.
FaaSNet is driven by the workload and infrastructure requirements of the FaaS
platform at one of the world's largest cloud providers, Alibaba Cloud Function
Compute. FaaSNet enables scalable container provisioning via a lightweight,
adaptive function tree (FT) structure. FaaSNet uses an I/O efficient, on-demand
fetching mechanism to further reduce provisioning costs at scale. We implement
and integrate FaaSNet in Alibaba Cloud Function Compute. Evaluation results
show that FaaSNet: (1) finishes provisioning 2500 function containers on 1000
virtual machines in 8.3 seconds, (2) scales 13.4x and 16.3x faster than Alibaba
Cloud's current FaaS platform and a state-of-the-art P2P container registry
(Kraken), respectively, and (3) sustains a bursty workload using 75.2% less
time than an optimized baseline.


Abstract:
  The practical application requests both accuracy and efficiency on
multi-person pose estimation algorithms. But the high accuracy and fast
inference speed are dominated by top-down methods and bottom-up methods
respectively. To make a better trade-off between accuracy and efficiency, we
propose a novel multi-person pose estimation framework, SIngle-network with
Mimicking and Point Learning for Bottom-up Human Pose Estimation (SIMPLE).
Specifically, in the training process, we enable SIMPLE to mimic the pose
knowledge from the high-performance top-down pipeline, which significantly
promotes SIMPLE's accuracy while maintaining its high efficiency during
inference. Besides, SIMPLE formulates human detection and pose estimation as a
unified point learning framework to complement each other in single-network.
This is quite different from previous works where the two tasks may interfere
with each other. To the best of our knowledge, both mimicking strategy between
different method types and unified point learning are firstly proposed in pose
estimation. In experiments, our approach achieves the new state-of-the-art
performance among bottom-up methods on the COCO, MPII and PoseTrack datasets.
Compared with the top-down approaches, SIMPLE has comparable accuracy and
faster inference speed.


Abstract:
  This work considers predicting the relational structure of a hypergraph for a
given set of vertices, as common for applications in particle physics,
biological systems and other complex combinatorial problems. A problem arises
from the number of possible multi-way relationships, or hyperedges, scaling in
$\mathcal{O}(2^n)$ for a set of $n$ elements. Simply storing an indicator
tensor for all relationships is already intractable for moderately sized $n$,
prompting previous approaches to restrict the number of vertices a hyperedge
connects. Instead, we propose a recurrent hypergraph neural network that
predicts the incidence matrix by iteratively refining an initial guess of the
solution. We leverage the property that most hypergraphs of interest are
sparsely connected and reduce the memory requirement to $\mathcal{O}(nk)$,
where $k$ is the maximum number of positive edges, i.e., edges that actually
exist. In order to counteract the linearly growing memory cost from training a
lengthening sequence of refinement steps, we further propose an algorithm that
applies backpropagation through time on randomly sampled subsequences. We
empirically show that our method can match an increase in the intrinsic
complexity without a performance decrease and demonstrate superior performance
compared to state-of-the-art models.


Abstract:
  In this work we improve the runtime of recent classical algorithms for strong
simulation of quantum circuits composed of Clifford and T gates. The
improvement is obtained by establishing a new upper bound on the stabilizer
rank of $m$ copies of the magic state
$|T\rangle=\sqrt{2}^{-1}(|0\rangle+e^{i\pi/4}|1\rangle)$ in the limit of large
$m$. In particular, we show that $|T\rangle^{\otimes m}$ can be exactly
expressed as a superposition of at most $O(2^{\alpha m})$ stabilizer states,
where $\alpha\leq 0.3963$, improving on the best previously known bound $\alpha
\leq 0.463$. This furnishes, via known techniques, a classical algorithm which
approximates output probabilities of an $n$-qubit Clifford + T circuit $U$ with
$m$ uses of the T gate to within a given inverse polynomial relative error
using a runtime $\mathrm{poly}(n,m)2^{\alpha m}$. We also provide improved
upper bounds on the stabilizer rank of symmetric product states
$|\psi\rangle^{\otimes m}$ more generally; as a consequence we obtain a strong
simulation algorithm for circuits consisting of Clifford gates and $m$
instances of any (fixed) single-qubit $Z$-rotation gate with runtime
$\text{poly}(n,m) 2^{m/2}$. We suggest a method to further improve the upper
bounds by constructing linear codes with certain properties.


Abstract:
  Continual learning is a machine learning sub-field specialized in settings
with non-iid data. Hence, the training data distribution is not static and
drifts through time. Those drifts might cause interferences in the trained
model and knowledge learned on previous states of the data distribution might
be forgotten. Continual learning's challenge is to create algorithms able to
learn an ever-growing amount of knowledge while dealing with data distribution
drifts.
  One implementation difficulty in these field is to create data loaders that
simulate non-iid scenarios. Indeed, data loaders are a key component for
continual algorithms. They should be carefully designed and reproducible. Small
errors in data loaders have a critical impact on algorithm results, e.g. with
bad preprocessing, wrong order of data or bad test set. Continuum is a simple
and efficient framework with numerous data loaders that avoid researcher to
spend time on designing data loader and eliminate time-consuming errors. Using
our proposed framework, it is possible to directly focus on the model design by
using the multiple scenarios and evaluation metrics implemented. Furthermore
the framework is easily extendable to add novel settings for specific needs.


Abstract:
  Convection and turbulence in core-collapse supernovae (CCSNe) are inherently
three-dimensional in nature. However, 3D simulations of CCSNe are
computationally demanding. Thus, it is valuable to modify simulations in
spherical symmetry to incorporate 3D effects using some parametric model. In
this paper, we report on the formulation and implementation of general
relativistic (GR) neutrino-driven turbulent convection in the spherically
symmetric core-collapse supernova code \texttt{GR1D}. This is based upon the
recently proposed method of Supernova Turbulence in Reduced-dimensionality
(\textit{STIR}) in Newtonian simulations from \cite{Couch2020_STIR}. When the
parameters of this model are calibrated to 3D simulations, we find that our GR
formulation of \textit{STIR} requires larger turbulent eddies to achieve a
shock evolution similar to the original \textit{STIR} model. We also find that
general relativity may alter the correspondence between progenitor mass and
successful vs.~failed explosions.


Abstract:
  We present some properties of the Weierstrass $\wp$-function associated to
the hexagonal (or triangular) lattice. In particular, with the help of an old
theorem of I.N. Baker \cite{B} on the characterization of meromorphic solutions
of the equation $X^3 + Y^3 = 1$ we determine the zeros of the function $\wp'(z)
\pm \sqrt{3}$.


Abstract:
  In a recent MNRAS article, Raposo-Pulido and Pelaez (RPP) designed a scheme
for obtaining very close seeds for solving the elliptic Kepler Equation with
the classical and the modified Newton-Rapshon methods. This implied an
important reduction in the number of iterations needed to reach a given
accuracy. However, RPP also made strong claims about the errors of their method
that are incorrect. In particular, they claim that their accuracy can always
reach the level $\sim5\varepsilon$, where $\varepsilon$ is the machine epsilon
(e.g. $\varepsilon=2.2\times10^{-16} $ in double precision), and that this
result is attained for all values of the eccentricity $e<1$ and the mean
anomaly $M\in[0,\pi]$, including for $e$ and $M$ that are arbitrarily close to
$1$ and $0$, respectively. However, we demonstrate both numerically and
analytically that any implementation of the classical or modified
Newton-Raphson methods for Kepler's equation, including those described by RPP,
have a limiting accuracy of the order $\sim\varepsilon/\sqrt{2(1-e)}$.
Therefore the errors of these implementations diverge in the limit $e\to1$, and
differ dramatically from the incorrect results given by RPP. Despite these
shortcomings, the RPP method can provide a very efficient option for reaching
such limiting accuracy. We also provide a limit that is valid for the accuracy
of any algorithm for solving Kepler equation, including schemes like bisection
that do not use derivatives. Moreover, similar results are also demonstrated
for the hyperbolic Kepler Equation. The methods described in this work can
provide guidelines for designing more accurate solutions of the elliptic and
hyperbolic Kepler equations.


Abstract:
  The deep crustal heating, associated with exothermal nuclear reactions, is
believed to be a key parameter for describing the thermal evolution of
accreting neutron stars. In this paper, we present the first thermodynamically
consistent calculations of the crustal heating for realistic compositions of
thermonuclear ashes. In contrast to previous studies based on the traditional
approach, we account for neutron hydrostatic/diffusion (nHD) equilibrium
condition imposed by superfluidity of neutrons in a major part of the inner
crust and rapid diffusion in the remaining part of the inner crust. We apply a
simplified reaction network to model nuclear evolution of various
multi-component thermonuclear burning ashes (superburst, KEPLER, and extreme
rp-process ashes) in the outer crust and calculate the deep crustal heating
energy release Q, parametrized by the pressure at the outer-inner crust
interface, P_{oi}. Using the general thermodynamic arguments, we set a lower
limit on Q, Q>0.13-0.2 MeV per baryon (an actual value depends on the ash
composition and the employed mass model).


Abstract:
  Studying the statistical properties of the large-scale structure in the
Universe with weak gravitational lensing is a prime goal of several current and
forthcoming galaxy surveys. The power that weak lensing has to constrain
cosmological parameters can be enhanced by considering statistics beyond
second-order shear correlation functions or power spectra. One such
higher-order probe that has proven successful in observational data is the
density split statistics (DSS), in which one analyses the mean shear profiles
around points that are classified according to their foreground galaxy density.
In this paper, we generalise the most accurate DSS model to allow for a broad
class of angular filter functions used for the classification of the different
local density regions. This approach is motivated by earlier findings showing
that an optimised filter can provide tighter constraints on model parameters
compared to the standard top-hat case. We build on large deviation theory
approaches and approximations thereof to model the matter density PDF, and on
perturbative calculations of higher-order moments of the density field. The
novel addition relies on the generalisation of these previously employed
calculations to allow for general filter functions and is validated on several
sets of numerical simulations. The revised model fits well the simulation
measurements, with a residual systematic offset that is small compared to the
statistical accuracy of current weak lensing surveys. The accuracy of the model
is slightly lower for a compensated filter than for a non-negative filter
function, and that it increases with the filter size. Using a Fisher matrix
approach, we find constraints comparable to the commonly used two-point cosmic
shear measures. Hence, our DSS model can be used in competitive analyses of
current cosmic shear data, while it may need refinements for forthcoming
lensing surveys.


Abstract:
  The software Randentropy is designed to estimate inequality in a random
system where several individuals interact moving among many communities and
producing dependent random quantities of an attribute. The overall inequality
is assessed by computing the Random Theil's Entropy. Firstly, the software
estimates a piecewise homogeneous Markov chain by identifying the
changing-points and the relative transition probability matrices. Secondly, it
estimates the multivariate distribution function of the attribute using a
copula function approach and finally, through a Monte Carlo algorithm,
evaluates the expected value of the Random Theil's Entropy. Possible
applications are discussed as related to the fields of finance and human
mobility


Abstract:
  Hierarchical computational methods for multiscale mechanics such as the
FE$^2$ and FE-FFT methods are generally accompanied by high computational
costs. Data-driven approaches are able to speed the process up significantly by
enabling to incorporate the effective micromechanical response in macroscale
simulations without the need of performing additional computations at each
Gauss point explicitly. Traditionally artificial neural networks (ANNs) have
been the surrogate modeling technique of choice in the solid mechanics
community. However they suffer from severe drawbacks due to their parametric
nature and suboptimal training and inference properties for the investigated
datasets in a three dimensional setting. These problems can be avoided using
local approximate Gaussian process regression (laGPR). This method can allow
the prediction of stress outputs at particular strain space locations by
training local regression models based on Gaussian processes, using only a
subset of the data for each local model, offering better and more reliable
accuracy than ANNs. A modified Newton-Raphson approach is proposed to
accommodate for the local nature of the laGPR approximation when solving the
global structural problem in a FE setting. Hence, the presented work offers a
complete and general framework enabling multiscale calculations combining a
data-driven constitutive prediction using laGPR, and macroscopic calculations
using an FE scheme that we test for finite-strain three-dimensional
hyperelastic problems.


Abstract:
  The recent advances in cancer immunotherapy boosted the development of
tumor-immune system models aiming to provide mechanistic understanding and
indicate more efficient treatment regimes. However, the complexity of such
models, their multi-scale dynamics and their overparameterized character
renders them inaccessible for wide utilization. In this work, the dynamics of a
fundamental model formulating the interactions of tumor cells with natural
killer cells, CD8$^+$ T cells and circulating lymphocytes is examined. It is
first shown that the long-term evolution of the system towards high-tumor or
tumor-free equilibria is determined by the dynamics of an initial
\emph{explosive stage} of tumor progression. Focusing on this stage, the
algorithmic Computational Singular Perturbation methodology is employed to
identify the underlying mechanisms confining the system's evolution towards the
equilibrium and the governing slow dynamics along them. It is shown that these
insights are preserved along different tumor-immune system and
patient-dependent realizations. Utilizing the obtained mechanistic
understanding, a novel reduced model is constructed in an algorithmic fashion,
which accurately predicts the dynamics of the system during the explosive stage
and includes half of the parameters of the detailed model. This present
analysis demonstrates the potential of algorithmic asymptotic analysis to
simplify the complex, overeparameterized and multi-scale nature of cancer
immunology models and to indicate the interactions and cell types to target for
more effective treatment development.


Abstract:
  Several studies point to the antimicrobial effects of ELF electromagnetic
fields. Such fields have accompanied life from the very beginning, and it is
possible that they played a significant role in its emergence and evolution.
However, the literature on the biological effects of ELF electromagnetic fields
is controversial, and we still lack an understanding of the complex mechanisms
that make such effects, observed in many experiments, possible. The Covid-19
pandemic has shown how fragile we are in the face of powerful processes
operating in the biosphere. We believe that understanding the role of ELF
electromagnetic fields in regulating the biosphere is important in our fight
against Covid-19, and research in this direction should be intensified.


Abstract:
  A simple, self-calibrating, rotating-waveplate polarimeter is largely
insensitive to light intensity fluctuations and is shown to be useful for
determining the Stokes parameters of light. This study shows how to minimize
the in situ self-calibration time, the measurement time and the measurement
uncertainty. The suggested methods are applied to measurements of spatial
variations in the linear and circular polarizations of laser light passing
through glass plates with a laser intensity dependent birefringence. These are
crucial measurements for the ACME electron electric dipole measurements,
requiring accuracies in circular and linear polarization fraction of about 0.1%
and 0.4%, with laser intensities up to 100 $\text{mW/mm}^2$ incident into the
polarimeter.


Abstract:
  Transverse spin angular momentum of light is a key concept in recent
nanophotonics to realize unidirectional light transport in waveguides by
spin-momentum locking. Herein we theoretically propose subwavelength
nanoparticle chain waveguides that efficiently sort optical spins with
engineerable spin density distributions. By arranging high-refractive-index
nanospheres of different sizes in a zigzag manner, directional optical spin
propagation is realized. The origin of the efficient spin transport is revealed
by analyzing the dispersion relation and spin angular momentum density
distributions. In contrast to conventional waveguides, the proposed asymmetric
waveguide can spatially separate up- and down-spins and locate one parity
inside and the other outside the structure. Moreover, robustness against
bending the waveguide and its application as an optical spin sorter are
presented. Compared to previous reports on spatial engineering of local spins
in photonic crystal waveguides, we achieved substantial miniaturization of the
entire footprint down to subwavelength scale.


Abstract:
  We investigate a correspondence between two formalisms for discrete
probabilistic modeling: probabilistic graphical models (PGMs) and tensor
networks (TNs), a powerful modeling framework for simulating complex quantum
systems. The graphical calculus of PGMs and TNs exhibits many similarities,
with discrete undirected graphical models (UGMs) being a special case of TNs.
However, more general probabilistic TN models such as Born machines (BMs)
employ complex-valued hidden states to produce novel forms of correlation among
the probabilities. While representing a new modeling resource for capturing
structure in discrete probability distributions, this behavior also renders the
direct application of standard PGM tools impossible. We aim to bridge this gap
by introducing a hybrid PGM-TN formalism that integrates quantum-like
correlations into PGM models in a principled manner, using the
physically-motivated concept of decoherence. We first prove that applying
decoherence to the entirety of a BM model converts it into a discrete UGM, and
conversely, that any subgraph of a discrete UGM can be represented as a
decohered BM. This method allows a broad family of probabilistic TN models to
be encoded as partially decohered BMs, a fact we leverage to combine the
representational strengths of both model families. We experimentally verify the
performance of such hybrid models in a sequential modeling task, and identify
promising uses of our method within the context of existing applications of
graphical models.


Abstract:
  Many researchers have studied false data injection (FDI) attacks in power
state estimation, but existing state estimation approaches are still highly
vulnerable to FDI attacks. In this paper, we investigate the problem of the
above three FDI attacks against dynamic power state estimation (DSE). Although
the three attacks were discovered in SSE several years ago, none of them has
been well addressed in static power state systems. In this research, we propose
two robust defense approaches against the above three efficient FDI attacks on
DSE. Compared to existing approaches, our proposed approaches have three major
differences and significant strengths: (1) they defend against the three FDI
attacks on dynamic power state estimation rather than static power state
estimation, (2) they give a robust estimator that can accurately extract a
subset of attack-free sensors for power state estimation, and (3) they adopt
the little-known Mahalanobis distance in the consistency check of power sensor
measurements, which is different from the Euclidean distance used in all the
existing studies on power state estimation. We mathematically prove that the
Mahalanobis distance is not only useful but also much better than the Euclidean
distance in the consistency check of power sensor measurements. Our time
complexity analysis shows that the two proposed robust defense approaches are
efficient. Moreover, in order to demonstrate the effectiveness of the proposed
approaches, we compare them with the three well-known approaches: the least
square approach, the Imhotep-SMT approach, and the MEE-UKF approach. Our
extensive experiments show that the proposed approaches further reduce the
estimation error by two orders of magnitude and four orders of magnitude
compared to the Imhotep-SMT approach and the least square approach,
respectively. Moreover, our approach is more stable than the MEE-UKF approach.


Abstract:
  We compare three populations commonly used in experiments by economists and
other social scientists: undergraduate students at a physical location (lab),
Amazon's Mechanical Turk (MTurk), and Prolific. The comparison is made along
three dimensions: the noise in the data due to inattention, the cost per
observation, and the elasticity of response. We draw samples from each
population, examining decisions in four one-shot games with varying tensions
between the individual and socially efficient choices. When there is no
tension, where individual and pro-social incentives coincide, noisy behavior
accounts for 60% of the observations on MTurk, 19% on Prolific, and 14% for the
lab. Taking costs into account, if noisy data is the only concern Prolific
dominates from an inferential power point of view, combining relatively low
noise with a cost per observation one fifth of the lab's. However, because the
lab population is more sensitive to treatment, across our main PD game
comparison the lab still outperforms both Prolific and MTurk.


Abstract:
  In this paper we derive a combinatorial formula for mixed Eulerian numbers in
type $A$ from Peterson Schubert calculus. We also provide a simple computation
for mixed $\Phi$-Eulerian numbers in arbitrary Lie types.


Abstract:
  Entanglement underpins a variety of quantum-enhanced communication, sensing,
and computing capabilities. Entanglement-assisted communication (EACOMM)
leverages entanglement pre-shared by communication parties to boost the rate of
classical information transmission. Pioneering theory works showed that EACOMM
can enable a communication rate well beyond the ultimate classical capacity of
optical communications, but an experimental demonstration of any EACOMM
advantage remains elusive. Here, we report the implementation of EACOMM
surpassing the classical capacity over lossy and noisy bosonic channels. We
construct a high-efficiency entanglement source and a phase-conjugate quantum
receiver to reap the benefit of pre-shared entanglement, despite entanglement
being broken by channel loss and noise. We show that EACOMM beats the
Holevo-Schumacher-Westmoreland capacity of classical communication by up to
14.6%, when both protocols are subject to the same power constraint at the
transmitter. As a practical performance benchmark, a classical communication
protocol without entanglement assistance is implemented, showing that EACOMM
can reduce the bit-error rate by up to 69% over the same bosonic channel. Our
work opens a route to provable quantum advantages in a wide range of quantum
information processing tasks.


Abstract:
  Pseudo-relevance feedback mechanisms, from Rocchio to the relevance models,
have shown the usefulness of expanding and reweighting the users' initial
queries using information occurring in an initial set of retrieved documents,
known as the pseudo-relevant set. Recently, dense retrieval -- through the use
of neural contextual language models such as BERT for analysing the documents'
and queries' contents and computing their relevance scores -- has shown a
promising performance on several information retrieval tasks still relying on
the traditional inverted index for identifying documents relevant to a query.
Two different dense retrieval families have emerged: the use of single embedded
representations for each passage and query (e.g. using BERT's [CLS] token), or
via multiple representations (e.g. using an embedding for each token of the
query and document). In this work, we conduct the first study into the
potential for multiple representation dense retrieval to be enhanced using
pseudo-relevance feedback. In particular, based on the pseudo-relevant set of
documents identified using a first-pass dense retrieval, we extract
representative feedback embeddings (using KMeans clustering) -- while ensuring
that these embeddings discriminate among passages (based on IDF) -- which are
then added to the query representation. These additional feedback embeddings
are shown to both enhance the effectiveness of a reranking as well as an
additional dense retrieval operation. Indeed, experiments on the MSMARCO
passage ranking dataset show that MAP can be improved by upto 26% on the TREC
2019 query set and 10% on the TREC 2020 query set by the application of our
proposed ColBERT-PRF method on a ColBERT dense retrieval approach.


Abstract:
  Increasingly large datasets are rapidly driving up the computational costs of
machine learning. Prototype generation methods aim to create a small set of
synthetic observations that accurately represent a training dataset but greatly
reduce the computational cost of learning from it. Assigning soft labels to
prototypes can allow increasingly small sets of prototypes to accurately
represent the original training dataset. Although foundational work on `less
than one'-shot learning has proven the theoretical plausibility of learning
with fewer than one observation per class, developing practical algorithms for
generating such prototypes remains an unexplored territory. We propose a novel,
modular method for generating soft-label prototypical lines that still
maintains representational accuracy even when there are fewer prototypes than
the number of classes in the data. In addition, we propose the Hierarchical
Soft-Label Prototype k-Nearest Neighbor classification algorithm based on these
prototypical lines. We show that our method maintains high classification
accuracy while greatly reducing the number of prototypes required to represent
a dataset, even when working with severely imbalanced and difficult data. Our
code is available at https://github.com/ilia10000/SLkNN.


Abstract:
  Many objects do not appear frequently enough in complex scenes (e.g., certain
handbags in living rooms) for training an accurate object detector, but are
often found frequently by themselves (e.g., in product images). Yet, these
object-centric images are not effectively leveraged for improving object
detection in scene-centric images. In this paper, we propose Mosaic of
Object-centric images as Scene-centric images (MosaicOS), a simple and novel
framework that is surprisingly effective at tackling the challenges of
long-tailed object detection. Keys to our approach are three-fold: (i) pseudo
scene-centric image construction from object-centric images for mitigating
domain differences, (ii) high-quality bounding box imputation using the
object-centric images' class labels, and (iii) a multi-stage training
procedure. On LVIS object detection (and instance segmentation), MosaicOS leads
to a massive 60% (and 23%) relative improvement in average precision for rare
object categories. We also show that our framework can be compatibly used with
other existing approaches to achieve even further gains. Our pre-trained models
are publicly available at https://github.com/czhang0528/MosaicOS/.


Abstract:
  Self-organized criticality (SOC) refers to the ability of complex systems to
evolve towards a 2nd-order phase transition at which interactions between
system components lead to scale-invariant events beneficial for system
performance. For the last two decades, considerable experimental evidence
accumulated that the mammalian cortex with its diversity in cell types and
connections might exhibit SOC. Here we review experimental findings of
isolated, layered cortex preparations to self-organize towards four dynamical
motifs identified in the cortex in vivo: up-states, oscillations, neuronal
avalanches, and coherence potentials. During up-states, the synchronization
observed for nested theta/gamma-oscillations embeds scale-invariant neuronal
avalanches that exhibit robust power law scaling in size with a slope of -3/2
and a critical branching parameter of 1. This dynamical coordination, tracked
in the local field potential (nLFP) and pyramidal neuron activity using
2-photon imaging, emerges autonomously in superficial layers of organotypic
cortex cultures and acute cortex slices, is homeostatically regulated, displays
separation of time scales, and reveals unique size vs. quiet time dependencies.
A threshold operation identifies coherence potentials; avalanches that in
addition maintain the precise time course of propagated synchrony. Avalanches
emerge under conditions of external driving. Control parameters are established
by the balance of excitation and inhibition (E/I) and the neuromodulator
dopamine. This rich dynamical repertoire is not observed in dissociated cortex
cultures, which lack cortical layers and exhibit dynamics similar to a
1st-order phase transition. The precise interactions between up-states, nested
oscillations, avalanches, and coherence potentials in superficial cortical
layers provide compelling evidence for SOC in the brain.


Abstract:
  Information flow measures, over the duration of a game, the audience's belief
of who will win, and thus can reflect the amount of surprise in a game. To
quantify the relationship between information flow and audiences' perceived
quality, we conduct a case study where subjects watch one of the world's
biggest esports events, LOL S10. In addition to eliciting information flow, we
also ask subjects to report their rating for each game. We find that the amount
of surprise in the end of the game plays a dominant role in predicting the
rating. This suggests the importance of incorporating when the surprise occurs,
in addition to the amount of surprise, in perceived quality models. For content
providers, it implies that everything else being equal, it is better for twists
to be more likely to happen toward the end of a show rather than uniformly
throughout.


Abstract:
  We study the one parameter family of potential functions $q\varphi^u$
associated with the geometric potential $\varphi^u$ for the geodesic flow of a
compact rank 1 surface of nonpositive curvature. For $q<1$ it is known that
there is a unique equilibrium state associated with $q\varphi^u$, and it has
full support. For $q > 1$ it is known that an invariant measure is an
equilibrium state if and only if it is supported on the singular set. We study
the critical value $q=1$ and show that the ergodic equilibrium states are
either the restriction to the regular set of the Liouville measure, or measures
supported on the singular set. In particular, when~$q = 1$, there is a unique
ergodic equilibrium state that gives positive measure to the regular set.


Abstract:
  Reconfigurable intelligent surfaces (RISs) are envisioned to be a disruptive
wireless communication technique that is capable of reconfiguring the wireless
propagation environment. In this paper, we study a far-field RIS-assisted
multiple-input single-output (MISO) communication system operating in free
space. To maximize the received power of the receiver from the physics and
electromagnetic nature point of view, an optimization, including beamforming of
the transmitter, phase shifts of the RIS, orientation and position of the RIS
is formulated and solved. After exploiting the property of line-of-sight (LoS),
we derive closed-form solutions of beamforming and phase shifts. For the
non-trivial RIS position optimization problem in arbitrary three-dimensional
space, a dimensional-reducing theory is proved. The simulation results show
that the proposed closed-form beamforming and phase shifts are near-optimal
solutions. Besides, the RIS significantly enhances the performance of the
communication system when it is deployed at the optimal position.


Abstract:
  We investigate the geometrical structure of Vaidya's spacetime in the case of
a white hole with decreasing mass, stabilising to a black hole in finite or
infinite time or evaporating completely. Our approach relies on a detailed
analysis of the ordinary differential equation describing the incoming
principal null geodesics, among which are the generators of the past horizon.
We devote special attention to the case of a complete evaporation in infinite
time and establish the existence of an asymptotic light-like singularity of the
conformal curvature, touching both the past space-like singularity and future
time-like infinity. This singularity is present independently of the decay rate
of the mass. We derive an explicit formula that relates directly the strength
of this null singularity to the asymptotic behaviour of the mass function.


Abstract:
  We define topological time crystals, a dynamical phase of periodically driven
quantum many-body systems capturing the coexistence of topological order with
the spontaneous breaking of discrete time-translation symmetry. We show that
many-body localization can stabilize this phase against generic perturbations
and establish some of its key features and signatures. We link topological and
ordinary time crystals through three complementary perspectives: higher-form
symmetries, quantum error-correcting codes, and a holographic correspondence.
We also propose an experimental realization of a surface-code-based topological
time crystal for the Google Sycamore processor.


Abstract:
  The paper concerns a new statistical method for assessing dissimilarity of
two random sets based on one realisation of each of them. The method focuses on
shapes of the components of the random sets, namely on the curvature of their
boundaries together with the ratios of their perimeters and areas. Theoretical
background is introduced and then, the method is described, justified by a
simulation study and applied to real data of two different types of tissue -
mammary cancer and mastopathy.


Abstract:
  In the events of peripheral dissociation of relativistic nuclei in the
nuclear track emulsion, it is possible to study the emerging ensembles of He
and H nuclei, including those from decays of unstable $^{8}$Be and $^{9}$B
nuclei, as well as the Hoyle state. These extremely short-lived states are
identified by invariant masses calculated from the angles in 2$\alpha$-pairs,
2$\alpha p$- and 3$\alpha$-triplets in the approximation of conservation of
momentum per nucleon of the primary nucleus. In the same approach, it is
possible to search for more complex states. This paper explores the correlation
between the formation of $^{8}$Be nuclei and the multiplicity of accompanying
$\alpha$-particles in the dissociation of relativistic $^{16}$O, $^{22}$Ne,
$^{28}$Si, and $^{197}$Au nuclei. On the above basis, estimates of this
correlation are presented for the unstable $^{9}$B nucleus and the Hoyle state.
The enhancement in the $^{8}$Be contribution to dissociation with the
$\alpha$-particle multiplicity has been found. Decays of $^{9}$B nuclei and
Hoyle states follow the same trend.


Abstract:
  Online federated learning (OFL) becomes an emerging learning framework, in
which edge nodes perform online learning with continuous streaming local data
and a server constructs a global model from the aggregated local models. Online
multiple kernel learning (OMKL), using a preselected set of P kernels, can be a
good candidate for OFL framework as it has provided an outstanding performance
with a low-complexity and scalability. Yet, an naive extension of OMKL into OFL
framework suffers from a heavy communication overhead that grows linearly with
P. In this paper, we propose a novel multiple kernel-based OFL (MK-OFL) as a
non-trivial extension of OMKL, which yields the same performance of the naive
extension with 1/P communication overhead reduction. We theoretically prove
that MK-OFL achieves the optimal sublinear regret bound when compared with the
best function in hindsight. Finally, we provide the numerical tests of our
approach on real-world datasets, which suggests its practicality.


Abstract:
  We develop a far-from-equilibrium hydrodynamic model to evolve
ultrarelativistic heavy-ion collisions in event-by-event simulations.
Anisotropic hydrodynamics is designed to better handle the strong and highly
anisotropic expansion during the early stages of the collision. The large
gradients cause conventional second-order viscous hydrodynamic approaches to
break down at early times. Anisotropic hydrodynamics evolves the large pressure
anisotropies present in the quark-gluon plasma non-perturbatively, which
prevents negative longitudinal pressures from developing even under extreme
conditions. This increased stability allows us to start anisotropic
hydrodynamics already at a very early longitudinal proper time to evolve the
pre-hydrodynamic stage. In current pre-hydrodynamic models, the equation of
state is not consistent with the QCD equation of state used in the subsequent
fluid dynamic stage. Since our approach avoids this inconsistency, we are able
to achieve a smooth transition to non-conformal viscous hydrodynamics as the
gradients decrease over time. For our first phenomenological application, we
apply our new simulation to model fluctuating Pb+Pb collisions at LHC energies
($\sqrt{s_\text{NN}} = 2.76$ TeV) and find that our preliminary calculations
for the hadronic observables are in excellent agreement with the experimental
data.


Abstract:
  A direct formulation of linear elasticity of cell complexes based on discrete
exterior calculus is presented. The primary unknown are displacements,
represented by primal vector-valued 0-cochain. Displacement differences and
internal forces are represented by primal vector-valued 1-cochain and dual
vector-valued 2-cochain, respectively. The macroscopic constitutive relation is
enforced at primal 0-cells with the help of musical isomorphisms mapping
cochains to smooth fields and vice versa. The balance of linear momentum is
established at primal 0-cells. The governing equations are solved as a Laplace
equation with a non-local and non-diagonal material Hodge star. Numerical
simulations of several classical problems with analytic solutions are presented
to validate the formulation. Good agreement with known solutions is obtained.
The formulation provides a method to calculate the relations between
displacement differences and internal forces for any lattice structure, when
the structure is required to follow a prescribed macroscopic elastic behaviour.
This is also the first and critical step in developing formulations for
dissipative processes in cell complexes.


Abstract:
  Magic-angle twisted bilayer graphene exhibits a panoply of many-body
phenomena that are intimately tied to the appearance of narrow and
well-isolated electronic bands. The microscopic ingredients that are
responsible for the complex experimental phenomenology include
electron-electron (phonon) interactions and non-trivial Bloch wavefunctions
associated with the narrow bands. Inspired by recent experiments, we focus on
two independent quantities that are considerably modified by Coulomb
interaction driven band renormalization, namely the density of states and the
minimal spatial extent associated with the Wannier functions. First, we show
that a filling-dependent enhancement of the density of states, caused by band
flattening, in combination with phonon-mediated attraction due to
electron-phonon umklapp processes, increases the tendency towards
superconducting pairing in a range of angles around magic-angle. Second, we
demonstrate that the minimal spatial extent associated with the Wannier
functions, which contributes towards increasing the superconducting phase
stiffness, also develops a non-trivial enhancement due to the interaction
induced renormalization of the Bloch wavefunctions. While our modeling of
superconductivity assumes a weak electron-phonon coupling and does not consider
many of the likely relevant correlation effects, it explains simply the
experimentally observed robustness of superconductivity in the wide range of
angles that occurs in the relevant range of fillings.


Abstract:
  We develop simple methods for constructing parameter priors for model choice
among Directed Acyclic Graphical (DAG) models. In particular, we introduce
several assumptions that permit the construction of parameter priors for a
large number of DAG models from a small set of assessments. We then present a
method for directly computing the marginal likelihood of every DAG model given
a random sample with no missing observations. We apply this methodology to
Gaussian DAG models which consist of a recursive set of linear regression
models. We show that the only parameter prior for complete Gaussian DAG models
that satisfies our assumptions is the normal-Wishart distribution. Our analysis
is based on the following new characterization of the Wishart distribution: let
$W$ be an $n \times n$, $n \ge 3$, positive-definite symmetric matrix of random
variables and $f(W)$ be a pdf of $W$. Then, f$(W)$ is a Wishart distribution if
and only if $W_{11} - W_{12} W_{22}^{-1} W'_{12}$ is independent of
$\{W_{12},W_{22}\}$ for every block partitioning $W_{11},W_{12}, W'_{12},
W_{22}$ of $W$. Similar characterizations of the normal and normal-Wishart
distributions are provided as well.


Abstract:
  We compute corrections to the hard thermal (or dense) loop photon
polarization tensor associated to a small mass $m$ of the fermions of an
electromagnetic plasma at high temperature $T$ (or chemical potential $\mu$).
To this aim we use the on-shell effective field theory, amended with mass
corrections. We also carry out the computation using transport theory, reaching
to the same result. Interemediate steps in the computations reveal the presence
of potential infrared divergencies. We use dimensional regularization, as it is
respectful with the gauge symmetry, and then show that all infrared
divergencies cancel in the final result. We compare the mass corrections with
both the power and two-loop corrections, and claim that they are equally
important if the mass is soft, that is, of order $e T$ (or $e \mu$), where $e$
is the gauge coupling constant, but are dominat if the mass obeys $ e T < m \ll
T $ (or $e \mu < m \ll \mu)$.


Abstract:
  The fractional quantum Hall effect has been considered as a puzzling quantum
many-body phenomenon that has yet to be fully explained. The plateau width and
excitation energy gap are particularly problematic. We report here that those
two are determined by degrees of multi-particle correlations among the skipping
electrons forming the edge current flowing in incompressible strips (ISs).
Consideration of the total angular momentum of correlated skipping electrons
and their images, which are introduced to eliminate the confining potential
within the IS, yields additional Zeeman energies that hierarchically split the
Landau levels (LLs) by correlation order. This level splitting produces all the
odd-denominator plateaus and explains the occurrence of fractional charges,
while the split distances representing the correlation strengths determine both
plateau widths and excitation energy gaps. With such a scheme, we explicitly
reproduce an experimental Hall resistivity curve for the lowest LL and reveal
the characteristics of the half-filling state.


Abstract:
  Kirigami, art of paper cutting, enables two-dimensional sheets transforming
into unique shapes which are also hard to reshape once with prescribed cutting
patterns. Rare kirigami designs manipulate cuts on three-dimensional objects to
compose periodic structures with programmability and/or re-programmability.
Here, we propose a new class of three-dimensional modular kirigami by
introducing cuts on cuboid-shaped objects, based on which constructing two
quasi-three-15 dimensional architected kirigamis with even-flat structural
form. We demonstrate the proposed architected kirigamis are with rich
mobilities triggered by kinematic bifurcations inherited from their composed
modular kirigami, and can undergo living-matter-like metamorphosis evolving
into miscellaneous transformable three-dimensional architectures and even a
pluripotent platform capable of being re-programmed into curvature different
surfaces through inverse design. Such 20 metamorphic structures could find
broad applications in reconfigurable metamaterials, transformable robots and
architectures.


Abstract:
  A set $\mathbb{Y}$ of elements (vertices or edges) in space is said to be a
$generator$ of a metric space if each element of the space is recognized by its
distances from the elements of $\mathbb{Y}$, uniquely. The generator with
minimum cardinality is known as the $basis$ of the metric space, and this
cardinality is the $dimension$ of the given space. In this article, we further
discuss these notions with respect to a heptagonal circular ladder. We show
that for a heptagonal circular ladder $\Gamma_{n}$, the edge metric dimension
is three and find that it equals its metric dimension. We also introduce a new
family of the convex polytope graph (denoted by $\Delta_{n}$) from a heptagonal
circular ladder and find its metric dimension. Furthermore, we prove that the
minimum generator (metric and edge metric) are independent for all of these
families of the convex polytopes.


Abstract:
  In theoretical computer science, conferences play an important role in the
scientific process. The decisions whether to accept or reject articles is taken
by the program committee (PC) members. Serving on a PC for the first time can
be a daunting experience. This guide will help new program-committee members to
understand how the system works, and provide useful tips and guidelines. It
discusses every phase of the paper-selection process, and the tasks associated
to it.


Abstract:
  We prove an $\Omega(n^2)$ lower bound on the query complexity of local
proportionality in the Robertson-Webb cake-cutting model. Local proportionality
requires that each agent prefer their allocation to the average of their
neighbors' allocations in some undirected social network. It is a weaker
fairness notion than envy-freeness, which also has query complexity
$\Omega(n^2)$, and generally incomparable to proportionality, which has query
complexity $\Theta(n \log n)$. This result separates the complexity of local
proportionality from that of ordinary proportionality, confirming the intuition
that finding a locally proportional allocation is a more difficult
computational problem.


Abstract:
  Real-time adaptation is imperative to the control of robots operating in
complex, dynamic environments. Adaptive control laws can endow even nonlinear
systems with good trajectory tracking performance, provided that any uncertain
dynamics terms are linearly parameterizable with known nonlinear features.
However, it is often difficult to specify such features a priori, such as for
aerodynamic disturbances on rotorcraft or interaction forces between a
manipulator arm and various objects. In this paper, we turn to data-driven
modeling with neural networks to learn, offline from past data, an adaptive
controller with an internal parametric model of these nonlinear features. Our
key insight is that we can better prepare the controller for deployment with
control-oriented meta-learning of features in closed-loop simulation, rather
than regression-oriented meta-learning of features to fit input-output data.
Specifically, we meta-learn the adaptive controller with closed-loop tracking
simulation as the base-learner and the average tracking error as the
meta-objective. With a nonlinear planar rotorcraft subject to wind, we
demonstrate that our adaptive controller outperforms other controllers trained
with regression-oriented meta-learning when deployed in closed-loop for
trajectory tracking control.


Abstract:
  High cost of training time caused by multi-step adversarial example
generation is a major challenge in adversarial training. Previous methods try
to reduce the computational burden of adversarial training using single-step
adversarial example generation schemes, which can effectively improve the
efficiency but also introduce the problem of catastrophic overfitting, where
the robust accuracy against Fast Gradient Sign Method (FGSM) can achieve nearby
100\% whereas the robust accuracy against Projected Gradient Descent (PGD)
suddenly drops to 0\% over a single epoch. To address this problem, we propose
a novel Fast Gradient Sign Method with PGD Regularization (FGSMPR) to boost the
efficiency of adversarial training without catastrophic overfitting. Our core
idea is that single-step adversarial training can not learn robust internal
representations of FGSM and PGD adversarial examples. Therefore, we design a
PGD regularization term to encourage similar embeddings of FGSM and PGD
adversarial examples. The experiments demonstrate that our proposed method can
train a robust deep network for L$_\infty$-perturbations with FGSM adversarial
training and reduce the gap to multi-step adversarial training.


Abstract:
  Quantum conference is a process of securely exchanging messages between three
or more parties, using quantum resources. A Measurement Device Independent
Quantum Dialogue (MDI-QD) protocol, which is secure against information
leakage, has been proposed (Quantum Information Processing 16.12 (2017): 305)
in 2017, is proven to be insecure against intercept-and-resend attack strategy.
We first modify this protocol and generalize this MDI-QD to a three-party
quantum conference and then to a multi-party quantum conference. We also
propose a protocol for quantum multi-party XOR computation. None of these three
protocols proposed here use entanglement as a resource and we prove the
correctness and security of our proposed protocols.


Abstract:
  Carmichael showed for sufficiently large $L$, that $F_L$ has at least one
prime divisor that is $\pm 1({\rm mod}\, L)$. For a given $F_L$, we will show
that a product of distinct odd prime divisors with that congruence condition is
a Fibonacci pseudoprime. Such pseudoprimes can be used in an attempt, here
unsuccessful, to find an example of a Baillie-PSW pseudoprime, i.e.\ an odd
Fibonacci pseudoprime that is congruent to $\pm 2({\rm mod}\, 5)$ and is also a
base-2 pseudoprime.


Abstract:
  Recent work has demonstrated the effectiveness of cross-lingual language
model pretraining for cross-lingual understanding. In this study, we present
the results of two larger multilingual masked language models, with 3.5B and
10.7B parameters. Our two new models dubbed XLM-R XL and XLM-R XXL outperform
XLM-R by 1.8% and 2.4% average accuracy on XNLI. Our model also outperforms the
RoBERTa-Large model on several English tasks of the GLUE benchmark by 0.3% on
average while handling 99 more languages. This suggests pretrained models with
larger capacity may obtain both strong performance on high-resource languages
while greatly improving low-resource languages. We make our code and models
publicly available.


Abstract:
  Dense retrieval has been shown to be effective for retrieving relevant
documents for Open Domain QA, surpassing popular sparse retrieval methods like
BM25. REALM (Guu et al., 2020) is an end-to-end dense retrieval system that
relies on MLM based pretraining for improved downstream QA efficiency across
multiple datasets. We study the finetuning of REALM on various QA tasks and
explore the limits of various hyperparameter and supervision choices. We find
that REALM was significantly undertrained when finetuning and simple
improvements in the training, supervision, and inference setups can
significantly benefit QA results and exceed the performance of other models
published post it. Our best model, REALM++, incorporates all the best working
findings and achieves significant QA accuracy improvements over baselines
(~5.5% absolute accuracy) without any model design changes. Additionally,
REALM++ matches the performance of large Open Domain QA models which have 3x
more parameters demonstrating the efficiency of the setup.


Abstract:
  Yellowballs (YBs) were first discovered during the Milky Way Project
citizen-science initiative (MWP; Simpson et al. 2012). MWP users noticed
compact, yellow regions in Spitzer Space Telescope mid-infrared (MIR) images of
the Milky Way plane and asked professional astronomers to explain these "yellow
balls." Follow-up work by Kerton et al. (2015) determined that YBs likely trace
compact photo-dissociation regions associated with massive and
intermediate-mass star formation. YBs were included as target objects in a
version of the Milky Way Project launched in 2016 (Jayasinghe et al. 2016),
which produced a listing of over 6000 YB locations. We have measured distances,
cross-match associations, physical properties, and MIR colors of ~500 YBs
within a pilot region covering the l= 30 - 40 degrees, b= +/- 1 degree region
of the Galactic plane. We find 20-30% of YBs in our pilot region contain
high-mass star formation capable of becoming expanding H II regions that
produce MIR bubbles. A majority of YBs represent intermediate-mass star-forming
regions whose placement in evolutionary diagrams suggest they are still
actively accreting, and may be precursors to optically-revealed Herbig Ae/Be
nebulae. Many of these intermediate-mass YBs were missed by surveys of massive
star-formation tracers and thus this catalog provides information for many new
sites of star formation. Future work will expand this pilot region analysis to
the entire YB catalog.


Abstract:
  In this article, we propose a method to realize the "delayed choice
experiment" using ultra-cold atoms. Here we attempt to probe the "welcher-Weg"
information without collapsing the wavefunction of the atom. This experiment
consists of components built around proven techniques that are put together in
novel configuration to preserve the coherence of the system during the
measurement. The Ramsey interference is used to establish the wave nature of
the atom and the particle nature of the atom is probed by detecting its
internal state by performing a nondemolition measurement using an ultra-high
finesse cavity. The coherence of the atom is preserved by adjusting the
atom-cavity interaction time such that the state of the atom is unchanged when
it emerges out of the cavity.


Abstract:
  A light-weight high-performance Deepfake detection method, called DefakeHop,
is proposed in this work. State-of-the-art Deepfake detection methods are built
upon deep neural networks. DefakeHop extracts features automatically using the
successive subspace learning (SSL) principle from various parts of face images.
The features are extracted by c/w Saab transform and further processed by our
feature distillation module using spatial dimension reduction and soft
classification for each channel to get a more concise description of the face.
Extensive experiments are conducted to demonstrate the effectiveness of the
proposed DefakeHop method. With a small model size of 42,845 parameters,
DefakeHop achieves state-of-the-art performance with the area under the ROC
curve (AUC) of 100%, 94.95%, and 90.56% on UADFV, Celeb-DF v1 and Celeb-DF v2
datasets, respectively.