Abstract:
  Neural networks allow us to model complex relationships between variables. We
show how to efficiently find extrema of a trained neural network in regression
problems. Finding the extremizing input of an approximated model is formulated
as the training of an additional neural network with a loss function that
minimizes when the extremizing input is achieved. We further show how to
incorporate additional constraints on the input vector such as limiting the
extrapolation of the extremizing input vector from the original training data
set. An instructional example of this approach using TensorFlow is included.


Abstract:
  In this work, we consider two-stage polynomial optimization problems under
uncertainty. In the first stage, one needs to decide upon the values of a
subset of optimization variables (control variables). In the second stage, the
uncertainty is revealed and the rest of optimization variables (state
variables) are set up as a solution to a known system of possibly non-linear
equations. This type of problem occurs, for instance, in optimization for
dynamical systems, such as electric power systems. We combine tools from
polynomial and robust optimization to provide a framework for general
adjustable robust polynomial optimization problems. In particular, we propose
an iterative algorithm to build a sequence of (approximately) robustly feasible
solutions with an improving objective value and verify robust feasibility or
infeasibility of the resulting solution under a semialgebraic uncertainty set.
At each iteration, the algorithm optimizes over a subset of the feasible set
and uses affine approximations of the second-stage equations while preserving
the non-linearity of other constraints. The algorithm allows for additional
simplifications in case of possibly non-convex quadratic problems under
ellipsoidal uncertainty. We implement our approach for AC Optimal Power Flow
and demonstrate the performance of our proposed method on Matpower instances.


Abstract:
  The inspiral and merger of two black holes produces a remnant black hole with
mass and spin determined by the properties of its parent black holes. Using the
inferred population properties of component black holes from the first two and
a half observing runs of Advanced LIGO and Virgo, we calculate the population
properties of the leftover remnant black holes. By integrating their rate of
formation over the age of the universe, we estimate the number density of
remnant black holes today. Using simple prescriptions for the cosmic star
formation rate and black hole inspiral delay times, we determine the number
density of this leftover black hole population to be $660_{-240}^{+440}
\mathrm{Mpc}^{-3}$, corresponding to $\sim 60,000$ black hole remnants per
Milky-Way-equivalent galaxy. The mass spectrum of these remnants starts at
$\sim 10 M_\odot$ and can be approximated by a decreasing exponential with
characteristic length $\sim 15 M_\odot$, the final spin distribution is sharply
peaked at $\chi_f\sim0.7$, and the kick velocities range from tens to thousands
of km/s. These kick velocities suggest that globular clusters and nuclear star
clusters may retain up to $3_{-2}^{+3}\%$ and $46_{-15}^{+17}\%$ of their
remnant black holes, respectively, while young star clusters would only retain
a few tenths of a percent. The estimates in this work assume that none of the
remnants participate in subsequent hierarchical mergers. If hierarchical
mergers occur, the overall number density would drop accordingly and the
remnant mass distribution shape would evolve over time. This population of
leftover black holes is an inescapable result from gravitational-wave
observations of binary black-hole mergers.


Abstract:
  Hall instability in electron magnetohydrodynamics is interpreted as the
shear-Hall instability driven jointly by helicoidal oscillations and shear in
the electron current velocity. This explanation suggests an antiparallel
orientation of the background magnetic field and vorticity of the current
velocity as the necessary condition for Hall instability. The condition is
tested and generally confirmed by numerical computations in plane slab
geometry. Unstable eigenmodes are localized in the spatial regions of the
antiparallel field and vorticity. Computations of the tearing-type mode of the
instability are complemented by (and generally agree with) asymptotic
analytical estimations for large Hall numbers. The stabilizing effect of
perfect conductor boundary conditions is found and explained. For large Hall
numbers, the growth rates approach the power law dependence $\sigma \propto
B^\alpha\eta^{1-\alpha}$ on the magnetic field ($B$) and diffusivity ($\eta$).
Almost all computations give the power index $\alpha = 3/4$ with one exception
of the tearing-type mode with vacuum boundary conditions for which case $\alpha
= 2/3$.


Abstract:
  We prove that for any integer $k\geq 2$ and $\varepsilon>0$, there is an
integer $\ell_0\geq 1$ such that any $k$-uniform hypergraph on $n$ vertices
with minimum codegree at least $(1/2+\varepsilon)n$ has a fractional
decomposition into tight cycles of length $\ell$ ($\ell$-cycles for short)
whenever $\ell\geq \ell_0$ and $n$ is large in terms of $\ell$. This is
essentially tight.
  This immediately yields also approximate integral decompositions for these
hypergraphs into $\ell$-cycles. Moreover, for graphs this even guarantees
integral decompositions into $\ell$-cycles and solves a problem posed by Glock,
K\"uhn and Osthus. For our proof, we introduce a new method for finding a set
of $\ell$-cycles such that every edge is contained in roughly the same number
of $\ell$-cycles from this set by exploiting that certain Markov chains are
rapidly mixing.


Abstract:
  We show that viewing graphs as sets of node features and incorporating
structural and positional information into a transformer architecture is able
to outperform representations learned with classical graph neural networks
(GNNs). Our model, GraphiT, encodes such information by (i) leveraging relative
positional encoding strategies in self-attention scores based on positive
definite kernels on graphs, and (ii) enumerating and encoding local
sub-structures such as paths of short length. We thoroughly evaluate these two
ideas on many classification and regression tasks, demonstrating the
effectiveness of each of them independently, as well as their combination. In
addition to performing well on standard benchmarks, our model also admits
natural visualization mechanisms for interpreting graph motifs explaining the
predictions, making it a potentially strong candidate for scientific
applications where interpretation is important. Code available at
https://github.com/inria-thoth/GraphiT.


Abstract:
  Let $n\ge 2$ and $k\ge 1$ be two integers. Let $M$ be an isometrically
immersed closed $n$-submanifold of co-dimension $k$ that is homotopic to a
point in a complete manifold $N$, where the sectional curvature of $N$ is no
more than $\delta<0$. We prove that the total squared mean curvature of $M$ in
$N$ and the first non-zero eigenvalue $\lambda_1(M)$ of $M$ satisfies
$$\lambda_1(M)\le n\left(\delta +\frac{1}{\operatorname{Vol} M}\int_M |H|^2
\operatorname{dvol}\right).$$ The equality implies that $M$ is minimally
immersed in a metric sphere after lifted to the universal cover of $N$. This
completely settles an open problem raised by E. Heintze in 1988.


Abstract:
  The development of Industrial Internet of Things systems (IIoT) requires
tools robust enough to cope with the complexity and heterogeneity of such
systems, which are supposed to work in safety-critical conditions. The
availability of methodologies to support early analysis, verification, and
validation is still an open issue in the research community. The early
real-time schedulability analysis can help quantify to what extent the desired
system's timing performance can eventually be achieved. In this paper, we
present CHESSIoT, a model-driven environment to support the design and analysis
of industrial IoT systems. CHESSIoT follows a multi-view, component-based
modelling approach with a comprehensive way to perform event-based modelling on
system components for code generation purposes employing an intermediate
ThingML model. To showcase the capability of the extension, we have designed
and analysed an Industrial real-time safety use case.


Abstract:
  Generalization is the ability of a model to predict on unseen domains and is
a fundamental task in machine learning. Several generalization bounds, both
theoretical and empirical have been proposed but they do not provide tight
bounds .In this work, we propose a simple yet effective method to predict the
generalization performance of a model by using the concept that models that are
robust to augmentations are more generalizable than those which are not. We
experiment with several augmentations and composition of augmentations to check
the generalization capacity of a model. We also provide a detailed motivation
behind the proposed method. The proposed generalization metric is calculated
based on the change in the output of the model after augmenting the input. The
proposed method was the first runner up solution for the NeurIPS competition on
Predicting Generalization in Deep Learning.


Abstract:
  A phase slip is a localized disturbance in the coherence of a superconductor
allowing an abrupt 2$\pi$ phase shift. Phase slips are a ubiquitous feature of
one-dimensional superconductors and also have an analogue in two-dimensions.
Here we present electrical transport measurements on boron-doped
nanocrystalline diamond (BNCD) microbridges where, despite their
three-dimensional macroscopic geometry, we find clear evidence of phase
slippage in both the resistance-temperature and voltage-current
characteristics. We attribute this behavior to the unusual microstructure of
BNCD. We argue that the columnar crystal structure of BNCD forms an intrinsic
Josephson junction array that supports a line of phase slippage across the
microbridge. The voltage-state in these bridges is metastable and we
demonstrate the ability to switch deterministically between different
superconducting states by applying electromagnetic noise pulses. This
metastability is remarkably similar to that observed in $\delta$-MoN nanowires,
but with a vastly greater response voltage.


Abstract:
  Despite the vast literature on Human Activity Recognition (HAR) with wearable
inertial sensor data, it is perhaps surprising that there are few studies
investigating semisupervised learning for HAR, particularly in a challenging
scenario with class imbalance problem. In this work, we present a new
benchmark, called A*HAR, towards semisupervised learning for class-imbalanced
HAR. We evaluate state-of-the-art semi-supervised learning method on A*HAR, by
combining Mean Teacher and Convolutional Neural Network. Interestingly, we find
that Mean Teacher boosts the overall performance when training the classifier
with fewer labelled samples and a large amount of unlabeled samples, but the
classifier falls short in handling unbalanced activities. These findings lead
to an interesting open problem, i.e., development of semi-supervised HAR
algorithms that are class-imbalance aware without any prior knowledge on the
class distribution for unlabeled samples. The dataset and benchmark evaluation
are released at https://github.com/I2RDL2/ASTAR-HAR for future research.


Abstract:
  We extend previous work on symbolic self-triggered control for
non-deterministic continuous-time nonlinear systems without stability
assumptions to a larger class of specifications. Our goal is to synthesise a
controller for two objectives: the first one is modelled as a right-recursive
LTL formula, and the second one is to ensure that the average communication
rate between the controller and the system stays below a given threshold. We
translate the control problem to solving a mean-payoff parity game played on a
discrete graph. Apart from extending the class of specifications, we propose a
heuristic method to shorten the computation time. Finally, we illustrate our
results on the example of a navigating nonholonomic robot with several
specifications.


Abstract:
  This paper formalizes connections between stability of polynomials and
convergence rates of Markov Chain Monte Carlo (MCMC) algorithms. We prove that
if a (multivariate) partition function is nonzero in a region around a real
point $\lambda$ then spectral independence holds at $\lambda$. As a
consequence, for Holant-type problems (e.g., spin systems) on bounded-degree
graphs, we obtain optimal $O(n\log n)$ mixing time bounds for the single-site
update Markov chain known as the Glauber dynamics. Our result significantly
improves the running time guarantees obtained via the polynomial interpolation
method of Barvinok (2017), refined by Patel and Regts (2017).
  There are a variety of applications of our results. In this paper, we focus
on Holant-type (i.e., edge-coloring) problems, including weighted edge covers
and weighted even subgraphs. For the weighted edge cover problem (and several
natural generalizations) we obtain an $O(n\log{n})$ sampling algorithm on
bounded-degree graphs. The even subgraphs problem corresponds to the
high-temperature expansion of the ferromagnetic Ising model. We obtain an
$O(n\log{n})$ sampling algorithm for the ferromagnetic Ising model with a
nonzero external field on bounded-degree graphs, which improves upon the
classical result of Jerrum and Sinclair (1993) for this class of graphs. We
obtain further applications to antiferromagnetic two-spin models on line
graphs, weighted graph homomorphisms, tensor networks, and more.


Abstract:
  We address an issue of how to accurately include the self energy effect of
the screened electron-electron Coulomb interaction in the phonon-mediated
superconductors from first principles. In the Eliashberg theory for
superconductors, self energy is usually decomposed using the $2\times 2$ Pauli
matrices in the electron-hole space. We examine how the diagonal ($\sigma_{0}$
and $\sigma_{3}$) components, which results in the quasiparticle correction to
the normal state, behave in the homogeneous electron gas in order to establish
a norm of treating those components in real metallic systems. Within the
$G_{0}W_{0}$ approximation, we point out that these components are non-analytic
near the Fermi surface but their directional derivatives and resulting
corrections to the quasiparticle velocity are nevertheless well defined. In the
low-energy spectrum, we observe large cancellation between effects of these
components and, without the numerically more tedious $\sigma_{3}$ component,
the effective mass is incorrectly increased. Feasible paths to manage this
cancellation in the ab initio Eliashberg calculations are discussed.


Abstract:
  In March 2021, Turkey withdrew from The Istanbul Convention, a human-rights
treaty that addresses violence against women, citing issues with the
convention's implicit recognition of sexual and gender minorities. In this
work, we trace disinformation campaigns related to the Istanbul Convention and
its associated Turkish law that circulate on divorced men's rights Facebook
groups. We find that these groups adjusted the narrative and focus of the
campaigns to appeal to a larger audience, which we refer to as "tactical
reframing." Initially, the men organized in a grass-roots manner to campaign
against the Turkish law that was passed to codify the convention, focusing on
one-sided custody of children and indefinite alimony. Later, they reframed
their campaign and began attacking the Istanbul Convention, highlighting its
acknowledgment of homosexuality. This case study highlights how disinformation
campaigns can be used to weaponize homophobia in order to limit the rights of
women. To the best of our knowledge, this is the first case study that analyzes
a narrative reframing in the context of a disinformation campaign on social
media.


Abstract:
  Age of Incorrect Information (AoII) is a newly introduced performance metric
that considers communication goals. Therefore, comparing with traditional
performance metrics and the recently introduced metric - Age of Information
(AoI), AoII achieves better performance in many real-life applications.
However, the fundamental nature of AoII has been elusive so far. In this paper,
we consider the AoII in a system where a transmitter sends updates about a
multi-state Markovian source to a remote receiver through an unreliable
channel. The communication goal is to minimize AoII subject to a power
constraint. We cast the problem into a Constrained Markov Decision Process
(CMDP) and prove that the optimal policy is a mixture of two deterministic
threshold policies. Afterward, by leveraging the notion of Relative Value
Iteration (RVI) and the structural properties of threshold policy, we propose
an efficient algorithm to find the threshold policies as well as the mixing
coefficient. Lastly, numerical results are laid out to highlight the
performance of AoII-optimal policy.


Abstract:
  We show that compact Riemannian manifolds, regarded as metric spaces with
their global geodesic distance, cannot contain a number of rigid structures
such as (a) arbitrarily large regular simplices or (b) arbitrarily long
sequences of points equidistant from pairs of points preceding them in the
sequence. All of this provides evidence that Riemannian metric spaces admit
what we term loose embeddings into finite-dimensional Euclidean spaces:
continuous maps that preserve both equality as well as inequality.
  We also prove a local-to-global principle for Riemannian-metric-space loose
embeddability: if every finite subspace thereof is loosely embeddable into a
common $\mathbb{R}^N$, then the metric space as a whole is loosely embeddable
into $\mathbb{R}^N$ in a weakened sense.


Abstract:
  We show that gapless spin liquids, which are potential candidates to describe
the ground state of frustrated Heisenberg models in two dimensions, become
trivial insulators on cylindrical geometries with an even number of legs. In
particular, we report calculations for Gutzwiller-projected fermionic states on
strips of square and kagome lattices. By choosing different boundary conditions
for the fermionic degrees of freedom, both gapless and gapped states may be
realized, the latter ones having a lower variational energy. The direct
evaluation of static and dynamical correlation functions, as well as overlaps
between different states, allows us to demonstrate the sharp difference between
the ground-state properties obtained within cylinders or directly in the
two-dimensional lattice. Our results shed light on the difficulty to detect
bona fide gapless spin liquids in such cylindrical geometries.


Abstract:
  We prove that any $n$-dimensional closed mean convex $\lambda$-hypersurface
is convex if $\lambda\le 0.$ This generalizes Guang's work on $2$-dimensional
strictly mean convex $\lambda$-hypersurfaces. As a corollary, we obtain a gap
theorem for closed $\lambda$-hypersurfaces with $\lambda\le 0.$


Abstract:
  We propose a Bayesian inference approach for static Origin-Destination
(OD)-estimation in large-scale networked transit systems. The approach finds
posterior distribution estimates of the OD-coefficients, which describe the
relative proportions of passengers travelling between origin and destination
locations, via a Hamiltonian Monte Carlo sampling procedure. We suggest two
different inference model formulations: the instantaneous-balance and
average-delay model. We discuss both models' sensitivity to various count
observation properties, and establish that the average-delay model is generally
more robust in determining the coefficient posteriors. The
instantaneous-balance model, however, requires lower resolution count
observations and produces comparably accurate estimates as the average-delay
model, pending that count observations are only moderately interfered by trend
fluctuations or the truncation of the observation window, and sufficient number
of dispersed data records are available. We demonstrate that the Bayesian
posterior distribution estimates provide quantifiable measures of the
estimation uncertainty and prediction quality of the model, whereas the point
estimates obtained from an alternative constrained quadratic programming
optimisation approach only provide the residual errors between the predictions
and observations. Moreover, the Bayesian approach proves more robust in scaling
to high-dimensional underdetermined problems. The Bayesian
instantaneous-balance OD-coefficient posteriors are determined for the New York
City (NYC) subway network, based on several years of entry and exit count
observations recorded at station turnstiles across the network. The
average-delay model proves intractable on the real-world test scenario, given
its computational time complexity and the incompleteness as well as coarseness
of the turnstile records.


Abstract:
  Thin films of topological insulators (TI) attract large attention because of
expected topological effects from the inter-surface hybridization of Dirac
points. However, these effects may be depleted by unexpectedly large energy
smearing $\Gamma$ of surface Dirac points by the random potential of abundant
Coulomb impurities. We show that in a typical TI film with large dielectric
constant $\sim 50$ sandwiched between two low dielectric constant layers, the
Rytova-Chaplik-Entin-Keldysh modification of the Coulomb potential of a charge
impurity allows a larger number of the film impurities to contribute to
$\Gamma$. As a result, $\Gamma$ is large and independent of the TI film
thickness $d$ for $d > 5$ nm. In thinner films $\Gamma$ grows with decreasing
$d$ due to reduction of screening by the hybridization gap. We study the
surface conductivity away from the neutrality point and at the neutrality
point. In the latter case, we find the maximum TI film thickness at which the
hybridization gap is still able to make a TI film insulating and allow
observation of the quantum spin Hall effect, $d_{\max} \sim 7$ nm.


Abstract:
  This work proposes a time-efficient Natural Gradient Descent method, called
TENGraD, with linear convergence guarantees. Computing the inverse of the
neural network's Fisher information matrix is expensive in NGD because the
Fisher matrix is large. Approximate NGD methods such as KFAC attempt to improve
NGD's running time and practical application by reducing the Fisher matrix
inversion cost with approximation. However, the approximations do not reduce
the overall time significantly and lead to less accurate parameter updates and
loss of curvature information. TENGraD improves the time efficiency of NGD by
computing Fisher block inverses with a computationally efficient covariance
factorization and reuse method. It computes the inverse of each block exactly
using the Woodbury matrix identity to preserve curvature information while
admitting (linear) fast convergence rates. Our experiments on image
classification tasks for state-of-the-art deep neural architecture on CIFAR-10,
CIFAR-100, and Fashion-MNIST show that TENGraD significantly outperforms
state-of-the-art NGD methods and often stochastic gradient descent in
wall-clock time.


Abstract:
  The end-to-end architecture has made promising progress in speech translation
(ST). However, the ST task is still challenging under low-resource conditions.
Most ST models have shown unsatisfactory results, especially in the absence of
word information from the source speech utterance. In this study, we survey
methods to improve ST performance without using source transcription, and
propose a learning framework that utilizes a language-independent universal
phone recognizer. The framework is based on an attention-based
sequence-to-sequence model, where the encoder generates the phonetic embeddings
and phone-aware acoustic representations, and the decoder controls the fusion
of the two embedding streams to produce the target token sequence. In addition
to investigating different fusion strategies, we explore the specific usage of
byte pair encoding (BPE), which compresses a phone sequence into a
syllable-like segmented sequence. Due to the conversion of symbols, a segmented
sequence represents not only pronunciation but also language-dependent
information lacking in phones. Experiments conducted on the Fisher
Spanish-English and Taigi-Mandarin drama corpora show that our method
outperforms the conformer-based baseline, and the performance is close to that
of the existing best method using source transcription.


Abstract:
  In this paper, when the order of $\theta$ is even, we prove that there exists
no central difference sets in $A_2(m,\theta)$ and establish some non-existence
results of central partial difference sets in $A_p(m,\theta)$ with $p>2$. When
the order of $\theta$ is odd, we construct central difference sets in
$A_2(m,\theta)$. Furthermore, we give some reduced linking systems of
difference sets in $A_2(m,\theta)$ by using the difference sets we constructed.
In the case $p>2$, we construct Latin square type central partial difference
sets in $A_p(m,\theta)$ by a similar method.


Abstract:
  Using the Laughlin's argument on a torus with two pin-holes, we numerically
demonstrate the bulk-edge correspondence of the fractional quantum Hall states
of anyons, where the many-body Chern number predicts the discontinuities of the
center-of-mass in the pumping phenomenon with edges. Through the process of
trading the magnetic flux for the statistical one (flux-attachment), the
general feature of the edge states remains unchanged as predicted by the Chern
number while the ground state degeneracy is discretely changed. This supports
the stability of the quantum Hall edge states in the adiabatic heuristic
principle.


Abstract:
  Inertial measurement units (IMUs) increasingly function as a basic component
of wearable sensor network (WSN)systems. IMU-based joint angle estimation (JAE)
is a relatively typical usage of IMUs, with extensive applications. However,
the issue that IMUs move with respect to their original placement during JAE is
still a research gap, and limits the robustness of deploying the technique in
real-world application scenarios. In this study, we propose to detect and
correct the IMU movement online in a relatively computationally lightweight
manner. Particularly, we first experimentally investigate the influence of IMU
movements. Second, we design the metrics for detecting IMU movements by
mathematically formulating how the IMU movement affects the IMU measurements.
Third, we determine the optimal thresholds of metrics by synthetic IMU data
from a significantly amended simulation model. Finally, a correction method is
proposed to correct the effects of IMU movements. We demonstrate our method on
both synthetic data and real-user data. The results demonstrate our method is a
promising solution to detecting and correcting IMU movements during JAE.


Abstract:
  It is non-trivial to recognize phase transitions and track dynamics inside a
stochastic process because of its intrinsic stochasticity. In this paper, we
employ the deep learning method to classify the phase orders and predict the
damping coefficient of fluctuating systems under Langevin's description. As a
concrete set-up, we demonstrate this paradigm for the scalar condensation in
QCD matter near the critical point, in which the order parameter of chiral
phase transition can be characterized in a $1+1$-dimensional Langevin equation
for $\sigma$ field. In a supervised learning manner, the Convolutional Neural
Networks(CNNs) accurately classify the first-order phase transition and
crossover based on $\sigma$ field configurations with fluctuations. Noise in
the stochastic process does not significantly hinder the performance of the
well-trained neural network for phase order recognition. For mixed dynamics
with diverse dynamical parameters, we further devise and train the machine to
predict the damping coefficients $\eta$ in a broad range. The results show that
it is robust to extract the dynamics from the bumpy field configurations.


Abstract:
  We propose RPSRNet - a novel end-to-end trainable deep neural network for
rigid point set registration. For this task, we use a novel $2^D$-tree
representation for the input point sets and a hierarchical deep feature
embedding in the neural network. An iterative transformation refinement module
in our network boosts the feature matching accuracy in the intermediate stages.
We achieve an inference speed of 12-15ms to register a pair of input point
clouds as large as 250K. Extensive evaluation on (i) KITTI LiDAR odometry and
(ii) ModelNet-40 datasets shows that our method outperforms prior
state-of-the-art methods - e.g., on the KITTI data set, DCP-v2 by1.3 and 1.5
times, and PointNetLK by 1.8 and 1.9 times better rotational and translational
accuracy respectively. Evaluation on ModelNet40 shows that RPSRNet is more
robust than other benchmark methods when the samples contain a significant
amount of noise and other disturbances. RPSRNet accurately registers point
clouds with non-uniform sampling densities, e.g., LiDAR data, which cannot be
processed by many existing deep-learning-based registration methods.


Abstract:
  Different from previous modelings of self-propelled particles, we develop a
method to propel the particles with a constant average velocity instead of a
constant force. This constant propulsion velocity (CPV) approach is validated
by its agreement with the conventional constant propulsion force (CPF) approach
in the flowing regime. However, the CPV approach shows its advantage of
accessing quasistatic flows of yield stress fluids with a vanishing propulsion
velocity, while the CPF approach is usually unable to because of finite system
size. Taking this advantage, we realize the cyclic self-propulsion and study
the evolution of the propulsion force with propelled particle displacement,
both in the quasistatic flow regime. By mapping shear stress and shear rate to
propulsion force and propulsion velocity, we find similar rheological behaviors
of self-propelled systems to sheared systems, including the yield force gap
between the CPF and CPV approaches, propulsion force overshoot,
reversible-irreversible transition under cyclic propulsion, and propulsion
bands in plastic flows. These similarities suggest the underlying connections
between self-propulsion and shear, although they act on systems in different
ways.


Abstract:
  In previous work, we constructed Fleming--Viot-type measure-valued diffusions
(and diffusions on a space of interval partitions of the unit interval $[0,1]$)
that are stationary with the Poisson--Dirichlet laws with parameters
$\alpha\in(0,1)$ and $\theta\geq 0$. In this paper, we complete the proof that
these processes resolve a conjecture by Feng and Sun (2010) by showing that the
processes of ranked atom sizes (or of ranked interval lengths) of these
diffusions are members of a two-parameter family of diffusions introduced by
Petrov (2009), extending a model by Ethier and Kurtz (1981) in the case
$\alpha=0$. The latter diffusions are continuum limits of up-down Chinese
restaurant processes.


Abstract:
  In this article, the definition on the observe ability and its relation to
the signal detecting performance are studied systematically for the linear
discrete-time(LDT) systems. Firstly, to define and analyze the observe ability
for the practical systems with the measured noise, six kinds of bounded noise
models are classified. For the noise energy bounded case, the observability
ellipsoid and the image observability ellipsoid are defined by the state
observed error and then a novel concept on the LDT systems, called as the
observe ability, is proposed. Based on that, some theorems and properties about
the observe ability and the signal detecting performances are given and proven,
and then the reason that to maximize the observe ability is to optimize the
signal detecting performances is established. Secondly, a dual relation between
the observability ellipsoid and the controllability ellipsoid, which volumes
and radii are respectively with some inverse relations, is stated and proven.
Accordingly, the analytical computing equations for the volume of the two
observability ellipsoids are got and some analytical shape factors of these
ellipsoids are deconstructed. Based on these effective compting for the
volumes, radii, and shape factors, analyzing and optimizing for the observe
ability can be carried out. Thirdly, to compare rationally the state observe
ability between the different systems or different system parameters, the
normalization of the output variables, the state variables, and the system
models are discussed. Finally, some numerical experiments and their results
show the effectiveness of the computing and comparing methods for the observe
ability.


Abstract:
  Personal and home sensors generate valuable information that could be used in
Smart Cities. Unfortunately, typically, this data is locked out and used only
by application/system developer. While vendors are to blame, one should
consider also the "binary nature" of data access. Specifically, either owner
has full control over her data (e.g. in a "closed system"), or she completely
looses control, when the data is "opened". In this context, we propose, a
semantic technologies-based, authorization and privacy control framework that
enables user to maintain flexible, yet manageable data access control policies.
The proposed approach is described in detail, including implementation and
testing.


Abstract:
  The Boltzmann equation is a fundamental kinetic equation that describes the
dynamics of dilute gas. In this paper we study the regularity of both dynamical
and steady Boltzmann equation in strictly convex domain with the
Cercignani-Lampis (C-L) boundary condition. The C-L boundary condition
describes the intermediate reflection law between diffuse reflection and
specular reflection via two accommodation coefficients. We construct local
weighted $C^1$ dynamical solution using repeated interaction through the
characteristic. When we assume small fluctuation to the wall temperature and
accommodation coefficients, we construct weighted $C^1$ steady solution.


Abstract:
  Anomalous heat transport in one-dimensional nanostructures, such as nanotubes
and nanowires, is a widely debated problem in condensed matter and statistical
physics, with contradicting pieces of evidence from experiments and
simulations. Using a comprehensive modeling approach, comprised of lattice
dynamics and molecular dynamics simulations, we proved that the infinite length
limit of the thermal conductivity of a (10,0) single-wall carbon nanotube is
finite but this limit is reached only for macroscopic lengths due to thermal
phonon mean free path of several millimeters. Our calculations showed that the
extremely high thermal conductivity of this system at room temperature is
dictated by quantum effects. Modal analysis showed that the divergent nature of
thermal conductivity, observed in one-dimensional model systems, is suppressed
in carbon nanotubes by anharmonic scattering channels provided by the flexural
and optical modes with polarization in the plane orthogonal to the transport
direction.


Abstract:
  Veracity of data posted on the microblog platforms has in recent years been a
subject of intensive study by professionals specializing in various fields of
informatics as well as sociology, particularly in the light of increasing
importance of online tools for news spreading. On Twitter and similar sites, it
is possible to report on ongoing situations globally with minimal delay, while
the cost of such reporting remains negligible. One of the most important
features of this social network is that content delivery can be customized to
allow users to focus only on news items covering subject matters they find
interesting. With this in mind, it becomes necessary to create verification
mechanisms that can ascertain whether the claims made on Twitter can be taken
seriously and prevent false content from spreading too far. This study
demonstrates an innovative System for verification of information that can
fulfill the role described above. The System is comprised of four mutually
connected modules: a legacy module, a trustworthiness classifier; a module
managing user authority, and a ranking procedure. All of the modules function
within an integrated framework and jointly contribute to an accurate
classification of messages and authors. Effectiveness of the solution was
evaluated empirically on a sample of Twitter users, with a strict 10-fold
evaluation procedure applied for each module. The findings indicate that the
solution successfully meets the primary objectives of the study and performs
its function as expected.


Abstract:
  The detection of low-energy deposition in the range of sub-eV through
ionization using germanium (Ge) with a bandgap of $\sim$0.7 eV requires
internal amplification of charge signal. This can be achieved through high
electric field which accelerates charge carriers to generate more charge
carriers. The minimum electric field required to generate internal charge
amplification is derived for different temperatures. A point contact Ge
detector provides extremely high electric field in proximity to the point
contact. We show the development of a planar point contact detector and its
performance. The field distribution is calculated for this planar point contact
detector. We demonstrate the required electric field can be achieved with a
point contact detector.


Abstract:
  There has been significant recent interest in devising verification
techniques for learning-enabled controllers (LECs) that manage safety-critical
systems. Given the opacity and lack of interpretability of the neural policies
that govern the behavior of such controllers, many existing approaches enforce
safety properties through the use of shields, a dynamic monitoring and repair
mechanism that ensures a LEC does not emit actions that would violate desired
safety conditions. These methods, however, have shown to have significant
scalability limitations because verification costs grow as problem
dimensionality and objective complexity increase. In this paper, we propose a
new automated verification pipeline capable of synthesizing high-quality safety
shields even when the problem domain involves hundreds of dimensions, or when
the desired objective involves stochastic perturbations, liveness
considerations, and other complex non-functional properties. Our key insight
involves separating safety verification from neural controller, using
pre-computed verified safety shields to constrain neural controller training
which does not only focus on safety. Experimental results over a range of
realistic high-dimensional deep RL benchmarks demonstrate the effectiveness of
our approach.


Abstract:
  The quantum approximate optimization algorithm (QAOA) is a hybrid
quantum-classical variational algorithm that offers the potential to handle
combinatorial optimization problems. Introducing constraints in such
combinatorial optimization problems poses a significant challenge in the
extensions of QAOA to support relevant larger-scale problems. This paper
introduces a quantum machine learning approach to learn the mixer Hamiltonian
required to hard constrain the search subspace. We show that this method can be
used for encoding any general form of constraints. One can directly plug the
learnt unitary into the QAOA framework using an adaptable ansatz. This
procedure gives the flexibility to control the depth of the circuit at the cost
of the accuracy of enforcing the constraint, thus having immediate application
in the Noisy Intermediate Scale Quantum (NISQ) era. We also develop an
intuitive metric that uses Wasserstein distance to assess the performance of
general approximate optimization algorithms with/without constraints. Finally,
using this metric, we evaluate the performance of the proposed algorithm.


Abstract:
  Recent approaches have exploited weaknesses in monolingual question answering
(QA) models by adding adversarial statements to the passage. These attacks
caused a reduction in state-of-the-art performance by almost 50%. In this
paper, we are the first to explore and successfully attack a multilingual QA
(MLQA) system pre-trained on multilingual BERT using several attack strategies
for the adversarial statement reducing performance by as much as 85%. We show
that the model gives priority to English and the language of the question
regardless of the other languages in the QA pair. Further, we also show that
adding our attack strategies during training helps alleviate the attacks.


Abstract:
  Ever-increasing interests to more accurate thermodynamic predictions of phase
diagrams motivate more reliable thermodynamic models to be developed. The
Modified Quasichemical Model within the two-sublattice Quadruplet Approximation
(MQMQA) was thus established in well response to these interests and
motivations. However, the model still needs to be further improved in order to
have better thermodynamic predictions of arbitrary reciprocal solutions. The
present paper proposes a new and generic formalism to characterize the Gibbs
energy of the ternary reciprocal quadruplet within the framework of the MQMQA.
The new formalism circumvents the problem spawned from solving the singular
matrix of mass equations. Resultantly, energy landscapes of reciprocal
solutions can be better defined everywhere in reciprocal composition spaces.
With the current improvement, the MQMQA is believed to be one of the most
reliable thermodynamic models for various types of solutions with or without
short-range ordering.


Abstract:
  We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data
analysis on both simulated and experimental thin-film data. We show that
crystal structure representations learned by a VAE reveal latent information,
such as the structural similarity of textured diffraction patterns. While other
artificial intelligence (AI) agents are effective at classifying XRD data into
known phases, a similarly conditioned VAE is uniquely effective at knowing what
it does not know, rapidly identifying novel phases and mixtures. These
capabilities demonstrate that a VAE is a valuable AI agent for materials
discovery and understanding XRD measurements both on-the-fly and during post
hoc analysis.


Abstract:
  Let $f$ be a polynomial over a global field $K$. For each $\alpha$ in $K$ and
$N$ in $\mathbb{Z}_{\geq 0}$ denote by $K_N(f,\alpha)$ the arboreal field
$K(f^{-N}(\alpha))$ and by $D_N(f,\alpha)$ its degree over $K$.
  It is conjectured that $D_N(f,\alpha)$ should grow as a double exponential
function of $N$, unless $f$ is post-critically finite (PCF), in which case
there are examples like $D_N(x^2,\alpha) \leq 4^{N}$. There is evidence
conditionally on Vojta's conjecture. However, before the present work, no
unconditional non-trivial lower bound was known for post-critically infinite
$f$. In the case $f$ is PCF, no non-trivial lower bound was known, not even
under Vojta's conjecture.
  In this paper we give two simple methods that turn the finiteness of the
critical orbit into an exploitable feature, also in the post-critically
infinite case. First, assuming GRH for number fields, we establish for all PCF
polynomials $f$ of degree at least $2$ and all $\alpha$ outside of the critical
orbits of $f$, the existence of a positive constant $c(f,\alpha)$ such that
$$D_N(f,\alpha) \geq \text{exp}(c(f,\alpha)N), $$ for all $N$, which is sharp
up to, possibly, improve the constant $c(f,\alpha)$.
  Second, we show unconditionally that if $f$ is post-critically infinite over
any number field $K$ and unicritical, then, for each $\alpha$ in $K$, there
exists a positive constant $c(f,\alpha)$ such that $$D_N(f,\alpha) \geq
\text{exp}(c(f,\alpha)N), $$ for all $N$. The main input here is to work modulo
a suitably chosen prime and use a construction available for PCF unicritical
polynomials with periodic critical orbit.


Abstract:
  Beam quality optimization in mammography traditionally considers detection of
a target obscured by quantum noise on a homogenous background. It can be argued
that this scheme does not correspond well to the clinical imaging task because
real mammographic images contain a complex superposition of anatomical
structures, resulting in anatomical noise that may dominate over quantum noise.
Using a newly developed spectral mammography system, we measured the
correlation and magnitude of the anatomical noise in a set of mammograms. The
results from these measurements were used as input to an observer-model
optimization that included quantum noise as well as anatomical noise. We found
that, within this framework, the detectability of tumors and
microcalcifications behaved very differently with respect to beam quality and
dose. The results for small microcalcifications were similar to what
traditional optimization methods would yield, which is to be expected since
quantum noise dominates over anatomical noise at high spatial frequencies. For
larger tumors, however, low-frequency anatomical noise was the limiting factor.
Because anatomical structure has similar energy dependence as tumor contrast,
optimal x-ray energy was significantly higher and the useful energy region
wider than traditional methods suggest. Measurements on a tissue phantom
confirmed these theoretical results. Furthermore, since quantum noise
constitutes only a small fraction of the noise, the dose could be reduced
substantially without sacrificing tumor detectability. Exposure settings used
clinically are therefore not necessarily optimal for this imaging task. The
impact of these findings on the mammographic imaging task as a whole is,
however, at this stage unclear.


Abstract:
  We demonstrate that the time evolution of the van Hove dynamical pair
correlation function is governed by adiabatic forces that arise from the free
energy and by superadiabatic forces that are induced by the flow of the van
Hove function. The superadiabatic forces consist of drag, viscous, and
structural contributions, as occur in active Brownian particles, in liquids
under shear and in lane forming mixtures. For hard sphere liquids we present a
power functional theory that predicts these universal force fields in
quantitative agreement with our Brownian dynamics simulation results.


Abstract:
  We present our current progress in the holographic computation of the
scattering amplitude for Deeply Virtual Compton Scattering (DVCS) processe, as
a function of the Mandelstam invariant $t$. We show that it is possible to
describe simultaneously the differential cross-section and total cross-section
of DVCS data with a single holographic model for the pomeron. Using data from
H1-ZEUS we obtained a $\chi^2_{dof} \sim 1.5$ for the best fit to the data.


Abstract:
  We develop a low scale left-right symmetric model based on $SU(3)_C\times
SU(2)_L\times SU(2)_R\times U(1)_{B-L}\times Z_2$ with a simplified Higgs
sector consisting of only one bidoublet and one $SU(2)_R$ doublet. In this
model, the tiny values of light neutrino masses are generated through an
inverse-seesaw mechanism. We emphasize that in this setup, the tree-level
flavor changing neutral current can be strongly suppressed, consistent with the
current experimental constraints. We show that the lightest $CP$-even Higgs
boson, which is like the standard model Higgs boson, and the next lightest
Higgs boson, $h'$, are generated from the neutral components of the bidoublet.
We show that the mass of the next lightest Higgs boson can be of an order a few
hundred~\text{GeVs}. We analyze the detection of $h'$ at the Large Hadron
Collider (LHC) for a center-of-mass energy $\sqrt{s}=14~\text{TeV}$ and
integrated luminosity $L_{\text{int}}=300~{\text{fb}}^{-1}$ via di-Higgs
channel: $h'\to hh\to b\bar{b}\gamma\gamma$ and also in the $ZZ$ channel:
$h'\to ZZ\to 4\ell~(\ell=e,~\mu)$ at an integrated luminosity
$L_{\text{int}}=3000~{\text{fb}}^{-1}$. We consider three benchmark points for
this analysis with $m_{h'}=250~\text{GeV},~400~\text{GeV}$, and
$600~\text{GeV}$. We show that promising signals with good statistical
significances can be obtained in di-Higgs channel, with $2\gamma+2b$-jets final
states.


Abstract:
  Broadcast consensus protocols (BCPs) are a model of computation, in which
anonymous, identical, finite-state agents compute by sending/receiving global
broadcasts. BCPs are known to compute all number predicates in
$\mathsf{NL}=\mathsf{NSPACE}(\log n)$ where $n$ is the number of agents. They
can be considered an extension of the well-established model of population
protocols. This paper investigates execution time characteristics of BCPs. We
show that every predicate computable by population protocols is computable by a
BCP with expected $\mathcal{O}(n \log n)$ interactions, which is asymptotically
optimal. We further show that every log-space, randomized Turing machine can be
simulated by a BCP with $\mathcal{O}(n \log n \cdot T)$ interactions in
expectation, where $T$ is the expected runtime of the Turing machine. This
allows us to characterise polynomial-time BCPs as computing exactly the number
predicates in $\mathsf{ZPL}$, i.e. predicates decidable by log-space bounded
randomised Turing machine with zero-error in expected polynomial time where the
input is encoded as unary.


Abstract:
  In this paper, we study an interesting combination of sleeping and
combinatorial stochastic bandits. In the mixed model studied here, at each
discrete time instant, an arbitrary \emph{availability set} is generated from a
fixed set of \emph{base} arms. An algorithm can select a subset of arms from
the \emph{availability set} (sleeping bandits) and receive the corresponding
reward along with semi-bandit feedback (combinatorial bandits).
  We adapt the well-known CUCB algorithm in the sleeping combinatorial bandits
setting and refer to it as \CSUCB. We prove -- under mild smoothness conditions
-- that the \CSUCB\ algorithm achieves an $O(\log (T))$ instance-dependent
regret guarantee. We further prove that (i) when the range of the rewards is
bounded, the regret guarantee of \CSUCB\ algorithm is $O(\sqrt{T \log (T)})$
and (ii) the instance-independent regret is $O(\sqrt[3]{T^2 \log(T)})$ in a
general setting. Our results are quite general and hold under general
environments -- such as non-additive reward functions, volatile arm
availability, a variable number of base-arms to be pulled -- arising in
practical applications. We validate the proven theoretical guarantees through
experiments.


Abstract:
  We show that if we factor the long exact sequence in cohomology of a cofiber
sequence of spectra into short exact sequences, then the $d_2$-differential in
the Adams spectral sequence of any one term is related in a precise way to
Yoneda composition with the 2-extension given by the complementary terms in the
long exact sequence. We use this to give a complete analysis of the Adams
spectral sequence for the connective image-of-$J$ spectrum, finishing a
calculation that was begun by D. Davis in 1975.


Abstract:
  Machine learning based data-driven technologies have shown impressive
performances in a variety of application domains. Most enterprises use data
from multiple sources to provide quality applications. The reliability of the
external data sources raises concerns for the security of the machine learning
techniques adopted. An attacker can tamper the training or test datasets to
subvert the predictions of models generated by these techniques. Data poisoning
is one such attack wherein the attacker tries to degrade the performance of a
classifier by manipulating the training data.
  In this work, we focus on label contamination attack in which an attacker
poisons the labels of data to compromise the functionality of the system. We
develop Gradient-based Data Subversion strategies to achieve model degradation
under the assumption that the attacker has limited-knowledge of the victim
model. We exploit the gradients of a differentiable convex loss function
(residual errors) with respect to the predicted label as a warm-start and
formulate different strategies to find a set of data instances to contaminate.
Further, we analyze the transferability of attacks and the susceptibility of
binary classifiers. Our experiments show that the proposed approach outperforms
the baselines and is computationally efficient.


Abstract:
  Two-component coupled Bose gas in a 1D optical lattice is examined. In
addition to the postulated Mott insulator and superfluid phases, multiple
bosonic components manifest spin degrees of freedom. Coupling of the components
in the Bose gas leads to substantial change in the previously observed spin
phases, giving rise to new effective spin Hamiltonian and unraveling remarkable
spin correlations. The system exhibiting ferromagnetic and non-ferromagnetic
spin phases for on-site intra-component interaction stronger than
inter-component interaction switches from first-order to second-order phase
transition between the spin phases upon introduction of coupling, on which is
dependent the transition width. For comparable on-site inter- and intra-
component interaction, with coupling, instead of one, two spin phases emerge
with a second-order phase transition. Exact diagonalization and Variational
Monte Carlo (VMC) with stochastic minimization on Entangled Plaquette State
(EPS) bestow a unique and enhanced perspective into the system beyond the scope
of a mean-field treatment.


Abstract:
  We define R-equivalence for group schemes over a semilocal ring and relate
this with rational properties. Two main cases are investigated: tori and
isotropic semisimple simply connected group schemes where we show in certain
cases that R-equivalence coincide with Karoubi-Villamayor equivalence and is
also related to the Kneser-Tits problem in this setting. Finally we construct
specialization maps for R-equivalence in the case of regular algebras
containing a field.


Abstract:
  Time-series representation learning is a fundamental task for time-series
analysis. While significant progress has been made to achieve accurate
representations for downstream applications, the learned representations often
lack interpretability and do not expose semantic meanings. Different from
previous efforts on the entangled feature space, we aim to extract the
semantic-rich temporal correlations in the latent interpretable factorized
representation of the data. Motivated by the success of disentangled
representation learning in computer vision, we study the possibility of
learning semantic-rich time-series representations, which remains unexplored
due to three main challenges: 1) sequential data structure introduces complex
temporal correlations and makes the latent representations hard to interpret,
2) sequential models suffer from KL vanishing problem, and 3) interpretable
semantic concepts for time-series often rely on multiple factors instead of
individuals. To bridge the gap, we propose Disentangle Time Series (DTS), a
novel disentanglement enhancement framework for sequential data. Specifically,
to generate hierarchical semantic concepts as the interpretable and
disentangled representation of time-series, DTS introduces multi-level
disentanglement strategies by covering both individual latent factors and group
semantic segments. We further theoretically show how to alleviate the KL
vanishing problem: DTS introduces a mutual information maximization term, while
preserving a heavier penalty on the total correlation and the dimension-wise KL
to keep the disentanglement property. Experimental results on various
real-world benchmark datasets demonstrate that the representations learned by
DTS achieve superior performance in downstream applications, with high
interpretability of semantic concepts.


Abstract:
  We consider the algebras generated by observables in quantum field theory
localized in regions in the null plane. For a scalar free field theory, we show
that the one-particle structure can be decomposed into a continuous direct
integral of lightlike fibres, and the modular operator decomposes accordingly.
This implies that a certain form of QNEC is valid in free fields involving the
causal completions of half-spaces on the null plane (null cuts). We also
compute the relative entropy of null cut algebras with respect to the vacuum
and some coherent states.


Abstract:
  We have come to rely on industrial-scale cyber-physical systems more and more
to manage tasks and machinery in safety-critical situations. Efficient,
reliable fault identification and management has become a critical factor in
the design of these increasingly sophisticated and complex devices. Teams of
co-operating software agents are one way to coordinate the flow of diagnostic
information gathered during fault-finding. By wielding domain knowledge of the
software architecture used to construct the system, agents build and refine
their beliefs about the location and root cause of faults. This paper examines
how agents constructed within the GORITE Multi-Agent Framework create and
refine their beliefs. We demonstrate three different belief structures
implemented within our Fault Diagnostic Engine, showing how each supports a
distinct aspect of the agent's reasoning. Using domain knowledge of the IEC
61499 Function Block architecture, agents are able to examine and rigorously
evaluate both individual components and entire subsystems.


Abstract:
  Providing high-speed Internet service using satellite network has attracted
researchers from both academia and industry mainly due to the characteristics
of Low Earth Orbit (LEO) satellite networks such as global coverage,
scalability, and lower transmission delay. With the recent advancements in the
Software-Defined Network (SDN), implementation of SDN in Non-Terrestrial
Networks (NTN) can help to achieve the set goals for 5G and beyond networks.
Since satellite networks have a distinct architecture, some of the traditional
protocols no longer remain useful. Therefore, to satisfy the diverse Quality of
Service (QoS) requirements for a variety of applications, we propose a novel
and centralized QoS-aware routing algorithm, called fybrrLink in which the
global view of the network in SDN is utilized. We implement a modified
Bresenham's algorithm and Dijkstra's algorithm to find the optimal path in a
significantly reduced computation time. Also, taking advantage of the
deterministic satellite constellation, we propose a flow rule transfer
algorithm and a topology monitoring algorithm. Further, fybrrLink is evaluated
with multiple NS3 simulations, and results confirm its supremacy over other
state-of-the-art algorithms.


Abstract:
  The progress in Query-focused Multi-Document Summarization (QMDS) has been
limited by the lack of sufficient largescale high-quality training datasets. We
present two QMDS training datasets, which we construct using two data
augmentation methods: (1) transferring the commonly used single-document
CNN/Daily Mail summarization dataset to create the QMDSCNN dataset, and (2)
mining search-query logs to create the QMDSIR dataset. These two datasets have
complementary properties, i.e., QMDSCNN has real summaries but queries are
simulated, while QMDSIR has real queries but simulated summaries. To cover both
these real summary and query aspects, we build abstractive end-to-end neural
network models on the combined datasets that yield new state-of-the-art
transfer results on DUC datasets. We also introduce new hierarchical encoders
that enable a more efficient encoding of the query together with multiple
documents. Empirical results demonstrate that our data augmentation and
encoding methods outperform baseline models on automatic metrics, as well as on
human evaluations along multiple attributes.


Abstract:
  Let $G$ be a Wheeler graph and $r$ be the number of runs in a Burrows-Wheeler
Transform of $G$, and suppose $G$ can be decomposed into $\upsilon$
edge-disjoint directed paths whose internal vertices each have in- and
out-degree exactly 1. We show how to store $G$ in $O (r + \upsilon)$ space such
that later, given a pattern $P$, in $O (|P| \log \log |G|)$ time we can count
the vertices of $G$ reachable by directed paths labelled $P$, and then report
those vertices in $O (\log \log |G|)$ time per vertex.


Abstract:
  The Griddy Gibbs sampling was proposed by Ritter and Tanner (1992) as a
computationally efficient approximation of the well-known Gibbs sampling
method. The algorithm is simple and effective and has been used successfully to
address problems in various fields of applied science. However, the approximate
nature of the algorithm has prevented it from being widely used: the Markov
chains generated by the Griddy Gibbs sampling method are not reversible in
general, so the existence and uniqueness of its invariant measure is not
guaranteed. Even when such an invariant measure uniquely exists, there was no
estimate of the distance between it and the probability distribution of
interest, hence no means to ensure the validity of the algorithm as a means to
sample from the true distribution.
  In this paper, we show, subject to some fairly natural conditions, that the
Griddy Gibbs method has a unique, invariant measure. Moreover, we provide $L^p$
estimates on the distance between this invariant measure and the corresponding
measure obtained from Gibbs sampling. These results provide a theoretical
foundation for the use of the Griddy Gibbs sampling method. We also address a
more general result about the sensitivity of invariant measures under small
perturbations on the transition probability. That is, if we replace the
transition probability $P$ of any Monte Carlo Markov chain by another
transition probability $Q$ where $Q$ is close to $P$, we can still estimate the
distance between the two invariant measures. The distinguishing feature between
our approach and previous work on convergence of perturbed Markov chain is that
by considering the invariant measures as fixed points of linear operators on
function spaces, we don't need to impose any further conditions on the rate of
convergence of the Markov chain.


Abstract:
  Quantum detector tomography is a fundamental technique for calibrating
quantum devices and performing quantum engineering tasks. In this paper, we
design optimal probe states for detector estimation based on the minimum upper
bound of the mean squared error (UMSE) and the maximum robustness. We establish
the minimum UMSE and the minimum condition number for quantum detectors and
provide concrete examples that can achieve optimal detector tomography. In
order to enhance estimation precision, we also propose a two-step adaptive
detector tomography algorithm and investigate how this adaptive strategy can be
used to achieve efficient estimation of quantum detectors. Moreover, the
superposition of coherent states are used as probe states for quantum detector
tomography and the estimation error is analyzed. Numerical results demonstrate
the effectiveness of both the proposed optimal and adaptive quantum detector
tomography methods.


Abstract:
  Nothing can physically travel faster than light in vacuum. This is why it has
been considered that there is no \v{C}erenkov radiation (\v{C}R) without an
effective refractive index due to some background field. In this Letter, we
theoretically predict \v{C}R in vacuum from a spatiotemporally modulated
boundary. We consider the modulation of traveling wave type and apply a uniform
electrostatic field on the boundary to generate electric dipoles. Since the
induced dipoles stick to the interface, they travel at the modulation speed.
When the grating travels faster than light, it emits \v{C}R. In order to
quantitatively examine this argument, we need to calculate the field scattered
at the boundary. We utilise a dynamical differential method, which we developed
in the previous paper, to quantitatively evaluate the field distribution in
such a situation. We can confirm that all scattered fields are evanescent if
the modulation speed is slower than light while some become propagating if the
modulation is faster than light.


Abstract:
  We show that Hertzian particle contacts are the underlying cause of the
as-yet-unexplained noninteger power laws in weakly nonlinear rheology. In the
medium amplitude oscillatory shear (MAOS) region, the cubic scaling of the
leading order nonlinear shear stress ($\sigma_\mathrm{3} \sim
\gamma_\mathrm{0}^{m_\mathrm{3}}$, $m_\mathrm{3}=3$) is the standard
expectation. Expanding on the work by Natalia et al. [J. Rheol. 64 625-635
(2020)], we report an extensive data set of noncubical, noninteger power law
scalings $m_\mathrm{3}$ for particle suspensions in two immiscible fluids with
a capillary attractive interaction, known as capillary suspensions. Here, we
show that distinct power law exponents are found for the storage and loss
moduli and these noninteger scalings occur at every secondary fluid
concentration for two different contact angles. These compelling results
indicate that the noninteger scalings are related to the underlying
microstructure of capillary suspensions. We show that the magnitude of the
third harmonic elastic stress scaling $m_\mathrm{3,elastic}$ originates from
Hertzian-like contacts in combination with the attractive capillary force. The
related third harmonic viscous stress scaling $m_\mathrm{3,viscous}$ is, found
to be associated with adhesive-controlled friction. These observations,
conducted for a wide range of compositions, can help explain previous reports
of noninteger scaling for materials involving particle contacts and offers a
new opportunity using the variable power law exponent of MAOS rheology to
reveal the physics of particle bonds and friction in the rheological response
under low deformation instead of at very high shear rates.


Abstract:
  Dot product latent space embedding is a common form of representation
learning in undirected graphs (e.g. social networks, co-occurrence networks).
We show that such models have problems dealing with 'intransitive' situations
where A is linked to B, B is linked to C but A is not linked to C. Such
situations occur in social networks when opposites attract (heterophily) and in
co-occurrence networks when there are substitute nodes (e.g. the presence of
Pepsi or Coke, but rarely both, in otherwise similar purchase baskets). We
present a simple expansion which we call the attract-repel (AR) decomposition:
a set of latent attributes on which similar nodes attract and another set of
latent attributes on which similar nodes repel. We demonstrate the AR
decomposition in real social networks and show that it can be used to measure
the amount of latent homophily and heterophily. In addition, it can be applied
to co-occurrence networks to discover roles in teams and find substitutable
ingredients in recipes.


Abstract:
  We present analysis of two type-I X-ray bursts observed by NuSTAR originating
from the very faint transient neutron star low-mass X-ray binary GRS
1741.9-2853 during a period of outburst in May 2020. We show that the
persistent emission can be modeled as an absorbed, Comptonized blackbody in
addition to Fe K$\alpha$ emission which can be attributed to relativistic disk
reflection. We measure a persistent bolometric, unabsorbed luminosity of
$L_{\mathrm{bol}}=7.03^{+0.04}_{-0.05}\times10^{36}\,\mathrm{erg\,s^{-1}}$,
assuming a distance of 7 kpc, corresponding to an Eddington ratio of $4.5\%$.
This persistent luminosity combined with light curve analysis leads us to infer
that the bursts were the result of pure He burning rather than mixed H/He
burning. Time-resolved spectroscopy reveals that the bolometric flux of the
first burst exhibits a double-peaked structure, placing the source within a
small population of accreting neutron stars which exhibit multiple-peaked
type-I X-ray bursts. We find that the second, brighter burst shows evidence for
photospheric radius expansion (PRE) and that at its peak, this PRE event had an
unabsorbed bolometric flux of
$F_{\mathrm{peak}}=2.94^{+0.28}_{-0.26}\times10^{-8}\,\mathrm{erg\,cm^{-2}\,s^{-1}}$.
This yields a new distance estimate of $d=9.0\pm0.5$ kpc, assuming that this
corresponds to the Eddington limit for pure He burning on the surface of a
canonical neutron star. Additionally, we performed a detailed timing analysis
which failed to find evidence for quasiperiodic oscillations or burst
oscillations, and we place an upper limit of $16\%$ on the rms variability
around 589 Hz, the frequency at which oscillations have previously been
reported.


Abstract:
  A low-energy magnetic dipole $(M1)$ spin-scissors resonance (SSR) located
just below the ordinary orbital scissors resonance (OSR) was recently predicted
in deformed nuclei within the Wigner Function Moments (WFM) approach. We
analyze this prediction using fully self-consistent Skyrme Quasiparticle Random
Phase Approximation (QRPA) method. Skyrme forces SkM*, SVbas and SG2 are
implemented to explore SSR and OSR in $^{160,162,164}$Dy and $^{232}$Th.
Accuracy of the method is justified by a good description of M1 spin-flip giant
resonance. The calculations show that isotopes $^{160,162,164}$Dy indeed have
at 1.5-2.4 MeV (below OSR) $I^{\pi}K=1^+1$ states with a large $M1$ spin
strength ($K$ is the projection of the total nuclear moment to the symmetry
z-axis). These states are almost fully exhausted by $pp[411\uparrow,
411\downarrow]$ and $nn[521\uparrow, 521\downarrow]$ spin-flip configurations
corresponding to $pp[2d_{3/2}, 2d_{5/2}]$ and $nn[2f_{5/2}, 2f_{7/2}]$
structures in the spherical limit. So the predicted SSR is actually reduced to
low-orbital (l=2,3) spin-flip states. Following our analysis and in
contradiction with WFM spin-scissors picture, deformation is not the principle
origin of the low-energy spin $M1$ states but only a factor affecting their
features. The spin and orbital strengths are generally mixed and exhibit the
interference: weak destructive in SSR range and strong constructive in OSR
range. In $^{232}$Th, the $M1$ spin strength is found very small. Two groups of
$I^{\pi}=1^+$ states observed experimentally at 2.4-4 MeV in $^{160,162,164}$Dy
and at 2-4 MeV in $^{232}$Th are mainly explained by fragmentation of the
orbital strength. Distributions of nuclear currents in QRPA states partly
correspond to the isovector orbital-scissors flow but not to spin-scissors one.


Abstract:
  Automatic identification of plant specimens from amateur photographs could
improve species range maps, thus supporting ecosystems research as well as
conservation efforts. However, classifying plant specimens based on image data
alone is challenging: some species exhibit large variations in visual
appearance, while at the same time different species are often visually
similar; additionally, species observations follow a highly imbalanced,
long-tailed distribution due to differences in abundance as well as observer
biases. On the other hand, most species observations are accompanied by side
information about the spatial, temporal and ecological context. Moreover,
biological species are not an unordered list of classes but embedded in a
hierarchical taxonomic structure. We propose a multimodal deep learning model
that takes into account these additional cues in a unified framework. Our
Digital Taxonomist is able to identify plant species in photographs better than
a classifier trained on the image content alone, the performance gained is over
6 percent points in terms of accuracy.


Abstract:
  We investigate how well BERT performs on predicting factuality in several
existing English datasets, encompassing various linguistic constructions.
Although BERT obtains a strong performance on most datasets, it does so by
exploiting common surface patterns that correlate with certain factuality
labels, and it fails on instances where pragmatic reasoning is necessary.
Contrary to what the high performance suggests, we are still far from having a
robust system for factuality prediction.


Abstract:
  In \cite[Theorem 2.5]{Bac16} Bachiller proved that if $(G, \cdot, \circ)$ is
a brace of order the power of a prime $p$ and the rank of $(G,\cdot)$ is
smaller than $p-1$, then the order of any element is the same in the additive
and multiplicative group. This means that in this case the isomorphism type of
$(G,\circ)$ determines the isomorphism type of $(G,\cdot)$. In this paper we
complement Bachiller's result in two directions. In Theorem 2.2 we prove that
if $(G, \cdot, \circ)$ is a brace of order the power of a prime $p$, then
$(G,\cdot)$ has small rank (i.e. $< p-1$) if and only if $(G,\circ)$ has small
rank. We also provide examples of groups of rank $p-1$ in which elements of
arbitrarily large order in the additive group become of prime order in the
multiplicative group. When the rank is larger, orders may increase.


Abstract:
  In traffic management, it is a very important issue to shorten the response
time by detecting the incidents (accident, vehicle breakdown, an object falling
on the road, etc.) and informing the corresponding personnel. In this study, an
anomaly detection framework for road junctions is proposed. The final judgment
is based on the trajectories followed by the vehicles. Trajectory information
is provided by vehicle detection and tracking algorithms on visual data
streamed from a fisheye camera. Deep learning algorithms are used for vehicle
detection, and Kalman Filter is used for tracking. To observe the trajectories
more accurately, the detected vehicle coordinates are transferred to the bird's
eye view coordinates using the lens distortion model prediction algorithm. The
system determines whether there is an abnormality in trajectories by comparing
historical trajectory data and instantaneous incoming data. The proposed system
has achieved 84.6% success in vehicle detection and 96.8% success in
abnormality detection on synthetic data. The system also works with a 97.3%
success rate in detecting abnormalities on real data.


Abstract:
  An \emph{equitable $(q, r)$-tree-coloring} of a graph $G$ is a $q$-coloring
of $G$ such that the subgraph induced by each color class is a forest of
maximum degree at most $r$ and the sizes of any two color classes differ by at
most $1.$ Let the \emph{strong equitable vertex $r$-arboricity} of a graph $G,$
denoted by $va^\equiv_r (G)$, be the minimum $p$ such that $G$ has an equitable
$(q, r)$-tree-coloring for every $q\geq p.$
  The values of $va^\equiv_1 (K_{n,n})$ were investigated by Tao and Lin and
Wu, Zhang, and Li where exact values of $va^\equiv_1 (K_{n,n})$ were found in
some special cases. In this paper, we extend their results by giving the exact
values of $va^\equiv_1 (K_{n,n})$ for all cases. In the process, we introduce a
new function related to an equitable coloring and obtain a more general result
by determining the exact value of each $va^\equiv_1 (K_{m,n})$ and $va^\equiv_1
(G)$ where $G$ is a balanced complete $k$-partite graph $K_{n,\ldots,n}.$


Abstract:
  We propose a near-term quantum simulator based on the fluxonium qubits
inductively coupled to form a chain. This system provides long coherence time,
large anharmonicity, and strong coupling, making it suitable to study Ising
spin models. At the half-flux quantum sweet spot, the system is described by
the transverse field Ising model (TFIM). We evaluate the propagation of qubit
excitations through the system. As disorder increases, the excitations become
localized. A single qubit measurement using the circuit QED methods is
sufficient to identify localization transition without introducing tunable
couplers. We argue that inductively coupled fluxoniums provide opportunities to
study localization and many-body effects in highly coherent quantum systems.


Abstract:
  First-principles calculations using density functional theory and two methods
in comparison, Quantum ESPRESSO and Siesta, are done on large supercells which
describe different placements of two identical adsorbed alkali metal atoms (of
either Na, or K species) on the monolayer of boron carbide BC3. The energy of
single-atom adsorption over the center of C6 ring, of the C4B2 hexagon and over
a boron atom have been preliminarily estimated, the effect of applying the
Grimme D2 correction on the adsorption characteristics evaluated, and the
comparison of these results with available data discussed. The interaction of
two identical Na or K atoms adsorbed at close enough distances (less than 10
Angstrom) is negligible if the adsorption occurs at the opposite sides of the
BC3 layer, but creates a steep repulsive potential at distances less than 8
Angstrom if both atoms are adsorbed on the same side of the monolayer.
Relaxation patterns resulting from the two K atoms being trapped at adjacent
adsorption sites in the lattice are explained. The results suggest that the
density of adsorbed K atoms on BC3 can be interestingly high.


Abstract:
  Image denoising is often empowered by accurate prior information. In recent
years, data-driven neural network priors have shown promising performance for
RGB natural image denoising. Compared to classic handcrafted priors (e.g.,
sparsity and total variation), the "deep priors" are learned using a large
number of training samples -- which can accurately model the complex image
generating process. However, data-driven priors are hard to acquire for
hyperspectral images (HSIs) due to the lack of training data. A remedy is to
use the so-called unsupervised deep image prior (DIP). Under the unsupervised
DIP framework, it is hypothesized and empirically demonstrated that proper
neural network structures are reasonable priors of certain types of images, and
the network weights can be learned without training data. Nonetheless, the most
effective unsupervised DIP structures were proposed for natural images instead
of HSIs. The performance of unsupervised DIP-based HSI denoising is limited by
a couple of serious challenges, namely, network structure design and network
complexity. This work puts forth an unsupervised DIP framework that is based on
the classic spatio-spectral decomposition of HSIs. Utilizing the so-called
linear mixture model of HSIs, two types of unsupervised DIPs, i.e., U-Net-like
network and fully-connected networks, are employed to model the abundance maps
and endmembers contained in the HSIs, respectively. This way, empirically
validated unsupervised DIP structures for natural images can be easily
incorporated for HSI denoising. Besides, the decomposition also substantially
reduces network complexity. An efficient alternating optimization algorithm is
proposed to handle the formulated denoising problem. Semi-real and real data
experiments are employed to showcase the effectiveness of the proposed
approach.


Abstract:
  We present experiments revealing the competing effect of quantum fluctuations
and of a coherent seed in the dynamics of a spin-1 Bose-Einstein condensate,
and discuss the relevance of a mean-field description of our system. We first
explore a near-equilibrium situation, where the mean-field equations can be
linearized around a fixed point corresponding to all atoms in the same Zeeman
state $m=0$. Preparing the system at this classical fixed point, we observe a
reversible dynamics triggered by quantum fluctuations, which cannot be
understood within a classical framework. We demonstrate that the classical
description becomes accurate provided a coherent seed of a few atoms only is
present in the other Zeeman states $m= \pm 1$. In a second regime characterized
by a strong non-linearity of the mean-field equations, we observe a collapse
dynamics driven by quantum fluctuations. This behavior cannot be accounted for
by a classical description and persists for a large range of initial states. We
show that all our experimental results can be explained with a semi-classical
description (truncated Wigner approximation), using stochastic classical
variables to model the quantum noise.


Abstract:
  TypeShift is a tool for visualizing linguistic patterns in the timing of
typing production. Language production is a complex process which draws on
linguistic, cognitive and motor skills. By visualizing holistic trends in the
typing process, TypeShift aims to elucidate the often noisy information signals
that are used to represent typing patterns, both at the word-level and
character-level. It accomplishes this by enabling a researcher to compare and
contrast specific linguistic phenomena, and compare an individual typing
session to multiple group averages. Finally, although TypeShift was originally
designed for typing data, it can easy be adapted to accommodate speech data, as
well. A web demo is available at https://angoodkind.shinyapps.io/TypeShift/.
The source code can be accessed at https://github.com/angoodkind/TypeShift.


Abstract:
  This paper considers the use of instruments to identify and estimate private
and social returns to education within a model of employer learning. What an
instrument identifies depends on whether it is hidden from, or transparent
(i.e., observed) to, the employers. A hidden instrument identifies private
returns to education, and a transparent instrument identifies social returns to
education. We use variation in compulsory schooling laws across non-central and
central municipalities in Norway to, respectively, construct hidden and
transparent instruments. We estimate a private return of 7.9%, of which 70% is
due to increased productivity and the remaining 30% is due to signaling.


Abstract:
  We study X-ray and soft gamma-ray spectra from the hard state of the
accreting black-hole binary MAXI J1820+070. We perform analysis of two joint
spectra from NuSTAR and INTEGRAL, covering the range of 3--650 keV, and of an
average joint spectrum over the rise of the hard state, covering the 3--2200
keV range. The spectra are well modelled by Comptonization of soft seed
photons. However, the distributions of the scattering electrons are not purely
thermal; we find they have substantial high-energy tails, well modelled as
power laws. The photon tail in the average spectrum is detected well beyond the
threshold for electron-positron pair production, 511 keV. This allows us to
calculate the rate of the electron-positron pair production and put a lower
limit on the size of the source from pair equilibrium. At the fitted Thomson
optical depth of the Comptonizing plasma, the limit is about 4 gravitational
radii. If we adopt the sizes estimated by us from the reflection spectroscopy
of $>$20 gravitational radii, the fractional pair abundance becomes much less
than unity. The low pair abundance is confirmed by the lack of both an
annihilation feature and of a pair absorption cutoff above 511 keV in the
average spectrum.


Abstract:
  We establish a connection between federated learning, a concept from machine
learning, and mean-field games, a concept from game theory and control theory.
In this analogy, the local federated learners are considered as the players and
the aggregation of the gradients in a central server is the mean-field effect.
We present federated learning as a differential game and discuss the properties
of the equilibrium of this game. We hope this novel view to federated learning
brings together researchers from these two distinct areas to work on
fundamental problems of large-scale distributed and privacy-preserving learning
algorithms.


Abstract:
  It is known that nuclear deformation plays an important role in inducing the
halo structure in neutron-rich nuclei by mixing several angular momentum
components. While previous theoretical studies on this problem in the
literature assume axially symmetric deformation, we here consider non-axially
symmetric deformations. With triaxial deformation, the $\Omega$ quantum number
is admixed in a single-particle wave function, where $\Omega$ is the projection
of the single-particle angular momentum on the symmetric axis, and the halo
structure may arise even when it is absent with the axially symmetric
deformation. In this way, the area of halo nuclei may be extended when triaxial
deformation is considered. We demonstrate this idea using a deformed
Woods-Saxon potential for nuclei with neutron number N=13 and 43.


Abstract:
  We report the first quasioptical simulations of wave beams in a hot plasma
using the quasioptical code PARADE (PAraxial RAy DEscription) [Phys. Plasmas
26, 072112 (2019)]. This code is unique in that it accounts for inhomogeneity
of the dissipation-rate across the beam and mode conversion simultaneously. We
show that the dissipation-rate inhomogeneity shifts beams relative to their
trajectories in cold plasma and that the two electromagnetic modes are coupled
via this process, an effect that was ignored in the past. We also propose a
simplified approach to accounting for the dissipation-rate inhomogeneity. This
approach is computationally inexpensive and simplifies analysis of actual
experiments.


Abstract:
  Autonomous vehicles are conceived to provide safe and secure services by
validating the safety standards as indicated by SOTIF-ISO/PAS-21448 (Safety of
the intended functionality). Keeping in this context, the perception of the
environment plays an instrumental role in conjunction with localization,
planning and control modules. As a pivotal algorithm in the perception stack,
object detection provides extensive insights into the autonomous vehicle's
surroundings. Camera and Lidar are extensively utilized for object detection
among different sensor modalities, but these exteroceptive sensors have
limitations in resolution and adverse weather conditions. In this work,
radar-based object detection is explored provides a counterpart sensor modality
to be deployed and used in adverse weather conditions. The radar gives complex
data; for this purpose, a channel boosting feature ensemble method with
transformer encoder-decoder network is proposed. The object detection task
using radar is formulated as a set prediction problem and evaluated on the
publicly available dataset in both good and good-bad weather conditions. The
proposed method's efficacy is extensively evaluated using the COCO evaluation
metric, and the best-proposed model surpasses its state-of-the-art counterpart
method by $12.55\%$ and $12.48\%$ in both good and good-bad weather conditions.


Abstract:
  Recent theoretical work has shown that the competition between coherent
unitary dynamics and stochastic measurements, performed by the environment,
along wavefunction trajectories can give rise to transitions in the
entanglement scaling. In this work, complementary to these previous studies, we
analyze a situation where the role of Hamiltonian and dissipative dynamics is
reversed. We consider an engineered dissipation, which stabilizes an entangled
phase of a quantum spin$-1/2$ chain, while competing single-particle or
interacting Hamiltonian dynamics induce a disentangled phase. Focusing on the
single-particle unitary dynamics, we find that the system undergoes an
entanglement transition from a logarithmic growth to an area law when the
competition ratio between the unitary evolution and the non-unitary dynamics
increases. We evidence that the transition manifests itself in state-dependent
observables at a finite competition ratio for Hamiltonian and measurement
dynamics. On the other hand, it is absent in trajectory-averaged steady-state
dynamics, governed by a Lindblad master equation: although purely dissipative
dynamics stabilizes an entangled state, for any non-vanishing Hamiltonian
contribution the system ends up irremediably in a disordered phase. In
addition, a single trajectory analysis reveals that the distribution of the
entanglement entropy constitutes an efficient indicator of the transition.
Complementarily, we explore the competition of the dissipation with coherent
dynamics generated by an interacting Hamiltonian, and demonstrate that the
entanglement transition also occurs in this second model. Our results suggest
that this type of transition takes place for a broader class of Hamiltonians,
underlining its robustness in monitored open quantum many-body systems.


Abstract:
  We establish connectedness criteria for graphs associated to monomials in
certain quotients of the mod 2 dual Steenrod algebra. We also investigate
questions about trees and Hamilton cycles in the context of these graphs.
Finally, we improve upon a known connection between the graph theoretic
interpretation of the mod 2 dual Steenrod algebra and its structure as a Hopf
algebra.


Abstract:
  Natural Language Inference (NLI) datasets contain examples with highly
ambiguous labels. While many research works do not pay much attention to this
fact, several recent efforts have been made to acknowledge and embrace the
existence of ambiguity, such as UNLI and ChaosNLI. In this paper, we explore
the option of training directly on the estimated label distribution of the
annotators in the NLI task, using a learning loss based on this ambiguity
distribution instead of the gold-labels. We prepare AmbiNLI, a trial dataset
obtained from readily available sources, and show it is possible to reduce
ChaosNLI divergence scores when finetuning on this data, a promising first step
towards learning how to capture linguistic ambiguity. Additionally, we show
that training on the same amount of data but targeting the ambiguity
distribution instead of gold-labels can result in models that achieve higher
performance and learn better representations for downstream tasks.


Abstract:
  Our work aimed at experimentally assessing the benefits of model ensembling
within the context of neural methods for passage reranking. Starting from
relatively standard neural models, we use a previous technique named Fast
Geometric Ensembling to generate multiple model instances from particular
training schedules, then focusing or attention on different types of approaches
for combining the results from the multiple model instances (e.g., averaging
the ranking scores, using fusion methods from the IR literature, or using
supervised learning-to-rank). Tests with the MS-MARCO dataset show that model
ensembling can indeed benefit the ranking quality, particularly with supervised
learning-to-rank although also with unsupervised rank aggregation.


Abstract:
  Present theories of irreversible energy losses and heat generation within
Li-ion cells are unsatisfactory because they are not compatible with energy
conservation. This work aims to provide a consistent theoretical treatment of
energy transport and losses in such devices. An energy conservation law is
derived from the Doyle-Fuller-Newman (DFN) model of a Li-ion cell using a
rigorous mathematical approach. The resulting law allows irreversible chemical
energy losses to be located to seven different regions of the cell, namely: (i)
the electrolyte, (ii) the anode particles, (iii) the cathode particles, (iv)
the solid parts of the anode (ohmic losses), (v) the solid parts of the cathode
(ohmic losses), (vi) the surfaces of the anode particles (polarisation losses),
and (vii) the surfaces of the cathode particles (polarisation losses).
Numerical solutions to the DFN model are used to validate the conservation law
in the cases of a drive cycle and constant current discharges, and to compare
the energy losses occurring in different locations. It is indicated how cell
design can be improved, for a specified set of operating conditions, by
comparing the magnitude of energy losses in the different regions of the cell.


Abstract:
  Propagation effects in the interstellar medium and intrinsic profile changes
can cause variability in the timing of pulsars, which limits the accuracy of
fundamental science done via pulsar timing. One of the best timing pulsars, PSR
J1713+0747, has gone through two `dip' events in its dispersion measure (DM)
time series. If these events reflect real changes in electron column density,
they should lead to multiple imaging. We show that the events are are well
fitted by an underdense corrugated sheet model, and look for associated
variability in the pulse profile using principal component analysis. We find
that there are transient pulse profile variations, but they vary in concert
with the dispersion measure, unlike what is expected from lensing due to a
corrugated sheet. The change is consistent in shape across profiles from both
the Greenbank and Arecibo radio observatories, and its amplitude appears to be
achromatic across the 820-MHz, 1.4-GHz, and 2.3-GHz bands, again unlike
expected from interference between lensed images. This result is puzzling. We
note that some of the predicted lensing effects would need higher time and
frequency resolution data than used in this analysis. Future events appear
likely, and storing baseband data or keeping multiple time-frequency
resolutions will allow more in-depth study of propagation effects and hence
improvements to pulsar timing accuracy.


Abstract:
  Imitation Learning (IL) is an effective framework to learn visuomotor skills
from offline demonstration data. However, IL methods often fail to generalize
to new scene configurations not covered by training data. On the other hand,
humans can manipulate objects in varying conditions. Key to such capability is
hand-eye coordination, a cognitive ability that enables humans to adaptively
direct their movements at task-relevant objects and be invariant to the
objects' absolute spatial location. In this work, we present a learnable action
space, Hand-eye Action Networks (HAN), that can approximate human's hand-eye
coordination behaviors by learning from human teleoperated demonstrations.
Through a set of challenging multi-stage manipulation tasks, we show that a
visuomotor policy equipped with HAN is able to inherit the key spatial
invariance property of hand-eye coordination and achieve zero-shot
generalization to new scene configurations. Additional materials available at
https://sites.google.com/stanford.edu/han


Abstract:
  As the applications of deep learning models on edge devices increase at an
accelerating pace, fast adaptation to various scenarios with varying resource
constraints has become a crucial aspect of model deployment. As a result, model
optimization strategies with adaptive configuration are becoming increasingly
popular. While single-shot quantized neural architecture search enjoys
flexibility in both model architecture and quantization policy, the combined
search space comes with many challenges, including instability when training
the weight-sharing supernet and difficulty in navigating the exponentially
growing search space. Existing methods tend to either limit the architecture
search space to a small set of options or limit the quantization policy search
space to fixed precision policies. To this end, we propose BatchQuant, a robust
quantizer formulation that allows fast and stable training of a compact,
single-shot, mixed-precision, weight-sharing supernet. We employ BatchQuant to
train a compact supernet (offering over $10^{76}$ quantized subnets) within
substantially fewer GPU hours than previous methods. Our approach,
Quantized-for-all (QFA), is the first to seamlessly extend one-shot
weight-sharing NAS supernet to support subnets with arbitrary ultra-low
bitwidth mixed-precision quantization policies without retraining. QFA opens up
new possibilities in joint hardware-aware neural architecture search and
quantization. We demonstrate the effectiveness of our method on ImageNet and
achieve SOTA Top-1 accuracy under a low complexity constraint ($<20$ MFLOPs).
The code and models will be made publicly available at
https://github.com/bhpfelix/QFA.


Abstract:
  We show that two families of equations, the generalized inviscid
Proudman-Johnson equation, and the $r$-Hunter-Saxton equation (recently
introduced by Cotter et al.) coincide for a certain range of parameters. This
gives a new geometric interpretation of these Proudman-Johnson equations as
geodesic equations of right invariant homogeneous $W^{1,r}$-Finsler metrics on
the diffeomorphism group. Generalizing a construction of Lenells for the
Hunter-Saxton equation, we analyze these equations using an isometry from the
diffeomorphism group to an appropriate subset of real-valued functions. Thereby
we show that the periodic case is equivalent to the geodesic equations on the
$L^r$-sphere in the space of functions, and the non-periodic case is equivalent
to a geodesic flow on a flat space. This allows us to give explicit solutions
to these equations in the non-periodic case, and answer several questions of
Cotter et al. regarding their limiting behavior.


Abstract:
  We work out the interplay between lowest-order perturbative computations in
the 't Hooft coupling, $g^2=g^2_{YM} N$, operator mixing, renormalization-group
(RG) improved ultraviolet (UV) asymptotics of leading-order (LO)
nonplanar/planar contributions to $2$-point correlators, and nonperturbative
large-$N$ expansion of perturbatively massless QCD-like theories. As concrete
examples, we compute to the lowest perturbative order in $SU(N)$ YM theory the
ratios, $r_i$, of LO-nonplanar to planar contributions to the $2$-point
correlators in the orthogonal basis in the coordinate representation of the
gauge-invariant dimension-$8$ scalar operators and all the twist-$2$ operators.
We demonstrate that -- if $\frac{\gamma_0}{\beta_0}$ has no LO-nonplanar
contribution, with $\gamma_0$ and $\beta_0$ the one-loop coefficients of the
anomalous-dimension matrix and beta function respectively -- $r_i$ actually
coincides with the corresponding ratio in the large-$N$ expansion of the
RG-improved UV asymptotics of the $2$-point correlators, provided that a
certain canonical nonresonant diagonal renormalization scheme exists for the
corresponding operators. Contrary to the aforementioned scalar operators, for
the first $10^3$ twist-$2$ operators we actually verify the above conditions,
and we get the universal value $r_i=-\frac{1}{N^2}$. Hence, nonperturbatively
such $r_i$ must coincide with the UV asymptotics of the ratio of the glueball
self-energy loop to the glueball tree contribution to the $2$-point correlators
above. As a consequence, the universality of $r_i$ reflects the universality of
the effective coupling in the nonperturbative large-$N$ YM theory for the
twist-$2$ operators in the coordinate representation.