Abstract:
  Multi-agent path finding (MAPF) determines an ensemble of collision-free
paths for multiple agents between their respective start and goal locations.
Among the available MAPF planners for workspace modeled as a graph, A*-based
approaches have been widely investigated due to their guarantees on
completeness and solution optimality, and have demonstrated their efficiency in
many scenarios. However, almost all of these A*-based methods assume that each
agent executes an action concurrently in that all agents start and stop
together. This article presents a natural generalization of MAPF with
asynchronous actions (MAPF-AA) where agents do not necessarily start and stop
concurrently. The main contribution of the work is a proposed approach called
Loosely Synchronized Search (LSS) that extends A*-based MAPF planners to handle
asynchronous actions. We show LSS is complete and finds an optimal solution if
one exists. We also combine LSS with other existing MAPF methods that aims to
trade-off optimality for computational efficiency. Numerical results are
presented to corroborate the performance of LSS and the applicability of the
proposed method is verified in the Robotarium, a remotely accessible swarm
robotics research platform.


Abstract:
  By definition, reciprocal matrices are tridiagonal $n$-by-$n$ matrices $A$
with constant main diagonal and such that $a_{i,i+1}a_{i+1,i}=1$ for
$i=1,\ldots,n-1$. For $n\leq 6$, we establish criteria under which the
numerical range generating curves (also called Kippenhahn curves) of such
matrices consist of elliptical components only. As a corollary, we also provide
a complete description of higher-rank numerical ranges when the criteria are
met.


Abstract:
  CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the
target data on a functional manifold. To that matter, we introduce a new
Functional Instance Normalization layer and residual mechanism, which together
disentangle image content from position on target manifold. We rely on naive
physics-inspired models to guide the training while allowing private
model/translations features. CoMoGAN can be used with any GAN backbone and
allows new types of image translation, such as cyclic image translation like
timelapse generation, or detached linear translation. On all datasets, it
outperforms the literature. Our code is available at
http://github.com/cv-rits/CoMoGAN .


Abstract:
  In this paper, we consider the predual spaces of weak Orlicz spaces. As an
application, we provide the Fefferman-Stein vector-valued maximal inequality
for the weak Orlicz spaces. In order to prove this statement, we introduced the
Orlicz-Lorentz spaces, and showed the boundedness of the Hardy-Littlewood
maximal operator on these spaces.


Abstract:
  Domain-specific quantitative modeling and analysis approaches are fundamental
in scenarios in which qualitative approaches are inappropriate or unfeasible.
In this paper, we present a tool-supported approach to quantitative graph-based
security risk modeling and analysis based on attack-defense trees. Our approach
is based on QFLan, a successful domain-specific approach to support
quantitative modeling and analysis of highly configurable systems, whose
domain-specific components have been decoupled to facilitate the instantiation
of the QFLan approach in the domain of graph-based security risk modeling and
analysis. Our approach incorporates distinctive features from three popular
kinds of attack trees, namely enhanced attack trees, capabilities-based attack
trees and attack countermeasure trees, into the domain-specific modeling
language. The result is a new framework, called RisQFLan, to support
quantitative security risk modeling and analysis based on attack-defense
diagrams. By offering either exact or statistical verification of probabilistic
attack scenarios, RisQFLan constitutes a significant novel contribution to the
existing toolsets in that domain. We validate our approach by highlighting the
additional features offered by RisQFLan in three illustrative case studies from
seminal approaches to graph-based security risk modeling analysis based on
attack trees.


Abstract:
  Application domains of Bayesian optimization include optimizing black-box
  functions or very complex functions. The functions we are interested in
describe
  complex real-world systems applied in industrial settings. Even though
  they do have explicit representations, standard optimization
  techniques fail to provide validated solutions and correctness
  guarantees for them.
  In this paper we present a combination of Bayesian optimisation and SMT-based
constraint solving to achieve safe and stable solutions with optimality
guarantees.


Abstract:
  Nitrogen is one of the most abundant metals in the interstellar medium (ISM),
and thus it constitutes an excellent test to study a variety of astrophysical
environments, ranging from nova to active galactic nuclei. We present a
detailed analysis of the gaseous component of the N K~edge using
high-resolution {\it XMM-Newton} spectra of 12 Galactic and 40 extragalactic
sources. For each source, we have estimated column densities for {\rm N}~{\sc
i}, {\rm N}~{\sc ii}, {\rm N}~{\sc iii}, {\rm N}~{\sc v}, {\rm N}~{\sc vi} and
{\rm N}~{\sc vii} ionic species, which trace the cold, warm and hot phases of
the local Galactic interstellar medium. We have found that the cold-warm
component column densities decrease with the Galactic latitude while the hot
component does not. Moreover, the cold column density distribution is in good
agreement with UV measurements. This is the first detailed analysis of the
nitrogen K-edge absorption due to ISM using high-resolution X-ray spectra.


Abstract:
  Traditional academic competitions that foster collaboration from student
communities all around the globe have either been postponed indefinitely or
cancelled due to restrictions posed upon in-person gathering. Owing to this,
virtual competitions are gaining importance as they provide the student
community with an outlet for exposure and academic growth whilst having the
convenience of being online and safe. Mars Society South Asia (MSSA) has
developed a software stack capable of replicating on-site competition tasks via
ROS-based simulations. The software enables users to perform necessary
simulations without them having to interact with ROS or Gazebo. Currently, the
software has been set out to be used for the Virtual Mars Rover Challenge
(VMRC), a competition that focuses on simulating the next generation of Mars
rovers and thereby providing the student community with a substitute to their
on-site competitions. The objective of this white paper is to explain the
software architecture, installation, usage, maintenance and ease of use which
is the singular most important factor in software adoption.


Abstract:
  Diabetes is one of the deadliest diseases in the world and affects nearly 10
percent of the global adult population. Fortunately, powerful new technologies
allow for a consistent and reliable treatment plan for people with diabetes.
One major development is a system called continuous blood glucose monitoring
(CGM). In this review, we look at three different continuous meal detection
algorithms that were developed using given CGM data from patients with
diabetes. From this analysis, an initial meal prediction algorithm was also
developed utilizing these methods.


Abstract:
  The transmission system operators (TSOs) are responsible to provide secure
and efficient access to the transmission system for all stakeholders. This task
is gradually getting challenging due to the demand growth, demand uncertainty,
rapid changes in generation mix, and market policies. Traditionally, the TSOs
try to maximize the technical performance of the transmission network via
building new overhead lines or physical hardening. However, obtaining public
acceptance for building new lines is not an easy step in this procedure. For
this reason, the TSOs try to capture the capabilities of existing assets. This
paper investigates the use of modular FACTS devices (to alter the line
characteristics) for improving the capability of transmission network in
serving the uncertain demand without the need for building new overhead lines.
The proposed method considers the uncertainty of demands and controls the
utilization of existing transmission assets. The mathematical results obtained
are validated with a complete non-linear simulation model of three transmission
networks.


Abstract:
  We consider the time-convex hull problem in the presence of two orthogonal
highways H. In this problem, the travelling speed on the highway is faster than
off the highway, and the time-convex hull of a point set P is the closure of P
with respect to the inclusion of shortest time-paths. In this paper, we provide
the algorithm for constructing the time-convex hull with two orthogonal
highways. We reach the optimal result of O(n log n) time for arbitrary highway
speed in the L1-metric. For the L2-metric with infinite highway speed, we hit
the goal of O(n log n) time as well.


Abstract:
  Structural credit assignment for recurrent learning is challenging. An
algorithm called RTRL can compute gradients for recurrent networks online but
is computationally intractable for large networks. Alternatives, such as BPTT,
are not online. In this work, we propose a credit-assignment algorithm --
\algoname{} -- that approximates the gradients for recurrent learning in
real-time using $O(n)$ operations and memory per-step. Our method builds on the
idea that for modular recurrent networks, composed of columns with scalar
states, it is sufficient for a parameter to only track its influence on the
state of its column. We empirically show that as long as connections between
columns are sparse, our method approximates the true gradient well. In the
special case when there are no connections between columns, the $O(n)$ gradient
estimate is exact. We demonstrate the utility of the approach for both
recurrent state learning and meta-learning by comparing the estimated gradient
to the true gradient on a synthetic test-bed.


Abstract:
  We show that for every countable recursively saturated model $M$ of Peano
Arithmetic and every subset $A \subseteq M$, there exists a full satisfaction
class $S_A \subset M^2$ such that $A$ is definable in $(M,S_A)$ without
parametres. It follows that in every such model, there exists a full
satisfaction class which makes every element definable and thus the expanded
model is minimal and rigid. On the other hand, we show that for every full
satisfaction class $S$ there are two elements which have the same arithmetical
type, but exactly one of them is in $S$. In particular, the automorphism group
of a model expanded with a satisfaction class is never equal to the
automorphism group of the original model. The analogue of many of the results
proved here for full satisfaction classes were obtained by Roman Kossak for
partial inductive satisfaction classes. However, most of the proofs relied
heavily on the induction scheme in a crucial way, so recapturing the results in
the setting of full satisfaction classes requires quite different arguments.


Abstract:
  The purpose of this paper is to illustrate the problem of energy and momentum
distributions of Van Stockum space-time within the framework of two different
theories of gravity, general relativity and teleparallel gravity.
  We have shown that for all homogeneous space-times with metric components
$g_{\mu\nu}$ being functions of time variable, $t$, alone and independent of
space variables the total gravitational energy for any finite volume is
identically zero. By working with general relativity, we have calculated the
energy-momentum density for Van Stockum space-time using double index complexes
and in the framework teleparallel gravity, we used the energy-momentum
complexes of Einstein, Bergmann-Thomson and Landau-Lifshitz. In our analysis,
we sustained that general relativity and teleparallel gravity are equivalent
theories of space-time under consideration. For space-time under consideration,
we have shown that different complexes of energy-momentum density do not
provide the same results neither in general relativity nor in teleparallel
gravity.


Abstract:
  In this work we marry multi-index Monte Carlo with ensemble Kalman filtering
(EnKF) to produce the multi-index EnKF method (MIEnKF). The MIEnKF method is
based on independent samples of four-coupled EnKF estimators on a multi-index
hierarchy of resolution levels, and it may be viewed as an extension of the
multilevel EnKF (MLEnKF) method developed by the same authors in 2020.
Multi-index here refers to a two-index method, consisting of a hierarchy of
EnKF estimators that are coupled in two degrees of freedom: time discretization
and ensemble size. Under certain assumptions, the MIEnKF method is proven to be
more tractable than EnKF and MLEnKF, and this is also verified in numerical
examples.


Abstract:
  The modeling of fast flavor evolution of neutrinos in dense environments has
been traditionally carried out by relying on a two flavor approximation for
simplicity. In addition, vacuum mixing has been deemed negligible. For the
first time, we highlight that the fast flavor evolution in three flavors is
intrinsically different from the one obtained in the two flavor approximation.
This is due to the exponential growth of flavor mixing in the $e$--$\mu$ and
$e$--$\tau$ sectors generated by the vacuum term in the Hamiltonian. As a
result, substantially larger flavor mixing is found in three flavors. Our
findings highlight that the two flavor approximation is not justified for fast
pairwise conversion, even if the angular distributions of non-electron type
neutrinos are initially identical.


Abstract:
  Our goal in this work is to generate realistic videos given just one initial
frame as input. Existing unsupervised approaches to this task do not consider
the fact that a video typically shows a 3D environment, and that this should
remain coherent from frame to frame even as the camera and objects move. We
address this by developing a model that first estimates the latent 3D structure
of the scene, including the segmentation of any moving objects. It then
predicts future frames by simulating the object and camera dynamics, and
rendering the resulting views. Importantly, it is trained end-to-end using only
the unsupervised objective of predicting future frames, without any 3D
information nor segmentation annotations. Experiments on two challenging
datasets of natural videos show that our model can estimate 3D structure and
motion segmentation from a single frame, and hence generate plausible and
varied predictions.


Abstract:
  Charge density waves (CDW) are modulations of the electron density and the
atomic lattice that develop in some crystalline materials at low temperature.
We report an unusual example of a CDW in BaFe$_2$Al$_9$ below 100 K. In
contrast to the canonical CDW phase transition, temperature dependent physical
properties of single crystals reveal a first-order phase transition. This is
accompanied by a discontinuous change in the size of the crystal lattice. In
fact, this large strain has catastrophic consequences for the crystals causing
them to physically shatter. Single crystal x-ray diffraction reveals
super-lattice peaks in the low-temperature phase signaling the development of a
CDW lattice modulation. No similar low-temperature transitions are observed in
BaCo$_2$Al$_9$. Electronic structure calculations provide one hint to the
different behavior of these two compounds; the d-orbital states in the Fe
compound are not completely filled. Iron compounds are renowned for their
magnetism and partly filled d-states play a key role. It is therefore
surprising that BaFe$_2$Al$_9$ develops a structural modulation instead at low
temperature instead of magnetic order.


Abstract:
  We study the scattering of open-beauty mesons and Goldstone bosons as
predicted by the chiral SU(3) Lagrangian. The impact of subleading order chiral
interactions to systems with $J^P= 0^+$ and $J^P=1^+$ quantum numbers is worked
out. We estimate the relevant low-energy coefficients from the open-charm
sector, for which their values have been determined previously from sets of QCD
lattice data. The leading order heavy-quark symmetry breaking effects are
estimated by matching the $B$-meson ground-state chiral mass formula to the
mass formula from the heavy-quark effective theory. We make refined predictions
for the flavor anti-triplet and sextet resonances that are generated
dynamically by coupled-channel interactions.


Abstract:
  The cold start problem in recommender systems is a long-standing challenge,
which requires recommending to new users (items) based on attributes without
any historical interaction records. In these recommendation systems, warm users
(items) have privileged collaborative signals of interaction records compared
to cold start users (items), and these Collaborative Filtering (CF) signals are
shown to have competing performance for recommendation. Many researchers
proposed to learn the correlation between collaborative signal embedding space
and the attribute embedding space to improve the cold start recommendation, in
which user and item categorical attributes are available in many online
platforms. However, the cold start recommendation is still limited by two
embedding spaces modeling and simple assumptions of space transformation. As
user-item interaction behaviors and user (item) attributes naturally form a
heterogeneous graph structure, in this paper, we propose a privileged graph
distillation model~(PGD). The teacher model is composed of a heterogeneous
graph structure for warm users and items with privileged CF links. The student
model is composed of an entity-attribute graph without CF links. Specifically,
the teacher model can learn better embeddings of each entity by injecting
complex higher-order relationships from the constructed heterogeneous graph.
The student model can learn the distilled output with privileged CF embeddings
from the teacher embeddings. Our proposed model is generally applicable to
different cold start scenarios with new user, new item, or new user-new item.
Finally, extensive experimental results on the real-world datasets clearly show
the effectiveness of our proposed model on different types of cold start
problems, with average $6.6\%, 5.6\%, $ and $17.1\%$ improvement over
state-of-the-art baselines on three datasets, respectively.


Abstract:
  We study discrete-time mirror descent applied to the unregularized empirical
risk in matrix sensing. In both the general case of rectangular matrices and
the particular case of positive semidefinite matrices, a simple potential-based
analysis in terms of the Bregman divergence allows us to establish convergence
of mirror descent -- with different choices of the mirror maps -- to a matrix
that, among all global minimizers of the empirical risk, minimizes a quantity
explicitly related to the nuclear norm, the Frobenius norm, and the von Neumann
entropy. In both cases, this characterization implies that mirror descent, a
first-order algorithm minimizing the unregularized empirical risk, recovers
low-rank matrices under the same set of assumptions that are sufficient to
guarantee recovery for nuclear-norm minimization. When the sensing matrices are
symmetric and commute, we show that gradient descent with full-rank factorized
parametrization is a first-order approximation to mirror descent, in which case
we obtain an explicit characterization of the implicit bias of gradient flow as
a by-product.


Abstract:
  An increase in radiotherapy-induced secondary malignancies has led to recent
developments in analytical modelling of out-of-field dose. These models must be
validated against measurements, but currently available datasets are outdated
or limited in scope. This study aimed to address these shortcomings by
producing a large dataset of out-of-field dose profiles measured with modern
equipment. A novel method was developed with the intention of allowing
physicists in all clinics to perform these measurements themselves using
commonly available dosimetry equipment. A standard 3D scanning water tank was
used to collect 36 extended profiles. Each profile was measured in two
sections, with the inner section measured with the beam directly incident on
the tank, and the outer section with the beam incident on a water-equivalent
phantom abutted next to the tank. The two sections were then stitched using a
novel feature-matching approach. The profiles were compared against linac
commissioning data and manually inspected for discontinuities in the overlap
region. The dataset is presented as a publicly accessible comma separated
variable file containing off-axis ratios at a range of off-axis distances. This
dataset may be applied to the development and validation of analytical models
of out-of-field dose. Additionally, it may be used to inform dose estimates to
radiosensitive implants and anatomy. Physicists are encouraged to perform these
out-of-field measurements in their own clinics and share their results with the
community.


Abstract:
  We come back to the construction of p-adic L-functions attached to cusp forms
of even weight k in the spirit of G. Stevens, R. Pollack [7] and M. Greenberg
[3] with a new unified presentation including the non-ordinary case. This
construction is based on Stevens's modular symbols rather than q-developments.
We review the proofs in order to obtain an effective algorithm guaranteeing a
given p-adic accuracy.


Abstract:
  In this paper, we propose self-reorganizing and rejuvenating convolutional
neural networks; a biologically inspired method for improving the computational
resource utilization of neural networks. The proposed method utilizes the
channel activations of a convolution layer in order to reorganize that layers
parameters. The reorganized parameters are clustered to avoid parameter
redundancies. As such, redundant neurons with similar activations are merged
leaving room for the remaining parameters to rejuvenate. The rejuvenated
parameters learn different features to supplement those learned by the
reorganized surviving parameters. As a result, the network capacity utilization
increases improving the baseline network performance without any changes to the
network structure. The proposed method can be applied to various network
architectures during the training stage, or applied to a pre-trained model
improving its performance. Experimental results showed that the proposed method
is model-agnostic and can be applied to any backbone architecture increasing
its performance due to the elevated utilization of the network capacity.


Abstract:
  Let $k$ be a field of characteristic two. We prove that a non constant monic
polynomial $f\in k[X]$ of degree $n$ is the minimal/characteristic polynomial
of a symmetric matrix with entries in $k$ if and only if it is not the product
of pairwise distinct inseparable irreducible polynomials. In this case, we
prove that $f$ is the minimal polynomial of a symmetric matrix of size $n$. We
also prove that any element $\alpha\in k_{alg}$ of degree $n\geq 1$ is the
eigenvalue of a symmetrix matrix of size $n$ or $n+1$, the first case happening
if and only if the minimal polynomial of $\alpha$ is not the product of
pairwise distinct inseparable irreducible polynomials.


Abstract:
  A concise method of deriving expressions for Gaussian-like solutions of the
paraxial and d'Alembert equations is presented. This method is based on the
Hankel transform. Choosing some Gaussian base functions with slight
modifications of the prefactor all basic beams of cylindrical character can be
easily obtained. This refers to Gaussian, Bessel-Gaussian, modified
Bessel-Gaussian, Laguerre-Gaussian and Kummer-Gaussian (i.e.,
Hypergeometric-Gaussian) beams although potentially other beams can come into
play as well. For instance a new type of a beam that can be derived in this way
is described through the incomplete gamma function so it may be called a
$\gamma$ beam.


Abstract:
  The latest measurement of the muon anomalous magnetic moment $a^{}_{\mu}
\equiv (g^{}_\mu - 2)/2$ at the Fermi Laboratory has found a $4.2\,\sigma$
discrepancy with the theoretical prediction of the Standard Model (SM).
Motivated by this exciting progress, we investigate in the present paper the
general one-loop contributions to $a^{}_\mu$ within the SM and beyond. First,
different from previous works, the analytical formulae of relevant loop
functions after integration are now derived and put into compact forms with the
help of the Passarino-Veltman functions. Second, given the interactions of muon
with new particles running in the loop, we clarify when the one-loop
contribution to $a^{}_\mu$ could take the correct positive sign as desired.
Third, possible divergences in the zero- and infinite-mass limits are examined,
and the absence of any divergences in the calculations leads to some
consistency conditions for the construction of ultraviolet complete models.
Applications of our general formulae to specific models, such as the SM, seesaw
models, $Z^\prime$ and leptoquark models, are also discussed.


Abstract:
  To the best of our knowledge, there are no specific calculations of
gravity-darkening exponents for white dwarfs in the literature. On the other
hand, the number of known eclipsing binaries whose components are tidally
and/or rotationally distorted white dwarfs is increasing year on year.
  Our main objective is to present the first theoretical approaches to the
problem of the distribution of temperatures on the surfaces of compact stars
distorted by rotation and/or tides in order to compare with relevant
observational data.
  We find discrepancies between the gravity-darkening exponents calculated with
our methods and the predictions of the von Zeipel theorem, particularly in the
cases of cold white dwarfs; although the discrepancy also applies to higher
effective temperatures under determined physical conditions. We find physical
connections between the gravity-darkening exponents calculated using our
modified method of triangles strategy with the convective efficiency (defined
here as the ratio of the convective to the total flux). A connection between
the entropy and the gravity-darkening coefficients is also found: variations of
the former cause changes in the way the temperature is distributed on distorted
stellar surfaces.
  On the other hand, we have generalised the von Zeipel theorem for the case of
hot white dwarfs. Such a generalisation allows us to predict that, under
certain circumstances, the value of the gravity-darkening exponent may be
smaller than 1.0, even in the case of high effective temperatures.


Abstract:
  We outline the development of a general-purpose Python-based data analysis
tool for OpenFOAM. Our implementation relies on the construction of OpenFOAM
applications that have bindings to data analysis libraries in Python. Double
precision data in OpenFOAM is cast to a NumPy array using the NumPy C-API and
Python modules may then be used for arbitrary data analysis and manipulation on
flow-field information. We highlight how the proposed wrapper may be used for
an in-situ online singular value decomposition (SVD) implemented in Python and
accessed from the OpenFOAM solver PimpleFOAM. Here, `in-situ' refers to a
programming paradigm that allows for a concurrent computation of the data
analysis on the same computational resources utilized for the partial
differential equation solver. In addition, to demonstrate data-parallel
analyses, we deploy a distributed SVD, which collects snapshot data across the
ranks of a distributed simulation to compute the global left singular vectors.
Crucially, both OpenFOAM and Python share the same message passing interface
(MPI) communicator for this deployment which allows Python objects and
functions to exchange NumPy arrays across ranks. Subsequently, we provide
scaling assessments of this distributed SVD on multiple nodes of Intel
Broadwell and KNL architectures for canonical test cases such as the large eddy
simulations of a backward facing step and a channel flow at friction Reynolds
number of 395. Finally, we demonstrate the deployment of a deep neural network
for compressing the flow-field information using an autoencoder to demonstrate
an ability to use state-of-the-art machine learning tools in the Python
ecosystem.


Abstract:
  Let $(f_1,f_2)$ a $2$-tuple of arithmetic functions and
$$\Delta_3(n,f_1,f_2):=\sup\limits_{\substack{(u_1,u_2) \in \mathbb{R}^{2}
\\(v_1,v_2) \in [0,1]^{2}}}\Big\lvert \sum\limits_{\substack{d_1 d_{2} \mid n
\\ e^{u_i}<d_i\leqslant e^{u_i+v_i}}}{f_1(d_1) f_{2}(d_{2})} \Big\rvert{\rm
.}$$ In this paper, we give an upper bound of the second moment of
$\Delta_3(n,\chi_1,\chi_2)$ when $\chi_1$ and $\chi_2$ are two non principal
Dirichlet characters, following methods developed by La Bret\`eche and
Tenenbaum. This upper bound is a main step for the asymptotic counting of the
number of ideals of norm fixed, which will be developped in another article.


Abstract:
  We review 15 years of theoretical and experimental work on the non-linear
response of glassy systems. We argue that an anomalous growth of the peak value
of non-linear susceptibilities is a signature of growing "amorphous order" in
the system, with spin-glasses as a case in point. Experimental results on
supercooled liquids are fully compatible with the RFOT prediction of compact
"glassites" of increasing volume as temperature is decreased, or as the system
ages. We clarify why such a behaviour is hard to explain within purely kinetic
theories of glass formation, despite recent claims to the contrary.


Abstract:
  The outer crust of a cold nonaccreting neutron star has been generally
assumed to be stratified into different layers, each of which consists of a
pure body-centered cubic ionic crystal in a charge compensating background of
highly degenerate electrons. The validity of this assumption is examined by
analyzing the stability of multinary ionic compounds in dense stellar matter.
It is thus shown that their stability against phase separation is uniquely
determined by their structure and their composition irrespective of the stellar
conditions. However, equilibrium with respect to weak and strong nuclear
processes imposes very stringent constraints on the composition of multinary
compounds, and thereby on their formation. By examining different cubic and
noncubic lattices, it is found that substitutional compounds having the same
structure as cesium chloride are the most likely to exist in the outer crust of
a nonaccreting neutron star. The presence of ternary compounds is also
investigated. Very accurate analytical expressions are obtained for the
threshold pressure, as well as for the densities of the different phases
irrespective of the degree of relativity of the electron gas. Finally,
numerical calculations of the ground-state structure and of the equation of
state of the outer crust of a cold nonaccreting neutron star are carried out
using recent experimental and microscopic nuclear mass tables.


Abstract:
  We study the Hardy identities and inequalities on Cartan-Hadamard manifolds
using the notion of a Bessel pair. These Hardy identities offer significantly
more information on the existence/nonexistence of the extremal functions of the
Hardy inequalities. These Hardy inequalities are in the spirit of
Brezis-V\'{a}zquez in the Euclidean spaces. As direct consequences, we
establish several Hardy type inequalities that provide substantial improvements
as well as simple understandings to many known Hardy inequalities and
Hardy-Poincar\'{e}-Sobolev type inequalities on hyperbolic spaces in the
literature.


Abstract:
  Crowdfunding offers inventors and entrepreneurs alternative access to
resources with which they can develop and realize their ideas. Besides helping
to secure capital, crowdfunding also connects creators with engaged early
supporters who provide public feedback. But does this process foster truly
innovative outcomes? Does the proliferation of crowdfunding in an industry make
it more innovative overall? Prior studies investigating the link between
crowdfunding and innovation do not compare traditional and crowdfunded products
and so while claims that crowdfunding supports innovation are theoretically
sound, they lack empirical backing. We address this gap using a unique dataset
of board games, an industry with significant crowdfunding activity in recent
years. Each game is described by how it combines fundamental mechanisms such as
dice-rolling, negotiation, and resource-management, from which we develop
quantitative measures of innovation in game design. Using these measures to
compare games, we find that crowdfunded games tend to be more distinctive from
previous games than their traditionally published counterparts. They are also
significantly more likely to implement novel combinations of mechanisms.
Crowdfunded games are not just transient experiments: subsequent games imitate
their novel ideas. These results hold in regression models controlling for game
and designer-level confounders. Our findings demonstrate that the innovative
potential of crowdfunding goes beyond individual products to entire industries,
as new ideas spill over to traditionally funded products.


Abstract:
  The color-magnitude diagrams (CMDs) of young star clusters show that,
particularly at ultraviolet wavelengths, their upper main sequences (MSs)
bifurcate into a sequence comprising the bulk population and a blue periphery.
The spatial distribution of stars is crucial to understand the reasons for
these distinct stellar populations. This study uses high-resolution photometric
data obtained with the Hubble Space Telescope to study the spatial
distributions of the stellar populations in seven Magellanic Cloud star
clusters. The cumulative radial number fractions of blue stars within four
clusters are strongly anti-correlated with those of the high-mass-ratio
binaries in the bifurcated region, with negative Pearson coefficients < -0.7.
Those clusters generally are young or in an early dynamical evolutionary stage.
In addition, a supporting N-body simulation suggests the increasing percentage
of blue-MS stars from the cluster centers to their outskirts may be associated
with the dissolution of soft binaries. This study provides a different
perspective to explore the MS bimodalities in young clusters and adds extra
puzzles. A more comprehensive study combined with detailed simulations is
needed in the future.


Abstract:
  This paper presents a comparison of two multi-fidelity methods for the
forward uncertainty quantification of a naval engineering problem.
Specifically, we consider the problem of quantifying the uncertainty of the
hydrodynamic resistance of a roll-on/roll-off passengers ferry advancing in
calm water and subject to two operational uncertainties (ship speed and
payload). The first four statistical moments (mean, variance, skewness,
kurtosis), and the probability density function for such quantity of interest
(QoI) are computed with two multi-fidelity methods, i.e., the Multi-Index
Stochastic Collocation (MISC) method and an adaptive multi-fidelity Stochastic
Radial Basis Functions (SRBF) algorithm. The QoI is evaluated via computational
fluid dynamics simulations, which are performed with the in-house unsteady
Reynolds-Averaged Navier-Stokes (RANS) multi-grid solver $\chi$navis. The
different fidelities employed by both methods are obtained by stopping the RANS
solver at different grid levels of the multi-grid cycle. The performance of
both methods are presented and discussed: in a nutshell, the findings suggest
that, at least for the current implementations of both algorithms, MISC could
be preferred whenever a limited computational budget is available, whereas for
a larger computational budget SRBFs seem to be preferable, thanks to its
robustness to the numerical noise in the evaluations of the QoI.


Abstract:
  Recently, pure transformer-based models have shown great potentials for
vision tasks such as image classification and detection. However, the design of
transformer networks is challenging. It has been observed that the depth,
embedding dimension, and number of heads can largely affect the performance of
vision transformers. Previous models configure these dimensions based upon
manual crafting. In this work, we propose a new one-shot architecture search
framework, namely AutoFormer, dedicated to vision transformer search.
AutoFormer entangles the weights of different blocks in the same layers during
supernet training. Benefiting from the strategy, the trained supernet allows
thousands of subnets to be very well-trained. Specifically, the performance of
these subnets with weights inherited from the supernet is comparable to those
retrained from scratch. Besides, the searched models, which we refer to
AutoFormers, surpass the recent state-of-the-arts such as ViT and DeiT. In
particular, AutoFormer-tiny/small/base achieve 74.7%/81.7%/82.4% top-1 accuracy
on ImageNet with 5.7M/22.9M/53.7M parameters, respectively. Lastly, we verify
the transferability of AutoFormer by providing the performance on downstream
benchmarks and distillation experiments. Code and models are available at
https://github.com/microsoft/AutoML.


Abstract:
  We give new parametrisations of elliptic curves in Weierstrass normal form
$y^2=x^3+ax^2+bx$ with torsion groups $\mathbb{Z}/10\mathbb{Z}$ and
$\mathbb{Z}/12\mathbb{Z}$ over $\mathbb{Q}$, and with $\mathbb{Z}/14\mathbb{Z}$
and $\mathbb{Z}/16\mathbb{Z}$ over quadratic fields. Even though the
parametrisations are equivalent to those given by Kubert and Rabarison,
respectively, with the new parametrisations we found three infinite families of
elliptic curves with torsion group $\mathbb{Z}/12\mathbb{Z}$ and positive rank.
Furthermore, we found elliptic curves with torsion group
$\mathbb{Z}/14\mathbb{Z}$ and rank $3$, which is a new record for such curves,
as well as some new elliptic curves with torsion group
$\mathbb{Z}/16\mathbb{Z}$ and rank $3$.


Abstract:
  We develop a new stock market index that captures the chaos existing in the
market by measuring the mutual changes of asset prices. This new index relies
on a tensor-based embedding of the stock market information, which in turn
frees it from the restrictive value- or capitalization-weighting assumptions
that commonly underlie other various popular indexes. We show that our index is
a robust estimator of the market volatility which enables us to characterize
the market by performing the task of segmentation with a high degree of
reliability. In addition, we analyze the dynamics and kinematics of the
realized market volatility as compared to the implied volatility by introducing
a time-dependent dynamical system model. Our computational results which
pertain to the time period from January 1990 to December 2019 imply that there
exist a bidirectional causal relation between the processes underlying the
realized and implied volatility of the stock market within the given time
period, where it is shown that the later has a stronger causal effect on the
former as compared to the opposite. This result connotes that the implied
volatility of the market plays a key role in characterization of the market's
realized volatility.


Abstract:
  The first measurement of the temperature dependence of the muon transfer rate
from muonic hydrogen to oxygen was performed by the FAMU collaboration in 2016.
The results provide evidence that the transfer rate rises with the temperature
in the range 104-300 K. This paper presents the results of the experiment done
in 2018 to extend the measurements towards lower (70 K) and higher (336 K)
temperatures. The 2018 results confirm the temperature dependence of the muon
transfer rate observed in 2016 and sets firm ground for comparison with the
theoretical predictions.


Abstract:
  We address the problem of strategyproof (SP) facility location mechanisms on
discrete trees. Our main result is a full characterization of onto and SP
mechanisms. In particular, we prove that when a single agent significantly
affects the outcome, the trajectory of the facility is almost contained in the
trajectory of the agent, and both move in the same direction along the common
edges. We show tight relations of our characterization to previous results on
discrete lines and on continuous trees. We then derive further implications of
the main result for infinite discrete lines.


Abstract:
  Most segmentation losses are arguably variants of the Cross-Entropy (CE) or
Dice losses. In the abundant segmentation literature, there is no clear
consensus as to which of these losses is a better choice, with varying
performances for each across different benchmarks and applications. In this
work, we develop a theoretical analysis that links these two types of losses,
exposing their advantages and weaknesses. First, we provide a
constrained-optimization perspective showing that CE and Dice share a much
deeper connection than previously thought: They both decompose into
label-marginal penalties and closely related ground-truth matching penalties.
Then, we provide bound relationships and an information-theoretic analysis,
which uncover hidden label-marginal biases: Dice has an intrinsic bias towards
specific extremely imbalanced solutions, whereas CE implicitly encourages the
ground-truth region proportions. Our theoretical results explain the wide
experimental evidence in the medical-imaging literature, whereby Dice losses
bring improvements for imbalanced segmentation. It also explains why CE
dominates natural-image problems with diverse class proportions, in which case
Dice might have difficulty adapting to different label-marginal distributions.
Based on our theoretical analysis, we propose a principled and simple solution,
which enables to control explicitly the label-marginal bias. Our loss
integrates CE with explicit ${\cal L}_1$ regularization, which encourages label
marginals to match target class proportions, thereby mitigating class imbalance
but without losing generality. Comprehensive experiments and ablation studies
over different losses and applications validate our theoretical analysis, as
well as the effectiveness of our explicit label-marginal regularizers.


Abstract:
  In this work, we prove an optimal global-in-time L p --L q estimate for
solutions to the Kramers-Fokker-Planck equation with short range potential in
dimension three. Our result shows that the decay rate as t $\rightarrow$
+$\infty$ is the same as the heat equation in x-variables and the divergence
rate as t $\rightarrow$ 0 + is related to the sub-ellipticity with loss of 1/3
derivatives of the Kramers-Fokker-Planck operator.


Abstract:
  We consider the strong solution of the 2D Navier-Stokes equations in a torus
subject to an additive noise. We implement a fully implicit time numerical
scheme and a finite element method in space. We prove that the rate of
convergence of the schemes is $\eta\in[0,1/2)$ in time and 1 in space. Let us
mention that the coefficient $\eta$ is equal to the time regularity of the
solution with values in $\LL^2$. Our method relies on the existence of finite
exponential moments for both the solution and its time approximation. Our main
idea is to use a discrete Gronwall lemma for the error estimate without any
localization.


Abstract:
  Loss functions are widely used to compare several competing forecasts.
However, forecast comparisons are often based on mismeasured proxy variables
for the true target. We introduce the concept of exact robustness to
measurement error for loss functions and fully characterize this class of loss
functions as the Bregman class. For such exactly robust loss functions,
forecast loss differences are on average unaffected by the use of proxy
variables and, thus, inference on conditional predictive ability can be carried
out as usual. Moreover, we show that more precise proxies give predictive
ability tests higher power in discriminating between competing forecasts.
Simulations illustrate the different behavior of exactly robust and non-robust
loss functions. An empirical application to US GDP growth rates demonstrates
that it is easier to discriminate between forecasts issued at different
horizons if a better proxy for GDP growth is used.


Abstract:
  The identification and counting of small graph patterns, called network
motifs, is a fundamental primitive in the analysis of networks, with
application in various domains, from social networks to neuroscience. Several
techniques have been designed to count the occurrences of motifs in static
networks, with recent work focusing on the computational challenges provided by
large networks. Modern networked datasets contain rich information, such as the
time at which the events modeled by the networks edges happened, which can
provide useful insights into the process modeled by the network. The analysis
of motifs in temporal networks, called temporal motifs, is becoming an
important component in the analysis of modern networked datasets. Several
methods have been recently designed to count the number of instances of
temporal motifs in temporal networks, which is even more challenging than its
counterpart for static networks. Such methods are either exact, and not
applicable to large networks, or approximate, but provide only weak guarantees
on the estimates they produce and do not scale to very large networks. In this
work we present an efficient and scalable algorithm to obtain rigorous
approximations of the count of temporal motifs. Our algorithm is based on a
simple but effective sampling approach, which renders our algorithm practical
for very large datasets. Our extensive experimental evaluation shows that our
algorithm provides estimates of temporal motif counts which are more accurate
than the state-of-the-art sampling algorithms, with significantly lower running
time than exact approaches, enabling the study of temporal motifs, of size
larger than the ones considered in previous works, on billion edges networks.


Abstract:
  Many important applications are modelled by differential equations with
positive solutions. However, it remains an outstanding open problem to develop
numerical methods that are both (i) of a high order of accuracy and (ii)
capable of preserving positivity. It is known that the two main families of
numerical methods, Runge-Kutta methods and multistep methods, face an order
barrier: if they preserve positivity, then they are constrained to low
accuracy: they cannot be better than first order. We propose novel methods that
overcome this barrier: our methods are of second order, and they are guaranteed
to preserve positivity. Our methods apply to a large class of differential
equations that have a special graph Laplacian structure, which we elucidate.
The equations need be neither linear nor autonomous and the graph Laplacian
need not be symmetric. This algebraic structure arises naturally in many
important applications where positivity is required. We showcase our new
methods on applications where standard high order methods fail to preserve
positivity, including infectious diseases, Markov processes, master equations
and chemical reactions.


Abstract:
  We study polarons in the one-dimensional Bond-Peierls electron-phonon model,
in which phonons on bonds of a lattice modulate the hopping of electrons
between lattice sites, and contrast the results to those known for the
Breathing-Mode Peierls problem. By inspecting the atomic limit, we show that
polaronic dressing and mass enhancement of Bond-Peierls polarons depend on the
momentum dependence of the phonons. For dispersionless phonons, Bond-Peierls
polarons are perfectly localized in the atomic limit, unlike their
Breathing-mode counterparts, because the carrier creates a string of phonon
excitations that can only be annihilated via processes that retrace the carrier
to its original site. However, inclusion of phonon dispersion leads to a
non-divergent polaron mass even in the atomic limit and depending on the form
of the phonon dispersion may lead to a transition to a non-zero-momentum ground
state akin to that found in the Breathing-mode Peierls model.


Abstract:
  By generalizing our automated algebra approach from homogeneous space to
harmonically trapped systems, we have calculated the fourth- and fifth-order
virial coefficients of universal spin-1/2 fermions in the unitary limit,
confined in an isotropic harmonic potential. We present results for said
coefficients as a function of trapping frequency (or, equivalently,
temperature), which compare favorably with previous Monte Carlo calculations
(available only at fourth order) as well as with our previous estimates in the
untrapped limit (high temperature, low frequency). We use our estimates of the
virial expansion, together with resummation techniques, to calculate the
compressibility and spin susceptibility.


Abstract:
  The ability to understand the ways to interact with objects from visual cues,
a.k.a. visual affordance, is essential to vision-guided robotic research. This
involves categorizing, segmenting and reasoning of visual affordance. Relevant
studies in 2D and 2.5D image domains have been made previously, however, a
truly functional understanding of object affordance requires learning and
prediction in the 3D physical domain, which is still absent in the community.
In this work, we present a 3D AffordanceNet dataset, a benchmark of 23k shapes
from 23 semantic object categories, annotated with 18 visual affordance
categories. Based on this dataset, we provide three benchmarking tasks for
evaluating visual affordance understanding, including full-shape, partial-view
and rotation-invariant affordance estimations. Three state-of-the-art point
cloud deep learning networks are evaluated on all tasks. In addition we also
investigate a semi-supervised learning setup to explore the possibility to
benefit from unlabeled data. Comprehensive results on our contributed dataset
show the promise of visual affordance understanding as a valuable yet
challenging benchmark.


Abstract:
  This paper presents a regularization theory for numerical computation of
polynomial greatest common divisors and a convergence analysis, along with a
detailed description of a blackbox-type algorithm. The root of the
ill-posedness in conventional GCD computation is identified by its geometry
where polynomials form differentiable manifolds entangled in a stratification
structure. With a proper regularization, the numerical GCD is proved to be
strongly well-posed. Most importantly, the numerical GCD solves the problem of
finding the GCD accurately using floating point arithmetic even if the data are
perturbed. A sensitivity measurement, error bounds at each computing stage, and
the overall convergence are established rigorously. The computing results of
selected test examples show that the algorithm and software appear to be robust
and accurate.


Abstract:
  Geometrically frustrated spin-chain compounds such as Ca3Co2O6 exhibit
extremely slow relaxation under a changing magnetic field. Consequently, both
low-temperature laboratory experiments and Monte Carlo simulations have shown
peculiar out-of-equilibrium magnetization curves, which arise from trapping in
metastable configurations. In this work we simulate this phenomenon in a
superconducting quantum annealing processor, allowing us to probe the impact of
quantum fluctuations on both equilibrium and dynamics of the system. Increasing
the quantum fluctuations with a transverse field reduces the impact of
metastable traps in out-of-equilibrium samples, and aids the development of
three-sublattice ferrimagnetic (up-up-down) long-range order. At equilibrium we
identify a finite-temperature shoulder in the 1/3-to-saturated phase
transition, promoted by quantum fluctuations but with entropic origin. This
work demonstrates the viability of dynamical as well as equilibrium studies of
frustrated magnetism using large-scale programmable quantum systems, and is
therefore an important step toward programmable simulation of dynamics in
materials using quantum hardware.


Abstract:
  Motivated by work of Haythorpe, Thomassen and the author showed that there
exists a positive constant $c$ such that there is an infinite family of
4-regular 4-connected graphs, each containing exactly $c$ hamiltonian cycles.
We complement this by proving that the same conclusion holds for planar
4-regular 3-connected graphs, although it does not hold for planar 4-regular
4-connected graphs by a result of Brinkmann and Van Cleemput, and that it holds
for 4-regular graphs of connectivity~2 with the constant $144 < c$, which we
believe to be minimal among all hamiltonian 4-regular graphs of sufficiently
large order. We then disprove a conjecture of Haythorpe by showing that for
every non-negative integer $k$ there is a 5-regular graph on $26 + 6k$ vertices
with $2^{k+10} \cdot 3^{k+3}$ hamiltonian cycles. We prove that for every $d
\ge 3$ there is an infinite family of hamiltonian 3-connected graphs with
minimum degree $d$, with a bounded number of hamiltonian cycles. It is shown
that if a 3-regular graph $G$ has a unique longest cycle $C$, at least two
components of $G - E(C)$ have an odd number of vertices on $C$, and that there
exist 3-regular graphs with exactly two such components.


Abstract:
  The objects under inspection, on a given probability space, are noise(-type)
Boolean algebras -- distributive non-empty sublattices of the lattice of all
complete sub-$\sigma$-fields, whose every element admits an independent
complement. Special attention is given to the spectral decompositions of the
algebras of operators generated by the conditional expectations of their
members (acting on $\mathrm{L}^2$).
  Atoms of the spectra are identified in explicit terms. For a reverse
filtration admitting an innovating sequence of equiprobable random signs, a
discreteness property of the spectral measure of the associated noise Boolean
algebra is shown to imply product-typeness.
  Noise projections on the spectral space are introduced, which correspond to
restricting a noise to a part of its domain space. They appear to play a
natural (albeit technical) r\^ole in the general analysis. In particular
through them manifests itself the tensor structure of the spectral
decomposition.
  The spectrum is precisely delineated in the classical case (i.e. when the
noise Boolean algebra is complete), a kind-of standard "symmetric Fock space"
form thereof is procured. The latter result leads to a new characterization of
classicality and blackness involving "spectral independence".


Abstract:
  This paper presents a study of the interaction between Alfv\'en modes and
zonal structures, considering a realistic ASDEX Upgrade equilibrium. The
results of gyrokinetic simulations with the global, electromagnetic,
particle-in-cell code ORB5 are presented, where the modes are driven unstable
by energetic particles with a bump-on-tail equilibrium distribution function,
with radial density gradient. Two regimes have been observed: at low energetic
particles concentration, the Alfv\'en mode saturates at much higher level in
presence of zonal structures; on the other hand at high energetic particles
concentration the difference is less pronounced. The former regime is
characterized by the zonal structure (identified as an energetic particle
driven geodesic acoustic mode), being more unstable than the Alfv\'en mode. In
the latter regime the Alfv\'en mode is more unstable than the zonal structure.
The theoretical explanation is given in terms of a 3-wave coupling of the
energetic particle driven geodesic acoustic mode and Alfv\'en mode, mediated by
the curvature-pressure coupling term of the energetic particles.


Abstract:
  We present a new scientific machine learning method that learns from data a
computationally inexpensive surrogate model for predicting the evolution of a
system governed by a time-dependent nonlinear partial differential equation
(PDE), an enabling technology for many computational algorithms used in
engineering settings. Our formulation generalizes to the PDE setting the
Operator Inference method previously developed in [B. Peherstorfer and K.
Willcox, Data-driven operator inference for non-intrusive projection-based
model reduction, Computer Methods in Applied Mechanics and Engineering, 306
(2016)] for systems governed by ordinary differential equations. The method
brings together two main elements. First, ideas from projection-based model
reduction are used to explicitly parametrize the learned model by
low-dimensional polynomial operators which reflect the known form of the
governing PDE. Second, supervised machine learning tools are used to infer from
data the reduced operators of this physics-informed parametrization. For
systems whose governing PDEs contain more general (non-polynomial)
nonlinearities, the learned model performance can be improved through the use
of lifting variable transformations, which expose polynomial structure in the
PDE. The proposed method is demonstrated on a three-dimensional combustion
simulation with over 18 million degrees of freedom, for which the learned
reduced models achieve accurate predictions with a dimension reduction of six
orders of magnitude and model runtime reduction of 5-6 orders of magnitude.


Abstract:
  Next-generation large-scale structure surveys will deliver a significant
increase in the precision of growth data, allowing us to use `agnostic' methods
to study the evolution of perturbations without the assumption of a
cosmological model. We focus on a particular machine learning tool, Gaussian
processes, to reconstruct the growth rate $f$, the root mean square of matter
fluctuations $\sigma_8$, and their product $f\sigma_8$. We apply this method to
simulated data, representing the precision of upcoming Stage IV galaxy surveys.
We extend the standard single-task approach to a multi-task approach that
reconstructs the three functions simultaneously, thereby taking into account
their inter-dependence. We find that this multi-task approach outperforms the
single-task approach for future surveys and will allow us to detect departures
from the standard model with higher significance. By contrast, the limited
sensitivity of current data severely hinders the use of agnostic methods, since
the Gaussian processes parameters need to be fine tuned in order to obtain
robust reconstructions.


Abstract:
  The non-exponential Schilder-type theorem in Backhoff-Veraguas, Lacker and
Tangpi [Ann. Appl. Probab., 30 (2020), pp. 1321-1367] is expressed as a
convergence result for path-dependent partial differential equations with
appropriate notions of generalized solutions. This entails a non-Markovian
counterpart to the vanishing viscosity method.
  We show uniqueness of maximal subsolutions for path-dependent viscous
Hamilton-Jacobi equations related to convex super-quadratic backward stochastic
differential equations.
  We establish well-posedness for the Hamilton-Jacobi-Bellman equation
associated to a Bolza problem of the calculus of variations with path-dependent
terminal cost. In particular, uniqueness among lower semi-continuous solutions
holds and state constraints are admitted.


Abstract:
  In three previous papers I showed that the series of the midpoints of the
times of all the X-ray flares of Sgr A* that have been detected so far harbor a
statistical trend termed pacemaker regularity. This means that X-ray flares are
detected more frequently around time points that are a subset of a periodic
grid on the time axis of period P_X=0.1032 day=149 minutes. The series of the
times of detection of the peaks of near IR (NIR) flares of the object are also
regulated by a pacemaker with a period of P_IR=0.028 day=41 minutes. Here, I
show that the series of the midpoints of the times of recorded NIR flares are
also regulated by a pacemaker of the period P_IRM=0.039 day=56 minutes. The two
pacemakers found in the previous papers were interpreted as signals of a star
that revolves around the black hole of Sgr A* in orbit with a mean radius of
~3.2 Schwarzschild radii of the black hole, here corrected to ~3.13. The
finding of the period of the third pacemaker is consistent with the suggested
revolving star model. Here, I present the specific orbit of the star as well as
a plausible description of its sidereal rotation. The model also implies that
the star has an unusual internal structure. I show that the discovery of the
GRAVITY collaboration of the motion of hotspots at distances from the black
hole that are of the order of very few Schwarzschild radii of it may well be
understood within the context of the revolving star model.


Abstract:
  Transformer-based pre-trained language models have significantly improved the
performance of various natural language processing (NLP) tasks in the recent
years. While effective and prevalent, these models are usually prohibitively
large for resource-limited deployment scenarios. A thread of research has thus
been working on applying network pruning techniques under the
pretrain-then-finetune paradigm widely adopted in NLP. However, the existing
pruning results on benchmark transformers, such as BERT, are not as remarkable
as the pruning results in the literature of convolutional neural networks
(CNNs). In particular, common wisdom in pruning CNN states that sparse pruning
technique compresses a model more than that obtained by reducing number of
channels and layers (Elsen et al., 2020; Zhu and Gupta, 2017), while existing
works on sparse pruning of BERT yields inferior results than its small-dense
counterparts such as TinyBERT (Jiao et al., 2020). In this work, we aim to fill
this gap by studying how knowledge are transferred and lost during the
pre-train, fine-tune, and pruning process, and proposing a knowledge-aware
sparse pruning process that achieves significantly superior results than
existing literature. We show for the first time that sparse pruning compresses
a BERT model significantly more than reducing its number of channels and
layers. Experiments on multiple data sets of GLUE benchmark show that our
method outperforms the leading competitors with a 20-times weight/FLOPs
compression and neglectable loss in prediction accuracy.


Abstract:
  A TL-LUE model modified with a radiation scalar (RTL-LUE) is developed in
this paper. The same maximum LUE is used for both sunlit and shaded leaves, and
the difference in LUE between sunlit and shaded leaf groups is determined by
the same radiation scalar. The RTL-LUE model was calibrated and validated at
global 169 FLUXNET eddy covariance (EC) sites. Results indicate that although
GPP simulations from the TL-LUE model match well with the EC GPP, the RTL-LUE
model can further improve the simulation, for half-hour, 8-day, and yearly time
scales. The TL-LUE model tends to overestimate GPP under conditions of high
incoming photosynthetically active radiation (PAR), because the
radiation-independent LUE values for both sunlit and shaded leaves are only
suitable for low-medium (e.g. average) incoming PAR conditions. The errors in
the RTL-LUE model show lower sensitivity to PAR, and its GPP simulations can
better track the diurnal and seasonal variations of EC GPP by alleviating the
overestimation at noon and growing seasons associated with the TL-LUE model.
This study demonstrates the necessity of considering a radiation scalar in GPP
simulation in LUE models even if the first-order effect of radiation is already
considered through differentiating sunlit and shaded leaves. The simple RTL-LUE
developed in this study would be a useful alternative to complex process-based
models for global carbon cycle research.


Abstract:
  Using task-specific pre-training and leveraging cross-lingual transfer are
two of the most popular ways to handle code-switched data. In this paper, we
aim to compare the effects of both for the task of sentiment analysis. We work
with two Dravidian Code-Switched languages - Tamil-Engish and Malayalam-English
and four different BERT based models. We compare the effects of task-specific
pre-training and cross-lingual transfer and find that task-specific
pre-training results in superior zero-shot and supervised performance when
compared to performance achieved by leveraging cross-lingual transfer from
multilingual BERT models.


Abstract:
  Online updating of the object model via samples from historical frames is of
great importance for accurate visual object tracking. Recent works mainly focus
on constructing effective and efficient updating methods while neglecting the
training samples for learning discriminative object models, which is also a key
part of a learning problem. In this paper, we propose the DeepMix that takes
historical samples' embeddings as input and generates augmented embeddings
online, enhancing the state-of-the-art online learning methods for visual
object tracking. More specifically, we first propose the online data
augmentation for tracking that online augments the historical samples through
object-aware filtering. Then, we propose MixNet which is an offline trained
network for performing online data augmentation within one-step, enhancing the
tracking accuracy while preserving high speeds of the state-of-the-art online
learning methods. The extensive experiments on three different tracking
frameworks, i.e., DiMP, DSiam, and SiamRPN++, and three large-scale and
challenging datasets, \ie, OTB-2015, LaSOT, and VOT, demonstrate the
effectiveness and advantages of the proposed method.


Abstract:
  Does a new product spread faster among heterogeneous or homogeneous
consumers? We analyze this question using the stochastic discrete Bass model,
in which consumers may differ in their individual external influence rates
$\{p_j \}$ and in their individual internal influence rates $\{ q_j \}$. When
the network is complete and the heterogeneity is only manifested in $\{p_j \}$
or only in $\{ q_j \}$, it always slows down the diffusion, compared to the
corresponding homogeneous network. When, however, consumers are heterogeneous
in both $\{p_j\}$ and $\{q_{j}\}$, heterogeneity slows down the diffusion in
some cases, but accelerates it in others. Moreover, the dominance between the
heterogeneous and homogeneous adoption levels is global in time in some cases,
but changes with time in others. Perhaps surprisingly, global dominance between
two networks is not always preserved under "additive transformations", such as
adding an identical node to both networks. When the network is not complete,
the effect of heterogeneity depends also on its spatial distribution within the
network.


Abstract:
  We derive an upper bound on the reliability function of mismatched decoding
for zero-rate codes. The bound is based on a result by Koml\'os that shows the
existence of a subcode with certain symmetry properties. The bound is shown to
coincide with the expurgated exponent at rate zero for a broad family of
channel-decoding metric pairs.


Abstract:
  For the first time, the hybrid Frequency Division Multiple Access (FDMA) Code
Division Multiple Access (CDMA) mode of the CAOS (i.e., Coded Access Optical
Sensor) camera is demonstrated. The FDMA CDMA mode is a time frequency double
signal encoding design for robust and faster linear High Dynamic Range (HDR)
image irradiance extraction. Specifically, it simultaneously combines the
strength of the FDMA-mode linear HDR Fast Fourier Transform (FFT) Digital
Signal Processing (DSP) based spectrum analysis with the CDMA mode provided
many simultaneous CAOS pixels high Signal to Noise Ratio (SNR) photo-detection.
The FDMA CDMA mode with P FDMA channels provides a faster camera operation
versus the linear HDR Frequency Modulation (FM) CDMA mode. Visible band imaging
experiments using a Digital Micromirror Device (DMD) based CAOS camera
demonstrate a P equal to 4 channels FDMA CDMA mode high quality image recovery
of a calibrated 64 dB 6 patches HDR target versus the CDMA and FM CDMA CAOS
modes that limit dynamic range and speed, respectively. Simultaneous dual image
capture capability of the FDMA-CDMA mode is also demonstrated for the first
time in Ultraviolet (UV) to Near Infrared (NIR) 350 to 1800 nm full spectrum
using Silicon (Si) and Germanium (Ge) point photo-detectors.


Abstract:
  We consider a linear Fermi-Pasta-Ulam-Tsingou lattice with random spatially
varying material coefficients. Using the methods of stochastic homogenization
we show that solutions with long wave initial data converge in an appropriate
sense to solutions of a wave equation. The convergence is strong and both
almost sure and in expectation, but the rate is quite slow. The technique
combines energy estimates with powerful classical results about random walks,
specifically the law of the iterated logarithm.


Abstract:
  Steganography is a solution for covert communication and blockchain is a p2p
network for data transmission, so the benefits of blockchain can be used in
steganography. In this paper, we discuss the advantages of blockchain in
steganography, which include the ability to embed hidden data without manual
change in the original data, as well as the readiness of the blockchain
platform for data transmission and storage, which eliminates the need for the
Steganographer to design and implement a new platform for data transmission and
storage. We have proposed two algorithms for steganography in blockchain, the
first one is a high-capacity algorithm for the key and the steganography
algorithm exchange and switching, and the second one is a medium-capacity
algorithm for embedding hidden data. Also, by reviewing the previous three
steganography schemes in blockchain, we have examined their drawback and have
showed that none of them are practical schemes for steganography in blockchain.
Then, we have explained the challenges of steganography in blockchain from the
steganographers and steganalyzers point of view.


Abstract:
  The exact reconstruction of many-body quantum systems is one of the major
challenges in modern physics, because it is impractical to overcome the
exponential complexity problem brought by high-dimensional quantum many-body
systems. Recently, machine learning techniques are well used to promote quantum
information research and quantum state tomography has been also developed by
neural network generative models. We propose a quantum state tomography method,
which is based on Bidirectional Gated Recurrent Unit neural network (BiGRU), to
learn and reconstruct both easy quantum states and hard quantum states in this
paper. We are able to use fewer measurement samples in our method to
reconstruct these quantum states and obtain high fidelity.


Abstract:
  Computer vision approaches are widely used by autonomous robotic systems to
sense the world around them and to guide their decision making as they perform
diverse tasks such as collision avoidance, search and rescue, and object
manipulation. High accuracy is critical, particularly for Human-on-the-loop
(HoTL) systems where decisions are made autonomously by the system, and humans
play only a supervisory role. Failures of the vision model can lead to
erroneous decisions with potentially life or death consequences. In this paper,
we propose a solution based upon adaptive autonomy levels, whereby the system
detects loss of reliability of these models and responds by temporarily
lowering its own autonomy levels and increasing engagement of the human in the
decision-making process. Our solution is applicable for vision-based tasks in
which humans have time to react and provide guidance. When implemented, our
approach would estimate the reliability of the vision task by considering
uncertainty in its model, and by performing covariate analysis to determine
when the current operating environment is ill-matched to the model's training
data. We provide examples from DroneResponse, in which small Unmanned Aerial
Systems are deployed for Emergency Response missions, and show how the vision
model's reliability would be used in addition to confidence scores to drive and
specify the behavior and adaptation of the system's autonomy. This workshop
paper outlines our proposed approach and describes open challenges at the
intersection of Computer Vision and Software Engineering for the safe and
reliable deployment of vision models in the decision making of autonomous
systems.


Abstract:
  We answer Mundici's problem number 3 (D. Mundici. Advanced {\L}ukasiewicz
calculus. Trends in Logic Vol. 35. Springer 2011, p. 235): Is the category of
locally finite MV-algebras equivalent to an equational class? We prove: (i) The
category of locally finite MV-algebras is not equivalent to any finitary
variety. (ii) More is true: the category of locally finite MV-algebras is not
equivalent to any finitely-sorted finitary quasi-variety. (iii) The category of
locally finite MV-algebras is equivalent to an infinitary variety; with
operations of at most countable arity. (iv) The category of locally finite
MV-algebras is equivalent to a countably-sorted finitary variety.
  Our proofs rest upon the duality between locally finite MV-algebras and the
category of multisets by R. Cignoli, E. J. Dubuc and D. Mundici, and
categorical characterisations of varieties and quasi-varieties proved by J.
Duskin, J. R. Isbell, F. W. Lawvere and others. In fact no knowledge on
MV-algebras is needed, apart from the aforementioned duality.


Abstract:
  In this Letter we construct the Hubble diagram for a Universe where dark
matter is universally charged under a dark non-linear electromagnetic force
which features a screening mechanism of the K-mouflage type for repulsive
forces. By resorting to the Newtonian approximation, we explicitly show that
the cosmological evolution generates an inhomogeneous Hubble diagram that
corresponds to a curvature dominated expansion at short distances and converges
to the cosmological one of $\Lambda$CDM. We discuss the potential impact of
this inhomogeneous profile on the Hubble tension. For completeness, we
explicitly show how the Newtonian approximation can be derived from an
inhomogeneous relativistic Lema\^itre model.


Abstract:
  We study the two-phase Stokes flow driven by surface tension for two fluids
of different viscosities, separated by an asymptotically flat interface
representable as graph of a differentiable function. The flow is assumed to be
two-dimensional with the fluids filling the entire space. We prove
well-posedness and parabolic smoothing in Sobolev spaces up to critical
regularity. The main technical tools are an analysis of nonlinear singular
integral operators arising from the hydrodynamic single and double layer
potential, spectral results on the corresponding integral operators, and
abstract results on nonlinear parabolic evolution equations.


Abstract:
  We give conditions for minimality of $\mathbb Z/N\mathbb Z$ extensions of a
rotation of angle $\alpha$ with one marked point, solving the problem for any
prime $N$: for $N=2$, these correspond to the Veech 1969 examples, for which a
necessary and sufficient condition was not known yet. We provide also a word
combinatorial criterion of minimality valid for general interval exchange
transformations, which applies to $\mathbb Z/N\mathbb Z$ extensions of any
interval exchange transformation with any number of marked points. Then we give
a condition for unique ergodicity of these extensions when the initial interval
exchange transformation is linearly recurrent and there are one or two marked
points.


Abstract:
  We have employed Gd55Co30NixAl15-x (x = 10, 5 and 0) amorphous microwires as
a model system to unravel the impact of multiple magnetic interactions on the
magnetism and the magnetocaloric behavior in Gd-alloy microwire systems. Our
study shows that in addition to the RKKY ferromagnetic (FM) interaction
(Gd-Gd), antiferromagnetic (AFM) interactions (Gd-Co, Gd-Ni) coexist and
contribute to the magnetic and magnetocaloric response of the system. The
dilution effect of Al element on the FM Gd-Gd interaction is responsible for
the decrease of the Curie temperature (TC), whereas the increase of the
saturation magnetization (MS) is originated from the reduced AFM Gd-Ni
interaction. A thorough analysis of critical exponents suggests that the
presence of the AFM interactions hinders the system to exhibit a long-range FM
order below the TC. Adjusting these interactions is shown to preserve the large
refrigerant capacity (RC) while tuning the TC over a wide temperature range,
which is desirable for active magnetic refrigeration.


Abstract:
  Low-light image enhancement aims to improve an image's visibility while
keeping its visual naturalness. Different from existing methods tending to
accomplish the relighting task directly by ignoring the fidelity and
naturalness recovery, we investigate the intrinsic degradation and relight the
low-light image while refining the details and color in two steps. Inspired by
the color image formulation (diffuse illumination color plus environment
illumination color), we first estimate the degradation from low-light inputs to
simulate the distortion of environment illumination color, and then refine the
content to recover the loss of diffuse illumination color. To this end, we
propose a novel Degradation-to-Refinement Generation Network (DRGN). Its
distinctive features can be summarized as 1) A novel two-step generation
network for degradation learning and content refinement. It is not only
superior to one-step methods, but also capable of synthesizing sufficient
paired samples to benefit the model training; 2) A multi-resolution fusion
network to represent the target information (degradation or contents) in a
multi-scale cooperative manner, which is more effective to address the complex
unmixing problems. Extensive experiments on both the enhancement task and joint
detection task have verified the effectiveness and efficiency of our proposed
method, surpassing the SOTA by \textit{0.70dB on average and 3.18\% in mAP},
respectively. The code is available at
\url{https://github.com/kuijiang0802/DRGN}.


Abstract:
  The entropy production scenarios due to electroweak phase transition (EWPT)
in the framework of the minimal extension of standard model namely two Higgs
doublet model(2HDM) is revisited. The possibility of first order phase
transition is discussed. Intense parameter scanning is done with the help of
BSMPT, a C++ package. Numerical calculations are performed in order to
calculate the entropy production with numerous benchmark points.


Abstract:
  Electrically actuated optomechanical resonators provide a route to
quantum-coherent, bidirectional conversion of microwave and optical photons.
Such devices could enable optical interconnection of quantum computers based on
qubits operating at microwave frequencies. Here we present a novel platform for
microwave-to-optical conversion comprising a photonic crystal cavity made of
single-crystal, piezoelectric gallium phosphide integrated on pre-fabricated
niobium circuits on an intrinsic silicon substrate. The devices exploit
spatially extended, sideband-resolved mechanical breathing modes at $\sim$ 3.2
GHz, with vacuum optomechanical coupling rates of up to $g_0/2\pi \approx$ 300
kHz. The mechanical modes are driven by integrated microwave electrodes via the
inverse piezoelectric effect. We estimate that the system could achieve an
electromechanical coupling rate to a superconducting transmon qubit of $\sim$
200 kHz. Our work represents a decisive step towards integration of
piezoelectro-optomechanical interfaces with superconducting quantum processors.


Abstract:
  Conventional approaches to image-text retrieval mainly focus on indexing
visual objects appearing in pictures but ignore the interactions between these
objects. Such objects occurrences and interactions are equivalently useful and
important in this field as they are usually mentioned in the text. Scene graph
presentation is a suitable method for the image-text matching challenge and
obtained good results due to its ability to capture the inter-relationship
information. Both images and text are represented in scene graph levels and
formulate the retrieval challenge as a scene graph matching challenge. In this
paper, we introduce the Local and Global Scene Graph Matching (LGSGM) model
that enhances the state-of-the-art method by integrating an extra graph
convolution network to capture the general information of a graph.
Specifically, for a pair of scene graphs of an image and its caption, two
separate models are used to learn the features of each graph's nodes and edges.
Then a Siamese-structure graph convolution model is employed to embed graphs
into vector forms. We finally combine the graph-level and the vector-level to
calculate the similarity of this image-text pair. The empirical experiments
show that our enhancement with the combination of levels can improve the
performance of the baseline method by increasing the recall by more than 10% on
the Flickr30k dataset.


Abstract:
  Proper scoring rules are commonly applied to quantify the accuracy of
distribution forecasts. Given an observation they assign a scalar score to each
distribution forecast, with the the lowest expected score attributed to the
true distribution. The energy and variogram scores are two rules that have
recently gained some popularity in multivariate settings because their
computation does not require a forecast to have parametric density function and
so they are broadly applicable. Here we conduct a simulation study to compare
the discrimination ability between the energy score and three variogram scores.
Compared with other studies, our simulation design is more realistic because it
is supported by a historical data set containing commodity prices, currencies
and interest rates, and our data generating processes include a diverse
selection of models with different marginal distributions, dependence
structure, and calibration windows. This facilitates a comprehensive comparison
of the performance of proper scoring rules in different settings. To compare
the scores we use three metrics: the mean relative score, error rate and a
generalised discrimination heuristic. Overall, we find that the variogram score
with parameter p=0.5 outperforms the energy score and the other two variogram
scores.


Abstract:
  It is conjectured that the canonical models of varieties (not of general
type) are bounded when the Iitaka volume is fixed. We confirm this conjecture
when the general fibers of the corresponding Iitaka fibration are in a fixed
bounded family of polarized Calabi-Yau pairs. As a consequence, we prove that
in this case, the fibration is birationally bounded, and when it has terminal
singularities, the corresponding minimal model is bounded in codimension 1.


Abstract:
  A Just-In-Time (JIT) defect prediction model is a classifier to predict if a
commit is defect-introducing. Recently, CC2Vec -- a deep learning approach for
Just-In-Time defect prediction -- has been proposed. However, CC2Vec requires
the whole dataset (i.e., training + testing) for model training, assuming that
all unlabelled testing datasets would be available beforehand, which does not
follow the key principles of just-in-time defect predictions. Our replication
study shows that, after excluding the testing dataset for model training, the
F-measure of CC2Vec is decreased by 38.5% for OpenStack and 45.7% for Qt,
highlighting the negative impact of excluding the testing dataset for
Just-In-Time defect prediction. In addition, CC2Vec cannot perform fine-grained
predictions at the line level (i.e., which lines are most risky for a given
commit).
  In this paper, we propose JITLine -- a Just-In-Time defect prediction
approach for predicting defect-introducing commits and identifying lines that
are associated with that defect-introducing commit (i.e., defective lines).
Through a case study of 37,524 commits from OpenStack and Qt, we find that our
JITLine approach is at least 26%-38% more accurate (F-measure), 17%-51% more
cost-effective (PCI@20%LOC), 70-100 times faster than the state-of-the-art
approaches (i.e., CC2Vec and DeepJIT) and the fine-grained predictions at the
line level by our approach are 133%-150% more accurate (Top-10 Accuracy) than
the baseline NLP approach. Therefore, our JITLine approach may help
practitioners to better prioritize defect-introducing commits and better
identify defective lines.


Abstract:
  Gaisi Takeuti introduced Boolean valued analysis around 1974 to provide
systematic applications of Boolean valued models of set theory to analysis.
Later, his methods were further developed by his followers, leading to solving
several open problems in analysis and algebra. Using the methods of Boolean
valued analysis, he further stepped forward to construct set theory based on
quantum logic, as the first step to construct "quantum mathematics", a
mathematics based on quantum logic. While it is known that the distributive law
does not apply to quantum logic, and the equality axiom turns out not to hold
in quantum set theory, he showed that the real numbers in quantum set theory
are in one-to-one correspondence with the self-adjoint operators on a Hilbert
space, or equivalently the physical quantities of the corresponding quantum
system. As quantum logic is intrinsic and empirical, the results of the quantum
set theory can be experimentally verified by quantum mechanics. In this paper,
we analyze Takeuti's mathematical world view underlying his program from two
perspectives: set theoretical foundations of modern mathematics and extending
the notion of sets to multi-valued logic. We outlook the present status of his
program, and envisage the further development of the program, by which we would
be able to take a huge step forward toward unraveling the mysteries of quantum
mechanics that have persisted for many years.


Abstract:
  We prove that the number of irreducible ordinary characters in the principal
$p$-block of a finite group $G$ of order divisible by $p$ is always at least
$2\sqrt{p-1}$. This confirms a conjecture of H\'{e}thelyi and K\"{u}lshammer
for principal blocks and provides an affirmative answer to Brauer's Problem 21
for principal blocks of bounded defect. Our proof relies on recent works of
Mar\'{o}ti and Malle-Mar\'{o}ti on bounding the conjugacy class number and the
number of $p'$-degree irreducible characters of finite groups, earlier works of
Brou\'{e}-Malle-Michel and Cabanes-Enguehard on the distribution of characters
into unipotent blocks and $e$-Harish-Chandra series of finite reductive groups,
and known cases of the Alperin-McKay conjecture.


Abstract:
  Diffusion channels are critical to determining the adoption scale which leads
to the ultimate impact of an innovation. The aim of this study is to develop an
integrative understanding of the impact of two diffusion channels (i.e.,
broadcasting vs virality) on innovation adoption. Using citations of a series
of classic algorithms and the time series of co-authorship as the footprints of
their diffusion trajectories, we propose a novel method to analyze the
intertwining relationships between broadcasting and virality in the innovation
diffusion process. Our findings show that broadcasting and virality have
similar diffusion power, but play different roles across diffusion stages.
Broadcasting is more powerful in the early stages but may be gradually caught
up or even surpassed by virality in the later period. Meanwhile, diffusion
speed in virality is significantly faster than broadcasting and members from
virality channels tend to adopt the same innovation repetitively.


Abstract:
  Achieving large-size two-dimensional (2D) crystals is key to fully exploiting
their remarkable functionalities and application potentials. Chemical vapor
deposition (CVD) growth of 2D semiconductors such as monolayer MoS2 has been
reported to be activated by halide salts, yet clear identification of the
underlying mechanism remains elusive. Here we provide unambiguous experimental
evidence showing that the MoS2 growth dynamics are halogen-dependent through
the Br{\o}nsted-Evans-Polanyi relation, based on which we build a growth model
by considering MoS2 edge passivation by halogens, and theoretically reproduces
the trend of our experimental observations. These mechanistic understandings
enable us to further optimize the fast growth of MoS2 and reach record-large
domain sizes that should facilitate practical applications.


Abstract:
  The spectrum of cosmic ray protons and electrons released by supernova
remnants throughout their evolution is poorly known, because of the difficulty
in accounting for particle escape and confinement in the downstream of a shock
front, where both adiabatic and radiative losses are present. Here we calculate
the spectrum of cosmic ray protons released during the evolution of supernovae
of different types, accounting for the escape from upstream and for adiabatic
losses of particles advected downstream of the shock and liberated at later
times. The same calculation is carried out for electrons. The magnetic field in
the post-shock region is calculated by using an analytic treatment of the
magnetic field amplification due to non--resonant and resonant streaming
instability and their saturation. We find that when the field is the result of
the growth of the cosmic-ray--driven non--resonant instability alone, the
spectrum of electrons and protons released by a supernova remnant are indeed
different, but such a difference becomes appreciable only at energies $\gtrsim
100-1000$ GeV, while observations of the electron spectrum require such a
difference to be present at energies as low as $\sim 10$ GeV. An effect at such
low energies requires substantial magnetic field amplification in the late
stages of the supernova remnant evolution (shock velocity $\ll 1000$ km/s),
perhaps not due to streaming instability but hydrodynamical processes. We
comment on the feasibility of such conditions and speculate on the possibility
that the difference in spectral shape between electrons and protons may reflect
either some unknown acceleration effect, or additional energy losses in cocoons
around the sources.


Abstract:
  The computational underpinnings of positive psychotic symptoms have recently
received significant attention. Candidate mechanisms include some combination
of maladaptive priors and reduced updating of these priors during perception. A
potential benefit of models with such mechanisms is their ability to link
multiple levels of explanation. This is key to improving how we understand the
experience of psychosis. Moreover, it points us towards more comprehensive
avenues for therapeutic research by providing a putative mechanism that could
allow for the generation of new treatments from first principles. In order to
demonstrate this, our conceptual paper will discuss the application of the
insights from previous computational models to an important and complex set of
evidence-based clinical interventions with strong social elements, such as
coordinated specialty care clinics in early psychosis and assertive community
treatment. These interventions may include but also go beyond
psychopharmacology, providing, we argue, structure and predictability for
patients experiencing psychosis. We develop the argument that this structure
and predictability directly counteract the relatively low precision afforded to
sensory information in psychosis, while also providing the patient more access
to external cognitive resources in the form of providers and the structure of
the programs themselves. We discuss how computational models explain the
resulting reduction in symptoms, as well as the predictions these models make
about potential responses of patients to modifications or to different
variations of these interventions. We also link, via the framework of
computational models, the experiences of patients and response to interventions
to putative neurobiology.


Abstract:
  As predictive models are increasingly being deployed in high-stakes decision
making (e.g., loan approvals), there has been growing interest in post hoc
techniques which provide recourse to affected individuals. These techniques
generate recourses under the assumption that the underlying predictive model
does not change. However, in practice, models are often regularly updated for a
variety of reasons (e.g., dataset shifts), thereby rendering previously
prescribed recourses ineffective. To address this problem, we propose a novel
framework, RObust Algorithmic Recourse (ROAR), that leverages adversarial
training for finding recourses that are robust to model shifts. To the best of
our knowledge, this work proposes the first solution to this critical problem.
We also carry out detailed theoretical analysis which underscores the
importance of constructing recourses that are robust to model shifts: 1) we
derive a lower bound on the probability of invalidation of recourses generated
by existing approaches which are not robust to model shifts. 2) we prove that
the additional cost incurred due to the robust recourses output by our
framework is bounded. Experimental evaluation on multiple synthetic and
real-world datasets demonstrates the efficacy of the proposed framework and
supports our theoretical findings.


Abstract:
  This paper presents a four-stage algorithm for the realization of
multi-input/multi-output (MIMO) switched linear systems (SLSs) from Markov
parameters. In the first stage, a linear time-varying (LTV) realization that is
topologically equivalent to the true SLS is derived from the Markov parameters
assuming that the submodels have a common MacMillan degree and a mild condition
on their dwell times holds. In the second stage, zero sets of LTV Hankel
matrices where the realized system has a linear time-invariant (LTI) pulse
response matching that of the original SLS are exploited to extract the
submodels, up to arbitrary similarity transformations, by a clustering
algorithm using a statistics that is invariant to similarity transformations.
Recovery is shown to be complete if the dwell times are sufficiently long and
some mild identifiability conditions are met. In the third stage, the switching
sequence is estimated by three schemes. The first scheme is based on
forward/backward corrections and works on the short segments. The second scheme
matches Markov parameter estimates to the true parameters for LTV systems and
works on the medium-to-long segments. The third scheme also matches Markov
parameters, but for LTI systems only and works on the very short segments. In
the fourth stage, the submodels estimated in Stage 2 are brought to a common
basis by applying a novel basis transformation method which is necessary before
performing output predictions to given inputs. A numerical example illustrates
the properties of the realization algorithm. A key role in this algorithm is
played by time-dependent switching sequences that partition the state-space
according to time, unlike many other works in the literature in which
partitioning is state and/or input dependent.


Abstract:
  Recent tracking-by-detection approaches use deep object detectors as target
detection baseline, because of their high performance on still images. For
effective video object tracking, object detection is integrated with a data
association step performed by either a custom design inference architecture or
an end-to-end joint training for tracking purpose. In this work, we adopt the
former approach and use the pre-trained Mask R-CNN deep object detector as the
baseline. We introduce a novel inference architecture placed on top of
FPN-ResNet101 backbone of Mask R-CNN to jointly perform detection and tracking,
without requiring additional training for tracking purpose. The proposed single
object tracker, TDIOT, applies an appearance similarity-based temporal matching
for data association. In order to tackle tracking discontinuities, we
incorporate a local search and matching module into the inference head layer
that exploits SiamFC for short term tracking. Moreover, in order to improve
robustness to scale changes, we introduce a scale adaptive region proposal
network that enables to search the target at an adaptively enlarged spatial
neighborhood specified by the trace of the target. In order to meet long term
tracking requirements, a low cost verification layer is incorporated into the
inference architecture to monitor presence of the target based on its LBP
histogram model. Performance evaluation on videos from VOT2016, VOT2018 and
VOT-LT2018 datasets demonstrate that TDIOT achieves higher accuracy compared to
the state-of-the-art short-term trackers while it provides comparable
performance in long term tracking.


Abstract:
  The "track event theory" (TET) has been developed in recent years as an
alternative to the phenomenological linear-quadratic model for cell survival
under exposure to ionizing radiation, particularly for heavy charged particles.
The TET is based on a few simple model assumptions including the possibility to
derive some of the model parameters from nanodosimetry. This work intends to
carve out more clearly the basic assumptions behind the TET and to critically
review the resulting mathematical model equations. It is demonstrated that the
model assumptions of Poisson distribution and statistical independence of the
frequency distributions of so-called one-track and two-track events follow from
the Poisson distribution of the number of tracks affecting the considered
target volume. It is also shown that the modified TET model equation used in
the literature for consideration of repair is inconsistent with the model
assumptions and requires an additional model parameter. Furthermore, the
derivation of the model parameters from nanodosimetric properties of particle
track structure is revealed to lead to a pure exponential dose dependence when
the potentially large number of relevant nanometric target volumes inside a
cell nucleus is accounted for.


Abstract:
  We study the hedging and valuation of European and American claims on a
non-traded asset $Y$, when a traded stock $S$ is available for hedging, with
$S$ and $Y$ following correlated geometric Brownian motions. This is an
incomplete market, often called a basis risk model. The market agent's risk
preferences are modelled using a so-called forward performance process (forward
utility), which is a time-decreasing utility of exponential type. Moreover, the
market agent (investor) does not know with certainty the values of the asset
price drifts. This market setting with drift parameter uncertainty is the
partial information scenario. We discuss the stochastic control problem
obtained by setting up the hedging portfolio and derive the optimal hedging
strategy. Furthermore, a (dual) forward indifference price representation of
the claim and its PDE are obtained. With these results, the residual risk
process representing the basis risk (hedging error), pay-off decompositions and
asymptotic expansions of the indifference price in the European case are
derived. We develop the analogous stochastic control and stopping problem with
an American claim and obtain the corresponding forward indifference price
valuation formula.