Abstract:
  We introduce the problem of constructing explicit variety evasive subspace
families. Given a family $\mathcal{F}$ of subvarieties of a projective or
affine space, a collection $\mathcal{H}$ of projective or affine $k$-subspaces
is $(\mathcal{F},\epsilon)$-evasive if for every $\mathcal{V}\in\mathcal{F}$,
all but at most $\epsilon$-fraction of $W\in\mathcal{H}$ intersect every
irreducible component of $\mathcal{V}$ with (at most) the expected dimension.
The problem of constructing such an explicit subspace family generalizes both
deterministic black-box polynomial identity testing (PIT) and the problem of
constructing explicit (weak) lossless rank condensers.
  Using Chow forms, we construct explicit $k$-subspace families of polynomial
size that are evasive for all varieties of bounded degree in a projective or
affine $n$-space. As one application, we obtain a complete derandomization of
Noether's normalization lemma for varieties of low degree in a projective or
affine $n$-space. In another application, we obtain a simple polynomial-time
black-box PIT algorithm for depth-4 arithmetic circuits with bounded top fan-in
and bottom fan-in that are not in the Sylvester-Gallai configuration, improving
and simplifying a result of Gupta (ECCC TR 14-130).
  As a complement of our explicit construction, we prove a tight lower bound
for the size of $k$-subspace families that are evasive for degree-$d$ varieties
in a projective $n$-space. When $n-k=n^{\Omega(1)}$, the lower bound is
superpolynomial unless $d$ is bounded. The proof uses a dimension-counting
argument on Chow varieties that parametrize projective subvarieties.


Abstract:
  We present the results of a 3D global magnetohydrodynamic (MHD) simulation of
an AM CVn system that was aimed at exploring eccentricity growth in the
accretion disc self-consistently from a first principles treatment of the MHD
turbulence. No significant eccentricity growth occurs in the simulation. In
order to investigate the reasons why, we ran 2D alpha disc simulations with
alpha values of 0.01, 0.1, and 0.2, and found that only the latter two exhibit
significant eccentricity growth. We present an equation expressing global
eccentricity evolution in terms of contributing forces and use it to analyze
the simulations. As expected, we find that the dominant term contributing to
the growth of eccentricity is the tidal gravity of the companion star. In the
2D simulations, the alpha viscosity directly contributes to eccentricity
growth. In contrast, the overall magnetic forces in the 3D simulation damp
eccentricity. We also analyzed the mode-coupling mechanism of Lubow, and
confirmed that the spiral wave excited by the 3:1 resonance was the dominant
contributor to eccentricity growth in the 2D $\alpha=0.1$ simulations, but
other waves also contribute significantly. We found that the $\alpha=0.1$ and
0.2 simulations had more relative mass at larger radii compared to the
$\alpha=0.01$ and 3D MHD simulation, which also had an effective $\alpha$ of
0.01. This suggests that in 3D MHD simulations without sufficient poloidal
magnetic flux, MRI turbulence does not saturate at a high enough $\alpha$ to
spread the disc to large enough radii to reproduce the superhumps observed in
real systems.


Abstract:
  The problem of open-set noisy labels denotes that part of training data have
a different label space that does not contain the true class. Lots of
approaches, e.g., loss correction and label correction, cannot handle such
open-set noisy labels well, since they need training data and test data to
share the same label space, which does not hold for learning with open-set
noisy labels. The state-of-the-art methods thus employ the sample selection
approach to handle open-set noisy labels, which tries to select clean data from
noisy data for network parameters updates. The discarded data are seen to be
mislabeled and do not participate in training. Such an approach is intuitive
and reasonable at first glance. However, a natural question could be raised
"can such data only be discarded during training?". In this paper, we show that
the answer is no. Specifically, we discuss that the instances of discarded data
could consist of some meaningful information for generalization. For this
reason, we do not abandon such data, but use instance correction to modify the
instances of the discarded data, which makes the predictions for the discarded
data consistent with given labels. Instance correction are performed by
targeted adversarial attacks. The corrected data are then exploited for
training to help generalization. In addition to the analytical results, a
series of empirical evidences are provided to justify our claims.


Abstract:
  We propose a new hash function QHFM based on controlled alternate quantum
walks with memory on cycles, where the jth message bit decides whether to run
quantum walk with one-step memory or to run quantum walk with two-step memory
at the jth time step, and the hash value is calculated from the resulting
probability distribution of the walker. Numerical simulation shows that the
proposed hash function has near-ideal statistical performance and is at least
on a par with the state-of-the-art hash functions based on quantum walks in
terms of sensitivity of hash value to message, diffusion and confusion
properties, uniform distribution property, and collision resistance property;
and theoretical analysis indicates that the time and space complexity of the
new scheme are not greater than those of its peers. The good performance of
QHFM suggests that quantum walks that differ not only in coin operators but
also in memory lengths can be combined to build good hash functions, which, in
turn, enriches the construction of controlled alternate quantum walks.


Abstract:
  Recent progress in self-supervised learning has resulted in models that are
capable of extracting rich representations from image collections without
requiring any explicit label supervision. However, to date the vast majority of
these approaches have restricted themselves to training on standard benchmark
datasets such as ImageNet. We argue that fine-grained visual categorization
problems, such as plant and animal species classification, provide an
informative testbed for self-supervised learning. In order to facilitate
progress in this area we present two new natural world visual classification
datasets, iNat2021 and NeWT. The former consists of 2.7M images from 10k
different species uploaded by users of the citizen science application
iNaturalist. We designed the latter, NeWT, in collaboration with domain experts
with the aim of benchmarking the performance of representation learning
algorithms on a suite of challenging natural world binary classification tasks
that go beyond standard species classification. These two new datasets allow us
to explore questions related to large-scale representation and transfer
learning in the context of fine-grained categories. We provide a comprehensive
analysis of feature extractors trained with and without supervision on ImageNet
and iNat2021, shedding light on the strengths and weaknesses of different
learned features across a diverse set of tasks. We find that features produced
by standard supervised methods still outperform those produced by
self-supervised approaches such as SimCLR. However, improved self-supervised
learning methods are constantly being released and the iNat2021 and NeWT
datasets are a valuable resource for tracking their progress.


Abstract:
  Smartphone apps for exposure notification and contact tracing have been shown
to be effective in controlling the COVID-19 pandemic. However, Bluetooth Low
Energy tokens similar to those broadcast by existing apps can still be picked
up far away from the transmitting device. In this paper, we present a new class
of methods for detecting whether or not two Wi-Fi-enabled devices are in
immediate physical proximity, i.e. 2 or fewer meters apart, as established by
the U.S. Centers for Disease Control and Prevention (CDC). Our goal is to
enhance the accuracy of smartphone-based exposure notification and contact
tracing systems. We present a set of binary machine learning classifiers that
take as input pairs of Wi-Fi RSSI fingerprints. We empirically verify that a
single classifier cannot generalize well to a range of different environments
with vastly different numbers of detectable Wi-Fi Access Points (APs). However,
specialized classifiers, tailored to situations where the number of detectable
APs falls within a certain range, are able to detect immediate physical
proximity significantly more accurately. As such, we design three classifiers
for situations with low, medium, and high numbers of detectable APs. These
classifiers distinguish between pairs of RSSI fingerprints recorded 2 or fewer
meters apart and pairs recorded further apart but still in Bluetooth range. We
characterize their balanced accuracy for this task to be between 66.8% and
77.8%.


Abstract:
  We give a complete classification of left invariant para-K\"ahler structures
on four-dimensional simply connected Lie groups up to an automorphism. As an
application we discuss some curvatures properties of the canonical connection
associated to these structures as flat, Ricci flat and existence of Ricci
solitons.


Abstract:
  This paper studies the convergence of a spatial semi-discretization for a
backward semilinear stochastic parabolic equation. The filtration is general,
and the spatial semi-discretization uses the standard continuous piecewise
linear element method. Firstly, higher regularity of the solution to the
continuous equation is derived. Secondly, the first-order spatial accuracy is
derived for the spatial semi-discretization. Thirdly, an application of the
theoretical result to a stochastic linear quadratic control problem is
presented.


Abstract:
  Quantum mechanics dictates the band-structure of materials that is essential
for functional electronic components. With increased miniaturization of devices
it becomes possible to exploit the full potential of quantum mechanics through
the principles of superpositions and entanglement. We propose a new class of
quantum rectifiers that can leverage entanglement to dramatically increase
performance by coupling two small spin chains through an effective double-slit
interface. Simulations show that rectification is enhanced by several orders of
magnitude even in small systems and should be realizable using several of the
quantum technology platforms currently available.


Abstract:
  Light bridges (LBs) are bright lanes that divide an umbra into multiple parts
in some sunspots. Persistent oscillatory bright fronts at a temperature of
$\sim$$10^5$ K are commonly observed above LBs in the 1400/1330 \AA~passbands
of the Interface Region Imaging Spectrograph (IRIS). Based on IRIS
observations, we report small-scale bright blobs from the oscillating bright
front above a light bridge. Some of these blobs reveal a clear acceleration,
whereas the others do not. The average speed of these blobs projected onto the
plane of sky is $71.7\pm14.7$ km s$^{-1}$, with an initial acceleration of
$1.9\pm1.3$ km s$^{-2}$. These blobs normally reach a projected distance of
3--7 Mm from their origin sites. From the transition region images we find an
average projected area of $0.57\pm0.37$ Mm$^{2}$ for the blobs. The blobs were
also detected in multi-passbands of the Solar Dynamics Observatory, but not in
the H$\alpha$ images. These blobs are likely to be plasma ejections, and we
investigate their kinematics and energetics. Through emission measure analyses,
the typical temperature and electron density of these blobs are found to be
around $10^{5.47}$ K and $10^{9.7}$ cm$^{-3}$, respectively. The estimated
kinetic and thermal energies are on the order of $10^{22.8}$ erg and
$10^{23.3}$ erg, respectively. These small-scale blobs appear to show three
different types of formation process. They are possibly triggered by induced
reconnection or release of enhanced magnetic tension due to interaction of
adjacent shocks, local magnetic reconnection between emerging magnetic bipoles
on the light bridge and surrounding unipolar umbral fields, and plasma
acceleration or instability caused by upward shocks, respectively.


Abstract:
  To realize spin wave logic gates programmable phase inverters are essential.
We image with phase-resolved Brillouin light scattering microscopy propagating
spin waves in a one-dimensional magnonic crystal consisting of dipolarly
coupled magnetic nanostripes. We demonstrate phase shifts upon a single
nanostripe of opposed magnetization. Using micromagnetic simulations we model
our experimental finding in a wide parameter space of bias fields and wave
vectors. We find that low-loss phase inversion is achieved, when the internal
field of the oppositely magnetized nanostripe is tuned such that the latter
supports a resonant standing spin wave mode with odd quantization number at the
given frequency. Our results are key for the realization of phase inverters
with optimized signal transmission.


Abstract:
  This work is the first to employ and adapt the image-to-image translation
concept based on conditional generative adversarial networks (cGAN) towards
learning a forward and an inverse solution operator of partial differential
equations (PDEs). Even though the proposed framework could be applied as a
surrogate model for the solution of any PDEs, here we focus on steady-state
solutions of coupled hydro-mechanical processes in heterogeneous porous media.
Strongly heterogeneous material properties, which translate to the
heterogeneity of coefficients of the PDEs and discontinuous features in the
solutions, require specialized techniques for the forward and inverse solution
of these problems. Additionally, parametrization of the spatially heterogeneous
coefficients is excessively difficult by using standard reduced order modeling
techniques. In this work, we overcome these challenges by employing the
image-to-image translation concept to learn the forward and inverse solution
operators and utilize a U-Net generator and a patch-based discriminator. Our
results show that the proposed data-driven reduced order model has competitive
predictive performance capabilities in accuracy and computational efficiency as
well as training time requirements compared to state-of-the-art data-driven
methods for both forward and inverse problems.


Abstract:
  Identifying the underlying mechanisms behind the excitation of transverse
oscillations in coronal loops is essential for their role as diagnostic tools
in coronal seismology and their potential use as wave heating mechanisms of the
solar corona. In this paper, we explore the concept of these transverse
oscillations being excited through a self-sustaining process, caused by
Alfv\'{e}nic vortex shedding from strong background flows interacting with
coronal loops. We show for the first time in 3D simulations that vortex
shedding can generate transverse oscillations in coronal loops, in the
direction perpendicular to the flow due to periodic "pushing" by the vortices.
By plotting the power spectral density we identify the excited frequencies of
these oscillations. We see that these frequencies are dependent both on the
speed of the flow, as well as the characteristics of the oscillating loop.
This, in addition to the fact that the background flow is constant and not
periodic, makes us treat this as a self-oscillating process. Finally, the
amplitudes of the excited oscillations are near constant in amplitude, and are
comparable with the observations of decay-less oscillations. This makes the
mechanism under consideration a possible interpretation of these undamped waves
in coronal loops.


Abstract:
  One-loop $W$ boson contributions to the decay $H\rightarrow Z\gamma$ in the
general $R_\xi$ gauge are presented. The analytical results are expressed in
terms of well-known Passarino-Veltman functions which their numerical
evaluations can be generated using {\tt LoopTools}. In the limit $d\rightarrow
4$, we have shown that these analytical results are independent of the
unphysical parameter $\xi$ and consistent with previous results. The gauge
parameter independence are also checked numerically for consistence. Our
results are also well stable with different values of $\xi =0, 1, 100,$ and
$\xi \rightarrow \infty$.


Abstract:
  The performance and cost of Bi-2212/Ag wire is limited by the large fraction
of silver matrix (~3:1) that is required in the oxide-powder-in-tube
fabrication process. An alternative fabrication process is being developed in
which fine-powder Bi-2212 is uni-axially compressed to form bars with a thin Ag
foil sheath. The fine powder naturally textures (aligns the a-b planes
perpendicular to the direction of compaction) with texture >80% using 200 MPa
compression. A billet is formed by stacking trapezoidal-cross-section bars in a
symmetric 8-12-16 pattern around a Ag rod and enclosing in a Ag-wall extrusion
can. The billet is extruded and drawn to fine wire. Results are presented on
present status of the development and testing.


Abstract:
  Identifying the roles of individual units is critical for understanding the
mechanism of convolutional neural networks (CNNs). However, it is challenging
to give the fully automatic and quantitative measures for effectiveness
assessment of individual units in CNN. To this end, we propose a novel method
for quantitatively clarifying the status and usefulness of single unit of CNN
in image classification tasks. The technical substance of our method is ranking
the importance of unit for each class in classification based on calculation of
specifically defined entropy using algebraic topological tools. It could be
implemented totally by machine without any human intervention. Some interesting
phenomena including certain kind of phase transition are observed via the
evolution of accuracy and loss of network in the successive ablation process of
units. All of the network units are divided into four categories according to
their performance on training and testing data. The role categorization is
excellent startpoint for network construction and simplification. The diverse
utility and contribution to the network generalization of units in
classification tasks are thoroughly illustrated by extensive experiments on
network (VGG) and dataset (ImageNet) with considerable scale. It is easy for
our method to have extensional applications on other network models and tasks
without essential difficulties.


Abstract:
  This paper develops new analytical process noise covariance models for both
absolute and relative spacecraft states. Process noise is always present when
propagating a spacecraft state due to dynamics modeling deficiencies.
Accurately modeling this noise is essential for sequential orbit determination
and improves satellite conjunction analysis. A common approach called state
noise compensation models process noise as zero-mean Gaussian white noise
accelerations. The resulting process noise covariance can be evaluated
numerically, which is computationally intensive, or through a widely used
analytical model that is restricted to an absolute Cartesian state and small
propagation intervals. Moreover, mathematically rigorous, analytical process
noise covariance models for relative spacecraft states are not currently
available. To address these limitations of the state of the art, new analytical
process noise covariance models are developed for state noise compensation for
both Cartesian and orbital element absolute state representations by leveraging
spacecraft relative dynamics models. Two frameworks are then presented for
modeling the process noise covariance of relative spacecraft states by assuming
either small or large interspacecraft separation. The presented techniques are
validated through numerical simulations.


Abstract:
  While game theory has been transformative for decision-making, the
assumptions made can be overly restrictive in certain instances. In this work,
we focus on some of the assumptions underlying rationality such as mutual
consistency and best response, and consider ways to relax these assumptions
using concepts from level-$k$ reasoning and quantal response equilibrium (QRE)
respectively. Specifically, we provide an information-theoretic two-parameter
model that can relax both mutual consistency and best response, but can recover
approximations of level-$k$, QRE, or typical Nash equilibrium behaviour in the
limiting cases. The proposed Quantal Hierarchy model is based on a recursive
form of the variational free energy principle, representing self-referential
games as (pseudo) sequential decisions. Bounds in player processing abilities
are captured as information costs, where future chains of reasoning are
discounted, implying a hierarchy of players where lower-level players have
fewer processing resources. We demonstrate the applicability of the proposed
model to several canonical economic games.


Abstract:
  Recently a conformally invariant action describing the Wilson-Fischer fixed
point in $D=4-\epsilon$ dimensions in the presence of a {\em finite} UV cutoff
was constructed \cite{Dutta}. In the present paper we construct two composite
operator perturbations of this action with definite scaling dimension also in
the presence of a finite cutoff. Thus the operator (as well as the fixed point
action) is well defined at all momenta $0\leq p\leq \infty$ and at low energies
they reduce to $\int_x \phi^2$ and $\int _x \phi^4$ respectively. The
construction includes terms up to $O(\lamda^2)$. In the presence of a finite
cutoff they mix with higher order irrelevant operators. The dimensions are also
calculated to this order and agree with known results.


Abstract:
  We consider the practicalities of defining, simulating, and characterizing
"Liquids" from a pedagogical standpoint based on atomistic computer
simulations. For simplicity and clarity we study two-dimensional systems
throughout. In addition to the infinite-ranged Lennard-Jones 12/6 potential we
consider two shorter-ranged families of pair potentials. At zero pressure one
of them includes just nearest neighbors. The other longer-ranged family
includes twelve additional neighbors. We find that these further neighbors can
help stabilize the liquid phase.
  What about liquids? To implement Wikipedia's definition of liquids as
conforming to their container we begin by formulating and imposing
smooth-container boundary conditions. To encourage conformation further we add
a vertical gravitational field. Gravity helps stabilize the relatively vague
liquid-gas interface. Gravity reduces the messiness associated with the
curiously-named "spinodal" (tensile) portion of the phase diagram. Our
simulations are mainly isothermal. We control the kinetic temperature with
Nos\'e-Hoover thermostating, extracting or injecting heat so as to impose a
mean kinetic temperature over time. Our simulations stabilizing density
gradients and the temperature provide critical-point estimates fully consistent
with previous efforts from free energy and Gibbs' ensemble simulations. This
agreement validates our approach.


Abstract:
  Recent work has highlighted the role of initialization scale in determining
the structure of the solutions that gradient methods converge to. In
particular, it was shown that large initialization leads to the neural tangent
kernel regime solution, whereas small initialization leads to so called "rich
regimes". However, the initialization structure is richer than the overall
scale alone and involves relative magnitudes of different weights and layers in
the network. Here we show that these relative scales, which we refer to as
initialization shape, play an important role in determining the learned model.
We develop a novel technique for deriving the inductive bias of gradient-flow
and use it to obtain closed-form implicit regularizers for multiple cases of
interest.


Abstract:
  The success of the current generation of Noisy Intermediate-Scale Quantum
(NISQ) hardware shows that quantum hardware may be able to tackle complex
problems even without error correction. One outstanding issue is that of
coherent errors arising from the increased complexity of these devices. These
errors can accumulate through a circuit, making their impact on algorithms hard
to predict and mitigate. Iterative algorithms like Quantum Imaginary Time
Evolution are susceptible to these errors. This article presents the
combination of both noise tailoring using Randomized Compiling and error
mitigation with a purification. We also show that Cycle Benchmarking gives an
estimate of the reliability of the purification. We apply this method to the
Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report
an energy estimation and a ground state infidelity both below 1\%. Our
methodology is general and can be used for other algorithms and platforms. We
show how combining noise tailoring and error mitigation will push forward the
performance of NISQ devices.


Abstract:
  For a compact set of actions, an invariant of Kushnirenko's entropy type is
chosen in such a way that on this set it is equal to zero, but will be infinity
for typical actions. As a consequence, we show that typical measure-preserving
transformations are not isomorphic to geometric shape exchange transformations.
This problem arose in connection with the result of Chaika and Davis about the
atypical nature of IETs.


Abstract:
  Neural language models exhibit impressive performance on a variety of tasks,
but their internal reasoning may be difficult to understand. Prior art aims to
uncover meaningful properties within model representations via probes, but it
is unclear how faithfully such probes portray information that the models
actually use. To overcome such limitations, we propose a technique, inspired by
causal analysis, for generating counterfactual embeddings within models. In
experiments testing our technique, we produce evidence that suggests some
BERT-based models use a tree-distance-like representation of syntax in
downstream prediction tasks.


Abstract:
  By ungauging a recently discovered lattice rotor model for Chern-Simons
theory, we create an exactly soluble path integral on spacetime lattice for
$U^\kappa(1)$ Symmetry Protected Topological (SPT) phases in $2+1$ dimensions
with a non-zero Hall conductance. We then convert the path integral on a $2+1$d
spacetime lattice into a $2$d Hamiltonian lattice model, and show that the
Hamiltonian consists of mutually commuting local projectors. We confirm the
non-zero Hall conductance by calculating the Chern number of the exact ground
state. It has recently been suggested that no commuting projector model can
host a nonzero Hall conductance. We evade this no-go theorem by considering a
rotor model, with a countably infinite number of states per site.


Abstract:
  With the need of fast retrieval speed and small memory footprint, document
hashing has been playing a crucial role in large-scale information retrieval.
To generate high-quality hashing code, both semantics and neighborhood
information are crucial. However, most existing methods leverage only one of
them or simply combine them via some intuitive criteria, lacking a theoretical
principle to guide the integration process. In this paper, we encode the
neighborhood information with a graph-induced Gaussian distribution, and
propose to integrate the two types of information with a graph-driven
generative model. To deal with the complicated correlations among documents, we
further propose a tree-structured approximation method for learning. Under the
approximation, we prove that the training objective can be decomposed into
terms involving only singleton or pairwise documents, enabling the model to be
trained as efficiently as uncorrelated ones. Extensive experimental results on
three benchmark datasets show that our method achieves superior performance
over state-of-the-art methods, demonstrating the effectiveness of the proposed
model for simultaneously preserving semantic and neighborhood information.\


Abstract:
  Toroidal rotation is well known to play significant roles in the edge
transport and L-H transition dynamics of tokamaks. Our recent calculation finds
that a sufficiently strong localized toroidal rotation can directly bring out
the formation of edge pressure pedestal with reversed magnetic shear that is
reminiscent of an H-mode plasma, purely through the effects of toroidal
rotation on the tokamak MHD equilibrium itself. In particular, the enhanced
edge toroidal rotation enables a substantial peaking of the parallel current
profile near edge in higher $\beta$ regimes, which leads to the flattening or
reversal of the local $q$ (safety factor) profile. Here the formation of
pressure pedestal along with the reversed magnetic shear region is shown to be
the natural outcome of the MHD tokamak equilibrium in a self-consistent
response to the presence of a localized toroidal rotation typically observed in
H-mode or QH-mode.


Abstract:
  The exchange of information between an open quantum system and its
environment allows us to discriminate among different kinds of dynamics, in
particular detecting memory effects to characterize non-Markovianity. Here, we
investigate the role played by the system-environment correlations and the
environmental evolution in the flow of information. First, we derive general
conditions ensuring that two generalized dephasing microscopic models of the
global system-environment evolution result exactly in the same open-system
dynamics, for any initial state of the system. Then, we use the trace distance
to quantify the distinct contributions to the information inside and outside
the open system in the two models. Our analysis clarifies how the interplay
between system-environment correlations and environmental-state
distinguishability can lead to the same information flow from and toward the
open system, despite significant qualitative and quantitative differences at
the level of the global evolution.


Abstract:
  Recently a non-supersymmetric conformal field theory with an exactly marginal
deformation in the large $N$ limit was constructed by
Chaudhuri-Choi-Rabinovici. On a non-supersymmetric conformal manifold, $c$
coefficient of the trace anomaly in four dimensions would generically change.
In this model, we, however, find that it does not change at the first
non-trivial order given by three-loop diagrams.


Abstract:
  We propose a logic of knowledge for impure simplicial complexes. Impure
simplicial complexes represent distributed systems under uncertainty over which
processes are still active (are alive) and which processes have failed or
crashed (are dead). Our work generalizes the logic of knowledge for pure
simplicial complexes, where all processes are alive, by Goubault et al. Our
logical semantics has a satisfaction relation defined simultaneously with a
definability relation. The latter restricts which formulas are allowed to have
a truth value: dead processes cannot know or be ignorant of any proposition,
and live processes cannot know or be ignorant of propositions involving
processes they know to be dead. The logic satisfies some but not all axioms and
rules of the modal logic S5. Impure simplicial complexes correspond to Kripke
models where each agent's accessibility relation is an equivalence relation on
a subset of the domain only, and otherwise empty, and where each propositional
variable is known by an agent. We also propose a notion of bisimulation for
impure simplexes and show bisimulation correspondence on certain finitary
simplexes. % Dynamic aspects of our semantics, such as how to formalize
possibly incomplete tasks and algorithms in distributed computing, is left for
future research.


Abstract:
  Missing node attributes is a common problem in real-world graphs. Graph
neural networks have been demonstrated powerful in graph representation
learning, however, they rely heavily on the completeness of graph information.
Few of them consider the incomplete node attributes, which can bring great
damage to the performance in practice. In this paper, we propose an innovative
node representation learning framework, Wasserstein graph diffusion (WGD), to
mitigate the problem. Instead of feature imputation, our method directly learns
node representations from the missing-attribute graphs. Specifically, we extend
the message passing schema in general graph neural networks to a Wasserstein
space derived from the decomposition of attribute matrices. We test WGD in node
classification tasks under two settings: missing whole attributes on some nodes
and missing only partial attributes on all nodes. In addition, we find WGD is
suitable to recover missing values and adapt it to tackle matrix completion
problems with graphs of users and items. Experimental results on both tasks
demonstrate the superiority of our method.


Abstract:
  Non-potential magnetic energy promptly released in solar flares is converted
to other forms of energy. This may include nonthermal energy of
flare-accelerated particles, thermal energy of heated flaring plasma, and
kinetic energy of eruptions, jets, up/down flows, and stochastic (turbulent)
plasma motions. The processes or parameters governing partitioning of the
released energy between these components is an open question. How these
components are distributed between distinct flaring loops and what controls
these spatial distributions is also unclear. Here, based on multi-wavelength
data and 3D modeling, we quantify the energy partitioning and spatial
distribution in the well observed SOL2014-02-16T064620 solar flare of class
C1.5. Nonthermal emissions of this flare displayed a simple impulsive
single-spike light curves lasting about 20\,s. In contrast, the thermal
emission demonstrated at least three distinct heating episodes, only one of
which was associated with the nonthermal component. The flare was accompanied
by up and down flows and substantial turbulent velocities. The results of our
analysis suggest that (i) the flare occurs in a multi-loop system that included
at least three distinct flux tubes; (ii) the released magnetic energy is
divided unevenly between the thermal and nonthermal components in these loops;
(iii) only one of these three flaring loops contains an energetically important
amount of nonthermal electrons, while two other loops remain thermal; (iv) the
amounts of direct plasma heating and that due to nonthermal electron loss are
comparable; (v) the kinetic energy in the flare footpoints constitute only a
minor fraction compared with the thermal and nonthermal energies.


Abstract:
  This article develops for the first time a rigorous analysis of Hibler's
model of sea ice dynamics. Identifying Hibler's ice stress as a quasilinear
second order operator and regarding Hibler's model as a quasilinear evolution
equation, it is shown that Hibler's coupled sea ice model, i.e., the model
coupling velocity, thickness and compactness of sea ice, is locally strongly
well-posed within the $L_q$-setting and also globally strongly well-posed for
initial data close to constant equilibria.


Abstract:
  Ensemble data from Earth system models has to be calibrated and
post-processed. I propose a novel member-by-member post-processing approach
with neural networks. I bridge ideas from ensemble data assimilation with
self-attention, resulting into the self-attentive ensemble transformer. Here,
interactions between ensemble members are represented as additive and dynamic
self-attentive part. As proof-of-concept, I regress global ECMWF ensemble
forecasts to 2-metre-temperature fields from the ERA5 reanalysis. I demonstrate
that the ensemble transformer can calibrate the ensemble spread and extract
additional information from the ensemble. As it is a member-by-member approach,
the ensemble transformer directly outputs multivariate and spatially-coherent
ensemble members. Therefore, self-attention and the transformer technique can
be a missing piece for a non-parametric post-processing of ensemble data with
neural networks.


Abstract:
  Cold spots are sub-wavelength regions which might emerge near a plasmonic
nanoantenna, should one or more components of some far-field illumination
cancel out with scattered light. With a simplest-case demonstration using two
dipolar scatterers, we show that by changing only the polarisation and
amplitude of two plane waves, a unique, zero-magnitude and super sub-wavelength
cold spot can be created anywhere in the space around a nanoantenna. This
technique is a means for ultra-fast, remote, and non-mechanical sub-wavelength
electric field manipulation.


Abstract:
  Bayesian inference allows to obtain useful information on the parameters of
models, either in computational statistics or more recently in the context of
Bayesian Neural Networks. The computational cost of usual Monte Carlo methods
for sampling a posteriori laws in Bayesian inference scales linearly with the
number of data points. One option to reduce it to a fraction of this cost is to
resort to mini-batching in conjunction with unadjusted discretizations of
Langevin dynamics, in which case only a random fraction of the data is used to
estimate the gradient. However, this leads to an additional noise in the
dynamics and hence a bias on the invariant measure which is sampled by the
Markov chain. We advocate using the so-called Adaptive Langevin dynamics, which
is a modification of standard inertial Langevin dynamics with a dynamical
friction which automatically corrects for the increased noise arising from
mini-batching. We investigate the practical relevance of the assumptions
underpinning Adaptive Langevin (constant covariance for the estimation of the
gradient), which are not satisfied in typical models of Bayesian inference, and
quantify the bias induced by minibatching in this case. We also show how to
extend AdL in order to systematically reduce the bias on the posterior
distribution by considering a dynamical friction depending on the current value
of the parameter to sample.


Abstract:
  In localization microscopy, subnanometer precision is possible but supporting
accuracy is challenging, and no study has demonstrated reliable traceability to
the International System of Units (SI). To do so, we measure the positions of
nanoscale apertures in a reference array by traceable atomic-force microscopy,
creating a master standard. We perform correlative measurements of this
standard by optical microscopy, correcting position errors from optical
aberrations by a Zernike calibration. We establish an uncertainty field due to
localization errors and scale uncertainty, with regions of position
traceability to within a 68 % coverage interval of +/- 1.0 nm. These results
enable localization metrology with high throughput, which we apply to measure
working standards, validating the subnanometer accuracy of lithographic pitch.


Abstract:
  Motivated by entropic optimal transport, time reversal of diffusion processes
is revisited. An integration by parts formula is derived for the carr\'e du
champ of a Markov process in an abstract space. It leads to a time reversal
formula for a wide class of diffusion processes in $ \mathbb{R}^n$ possibly
with singular drifts, extending the already known results in this domain.
  The proof of the integration by parts formula relies on stochastic
derivatives. Then, this formula is applied to compute the semimartingale
characteristics of the time-reversed $P^*$ of a diffusion measure $P$ provided
that the relative entropy of $P$ with respect to another diffusion measure $R$
is finite, and the semimartingale characteristics of the time-reversed $R^*$
are known (for instance when the reference path measure $R$ is reversible).
  As an illustration of the robustness of this method, the integration by parts
formula is also employed to derive a time-reversal formula for a random walk on
a graph.


Abstract:
  Collecting and aggregating information from several probability measures or
histograms is a fundamental task in machine learning. One of the popular
solution methods for this task is to compute the barycenter of the probability
measures under the Wasserstein metric. However, approximating the Wasserstein
barycenter is numerically challenging because of the curse of dimensionality.
This paper proposes the projection robust Wasserstein barycenter (PRWB) that
has the potential to mitigate the curse of dimensionality. Since PRWB is
numerically very challenging to solve, we further propose a relaxed PRWB
(RPRWB) model, which is more tractable. The RPRWB projects the probability
measures onto a lower-dimensional subspace that maximizes the Wasserstein
barycenter objective. The resulting problem is a max-min problem over the
Stiefel manifold. By combining the iterative Bregman projection algorithm and
Riemannian optimization, we propose two new algorithms for computing the RPRWB.
The complexity of arithmetic operations of the proposed algorithms for
obtaining an $\epsilon$-stationary solution is analyzed. We incorporate the
RPRWB into a discrete distribution clustering algorithm, and the numerical
results on real text datasets confirm that our RPRWB model helps improve the
clustering performance significantly.


Abstract:
  Context. We report the discovery of VVV-CL160, a new nearby globular cluster
(GC) with extreme kinematics, located in the Galactic plane at $l = 10.1477$
deg, $b = 0.2999$ deg. Aims. We aim to characterize the physical properties of
this new GC and place it in the context of the Milky Way, exploring its
possible connection with the known GC NGC 6544 and with the Hrid halo stream.
Methods. VVV-CL160 was originally detected in the VISTA Variables in the V\'ia
L\'actea (VVV) survey. We use the proper motions (PMs) from the updated VVV
Infrared Astrometric Catalog (VIRAC2) to select GC members and make deep
near-infrared color-magnitude diagrams (CMDs) to study the cluster properties.
We also fit King models to the decontaminated sample to determine the GC
structural parameters. Results. VVV-CL160 has an unusually large PM for a
Galactic GC as measured with VIRAC2 and Gaia EDR3: $\mu_{\alpha}\cos(\delta)$ =
$-2.3 \pm 0.1 $ mas yr$^{-1}$ and $\mu_{\delta}$ = $-16.8 \pm 0.1 $ mas
yr$^{-1}$. The kinematics are similar to those of the known GC NGC 6544 and the
Hrid halo stream. We estimate a reddening of $E(J-K) = 1.95$ mag and an
extinction of $A_{k}= 1.40$ mag for VVV-CL160. We also measure a distance
modulus of $(m-M) = 13.01$ mag and a distance of $D_{\odot} = 4.0 \pm 0.5$ kpc.
This places the GC at $z=29$ pc above the Galactic plane and at a
galactocentric distance of $R_G=4.2$ kpc. We also measure a metallicity of
$[Fe/H] = -1.4 \pm 0.2$ dex for an adopted age of $t=12$ Gyr; King model fits
of the PM-decontaminated sample reveal a concentrated GC, with core radius
$r_{c}= 22.8"$ and tidal radius $r_{t}= 50'$. .... We also explore the possible
association of this new GC with other GCs and halo streams. Conclusions. Based
on the locations and kinematics, we suggest that VVV-CL160, along with NGC
6544, may be associated with the extension of the Hrid halo stream.


Abstract:
  A rank-$r$ integer matrix $A$ is $\Delta$-modular if the determinant of each
$r \times r$ submatrix has absolute value at most $\Delta$. The class of
$1$-modular, or unimodular, matrices is of fundamental significance in both
integer programming theory and matroid theory. A 1957 result of Heller shows
that the maximum number of nonzero, pairwise non-parallel rows of a rank-$r$
unimodular matrix is ${r + 1 \choose 2}$. We prove that, for each sufficiently
large integer $r$, the maximum number of nonzero, pairwise non-parallel rows of
a rank-$r$ $2$-modular matrix is ${r + 2 \choose 2} - 2$.


Abstract:
  Remote sensing images and techniques are powerful tools to investigate earth
surface. Data quality is the key to enhance remote sensing applications and
obtaining a clear and noise-free set of data is very difficult in most
situations due to the varying acquisition (e.g., atmosphere and season),
sensor, and platform (e.g., satellite angles and sensor characteristics)
conditions. With the increasing development of satellites, nowadays Terabytes
of remote sensing images can be acquired every day. Therefore, information and
data fusion can be particularly important in the remote sensing community. The
fusion integrates data from various sources acquired asynchronously for
information extraction, analysis, and quality improvement. In this chapter, we
aim to discuss the theory of spatiotemporal fusion by investigating previous
works, in addition to describing the basic concepts and some of its
applications by summarizing our prior and ongoing works.


Abstract:
  Deep neural networks give state-of-the-art accuracy for reconstructing images
from few and noisy measurements, a problem arising for example in accelerated
magnetic resonance imaging (MRI). However, recent works have raised concerns
that deep-learning-based image reconstruction methods are sensitive to
perturbations and are less robust than traditional methods: Neural networks (i)
may be sensitive to small, yet adversarially-selected perturbations, (ii) may
perform poorly under distribution shifts, and (iii) may fail to recover small
but important features in an image. In order to understand the sensitivity to
such perturbations, in this work, we measure the robustness of different
approaches for image reconstruction including trained and un-trained neural
networks as well as traditional sparsity-based methods. We find, contrary to
prior works, that both trained and un-trained methods are vulnerable to
adversarial perturbations. Moreover, both trained and un-trained methods tuned
for a particular dataset suffer very similarly from distribution shifts.
Finally, we demonstrate that an image reconstruction method that achieves
higher reconstruction quality, also performs better in terms of accurately
recovering fine details. Our results indicate that the state-of-the-art
deep-learning-based image reconstruction methods provide improved performance
than traditional methods without compromising robustness.


Abstract:
  Efficiently approximating local curvature information of the loss function is
a key tool for optimization and compression of deep neural networks. Yet, most
existing methods to approximate second-order information have high
computational or storage costs, which can limit their practicality. In this
work, we investigate matrix-free, linear-time approaches for estimating
Inverse-Hessian Vector Products (IHVPs) for the case when the Hessian can be
approximated as a sum of rank-one matrices, as in the classic approximation of
the Hessian by the empirical Fisher matrix. We propose two new algorithms as
part of a framework called M-FAC: the first algorithm is tailored towards
network compression and can compute the IHVP for dimension $d$, if the Hessian
is given as a sum of $m$ rank-one matrices, using $O(dm^2)$ precomputation,
$O(dm)$ cost for computing the IHVP, and query cost $O(m)$ for any single
element of the inverse Hessian. The second algorithm targets an optimization
setting, where we wish to compute the product between the inverse Hessian,
estimated over a sliding window of optimization steps, and a given gradient
direction, as required for preconditioned SGD. We give an algorithm with cost
$O(dm + m^2)$ for computing the IHVP and $O(dm + m^3)$ for adding or removing
any gradient from the sliding window. These two algorithms yield
state-of-the-art results for network pruning and optimization with lower
computational overhead relative to existing second-order methods.
Implementations are available at [9] and [17].


Abstract:
  With a growing number of cores per socket in modern data-centers where
multi-tenancy of a diverse set of applications must be efficiently supported,
effective sharing of the last level cache is a very important problem. This is
challenging because modern workloads exhibit dynamic phase behavior - their
cache requirements & sensitivity vary across different execution points. To
tackle this problem, we propose Com-CAS, a compiler-guided cache apportioning
system that provides smart cache allocation to co-executing applications in a
system. The front-end of Com-CAS is primarily a compiler-framework equipped
with learning mechanisms to predict cache requirements, while the backend
consists of an allocation framework with a pro-active scheduler that apportions
cache dynamically to co-executing applications. Our system improved average
throughput by 21%, with a maximum of 54% while maintaining the worst individual
application execution time degradation within 15% to meet SLA requirements.


Abstract:
  The performance of algorithms for neural architecture search strongly depends
on the parametrization of the search space. We use contrastive learning to
identify networks across different initializations based on their data
Jacobians, and automatically produce the first architecture embeddings
independent from the parametrization of the search space. Using our contrastive
embeddings, we show that traditional black-box optimization algorithms, without
modification, can reach state-of-the-art performance in Neural Architecture
Search. As our method provides a unified embedding space, we perform for the
first time transfer learning between search spaces. Finally, we show the
evolution of embeddings during training, motivating future studies into using
embeddings at different training stages to gain a deeper understanding of the
networks in a search space.


Abstract:
  The field of natural language understanding has experienced exponential
progress in the last few years, with impressive results in several tasks. This
success has motivated researchers to study the underlying knowledge encoded by
these models. Despite this, attempts to understand their semantic capabilities
have not been successful, often leading to non-conclusive, or contradictory
conclusions among different works. Via a probing classifier, we extract the
underlying knowledge graph of nine of the most influential language models of
the last years, including word embeddings, text generators, and context
encoders. This probe is based on concept relatedness, grounded on WordNet. Our
results reveal that all the models encode this knowledge, but suffer from
several inaccuracies. Furthermore, we show that the different architectures and
training strategies lead to different model biases. We conduct a systematic
evaluation to discover specific factors that explain why some concepts are
challenging. We hope our insights will motivate the development of models that
capture concepts more precisely.


Abstract:
  Convolutional Neural Networks (CNNs) are successful deep learning models in
the field of computer vision. To get the maximum advantage of CNN model for
Human Action Recognition (HAR) using inertial sensor data, in this paper, we
use 4 types of spatial domain methods for transforming inertial sensor data to
activity images, which are then utilized in a novel fusion framework. These
four types of activity images are Signal Images (SI), Gramian Angular Field
(GAF) Images, Markov Transition Field (MTF) Images and Recurrence Plot (RP)
Images. Furthermore, for creating a multimodal fusion framework and to exploit
activity image, we made each type of activity images multimodal by convolving
with two spatial domain filters : Prewitt filter and High-boost filter.
Resnet-18, a CNN model, is used to learn deep features from multi-modalities.
Learned features are extracted from the last pooling layer of each ReNet and
then fused by canonical correlation based fusion (CCF) for improving the
accuracy of human action recognition. These highly informative features are
served as input to a multiclass Support Vector Machine (SVM). Experimental
results on three publicly available inertial datasets show the superiority of
the proposed method over the current state-of-the-art.


Abstract:
  The paper presents a new approach to multiview video coding using Screen
Content Coding. It is assumed that for a time instant the frames corresponding
to all views are packed into a single frame, i.e. the frame-compatible approach
to multiview coding is applied. For such coding scenario, the paper
demonstrates that Screen Content Coding can be efficiently used for multiview
video coding. Two approaches are considered: the first using standard HEVC
Screen Content Coding, and the second using Advanced Screen Content Coding. The
latter is the original proposal of the authors that exploits quarter-pel motion
vectors and other nonstandard extensions of HEVC Screen Content Coding. The
experimental results demonstrate that multiview video coding even using
standard HEVC Screen Content Coding is much more efficient than simulcast HEVC
coding. The proposed Advanced Screen Content Coding provides virtually the same
coding efficiency as MV-HEVC, which is the state-of-the-art multiview video
compression technique. The authors suggest that Advanced Screen Content Coding
can be efficiently used within the new Versatile Video Coding (VVC) technology.
Nevertheless a reference multiview extension of VVC does not exist yet,
therefore, for VVC-based coding, the experimental comparisons are left for
future work.


Abstract:
  Motivated by the observation of the first hidden charm pentaquarks by the
LHCb collaboration in 2015 and the updated analysis with an order-of-magnitude
larger data set in 2019, we estimate their cross sections for the prompt
production as well as their heavy quark spin partners, in the
$\Sigma_c^{(*)}\bar{D}^{(*)}$ hadronic molecular picture, at the center-of-mass
energy $7~\mathrm{TeV}$ in the $pp$ collision. Their cross sections are several
$\mathrm{nb}$ and we would expect several tens hidden charm pentaquark events
in the LHC based on its current integrated luminosity. The cross sections show
a sizable deviation of the cross sections for hidden charm pentaquarks with the
third isospin component $I_z=+\frac{1}{2}$ ($P_c^+$) from those with
$I_z=-\frac{1}{2}$ ($P_c^0$). The cross sections decrease dramatically with the
increasing transverse momentum. Our study can also tell where to search for the
missing hidden charm pentaquarks. The confirmation of the complete hidden charm
pentaquarks in the heavy quark symmetry would further verify their
$\Sigma_c^{(*)}\bar{D}^{(*)}$ molecular interpretation. In addition, the
relative strength among these cross sections for pentaquarks can help us to
identify the quantum numbers of the $P_c(4440)$ and $P_c(4457)$.


Abstract:
  This work studies the quantitative stability of the quadratic optimal
transport map between a fixed probability density $\rho$ and a probability
measure $\mu$ on R^d , which we denote T$\mu$. Assuming that the source density
$\rho$ is bounded from above and below on a compact convex set, we prove that
the map $\mu$ $\rightarrow$ T$\mu$ is bi-H{\"o}lder continuous on large
families of probability measures, such as the set of probability measures whose
moment of order p > d is bounded by some constant. These stability estimates
show that the linearized optimal transport metric W2,$\rho$($\mu$, $\nu$) =
T$\mu$ -- T$\nu$ L 2 ($\rho$,R d) is bi-H{\"o}lder equivalent to the
2-Wasserstein distance on such sets, justifiying its use in applications.


Abstract:
  Speedrunning in general means to play a video game fast, i.e. using all means
at one's disposal to achieve a given goal in the least amount of time possible.
To do so, a speedrun must be planned in advance, or routed, as it is referred
to by the community. This paper focuses on discovering challenges and defining
models needed when trying to approach the problem of routing algorithmically.
It provides an overview of relevant speedrunning literature, extracting vital
information and formulating criticism. Important categorizations are pointed
out and a nomenclature is build to support professional discussion. Different
concepts of graph representations are presented and their potential is
discussed with regard to solving the speedrun routing optimization problem.
Visions both for problem modeling as well as solving are presented and assessed
regarding suitability and expected challenges. This results in a vision of
potential solutions and what will be addressed in the future.


Abstract:
  Many engineering problems involve the optimization of computationally
expensive models for which derivative information is not readily available. The
Bayesian optimization (BO) framework is a particularly promising approach for
solving these problems, which uses Gaussian process (GP) models and an expected
utility function to systematically tradeoff between exploitation and
exploration of the design space. BO, however, is fundamentally limited by the
black-box model assumption that does not take into account any underlying
problem structure. In this paper, we propose a new algorithm, COBALT, for
constrained grey-box optimization problems that combines multivariate GP models
with a novel constrained expected utility function whose structure can be
exploited by state-of-the-art nonlinear programming solvers. COBALT is compared
to traditional BO on seven test problems including the calibration of a
genome-scale bioreactor model to experimental data. Overall, COBALT shows very
promising performance on both unconstrained and constrained test problems.


Abstract:
  Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar
induction. However, to reduce computational complexity, they make a strong
independence assumption on the generation of the child word and thus bilexical
dependencies are ignored. In this paper, we propose an approach to parameterize
L-PCFGs without making implausible independence assumptions. Our approach
directly models bilexical dependencies and meanwhile reduces both learning and
representation complexities of L-PCFGs. Experimental results on the English WSJ
dataset confirm the effectiveness of our approach in improving both running
speed and unsupervised parsing performance.


Abstract:
  A common practice in many auctions is to offer bidders an opportunity to
improve their bids, known as a Best and Final Offer (BAFO) stage. This final
bid can depend on new information provided about either the asset or the
competitors. This paper examines the effects of new information regarding
competitors, seeking to determine what information the auctioneer should
provide assuming the set of allowable bids is discrete. The rational strategy
profile that maximizes the revenue of the auctioneer is the one where each
bidder makes the highest possible bid that is lower than his valuation of the
item. This strategy profile is an equilibrium for a large enough number of
bidders, regardless of the information released. We compare the number of
bidders needed for this profile to be an equilibrium under different
information settings. We find that it becomes an equilibrium with fewer bidders
when less additional information is made available to the bidders regarding the
competition. It follows that when the number of bidders is a priori unknown,
there are some advantages to the auctioneer to not reveal information.


Abstract:
  Using isobaric Monte Carlo simulations, we map out the entire phase diagram
of a system of hard cylindrical particles of length $L$ and diameter $D$, using
an improved algorithm to identify the overlap condition between two cylinders.
Both the prolate $L/D>1$ and the oblate $L/D<1$ phase diagrams are reported
with no solution of continuity. In the prolate $L/D>1$ case, we find
intermediate nematic \textrm{N} and smectic \textrm{SmA} phases in addition to
a low density isotropic \textrm{I} and a high density crystal \textrm{X} phase,
with \textrm{I-N-SmA} and \textrm{I-SmA-X} triple points. An apparent columnar
phase \textrm{C} is shown to be metastable as in the case of spherocylinders.
In the oblate $L/D<1$ case, we find stable intermediate cubatic \textrm{Cub},
nematic \textrm{N}, and columnar \textrm{C} phases with \textrm{I-N-Cub},
\textrm{N-Cub-C}, and \textrm{I-Cub-C} triple points. Comparison with previous
numerical and analytical studies is discussed. The present study, accounting
for the explicit cylindrical shape, paves the way to more sophisticated models
with important biological applications, such as viruses and nucleosomes.


Abstract:
  Person re-identification (re-ID) in the scenario with large spatial and
temporal spans has not been fully explored. This is partially because that,
existing benchmark datasets were mainly collected with limited spatial and
temporal ranges, e.g., using videos recorded in a few days by cameras in a
specific region of the campus. Such limited spatial and temporal ranges make it
hard to simulate the difficulties of person re-ID in real scenarios. In this
work, we contribute a novel Large-scale Spatio-Temporal LaST person re-ID
dataset, including 10,862 identities with more than 228k images. Compared with
existing datasets, LaST presents more challenging and high-diversity re-ID
settings, and significantly larger spatial and temporal ranges. For instance,
each person can appear in different cities or countries, and in various time
slots from daytime to night, and in different seasons from spring to winter. To
our best knowledge, LaST is a novel person re-ID dataset with the largest
spatio-temporal ranges. Based on LaST, we verified its challenge by conducting
a comprehensive performance evaluation of 14 re-ID algorithms. We further
propose an easy-to-implement baseline that works well on such challenging re-ID
setting. We also verified that models pre-trained on LaST can generalize well
on existing datasets with short-term and cloth-changing scenarios. We expect
LaST to inspire future works toward more realistic and challenging re-ID tasks.
More information about the dataset is available at
https://github.com/shuxjweb/last.git.


Abstract:
  We construct a symmetric invertible binary pairing function $F(m,n)$ on the
set of positive integers with a property of $F(m,n)=F(n,m)$. Then we provide a
complete proof of its symmetry and bijectivity, from which the construction of
symmetric invertible binary pairing functions on any custom set of integers
could be seen.


Abstract:
  This paper presents OpenCV2X, the first publicly available, open-source
simulation model of the Third Generation Partnership Project (3GPP) Release 14
Cellular Vehicle to Everything (C-V2X) sidelink, which forms the basis for 5G
NR Mode 2 under later releases. This model is fully compliant with the existing
vehicular service and application layers, including messaging sets as defined
by the automotive and standards communities providing a fully standardised,
cross-layer communication model. Using this model, we show how the current
sidelink scheduling mechanism performs poorly when scheduling applications with
highly aperiodic communication characteristics, such as ETSI Cooperative
Awareness Messages (CAMs). We then provide the first indepth evaluation of
dedicated per-packet aperiodic scheduling mechanisms, in contrast to schemes
that parameterise the existing algorithm. This paper highlights that the level
of aperiodicity exhibited by the application model greatly impacts scheduling
performance. Finally, we analyse how such scheduling mechanisms might co-exist.


Abstract:
  The variational quantum eigensolver (VQE) is one of the most representative
quantum algorithms in the noisy intermediate-size quantum (NISQ) era, and is
generally speculated to deliver one of the first quantum advantages for the
ground-state simulations of some non-trivial Hamiltonians. However, short
quantum coherence time and limited availability of quantum hardware resources
in the NISQ hardware strongly restrain the capacity and expressiveness of VQEs.
In this Letter, we introduce the variational quantum-neural hybrid eigensolver
(VQNHE) in which the shallow-circuit quantum ansatz can be further enhanced by
classical post-processing with neural networks. We show that VQNHE consistently
and significantly outperforms VQE in simulating ground-state energies of
quantum spins and molecules given the same amount of quantum resources. More
importantly, we demonstrate that for arbitrary post-processing neural
functions, VQNHE only incurs an polynomial overhead of processing time and
represents the first scalable method to exponentially accelerate VQE with
non-unitary post-processing that can be efficiently implemented in the NISQ
era.


Abstract:
  It is good practice to name test methods such that they are comprehensible to
developers; they must be written in such a way that their purpose and
functionality are clear to those who will maintain them. Unfortunately, there
is little automated support for writing or maintaining the names of test
methods. This can lead to inconsistent and low-quality test names and increase
the maintenance cost of supporting these methods. Due to this risk, it is
essential to help developers in maintaining their test method names over time.
In this paper, we use grammar patterns, and how they relate to test method
behavior, to understand test naming practices. This data will be used to
support an automated tool for maintaining test names.


Abstract:
  This paper proposes a general destriping framework using flatness
constraints, where we can handle various regularization functions in a unified
manner. Removing stripe noise, i.e., destriping, from remote sensing images is
an essential task in terms of visual quality and subsequent processing. Most of
the existing methods are designed by combining a particular image
regularization with a stripe noise characterization that cooperates with the
regularization, which precludes us to examine different regularizations to
adapt to various target images. To resolve this, we formulate the destriping
problem as a convex optimization problem involving a general form of image
regularization and the flatness constraints, a newly introduced stripe noise
characterization. This strong characterization enables us to consistently
capture the nature of stripe noise, regardless of the choice of image
regularization. For solving the optimization problem, we also develop an
efficient algorithm based on a diagonally preconditioned primal-dual splitting
algorithm (DP-PDS), which can automatically adjust the stepsizes. The
effectiveness of our framework is demonstrated through destriping experiments,
where we comprehensively compare combinations of image regularizations and
stripe noise characterizations using hyperspectral images (HSI) and infrared
(IR) videos.


Abstract:
  Biology offers many examples of large-scale, complex, concurrent systems:
many processes take place in parallel, compete on resources and influence each
other's behavior. The scalable modeling of biological systems continues to be a
very active field of research. In this paper we introduce a new approach based
on Event-B, a state-based formal method with refinement as its central
ingredient, allowing us to check for model consistency step-by-step in an
automated way. Our approach based on functions leads to an elegant and concise
modeling method. We demonstrate this approach by constructing what is, to our
knowledge, the largest ever built Event-B model, describing the ErbB signaling
pathway, a key evolutionary pathway with a significant role in development and
in many types of cancer. The Event-B model for the ErbB pathway describes 1320
molecular reactions through 242 events.


Abstract:
  Computer vision and image processing address many challenging applications.
While the last decade has seen deep neural network architectures
revolutionizing those fields, early methods relied on 'classic', i.e.,
non-learned approaches. In this study, we explore the differences between
classic and deep learning (DL) algorithms to gain new insight regarding which
is more suitable for a given application. The focus is on two challenging
ill-posed problems, namely faint edge detection and multispectral image
registration, studying recent state-of-the-art DL and classic solutions. While
those DL algorithms outperform classic methods in terms of accuracy and
development time, they tend to have higher resource requirements and are unable
to perform outside their training space. Moreover, classic algorithms are more
transparent, which facilitates their adoption for real-life applications. As
both classes of approaches have unique strengths and limitations, the choice of
a solution is clearly application dependent.


Abstract:
  Recently, flow-based methods have achieved promising success in video frame
interpolation. However, electron microscopic (EM) images suffer from unstable
image quality, low PSNR, and disorderly deformation. Existing flow-based
interpolation methods cannot precisely compute optical flow for EM images since
only predicting each position's unique offset. To overcome these problems, we
propose a novel interpolation framework for EM images that progressively
synthesizes interpolated features in a coarse-to-fine manner. First, we extract
missing intermediate features by the proposed temporal spatial-adaptive (TSA)
interpolation module. The TSA interpolation module aggregates temporal contexts
and then adaptively samples the spatial-related features with the proposed
residual spatial adaptive block. Second, we introduce a stacked deformable
refinement block (SDRB) further enhance the reconstruction quality, which is
aware of the matching positions and relevant features from input frames with
the feedback mechanism. Experimental results demonstrate the superior
performance of our approach compared to previous works, both quantitatively and
qualitatively.


Abstract:
  The underlying theme of this paper is to explore the various facets of power
systems data through the lens of graph signal processing (GSP), laying down the
foundations of the Grid-GSP framework. Grid-GSP provides an interpretation for
the spatio-temporal properties of voltage phasor measurements, by showing how
the well-known power systems modeling supports a generative low-pass graph
filter model for the state variables, namely the voltage phasors. Using the
model we formalize the empirical observation that voltage phasor measurement
data lie in a low-dimensional subspace and tie their spatio-temporal structure
to generator voltage dynamics. The Grid-GSP generative model is then
successfully employed to investigate the problems pertaining to the grid of
data sampling and interpolation, network inference, detection of anomalies and
data compression. Numerical results on a large synthetic grid that mimics the
real-grid of the state of Texas, ACTIVSg2000, and on real-world measurements
from ISO-New England verify the efficacy of applying Grid-GSP methods to
electric grid data.


Abstract:
  In this paper, we study both convergence and bounded variation properties of
a new fully discrete conservative Lagrangian--Eulerian scheme to the entropy
solution in the sense of Kruzhkov (scalar case) by using a weak asymptotic
analysis. We discuss theoretical developments on the conception of no-flow
curves for hyperbolic problems within scientific computing. The resulting
algorithms have been proven to be effective to study nonlinear wave formations
and rarefaction interactions. We present experiments to a study based on the
use of the Wasserstein distance to show the effectiveness of the no-flow curves
approach in the cases of shock interaction with an entropy wave related to the
inviscid Burgers' model problem and to a 2x2 nonlocal traffic flow symmetric
system of type Keyfitz--Kranzer.


Abstract:
  Recent developments in graph theoretic analysis of complex networks have led
to deeper understanding of brain networks. Many complex networks show similar
macroscopic behaviors despite differences in the microscopic details. Probably
two most often observed characteristics of complex networks are scale-free and
small-world properties. In this paper, we will explore whether brain networks
follow scale-free and small-worldness among other graph theory properties.


Abstract:
  Motivated by previous works on a Floquet version of the PXP model [Mukherjee
{\it et al.} Phys. Rev. B 102, 075123 (2020), Mukherjee {\it et al.} Phys. Rev.
B 101, 245107 (2020)], we study a one-dimensional spin-$1/2$ lattice model with
three-spin interactions in the same constrained Hilbert space (where all
configurations with two adjacent $S^z=\uparrow$ spins are excluded). We show
that this model possesses an extensive fragmentation of the Hilbert space which
leads to a breakdown of thermalization upon unitary evolution starting from a
large class of simple initial states. Despite the non-integrable nature of the
Hamiltonian, many of its high-energy eigenstates admit a quasiparticle
description. A class of these, which we dub as "bubble eigenstates", have
integer eigenvalues (including mid-spectrum zero modes) and strictly localized
quasiparticles while another class contains mobile quasiparticles leading to a
dispersion in momentum space. Other anomalous eigenstates that arise due to a
{\it secondary} fragmentation mechanism, including those that lead to flat
bands in momentum space due to destructive quantum interference, are also
discussed. The consequences of adding a (non-commuting) staggered magnetic
field and a PXP term respectively to this model, where the former preserves the
Hilbert space fragmentation while the latter destroys it, are discussed. A
Floquet version with time-dependent staggered field also evades thermalization
with additional features like freezing of exponentially many states at special
drive frequencies. Finally, we map the model to a $U(1)$ lattice gauge theory
coupled to dynamical fermions and discuss the interpretation of some of these
anomalous states in this language. A class of gauge-invariant states show
reduced mobility of the elementary charged excitations with only certain
charge-neutral objects being mobile suggesting a connection to fractons.


Abstract:
  The recently discovered monad, Tx = Selection (x -> r) -> r, provides an
elegant way to finnd optimal strategies in sequential games. During this
thesis, a library was developed which provides a set of useful functions using
the selection monad to compute optimal games and AIs for sequential games. In
order to explore the selection monads ability to support these AI
implementations, three example case studies were developed using Haskell: The
two-player game Connect Four, a Sudoku solver and a simplified version of
Chess. These case studies show how to elegantly implement a game AI.
Furthermore, a performance analysis of these case studies was done, identifying
the major points where performance can be increased.


Abstract:
  Amenability is a notion of facial exposedness for convex cones that is
stronger than being facially dual complete (or "nice") which is, in turn,
stronger than merely being facially exposed. Hyperbolicity cones are a family
of algebraically structured closed convex cones that contain all spectrahedra
(linear sections of positive semidefinite cones) as special cases. It is known
that all spectrahedra are amenable. We establish that all hyperbolicity cones
are amenable. As part of the argument, we show that any face of a hyperbolicity
cone is a hyperbolicity cone. As a corollary, we show that the intersection of
two hyperbolicity cones, not necessarily sharing a common relative interior
point, is a hyperbolicity cone.


Abstract:
  A two-component-two-dimensional coupled with one-component-three-dimensional
(2C2Dcw1C3D) flow may also be called a real Schur flow (RSF), as its velocity
gradient is uniformly of real Schur form, the latter being the intrinsic local
property of any general flows. The thermodynamic and `vortic' fine structures
of RSF are exposed and, in particular, the complete set of equations governing
a (viscous and/or driven) 2C2Dcw1C3D flow are derived. The Lie invariances of
the decomposed vorticity 2-forms of RSFs in $d$-dimensional Euclidean space
$\mathbb{E}^d$ for any interger $d\ge 3$ are also proven, and many
Lie-invariant fine results, such as those of the combinations of the entropic
and vortic quantities, including the invariances of the decomposed Ertel
potential vorticity (and their multiplications by any interger powers of
entropy) 3-forms, then follow.


Abstract:
  This article addresses extraction of physically meaningful information from
STEM EELS and EDX spectrum-images using methods of Multivariate Statistical
Analysis. The problem is interpreted in terms of data distribution in a
multi-dimensional factor space, which allows for a straightforward and
intuitively clear comparison of various approaches. A new computationally
efficient and robust method for finding physically meaningful endmembers in
spectrum-image datasets is presented. The method combines the geometrical
approach of Vertex Component Analysis with the statistical approach of Bayesian
inference. The algorithm is described in detail at an example of EELS
spectrum-imaging of a multi-compound CMOS transistor.


Abstract:
  Protoplanetary disks are thought to evolve viscously, where the disk mass -
the reservoir available for planet formation - decreases over time as material
is accreted onto the central star. Observations show a correlation between dust
mass and the stellar accretion rate, as expected from viscous theory. However,
the gas mass inferred from 13CO and C18O line fluxes, which should be a more
direct measure, shows no such correlation. Using thermochemical DALI models, we
investigate how 13CO and C18O J=3-2 line fluxes change over time in a viscously
evolving disk. We also investigate if the chemical conversion of CO through
grain-surface chemistry combined with viscous evolution can explain the
observations of disks in Lupus. The 13CO and C18O 3-2 line fluxes increase over
time due to their optically thick emitting regions growing in size as the disk
expands viscously. The C18O 3-2 emission is optically thin throughout the disk
for only a subset of our models (Mdisk (t = 1 Myr) < 1e-3 Msun). For these
disks the integrated C18O flux decreases with time, similar to the disk mass.
The C18O 3-2 fluxes for the bulk of the disks in Lupus (with Mdust < 5e-5 Msun)
can be reproduced to within a factor of ~2 with viscously evolving disks in
which CO is converted into other species through grain-surface chemistry driven
by a cosmic-ray ionization rate zeta_cr ~ 5e-17 - 1e-16 s^-1. However,
explaining the stacked C18O upper limits requires a lower average abundance
than our models can produce and they cannot explain the observed 13CO fluxes,
which, for most disks, are more than an order of magnitude fainter than what
our models predict. Reconciling the 13CO fluxes of viscously evolving disks
with the observations requires either a combination of efficient vertical
mixing and a high zeta_cr or low mass disks (Mdust < 3e-5 Msun) being much
thinner and/or smaller than their more massive counterparts.


Abstract:
  Electrospinning has exhibited excellent benefits to treat the trauma for
tissue engineering due to its produced micro/nano fibrous structure. It can
effectively adhere to the tissue surface for long-term continuous therapy. This
paper develops a robotic electrospinning platform for endoluminal therapy. The
platform consists of a continuum manipulator, the electrospinning device, and
the actuation unit. The continuum manipulator has two bending sections to
facilitate the steering of the tip needle for a controllable spinning
direction. Non-circular joint profile is carefully designed to enable a
constant length of the centreline of a continuum manipulator for stable fluid
transmission inside it. Experiments are performed on a bronchus phantom, and
the steering ability and bending limitation in each direction are also
investigated. The endoluminal electrospinning is also fulfilled by a trajectory
following and points targeting experiments. The effective adhesive area of the
produced fibre is also illustrated. The proposed robotic electrospinning shows
its feasibility to precisely spread more therapeutic drug to construct fibrous
structure for potential endoluminal treatment.


Abstract:
  Extended Affine (EA) equivalence is the equivalence relation between two
vectorial Boolean functions $F$ and $G$ such that there exist two affine
permutations $A$, $B$, and an affine function $C$ satisfying $G = A \circ F
\circ B + C$. While a priori simple, it is very difficult in practice to test
whether two functions are EA-equivalent. This problem has two variants:
EA-testing deals with figuring out whether the two functions can be
EA-equivalent, and EA-recovery is about recovering the tuple $(A,B,C)$ if it
exists.
  In this paper, we present a new efficient algorithm that efficiently solves
the EA-recovery problem for quadratic functions. Though its worst-case
complexity is obtained when dealing with APN functions, it supersedes all
previously known algorithms in terms of performance, even in this case. This
approach is based on the Jacobian matrix of the functions, a tool whose study
in this context can be of independent interest.
  In order to tackle EA-testing efficiently, the best approach in practice
relies on class invariants. We provide an overview of the literature on said
invariants along with a new one based on the \emph{ortho-derivative} which is
applicable to quadratic APN functions, a specific type of functions that is of
great interest, and of which tens of thousands need to be sorted into distinct
EA-classes. Our ortho-derivative-based invariant is both very fast to compute,
and highly discriminating.


Abstract:
  Cosmic shear estimation is an essential scientific goal for large galaxy
surveys. It refers to the coherent distortion of distant galaxy images due to
weak gravitational lensing along the line of sight. It can be used as a tracer
of the matter distribution in the Universe. The unbiased estimation of the
local value of the cosmic shear can be obtained via Bayesian analysis which
relies on robust estimation of the galaxies ellipticity (shape) posterior
distribution. This is not a simple problem as, among other things, the images
may be corrupted with strong background noise. For current and coming surveys,
another central issue in galaxy shape determination is the treatment of
statistically dominant overlapping (blended) objects. We propose a Bayesian
Convolutional Neural Network based on Monte-Carlo Dropout to reliably estimate
the ellipticity of galaxies and the corresponding measurement uncertainties. We
show that while a convolutional network can be trained to correctly estimate
well calibrated aleatoric uncertainty, -- the uncertainty due to the presence
of noise in the images -- it is unable to generate a trustworthy ellipticity
distribution when exposed to previously unseen data (i.e. here, blended
scenes). By introducing a Bayesian Neural Network, we show how to reliably
estimate the posterior predictive distribution of ellipticities along with
robust estimation of epistemic uncertainties. Experiments also show that
epistemic uncertainty can detect inconsistent predictions due to unknown
blended scenes.


Abstract:
  Binary stars are abundant in nearby galaxies, but are typically unaccounted
for in simulations of the high redshift Universe. Stellar population synthesis
models that include the effects of binary evolution result in greater relative
abundances of ionizing photons that could significantly affect the ambient
ionizing background during the epoch of hydrogen reionization, additionally
leading to differences in galaxy gas content and star formation. We use
hydrodynamic cosmological simulations including in situ multifrequency
radiative transfer to evaluate the effects of a high binary fraction in
reionization-era galaxies on traits of the early intergalactic medium and the
abundance of H I and He II ionizing photons. We further extend this to analyze
the traits of enriched gas. In comparing metrics generated using a fiducial
simulation assuming single stars with one incorporating a high binary fraction,
we find that binary stars cause H I reionization to complete earlier and at an
accelerated pace, while also increasing the abundances of high-ionization
metals (C IV and Si IV) in simulated absorption spectra while reducing the
abundance of low-ionization states (O I, Si II, and C II). However, through
increased photoheating of galactic and circumgalactic gas, they simultaneously
reduce the rate of star formation in low-mass galaxies, slowing the ongoing
process of enrichment and suppressing their own ionizing background. This
potentially contributes to a slower He II reionization process at $z\geq5$, and
further indicates that self-regulation of galaxies could be underestimated when
neglecting binary stellar evolution.


Abstract:
  We present a concept for a machine-learning classification of hard X-ray
(HXR) emissions from solar flares observed by the Reuven Ramaty High Energy
Solar Spectroscopic Imager (RHESSI), identifying flares that are either
occulted by the solar limb or located on the solar disk. Although HXR
observations of occulted flares are important for particle-acceleration
studies, HXR data analyses for past observations were time consuming and
required specialized expertise. Machine-learning techniques are promising for
this situation, and we constructed a sample model to demonstrate the concept
using a deep-learning technique. Input data to the model are HXR spectrograms
that are easily produced from RHESSI data. The model can detect occulted flares
without the need for image reconstruction nor for visual inspection by experts.
A technique of convolutional neural networks was used in this model by
regarding the input data as images. Our model achieved a classification
accuracy better than 90 %, and the ability for the application of the method to
either event screening or for an event alert for occulted flares was
successfully demonstrated.


Abstract:
  The mathematical similarities between non-relativistic wavefunction
propagation in quantum mechanics and image propagation in scalar diffraction
theory are used to develop a novel understanding of time and paths through
spacetime as a whole. It is well known that Feynman's original derivation of
the path integral formulation of non-relativistic quantum mechanics uses
time-slicing to calculate amplitudes as sums over all possible paths through
space, but along a definite curve through time. Here, a 3+1D spacetime wave
distribution and its 4-momentum dual are formally developed which have no
external time parameter and therefore cannot change or evolve in the usual
sense. Time is thus seen "from the outside". A given 3+1D momentum
representation of a system encodes complete dynamical information, describing
the system's spacetime behavior as a whole. A comparison is made to the
mathematics of holograms, and properties of motion for simple systems are
derived.


Abstract:
  Existing open-source modeling frameworks dedicated to energy systems
optimization typically utilize (mixed-integer) linear programming ((MI)LP)
formulations, which lack modeling freedom for technical system design and
operation. We present COMANDO, an open-source Python package for
component-oriented modeling and optimization for nonlinear design and operation
of integrated energy systems. COMANDO allows to assemble system models from
component models including nonlinear, dynamic and discrete characteristics.
Based on a single system model, different deterministic and stochastic problem
formulations can be obtained by varying objective function and underlying data,
and by applying automatic or manual reformulations. The flexible open-source
implementation allows for the integration of customized routines required to
solve challenging problems, e.g., initialization, problem decomposition, or
sequential solution strategies. We demonstrate features of COMANDO via case
studies, including automated linearization, dynamic optimization, stochastic
programming, and the use of nonlinear artificial neural networks as surrogate
models in a reduced-space formulation for deterministic global optimization.


Abstract:
  The emerging large-scale and data-hungry algorithms require the computations
to be delegated from a central server to several worker nodes. One major
challenge in the distributed computations is to tackle delays and failures
caused by the stragglers. To address this challenge, introducing efficient
amount of redundant computations via distributed coded computation has received
significant attention. Recent approaches in this area have mainly focused on
introducing minimum computational redundancies to tolerate certain number of
stragglers. To the best of our knowledge, the current literature lacks a
unified end-to-end design in a heterogeneous setting where the workers can vary
in their computation and communication capabilities. The contribution of this
paper is to devise a novel framework for joint scheduling-coding, in a setting
where the workers and the arrival of stream computational jobs are based on
stochastic models. In our initial joint scheme, we propose a systematic
framework that illustrates how to select a set of workers and how to split the
computational load among the selected workers based on their differences in
order to minimize the average in-order job execution delay. Through
simulations, we demonstrate that the performance of our framework is
dramatically better than the performance of naive method that splits the
computational load uniformly among the workers, and it is close to the ideal
performance.


Abstract:
  We study the realisation of higher-form symmetries in the holographic dual of
gauge theories coupled to probe matter in the fundamental. We particularly
focus on the dual of U(N) gauge theory coupled to fundamental matter. We
demonstrate the existence of a continuous 1-form symmetry associated with the
conservation of magnetic flux and show that this symmetry is spontaneously
broken in the IR when the flavour degrees of freedom are gapped. We numerically
compute the spectral function of the 2-form current and demonstrate the
existence of the associated Goldstone mode. We compare to expectations at
weak-coupling.


Abstract:
  We consider the motion of an electron in an atom subjected to a strong
linearly polarized laser field. We identify the invariant structures organizing
a very specific subset of trajectories, namely recollisions. Recollisions are
trajectories which first escape the ionic core (i.e., ionize) and later return
to this ionic core, for instance, to transfer the energy gained during the
large excursion away from the core to bound electrons. We consider the role
played by the directions transverse to the polarization direction in the
recollision process. We compute the family of two-dimensional invariant tori
associated with a specific hyperbolic-elliptic periodic orbit and their stable
and unstable manifolds. We show that these manifolds organize recollisions in
phase space.


Abstract:
  The Operating Room Scheduling (ORS) problem is the task of assigning patients
to operating rooms, taking into account different specialties, lengths and
priority scores of each planned surgery, operating room session durations, and
the availability of beds for the entire length of stay both in the Intensive
Care Unit and in the wards. A proper solution to the ORS problem is of primary
importance for the healthcare service quality and the satisfaction of patients
in hospital environments. In this paper we first present a solution to the
problem based on Answer Set Programming (ASP). The solution is tested on
benchmarks with realistic sizes and parameters, on three scenarios for the
target length on 5-day scheduling, common in small-medium sized hospitals, and
results show that ASP is a suitable solving methodology for the ORS problem in
such setting. Then, we also performed a scalability analysis on the schedule
length up to 15 days, which still shows the suitability of our solution also on
longer plan horizons. Moreover, we also present an ASP solution for the
rescheduling problem, i.e. when the off-line schedule cannot be completed for
some reason. Finally, we introduce a web framework for managing ORS problems
via ASP that allows a user to insert the main parameters of the problem, solve
a specific instance, and show results graphically in real-time. Under
consideration in Theory and Practice of Logic Programming (TPLP).


Abstract:
  We report on the first results of the Noble and Alkali Spin Detectors for
Ultralight Coherent darK matter (NASDUCK) collaboration. We search for the
interactions of Axion-Like Particles (ALPs) with atomic spins using an
earth-based precision quantum detector as it traverses through the galactic
dark matter halo. The detector is composed of spin-polarized xenon gas which
can coherently interact with a background ALP dark matter field and an in-situ
rubidium Floquet optical-magnetometer. Conducting a five months-long search, we
derive new constraints on ALP-proton and ALP-neutron interactions in the
$4\times 10^{-15}-4\times 10^{-12}{~\rm eV/c^2}$ mass range. Our limits on the
ALP-proton (ALP-neutron) couplings improve upon previous terrestrial bounds by
up to 3 orders of magnitude for masses above $4\times 10^{-14}{~\rm eV/c^2}$
($4\times 10^{-13}{~\rm eV/c^2}$). Moreover, barring the uncertain supernova
constraints, the ALP-proton bound improves on all existing terrestrial and
astrophysical limits, partially closing the unexplored region for couplings in
the range $10^{-6}~{\rm GeV^{-1}}$ to $2\times 10^{-5}~{\rm GeV^{-1}}$.
Finally, we also cast bounds on pseudo-scalar dark matter models in which dark
matter is quadratically-coupled to the nucleons.


Abstract:
  In this paper, we investigate properties of orbits of Hermann actions as
submanifolds without assuming the commutability of involutions which define
Hermann actions. In particular, we compute the second fundamental form of
orbits of Hermann action, and give a sufficient condition for orbits of Hermann
action to be weakly reflective (resp. arid) submanifolds.


Abstract:
  In this paper, we explore the impact of a galactic bar on the inspiral
time-scale of a massive perturber (MP) within a Milky Way-like galaxy. We
integrate the orbit of MPs in a multi-component galaxy model via a
semi-analytical approach including an accurate treatment for dynamical friction
generalized to rotationally supported backgrounds. We compare the MP evolution
in a galaxy featuring a Milky Way-like rotating bar to the evolution within an
analogous axisymmetric galaxy without the bar. We find that the bar presence
may significantly affect the inspiral, sometimes making it shorter by a factor
of a few, sometimes hindering it for a Hubble time, implying that dynamical
friction alone is greatly insufficient to fully characterize the orbital decay.
The effect of the bar is more prominent for initially in-plane, prograde MPs,
especially those crossing the bar co-rotation radius or outer Lindblad
resonance. In the barred galaxy, we find the sinking of the most massive MPs
(>~10^7.5 Msun) approaching the galaxy from large separations (>~8 kpc) to be
most efficiently hampered. Neglecting the effect of global torques associated
to the non-symmetric mass distribution is thus not advisable even within our
idealized, smooth Milky Way model, and it should be avoided when dealing with
more complex and realistic galaxy systems. This has important implications for
the orbital decay of massive black holes in late-type spirals, the natural
candidate sources to be detected with the Laser Interferometer Space Antenna
(LISA).


Abstract:
  We present a combined theoretical and experimental study of X-ray optical
wave mixing. This class of nonlinear phenomena combines the strengths of
spectroscopic techniques from the optical domain, with the high-resolution
capabilities of X-rays. In particular, the spectroscopic sensitivity of these
phenomena can be exploited to selectively probe valence dynamics. Specifically,
we focus on the effect of X-ray parametric down-conversion. We present a
theoretical description of the process, from which we deduce the observable
nonlinear response of valence charges. Subsequently, we simulate scattering
patterns for realistic conditions and identify characteristic signatures of the
nonlinear conversion. For the observation of this signature, we present a
dedicated experimental setup and results of a detailed investigation. However,
we do not find evidence of the nonlinear effect. This finding stands in strong
contradiction to previous claims of proof-of-principle demonstrations.
Nevertheless, we are optimistic to employ related X-ray optical wave mixing
processes on the basis of the methods presented here for probing valence
dynamics in the future.


Abstract:
  Estimation of prevalence of undocumented SARS-CoV-2 infections is critical
for understanding the overall impact of the Covid-19 disease. In fact,
unveiling uncounted cases has fundamental implications for public policy
interventions strategies. In the present work, we show a basic yet effective
approach to estimate the actual number of people infected by Sars-Cov-2, by
using epidemiological raw data reported by official health institutions in the
largest EU countries and USA.


Abstract:
  We discuss the rejection-free event-chain Monte-Carlo algorithm and several
applications to dense soft matter systems. Event-chain Monte-Carlo is an
alternative to standard local Markov-chain Monte-Carlo schemes, which are based
on detailed balance, for example the well-known Metropolis-Hastings algorithm.
Event-chain Monte-Carlo is a Markov chain Monte-Carlo scheme that uses
so-called lifting moves to achieve global balance without rejections (maximal
global balance). It has been originally developed for hard sphere systems but
is applicable to many soft matter systems and particularly suited for dense
soft matter systems with hard core interactions, where it gives significant
performance gains compared to a local Monte-Carlo simulation. The algorithm can
be generalized to deal with soft interactions and with three-particle
interactions, as they naturally arise, for example, in bead-spring models of
polymers with bending rigidity. We present results for polymer melts, where the
event-chain algorithm can be used for an efficient initialization. We then move
on to large systems of semiflexible polymers that form bundles by attractive
interactions and can serve as model systems for actin filaments in the
cytoskeleton. The event chain algorithm shows that these systems form networks
of bundles which coarsen similar to a foam. Finally, we present results on
liquid crystal systems, where the event-chain algorithm can equilibrate large
systems containing additional colloidal disks very efficiently, which reveals
the parallel chaining of disks.