Abstract:
  We use Feireisl-Lions theory to deduce the existence of weak solutions to a
system describing the dynamics of a linear oscillator containing a Newtonian
compressible fluid. The appropriate Navier-Stokes equation is considered on a
domain whose movement has one degree of freedom. The equation is paired with
the Newton law and we assume a no-slip boundary condition.


Abstract:
  Wavelength transduction of single-photon signals is indispensable to
networked quantum applications, particularly those incorporating quantum
memories. Lithium niobate nanophotonic devices have demonstrated favorable
linear, nonlinear, and electro-optical properties to deliver this crucial
function while offering superiror efficiency, integrability, and scalability.
Yet, their quantum noise level--an crucial metric for any single-photon based
application--has yet to be understood. In this work, we report the first study
with the focus on telecom to near-visible conversion driven by a telecom pump
of small detuning, for practical considerations in distributed quantum
processing over fiber networks. Our results find the noise level to be on the
order of $10^{-4}$ photons per time-frequency mode for high conversion,
allowing faithful pulsed operations. Through carefully analyzing the origins of
such noise and each's dependence on the pump power and wavelength detuning, we
have also identified a formula for noise suppression to $10^{-5}$ photons per
mode. Our results assert a viable, low-cost, and modular approach to networked
quantum processing and beyond using lithium niobate nanophotonics.


Abstract:
  Walking while using a smartphone is becoming a major pedestrian safety
concern as people may unknowingly bump into various obstacles that could lead
to severe injuries. In this paper, we propose ObstacleWatch, an acoustic-based
obstacle collision detection system to improve the safety of pedestrians who
are engaged in smartphone usage while walking. ObstacleWatch leverages the
advanced audio hardware of the smartphone to sense the surrounding obstacles
and infers fine-grained information about the frontal obstacle for collision
detection. In particular, our system emits well-designed inaudible beep signals
from the smartphone built-in speaker and listens to the reflections with the
stereo recording of the smartphone. By analyzing the reflected signals received
at two microphones, ObstacleWatch is able to extract fine-grained information
of the frontal obstacle including the distance, angle, and size for detecting
the possible collisions and to alert users. Our experimental evaluation under
two real-world environments with different types of phones and obstacles shows
that ObstacleWatch achieves over 92% accuracy in predicting obstacle collisions
with distance estimation errors at about 2 cm. Results also show that
ObstacleWatch is robust to different sizes of objects and is compatible with
different phone models with low energy consumption.


Abstract:
  In this work we look into adding a new language to a multilingual NMT system
in an unsupervised fashion. Under the utilization of pre-trained cross-lingual
word embeddings we seek to exploit a language independent multilingual sentence
representation to easily generalize to a new language. While using
cross-lingual embeddings for word lookup we decode from a yet entirely unseen
source language in a process we call blind decoding. Blindly decoding from
Portuguese using a basesystem containing several Romance languages we achieve
scores of 36.4 BLEU for Portuguese-English and 12.8 BLEU for Russian-English.
In an attempt to train the mapping from the encoder sentence representation to
a new target language we use our model as an autoencoder. Merely training to
translate from Portuguese to Portuguese while freezing the encoder we achieve
26 BLEU on English-Portuguese, and up to 28 BLEU when adding artificial noise
to the input. Lastly we explore a more practical adaptation approach through
non-iterative backtranslation, exploiting our model's ability to produce high
quality translations through blind decoding. This yields us up to 34.6 BLEU on
English-Portuguese, attaining near parity with a model adapted on real
bilingual data.


Abstract:
  We report here on experiments and simulations examining the effect of
changing wall friction on the gravity-driven flow of spherical particles in a
vertical hopper. In 2D experiments and simulations, we observe that the
exponent of the expected power-law scaling of mass flow rate with opening size
(known as Beverloo's law) decreases as the coefficient of friction between
particles and wall increases, whereas Beverloo scaling works as expected in 3D.
In our 2D experiments, we find that wall friction plays the biggest role in a
region near the outlet comparable in height to the largest opening size.
However, wall friction is not the only factor determining a constant rate of
flow, as we observe a near-constant mass outflow rate in the 2D simulations
even when wall friction is set to zero. We show in our simulations that an
increase in wall friction leaves packing fractions relatively unchanged, while
average particle velocities become independent of opening size as the
coefficient of friction increases. We track the spatial pattern of
time-averaged particle velocities and accelerations inside the hopper. We
observe that the hemisphere-like region above the opening where particles begin
to accelerate is largely independent of opening size at finite wall friction.
However, the magnitude of particle accelerations decreases significantly as
wall friction increases, which in turn results in mean sphere velocities that
no longer scale with opening size, consistent with our observations of mass
flow rate scaling. The case of zero wall friction is anomalous, in that most of
the acceleration takes place near the outlet.


Abstract:
  The analytical and numerical analysis of the dynamics of charged particles in
the field of an intensive transverse electromagnetic wave in a vacuum presented
in the article. Identifies the conditions for resonant acceleration of
particles. These conditions are formulated. The features and the mechanism of
this acceleration are discussed.


Abstract:
  The outbreak of COVID-19 led to a record-breaking race to develop a vaccine.
However, the limited vaccine capacity creates another massive challenge: how to
distribute vaccines to mitigate the near-end impact of the pandemic? In the
United States in particular, the new Biden administration is launching mass
vaccination sites across the country, raising the obvious question of where to
locate these clinics to maximize the effectiveness of the vaccination campaign.
This paper tackles this question with a novel data-driven approach to optimize
COVID-19 vaccine distribution. We first augment a state-of-the-art
epidemiological model, called DELPHI, to capture the effects of vaccinations
and the variability in mortality rates across age groups. We then integrate
this predictive model into a prescriptive model to optimize the location of
vaccination sites and subsequent vaccine allocation. The model is formulated as
a bilinear, non-convex optimization model. To solve it, we propose a coordinate
descent algorithm that iterates between optimizing vaccine distribution and
simulating the dynamics of the pandemic. As compared to benchmarks based on
demographic and epidemiological information, the proposed optimization approach
increases the effectiveness of the vaccination campaign by an estimated $20\%$,
saving an extra $4000$ extra lives in the United States over a three-month
period. The proposed solution achieves critical fairness objectives -- by
reducing the death toll of the pandemic in several states without hurting
others -- and is highly robust to uncertainties and forecast errors -- by
achieving similar benefits under a vast range of perturbations.


Abstract:
  In this paper, we present the use of Model Predictive Control (MPC) based on
Reinforcement Learning (RL) to find the optimal policy for a multi-agent
battery storage system. A time-varying prediction of the power price and
production-demand uncertainty are considered. We focus on optimizing an
economic objective cost while avoiding very low or very high state of charge,
which can damage the battery. We consider the bounded power provided by the
main grid and the constraints on the power input and state of each agent. A
parametrized MPC-scheme is used as a function approximator for the
deterministic policy gradient method and RL optimizes the closed-loop
performance by updating the parameters. Simulation results demonstrate that the
proposed method is able to tackle the constraints and deliver the optimal
policy.


Abstract:
  The mass contained in an arbitrary spacetime in general relativity is not
well defined. However, for asymptotically flat spacetimes various definitions
of mass have been proposed. In this paper I consider eight masses and show that
some of them correspond to the active gravitational mass while the others
correspond to the inertial mass. For example, the ADM mass corresponds to the
inertial mass while the M$\o$ller mass corresponds to the active gravitational
mass. In general the inertial and active gravitational masses are not equal. If
the spacetime is vacuum at large $r$ the Einstein equations force the inertial
and active gravitational masses to be the same. The Einstein equations also
force the masses to be the same if any matter that extends out to large $r$
satisfies the weak, strong or dominant energy condition. I also examine the
contributions of the inertial and active gravitational masses to the
gravitational redshift, the deflection of light, the Shapiro time delay, the
precession of perihelia and to the motion of test bodies in the spacetime.


Abstract:
  We test the adequacy of ultraviolet (UV) spectra for characterizing the outer
structure of Type Ia supernova (SN) ejecta. For this purpose, we perform
spectroscopic analysis for ASASSN-14lp, a normal SN Ia showing low continuum in
the mid-UV regime. To explain the strong UV suppression, two possible origins
have been investigated by mapping the chemical profiles over a significant part
of their ejecta. We fit the spectral time series with mid-UV coverage obtained
before and around maximum light by HST, supplemented with ground-based optical
observations for the earliest epochs. The synthetic spectra are calculated with
the one dimensional MC radiative-transfer code TARDIS from self-consistent
ejecta models. Among several physical parameters, we constrain the abundance
profiles of nine chemical elements. We find that a distribution of $^{56}$Ni
(and other iron-group elements) that extends toward the highest velocities
reproduces the observed UV flux well. The presence of radioactive material in
the outer layers of the ejecta, if confirmed, implies strong constraints on the
possible explosion scenarios. We investigate the impact of the inferred
$^{56}$Ni distribution on the early light curves with the radiative transfer
code TURTLS, and confront the results with the observed light curves of
ASASSN-14lp. The inferred abundances are not in conflict with the observed
photometry. We also test whether the UV suppression can be reproduced if the
radiation at the photosphere is significantly lower in the UV regime than the
pure Planck function. In this case, solar metallicity might be sufficient
enough at the highest velocities to reproduce the UV suppression.


Abstract:
  Medical image registration and segmentation are two of the most frequent
tasks in medical image analysis. As these tasks are complementary and
correlated, it would be beneficial to apply them simultaneously in a joint
manner. In this paper, we formulate registration and segmentation as a joint
problem via a Multi-Task Learning (MTL) setting, allowing these tasks to
leverage their strengths and mitigate their weaknesses through the sharing of
beneficial information. We propose to merge these tasks not only on the loss
level, but on the architectural level as well. We studied this approach in the
context of adaptive image-guided radiotherapy for prostate cancer, where
planning and follow-up CT images as well as their corresponding contours are
available for training. The study involves two datasets from different
manufacturers and institutes. The first dataset was divided into training (12
patients) and validation (6 patients), and was used to optimize and validate
the methodology, while the second dataset (14 patients) was used as an
independent test set. We carried out an extensive quantitative comparison
between the quality of the automatically generated contours from different
network architectures as well as loss weighting methods. Moreover, we evaluated
the quality of the generated deformation vector field (DVF). We show that MTL
algorithms outperform their Single-Task Learning (STL) counterparts and achieve
better generalization on the independent test set. The best algorithm achieved
a mean surface distance of $1.06 \pm 0.3$ mm, $1.27 \pm 0.4$ mm, $0.91 \pm 0.4$
mm, and $1.76 \pm 0.8$ mm on the validation set for the prostate, seminal
vesicles, bladder, and rectum, respectively. The high accuracy of the proposed
method combined with the fast inference speed, makes it a promising method for
automatic re-contouring of follow-up scans for adaptive radiotherapy.


Abstract:
  Transductive zero-shot learning (T-ZSL) which could alleviate the domain
shift problem in existing ZSL works, has received much attention recently.
However, an open problem in T-ZSL: how to effectively make use of unseen-class
samples for training, still remains. Addressing this problem, we first
empirically analyze the roles of unseen-class samples with different degrees of
hardness in the training process based on the uneven prediction phenomenon
found in many ZSL methods, resulting in three observations. Then, we propose
two hardness sampling approaches for selecting a subset of diverse and hard
samples from a given unseen-class dataset according to these observations. The
first one identifies the samples based on the class-level frequency of the
model predictions while the second enhances the former by normalizing the class
frequency via an approximate class prior estimated by an explored prior
estimation algorithm. Finally, we design a new Self-Training framework with
Hardness Sampling for T-ZSL, called STHS, where an arbitrary inductive ZSL
method could be seamlessly embedded and it is iteratively trained with
unseen-class samples selected by the hardness sampling approach. We introduce
two typical ZSL methods into the STHS framework and extensive experiments
demonstrate that the derived T-ZSL methods outperform many state-of-the-art
methods on three public benchmarks. Besides, we note that the unseen-class
dataset is separately used for training in some existing transductive
generalized ZSL (T-GZSL) methods, which is not strict for a GZSL task. Hence,
we suggest a more strict T-GZSL data setting and establish a competitive
baseline on this setting by introducing the proposed STHS framework to T-GZSL.


Abstract:
  Let $(\xi_k,\eta_k)_{k\in\mathbb{N}}$ be independent identically distributed
random vectors with arbitrarily dependent positive components. We call a
(globally) perturbed random walk a random sequence $T:=(T_k)_{k\in\mathbb{N}}$
defined by $T_k:=\xi_1+\ldots+\xi_{k-1}+\eta_k$ for $k\in\mathbb{N}$. Consider
a general branching process generated by $T$ and denote by $N_j(t)$ the number
of the $j$th generation individuals with birth times $\leq t$. We treat early
generations, that is, fixed generations $j$ which do not depend on $t$. In this
setting we prove counterparts for $\mathbb{E}N_j$ of the Blackwell theorem and
the key renewal theorem, prove a strong law of large numbers for $N_j$, find
the first-order asymptotics for the variance of $N_j$. Also, we prove a
functional limit theorem for the vector-valued process $(N_1(ut),\ldots,
N_j(ut))_{u\geq 0}$, properly normalized and centered, as $t\to\infty$. The
limit is a vector-valued Gaussian process whose components are integrated
Brownian motions.


Abstract:
  The augmented Lagrangian method (ALM) is one of the most useful methods for
constrained optimization. Its convergence has been well established under
convexity assumptions or smoothness assumptions, or under both assumptions. ALM
may experience oscillations and divergence when the underlying problem is
simultaneously nonconvex and nonsmooth. In this paper, we consider the linearly
constrained problem with a nonconvex (in particular, weakly convex) and
nonsmooth objective. We modify ALM to use a Moreau envelope of the augmented
Lagrangian and establish its convergence under conditions that are weaker than
those in the literature. We call it the Moreau envelope augmented Lagrangian
(MEAL) method. We also show that the iteration complexity of MEAL is
$o(\varepsilon^{-2})$ to yield an $\varepsilon$-accurate first-order stationary
point. We establish its whole sequence convergence (regardless of the initial
guess) and a rate when a Kurdyka-Lojasiewicz property is assumed. Moreover,
when the subproblem of MEAL has no closed-form solution and is difficult to
solve, we propose two practical variants of MEAL, an inexact version called
iMEAL with an approximate proximal update, and a linearized version called
LiMEAL for the constrained problem with a composite objective. Their
convergence is also established.


Abstract:
  In this effort, a novel operator theoretic framework is developed for
data-driven solution of optimal control problems. The developed methods focus
on the use of trajectories (i.e., time-series) as the fundamental unit of data
for the resolution of optimal control problems in dynamical systems. Trajectory
information in the dynamical systems is embedded in a reproducing kernel
Hilbert space (RKHS) through what are called occupation kernels. The occupation
kernels are tied to the dynamics of the system through the densely defined
Liouville operator. The pairing of Liouville operators and occupation kernels
allows for lifting of nonlinear finite-dimensional optimal control problems
into the space of infinite-dimensional linear programs over RKHSs.


Abstract:
  The fifth generation (5G) wireless technology is primarily designed to
address a wide range of use cases categorized into the enhanced mobile
broadband (eMBB), ultra-reliable and low latency communication (URLLC), and
massive machine-type communication (mMTC). Nevertheless, there are a few other
use cases which are in-between these main use cases such as industrial wireless
sensor networks, video surveillance, or wearables. In order to efficiently
serve such use cases, in Release 17, the 3rd generation partnership project
(3GPP) introduced the reduced capability NR devices (NR-RedCap) with lower cost
and complexity, smaller form factor and longer battery life compared to regular
NR devices. However, one key potential consequence of device cost and
complexity reduction is the coverage loss. In this paper, we provide a
comprehensive evaluation of NR RedCap coverage for different physical channels
and initial access messages to identify the channels/messages that are
potentially coverage limiting for RedCap UEs. We perform the coverage
evaluations for RedCap UEs operating in three different scenarios, namely
Rural, Urban and Indoor with carrier frequencies 700 MHz, 2.6 GHz and 28 GHz,
respectively. Our results confirm that for all the considered scenarios, the
amounts of required coverage recovery for RedCap channels are either less than
1 dB or can be compensated by considering smaller data rate targets for RedCap
use cases.


Abstract:
  Using a extended version of the Quantum Hadrodynamics (QHD), I propose a new
microscopic equation of state (EoS) able to correctly reproduce the main
properties of symmetric nuclear matter at the saturation density, as well
produce massive neutron stars and satisfactory results for the radius and the
tidal parameter $\Lambda$. I show that even when hyperons are present, this EoS
is able to reproduce at least 2.00 solar masses neutron star. The constraints
about the radius of a $2.00M_\odot$ and the minimum mass that enable direct
Urca effect are also checked.


Abstract:
  This paper is concerned with high moment and pathwise error estimates for
both velocity and pressure approximations of the Euler-Maruyama scheme for time
discretization and its two fully discrete mixed finite element discretizations.
The main idea for deriving the high moment error estimates for the velocity
approximation is to use a bootstrap technique starting from the second moment
error estimate. The pathwise error estimate, which is sub-optimal in the energy
norm, is obtained by using Kolmogorov's theorem based on the high moment error
estimates. Unlike for the velocity error estimate, the higher moment and
pathwise error estimates for the pressure approximation are derived in a
time-averaged norm. In addition, the impact of noise types on the rates of
convergence for both velocity and pressure approximations is also addressed.


Abstract:
  Machine Learning (ML) models are known to be vulnerable to adversarial inputs
and researchers have demonstrated that even production systems, such as
self-driving cars and ML-as-a-service offerings, are susceptible. These systems
represent a target for bad actors. Their disruption can cause real physical and
economic harm. When attacks on production ML systems occur, the ability to
attribute the attack to the responsible threat group is a critical step in
formulating a response and holding the attackers accountable. We pose the
following question: can adversarially perturbed inputs be attributed to the
particular methods used to generate the attack? In other words, is there a way
to find a signal in these attacks that exposes the attack algorithm, model
architecture, or hyperparameters used in the attack? We introduce the concept
of adversarial attack attribution and create a simple supervised learning
experimental framework to examine the feasibility of discovering attributable
signals in adversarial attacks. We find that it is possible to differentiate
attacks generated with different attack algorithms, models, and hyperparameters
on both the CIFAR-10 and MNIST datasets.


Abstract:
  In steady-state, the plasma loss rate to the chamber wall is balanced by the
ionization rate in the hot-filament discharges. This balance in the loss rate
and ionization rate maintains the quasi--neutrality of the bulk plasma. In this
report, we have studied the properties of bulk plasma in the presence of an
auxiliary (additional) biased metal disk, which is working as a sink, in
low-pressure helium plasma. A single Langmuir probe and emissive probe are used
to characterize the plasma for various biases (positive and negative) to the
metal disk, which was placed along the discharge axis inside the plasma. It is
observed that only a positively biased disk increases the plasma potential,
electron temperature, and plasma density. Moreover, the plasma parameters
remain unaltered when the disk is negatively biased. The observed results for
two different-sized positively metal disks are compared with an available
theoretical model and found an opposite behavior of plasma density variation
with the disk bias voltages at given discharge condition. The role of the
primary energetic electron population in determining the plasma parameters is
discussed. These experimental results are qualitatively explained on the basis
of electrostatic confinement arising due to the loss of electrons to a biased
metal disk electrode.


Abstract:
  Language models are the foundation of current neural network-based models for
natural language understanding and generation. However, research on the
intrinsic performance of language models on African languages has been
extremely limited, which is made more challenging by the lack of large or
standardised training and evaluation sets that exist for English and other
high-resource languages. In this paper, we evaluate the performance of
open-vocabulary language models on low-resource South African languages, using
byte-pair encoding to handle the rich morphology of these languages. We
evaluate different variants of n-gram models, feedforward neural networks,
recurrent neural networks (RNNs), and Transformers on small-scale datasets.
Overall, well-regularized RNNs give the best performance across two isiZulu and
one Sepedi datasets. Multilingual training further improves performance on
these datasets. We hope that this research will open new avenues for research
into multilingual and low-resource language modelling for African languages.


Abstract:
  We argue that a superconducting state with a Fermi-surface of Bogoliubov
quasiparticles, a Bogoliubov Fermi-surface (BG-FS), can be identified by the
dependence of physical quantities on disorder. In particular, we show that a
linear dependence of the residual density of states at weak disorder
distinguishes a BG-FS state from other nodal superconducting states. We further
demonstrate the stability of supercurrent against impurities and a
characteristic Drude-like behavior of the optical conductivity. Our results can
be directly applied to electron irradiation experiments on candidate materials
of BG-FSs, including Sr$_2$RuO$_4$, FeSe$_{1-x}$S$_x$, and UBe$_{13}$.


Abstract:
  We study the effects of stochastic resetting on geometric Brownian motion
(GBM), a canonical stochastic multiplicative process for non-stationary and
non-ergodic dynamics. Resetting is a sudden interruption of a process, which
consecutively renews its dynamics. We show that, although resetting renders GBM
stationary, the resulting process remains non-ergodic. Quite surprisingly, the
effect of resetting is pivotal in manifesting the non-ergodic behavior. In
particular, we observe three different long-time regimes: a quenched state, an
unstable and a stable annealed state depending on the resetting strength.
Notably, in the last regime, the system is self-averaging and thus the sample
average will always mimic ergodic behavior establishing a stand alone feature
for GBM under resetting. Crucially, the above-mentioned regimes are well
separated by a self-averaging time period which can be minimized by an optimal
resetting rate. Our results can be useful to interpret data emanating from
stock market collapse or reconstitution of investment portfolios.


Abstract:
  Markov Decision Processes are classically solved using Value Iteration and
Policy Iteration algorithms. Recent interest in Reinforcement Learning has
motivated the study of methods inspired by optimization, such as gradient
ascent. Among these, a popular algorithm is the Natural Policy Gradient, which
is a mirror descent variant for MDPs. This algorithm forms the basis of several
popular Reinforcement Learning algorithms such as Natural actor-critic, TRPO,
PPO, etc, and so is being studied with growing interest. It has been shown that
Natural Policy Gradient with constant step size converges with a sublinear rate
of O(1/k) to the global optimal. In this paper, we present improved finite time
convergence bounds, and show that this algorithm has geometric (also known as
linear) asymptotic convergence rate. We further improve this convergence result
by introducing a variant of Natural Policy Gradient with adaptive step sizes.
Finally, we compare different variants of policy gradient methods
experimentally.


Abstract:
  In 2017 Skabelund constructed two new examples of maximal curves
$\tilde{\mathcal{S}}_q$ and $\tilde{\mathcal{R}}_q$ as covers of the Suzuki and
Ree curves, respectively. The resulting Skabelund curves are analogous to the
Giulietti-Korchm\'aros cover of the Hermitian curve. In this paper a complete
characterization of all Galois subcovers of the Skabelund curves
$\tilde{\mathcal{S}}_q$ and $\tilde{\mathcal{R}}_q$ is given. Calculating the
genera of the corresponding curves, we find new additions to the list of known
genera of maximal curves over finite fields.


Abstract:
  Efficient and precise prediction of plasticity by data-driven models relies
on appropriate data preparation and a well-designed model. Here we introduce an
unsupervised machine learning-based data preparation method to maximize the
trainability of crystal orientation evolution data during deformation. For
Taylor model crystal plasticity data, the preconditioning procedure improves
the test score of an artificial neural network from 0.831 to 0.999, while
decreasing the training iterations by an order of magnitude. The efficacy of
the approach was further improved with a recurrent neural network. Electron
backscattered (EBSD) lab measurements of crystal rotation during rolling were
compared with the results of the surrogate model, and despite error introduced
by Taylor model simplifying assumptions, very reasonable agreement between the
surrogate model and experiment was observed. Our method is foundational for
further data-driven studies, enabling the efficient and precise prediction of
texture evolution from experimental and simulated crystal plasticity results.


Abstract:
  Cyber threat and attack intelligence information are available in
non-standard format from heterogeneous sources. Comprehending them and
utilizing them for threat intelligence extraction requires engaging security
experts. Knowledge graphs enable converting this unstructured information from
heterogeneous sources into a structured representation of data and factual
knowledge for several downstream tasks such as predicting missing information
and future threat trends. Existing large-scale knowledge graphs mainly focus on
general classes of entities and relationships between them. Open-source
knowledge graphs for the security domain do not exist. To fill this gap, we've
built \textsf{TINKER} - a knowledge graph for threat intelligence
(\textbf{T}hreat \textbf{IN}telligence \textbf{K}nowl\textbf{E}dge
g\textbf{R}aph). \textsf{TINKER} is generated using RDF triples describing
entities and relations from tokenized unstructured natural language text from
83 threat reports published between 2006-2021. We built \textsf{TINKER} using
classes and properties defined by open-source malware ontology and using
hand-annotated RDF triples. We also discuss ongoing research and challenges
faced while creating \textsf{TINKER}.


Abstract:
  We generalize standard credal set models for imprecise probabilities to
include higher order credal sets -- confidences about confidences. In doing so,
we specify how an agent's higher order confidences (credal sets) update upon
observing an event. Our model begins to address standard issues with imprecise
probability models, like Dilation and Belief Inertia. We conjecture that when
higher order credal sets contain all possible probability functions, then in
the limiting case the highest order confidences converge to form a uniform
distribution over the first order credal set, where we define uniformity in
terms of the statistical distance metric (total variation distance). Finite
simulation supports the conjecture. We further suggest that this convergence
presents the total-variation-uniform distribution as a natural, privileged
prior for statistical hypothesis testing.


Abstract:
  We consider the inverse Seesaw scenario for neutrino masses with the
approximate Lepton number symmetry broken dynamically by a scalar with Lepton
number two. We show that the Majoron associated to the spontaneous symmetry
breaking can alleviate the Hubble tension through its contribution to $\Delta
N_\text{eff}$ and late decays to neutrinos. Among the additional fermionic
states required for realizing the inverse Seesaw mechanism, sterile neutrinos
at the keV-MeV scale can account for all the dark matter component of the
Universe if produced via freeze-in from the decays of heavier degrees of
freedom.


Abstract:
  In any type II superstring background, the supergravity vertex operators in
the pure spinor formalism are described by a gauge superfield. In this paper,
we obtain for the first time an explicit expression for this superfield in an
$AdS_5 \times S^5$ background. Previously, the vertex operators were only known
close to the boundary of $AdS_5$ or in the minus eight picture. Our strategy
for the computation was to apply eight picture raising operators in the minus
eight picture vertices. In the process, a huge number of terms are generated
and we have developed numerical techniques to perform intermediary
simplifications. Alternatively, the same numerical techniques can be used to
compute the vertices directly in the zero picture by constructing a basis of
invariants and fitting for the coefficients. One motivation for constructing
the vertex operators is the computation of $AdS_5 \times S^5$ string
amplitudes.


Abstract:
  In this article we investigate the fibers of relative $D$-modules. In general
we prove that there exists an open, Zariski dense subset of the vanishing set
of the annihilator over which the fibers of a cyclic relative $D$-module are
non-zero. Next we restrict our attention to relatively holonomic $D$-modules.
For this class we prove that the fiber over every point in the vanishing set of
the annihilator is non-zero. As a consequence we obtain new proofs of a
conjecture of Budur which was recently proven by Budur, van der Veer, Wu and
Zhou, as well as a new proof of a theorem of Maisonobe. Moreover, we also
obtain a diagonal specialization result for Bernstein-Sato ideals.


Abstract:
  Riemannian manifolds provide a principled way to model nonlinear geometric
structure inherent in data. A Riemannian metric on said manifolds determines
geometry-aware shortest paths and provides the means to define statistical
models accordingly. However, these operations are typically computationally
demanding. To ease this computational burden, we advocate probabilistic
numerical methods for Riemannian statistics. In particular, we focus on
Bayesian quadrature (BQ) to numerically compute integrals over normal laws on
Riemannian manifolds learned from data. In this task, each function evaluation
relies on the solution of an expensive initial value problem. We show that by
leveraging both prior knowledge and an active exploration scheme, BQ
significantly reduces the number of required evaluations and thus outperforms
Monte Carlo methods on a wide range of integration problems. As a concrete
application, we highlight the merits of adopting Riemannian geometry with our
proposed framework on a nonlinear dataset from molecular dynamics.


Abstract:
  Gravitational instabilities can drive small-scale turbulence and large-scale
spiral arms in massive gaseous disks under conditions of slow radiative
cooling. These motions affect the observed disk morphology, its mass accretion
rate and variability, and could control the process of planet formation via
dust grain concentration, processing, and collisional fragmentation. We study
gravito-turbulence and its associated spiral structure in thin gaseous disks
subject to a prescribed cooling law. We characterize the morphology, coherence,
and propagation of the spirals and examine when the flow deviates from viscous
disk models. We used the finite-volume code Pluto to integrate the equations of
self-gravitating hydrodynamics in three-dimensional spherical geometry. The gas
was cooled over longer-than-orbital timescales to trigger the gravitational
instability and sustain turbulence. We ran models for various disk masses and
cooling rates. In all cases considered, the turbulent gravitational stress
transports angular momentum outward at a rate compatible with viscous disk
theory. The dissipation of orbital energy happens via shocks in spiral density
wakes, heating the disk back to a marginally stable thermal equilibrium. These
wakes drive vertical motions and contribute to mix material from the disk with
its corona. They are formed and destroyed intermittently, and they nearly
corotate with the gas at every radius. As a consequence, large-scale spiral
arms exhibit no long-term global coherence, and energy thermalization is an
essentially local process. In the absence of radial substructures or tidal
forcing, and provided a local cooling law, gravito-turbulence reduces to a
local phenomenon in thin gaseous disks.


Abstract:
  For many reinforcement learning (RL) applications, specifying a reward is
difficult. This paper considers an RL setting where the agent obtains
information about the reward only by querying an expert that can, for example,
evaluate individual states or provide binary preferences over trajectories.
From such expensive feedback, we aim to learn a model of the reward that allows
standard RL algorithms to achieve high expected returns with as few expert
queries as possible. To this end, we propose Information Directed Reward
Learning (IDRL), which uses a Bayesian model of the reward and selects queries
that maximize the information gain about the difference in return between
plausibly optimal policies. In contrast to prior active reward learning methods
designed for specific types of queries, IDRL naturally accommodates different
query types. Moreover, it achieves similar or better performance with
significantly fewer queries by shifting the focus from reducing the reward
approximation error to improving the policy induced by the reward model. We
support our findings with extensive evaluations in multiple environments and
with different query types.


Abstract:
  The growing popularity of online fundraising (aka "crowdfunding") has
attracted significant research on the subject. In contrast to previous studies
that attempt to predict the success of crowdfunded projects based on specific
characteristics of the projects and their creators, we present a more general
approach that focuses on crowd dynamics and is robust to the particularities of
different crowdfunding platforms. We rely on a multi-method analysis to
investigate the correlates, predictive importance, and quasi-causal effects of
features that describe crowd dynamics in determining the success of crowdfunded
projects. By applying a multi-method analysis to a study of fundraising in
three different online markets, we uncover general crowd dynamics that
ultimately decide which projects will succeed. In all analyses and across the
three different platforms, we consistently find that funders' behavioural
signals (1) are significantly correlated with fundraising success; (2)
approximate fundraising outcomes better than the characteristics of projects
and their creators such as credit grade, company valuation, and subject domain;
and (3) have significant quasi-causal effects on fundraising outcomes while
controlling for potentially confounding project variables. By showing that
universal features deduced from crowd behaviour are predictive of fundraising
success on different crowdfunding platforms, our work provides design-relevant
insights about novel types of collective decision-making online. This research
inspires thus potential ways to leverage cues from the crowd and catalyses
research into crowd-aware system design.


Abstract:
  We use a recent numerical security proof technique to analyse three different
postselection strategies for continuous-variable quantum key distribution
protocols (CV-QKD) with quadrature phase-shift keying modulation. For all
postselection strategies studied we provide novel analytical results for the
operators that define the respective regions in phase space. In both, the
untrusted and trusted detector model, a new, cross-shaped postselection
strategy, clearly outperforms a state-of-the-art radial postselection scheme
for higher transmission distances and higher values of noise. Motivated by the
high computational effort for the error-correction phase we also studied the
case when a large fraction of the raw key is eliminated by postselection: We
observe that the secure key rate in case that only $20\%$ of the raw key passes
the cross-shaped postselection is still roughly $80\%$ of the secure key rate
without postselection for low values of excess noise and roughly $95\%$ for
higher values of excess noise. Furthermore, we examine a strategy with radial
and angular postselection that combines both the advantages of state-of-the-art
radial postselection schemes and our cross-shaped strategy at the cost of
higher complexity. Employing the cross-shaped postselection strategy, that can
easily be introduced in the data processing, both new and existing CV-QKD
systems can improve the achievable secure key rates significantly.


Abstract:
  Subaru Strategic Program with the Hyper-Suprime Cam (HSC-SSP) has proven to
be successful with its extremely-wide area coverage in past years. Taking
advantages of this feature, we report initial results from exploration and
research of expansive over- and under-dense structures at $z=$ 0.3-1 based on
the second Public Data Release where optical 5-band photometric data for $\sim$
eight million sources with $i<23$ mag are available over $\sim360$ square
degrees. We not only confirm known superclusters but also find candidates of
titanic over- and under-dense regions out to $z=1$. The mock data analysis
suggests that the density peaks would involve one or more massive dark matter
haloes ($>10^{14}$ M$_\odot$) of the redshift, and the density troughs tend to
be empty of massive haloes over $>10$ comoving Mpc. Besides, the density peaks
and troughs at $z<0.6$ are in part identified as positive and negative weak
lensing signals respectively, in mean tangential shear profiles, showing a good
agreement with those inferred from the full-sky weak lensing simulation. The
coming extensive spectroscopic surveys will be able to resolve these colossal
structures in three-dimensional space. The number density information over the
entire survey field will be available as grid-point data on the website of the
HSC-SSP data release (https://hsc.mtk.nao.ac.jp/ssp/data-release/).


Abstract:
  Pre-training and fine-tuning have achieved remarkable success in many
downstream natural language processing (NLP) tasks. Recently, pre-training
methods tailored for information retrieval (IR) have also been explored, and
the latest success is the PROP method which has reached new SOTA on a variety
of ad-hoc retrieval benchmarks. The basic idea of PROP is to construct the
\textit{representative words prediction} (ROP) task for pre-training inspired
by the query likelihood model. Despite its exciting performance, the
effectiveness of PROP might be bounded by the classical unigram language model
adopted in the ROP task construction process. To tackle this problem, we
propose a bootstrapped pre-training method (namely B-PROP) based on BERT for
ad-hoc retrieval. The key idea is to use the powerful contextual language model
BERT to replace the classical unigram language model for the ROP task
construction, and re-train BERT itself towards the tailored objective for IR.
Specifically, we introduce a novel contrastive method, inspired by the
divergence-from-randomness idea, to leverage BERT's self-attention mechanism to
sample representative words from the document. By further fine-tuning on
downstream ad-hoc retrieval tasks, our method achieves significant improvements
over baselines without pre-training or with other pre-training methods, and
further pushes forward the SOTA on a variety of ad-hoc retrieval tasks.


Abstract:
  By analyzing photometric and spectroscopic time series in this paper, we show
that the pulsator V764 Mon, assumed to be the brightest RR Lyrae star in the
sky, is in fact a rapidly rotating delta Scuti star with an unusually long
dominant period (P1=0.29 d). Our spectroscopy confirmed the discovery of the
Gaia satellite about the binarity of V764 Mon. In the case of HY Com, a `bona
fide' RRc star, we present its first complete radial velocity curve.
Additionally, we found that the star continues its strong phase variation
reported before.


Abstract:
  Quantum computing can efficiently simulate Hamiltonian dynamics of many-body
quantum physics, a task that is generally intractable with classical computers.
The hardness lies at the ubiquitous anti-commutative relations of quantum
operators, in corresponding with the notorious negative sign problem in
classical simulation. Intuitively, Hamiltonians with more commutative terms are
also easier to simulate on a quantum computer, and anti-commutative relations
generally cause more errors, such as in the product formula method. Here, we
theoretically explore the role of anti-commutative relation in Hamiltonian
simulation. We find that, contrary to our intuition, anti-commutative relations
could also reduce the hardness of Hamiltonian simulation. Specifically,
Hamiltonians with mutually anti-commutative terms are easy to simulate, as what
happens with ones consisting of mutually commutative terms. Such a property is
further utilized to reduce the algorithmic error or the gate complexity in the
truncated Taylor series quantum algorithm for general problems. Moreover, we
propose two modified linear combinations of unitaries methods tailored for
Hamiltonians with different degrees of anti-commutation. We numerically verify
that the proposed methods exploiting anti-commutative relations could
significantly improve the simulation accuracy of electronic Hamiltonians. Our
work sheds light on the roles of commutative and anti-commutative relations in
simulating quantum systems.


Abstract:
  The deep extension of the restricted Boltzmann machine (RBM), known as the
deep Boltzmann machine (DBM), is an expressive family of machine learning
models which can serve as compact representations of complex probability
distributions. However, jointly training DBMs in the unsupervised setting has
proven to be a formidable task. A recent technique we have proposed, called
mode-assisted training, has shown great success in improving the unsupervised
training of RBMs. Here, we show that the performance gains of the mode-assisted
training are even more dramatic for DBMs. In fact, DBMs jointly trained with
the mode-assisted algorithm can represent the same data set with orders of
magnitude lower number of total parameters compared to state-of-the-art
training procedures and even with respect to RBMs, provided a fan-in network
topology is also introduced. This substantial saving in number of parameters
makes this training method very appealing also for hardware implementations.


Abstract:
  Probability forecasts for binary events play a central role in many
applications. Their quality is commonly assessed with proper scoring rules,
which assign forecasts a numerical score such that a correct forecast achieves
a minimal expected score. In this paper, we construct e-values for testing the
statistical significance of score differences of competing forecasts in
sequential settings. E-values have been proposed as an alternative to p-values
for hypothesis testing, and they can easily be transformed into conservative
p-values by taking the multiplicative inverse. The e-values proposed in this
article are valid in finite samples without any assumptions on the data
generating processes. They also allow optional stopping, so a forecast user may
decide to interrupt evaluation taking into account the available data at any
time and still draw statistically valid inference, which is generally not true
for classical p-value based tests. In a case study on postprocessing of
precipitation forecasts, state-of-the-art forecasts dominance tests and
e-values lead to the same conclusions.


Abstract:
  We prove an upper bound on the rank of the abelianised revised fundamental
group (called "revised first Betti number") of a compact $RCD^{*}(K,N)$ space,
in the same spirit of the celebrated Gromov-Gallot upper bound on the first
Betti number for a smooth compact Riemannian manifold with Ricci curvature
bounded below. When the synthetic lower Ricci bound is close enough to
(negative) zero and the aforementioned upper bound on the revised first Betti
number is saturated (i.e. equal to the integer part of $N$, denoted by $\lfloor
N \rfloor$), then we establish a torus stability result stating that the space
is $\lfloor N \rfloor$-rectifiable as a metric measure space, and a finite
cover must be mGH-close to an $\lfloor N \rfloor$-dimensional flat torus;
moreover, in case $N$ is an integer, we prove that the space itself is
bi-H\"older homeomorphic to a flat torus. This second result extends to the
class of non-smooth $RCD^{*}(-\delta, N)$ spaces a celebrated torus stability
theorem by Colding (later refined by Cheeger-Colding).


Abstract:
  Customers of machine learning systems demand accountability from the
companies employing these algorithms for various prediction tasks.
Accountability requires understanding of system limit and condition of
erroneous predictions, as customers are often interested in understanding the
incorrect predictions, and model developers are absorbed in finding methods
that can be used to get incremental improvements to an existing system.
Therefore, we propose an accountable error characterization method, AEC, to
understand when and where errors occur within the existing black-box models.
AEC, as constructed with human-understandable linguistic features, allows the
model developers to automatically identify the main sources of errors for a
given classification system. It can also be used to sample for the set of most
informative input points for a next round of training. We perform error
detection for a sentiment analysis task using AEC as a case study. Our results
on the sample sentiment task show that AEC is able to characterize erroneous
predictions into human understandable categories and also achieves promising
results on selecting erroneous samples when compared with the uncertainty-based
sampling.


Abstract:
  The paper deals with the H2-norm and associated energy or power measurements
for a class of processes known as CSVIU (Control and State Variation Increase
Uncertainty). These are system models for which a stochastic process conveys
the underlying uncertainties, and are able to give rise to cautious controls.
The paper delves into the non-controlled version and fundamental system and
norms notions associated with stochastic stability and mean-square convergence.
One pillar of the study is the connection between the finiteness of one of
these norms or a limited energy measurement growth with the corresponding
stochastic stability notions. A detectability concept ties these notions, and
the analysis of linear-positive operators plays a fundamental role. The
introduction of various H2-norms and energy measurement performance criteria
allows one to span the focus from transient to long-run behavior. As the
discount parameter turns into a counter-discount, the criteria enforce stricter
requirements on the second-moment steady state errors and on the exponential
convergence rate. A tidy connection among this H2-performance measures cast
employs a unifying vanishing discount reasoning.


Abstract:
  A nilmanifold is a (left) quotient of a nilpotent Lie group by a cocompact
lattice. A hypercomplex structure on a manifold is a triple of complex
structure operators satisfying the quaternionic relations. A hypercomplex
nilmanifold is a compact quotient of a nilpotent Lie group equipped with a
left-invariant hypercomplex structure. Such a manifold admits a whole
2-dimensional sphere $S^2$ of complex structures induced by quaternions. We
prove that for any hypercomplex nilmanifold $M$ and a generic complex structure
$L\in S^2$, the complex manifold $(M,L)$ has algebraic dimension 0. A stronger
result is proven when the hypercomplex nilmanifold is abelian. Consider the Lie
algebra of left-invariant vector fields of Hodge type (1,0) on the
corresponding nilpotent Lie group with respect to some complex structure $I\in
S^2$. A hypercomplex nilmanifold is called abelian when this Lie algebra is
abelian. We prove that all complex subvarieties of $(M,L)$ for generic $L\in
S^2$ on a hypercomplex abelian nilmanifold are also hypercomplex nilmanifolds.


Abstract:
  Given the increasing data collection capabilities and limited computing
resources of future collider experiments, interest in using generative neural
networks for the fast simulation of collider events is growing. In our previous
study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for
generating photon showers in a high-granularity calorimeter showed a high
accuracy modeling of various global differential shower distributions. In this
work, we investigate how the BIB-AE encodes this physics information in its
latent space. Our understanding of this encoding allows us to propose methods
to optimize the generation performance further, for example, by altering latent
space sampling or by suggesting specific changes to hyperparameters. In
particular, we improve the modeling of the shower shape along the particle
incident axis.


Abstract:
  Recent progress in nanofabrication has led to the emergence of
three-dimensional magnetic nanostructures as a vibrant field of research. This
includes the study of three-dimensional arrays of interconnected magnetic
nanowires with tunable artificial spin-ice properties. Prominent examples of
such structures are magnetic buckyball nanoarchitectures, which consist of
ferromagnetic nanowires connected at vertex positions corresponding to those of
a C60 molecule. These structures can be regarded as prototypes for the study of
the transition from two- to three-dimensional spin-ice lattices. In spite of
their significance for three-dimensional nanomagnetism, little is known about
the micromagnetic properties of buckyball nanostructures. By means of
finite-element micromagnetic simulations, we investigate the magnetization
structures and the hysteretic properties of several sub-micron-sized magnetic
buckyballs. Similar to ordinary artificial spin ice lattices, the array can be
magnetized in a variety of zero-field states with vertices exhibiting different
degrees of magnetic frustration. Remarkably, and unlike planar geometries,
magnetically frustrated states can be reversibly created and dissolved by
applying an external magnetic field. This easiness to insert and remove
defect-like magnetic charges, made possible by the angle-selectivity of the
field-induced switching of individual nanowires, demonstrates a potentially
significant advantage of three-dimensional nanomagnetism compared to planar
geometries. The control provided by the ability to switch between ice-rule
obeying and magnetically frustrated structures could be an important feature of
future applications, including magnonic devices exploiting differences in the
fundamental frequencies of these configurations.


Abstract:
  This perspective article elucidates both the importance and the implications
of relativistic spacetime crystals as well as the renormalized blended
coordinates transformation. It alludes to possible applications in materials
science, condensed matter physics and quantum gravity.


Abstract:
  The task of assigning semantic classes and track identities to every pixel in
a video is called video panoptic segmentation. Our work is the first that
targets this task in a real-world setting requiring dense interpretation in
both spatial and temporal domains. As the ground-truth for this task is
difficult and expensive to obtain, existing datasets are either constructed
synthetically or only sparsely annotated within short video clips. To overcome
this, we introduce a new benchmark encompassing two datasets, KITTI-STEP, and
MOTChallenge-STEP. The datasets contain long video sequences, providing
challenging examples and a test-bed for studying long-term pixel-precise
segmentation and tracking under real-world conditions. We further propose a
novel evaluation metric Segmentation and Tracking Quality (STQ) that fairly
balances semantic and tracking aspects of this task and is more appropriate for
evaluating sequences of arbitrary length. Finally, we provide several baselines
to evaluate the status of existing methods on this new challenging dataset. We
have made our datasets, metric, benchmark servers, and baselines publicly
available, and hope this will inspire future research.


Abstract:
  Third-party software, or skills, are essential components in Smart Personal
Assistants (SPA). The number of skills has grown rapidly, dominated by a
changing environment that has no clear business model. Skills can access
personal information and this may pose a risk to users. However, there is
little information about how this ecosystem works, let alone the tools that can
facilitate its study. In this paper, we present the largest systematic
measurement of the Amazon Alexa skill ecosystem to date. We study developers'
practices in this ecosystem, including how they collect and justify the need
for sensitive information, by designing a methodology to identify
over-privileged skills with broken privacy policies. We collect 199,295 Alexa
skills and uncover that around 43% of the skills (and 50% of the developers)
that request these permissions follow bad privacy practices, including
(partially) broken data permissions traceability. In order to perform this kind
of analysis at scale, we present SkillVet that leverages machine learning and
natural language processing techniques, and generates high-accuracy prediction
sets. We report a number of concerning practices including how developers can
bypass Alexa's permission system through account linking and conversational
skills, and offer recommendations on how to improve transparency, privacy and
security. Resulting from the responsible disclosure we have conducted,13% of
the reported issues no longer pose a threat at submission time.


Abstract:
  We introduce a new network marker for climate network analysis. It is based
upon an available special definition of local clustering coefficient for
weighted correlation networks, which was previously introduced in the
neuroscience context and aimed at compensating for uninformative correlations
caused by indirect interactions. We modify this definition further by replacing
Pearson's pairwise correlation coefficients and Pearson's three-way partial
correlation coefficients by the respective Kendall's rank correlations. This
reduces statistical sample size requirements to compute the correlations, which
translates into the possibility of using shorter time windows and hence into
shorter response time of the real-time climate network analysis. We compare
this proposed network marker to the conventional local clustering coefficient
based on unweighted networks obtained by thresholding the correlation matrix.
We show several examples where the new marker is found to be better associated
to tropical cyclones than the unweighted local clustering coefficient.


Abstract:
  A previously published covariant decomposition of the Levi-Civita tensor has
demonstrated the strong mathematical parallel between gravity and $N=2$
Yang-Mills theory. We use this to argue that that the Lorentz gauge theory has
a condensate vacuum of lower energy than the perturbative vacuum. Adapting our
previously published Clairaut-based treatment of QCD, we go on to study the
implications for second quantisation.


Abstract:
  Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial
system, is mushrooming. One and a half years ago the total value locked in DeFi
systems was approximately $700$m USD, now, as of September 2021, it stands at
around $100$bn USD. The frenetic evolution of the ecosystem has created
challenges in understanding the basic principles of these systems and their
security risks. In this Systematization of Knowledge (SoK) we delineate the
DeFi ecosystem along the following axes: its primitives, its operational
protocol types and its security. We provide a distinction between technical
security, which has a healthy literature, and economic security, which is
largely unexplored, connecting the latter with new models and thereby
synthesizing insights from computer science, economics and finance. Finally, we
outline the open research challenges in the ecosystem across these security
types.


Abstract:
  10 {\mu}m lasing is studied in a compact CO2-He cell pressurized up to 15 atm
when optically pumped by a ~50 mJ Fe:ZnSe laser tunable around 4.3 {\mu}m. The
optimal pump wavelength and partial pressure of CO2 for generating 10 {\mu}m
pulses are found to be ~4.4 {\mu}m and 0.75 atm, respectively. Without cavity
optimization, the optical-to-optical conversion efficiency reached ~10% at a
total pressure of 7 atm. The gain lifetime is measured to be ~1 {\mu}s at
pressures above 10 atm, indicating the feasibility of using high-pressure
optically pumped CO2 for the efficient amplification of picosecond 10 {\mu}m
pulses.


Abstract:
  We present a unified framework for minimizing average completion time for
many seemingly disparate online scheduling problems, such as the traveling
repairperson problems (TRP), dial-a-ride problems (DARP), and scheduling on
unrelated machines.
  We construct a simple algorithm that handles all these scheduling problems,
by computing and later executing auxiliary schedules, each optimizing a certain
function on already seen prefix of the input. The optimized function resembles
a prize-collecting variant of the original scheduling problem. By a careful
analysis of the interplay between these auxiliary schedules, and later
employing the resulting inequalities in a factor-revealing linear program, we
obtain improved bounds on the competitive ratio for all these scheduling
problems.
  In particular, our techniques yield a $4$-competitive deterministic algorithm
for all previously studied variants of online TRP and DARP, and a
$3$-competitive one for the scheduling on unrelated machines (also with
precedence constraints). This improves over currently best ratios for these
problems that are $5.14$ and $4$, respectively. We also show how to use
randomization to further reduce the competitive ratios to $1+2/\ln 3 < 2.821$
and $1+1/\ln 2 < 2.443$, respectively. The randomized bounds also substantially
improve the current state of the art. Our upper bound for DARP contradicts the
lower bound of 3 given by Fink et al. (Inf. Process. Lett. 2009); we pinpoint a
flaw in their proof.


Abstract:
  This paper introduces the Simulated Jet Engine Bracket Dataset (SimJEB): a
new, public collection of crowdsourced mechanical brackets and accompanying
structural simulations. SimJEB is applicable to a wide range of geometry
processing tasks; the complexity of the shapes in SimJEB offer a challenge to
automated geometry cleaning and meshing, while categorical labels and
structural simulations facilitate classification and regression (i.e.
engineering surrogate modeling). In contrast to existing shape collections,
SimJEB's models are all designed for the same engineering function and thus
have consistent structural loads and support conditions. On the other hand,
SimJEB models are more complex, diverse, and realistic than the synthetically
generated datasets commonly used in parametric surrogate model evaluation. The
designs in SimJEB were derived from submissions to the GrabCAD Jet Engine
Bracket Challenge: an open engineering design competition with over 700
hand-designed CAD entries from 320 designers representing 56 countries. Each
model has been cleaned, categorized, meshed, and simulated with finite element
analysis according to the original competition specifications. The result is a
collection of 381 diverse, high-quality and application-focused designs for
advancing geometric deep learning, engineering surrogate modeling, automated
cleaning and related geometry processing tasks.


Abstract:
  Several governments introduced or promoted the use of contact-tracing apps
during the ongoing COVID-19 pandemic. In Germany, the related app is called
Corona-Warn-App, and by end of 2020, it had 22.8 million downloads. Contact
tracing is a promising approach for containing the spread of the novel
coronavirus. It is only effective if there is a large user base, which brings
new challenges like app users unfamiliar with using smartphones or apps. As
Corona-Warn-App is voluntary to use, reaching many users and gaining a positive
public perception is crucial for its effectiveness. Based on app reviews and
tweets, we are analyzing the public perception of Corona-Warn-App. We collected
and analyzed all 78,963 app reviews for the Android and iOS versions from
release (June 2020) to beginning of February 2021, as well as all original
tweets until February 2021 containing #CoronaWarnApp (43,082). For the reviews,
the most common words and n-grams point towards technical issues, but it
remains unclear, to what extent this is due to the app itself, the used
Exposure Notification Framework, system settings on the user's phone, or the
user's misinterpretations of app content. For Twitter data, overall, based on
tweet content, frequent hashtags, and interactions with tweets, we conclude
that the German Twitter-sphere widely reports adopting the app and promotes its
use.


Abstract:
  The proximity effect from a spin-triplet $p_x$-wave superconductor to a dirty
normal-metal has been shown to result in various unusual electromagnetic
properties, reflecting a cooperative relation between topologically protected
zero-energy quasiparticles and odd-frequency Cooper pairs. However, because of
a lack of candidate materials for spin-triplet $p_x$-wave superconductors,
observing this effect has been difficult. In this paper, we demonstrate that
the anomalous proximity effect, which is essentially equivalent to that of a
spin-triplet $p_x$-wave superconductor, can occur in a semiconductor/high-$T_c$
cuprate superconductor hybrid device in which two potentials coexist: a
spin-singlet $d$-wave pair potential and a spin--orbit coupling potential
sustaining the persistent spin-helix state. As a result, we propose an
alternative and promising route to observe the anomalous proximity effect
related to the profound nature of topologically protected quasiparticles and
odd-frequency Cooper pairs.


Abstract:
  The dipole anisotropy in the Cosmic Microwave Background Radiation (CMBR) has
given a peculiar velocity vector 370 km s$^{-1}$ along
$l=264^\circ,b=48^\circ$. However, some other dipoles, for instance, from the
number counts, sky brightness or redshift distributions in large samples of
distant Active Galactic Nuclei (AGNs), have yielded values of the peculiar
velocity many times larger than that from the CMBR, though surprisingly, in all
cases the directions agreed with the CMBR dipole. Here we determine our
peculiar motion from a sample of ~0.28 million AGNs, selected from the Mid
Infra Red Active Galactic Nuclei (MIRAGN) sample comprising more than a million
sources. From this, we find a peculiar velocity, which is more than four times
the CMBR value, although the direction seems to be within $\sim 2\sigma$ of the
CMBR dipole. A genuine value of the solar peculiar velocity should be the same
irrespective of the data or the technique employed to estimate it. Therefore,
such discordant dipole amplitudes, might mean that the explanation for these
dipoles, including that of the CMBR, might in fact be something else. But, the
observed fact that the direction in all cases, is the same, though obtained
from completely independent surveys using different instruments and techniques,
by different sets of people employing different computing routines, might
nonetheless indicate that these dipoles are not merely due to some systematics,
otherwise why would they all be pointing along the same direction. It might
instead suggest a preferred direction in the Universe, implying a genuine
anisotropy, which would violate the Cosmological Principle, the core of the
modern cosmology.


Abstract:
  We present 3D general relativistic magnetohydrodynamic(GRMHD) simulations of
zero angular momentum accretion around a rapidly rotating black hole, modified
by the presence of initially uniform magnetic fields. We consider serveral
angles between the magnetic field direction and the black hole spin. In the
resulting flows, the midplane dynamics are governed by magnetic
reconnection-driven turbulence in a magnetically arrested (or a nearly
arrested) state. Electromagnetic jets with outflow efficiencies ~10-200% occupy
the polar regions, reaching several hundred gravitational radii before they
dissipate due to the kink instability. The jet directions fluctuate in time and
can be tilted by as much as ~30 degrees with respect to black hole spin, but
this tilt does not depend strongly on the tilt of the initial magnetic field. A
jet forms even when there is no initial net vertical magnetic flux since
turbulent, horizon-scale fluctuations can generate a net vertical field
locally. Peak jet power is obtained for an initial magnetic field tilted by
40-80 degrees with respect to the black hole spin because this maximizes the
amount of magnetic flux that can reach the black hole. These simulations may be
a reasonable model for low luminosity black hole accretion flows such as Sgr A*
or M87.


Abstract:
  Large Pre-trained Language Models (PLMs) have become ubiquitous in the
development of language understanding technology and lie at the heart of many
artificial intelligence advances. While advances reported for English using
PLMs are unprecedented, reported advances using PLMs in Hebrew are few and far
between. The problem is twofold. First, Hebrew resources available for training
NLP models are not at the same order of magnitude as their English
counterparts. Second, there are no accepted tasks and benchmarks to evaluate
the progress of Hebrew PLMs on. In this work we aim to remedy both aspects.
First, we present AlephBERT, a large pre-trained language model for Modern
Hebrew, which is trained on larger vocabulary and a larger dataset than any
Hebrew PLM before. Second, using AlephBERT we present new state-of-the-art
results on multiple Hebrew tasks and benchmarks, including: Segmentation,
Part-of-Speech Tagging, full Morphological Tagging, Named-Entity Recognition
and Sentiment Analysis. We make our AlephBERT model publicly available,
providing a single point of entry for the development of Hebrew NLP
applications.


Abstract:
  The goal of this minireview is restricted to describe how the
Kolmogorov-Johnson-Mehl-Avrami model has evolved from its birth up to the
present day. The model, which dates back to the late of 1930s, has the purpose
of describing the kinetics of a phase transformation. Given the nature of this
article, although there are hundreds (if not thousands) of experimental data
concerning the most disparate topics, which are interpreted on the basis of the
KJMA model, no arguments relating to these, will be touched upon. Starting from
the ingenious concept of phantom nuclei, firstly introduced by Avrami to get
the exact kinetics, we review theoretical approaches which overcome such
concept. We will show how spatial correlation among nuclei plays a fundamental
role in these modelings.


Abstract:
  The layered perovskite YBaCuFeO5 (YBCFO) is considered one of the best
candidates to high-temperature chiral multiferroics with strong magnetoelectric
coupling. In RBaCuFeO5 perovskites (R: rare-earth or Y) A-site cations are
fully ordered whereas their magnetic properties strongly depend on the
preparation process. They exhibit partial cationic disorder at the B-site that
generates a magnetic spiral stabilized through directionally assisted long
range coupling between canted locally frustrated spins. Moreover the
orientation of its magnetic spiral can be critical for the magnetoelectric
response of this chiral magnetic oxide. We have synthesized and studied
YBaCuFe1-xMnxO5 samples doped with Mn, with the aim of increasing spin-orbit
coupling effects, and found that the overall Fe/Cu cation disorder at the
B-sites can be increased by doping without changing the sample preparation
process. In YBaCuFe1-xMnxO5 samples prepared under the same conditions, the T-x
magnetic phase diagram have been constructed in the range 10K-500K combining
magnetometry, X-ray and neutron powder diffraction measurements. The tilting
angles of the spins in the collinear, {\theta}col , and spiral phases,
{\theta}spiral, barely vary with temperature. In the collinear phase
{\theta}col is also independent of the Mn content. In contrast, the presence of
Mn produces a progressive reorientation of the plane of the magnetic helix in
the incommensurate phase, capable to transform the helicoidal spin ordering
into a cycloidal one, which may critically determine the ferroelectric and
magnetoelectric behavior in these compounds. Some of the observations are of
interest for engineering and developing this family of potential
high-temperature multiferroics.


Abstract:
  Despite their consequential applications, metastable states of antibranes in
warped throats are not yet fully understood. In this thesis, we provide new
information on various aspects of these metastable antibranes through
applications of the blackfold effective theory for higher-dimensional black
holes. As concrete examples, we study the conjectured metastable state of
polarised anti-D3 branes at the tip of the Klebanov-Strassler (KS) throat in
type IIB supergravity and the analogous state of polarised anti-M2 branes at
the tip of the Cvetic-Gibbons-Lu-Pope (CGLP) throat in eleven-dimensional
supergravity.
  For anti-D3 branes in KS throat, we provide novel evidence for the existence
of the metastable state exactly where no-go theorems are lifted. In the
extremal limit, we recover directly in supergravity the metastable states
originally discovered by Kachru, Pearson, and Verlinde (KPV). Away from
extremality, we uncover a metastable wrapped black NS5 state. We observe that
such metastability is lost when the wrapped NS5 is heated sufficiently that its
horizon geometry resembles that of a black anti-D3. We study the classical
stability of the KPV state under generic long-wavelength deformations. We
observe that, with regards to considered perturbations and regime of
parameters, the state is classically stable. A study of anti-M2 branes in CGLP
throat reveals many similarities to that of the anti-D3 branes. We recover
directly in supergravity the Klebanov-Pufu (KP) state at extremality, and our
finite temperature results fit suggestively well with known, complementary
no-go theorems. However, we discover an unexpected, exotic pattern of thermal
transitions of the KP state different from that of the KPV.
  This thesis contains also a pedagogical introduction to the blackfold
formalism, focusing on aspects immediately relevant to applications to
metastable antibranes.


Abstract:
  In this paper, a relay-aided two-phase transmission protocol for the smart
factory scenario is proposed. This protocol aims at enabling all robots'
ultra-reliable target number of uplink critical data transmission within a
latency constraint by jointly optimizing the relay selection, resource block
(RB) assignment, and transmit power allocation. Such protocol design is
formulated as a mixed-integer and strictly non-convex problem where
optimization variables are mutual coupling, which is definitely challenging.
Instead of conventional methods designed for solving the problem, we leverage
the properties of the relative entropy function to equivalently transform the
problem without introducing extra constraints. As the packet error probability
requirements of each robot under two possible transmission modes are coupled in
one overall reliability constraint, the big-M technique is applied to decouple
it into two corresponding reliability constraints. One is for direct
transmission mode, and the other is for cooperative transmission mode.
Moreover, both non-convex penalty (NCP) and quadratic penalty (QP) approaches
are utilized to deal with the binary indicator constraints. Based on such
penalty methods, a sequence of penalized approximated convex problems can be
iteratively solved for sub-optimal solutions. Numerical results demonstrate the
efficiency of such two penalty methods from the perspectives of sub-optimal
values of total transmit power and convergence rate. Further, the impacts of
reliability, the number and location of relays, the number of robots, the
target number of data bits on the total power consumption are analyzed.


Abstract:
  We analytically study shock wave in the Josephson transmission line (JTL) in
the presence of ohmic dissipation. When ohmic resistors shunt the Josephson
junctions (JJ) or are introduced in series with the ground capacitors the shock
is broadened. When ohmic resistors are in series with the JJ, the shock remains
sharp, same as it was in the absence of dissipation. In all the cases
considered, ohmic resistors don't influence the shock propagation velocity. We
study an alternative to the shock wave - an expansion fan - in the framework of
the simple wave approximation for the dissipationless JTL and formulate the
generalization of the approximation for the JTL with ohmic dissipation.


Abstract:
  The world is facing a tough situation due to the catastrophic pandemic caused
by novel coronavirus (COVID-19). The number people affected by this virus are
increasing exponentially day by day and the number has already crossed 6.4
million. As no vaccine has been discovered yet, the early detection of patients
and isolation is the only and most effective way to reduce the spread of the
virus. Detecting infected persons from chest X-Ray by using Deep Neural
Networks, can be applied as a time and laborsaving solution. In this study, we
tried to detect Covid-19 by classification of Covid-19, pneumonia and normal
chest X-Rays. We used five different Convolutional Pre-Trained Neural Network
models (VGG16, VGG19, Xception, InceptionV3 and Resnet50) and compared their
performance. VGG16 and VGG19 shows precise performance in classification. Both
models can classify between three kinds of X-Rays with an accuracy over 92%.
Another part of our study was to find the impact of weather factors
(temperature, humidity, sun hour and wind speed) on this pandemic using
Decision Tree Regressor. We found that temperature, humidity and sun-hour
jointly hold 85.88% impact on escalation of Covid-19 and 91.89% impact on death
due to Covid-19 where humidity has 8.09% impact on death. We also tried to
predict the death of an individual based on age, gender, country, and location
due to COVID-19 using the LogisticRegression, which can predict death of an
individual with a model accuracy of 94.40%.


Abstract:
  Single-particle x-ray diffractive imaging (SPI) of small (bio-)nanoparticles
(NPs) requires optimized injectors to collect sufficient diffraction patterns
to reconstruct the NP structure with high resolution. Typically,
aerodynamic-lens-stack injectors are used for single NP injection. However,
current injectors were developed for larger NPs ($\gg\!100$ nm) and their
ability to generate high-density NP beams suffers with decreasing NP size.
Here, an aerodynamic-lens-stack injector with variable geometry and the
geometry-optimization procedure are presented. The optimization for 50 nm gold
NP (AuNP) injection using a numerical simulation infrastructure capable of
calculating the carrier gas flow and the particle trajectories through the
injector is introduced. The simulations are experimentally validated using
spherical AuNPs and sucrose NPs. In addition, the optimized injector is
compared to the standard-installation "Uppsala-injector" for AuNPs and results
for these heavy particles show a shift in the particle-beam focus position
rather than a change in beam size, which results in a lower gas background for
the optimized injector. Optimized aerodynamic-lens stack injectors will allow
to increase NP beam density, reduce the gas background, discover the limits of
current injectors, and contribute to structure determination of small NPs using
SPI.


Abstract:
  Despite significant improvements over the last few years, cloud-based
healthcare applications continue to suffer from poor adoption due to their
limitations in meeting stringent security, privacy, and quality of service
requirements (such as low latency). The edge computing trend, along with
techniques for distributed machine learning such as federated learning, have
gained popularity as a viable solution in such settings. In this paper, we
leverage the capabilities of edge computing in medicine by analyzing and
evaluating the potential of intelligent processing of clinical visual data at
the edge allowing the remote healthcare centers, lacking advanced diagnostic
facilities, to benefit from the multi-modal data securely. To this aim, we
utilize the emerging concept of clustered federated learning (CFL) for an
automatic diagnosis of COVID-19. Such an automated system can help reduce the
burden on healthcare systems across the world that has been under a lot of
stress since the COVID-19 pandemic emerged in late 2019. We evaluate the
performance of the proposed framework under different experimental setups on
two benchmark datasets. Promising results are obtained on both datasets
resulting in comparable results against the central baseline where the
specialized models (i.e., each on a specific type of COVID-19 imagery) are
trained with central data, and improvements of 16\% and 11\% in overall
F1-Scores have been achieved over the multi-modal model trained in the
conventional Federated Learning setup on X-ray and Ultrasound datasets,
respectively. We also discuss in detail the associated challenges,
technologies, tools, and techniques available for deploying ML at the edge in
such privacy and delay-sensitive applications.


Abstract:
  The goal of this paper is to propose a framework for representing and
reasoning about the rules governing a combinatorial exchange. Such a framework
is at first interest as long as we want to build up digital marketplaces based
on auction, a widely used mechanism for automated transactions. Combinatorial
exchange is the most general case of auctions, mixing the double and
combinatorial variants: agents bid to trade bundles of goods. Hence the
framework should fulfill two requirements: (i) it should enable bidders to
express their bids on combinations of goods and (ii) it should allow describing
the rules governing some market, namely the legal bids, the allocation and
payment rules. To do so, we define a logical language in the spirit of the Game
Description Language: the Combinatorial Exchange Description Language is the
first language for describing combinatorial exchange in a logical framework.
The contribution is two-fold: first, we illustrate the general dimension by
representing different kinds of protocols, and second, we show how to reason
about auction properties in this machine-processable language.


Abstract:
  Active learning continues to remain significant in the industry since it is
data efficient. Not only is it cost effective on a constrained budget,
continuous refinement of the model allows for early detection and resolution of
failure scenarios during the model development stage. Identifying and fixing
failures with the model is crucial as industrial applications demand that the
underlying model performs accurately in all foreseeable use cases. One popular
state-of-the-art technique that specializes in continuously refining the model
via failure identification is Learning Loss. Although simple and elegant, this
approach is empirically motivated. Our paper develops a foundation for Learning
Loss which enables us to propose a novel modification we call LearningLoss++.
We show that gradients are crucial in interpreting how Learning Loss works,
with rigorous analysis and comparison of the gradients between Learning Loss
and LearningLoss++. We also propose a convolutional architecture that combines
features at different scales to predict the loss. We validate LearningLoss++
for regression on the task of human pose estimation (using MPII and LSP
datasets), as done in Learning Loss. We show that LearningLoss++ outperforms in
identifying scenarios where the model is likely to perform poorly, which on
model refinement translates into reliable performance in the open world.


Abstract:
  We study voltage controllable superconducting state in multi-terminal bridge
composed of the dirty superconductor/pure normal metal (SN) bilayer and pure
normal metal. In the proposed system small control current $I_{ctrl}$ flows via
normal bridge, creates voltage drop $V$ and modifies distribution function of
electrons in connected SN bilayer. In case of long normal bridge the voltage
induced nonequilibrium effects could be interpreted in terms of increased local
electron temperature. In this limit we experimentally find large sensitivity of
critical curent $I_c$ of Cu/MoN/Pt-Cu bridge to $I_{ctrl}$ and relatively large
current gain which originate from steep dependence of $I_c$ on temperature and
large $I_c$ (comparable with theoretical depairing current of superconducting
bridge). In the short normal bridge deviation from equilibrium cannot be
described by simple increase of local temperature but we also theoretically
find large sensitivity of $I_c$ to control current/voltage. In this limit we
predict existence at finite $V$ of so called in-plane Fulde-Ferrell state with
spontaneous currents in SN bilayer. We argue that its appearance is connected
with voltage induced paramagnetic response in N layer.


Abstract:
  A unified explicit form for difference formulas to approximate the fractional
and classical derivatives is presented. The formula gives finite difference
approximations for any classical derivatives with a desired order of accuracy
at nodal point in the computational domain. It also gives Gr\"unwald type
approximations for fractional derivatives with arbitrary order of approximation
at any point. Thus, this explicit unifies approximations of both types of
derivatives. Moreover, for classical derivatives, it provides various finite
difference formulas such as forward, backward, central, staggered, compact,
non-compact etc. Efficient computations of the coefficients of the difference
formulas are also presented that lead to automating the solution process of
differential equations with a given higher order accuracy. Some basic
applications are presented to demonstrate the usefulness of this unified
formulation.


Abstract:
  Recent strategies achieved ensembling "for free" by fitting concurrently
diverse subnetworks inside a single base network. The main idea during training
is that each subnetwork learns to classify only one of the multiple inputs
simultaneously provided. However, the question of how to best mix these
multiple inputs has not been studied so far. In this paper, we introduce MixMo,
a new generalized framework for learning multi-input multi-output deep
subnetworks. Our key motivation is to replace the suboptimal summing operation
hidden in previous approaches by a more appropriate mixing mechanism. For that
purpose, we draw inspiration from successful mixed sample data augmentations.
We show that binary mixing in features - particularly with rectangular patches
from CutMix - enhances results by making subnetworks stronger and more diverse.
We improve state of the art for image classification on CIFAR-100 and Tiny
ImageNet datasets. Our easy to implement models notably outperform data
augmented deep ensembles, without the inference and memory overheads. As we
operate in features and simply better leverage the expressiveness of large
networks, we open a new line of research complementary to previous works.


Abstract:
  Effects from nonstandard corrections to Newtonian gravity, at large scale,
can be investigated using the cosmological structure formation. In particular,
it is possible to show if and how a logarithmic correction (as that induced
from nonlocal gravity) modifies the clustering properties of galaxies and of
clusters of galaxies. The thermodynamics of such systems can be used to obtain
important information about the effects of such modification on clustering. We
will compare its effects with observational data and it will be demonstrated
that the observations seem to point to a characteristic scale where such a
logarithmic correction might be in play at galactic scales. However, at larger
scales such statistical inferences are much weaker, so that a fully reliable
statistical evidence for this kind of corrections cannot be stated without
further investigations and the use of more varied and precise cosmological and
astrophysical probes.


Abstract:
  Recent research has shown that non-additive image steganographic frameworks
effectively improve security performance through adjusting distortion
distribution. However, as far as we know, all of the existing non-additive
proposals are based on handcrafted policies, and can only be applied to a
specific image domain, which heavily prevent non-additive steganography from
releasing its full potentiality. In this paper, we propose an automatic
non-additive steganographic distortion learning framework called MCTSteg to
remove the above restrictions. Guided by the reinforcement learning paradigm,
we combine Monte Carlo Tree Search (MCTS) and steganalyzer-based environmental
model to build MCTSteg. MCTS makes sequential decisions to adjust distortion
distribution without human intervention. Our proposed environmental model is
used to obtain feedbacks from each decision. Due to its self-learning
characteristic and domain-independent reward function, MCTSteg has become the
first reported universal non-additive steganographic framework which can work
in both spatial and JPEG domains. Extensive experimental results show that
MCTSteg can effectively withstand the detection of both hand-crafted
feature-based and deep-learning-based steganalyzers. In both spatial and JPEG
domains, the security performance of MCTSteg steadily outperforms the state of
the art by a clear margin under different scenarios.


Abstract:
  State-of-the-art approaches to calculate the electron-phonon and the
phonon-electron self-energy are based on a mean-field approximation for the
interacting electronic system. This approach introduces an overscreening error
which results in an underestimation of the electron-phonon coupling strength.
We introduce a theoretical and numerical approach for the calculation of the
phonon-electron self-energy without the overscreening error. Starting from the
out-of-equilibrium Kadanoff-Baym equations for the phonon propagator, we
discuss and compare the ovescreened (i.e., symmetrically screened) and
overscreening--free (i.e., asymmetrically screened) cases. We point out that
the difficulty in treating the latter stems from the static approximation to
the dielectric function and from the need to obtain a self-energy that
preserves the elementary scattering processes. We solve both problems in the
equilibrium case by considering a manifestly symmetric form of the correct
self-energy which can be easily calculated numerically and yields an
overscreening--free coupling strength. Finally, we describe the numerical
implementation of this treatment into the first--principles Yambo code for the
calculations of phonon linewidths.


Abstract:
  Ab initio molecular dynamics (AIMD) with hybrid density functionals and plane
wave basis is computationally expensive due to the high computational cost of
exact exchange energy evaluation. Recently, we proposed a strategy to combine
adaptively compressed exchange (ACE) operator formulation and multiple time
step (MTS) integration scheme to reduce the computational cost significantly
[J. Chem. Phys. 151, 151102 (2019)]. However, it was found that the
construction of the ACE operator, which has to be done at least once in every
MD time step, is computationally expensive. In the present work, systematic
improvements are introduced to further speed-up by employing localized orbitals
for the construction of the ACE operator. By this, we could achieve a
computational speed-up of an order of magnitude for a periodic system
containing 32-water molecules. Benchmark calculations were carried out to show
the accuracy and efficiency of the method in predicting the structural and
dynamical properties of bulk water. To demonstrate the applicability,
computationally intensive free energy computations at the level of hybrid
density functional theory were performed to investigate (a) methyl formate
hydrolysis reaction in neutral aqueous medium and (b) proton transfer reaction
within the active site residues of class-C $\beta$-lactamase enzyme.


Abstract:
  We theoretically investigate the apparent contact angle of droplets on liquid
infused surfaces as a function of the relative size of the wetting ridge and
the deposited droplet. We provide an intuitive geometrical interpretation
whereby the variation in the apparent contact angle is due to the rotation of
the Neumann triangle. We also derive linear and quadratic corrections to the
apparent contact angle as power series expansion in terms of pressure
differences between the lubricant, droplet and gas phases. These expressions
are much simpler and more compact compared to those previously derived by
Semprebon et al. [Soft Matter, 2017, 13, 101-110].


Abstract:
  Self-attention has the promise of improving computer vision systems due to
parameter-independent scaling of receptive fields and content-dependent
interactions, in contrast to parameter-dependent scaling and
content-independent interactions of convolutions. Self-attention models have
recently been shown to have encouraging improvements on accuracy-parameter
trade-offs compared to baseline convolutional models such as ResNet-50. In this
work, we aim to develop self-attention models that can outperform not just the
canonical baseline models, but even the high-performing convolutional models.
We propose two extensions to self-attention that, in conjunction with a more
efficient implementation of self-attention, improve the speed, memory usage,
and accuracy of these models. We leverage these improvements to develop a new
self-attention model family, HaloNets, which reach state-of-the-art accuracies
on the parameter-limited setting of the ImageNet classification benchmark. In
preliminary transfer learning experiments, we find that HaloNet models
outperform much larger models and have better inference performance. On harder
tasks such as object detection and instance segmentation, our simple local
self-attention and convolutional hybrids show improvements over very strong
baselines. These results mark another step in demonstrating the efficacy of
self-attention models on settings traditionally dominated by convolutional
models.


Abstract:
  We introduce an additive basis of the integral cohomology ring of the
Peterson variety which reflects the geometry of certain subvarieties of the
Peterson variety. We explain the positivity of the structure constants from a
geometric viewpoint, and provide a manifestly positive combinatorial formula
for them. We also prove that our basis coincides with the additive basis
introduced by Harada-Tymoczko.


Abstract:
  We study non-linear optical effects in electron systems with and without
inversion symmetry in a Fabry-Perot cavity. General photon up- and
down-conversion processes are modeled by the coupling of a noninteracting
lattice model to two modes of the quantized light field. Effective descriptions
retaining the most relevant states are devised via downfolding and a
generalized Householder transformation. These models are used to relate the
transition amplitudes for even order photon-conversion processes to the shift
vector, a topological quantity describing the difference in polarization
between the valence and conduction band in non-centrosymmetric systems. We also
demonstrate that the truncated models, despite their small Hilbert space,
capture correlation effects induced by the photons in the electronic subsystem.


Abstract:
  In recent years, blockchain has grown in popularity due to its singular
attributes, enabling the development of new innovative decentralized
applications. But when companies consider leveraging blockchain for their
applications, the plethora of possible choices and the difficulty of
integrating blockchain into architectures can hinder its adoption. Our research
project aims to ease the adoption of blockchain into companies, notably with
the construction of an automated decision process to solve this issue in which
requirements are first-class citizens, a knowledge base containing
architectural patterns and blockchains refined over time, and an architecture
generator able to process outputs into architectural stubs. This paper will
also present our current progression on this decision process, by introducing
the preliminary version that is able to choose the most suitable blockchain
between multiple choices and our process-driven benchmarking tool.


Abstract:
  A leader-follower framework is proposed for multi-robot navigation of large
scale teams where the leader agents corral the follower agents. A group of
leaders is modeled as a 2D deformable object where discrete masses (i.e.,
leader robots) are interconnected by springs and dampers. A time-varying domain
is defined by the positions of leaders while the external forces induce
deformations of the domain from its nominal configuration. The team of
followers is performing coverage over the time-varying domain by employing a
perspective transformation that maps between the nominal and deformed
configurations. A decentralized control strategy is proposed where a leader
only takes local sensing information and information about its neighbors
(connected by virtual springs and dampers), and a follower only needs partial
information about leaders and information about its Delaunay neighbors.


Abstract:
  In the era of digitization, different actors in agriculture produce numerous
data. Such data contains already latent historical knowledge in the domain.
This knowledge enables us to precisely study natural hazards within global or
local aspects, and then improve the risk prevention tasks and augment the
yield, which helps to tackle the challenge of growing population and changing
alimentary habits. In particular, French Plants Health Bulletins (BSV, for its
name in French Bulletin de Sant{\'e} du V{\'e}g{\'e}tal) give information about
the development stages of phytosanitary risks in agricultural production.
However, they are written in natural language, thus, machines and human cannot
exploit them as efficiently as it could be. Natural language processing (NLP)
technologies aim to automatically process and analyze large amounts of natural
language data. Since the 2010s, with the increases in computational power and
parallelization, representation learning and deep learning methods became
widespread in NLP. Recent advancements Bidirectional Encoder Representations
from Transformers (BERT) inspire us to rethink of knowledge representation and
natural language understanding in plant health management domain. The goal in
this work is to propose a BERT-based approach to automatically classify the BSV
to make their data easily indexable. We sampled 200 BSV to finetune the
pretrained BERT language models and classify them as pest or/and disease and we
show preliminary results.