Abstract:
  We study the behavior of two-flavor dense quark matter under the influence of
an external magnetic field in the framework of a nonlocal chiral quark model
with separable interactions. The nonlocality is incorporated in the model by
using a Gaussian form factor. It is found that for low and moderate values of
magnetic field there is a decrease of the critical chiral restoration chemical
potential $\mu_c$, i.e. an inverse magnetic catalysis effect is observed. For
larger values of $eB$ the behavior of $\mu_c$ becomes more or less flat,
depending on the parametrization. Within the considered parametrization range
we do not find a significant growth of the critical chemical potential for
large magnetic fields, as occurs in the case of the local NJL model.


Abstract:
  Oxides-on-metals are the well-known "inverse" catalysts. Fundamental
knowledge on the structural transformations that occur in such systems under
oxidizing and reducing conditions is crucial for maximizing their catalytic
activity. Sintering of oxide species is one of the effects that may, on one
hand, lead to catalyst deactivation, but on the other constitutes a powerful
means for fine-tuning their size and structure - which is one of the major
challenges in the field. In this Letter, we show that the structure of
well-dispersed bilayer iron monoxide (FeO) islands grown on Ru(0001), which
constitutes a model "inverse" catalyst, can be optimized by thermally-driven
sintering and phase transition. The structural transformation occurs via the
Ostwald ripening mechanism, as observed in real time using in situ low energy
electron microscopy (LEEM). The structure of the islands before and after the
transformation is determined using ex situ experimental methods, as well as
theoretical calculations.


Abstract:
  We introduce a notion of smooth fields of operators following the notion of
smooth fields of Hilbert spaces recently defined by L. Lempert and R.
Sz\H{o}oke arXiv:1004.4863(2) . Formally, if $\nabla$ is the connection of a
smooth field of Hilbert spaces we show that $\hat\nabla=[\nabla,\cdot]$ defines
a connection on a suitable space of fields of operators. In order to provide
examples we prove that, if $u$ is a suitable constant of motion of
$h(q,p)=\|q\|^2$ (i.e.\ $\{u,h\}=0$), then $\mathfrak{Op}(u)$ is a smooth field
of operators over the open interval $(0,\infty)$, where $\mathfrak{Op}$ denotes
the canonical quantization (Weyl calculus). Moreover, in such case we show that
we can compute derivatives using the formula
$\hat\nabla_{X_0}(\mathfrak{Op}(u))=\mathfrak{Op}(\tilde\nabla_{X_0}(u))$,
where $\tilde\nabla$ is a Poisson connection on the Poisson algebra of
constants of motion and $X_0=2\lambda\frac{\partial}{\partial \lambda}$. We
also introduce a notion of smooth field of $C^*$-algebras and we give an
example using Hilbert modules theory.


Abstract:
  Existing point-cloud based 3D object detectors use convolution-like operators
to process information in a local neighbourhood with fixed-weight kernels and
aggregate global context hierarchically. However, non-local neural networks and
self-attention for 2D vision have shown that explicitly modeling long-range
interactions can lead to more robust and competitive models. In this paper, we
propose two variants of self-attention for contextual modeling in 3D object
detection by augmenting convolutional features with self-attention features. We
first incorporate the pairwise self-attention mechanism into the current
state-of-the-art BEV, voxel and point-based detectors and show consistent
improvement over strong baseline models of up to 1.5 3D AP while simultaneously
reducing their parameter footprint and computational cost by 15-80% and 30-50%,
respectively, on the KITTI validation set. We next propose a self-attention
variant that samples a subset of the most representative features by learning
deformations over randomly sampled locations. This not only allows us to scale
explicit global contextual modeling to larger point-clouds, but also leads to
more discriminative and informative feature descriptors. Our method can be
flexibly applied to most state-of-the-art detectors with increased accuracy and
parameter and compute efficiency. We show our proposed method improves 3D
object detection performance on KITTI, nuScenes and Waymo Open datasets. Code
is available at https://github.com/AutoVision-cloud/SA-Det3D.


Abstract:
  In current object detection, algorithms require the object to be directly
visible in order to be detected. As humans, however, we intuitively use visual
cues caused by the respective object to already make assumptions about its
appearance. In the context of driving, such cues can be shadows during the day
and often light reflections at night. In this paper, we study the problem of
how to map this intuitive human behavior to computer vision algorithms to
detect oncoming vehicles at night just from the light reflections they cause by
their headlights. For that, we present an extensive open-source dataset
containing 59746 annotated grayscale images out of 346 different scenes in a
rural environment at night. In these images, all oncoming vehicles, their
corresponding light objects (e.g., headlamps), and their respective light
reflections (e.g., light reflections on guardrails) are labeled. In this
context, we discuss the characteristics of the dataset and the challenges in
objectively describing visual cues such as light reflections. We provide
different metrics for different ways to approach the task and report the
results we achieved using state-of-the-art and custom object detection models
as a first benchmark. With that, we want to bring attention to a new and so far
neglected field in computer vision research, encourage more researchers to
tackle the problem, and thereby further close the gap between human performance
and computer vision systems.


Abstract:
  The dependencies of the B$_{i}$O$_{i}$ defect concentration on doping,
irradiation fluence and particle type in p-type silicon diodes have been
investigated. We evidenced that large data scattering occurs for fluences above
$10^{12}$ 1 MeV neutrons/cm$^2$, becoming significant larger for higher
fluences. We show that the B$_{i}$O$_{i}$ defect is metastable, with two
configurations A and B, of which only A is detected by Deep Level Transient
Spectroscopy and Thermally Stimulated Currents techniques. The defect's
electrical activity is influenced by the inherent variations in ambient and
procedural experimental conditions, resulting not only in a large scattering of
the results coming from the same type of measurement but making any correlation
between different types of experiments difficult. It is evidenced that the
variations in [B$_{i}$O$_{i}^\mathrm{A}$] are triggered by subjecting the
samples to an excess of carriers, by either heating or an inherent short
exposure to ambient light when manipulating the samples prior to experiments.
It causes $\approx$7h variations in both, the [B$_{i}$O$_{i}^\mathrm{A}$] and
in the effective space charge. The analyses of structural damage in a diode
irradiated with 10$^{19}$ 1 MeV neutrons/cm$^2$ revealed that the Si structure
remains crystalline and vacancies and interstitials organize in parallel tracks
normal to the Si-SiO$_{2}$ interface.


Abstract:
  Learning with an objective to minimize the mismatch with a reference
distribution has been shown to be useful for generative modeling and imitation
learning. In this paper, we investigate whether one such objective, the
Wasserstein-1 distance between a policy's state visitation distribution and a
target distribution, can be utilized effectively for reinforcement learning
(RL) tasks. Specifically, this paper focuses on goal-conditioned reinforcement
learning where the idealized (unachievable) target distribution has full
measure at the goal. This paper introduces a quasimetric specific to Markov
Decision Processes (MDPs) and uses this quasimetric to estimate the above
Wasserstein-1 distance. It further shows that the policy that minimizes this
Wasserstein-1 distance is the policy that reaches the goal in as few steps as
possible. Our approach, termed Adversarial Intrinsic Motivation (AIM),
estimates this Wasserstein-1 distance through its dual objective and uses it to
compute a supplemental reward function. Our experiments show that this reward
function changes smoothly with respect to transitions in the MDP and directs
the agent's exploration to find the goal efficiently. Additionally, we combine
AIM with Hindsight Experience Replay (HER) and show that the resulting
algorithm accelerates learning significantly on several simulated robotics
tasks when compared to other rewards that encourage exploration or accelerate
learning.


Abstract:
  The conventional spectral mapping theorem for quantum walks can only be
applied for walks employing a shift operator whose square is the identity. This
theorem gives most of the eigenvalues of the time evolution $U$ by lifting the
eigenvalues of an induced self-adjoint matrix $T$ onto the unit circle on the
complex plane. We acquire a new spectral mapping theorem for the Grover walk
with a shift operator whose cube is the identity on finite graphs. Moreover,
graphs we can consider for a quantum walk with such a shift operator is
characterized by a triangulation. We call these graphs triangulable graphs in
this paper. One of the differences between our spectral mapping theorem and the
conventional one is that lifting the eigenvalues of $T-1/2$ onto the unit
circle gives most of the eigenvalues of $U$.


Abstract:
  A lingering mystery in core-collapse supernova theory is how collective
neutrino oscillations affect the dynamics. All previously identified flavor
instabilities, some of which might make the effects considerable, are
essentially collisionless phenomena. Here it is shown that collisional
instabilities exist as well. They are associated with asymmetries between the
neutrino and antineutrino interaction rates, are possibly prevalent deep inside
supernovae, and pose an unusual instance of decoherent interactions with a
thermal environment causing the sustained growth of quantum coherence.


Abstract:
  The constraint satisfaction problem (CSP) has important applications in
computer science and AI. In particular, infinite-domain CSPs have been
intensively used in subareas of AI such as spatio-temporal reasoning. Since
constraint satisfaction is a computationally hard problem, much work has been
devoted to identifying restricted problems that are efficiently solvable. One
way of doing this is to restrict the interactions of variables and constraints,
and a highly successful approach is to bound the treewidth of the underlying
primal graph. Bodirsky & Dalmau [J. Comput. System. Sci. 79(1), 2013] and Huang
et al. [Artif. Intell. 195, 2013] proved that CSP$(\Gamma)$ can be solved in
$n^{f(w)}$ time (where $n$ is the size of the instance, $w$ is the treewidth of
the primal graph and $f$ is a computable function) for certain classes of
constraint languages $\Gamma$. We improve this bound to $f(w) \cdot n^{O(1)}$,
where the function $f$ only depends on the language $\Gamma$, for CSPs whose
basic relations have the patchwork property. Hence, such problems are
fixed-parameter tractable and our algorithm is asymptotically faster than the
previous ones. Additionally, our approach is not restricted to binary
constraints, so it is applicable to a strictly larger class of problems than
that of Huang et al. However, there exist natural problems that are covered by
Bodirsky & Dalmau's algorithm but not by ours, and we begin investigating ways
of generalising our results to larger families of languages. We also analyse
our algorithm with respect to its running time and show that it is optimal
(under the Exponential Time Hypothesis) for certain languages such as Allen's
Interval Algebra.


Abstract:
  The COVID-19 pandemic has resulted in more than 125 million infections and
more than 2.7 million casualties. In this paper, we attempt to classify covid
vs non-covid cough sounds using signal processing and deep learning methods.
Air turbulence, the vibration of tissues, movement of fluid through airways,
opening, and closure of glottis are some of the causes for the production of
the acoustic sound signals during cough. Does the COVID-19 alter the acoustic
characteristics of breath, cough, and speech sounds produced through the
respiratory system? This is an open question waiting for answers. In this
paper, we incorporated novel data augmentation methods for cough sound
augmentation and multiple deep neural network architectures and methods along
with handcrafted features. Our proposed system gives 14% absolute improvement
in area under the curve (AUC). The proposed system is developed as part of
Interspeech 2021 special sessions and challenges viz. diagnosing of COVID-19
using acoustics (DiCOVA). Our proposed method secured the 5th position on the
leaderboard among 29 participants.


Abstract:
  Emerging applications of collaborative autonomy, such as Multi-Target
Tracking, Unknown Map Exploration, and Persistent Surveillance, require robots
plan paths to navigate an environment while maximizing the information
collected via on-board sensors. In this paper, we consider such information
acquisition tasks but in adversarial environments, where attacks may
temporarily disable the robots' sensors. We propose the first receding horizon
algorithm, aiming for robust and adaptive multi-robot planning against any
number of attacks, which we call Resilient Active Information acquisitioN
(RAIN). RAIN calls, in an online fashion, a Robust Trajectory Planning (RTP)
subroutine which plans attack-robust control inputs over a look-ahead planning
horizon. We quantify RTP's performance by bounding its suboptimality. We base
our theoretical analysis on notions of curvature introduced in combinatorial
optimization. We evaluate RAIN in three information acquisition scenarios:
Multi-Target Tracking, Occupancy Grid Mapping, and Persistent Surveillance. The
scenarios are simulated in C++ and a Unity-based simulator. In all simulations,
RAIN runs in real-time, and exhibits superior performance against a
state-of-the-art baseline information acquisition algorithm, even in the
presence of a high number of attacks. We also demonstrate RAIN's robustness and
effectiveness against varying models of attacks (worst-case and random), as
well as, varying replanning rates.


Abstract:
  Machine learning and deep learning have shown great promise in mobile sensing
applications, including Human Activity Recognition. However, the performance of
such models in real-world settings largely depends on the availability of large
datasets that captures diverse behaviors. Recently, studies in computer vision
and natural language processing have shown that leveraging massive amounts of
unlabeled data enables performance on par with state-of-the-art supervised
models.
  In this work, we present SelfHAR, a semi-supervised model that effectively
learns to leverage unlabeled mobile sensing datasets to complement small
labeled datasets. Our approach combines teacher-student self-training, which
distills the knowledge of unlabeled and labeled datasets while allowing for
data augmentation, and multi-task self-supervision, which learns robust
signal-level representations by predicting distorted versions of the input.
  We evaluated SelfHAR on various HAR datasets and showed state-of-the-art
performance over supervised and previous semi-supervised approaches, with up to
12% increase in F1 score using the same number of model parameters at
inference. Furthermore, SelfHAR is data-efficient, reaching similar performance
using up to 10 times less labeled data compared to supervised approaches. Our
work not only achieves state-of-the-art performance in a diverse set of HAR
datasets, but also sheds light on how pre-training tasks may affect downstream
performance.


Abstract:
  State-of-the-art approaches to design, develop and optimize software
packet-processing programs are based on static compilation: the compiler's
input is a description of the forwarding plane semantics and the output is a
binary that can accommodate any control plane configuration or input traffic.
In this paper, we demonstrate that tracking control plane actions and
packet-level traffic dynamics at run time opens up new opportunities for code
specialization. We present Morpheus, a system working alongside static
compilers that continuously optimizes the targeted networking code. We
introduce a number of new techniques, from static code analysis to adaptive
code instrumentation, and we implement a toolbox of domain specific
optimizations that are not restricted to a specific data plane framework or
programming language. We apply Morpheus to several eBPF and DPDK programs
including Katran, Facebook's production-grade load balancer. We compare
Morpheus against state-of-the-art optimization frameworks and show that it can
bring up to 2x throughput improvement, while halving the 99th percentile
latency.


Abstract:
  Self-driving technology is expected to revolutionize different sectors and is
seen as the natural evolution of road vehicles. In the last years, real-world
validation of designed and virtually tested solutions is growing in importance
since simulated environments will never fully replicate all the aspects that
can affect results in the real world. To this end, this paper presents our
prototype platform for experimental research on connected and autonomous
driving projects. In detail, the paper presents the overall architecture of the
vehicle focusing both on mechanical aspects related to remote actuation and
sensors set-up and software aspects by means of a comprehensive description of
the main algorithms required for autonomous driving as ego-localization,
environment perception, motion planning, and actuation. Finally, experimental
tests conducted in an urban-like environment are reported to validate and
assess the performances of the overall system.


Abstract:
  Multi-Task Learning has emerged as a methodology in which multiple tasks are
jointly learned by a shared learning algorithm, such as a DNN. MTL is based on
the assumption that the tasks under consideration are related; therefore it
exploits shared knowledge for improving performance on each individual task.
Tasks are generally considered to be homogeneous, i.e., to refer to the same
type of problem. Moreover, MTL is usually based on ground truth annotations
with full, or partial overlap across tasks. In this work, we deal with
heterogeneous MTL, simultaneously addressing detection, classification &
regression problems. We explore task-relatedness as a means for co-training, in
a weakly-supervised way, tasks that contain little, or even non-overlapping
annotations. Task-relatedness is introduced in MTL, either explicitly through
prior expert knowledge, or through data-driven studies. We propose a novel
distribution matching approach, in which knowledge exchange is enabled between
tasks, via matching of their predictions' distributions. Based on this
approach, we build FaceBehaviorNet, the first framework for large-scale face
analysis, by jointly learning all facial behavior tasks. We develop case
studies for: i) continuous affect estimation, action unit detection, basic
emotion recognition; ii) attribute detection, face identification.
  We illustrate that co-training via task relatedness alleviates negative
transfer. Since FaceBehaviorNet learns features that encapsulate all aspects of
facial behavior, we conduct zero-/few-shot learning to perform tasks beyond the
ones that it has been trained for, such as compound emotion recognition. By
conducting a very large experimental study, utilizing 10 databases, we
illustrate that our approach outperforms, by large margins, the
state-of-the-art in all tasks and in all databases, even in these which have
not been used in its training.


Abstract:
  The central challenge for describing the dynamics in open quantum systems is
that the Hilbert space of typical environments is too large to be treated
exactly. In some cases, such as when the environment has a short memory time or
only interacts weakly with the system, approximate descriptions of the system
are possible. Beyond these, numerically exact methods exist, but these are
typically restricted to baths with Gaussian correlations, such as
non-interacting bosons. Here we present a numerically exact method for
simulating open quantum systems with arbitrary environments which consist of a
set of independent degrees of freedom. Our approach automatically reduces the
large number of environmental degrees of freedom to those which are most
relevant. Specifically, we show how the process tensor -- which describes the
effect of the environment -- can be iteratively constructed and compressed
using matrix product state techniques. We demonstrate the power of this method
by applying it to problems with bosonic, fermionic, and spin environments:
electron transport, phonon effects and radiative decay in quantum dots, central
spin dynamics, anharmonic environments, dispersive coupling to time-dependent
lossy cavity modes, and superradiance. The versatility and efficiency of our
automated compression of environments (ACE) method provides a practical
general-purpose tool for open quantum systems.


Abstract:
  Early methods in the rapidly developing field of neural architecture search
(NAS) required fully training thousands of neural networks. To reduce this
extreme computational cost, dozens of techniques have since been proposed to
predict the final performance of neural architectures. Despite the success of
such performance prediction methods, it is not well-understood how different
families of techniques compare to one another, due to the lack of an
agreed-upon evaluation metric and optimization for different constraints on the
initialization time and query time. In this work, we give the first large-scale
study of performance predictors by analyzing 31 techniques ranging from
learning curve extrapolation, to weight-sharing, to supervised learning, to
"zero-cost" proxies. We test a number of correlation- and rank-based
performance measures in a variety of settings, as well as the ability of each
technique to speed up predictor-based NAS frameworks. Our results act as
recommendations for the best predictors to use in different settings, and we
show that certain families of predictors can be combined to achieve even better
predictive power, opening up promising research directions. Our code, featuring
a library of 31 performance predictors, is available at
https://github.com/automl/naslib.


Abstract:
  This document presents in detail the derivation of the formulas that describe
the resonance driving terms and the tune spread with amplitude generated by the
beam-beam long range interactions and the DC wire compensators in cyclical
machines. This analysis make use of the weak-strong approximation.


Abstract:
  For a given Lipschitz domain $\Omega$, it is a classical result that the
trace space of $W^{1,p}(\Omega)$ is $W^{1-1/p,p}(\partial\Omega)$, namely any
$W^{1,p}(\Omega)$ function has a well-defined $W^{1-1/p,p}(\partial\Omega)$
trace on its codimension-1 boundary $\partial\Omega$ and any
$W^{1-1/p,p}(\partial\Omega)$ function on $\partial\Omega$ can be extended to a
$W^{1,p}(\Omega)$ function. Recently, Dyda and Kassmann (2019) characterize the
trace space for nonlocal Dirichlet problems involving integrodifferential
operators with infinite interaction ranges, where the boundary datum is
provided on the whole complement of the given domain
$\mathbb{R}^d\backslash\Omega$. In this work, we study function spaces for
nonlocal Dirichlet problems with a finite range of nonlocal interactions, which
naturally serves a bridging role between the classical local PDE problem and
the nonlocal problem with infinite interaction ranges. For these nonlocal
Dirichlet problems, the boundary conditions are normally imposed on a region
with finite thickness volume which lies outside of the domain. We introduce a
function space on the volumetric boundary region that serves as a trace space
for these nonlocal problems and study the related extension results. Moreover,
we discuss the consistency of the new nonlocal trace space with the classical
$W^{1-1/p,p}(\partial\Omega)$ space as the size of nonlocal interaction tends
to zero. In making this connection, we conduct an investigation on the
relations between nonlocal interactions on a larger domain and the induced
interactions on its subdomain. The various forms of trace, embedding and
extension theorems may then be viewed as consequences in different scaling
limits.


Abstract:
  Some reinforcement learning methods suffer from high sample complexity
causing them to not be practical in real-world situations. $Q$-function reuse,
a transfer learning method, is one way to reduce the sample complexity of
learning, potentially improving usefulness of existing algorithms. Prior work
has shown the empirical effectiveness of $Q$-function reuse for various
environments when applied to model-free algorithms. To the best of our
knowledge, there has been no theoretical work showing the regret of
$Q$-function reuse when applied to the tabular, model-free setting. We aim to
bridge the gap between theoretical and empirical work in $Q$-function reuse by
providing some theoretical insights on the effectiveness of $Q$-function reuse
when applied to the $Q$-learning with UCB-Hoeffding algorithm. Our main
contribution is showing that in a specific case if $Q$-function reuse is
applied to the $Q$-learning with UCB-Hoeffding algorithm it has a regret that
is independent of the state or action space. We also provide empirical results
supporting our theoretical findings.


Abstract:
  We investigate experimentally and theoretically diffusiophoretic separation
of negatively charged particles in a rectangular channel flow, driven by CO2
dissolution from one side-wall. Since the negatively charged particles create
an exclusion zone near the boundary where CO2 is introduced, we model the
problem by applying a shear flow approximation in a two-dimensional
configuration. From the form of the equations we define a similarity variable
to transform the reaction-diffusion equations for CO2 and ions and the
advection-diffusion equation for the particle distribution to ordinary
differential equations. The definition of the similarity variable suggests a
characteristic length scale for the particle exclusion zone. We consider
height-averaged flow behaviors in rectangular channels to rationalize and
connect our experimental observations with the model, by calculating the wall
shear rate as functions of channel dimensions. Our observations and the
theoretical model provide the design parameters such as flow speed, channel
dimensions and CO2 pressure for the in-flow water cleaning systems.


Abstract:
  Being fundamentally a non-equilibrium process, synchronization comes with
unavoidable energy costs and has to be maintained under the constraint of
limited resources. Such resource constraints are often reflected as a finite
coupling budget available in a network to facilitate interaction and
communication. Here, we show that introducing temporal variation in the network
structure can lead to efficient synchronization even when stable synchrony is
impossible in any static network under the given budget, thereby demonstrating
a fundamental advantage of temporal networks. The temporal networks generated
by our open-loop design are versatile in the sense of promoting synchronization
for systems with vastly different dynamics, including periodic and chaotic
dynamics in both discrete-time and continuous-time models. Furthermore, we link
the dynamic stabilization effect of the changing topology to the curvature of
the master stability function, which provides analytical insights into
synchronization on temporal networks in general. In particular, our results
shed light on the effect of network switching rate and explain why certain
temporal networks synchronize only for intermediate switching rate.


Abstract:
  In late 2019, a novel coronavirus, the SARS-CoV-2 outbreak was identified in
Wuhan, China and later spread to every corner of the globe. Whilst the number
of infection-induced deaths in Ghana, West Africa are minimal when compared
with the rest of the world, the impact on the local health service is still
significant. Compartmental models are a useful framework for investigating
transmission of diseases in societies. To understand how the infection will
spread and how to limit the outbreak. We have developed a modified SEIR
compartmental model with nine compartments (CoVCom9) to describe the dynamics
of SARS-CoV-2 transmission in Ghana. We have carried out a detailed
mathematical analysis of the CoVCom9, including the derivation of the basic
reproduction number, $\mathcal{R}_{0}$. In particular, we have shown that the
disease-free equilibrium is globally asymptotically stable when
$\mathcal{R}_{0}<1$ via a candidate Lyapunov function. Using the SARS-CoV-2
reported data for confirmed-positive cases and deaths from March 13 to August
10, 2020, we have parametrised the CoVCom9 model. The results of this fit show
good agreement with data. We used Latin hypercube sampling-rank correlation
coefficient (LHS-PRCC) to investigate the uncertainty and sensitivity of
$\mathcal{R}_{0}$ since the results derived are significant in controlling the
spread of SARS-CoV-2. We estimate that over this five month period, the basic
reproduction number is given by $\mathcal{R}_{0} = 3.110$, with the 95\%
confidence interval being $2.042 \leq \mathcal{R}_0 \leq 3.240$, and the mean
value being $\mathcal{R}_{0}=2.623$. Of the 32 parameters in the model, we find
that just six have a significant influence on $\mathcal{R}_{0}$, these include
the rate of testing, where an increasing testing rate contributes to the
reduction of $\mathcal{R}_{0}$.


Abstract:
  Unsupervised representation learning achieves promising performances in
pre-training representations for object detectors. However, previous approaches
are mainly designed for image-level classification, leading to suboptimal
detection performance. To bridge the performance gap, this work proposes a
simple yet effective representation learning method for object detection, named
patch re-identification (Re-ID), which can be treated as a contrastive pretext
task to learn location-discriminative representation unsupervisedly, possessing
appealing advantages compared to its counterparts. Firstly, unlike
fully-supervised person Re-ID that matches a human identity in different camera
views, patch Re-ID treats an important patch as a pseudo identity and
contrastively learns its correspondence in two different image views, where the
pseudo identity has different translations and transformations, enabling to
learn discriminative features for object detection. Secondly, patch Re-ID is
performed in Deeply Unsupervised manner to learn multi-level representations,
appealing to object detection. Thirdly, extensive experiments show that our
method significantly outperforms its counterparts on COCO in all settings, such
as different training iterations and data percentages. For example, Mask R-CNN
initialized with our representation surpasses MoCo v2 and even its
fully-supervised counterparts in all setups of training iterations (e.g. 2.1
and 1.1 mAP improvement compared to MoCo v2 in 12k and 90k iterations
respectively). Code will be released at https://github.com/dingjiansw101/DUPR.


Abstract:
  We present a comprehensive ultrasound study of the prototypical heavy-fermion
material CeRhIn$_5$, examining the origin of the enigmatic 30 T transition. For
a field applied at 2$^\circ$ from the $c$ axis, we observed two sharp anomalies
in the sound velocity, at $B_m \approx$ 20 T and $B^* \approx$ 30 T, in all the
symmetry-breaking ultrasound modes at low temperatures. The lower-field anomaly
corresponds to the well-known first-order metamagnetic
incommensurate-to-commensurate transition. The higher-field anomaly takes place
at 30 T, where an electronic-nematic transition was previously suggested to
occur. Both anomalies, observed only within the antiferromagnetic state, are of
similar shape, but the corresponding changes of the ultrasound velocity have
opposite signs. Based on our experimental results, we suggest that a
field-induced magnetic transition from a commensurate to another incommensurate
antiferromagnetic state occurs at $B^*$. With further increasing the field
angle from the $c$ axis, the anomaly at $B^*$ slowly shifts to higher fields,
broadens, and becomes smaller in magnitude. Traced up to 30$^\circ$ from the
$c$ axis, it is no longer observed at 40$^\circ$ below 36 T.


Abstract:
  The Earth's magnetosphere represents a natural plasma laboratory that allows
us to study the behavior of particle distribution functions in the absence of
Coulomb collisions, typically described by the Kappa distributions. We have
investigated the properties of these functions for ions and electrons in
different magnetospheric regions, thereby making it possible to reveal the
$\kappa$-parameters for a wide range of plasma beta ($\beta$) values (from
$10^{-3}$ to $10^{2}$). This was done using simultaneous ion and electron
measurements from the five Time History of Events and Macroscale Interactions
during Substorms (THEMIS) spacecraft spanning the years 2008 to 2018. It was
found that for a fixed plasma $\beta$, the $\kappa$-index and core energy
($E_c$) of the distribution can be modeled by the power-law
$\kappa=AE_c^\gamma$ for both species, and the relation between $\beta$,
$\kappa$, and $E_c$ is much more complex than earlier reported: both $A$ and
$\gamma$ exhibit systematic dependencies with $\beta$. Our results indicate
that $\beta \sim 0.1-0.3$ is a range where the plasma is more dynamic since it
is influenced by both the magnetic field and temperature fluctuations, which
suggests that the transition between magnetically dominated plasmas to
kinetically dominated plasmas occurs at these values of $\beta$. For $\beta > 1
$, both $A$ and $\gamma$ take nearly constant values, a feature that is
especially notable for the electrons and might be related to their
demagnetization. The relation between $\beta$, $\kappa$, and $E_c$ that we
present is an important result that can be used by theoretical models in the
future.


Abstract:
  Conditionally on the $abc$ conjecture, we generalize previous work of Clark
and the author to show that a superelliptic curve $C: y^n = f(x)$ of
sufficiently high genus has infinitely many twists violating the Hasse
Principle if and only if $f(x)$ has no $\mathbb Q$-rational roots. We also show
unconditionally that a curve defined by $C: y^{pN}=f(x)$ has infinitely many
twists violating the Hasse Principle over any number field $k$ such that $k$
contains the $p$th roots of unity and $f(x)$ has no $k$-rational roots.


Abstract:
  The growth rate in the amount of biomedical documents is staggering.
Unlocking information trapped in these documents can enable researchers and
practitioners to operate confidently in the information world. Biomedical NER,
the task of recognising biomedical names, is usually employed as the first step
of the NLP pipeline. Standard NER models, based on sequence tagging technique,
are good at recognising short entity mentions in the generic domain. However,
there are several open challenges of applying these models to recognise
biomedical names: 1) Biomedical names may contain complex inner structure
(discontinuity and overlapping) which cannot be recognised using standard
sequence tagging technique; 2) The training of NER models usually requires
large amount of labelled data, which are difficult to obtain in the biomedical
domain; and, 3) Commonly used language representation models are pre-trained on
generic data; a domain shift therefore exists between these models and target
biomedical data. To deal with these challenges, we explore several research
directions and make the following contributions: 1) we propose a
transition-based NER model which can recognise discontinuous mentions; 2) We
develop a cost-effective approach that nominates the suitable pre-training
data; and, 3) We design several data augmentation methods for NER. Our
contributions have obvious practical implications, especially when new
biomedical applications are needed. Our proposed data augmentation methods can
help the NER model achieve decent performance, requiring only a small amount of
labelled data. Our investigation regarding selecting pre-training data can
improve the model by incorporating language representation models, which are
pre-trained using in-domain data. Finally, our proposed transition-based NER
model can further improve the performance by recognising discontinuous
mentions.


Abstract:
  Ultrasound (US) images usually contain identifying information outside the
ultrasound fan area and manual annotations placed by the sonographers during
exams. For those images to be exploitable in a Deep Learning framework, one
needs to first delineate the border of the fan which delimits the ultrasound
fan area and then remove other annotations inside. We propose a parametric
probabilistic approach for the first task. We make use of this method to
generate a training data set with segmentation masks of the region of interest
(ROI) and train a U-Net to perform the same task in a supervised way, thus
considerably reducing computational time of the method, one hundred and sixty
times faster. These images are then processed with existing inpainting methods
to remove annotations present inside the fan area. To the best of our
knowledge, this is the first parametric approach to quickly detect the fan in
an ultrasound image without any other information than the image itself.


Abstract:
  Robotic tasks such as manipulation with visual inputs require image features
that capture the physical properties of the scene, e.g., the position and
configuration of objects. Recently, it has been suggested to learn such
features in an unsupervised manner from simulated, self-supervised, robot
interaction; the idea being that high-level physical properties are well
captured by modern physical simulators, and their representation from visual
inputs may transfer well to the real world. In particular, learning methods
based on noise contrastive estimation have shown promising results. To
robustify the simulation-to-real transfer, domain randomization (DR) was
suggested for learning features that are invariant to irrelevant visual
properties such as textures or lighting. In this work, however, we show that a
naive application of DR to unsupervised learning based on contrastive
estimation does not promote invariance, as the loss function maximizes mutual
information between the features and both the relevant and irrelevant visual
properties. We propose a simple modification of the contrastive loss to fix
this, exploiting the fact that we can control the simulated randomization of
visual properties. Our approach learns physical features that are significantly
more robust to visual domain variation, as we demonstrate using both rigid and
non-rigid objects.


Abstract:
  We show that a partition of the unity (or POVM) on a Hilbert space that is
almost orthogonal is close to an orthogonal partition of unity in the same von
Neumann algebra. This generalizes to infinite dimension and slightly
strengthens previous results in matrix algebras by Kempe-Vidick and
Ji-Natarajan-Vidick-Wright-Yuen.
  We also generalize to infinite dimension a duality result between POVMs and
minimal majorants of finite subsets in the predual of a von Neumann algebra.


Abstract:
  We investigate entanglement of the Hawking radiation in a dispersive model
with subluminal dispersion. In this model, feature of the Hawking radiation is
represented by three mode Bogoliubov transformation connecting the in-vacuum
state and the out-state. We obtain the exact form of the tripartite in-vacuum
state which encodes structure of multipartite entanglement. Bogoliubov
coefficients are computed by numerical calculation of the wave equation with
subluminal dispersion and it is found that genuine tripartite entanglement
persists in whole frequency range up to the cutoff arisen from the subluminal
dispersion. In the low frequency region, amount of genuine tripartite
entanglement is small compared to bipartite entanglement between the Hawking
particle and its partner mode, and the deviation from the thermal spectrum is
negligible. On the other hand, in the high frequency region near the cutoff,
entanglement is equally shared by two pairs of three modes and the partner
picture of the Hawking radiation does not hold.


Abstract:
  We investigate scaling and efficiency of the deep neural network multigrid
method (DNN-MG).
  DNN-MG is a novel neural network-based technique for the simulation of the
Navier-Stokes equations that combines an adaptive geometric multigrid solver,
i.e. a highly efficient
  classical solution scheme, with a recurrent neural network with memory.
  The neural network replaces in DNN-MG one or multiple finest multigrid layers
and provides a correction for the classical solve in the next time step.
  This leads to little degradation in the solution quality while substantially
reducing the overall computational costs.
  At the same time, the use of the multigrid solver at the coarse scales allows
for a compact network that is easy to train, generalizes well, and allows for
the incorporation of physical constraints.
  Previous work on DNN-MG focused on the overall scheme and how to enforce
divergence freedom in the solution.
  In this work, we investigate how the network size affects training and
solution quality and the overall runtime of the computations.
  Our results demonstrate that larger networks are able to capture the
  flow behavior better while requiring only little additional training time.
  At runtime, the use of the neural network correction can even reduce the
computation time compared to a classical multigrid simulation through a faster
convergence of the nonlinear solve that is required at every time step.


Abstract:
  Inferring causal individual treatment effect (ITE) from observational data is
a challenging problem whose difficulty is exacerbated by the presence of
treatment assignment bias. In this work, we propose a new way to estimate the
ITE using the domain generalization framework of invariant risk minimization
(IRM). IRM uses data from multiple domains, learns predictors that do not
exploit spurious domain-dependent factors, and generalizes better to unseen
domains. We propose an IRM-based ITE estimator aimed at tackling treatment
assignment bias when there is little support overlap between the control group
and the treatment group. We accomplish this by creating diversity: given a
single dataset, we split the data into multiple domains artificially. These
diverse domains are then exploited by IRM to more effectively generalize
regression-based models to data regions that lack support overlap. We show
gains over classical regression approaches to ITE estimation in settings when
support mismatch is more pronounced.


Abstract:
  Bandit algorithms are increasingly used in real-world sequential
decision-making problems. Associated with this is an increased desire to be
able to use the resulting datasets to answer scientific questions like: Did one
type of ad lead to more purchases? In which contexts is a mobile health
intervention effective? However, classical statistical approaches fail to
provide valid confidence intervals when used with data collected with bandit
algorithms. Alternative methods have recently been developed for simple models
(e.g., comparison of means). Yet there is a lack of general methods for
conducting statistical inference using more complex models on data collected
with (contextual) bandit algorithms; for example, current methods cannot be
used for valid inference on parameters in a logistic regression model for a
binary reward. In this work, we develop theory justifying the use of
M-estimators -- which includes estimators based on empirical risk minimization
as well as maximum likelihood -- on data collected with adaptive algorithms,
including (contextual) bandit algorithms. Specifically, we show that
M-estimators, modified with particular adaptive weights, can be used to
construct asymptotically valid confidence regions for a variety of inferential
targets.


Abstract:
  Fast outflows and their interaction with slow shells (generally known as the
fossil circumstellar envelope of asymptotic giant branch stars) play an
important role in the structure and kinematics of protoplanetary and planetary
nebulae (pPNe, PNe). To properly study their effects within these objects, we
also need to observe the intermediate-temperature gas, which is only detectable
in the far-infrared (FIR) and submillimetre (submm) transitions. We study the
physical conditions of the outflows presented in a number of pPNe and PNe, with
a focus on their temperature and excitation states. We carried out
Herschel/HIFI observations in the submm lines of 12CO in nine pPNe and nine PNe
and complemented them with low-J CO spectra obtained with the IRAM 30m
telescope and taken from the literature. The spectral resolution of HIFI allows
us to identify and measure the different nebular components in the line
profiles. The comparison with large velocity gradient (LVG) model predictions
was used to estimate the physical conditions of the warm gas in the nebulae,
such as excitation conditions, temperature, and density. We found high kinetic
temperatures for the fast winds of pPNe, typically reaching between 75 K and
200 K. In contrast, the high-velocity gas in the sampled PNe is colder, with
characteristic temperatures between 25 K and 75 K, and it is found in a lower
excitation state. We interpret this correlation of the kinetic temperature and
excitation state of fast outflows with the amount of time elapsed since their
acceleration (probably driven by shocks) as a consequence of the cooling that
occurred during the pPN phase.


Abstract:
  Image registration plays an important role in medical image analysis.
Conventional optimization based methods provide an accurate estimation due to
the iterative process at the cost of expensive computation. Deep learning
methods such as learn-to-map are much faster but either iterative or
coarse-to-fine approach is required to improve accuracy for handling large
motions. In this work, we proposed to learn a registration optimizer via a
multi-scale neural ODE model. The inference consists of iterative gradient
updates similar to a conventional gradient descent optimizer but in a much
faster way, because the neural ODE learns from the training data to adapt the
gradient efficiently at each iteration. Furthermore, we proposed to learn a
modal-independent similarity metric to address image appearance variations
across different image contrasts. We performed evaluations through extensive
experiments in the context of multi-contrast 3D MR images from both public and
private data sources and demonstrate the superior performance of our proposed
methods.


Abstract:
  Till now geometric structures don't play a major role in cryptography.
Gilbert, MacWilliams and Sloane introduced an authentication scheme in the
projective plane and showed its perfectness in the sense of Shannon. In
arXiv:2102.10321 we introduced an encryption scheme in the M\"obius plane and
showed that it fulfills Shannon's requirement of perfectness in first
approximation and also the requirement of completeness according to Kam and
Davida. In this paper we will apply a similar approach to define encryption
schemes in the geometries of the Laguerre plande and the Minkowski plane. We
will show that the encryption scheme in the Laguerre geometry meets Shannon's
requirement of perfectness sharp and that the encryption scheme in the
Minkowski geometry meets this requirement in first approximation. The Laguerre
cipher also fulfills the requirement of completeness according to Kam and
Davida.


Abstract:
  With the advent of deep learning algorithms, fully automated radiological
image analysis is within reach. In spine imaging, several atlas- and
shape-based as well as deep learning segmentation algorithms have been
proposed, allowing for subsequent automated analysis of morphology and
pathology. The first Large Scale Vertebrae Segmentation Challenge (VerSe 2019)
showed that these perform well on normal anatomy, but fail in variants not
frequently present in the training dataset. Building on that experience, we
report on the largely increased VerSe 2020 dataset and results from the second
iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020
comprises annotated spine computed tomography (CT) images from 300 subjects
with 4142 fully visualized and annotated vertebrae, collected across multiple
centres from four different scanner manufacturers, enriched with cases that
exhibit anatomical variants such as enumeration abnormalities (n=77) and
transitional vertebrae (n=161). Metadata includes vertebral labelling
information, voxel-level segmentation masks obtained with a human-machine
hybrid algorithm and anatomical ratings, to enable the development and
benchmarking of robust and accurate segmentation algorithms.


Abstract:
  Increasing the level of automation in pharmaceutical laboratories and
production facilities plays a crucial role in delivering medicine to patients.
However, the particular requirements of this field make it challenging to adapt
cutting-edge technologies present in other industries. This article provides an
overview of relevant approaches and how they can be utilized in the
pharmaceutical industry, especially in development laboratories. Recent
advancements include the application of flexible mobile manipulators capable of
handling complex tasks. However, integrating devices from many different
vendors into an end-to-end automation system is complicated due to the
diversity of interfaces. Therefore, various approaches for standardization are
considered in this article, and a concept is proposed for taking them a step
further. This concept enables a mobile manipulator with a vision system to
"learn" the pose of each device and - utilizing a barcode - fetch interface
information from a universal cloud database. This information includes control
and communication protocol definitions and a representation of robot actions
needed to operate the device. In order to define the movements in relation to
the device, devices have to feature - besides the barcode - a fiducial marker
as standard. The concept will be elaborated following appropriate research
activities in follow-up papers.


Abstract:
  It has been a half-decade since the first direct detection of gravitational
waves, which signifies the coming of the era of the gravitational-wave
astronomy and gravitational-wave cosmology. The increasing number of the
detected gravitational-wave events has revealed the promising capability of
constraining various aspects of cosmology, astronomy, and gravity. Due to the
limited space in this review article, we will briefly summarize the recent
progress over the past five years, but with a special focus on some of our own
work for the Key Project ``Physics associated with the gravitational waves''
supported by the National Natural Science Foundation of China. In particular,
(1) we have presented the mechanism of the gravitational-wave production during
some physical processes of the early Universe, such as inflation, preheating
and phase transition, and the cosmological implications of gravitational-wave
measurements; (2) we have put constraints on the neutron star maximum mass
according to GW170817 observations; (3) we have developed a numerical
relativity algorithm based on the finite element method and a waveform model
for the binary black hole coalescence along an eccentric orbit.


Abstract:
  Despite tremendous efforts, it is very challenging to generate a robust model
to assist in the accurate quantification assessment of COVID-19 on chest CT
images. Due to the nature of blurred boundaries, the supervised segmentation
methods usually suffer from annotation biases. To support unbiased lesion
localisation and to minimise the labeling costs, we propose a data-driven
framework supervised by only image-level labels. The framework can explicitly
separate potential lesions from original images, with the help of a generative
adversarial network and a lesion-specific decoder. Experiments on two COVID-19
datasets demonstrate the effectiveness of the proposed framework and its
superior performance to several existing methods.


Abstract:
  We discuss the obstruction to the construction of a multiparticle field
theory on a $\kappa$-Minkowski noncommutative spacetime: the existence of
multilocal functions which respect the deformed symmetries of the problem. This
construction is only possible for a light-like version of the commutation
relations, if one requires invariance of the tensor product algebra under the
coaction of the $\kappa$-Poincar\'e group. This necessitates a braided tensor
product. We study the representations of this product, and prove that
$\kappa$-Poincar\'e-invariant N-point functions belong to an Abelian
subalgebra, and are therefore commutative. We use this construction to define
the 2-point Whightman and Pauli--Jordan functions, which turn out to be
identical to the undeformed ones. We finally outline how to construct a free
scalar $\kappa$-Poincar\'e-invariant quantum field theory, and identify some
open problems.


Abstract:
  We investigate the special case of diamond relay comprising a Gaussian
channel with identical frequency response from the user to the relays and
fronthaul links with limited rate from the relays to the destination. We use
the oblivious compress and forward (CF) with distributed compression and decode
and forward (DF) where each relay decodes the whole message and sends half of
its bits to the destination. We derive achievable rate by using time-sharing
between DF and CF. It is proved that optimal CF-DF time sharing is advantageous
over superposition of CF and DF. The optimal time sharing proportion between DF
and CF and power and rate allocations are different at each frequency and are
fully determined.


Abstract:
  Reasoning about information from multiple parts of a passage to derive an
answer is an open challenge for reading-comprehension models. In this paper, we
present an approach that reasons about complex questions by decomposing them to
simpler subquestions that can take advantage of single-span extraction
reading-comprehension models, and derives the final answer according to
instructions in a predefined reasoning template. We focus on subtraction-based
arithmetic questions and evaluate our approach on a subset of the DROP dataset.
We show that our approach is competitive with the state-of-the-art while being
interpretable and requires little supervision


Abstract:
  Scalar Field Dark Matter (SFDM) comprised of ultralight bosons has attracted
great interest as an alternative to standard, collisionless Cold Dark Matter
(CDM) because of its novel structure-formation dynamics, described by the
coupled Schr\"odinger-Poisson equations. In the free-field ("fuzzy") limit of
SFDM (FDM), structure is inhibited below the de Broglie wavelength, but
resembles CDM on larger scales. Virialized haloes have "solitonic" cores of
radius $\sim\lambda_\text{deB}$, surrounded by CDM-like envelopes. When a
strong enough repulsive self-interaction (SI) is also present, structure can be
inhibited below a second length scale, $\lambda_\text{SI}$, with
$\lambda_\text{SI}> \lambda_\text{deB}$ -- called the Thomas-Fermi (TF) regime.
FDM dynamics differs from CDM because of quantum pressure, and SFDM-TF differs
further by adding SI pressure. In the small-$\lambda_\text{deB}$ limit,
however, we can model all three by fluid conservation equations for a
compressible, $\gamma=5/3$ ideal gas, with ideal gas pressure sourced by
internal velocity dispersion and, for the TF regime, an added SI pressure,
$P_\text{SI}\propto \rho^2$. We use these fluid equations to simulate halo
formation from gravitational collapse in 1D, spherical symmetry, demonstrating
for the first time that SFDM-TF haloes form with cores the size of
$R_\text{TF}$, the radius of an SI-pressure-supported $(n=1)$-polytrope,
surrounded by CDM-like envelopes. In comparison with rotation curves of dwarf
galaxies in the local Universe, SFDM-TF haloes pass the ["too-big-to-fail" +
"cusp-core"]-test if $R_\text{TF}\gtrsim 1$ kpc.


Abstract:
  In single-particle entanglement (SPE) two degrees of freedom of a single
particle are entangled. SPE is a resource that can be exploited both in quantum
communication protocols and in experimental tests of noncontextuality based on
the Kochen-Specker theorem. SPE can be certified via a test of quantum
contextuality based on Bell inequalities. Experiments of Bell-like inequality
violation by single particle entangled systems may be affected by an analogue
of the locality loophole in this context, due to the presence of unavoidable
non-idealities in the experimental devices which actually produce unwanted
correlations between the two observables that are simultaneously measured. This
issue is tackled here by quantitatively analyzing the behaviour of realistic
devices in SPE experiments with photons. In particular, we show how it is
possible to provide a semi-device independent randomness certification of
realistic quantum random number generators based on Bell inequality violation
by SPE states of photons. The analysis is further enlarged to encompass, with a
Markovian model, memory effects due to dead time, dark counts and afterpulsing
affecting single photon detectors, in particular when not dealing with
coincidence measurements. An unbiased estimator is also proposed for quantum
transition probabilities out of the collection of experimental data.


Abstract:
  This study investigates dynamic system-optimal (DSO) and dynamic user
equilibrium (DUE) traffic assignment of departure/arrival-time choices in a
corridor network. The morning commute problems with a many-to-one pattern of
origin-destination demand and the evening commute problems with a one-to-many
pattern are considered. Specifically, a novel approach to derive closed-form
solutions for both DSO and DUE problems is developed. We first derive a
closed-form solution to the DSO problem based on the regularities of the cost
and flow variables at an optimal state. By utilizing this solution, we prove
that the queuing delay at a bottleneck in a DUE solution is equal to an optimal
toll that eliminates the queue in a DSO solution under certain conditions of a
schedule delay function. This enables us to derive a closed-form DUE solution
by using the DSO solution. We also show the theoretical relationship between
the DSO and DUE assignment. Numerical examples are provided to illustrate and
verify the analytical results.


Abstract:
  This paper studies how to learn parameters in diagonal Gaussian mixture
models. The problem can be formulated as computing incomplete symmetric tensor
decompositions. We use generating polynomials to compute incomplete symmetric
tensor decompositions and approximations. Then the tensor approximation method
is used to learn diagonal Gaussian mixture models. We also do the stability
analysis. When the first and third order moments are sufficiently accurate, we
show that the obtained parameters for the Gaussian mixture models are also
highly accurate. Numerical experiments are also provided.


Abstract:
  High-throughput technologies to collect field data have made observations
possible at scale in several branches of life sciences. The data collected can
range from the molecular level (genotypes) to physiological (phenotypic traits)
and environmental observations (e.g., weather, soil conditions). These vast
swathes of data, collectively referred to as phenomics data, represent a
treasure trove of key scientific knowledge on the dynamics of the underlying
biological system. However, extracting information and insights from these
complex datasets remains a significant challenge owing to their
multidimensionality and lack of prior knowledge about their complex structure.
In this paper, we present Pheno-Mapper, an interactive toolbox for the
exploratory analysis and visualization of large-scale phenomics data. Our
approach uses the mapper framework to perform a topological analysis of the
data, and subsequently render visual representations with built-in data
analysis and machine learning capabilities. We demonstrate the utility of this
new tool on real-world plant (e.g., maize) phenomics datasets. In comparison to
existing approaches, the main advantage of Pheno-Mapper is that it provides
rich, interactive capabilities in the exploratory analysis of phenomics data,
and it integrates visual analytics with data analysis and machine learning in
an easily extensible way. In particular, Pheno-Mapper allows the interactive
selection of subpopulations guided by a topological summary of the data and
applies data mining and machine learning to these selected subpopulations for
in-depth exploration.


Abstract:
  Federated learning provides a communication-efficient and privacy-preserving
training process by enabling learning statistical models with massive
participants while keeping their data in local clients. However, standard
federated learning techniques that naively minimize an average loss function
are vulnerable to data corruptions from outliers, systematic mislabeling, or
even adversaries. In addition, it is often prohibited for service providers to
verify the quality of data samples due to the increasing concern of user data
privacy. In this paper, we address this challenge by proposing Auto-weighted
Robust Federated Learning (arfl), a novel approach that jointly learns the
global model and the weights of local updates to provide robustness against
corrupted data sources. We prove a learning bound on the expected risk with
respect to the predictor and the weights of clients, which guides the
definition of the objective for robust federated learning. The weights are
allocated by comparing the empirical loss of a client with the average loss of
the best p clients (p-average), thus we can downweight the clients with
significantly high losses, thereby lower their contributions to the global
model. We show that this approach achieves robustness when the data of
corrupted clients is distributed differently from benign ones. To optimize the
objective function, we propose a communication-efficient algorithm based on the
blockwise minimization paradigm. We conduct experiments on multiple benchmark
datasets, including CIFAR-10, FEMNIST and Shakespeare, considering different
deep neural network models. The results show that our solution is robust
against different scenarios including label shuffling, label flipping and noisy
features, and outperforms the state-of-the-art methods in most scenarios.


Abstract:
  Since the appearance of Einstein's paper {\em"On the Electrodynamics of
Moving Bodies"} and the birth of special relativity, it is understood that the
theory was basically coded within Maxwell's equations. The celebrated
mass-energy equivalence relation, $E=mc^2$, is derived by Einstein using
thought experiments involving the kinematics of the emission of light
(electromagnetic energy) and the relativity principle. Text book derivations
often follow paths similar to Einstein's, or the analysis of the kinematics of
particle collisions interpreted from the perspective of different inertial
frames. All the same, in such derivations the direct dynamical link with
hypothetical fundamental fields describing matter (e.g. Maxwell theory or
other) is overshadowed by the use of powerful symmetry arguments, kinematics,
and the relativity principle.
  Here we show that the formula can be derived directly form the dynamical
equations of a massless matter model confined in a box (which can be thought of
as a toy model of a composite particle). The only assumptions in the derivation
are that the field equations hold and the energy-momentum tensor admits a
universal interpretation in arbitrary coordinate systems. The mass-energy
equivalence relation follows from the inertia or (taking the equivalence
principle for granted) weight of confined field radiation. The present
derivation offers an interesting pedagogical perspective on the formula
providing a simple toy model on the origin of mass and a natural bridge to the
foundations of general relativity.


Abstract:
  We use an enhanced methodology combining specific forms of AI techniques,
opinion mining and artificial mathematical intelligence (AMI), with public data
on the spread of the coronavirus SARS-CoV-2 and the incidence of COVID-19
disease in Colombia during the first three months since the first reported
positive case. The results obtained, together with conceptual tools coming from
the global taxonomy of fundamental cognitive mechanisms emerging in AMI and
with suitable contextual information from Colombian public health and
mainstream social media, allowed us to stating specific preventive guidelines
for a better restructuring of initial safe and stable life conditions in
Colombia, and in an extended manner in similar Latin American Countries. More
specifically, we describe three major guidelines: 1) regular creative
visualization and effective planning, 2) the continuous use of constructive
linguistic frameworks, and 3) frequent and moderate use of kinesthetic
routines. They should be understood as effective tools from a cognitive and
behavioural perspective, rather than from a biological one. Even more, the
first two guidelines should be acknowledged in integral cooperation with the
third one regarding the global effect of COVID-19 in human beings as a whole,
this includes the mind and body.


Abstract:
  A new model for calculating the Casimir-Lifshitz force per unit length for
two dielectric rods is proposed, based on the Green function method of
classical electrodynamics and the Lorentz model for permittivity.


Abstract:
  The \emph{ab initio} path integral Monte Carlo (PIMC) approach is one of the
most successful methods in quantum many-body theory. A particular strength of
this method is its straightforward access to imaginary-time correlation
functions (ITCF). For example, the well-known density-density ITCF
$F(\mathbf{q},\tau)$ allows one to estimate the linear response of a given
system for all wave vectors $\mathbf{q}$ from a single simulation of the
unperturbed system. Moreover, it constitutes the basis for the reconstruction
of the dynamic structure factor $S(\mathbf{q},\omega)$ -- a key quantity in
state-of-the-art scattering experiments. In this work, we present analogous
relations between the nonlinear density response in quadratic and cubic order
of the perturbation strength and generalized ITCFs measuring correlations
between up to four imaginary-time arguments. As a practical demonstration of
our new approach, we carry out simulations of the warm dense electron gas and
find excellent agreement with previous PIMC results that had been obtained with
substantially larger computational effort. In addition, we give a relation
between a cubic ITCF and the triple dynamic structure factor
$S(\mathbf{q}_1,\omega_1;\mathbf{q}_2,\omega_2)$, which evokes the enticing
possibility to study dynamic three-body effects on an \emph{ab initio} level.


Abstract:
  In this paper, we introduce and prove asymptotic normality for a new
nonparametric estimator of continuous treatment effects. Specifically, we
estimate the average dose-response function - the expected value of an outcome
of interest at a particular level of the treatment level. We utilize tools from
both the double debiased machine learning (DML) and the automatic double
machine learning (ADML) literatures to construct our estimator. Our estimator
utilizes a novel debiasing method that leads to nice theoretical stability and
balancing properties. In simulations our estimator performs well compared to
current methods.


Abstract:
  The so-called Froehlich entropy is the entropy variation of a material under
the application of an electric field. This quantity can be calculated, under
suitable hypotheses, directly from the measured real part of the dielectric
function. Although Froehlich entropy is defined for a biased sample, a
straightforward physical interpretation correlates it to the state of order of
the considered physical system in absence of field. When Froehlich entropy is
calculated from experimental results, its trend is able to give several
information about the evolution in temperature of the explored compound,
especially of its phase transition features. We here provide a comprehensive
review of the physical systems (dipolar liquids and nematicons, organic
molecular crystals, metallic nanoparticles, inorganic disordered
ferroelectrics, etc.) where this approach has been exploited with the aim of
evaluating their state of order. The variety of compounds where this method has
been applied demonstrates that the estimation of the Froehlich entropy can be
considered a trustworthy tool for carrying out study on the state of order of
different classes of materials. Indeed Froehlich entropy evaluation can be
considered a fruitful and reliable investigation technique which can be
exploited alongside more usual experimental approaches.


Abstract:
  Most of the Blockchain permissioned systems employ Byzantine fault-tolerance
(BFT) consensus protocols to ensure that honest validators agree on the order
for appending entries to their ledgers. In this paper, we study the performance
and the scalability of prominent consensus protocols, namely PBFT, Tendermint,
HotStuff, and Streamlet, both analytically via load formulas and practically
via implementation and evaluation. Under identical conditions, we identify the
bottlenecks of these consensus protocols and show that these protocols do not
scale well as the number of validators increases. Our investigation points to
the communication complexity as the culprit. Even when there is enough network
bandwidth, the CPU cost of serialization and deserialization of the messages
limits the throughput and increases the latency of the protocols. To alleviate
the bottlenecks, the most useful techniques include reducing the communication
complexity, rotating the hotspot of communications, and pipelining across
consensus instances.


Abstract:
  Image and video synthesis are closely related areas aiming at generating
content from noise. While rapid progress has been demonstrated in improving
image-based models to handle large resolutions, high-quality renderings, and
wide variations in image content, achieving comparable video generation results
remains problematic. We present a framework that leverages contemporary image
generators to render high-resolution videos. We frame the video synthesis
problem as discovering a trajectory in the latent space of a pre-trained and
fixed image generator. Not only does such a framework render high-resolution
videos, but it also is an order of magnitude more computationally efficient. We
introduce a motion generator that discovers the desired trajectory, in which
content and motion are disentangled. With such a representation, our framework
allows for a broad range of applications, including content and motion
manipulation. Furthermore, we introduce a new task, which we call cross-domain
video synthesis, in which the image and motion generators are trained on
disjoint datasets belonging to different domains. This allows for generating
moving objects for which the desired video data is not available. Extensive
experiments on various datasets demonstrate the advantages of our methods over
existing video generation techniques. Code will be released at
https://github.com/snap-research/MoCoGAN-HD.


Abstract:
  We construct self-adjoint Laplacians and symmetric Markov semigroups on
partially hyperbolic attractors and on hyperbolic attractors with
singularities, endowed with Gibbs u-measures. If the measure has full support,
we can also guarantee the existence of an associated symmetric Hunt diffusion
process. In the special case of partially hyperbolic diffeomorphisms induced by
geodesic flows on manifolds of negative sectional curvature the Laplacians we
consider are self-adjoint extensions of well-known classical leafwise
Laplacians.


Abstract:
  This work revisits the production of vorticity at an interface separating two
immiscible incompressible fluids. A new decomposition of the vorticity flux is
proposed in a two-dimensional context which allows to compute explicitly such a
quantity in terms of surface tension $\sigma$, viscosity $\mu$ and gravity $g$.
This approach is then applied in the context of gravito-capillary waves. It
leads to analytical results already known but from a new perspective, provides
some quantitative predictions at short time that can be a good test for
numerical codes. Finally it is a mean to obtain a qualitative understanding of
direct numerical simulation results.


Abstract:
  Majorana fermions exist on the boundaries of two-dimensional topological
superconductors (TSCs) as charge-neutral quasi-particles. The neutrality makes
the detection of such states challenging from both experimental and theoretical
points of view. Current methods largely rely on transport measurements in which
Majorana fermions manifest themselves by inducing electron-pair tunneling at
the lead-contacting point. Here we show that chiral Majorana fermions in TSCs
generate {enhanced} local optical response. The features of local optical
conductivity distinguish them not only from trivial superconductors or
insulators but also from normal fermion edge states such as those in quantum
Hall systems. Our results provide a new applicable method to detect dispersive
Majorana fermions and may lead to a novel direction of this research field.


Abstract:
  In an order-driven financial market, the price of a financial asset is
discovered through the interaction of orders - requests to buy or sell at a
particular price - that are posted to the public limit order book (LOB).
Therefore, LOB data is extremely valuable for modelling market dynamics.
However, LOB data is not freely accessible, which poses a challenge to market
participants and researchers wishing to exploit this information. Fortunately,
trades and quotes (TAQ) data - orders arriving at the top of the LOB, and
trades executing in the market - are more readily available. In this paper, we
present the LOB recreation model, a first attempt from a deep learning
perspective to recreate the top five price levels of the LOB for small-tick
stocks using only TAQ data. Volumes of orders sitting deep in the LOB are
predicted by combining outputs from: (1) a history compiler that uses a Gated
Recurrent Unit (GRU) module to selectively compile prediction relevant quote
history; (2) a market events simulator, which uses an Ordinary Differential
Equation Recurrent Neural Network (ODE-RNN) to simulate the accumulation of net
order arrivals; and (3) a weighting scheme to adaptively combine the
predictions generated by (1) and (2). By the paradigm of transfer learning, the
source model trained on one stock can be fine-tuned to enable application to
other financial assets of the same class with much lower demand on additional
data. Comprehensive experiments conducted on two real world intraday LOB
datasets demonstrate that the proposed model can efficiently recreate the LOB
with high accuracy using only TAQ data as input.


Abstract:
  The definition of the Jones polynomial in the 80's gave rise to a large
family of so-called quantum link invariants, based on quantum groups. These
quantum invariants are all controlled by the same two-variable invariant (the
HOMFLY-PT polynomial), which also specializes to the older Alexander
polynomial. Building upon quantum Schur--Weyl duality and variants of this
phenomenon, I will explain an algebraic setup that allows for global
definitions of these quantum polynomials, and discuss extensions of these
quantum objects designed to encompass all of the mentioned invariants,
including the HOMFLY-PT polynomial.


Abstract:
  Entanglement shared among multiple parties presents complex challenges for
the characterisation of different types of entanglement. One of the most basic
insights is the fact that some mixed states can feature entanglement across
every possible cut of a multipartite system, yet can be produced via a mixture
of partially separable states. To distinguish states that genuinely cannot be
produced from mixing partially separable states, the term genuine multipartite
entanglement was coined. All these considerations originate in a paradigm where
only a single copy of the state is distributed and locally acted upon. In
contrast, advances in quantum technologies prompt the question of how this
picture changes when multiple copies of the same state become locally
accessible. Here we show that multiple copies unlock genuine multipartite
entanglement from partially separable states, even from undistillable
ensembles, and even more than two copies can be required to observe this
effect. With these findings, we characterise the notion of partial separability
in the paradigm of multiple copies and conjecture a strict hierarchy of
activatable states and an asymptotic collapse of hierarchy.


Abstract:
  This article describes a volumetric approach for procedural shape modeling
and a new Procedural Shape Modeling Language (PSML) that facilitates the
specification of these models. PSML provides programmers the ability to
describe shapes in terms of their 3D elements where each element may be a
semantic group of 3D objects, e.g., a brick wall, or an indivisible object,
e.g., an individual brick. Modeling shapes in this manner facilitates the
creation of models that more closely approximate the organization and structure
of their real-world counterparts. As such, users may query these models for
volumetric information such as the number, position, orientation and volume of
3D elements which cannot be provided using surface based model-building
techniques. PSML also provides a number of new language-specific capabilities
that allow for a rich variety of context-sensitive behaviors and
post-processing functions. These capabilities include an object-oriented
approach for model design, methods for querying the model for component-based
information and the ability to access model elements and components to perform
Boolean operations on the model parts. PSML is open-source and includes freely
available tutorial videos, demonstration code and an integrated development
environment to support writing PSML programs.


Abstract:
  Temporary school closures caused by the Covid-19 pandemic have posed new
challenges for many teachers and students worldwide. Especially the abrupt
shift to online distance learning posed many obstacles to be overcome and it
particularly complicated the implementation of Educational Robotics activities.
Such activities usually comprise a variety of different learning artifacts,
which were not accessible to many students during the period of school closure.
Moreover, online distance learning considerably limits the possibilities for
students to interact with their peers and teachers. In an attempt to address
these issues, this work presents the development of an Educational Robotics
activity particularly conceived for online distance learning in primary school.
The devised activities are based on pen and paper approaches that are
complemented by commonly used social media to facilitate communication and
collaboration. They were proposed to 13 students, as a way to continue ER
activities in online distance learning over the time period of four weeks.


Abstract:
  Chemical element mapping is an imaging tool that provides essential
information on composite materials and it is crucial for a broad range of
fields ranging from fundamental science to numerous applications. Methods that
exploit x-ray fluorescence are very advantageous and are widely used, but
require focusing of the input beam and raster scanning of the sample. Thus the
methods are slow and exhibit limited resolution due to focusing challenges. We
demonstrate a new focusing free x-ray fluorescence method based ghost imaging
that overcomes those limitations. We combine our procedure with compressed
sensing to reduce the measurement time and the exposure to radiation by more
than 80%. Since our method does not require focusing, it opens the possibility
for improving the resolution and image quality of chemical element maps with
tabletop x-ray sources and for extending the applicability of x-ray
fluorescence detection to new fields such as medical imaging and homeland
security applications


Abstract:
  Phase change memories (PCM) is an emerging type of non-volatile memory that
has shown a strong presence in the data-storage market. This technology has
recently attracted significant research interest in the development of non-Von
Neumann computing architectures such as in-memory and neuromorphic computing.
Research in these areas has been primarily motivated by the scalability
potential of phase change materials and their compatibility with industrial
nanofabrication processes. In this work, we are presenting our development of
crossbar phase change memory arrays through the electrodeposition of GeSbTe
(GST). We show that GST can be electrodeposited in microfabricated TiN crossbar
arrays using a scalable process. Our phase switching test of the
electrodeposited materials have shown that a SET/RESET resistance ratio of 2-3
orders of magnitude is achievable with a switching endurance of around 80
cycles. These results represent the first phase switching of electrodeposited
GeSbTe in microfabricated crossbar arrays. Our work paves the way towards
developing large memory arrays involving electrodeposited materials for passive
selectors and phase switching devices. It also opens opportunities for
developing a variety of different electronic devices using electrodeposited
materials.


Abstract:
  Aligning humans' assessment of what a robot can do with its true capability
is crucial for establishing a common ground between human and robot partners
when they collaborate on a joint task. In this work, we propose an approach to
calibrate humans' estimate of a robot's reachable workspace through a small
number of demonstrations before collaboration. We develop a novel motion
planning method, REMP (Reachability-Expressive Motion Planning), which jointly
optimizes the physical cost and the expressiveness of robot motion to reveal
the robot's motion capability to a human observer. Our experiments with human
participants demonstrate that a short calibration using REMP can effectively
bridge the gap between what a non-expert user thinks a robot can reach and the
ground-truth. We show that this calibration procedure not only results in
better user perception, but also promotes more efficient human-robot
collaborations in a subsequent joint task.


Abstract:
  The oscillation frequencies observed in Sun-like stars are susceptible to
being shifted by magnetic activity effects. The measured shifts depend on a
complex relationship involving the mode type, the field strength and spatial
distribution of activity, as well as the inclination angle of the star.
Evidence of these shifts is also present in frequency separation ratios which
are often used when inferring global properties of stars in order to avoid
surface effects. However, one assumption when using frequency ratios for this
purpose is that there are no near-surface perturbations that are
non-spherically symmetric. In this work, we studied the impact on inferred
stellar properties when using frequency ratios that are influenced by
non-homogeneous activity distributions. We generate several sets of artificial
oscillation frequencies with various amounts of shift and determine stellar
properties using two separate pipelines. We find that for asteroseismic
observations of Sun-like targets we can expect magnetic activity to affect mode
frequencies which will bias the results from stellar modelling analysis.
Although for most stellar properties this offset should be small, typically
less than 0.5% in mass, estimates of age and central hydrogen content can have
an error of up to 5% and 3% respectively. We expect a larger frequency shift
and therefore larger bias for more active stars. We also warn that for stars
with very high or low inclination angles, the response of modes to activity is
more easily observable in the separation ratios and hence will incur a larger
bias.


Abstract:
  We present a Helmholtz Decomposition for many n-dimensional, continuously
differentiable vector fields on unbounded domains that do not decay at
infinity. Existing methods are restricted to fields not growing faster than
polynomially and require solving n-dimensional volume integrals over unbounded
domains. With our method only one-dimensional integrals have to be solved to
derive gradient and rotation potentials.
  Analytical solutions are obtained for smooth vector fields $f(x)$ whose
components are separable into a product of two functions: $f_k(x) = u_k(x_k)
\cdot v_k(x_{\neq k})$, where $u_k(x_k)$ depends only on $x_k$ and $v_k(x_{\neq
k})$ depends not on $x_k$. Additionally, an integer $\lambda_k$ must exist such
that the $2\lambda_k$-th integral of one of the functions times the
$\lambda_k$-th power of the Laplacian applied to the other function yields a
product that is a multiple of the original product. A similar condition is
well-known from repeated partial integration, where the calculation can be
finalized if the shifting of derivatives yields a multiple of the original
integrand.
  Also linear combinations of such vector fields can be decomposed. These
conditions include periodic and exponential functions, combinations of
polynomials with arbitrary integrable functions, their products and linear
combinations, and examples such as Lorenz or R\"ossler attractor.


Abstract:
  Recently there has been much interest in the chirality induced spin
selectivity effect, whereby electron spin polarisation, which is dependent on
the molecular chirality, is produced in electrode-molecule electron transfer
processes. Naturally, one might consider if a similar effect can be observed in
simple molecular charge transfer reactions, for example in light-induced
electron transfer from an electron donor to an electron acceptor. In this work
we explore the effect of electron transfer on spins in chiral single radicals
and chiral radical pairs using Nakajima-Zwanzig theory. In these cases
chirality, in conjuction with spin-orbit coupling, does not lead to spin
polarisation, but instead the electron transfer generates quantum coherence
between spins states. In principle, this chirality induced spin coherence could
manifest in a range of experiments, and in particular we demonstrate that the
OOP-ESEEM pulse EPR experiment would be able to detect this effect in oriented
radical pairs.


Abstract:
  We explore creating automated, personalized feedback in an intelligent
tutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts
in student answers in order to achieve better student learning gains. Although
automatic methods for providing personalized feedback exist, they do not
explicitly inform students about which concepts in their answers are correct or
incorrect. Our approach involves decomposing students answers using neural
discourse segmentation and classification techniques. This decomposition yields
a relational graph over all discourse units covered by the reference solutions
and student answers. We use this inferred relational graph structure and a
neural classifier to match student answers with reference solutions and
generate personalized feedback. Although the process is completely automated
and data-driven, the personalized feedback generated is highly contextual,
domain-aware and effectively targets each student's misconceptions and
knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that
our approach results in high-quality feedback and significantly improved
student learning gains.


Abstract:
  Generic scaling laws, such as the Kolmogorov's 5/3-law, are milestone
achievements of turbulence research in classical fluids. For quantum fluids
such as atomic Bose-Einstein condensates, superfluid helium, and superfluid
neutron stars, turbulence can also exist in the presence of a chaotic tangle of
evolving quantized vortex lines. However, due to the lack of suitable
experimental tools to directly probe the vortex-tangle motion, so far little is
known about possible scaling laws that characterize the velocity correlations
and trajectory statistics of the vortices in quantum-fluid turbulence (QT).
Acquiring such knowledge could greatly benefit the development of advanced
statistical models of QT. Here we report an experiment where a tangle of
vortices in superfluid $^4$He are decorated with solidified deuterium tracer
particles. Under experimental conditions where these tracers follow the motion
of the vortices, we observed an apparent superdiffusion of the vortices. Our
analysis shows that this superdiffusion is not due to L\'{e}vy flights, i.e.,
long-distance hops that are known to be responsible for superdiffusion of
random walkers. Instead, a previously unknown power-law scaling of the
vortex-velocity temporal correlation is uncovered as the cause. This finding
may motivate future research on hidden scaling laws in QT.


Abstract:
  We study the matter-coupled equations of motion for cosmological NS massless
fields including all $\alpha'$ corrections in an O$(d,d)$ duality invariant
approach, with emphasis on the Kalb-Ramond two-form field $B_{(2)}$ and its
source. Solutions for the vacuum and matter case are found and the
corresponding Einstein frame cosmologies are discussed. We also show that the
ansatz for $B_{(2)}$ required by the duality invariant framework implies that
the two-form is non-isotropic.


Abstract:
  In this work we investigate the nonperturbative decay dynamics of a quantum
emitter coupled to a composite right/left handed transmission line (CRLH-TL).
Our theory captures the contributions from the different spectral features of
the waveguide, providing an accurate prediction beyond the weak coupling
regime, and illustrating the multiple possibilities offered by the nontrivial
dispersion of metamaterial waveguides. We show that the waveguide is
characterized by a band-gap with two asymmetric edges: (i) a mu-near-zero (MNZ)
band edge, where spontaneous emission is inhibited and an unstable pole is
smoothly transformed into a bound state, and (ii) an epsilon-near-zero (ENZ)
band edge, where the decay rate diverges and unstable and real (bound state)
poles coexist. In both cases, branch cut singularities contribute with
fractional decay dynamics whose nature depend on the properties of the
band-edges.


Abstract:
  Video compression is a basic requirement for consumer and professional video
applications alike. Video coding standards such as H.264/AVC and H.265/HEVC are
widely deployed in the market to enable efficient use of bandwidth and storage
for many video applications. To reduce the coding artifacts and improve the
compression efficiency, neural network based loop filtering of the
reconstructed video has been developed in the literature. However, loop
filtering is a challenging task due to the variation in video content and
sampling densities. In this paper, we propose a on-line scaling based
multi-density attention network for loop filtering in video compression. The
core of our approach lies in several aspects: (a) parallel multi-resolution
convolution streams for extracting multi-density features, (b) single attention
branch to learn the sample correlations and generate mask maps, (c) a
channel-mutual attention procedure to fuse the data from multiple branches, (d)
on-line scaling technique to further optimize the output results of network
according to the actual signal. The proposed multi-density attention network
learns rich features from multiple sampling densities and performs robustly on
video content of different resolutions. Moreover, the online scaling process
enhances the signal adaptability of the off-line pre-trained model.
Experimental results show that 10.18% bit-rate reduction at the same video
quality can be achieved over the latest Versatile Video Coding (VVC) standard.
The objective performance of the proposed algorithm outperforms the
state-of-the-art methods and the subjective quality improvement is obvious in
terms of detail preservation and artifact alleviation.


Abstract:
  The polarization and strain response of ferroelectric materials at fields
below the macroscopic coercive field is of a paramount importance for the
operation of many electronic devices. The response of real ferroelectric and
related materials is in general complex and difficult to interpret. The reason
for this is that many processes in a ferroelectric material contribute to its
properties, often concurrently. Examples include motion of ferroelectric and
ferroelastic domains, presence of domains within domains, dynamics of different
types of polar nano-entities, interaction of polar nano-entities (e.g., polar
nanoregions in relaxors) with the strain and polarization within domains,
motion of defects and rearrangement of defect clusters and their interaction
with polarization and strain. One signature of these processes is nonlinearity
of the strain and polarization. Most ferroelectrics exhibit nonlinear response
at all practical field levels meaning that the apparent material coefficients
depend on the amplitude of the driving excitation. In this paper we show that
an investigation of nonlinear behavior is a sensitive way to study various
mechanisms operating in dielectric and piezoelectric materials. We review the
basic formalism of the nonlinear description of polarization and strain, give a
physical interpretation of different terms and illustrate this approach on
numerous examples of relaxors, relaxor ferroelectrics, hard and soft
ferroelectrics, and morphotropic phase boundary compositions. An experimental
approach based on a lock-in technique that is well-suited for such studies is
also discussed.


Abstract:
  Efficient devices for light harvesting and photon sensing are fundamental
building blocks of basic energy science and many essential technologies. Recent
efforts have turned to biomimicry to design the next generation of
light-capturing devices, partially fueled by an appreciation of the fantastic
efficiency of the initial stages of natural photosynthetic systems at capturing
photons. In such systems extended excitonic states are thought to play a
fundamental functional role, inducing cooperative coherent effects, such as
superabsorption of light and supertransfer of photoexcitations. Inspired by
this observation, we design an artificial light-harvesting and photodetection
device that maximally harnesses cooperative effects to enhance efficiency. The
design relies on separating absorption and transfer processes (energetically
and spatially) in order to overcome the fundamental obstacle to exploiting
cooperative effects to enhance light capture: the enhanced emission processes
that accompany superabsorption. This engineered separation of processes greatly
improves the efficiency and the scalability of the system.


Abstract:
  The machine learning publication process is broken, of that there can be no
doubt. Many of these flaws are attributed to the current workflow: LaTeX to PDF
to reviewers to camera ready PDF. This has understandably resulted in the
desire for new forms of publications; ones that can increase inclusively,
accessibility and pedagogical strength. However, this venture fails to address
the origins of these inadequacies in the contemporary paper workflow. The
paper, being the basic unit of academic research, is merely how problems in the
publication and research ecosystem manifest; but is not itself responsible for
them. Not only will simply replacing or augmenting papers with different
formats not fix existing problems; when used as a band-aid without systemic
changes, will likely exacerbate the existing inequities. In this work, we argue
that the root cause of hindrances in the accessibility of machine learning
research lies not in the paper workflow but within the misaligned incentives
behind the publishing and research processes. We discuss these problems and
argue that the paper is the optimal workflow. We also highlight some potential
solutions for the incentivization problems.


Abstract:
  We are concerned with the focusing $L^2$-critical nonlinear Schr\"odinger
equations in $\mathbb{R}^d$ for $d=1,2$. The uniqueness is proved for a large
energy class of multi-bubble blow-up solutions, which converge to a sum of $K$
pseudo-conformal blow-up solutions particularly with low rate $(T-t)^{0+}$, as
$t\to T$, $1\leq K<\infty$. Moreover, we also prove the uniqueness in the
energy class of multi-solitons which converge to a sum of $K$ solitary waves
with convergence rate $(1/t)^{2+}$, as $t\to \infty$. The uniqueness class is
further enlarged to contain the multi-solitons with even lower convergence rate
$(1/t)^{\frac 12+}$ in the pseudo-conformal space. The proof is mainly based on
the pseudo-conformal invariance and the monotonicity properties of several
functionals adapted to the multi-bubble case, the latter is crucial towards the
upgradation of the convergence to the fast exponential decay rate.


Abstract:
  We discuss an approach to the \emph{covering} and \emph{vanishing} theorems
for the comparison map from bounded cohomology to singular cohomology, based on
the observation that the comparison map is the coassembly map for bounded
cohomology.


Abstract:
  We define a Mellin amplitude for CFT$_1$ four-point functions. Its analytical
properties are inferred from physical requirements on the correlator. We
discuss the analytic continuation that is necessary for a fully nonperturbative
definition of the Mellin transform. The resulting bounded, meromorphic function
of a single complex variable is used to derive an infinite set of
nonperturbative sum rules for CFT data of exchanged operators, which we test on
known examples. We then consider the perturbative setup produced by quartic
interactions with an arbitrary number of derivatives in a bulk AdS$_2$ field
theory. With our formalism, we obtain a closed-form expression for the Mellin
transform of tree-level contact interactions and for the first correction to
the scaling dimension of "two-particle" operators exchanged in the generalized
free field theory correlator.