Abstract:
  We study the Duffing equation and its generalizations with polynomial
nonlinearities. Recently, we have demonstrated that metamorphoses of the
amplitude response curves, computed by asymptotic methods in implicit form as
$F\left( \Omega ,\ A\right) =0$, permit prediction of qualitative changes of
dynamics occurring at singular points of the implicit curve $F\left(\Omega ,\
A\right) =0$. In the present work we determine a global structure of singular
points of the amplitude profiles computing bifurcation sets, i.e. sets
containing all points in the parameter space for which the amplitude profile
has a singular point. We connect our work with independent research on
tangential points on amplitude profiles, associated with jump phenomena,
characteristic for the Duffing equation. We also show that our techniques can
be applied to solutions of form $\Omega _{\pm }=f_{\pm }\left( A\right) $,
obtained within other asymptotic approaches.


Abstract:
  We show a partial Boolean function $f$ together with an input $x\in
f^{-1}\left(*\right)$ such that both $C_{\bar{0}}\left(f,x\right)$ and
$C_{\bar{1}}\left(f,x\right)$ are at least
$C\left(f\right)^{2-o\left(1\right)}$. Due to recent results by Ben-David,
G\"{o}\"{o}s, Jain, and Kothari, this result implies several other separations
in query and communication complexity. For example, it gives a function $f$
with $C(f)=\Omega(deg^{2-o\left(1\right)}(f))$ where $C$ and $deg$ denote
certificate complexity and polynomial degree of $f$. (This is the first
improvement over a separation between $C(f)$ and $deg(f)$ by Kushilevitz and
Nisan in 1995.) Other implications of this result are an improved separation
between sensitivity and polynomial degree, a near-optimal lower bound on
conondeterministic communication complexity for Clique vs. Independent Set
problem and a near-optimal lower bound on complexity of Alon--Saks--Seymour
problem in graph theory.


Abstract:
  Tactoids are spindle shaped-droplets of a uniaxial nematic phase suspended in
the co-existing isotropic phase. They are found in dispersions of a wide
variety of elongated colloidal particles, including actin, fd virus, carbon
nanotubes, vanadium peroxide and chitin nanocrystals. Recent experiments on
tactoids of chitin nanocrystals in water show that electric fields can very
strongly elongate tactoids even though the dielectric properties of the
co-existing isotropic and nematic phases differ only subtly. We develop a model
for partially bipolar tactoids, where the degree of bipolarness of the director
field is free to adjust to optimize the sum of the elastic, surface and Coulomb
energies of the system. By means of a combination of a scaling analysis and a
numerical study, we investigate the elongation and director field's behavior of
the tactoids as a function of their size, the strength of the electric field,
the surface tension, anchoring strength, the elastic constants and the electric
susceptibility anisotropy. We find that tactoids cannot elongate significantly
due to an external electric field, unless the director field is bipolar or
quasi bipolar and is somehow frozen in the field-free configuration. Presuming
that this is the case, we find reasonable agreement with experimental data.


Abstract:
  Sparse tensors appear frequently in distributed deep learning, either as a
direct artifact of the deep neural network's gradients, or as a result of an
explicit sparsification process. Existing communication primitives are agnostic
to the peculiarities of deep learning; consequently, they impose unnecessary
communication overhead. This paper introduces DeepReduce, a versatile framework
for the compressed communication of sparse tensors, tailored for distributed
deep learning. DeepReduce decomposes sparse tensors in two sets, values and
indices, and allows both independent and combined compression of these sets. We
support a variety of common compressors, such as Deflate for values, or
run-length encoding for indices. We also propose two novel compression schemes
that achieve superior results: curve fitting-based for values and bloom
filter-based for indices. DeepReduce is orthogonal to existing gradient
sparsifiers and can be applied in conjunction with them, transparently to the
end-user, to significantly lower the communication overhead. As proof of
concept, we implement our approach on Tensorflow and PyTorch. Our experiments
with large real models demonstrate that DeepReduce transmits fewer data and
imposes lower computational overhead than existing methods, without affecting
the training accuracy.


Abstract:
  Different properties of dark matter haloes, including growth rate,
concentration, interaction history, and spin, correlate with environment in
unique, scale-dependent ways. While these halo properties are not directly
observable, galaxies will inherit their host haloes' correlations with
environment. In this paper, we show how these characteristic environmental
signatures allow using measurements of galaxy environment to constrain which
dark matter halo properties are most tightly connected to observable galaxy
properties. We show that different halo properties beyond mass imprint distinct
scale-dependent signatures in both the galaxy two-point correlation function
and the distribution of distances to galaxies' kth nearest neighbours, with
features strong enough to be accessible even with low-resolution (e.g., grism)
spectroscopy at higher redshifts. As an application, we compute observed
two-point correlation functions for galaxies binned by half-mass radius at z=0
from the Sloan Digital Sky Survey, showing that classic galaxy size models
(i.e., galaxy size being proportional to halo spin) as well as other recent
proposals show significant tensions with observational data. We show that the
agreement with observed clustering can be improved with a simple empirical
model in which galaxy size correlates with halo growth.


Abstract:
  SARS-CoV-2, the causative virus of COVID-19 continues to cause an ongoing
global pandemic. Therapeutics are still needed to treat mild and severe
COVID-19. Drug repurposing provides an opportunity to deploy drugs for COVID-19
more rapidly than developing novel therapeutics. Some existing drugs have shown
promise for treating COVID-19 in clinical trials. This in silico study uses
structural similarity to clinical trial drugs to identify two drugs with
potential applications to treat early COVID-19. We apply in silico validation
to suggest a possible mechanism of action for both. Triamcinolone is a
corticosteroid structurally similar to Dexamethasone. Gallopamil is a calcium
channel blocker structurally similar to Verapamil. We propose that both these
drugs could be useful to treat early COVID-19 infection due to the proximity of
their targets within a SARS-CoV-2-induced protein-protein interaction network
to kinases active in early infection, and the APOA1 protein which is linked to
the spread of COVID-19.


Abstract:
  For NP-hard combinatorial optimization problems, it is usually difficult to
find high-quality solutions in polynomial time. The design of either an exact
algorithm or an approximate algorithm for these problems often requires
significantly specialized knowledge. Recently, deep learning methods provide
new directions to solve such problems. In this paper, an end-to-end deep
reinforcement learning framework is proposed to solve this type of
combinatorial optimization problems. This framework can be applied to different
problems with only slight changes of input (for example, for a traveling
salesman problem (TSP), the input is the two-dimensional coordinates of nodes;
while for a capacity-constrained vehicle routing problem (CVRP), the input is
simply changed to three-dimensional vectors including the two-dimensional
coordinates and the customer demands of nodes), masks and decoder context
vectors. The proposed framework is aiming to improve the models in literacy in
terms of the neural network model and the training algorithm. The solution
quality of TSP and the CVRP up to 100 nodes are significantly improved via our
framework. Specifically, the average optimality gap is reduced from 4.53\%
(reported best \cite{R22}) to 3.67\% for TSP with 100 nodes and from 7.34\%
(reported best \cite{R22}) to 6.68\% for CVRP with 100 nodes when using the
greedy decoding strategy. Furthermore, our framework uses about 1/3$\sim$3/4
training samples compared with other existing learning methods while achieving
better results. The results performed on randomly generated instances and the
benchmark instances from TSPLIB and CVRPLIB confirm that our framework has a
linear running time on the problem size (number of nodes) during the testing
phase, and has a good generalization performance from random instance training
to real-world instance testing.


Abstract:
  A nanoparticle (NP) immersed in biological media rapidly forms a corona of
adsorbed proteins, which later controls the eventual fate of the particle and
the route through which adverse outcomes may occur. The composition and
timescale for the formation of this corona are both highly dependent on both
the NP and its environment. The deposition of proteins on the surface of the NP
is related to processes of random sequential adsorption and, based on this
model, we develop a rate-equation treatment for the formation of a corona
represented by hard spheres on spherical and cylindrical NPs. We find that the
geometry of the NP significantly alters the composition of the corona through a
process independent of the rate constants assumed for adsorption and desorption
of proteins, with the radius and shape of the NP both influencing the corona.
Moreover, we demonstrate that in the condition of strong binding such that the
adsorption is effectively irreversible the corona content reflects the protein
mobility and concentration in solution rather than their binding affinity.


Abstract:
  Travel demand forecasting is an essential part of transportation planning and
management. The four-step travel model is the traditional and most-common
procedure utilized for travel demand forecasting, and many models have been
proposed in the literature to describe each step separately. However, there is
still a lack of a unified modeling framework that can successfully describe the
collective choice behavior of travelers interacting with each other at
different steps. This study uses the free utility model, whose objective
function is mathematically consistent with the Helmholtz free energy in
physics, to separately and simultaneously describe travelers' mode,
destination, and route choice behaviors. The free utility model's basic
assumption is that the travelers will trade off the expected utility and
information-processing cost to maximize their own utility. This model provides
not only a unified modeling framework for traveler choice behavior, but also
provides a new perspective for understanding the user equilibrium model in
transportation science and the potential game model in game theory.


Abstract:
  Explainable AI (XAI) is a research area whose objective is to increase
trustworthiness and to enlighten the hidden mechanism of opaque machine
learning techniques. This becomes increasingly important in case such models
are applied to the chemistry domain, for its potential impact on humans'
health, e.g, toxicity analysis in pharmacology. In this paper, we present a
novel approach to tackle explainability of deep graph networks in the context
of molecule property prediction t asks, named MEG (Molecular Explanation
Generator). We generate informative counterfactual explanations for a specific
prediction under the form of (valid) compounds with high structural similarity
and different predicted properties. Given a trained DGN, we train a
reinforcement learning based generator to output counterfactual explanations.
At each step, MEG feeds the current candidate counterfactual into the DGN,
collects the prediction and uses it to reward the RL agent to guide the
exploration. Furthermore, we restrict the action space of the agent in order to
only keep actions that maintain the molecule in a valid state. We discuss the
results showing how the model can convey non-ML experts with key insights into
the learning model focus in the neighbourhood of a molecule.


Abstract:
  Motivation: Early detection and isolation of COVID-19 patients are essential
for successful implementation of mitigation strategies and eventually curbing
the disease spread. With a limited number of daily COVID19 tests performed in
every country, simulating the COVID-19 spread along with the potential effect
of each mitigation strategy currently remains one of the most effective ways in
managing the healthcare system and guiding policy-makers. We introduce
COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that
evaluates the current mitigation measures that are applied to a region and
provides suggestions on what strength the upcoming mitigation measure should
be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a
geographical region by simulating the average number of new infections caused
by an infected person considering the effect of external factors, such as
environmental conditions (e.g., climate, temperature, humidity) and mitigation
measures.
  Results: Using Switzerland as a case study, COVIDHunter estimates that the
policy-makers need to keep the current mitigation measures for at least 30 days
to prevent demand from quickly exceeding existing hospital capacity. Relaxing
the mitigation measures by 50% for 30 days increases both the daily capacity
need for hospital beds and daily number of deaths exponentially by an average
of 23.8x, who may occupy ICU beds and ventilators for a period of time. Unlike
existing models, the COVIDHunter model accurately monitors and predicts the
daily number of cases, hospitalizations, and deaths due to COVID-19. Our model
is flexible to configure and simple to modify for modeling different scenarios
under different environmental conditions and mitigation measures.
  Availability: https://github.com/CMU-SAFARI/COVIDHunter


Abstract:
  Masses of classical Cepheids of 3 to 11 M$\odot$ are predicted by theory but
those measured, clump between 3.6 and 5 M$\odot$. As a result, their
mass-luminosity relation is poorly constrained, impeding our understanding of
basic stellar physics and the Leavitt Law. All Cepheid masses come from the
analysis of 11 binary systems, including only 5 double-lined and well-suited
for accurate dynamical mass determination. We present a project to analyze a
new, numerous group of Cepheids in double-lined binary (SB2) systems to provide
mass determinations in a wide mass interval and study their evolution. We
analyze a sample of 41 candidate binary LMC Cepheids spread along the P-L
relation, that are likely accompanied by luminous red giants, and present
indirect and direct indicators of their binarity. In a spectroscopic study of a
subsample of 18 brightest candidates, for 16 we detected lines of two
components in the spectra, already quadrupling the number of Cepheids in SB2
systems. Observations of the whole sample may thus lead to quadrupling all the
Cepheid mass estimates available now. For the majority of our candidates,
erratic intrinsic period changes dominate over the light travel-time effect due
to binarity. However, the latter may explain the periodic phase modulation for
4 Cepheids. Our project paves the way for future accurate dynamical mass
determinations of Cepheids in the LMC, Milky Way, and other galaxies, which
will potentially increase the number of known Cepheid masses even 10-fold,
hugely improving our knowledge about these important stars.


Abstract:
  In the context of the ClearMind project, we measured the scintillating
properties, as induced from from gamma ray interactions, of today available
PbWO4 crystal. We measured scintillation s yields and time constants by
measuring the signal shape measured on a fast photo-multiplier and
deconvoluting it from the instrumental effects. For the doped crystals at room
temperature, we measured a fast scintillation component, with time constants of
2 ns, 55 percent of the total light yield, and a slow component of 6 ns. We
observe a significant increase of the light yield for the slow component when
the temperature decreases and simultaneous increase of the time constants, but
no increase in the fast component light yield. Our measurements reproduce the
main qualitative features of PbWO4 crystals quoted in the literature.
Quantitatively though, we measured significantly shorter time constants and
larger light yields. This is explained by a rigorous treatment of the
instrumental contributions in the measurements. Results are discussed and
prospect for future developments, tailored for the ClearMind project, are
presented.


Abstract:
  Many previous audio-visual voice-related works focus on speech, ignoring the
singing voice in the growing number of musical video streams on the Internet.
For processing diverse musical video data, voice activity detection is a
necessary step. This paper attempts to detect the speech and singing voices of
target performers in musical video streams using audiovisual information. To
integrate information of audio and visual modalities, a multi-branch network is
proposed to learn audio and image representations, and the representations are
fused by attention based on semantic similarity to shape the acoustic
representations through the probability of anchor vocalization. Experiments
show the proposed audio-visual multi-branch network far outperforms the
audio-only model in challenging acoustic environments, indicating the
cross-modal information fusion based on semantic correlation is sensible and
successful.


Abstract:
  We present Supervision by Registration and Triangulation (SRT), an
unsupervised approach that utilizes unlabeled multi-view video to improve the
accuracy and precision of landmark detectors. Being able to utilize unlabeled
data enables our detectors to learn from massive amounts of unlabeled data
freely available and not be limited by the quality and quantity of manual human
annotations. To utilize unlabeled data, there are two key observations: (1) the
detections of the same landmark in adjacent frames should be coherent with
registration, i.e., optical flow. (2) the detections of the same landmark in
multiple synchronized and geometrically calibrated views should correspond to a
single 3D point, i.e., multi-view consistency. Registration and multi-view
consistency are sources of supervision that do not require manual labeling,
thus it can be leveraged to augment existing training data during detector
training. End-to-end training is made possible by differentiable registration
and 3D triangulation modules. Experiments with 11 datasets and a newly proposed
metric to measure precision demonstrate accuracy and precision improvements in
landmark detection on both images and video. Code is available at
https://github.com/D-X-Y/landmark-detection.


Abstract:
  The quantum singular value transformation is a powerful quantum algorithm
that allows one to apply a polynomial transformation to the singular values of
a matrix that is embedded as a block of a unitary transformation. This paper
shows how to perform the quantum singular value transformation for a matrix
that can be embedded as a block of a Hamiltonian. The transformation can be
implemented in a purely Hamiltonian context by the alternating application of
Hamiltonians for chosen intervals: it is an example of the Quantum Alternating
Operator Ansatz (generalized QAOA). We also show how to use the Hamiltonian
quantum singular value transformation to perform inverse block encoding to
implement a unitary of which a given Hamiltonian is a block. Inverse block
encoding leads to novel procedures for matrix multiplication and for solving
differential equations on quantum information processors in a purely
Hamiltonian fashion.


Abstract:
  A coming resurgence of super heavy-lift launch vehicles has precipitated an
immense interest in the future of crewed spaceflight and even future
colonisation efforts. While it is true that a bright future awaits this sector,
driven by commercial ventures and the reignited interest of old space-faring
nations, and the joining of new ones, little of this attention has been
reserved for the science-centric applications of these launchers. The Arcanum
mission is a proposal to use these vehicles to deliver an L-class observatory
into a highly eccentric orbit around Neptune, with a wide-ranging suite of
science goals and instrumentation tackling Solar System science, planetary
science, Kuiper Belt Objects and exoplanet systems.


Abstract:
  Reconstructing dynamic, time-varying scenes with computed tomography (4D-CT)
is a challenging and ill-posed problem common to industrial and medical
settings. Existing 4D-CT reconstructions are designed for sparse sampling
schemes that require fast CT scanners to capture multiple, rapid revolutions
around the scene in order to generate high quality results. However, if the
scene is moving too fast, then the sampling occurs along a limited view and is
difficult to reconstruct due to spatiotemporal ambiguities. In this work, we
design a reconstruction pipeline using implicit neural representations coupled
with a novel parametric motion field warping to perform limited view 4D-CT
reconstruction of rapidly deforming scenes. Importantly, we utilize a
differentiable analysis-by-synthesis approach to compare with captured x-ray
sinogram data in a self-supervised fashion. Thus, our resulting optimization
method requires no training data to reconstruct the scene. We demonstrate that
our proposed system robustly reconstructs scenes containing deformable and
periodic motion and validate against state-of-the-art baselines. Further, we
demonstrate an ability to reconstruct continuous spatiotemporal representations
of our scenes and upsample them to arbitrary volumes and frame rates
post-optimization. This research opens a new avenue for implicit neural
representations in computed tomography reconstruction in general.


Abstract:
  A cornerstone of quantum mechanics is the characterisation of symmetries
provided by Wigner's theorem. Wigner's theorem establishes that every symmetry
of the quantum state space must be either a unitary transformation, or an
antiunitary transformation. Here we extend Wigner's theorem from quantum states
to quantum evolutions, including both the deterministic evolution associated to
the dynamics of closed systems, and the stochastic evolutions associated to the
outcomes of quantum measurements. We prove that every symmetry of the space of
quantum evolutions can be decomposed into two state space symmetries that are
either both unitary or both antiunitary. Building on this result, we show that
it is impossible to extend the time reversal symmetry of unitary quantum
dynamics to a symmetry of the full set of quantum evolutions. Our no-go theorem
implies that any time symmetric formulation of quantum theory must either
restrict the set of the allowed evolutions, or modify the operational
interpretation of quantum states and processes. Here we propose a time
symmetric formulation of quantum theory where the allowed quantum evolutions
are restricted to a suitable set, which includes both unitary evolution and
projective measurements, but excludes the deterministic preparation of pure
states. The standard operational formulation of quantum theory can be retrieved
from this time symmetric version by introducing an operation of conditioning on
the outcomes of past experiments.


Abstract:
  Extending Aubin's construction of metrics with constant negative scalar
curvature, we prove that every $n$-dimensional closed manifold admits a
Riemannian metric with constant negative scalar-Weyl curvature, that is
$R+t|W|, t\in\mathbb{R}$. In particular, there are no topological obstructions
for metrics with $\varepsilon$-pinched Weyl curvature and negative scalar
curvature.


Abstract:
  We propose a new general training technique for attention mechanisms based on
virtual adversarial training (VAT). VAT can compute adversarial perturbations
from unlabeled data in a semi-supervised setting for the attention mechanisms
that have been reported in previous studies to be vulnerable to perturbations.
Empirical experiments reveal that our technique (1) provides significantly
better prediction performance compared to not only conventional adversarial
training-based techniques but also VAT-based techniques in a semi-supervised
setting, (2) demonstrates a stronger correlation with the word importance and
better agreement with evidence provided by humans, and (3) gains in performance
with increasing amounts of unlabeled data.


Abstract:
  The $q$-Onsager algebra $O_q$ has a presentation involving two generators
$W_0$, $W_1$ and two relations, called the $q$-Dolan/Grady relations. The
alternating central extension $\mathcal O_q$ has a presentation involving the
alternating generators $\lbrace \mathcal W_{-k}\rbrace_{k=0}^\infty$, $\lbrace
\mathcal W_{k+1}\rbrace_{k=0}^\infty$, $ \lbrace \mathcal
G_{k+1}\rbrace_{k=0}^\infty$, $\lbrace \mathcal {\tilde
G}_{k+1}\rbrace_{k=0}^\infty$ and a large number of relations. Let $\langle
\mathcal W_0, \mathcal W_1 \rangle$ denote the subalgebra of $\mathcal O_q$
generated by $\mathcal W_0$, $\mathcal W_1$. It is known that there exists an
algebra isomorphism $O_q \to \langle \mathcal W_0, \mathcal W_1 \rangle$ that
sends $W_0\mapsto \mathcal W_0$ and $W_1 \mapsto \mathcal W_1$. It is known
that the center $\mathcal Z$ of $\mathcal O_q$ is isomorphic to a polynomial
algebra in countably many variables.
  It is known that the multiplication map $\langle \mathcal W_0, \mathcal W_1
\rangle \otimes \mathcal Z \to \mathcal O_q$, $ w \otimes z \mapsto wz$ is an
isomorphism of algebras. We call this isomorphism the standard tensor product
factorization of $\mathcal O_q$. In the study of $\mathcal O_q$ there are two
natural points of view: we can start with the alternating generators, or we can
start with the standard tensor product factorization. It is not obvious how
these two points of view are related. The goal of the paper is to describe this
relationship. We give seven main results; the principal one is an attractive
factorization of the generating function for some algebraically independent
elements that generate $\mathcal Z$.


Abstract:
  Triangle counting is a fundamental technique in network analysis, that has
received much attention in various input models. The vast majority of triangle
counting algorithms are targeted to static graphs. Yet, many real-world graphs
are directed and temporal, where edges come with timestamps. Temporal triangles
yield much more information, since they account for both the graph topology and
the timestamps.
  Temporal triangle counting has seen a few recent results, but there are
varying definitions of temporal triangles. In all cases, temporal triangle
patterns enforce constraints on the time interval between edges (in the
triangle). We define a general notion $(\delta_{1,3}, \delta_{1,2},
\delta_{2,3})$-temporal triangles that allows for separate time constraints for
all pairs of edges.
  Our main result is a new algorithm, DOTTT (Degeneracy Oriented Temporal
Triangle Totaler), that exactly counts all directed variants of $(\delta_{1,3},
\delta_{1,2}, \delta_{2,3})$-temporal triangles. Using the classic idea of
degeneracy ordering with careful combinatorial arguments, we can prove that
DOTTT runs in $O(m\kappa\log m)$ time, where $m$ is the number of (temporal)
edges and $\kappa$ is the graph degeneracy (max core number). Up to log
factors, this matches the running time of the best static triangle counters.
Moreover, this running time is better than existing.
  DOTTT has excellent practical behavior and runs twice as fast as existing
state-of-the-art temporal triangle counters (and is also more general). For
example, DOTTT computes all types of temporal queries in Bitcoin temporal
network with half a billion edges in less than an hour on a commodity machine.


Abstract:
  Recent works have shown that deep neural networks benefit from multi-task
learning by learning a shared representation across several related tasks.
However, performance of such systems depend on relative weighting between
various losses involved during training. Prior works on loss weighting schemes
assume that instances are equally easy or hard for all tasks. In order to break
this assumption, we let the training process dictate the optimal weighting of
tasks for every instance in the dataset. More specifically, we equip every
instance in the dataset with a set of learnable parameters (instance-level task
parameters) where the cardinality is equal to the number of tasks learned by
the model. These parameters model the weighting of each task for an instance.
They are updated by gradient descent and do not require hand-crafted rules. We
conduct extensive experiments on SURREAL and CityScapes datasets, for human
shape and pose estimation, depth estimation and semantic segmentation tasks. In
these tasks, our approach outperforms recent dynamic loss weighting approaches,
e.g. reducing surface estimation errors by 8.97% on SURREAL. When applied to
datasets where one or more tasks can have noisy annotations, the proposed
method learns to prioritize learning from clean labels for a given task, e.g.
reducing surface estimation errors by up to 60%. We also show that we can
reliably detect corrupt labels for a given task as a by-product from learned
instance-level task parameters.


Abstract:
  We investigate a class of reducible yet indecomposable modules over the $N=2$
superconformal algebras. These so-called staggered modules exhibit a
non-diagonalisable action of the Virasoro mode $L_{0}$. Using recent results on
the coset construction of $N=2$ minimal models, we explicitly construct such
modules for central charges $c = -1$ and $c = -6$. We also describe
spectral-flow orbits and symmetries of the families of staggered modules which
arise via the coset.


Abstract:
  Building on previous developments, we show that the Diagrammatic Monte Carlo
technique allows to compute finite temperature response functions directly on
the real-frequency axis within any field-theoretical formulation of the
interacting fermion problem. There are no limitations on the type and nature of
the system's action or whether partial summation and self-consistent treatment
of certain diagram classes are used. In particular, by eliminating the need for
numerical analytic continuation from a Matsubara representation, our scheme
allows to study spectral densities of arbitrary complexity with controlled
accuracy in models with frequency-dependent effective interactions. For
illustrative purposes we consider the problem of the plasmon line-width in a
homogeneous electron gas (jellium).


Abstract:
  I generalize a theorem of Predd, et al.~(2009) on domination and strictly
proper scoring rules to the case of non-additive scoring rules.


Abstract:
  We study the problem of online network change point detection. In this
setting, a collection of independent Bernoulli networks is collected
sequentially, and the underlying distributions change when a change point
occurs. The goal is to detect the change point as quickly as possible, if it
exists, subject to a constraint on the number or probability of false alarms.
In this paper, on the detection delay, we establish a minimax lower bound and
two upper bounds based on NP-hard algorithms and polynomial-time algorithms,
i.e., \[ \mbox{detection delay} \begin{cases} \gtrsim \log(1/\alpha)
\frac{\max\{r^2/n, \, 1\}}{\kappa_0^2 n \rho},\\ \lesssim \log(\Delta/\alpha)
\frac{\max\{r^2/n, \, \log(r)\}}{\kappa_0^2 n \rho}, & \mbox{with NP-hard
algorithms},\\ \lesssim \log(\Delta/\alpha) \frac{r}{\kappa_0^2 n \rho}, &
\mbox{with polynomial-time algorithms}, \end{cases} \] where $\kappa_0, n,
\rho, r$ and $\alpha$ are the normalised jump size, network size, entrywise
sparsity, rank sparsity and the overall Type-I error upper bound. All the model
parameters are allowed to vary as $\Delta$, the location of the change point,
diverges. The polynomial-time algorithms are novel procedures that we propose
in this paper, designed for quick detection under two different forms of Type-I
error control. The first is based on controlling the overall probability of a
false alarm when there are no change points, and the second is based on
specifying a lower bound on the expected time of the first false alarm.
Extensive experiments show that, under different scenarios and the
aforementioned forms of Type-I error control, our proposed approaches
outperform state-of-the-art methods.


Abstract:
  Over the years researchers have studied the evolution of Electrocardiogram
(ECG) and the complex classification of cardiovascular diseases. This review
focuses on the evolution of the ECG, and covers the most recent signal
processing schemes with milestones over last 150 years in a systematic manner.
Development phases of ECG, ECG leads, portable ECG monitors, Signal Processing
Schemes and the Complex Transformations are discussed. It also provides
recommendations for the inclusion of certain important points based on the
review.


Abstract:
  The density ratio model (DRM) provides a flexible and useful platform for
combining information from multiple sources. In this paper, we consider
statistical inference under two-sample DRMs with additional parameters defined
through and/or additional auxiliary information expressed as estimating
equations. We examine the asymptotic properties of the maximum empirical
likelihood estimators (MELEs) of the unknown parameters in the DRMs and/or
defined through estimating equations, and establish the chi-square limiting
distributions for the empirical likelihood ratio (ELR) statistics. We show that
the asymptotic variance of the MELEs of the unknown parameters does not
decrease if one estimating equation is dropped. Similar properties are obtained
for inferences on the cumulative distribution function and quantiles of each of
the populations involved. We also propose an ELR test for the validity and
usefulness of the auxiliary information. Simulation studies show that correctly
specified estimating equations for the auxiliary information result in more
efficient estimators and shorter confidence intervals. Two real-data examples
are used for illustrations.


Abstract:
  Denote by $Sof(G)$ the space of sofic representations of a countable group
$G$. This space is known by a result of the second author, to have a
convex-like structure. We show that, in this space, minimal faces are extreme
points. We then construct uncountable many extreme points for
$Sof(\mathbb{F}_2)$ and show that there exists a decreasing chain of closed
faces with empty intersection. Finally we construct a strangely looking sofic
representation in $Sof(\mathbb{F}_2)$ that we believe it is outside of the
closure of the convex hull of extreme points.


Abstract:
  Decentralized training of deep learning models is a key element for enabling
data privacy and on-device learning over networks. In realistic learning
scenarios, the presence of heterogeneity across different clients' local
datasets poses an optimization challenge and may severely deteriorate the
generalization performance. In this paper, we investigate and identify the
limitation of several decentralized optimization algorithms for different
degrees of data heterogeneity. We propose a novel momentum-based method to
mitigate this decentralized training difficulty. We show in extensive empirical
experiments on various CV/NLP datasets (CIFAR-10, ImageNet, and AG News) and
several network topologies (Ring and Social Network) that our method is much
more robust to the heterogeneity of clients' data than other existing methods,
by a significant improvement in test performance ($1\% \!-\! 20\%$). Our code
is publicly available.


Abstract:
  The phase-field method has been established as a de facto standard for
simulating the microstructural evolution of materials. In quantitative modeling
the assessment and compilation of thermodynamic/kinetic data is largely
dominated by the CALPHAD approach, which has produced a large set of
experimentally and computationally generated Gibbs free energy and atomic
mobility data in a standardized format: the thermodynamic database (TDB) file
format. Harnessing this data for the purpose of phase-field modeling is an
ongoing effort encompassing a wide variety of approaches. In this paper, we aim
to directly link CALPHAD data to the phase-field method, without intermediate
fitting or interpolation steps. We introduce a model based on the
Kim-Kim-Suzuki (KKS) approach. This model includes sublattice site fractions
and can directly utilize data from TDB files. Using this approach, we
demonstrate the model on the U-Zr and Mo-Ni-Re systems.


Abstract:
  General Relativity (GR) represents the most recent theory of gravity, on
which all modern astrophysics is based, including some of the most astonishing
results of physics research. Nevertheless, its study is limited to university
courses, while being ignored at high school level. To introduceGR in high
school one of the approaches that can be used is the so-called rubber sheet
analogy, i.e. comparing the space-time to a rubber sheet which deforms under a
weight. In this paper we analyze the efficacy of an activity for high school
students held at the Department of Mathematics and Physics of Roma Tre
University that adopts the rubber sheet analogy to address several topics
related to gravity. We present the results of the questionnaires we
administered to investigate the understanding of the topics treated to over 150
Italian high school students who participated in this activity.


Abstract:
  We study objective robustness failures, a type of out-of-distribution
robustness failure in reinforcement learning (RL). Objective robustness
failures occur when an RL agent retains its capabilities out-of-distribution
yet pursues the wrong objective. This kind of failure presents different risks
than the robustness problems usually considered in the literature, since it
involves agents that leverage their capabilities to pursue the wrong objective
rather than simply failing to do anything useful. We provide the first explicit
empirical demonstrations of objective robustness failures and present a partial
characterization of its causes.


Abstract:
  X-ray detectors made of crystalline silicon have several advantages including
low dark currents, fast charge collection and high energy resolution. For
high-energy x-rays, however, silicon suffers from its low atomic number, which
might result in low detection efficiency, as well as low energy and spatial
resolution due to Compton scattering. We have used a monte-carlo model to
investigate the feasibility of a detector for pediatric CT with 30 to 40 mm of
silicon using x-ray spectra ranging from 80 to 140 kVp. A detection efficiency
of 0.74 was found at 80 kVp, provided the noise threshold could be set low.
Scattered photons were efficiently blocked by a thin metal shielding between
the detector units, and Compton scattering in the detector could be well
separated from photo absorption at 80 kVp. Hence, the detector is feasible at
low acceleration voltages, which is also suitable for pediatric imaging. We
conclude that silicon detectors may be an alternative to other designs for this
special case.


Abstract:
  Multi-objective controller synthesis concerns the problem of computing an
optimal controller subject to multiple (possibly conflicting) objective
properties. The relative importance of objectives is often specified by human
decision-makers. However, there is inherent uncertainty in human preferences
(e.g., due to different preference elicitation methods). In this paper, we
formalize the notion of uncertain human preferences and present a novel
approach that accounts for uncertain human preferences in the multi-objective
controller synthesis for Markov decision processes (MDPs). Our approach is
based on mixed-integer linear programming (MILP) and synthesizes a sound,
optimally permissive multi-strategy with respect to a multi-objective property
and an uncertain set of human preferences. Experimental results on a range of
large case studies show that our MILP-based approach is feasible and scalable
to synthesize sound, optimally permissive multi-strategies with varying MDP
model sizes and uncertainty levels of human preferences. Evaluation via an
online user study also demonstrates the quality and benefits of synthesized
(multi-)strategies.


Abstract:
  In this work, we study magnetic field effects on neutron star matter
containing the baryon octet and additional heavier spin 3/2 baryons (the
$\Delta$'s). We make use of two different relativistic hadronic models that
contain an additional vector-isovector self interaction for the mesons: one
version of a relativistic mean field (RMF) model and the Chiral Mean Field
(CMF) model. We find that both the additional interaction and a strong magnetic
field enhance the $\Delta$ baryon population in dense matter, while decreasing
the relative density of hyperons. At the same time that the vector-isovector
meson interaction modifies neutron-star masses very little ($<0.1~M_\odot$), it
decreases their radii considerably, allowing both models to be in better
agreement with observations. Together, these features indicate that magnetic
neutron stars are likely to contain $\Delta$ baryons in their interior.


Abstract:
  Proof-of-Work (PoW) is a fundamental underlying technology behind most major
blockchain cryptocurrencies. It has been previously pointed out that quantum
devices provide a computational advantage in performing PoW in the context of
Bitcoin. Here we make the case that this quantum advantage extends not only to
all existing PoW mechanisms, but to any possible PoW as well. This has strong
consequences regarding both quantum-based attacks on the integrity of the
entirety of the blockchain, as well as more legitimate uses of quantum
computation for the purpose of mining Bitcoin and other cryptocurrencies. For
the first case, we estimate when these quantum attacks will become feasible,
for various cryptocurrencies, and discuss the impact of such attacks. For the
latter, we derive a precise formula to calculate the economic incentive for
switching to quantum-based cryptocurrency miners. Using this formula, we
analyze several test scenarios, and conclude that investing in quantum hardware
for cryptocurrency mining has the potential to pay off immensely.


Abstract:
  In this article we study a \emph{non-directed} polymer model in dimension
$d\ge 2$: we consider a simple symmetric random walk on $\mathbb{Z}^d$ which
interacts with a random environment, represented by i.i.d. random variables
$(\omega_x)_{x\in \mathbb{Z}^d}$. The model consists in modifying the law of
the random walk up to time (or length) $N$ by the exponential of $\sum_{x\in
\mathcal{R}_N}\beta (\omega_x-h)$ where $\mathcal{R}_N$ is the range of the
walk, \textit{i.e.} the set of visited sites up to time $N$, and $\beta\geq
0,\, h\in \mathbb{R}$ are two parameters. We study the behavior of the model in
a weak-coupling regime, that is taking $\beta:=\beta_N$ vanishing as the length
$N$ goes to infinity, and in the case where the random variables $\omega$ have
a heavy tail with exponent $\alpha\in (0,d)$. We are able to obtain precisely
the behavior of polymer trajectories under all possible weak-coupling regimes
$\beta_N = \hat \beta N^{-\gamma}$ with $\gamma \geq 0$: we find the correct
transversal fluctuation exponent $\xi$ for the polymer (it depends on $\alpha$
and $\gamma$) and we give the limiting distribution of the rescaled
log-partition function. This extends existing works to the non-directed case
and to higher dimensions.


Abstract:
  In this paper, we aim to study the main properties and luminosity function
(LF) of the [OII] emitters detected in the OTELO survey in order to
characterise the star formation processes in low-mass galaxies at $z\sim1.43$
and to constrain the faint-end of the LF.
  Here, we describe the selection method and analysis of the emitters obtained
from narrow-band scanning techniques. In addition, we present several relevant
properties of the emitters and discuss the selection biases and uncertainties
in the determination of the LF and the star formation rate density (SFRD).
  We confirmed a total of 60 sources from a preliminary list of 332 candidates
as [OII] emitters. Approximately 93% of the emitters have masses in the range
of $10^{8}<M_{*}/{\rm M_{\odot}}<10^{9}$. All of our emitters are classified as
late-type galaxies, with a lower value of $(u-v)$\, when compared with the rest
of the emitters of the OTELO survey. We find that the cosmic variance strongly
affects the normalisation ($\phi^*$) of the LF and explains the discrepancy of
our results when compared with those obtained from surveys of much larger
volumes. However, we are able to determine the faint-end slope of the LF,
namely, $\alpha=-1.42\pm0.06$, by sampling the LF down to $\sim1$\,dex lower
than in previous works. We present our calculation of the SFRD of our sample
and compare it to the value obtained in previous studies from the literature.


Abstract:
  We report that spin supercurrents can be induced in diffusive SFS Josephson
junctions without any magnetic misalignment or intrinsic spin orbit coupling.
Instead, the pathway to spin triplet generation is provided via geometric
curvature, and results in a long ranged Josephson effect. In addition, the
curvature is shown to induce a dynamically tunable $0-\pi$ transition in the
junction. We provide the analytic framework and discuss potential experimental
and innovation implications.


Abstract:
  In this paper, we open up new avenues for visual servoing systems built upon
the Path Integral (PI) optimal control theory, in which the non-linear partial
differential equation (PDE) can be transformed into an expectation over all
possible trajectories using the Feynman-Kac (FK) lemma. More precisely, we
propose an MPPI-VS control strategy, a real-time and inversion-free control
strategy on the basis of sampling-based model predictive control (namely, Model
Predictive Path Integral (MPPI) control) algorithm, for both image-based, 3D
point, and position-based visual servoing techniques, taking into account the
system constraints (such as visibility, 3D, and control constraints) and
parametric uncertainties associated with the robot and camera models as well as
measurement noise. Contrary to classical visual servoing control schemes, our
control strategy directly utilizes the approximation of the interaction matrix,
without the need for estimating the interaction matrix inversion or performing
the pseudo-inversion. We validate the MPPI-VS control strategy as well as the
classical control schemes on a 6-DoF Cartesian robot with an eye-in-hand camera
based on the utilization of four points in the image plane as visual features.
To better assess and demonstrate the robustness and potential advantages of our
proposed control strategy compared to classical schemes, intensive simulations
under various operating conditions are carried out and then discussed. The
obtained results demonstrate the effectiveness and capability of the proposed
scheme in coping easily with the system constraints, as well as its robustness
in the presence of large errors in camera parameters and measurements.


Abstract:
  Recent improvements in deep learning models and their practical applications
have raised concerns about the robustness of these models against adversarial
examples. Adversarial training (AT) has been shown effective to reach a robust
model against the attack that is used during training. However, it usually
fails against other attacks, i.e. the model overfits to the training attack
scheme. In this paper, we propose a simple modification to the AT that
mitigates the mentioned issue. More specifically, we minimize the perturbation
$\ell_p$ norm while maximizing the classification loss in the Lagrangian form.
We argue that crafting adversarial examples based on this scheme results in
enhanced attack generalization in the learned model. We compare our final model
robust accuracy against attacks that were not used during training to closely
related state-of-the-art AT methods. This comparison demonstrates that our
average robust accuracy against unseen attacks is 5.9% higher in the CIFAR-10
dataset and is 3.2% higher in the ImageNet-100 dataset than corresponding
state-of-the-art methods. We also demonstrate that our attack is faster than
other attack schemes that are designed for unseen attack generalization, and
conclude that it is feasible for large-scale datasets.


Abstract:
  At high Reynolds number, the interaction between two vortex tubes leads to
intense velocity gradients, which are at the heart of fluid turbulence. This
vorticity amplification comes about through two different instability
mechanisms of the initial vortex tubes, assumed anti-parallel and with a mirror
plane of symmetry. At moderate Reynolds number, the tubes destabilize via a
Crow instability, with the nonlinear development leading to strong flattening
of the cores into thin sheets. These sheets then break down into filaments
which can repeat the process. At higher Reynolds number, the instability
proceeds via the elliptical instability, producing vortex tubes that are
perpendicular to the original tube directions. In this work, we demonstrate
that these same transition between Crow and Elliptical instability occurs at
moderate Reynolds number when we vary the initial angle $\beta$ between two
straight vortex tubes. We demonstrate that when the angle between the two tubes
is close to $\pi/2$, the interaction between tubes leads to the formation of
thin vortex sheets. The subsequent breakdown of these sheets involves a
twisting of the paired sheets, followed by the appearance of a localized cloud
of small scale vortex structures. At smaller values of the angle $\beta$
between the two tubes, the breakdown mechanism changes to an elliptic
cascade-like mechanism. Whereas the interaction of two vortices depends on the
initial condition, the rapid formation of fine-scales vortex structures appears
to be a robust feature, possibly universal at very high Reynolds numbers.


Abstract:
  Emulator is widely used to build dynamic analysis frameworks due to its
fine-grained tracing capability, full system monitoring functionality, and
scalability of running on different operating systemsand architectures.
However, whether the emulator is consistent with real devices is unknown. To
understand this problem, we aim to automatically locate inconsistent
instructions, which behave differently between emulators and real devices.
  We target ARM architecture, which provides machine readable specification.
Based on the specification, we propose a test case generator by designing and
implementing the first symbolic execution engine for ARM architecture
specification language (ASL). We generate 2,774,649 representative instruction
streams and conduct differential testing with these instruction streams between
four ARM real devices in different architecture versions (i.e., ARMv5, ARMv6,
ARMv7-a, and ARMv8-a) and the state-of-the-art emulators (i.e., QEMU). We
locate 155,642 inconsistent instruction streams, which cover 30% of all
instruction encodings and 47.8% of the instructions. We find undefined
implementation in ARM manual and implementation bugs of QEMU are the major
causes of inconsistencies. Furthermore, we discover four QEMU bugs, which are
confirmed and patched by thedevelopers, covering 13 instruction encodings
including the most commonly used ones (e.g.,STR,BLX). With the inconsistent
instructions, we build three security applications and demonstrate
thecapability of these instructions on detecting emulators, anti-emulation, and
anti-fuzzing.


Abstract:
  We develop a special phase field/diffusive interface method to model the
nuclear architecture reorganization process. In particular, we use a Lagrange
multiplier approach in the phase field model to preserve the specific physical
and geometrical constraints for the biological events. We develop several
efficient and robust linear and weakly nonlinear schemes for this new model. To
validate the model and numerical methods, we present ample numerical
simulations which in particular reproduce several processes of nuclear
architecture reorganization from the experiment literature.


Abstract:
  Episode discovery from an event is a popular framework for data mining tasks
and has many real-world applications. An episode is a partially ordered set of
objects (e.g., item, node), and each object is associated with an event type.
This episode can also be considered as a complex event sub-sequence.
High-utility episode mining is an interesting utility-driven mining task in the
real world. Traditional episode mining algorithms, by setting a threshold,
usually return a huge episode that is neither intuitive nor saves time. In
general, finding a suitable threshold in a pattern-mining algorithm is a
trivial and time-consuming task. In this paper, we propose a novel algorithm,
called Top-K High Utility Episode (THUE) mining within the complex event
sequence, which redefines the previous mining task by obtaining the K highest
episodes. We introduce several threshold-raising strategies and optimize the
episode-weighted utilization upper bounds to speed up the mining process and
effectively reduce the memory cost. Finally, the experimental results on both
real-life and synthetic datasets reveal that the THUE algorithm can offer six
to eight orders of magnitude running time performance improvement over the
state-of-the-art algorithm and has low memory consumption.


Abstract:
  As the potential applications of Artificial Intelligence (AI) in the
financial sector increases, ethical issues become gradually latent. The
distrust of individuals, social groups, and governments about the risks arising
from Fintech's activities is growing. Due to this scenario, the preparation of
recommendations and Ethics Guidelines is increasing and the risks of being
chosen the principles and ethical values most appropriate to companies are
high. Thus, this exploratory research aims to analyze the benefits of the
application of the stakeholder theory and the idea of Social License to build
an environment of trust and for the realization of ethical principles by
Fintech. The formation of a Fintech association for the creation of a Social
License will allow early-stage Fintech to participate from the beginning of its
activities in the elaboration of a dynamic ethical code and with the
participation of stakeholders.


Abstract:
  Automatic evaluation remains an open research question in Natural Language
Generation. In the context of Sentence Simplification, this is particularly
challenging: the task requires by nature to replace complex words with simpler
ones that shares the same meaning. This limits the effectiveness of n-gram
based metrics like BLEU. Going hand in hand with the recent advances in NLG,
new metrics have been proposed, such as BERTScore for Machine Translation. In
summarization, the QuestEval metric proposes to automatically compare two texts
by questioning them.
  In this paper, we first propose a simple modification of QuestEval allowing
it to tackle Sentence Simplification. We then extensively evaluate the
correlations w.r.t. human judgement for several metrics including the recent
BERTScore and QuestEval, and show that the latter obtain state-of-the-art
correlations, outperforming standard metrics like BLEU and SARI. More
importantly, we also show that a large part of the correlations are actually
spurious for all the metrics. To investigate this phenomenon further, we
release a new corpus of evaluated simplifications, this time not generated by
systems but instead, written by humans. This allows us to remove the spurious
correlations and draw very different conclusions from the original ones,
resulting in a better understanding of these metrics. In particular, we raise
concerns about very low correlations for most of traditional metrics. Our
results show that the only significant measure of the Meaning Preservation is
our adaptation of QuestEval.


Abstract:
  The Talbot effect, also referred to as self-imaging or lensless imaging, was
originally discovered in the 1830's by Henry Fox Talbot. Over the years,
various investigators have found different aspects of this phenomenon, and a
theory of the Talbot effect capable of explaining the various observations
based on the classical theory of diffraction has emerged. Unfortunately, many
of the standard Optics textbooks do not discuss the Talbot effect. The goal of
the present paper is to bring to the reader's attention the essential features
as well as an elementary explanation of this wonderful phenomenon.


Abstract:
  This paper explores multi-armed bandit (MAB) strategies in very short horizon
scenarios, i.e., when the bandit strategy is only allowed very few interactions
with the environment. This is an understudied setting in the MAB literature
with many applications in the context of games, such as player modeling.
Specifically, we pursue three different ideas. First, we explore the use of
regression oracles, which replace the simple average used in strategies such as
epsilon-greedy with linear regression models. Second, we examine different
exploration patterns such as forced exploration phases. Finally, we introduce a
new variant of the UCB1 strategy called UCBT that has interesting properties
and no tunable parameters. We present experimental results in a domain
motivated by exergames, where the goal is to maximize a player's daily steps.
Our results show that the combination of epsilon-greedy or epsilon-decreasing
with regression oracles outperforms all other tested strategies in the short
horizon setting.


Abstract:
  We performed a large-scale mapping observation toward the W33 complex and its
surroundings, covering an area of $1.3^\circ \times 1.0^\circ$ , in $^{12}$CO
(1-0), $^{13}$CO (1-0), and C$^{18}$O (1-0) lines from the Purple Mountain
Observatory (PMO). We found a new hub--filament system ranging from 30 to 38.5
\kms located at the W33 complex. Three supercritical filaments are directly
converging into the central hub W33. Velocity gradients are detected along the
filaments and the accretion rates are in order of $\rm 10^{-3}\,M_\odot\,
yr^{-1}$. The central hub W33 has a total mass of $\rm\sim
1.8\times10^5\,M_\odot$, accounting for $\sim 60\%$ of the mass of the
hub--filament system. This indicates that the central hub is the mass reservoir
of the hub-filament system. Furthermore, 49 ATLASGAL clumps are associated with
the hub--filament system. We find $57\%$ of the clumps to be situated in the
central hub W33 and clustered at the intersections between the filaments and
the W33 complex. Moreover, the distribution of Class I young stellar objects
(YSOs) forms a structure resembling the hub--filament system and peaks at where
the clumps group; it seems to suggest that the mechanisms of clump formation
and star formation in this region are correlated. Gas flows along the filaments
are likely to feed the materials into the intersections and lead to the
clustering and formation of the clumps in the hub--filament system W33. The
star formation in the intersections between the filaments and the W33 complex
might be triggered by the motion of gas converging into the intersections.


Abstract:
  The breakthrough of contrastive learning (CL) has fueled the recent success
of self-supervised learning (SSL) in high-level vision tasks on RGB images.
However, CL is still ill-defined for low-level vision tasks, such as joint
demosaicking and denoising (JDD), in the RAW domain. To bridge this
methodological gap, we present a novel CL approach on RAW images, residual
contrastive learning (RCL), which aims to learn meaningful representations for
JDD. Our work is built on the assumption that noise contained in each RAW image
is signal-dependent, thus two crops from the same RAW image should have more
similar noise distribution than two crops from different RAW images. We use
residuals as a discriminative feature and the earth mover's distance to measure
the distribution divergence for the contrastive loss. To evaluate the proposed
CL strategy, we simulate a series of unsupervised JDD experiments with
large-scale data corrupted by synthetic signal-dependent noise, where we set a
new benchmark for unsupervised JDD tasks with unknown (random) noise variance.
Our empirical study not only validates that CL can be applied on distributions
(c.f. features), but also exposes the lack of robustness of previous non-ML and
SSL JDD methods when the statistics of the noise are unknown, thus providing
some further insight into signal-dependent noise problems.


Abstract:
  In this paper, we obtain two Lichnerowicz type formulas for sub-signature
operators. And we give the proof of Kastler-Kalau-Walze type theorems for
sub-signature operators on 4-dimensional and 6-dimensional compact manifolds
with (resp.without) boundary.


Abstract:
  The Alcubierre warp drive metric is a spacetime geometry featuring a
spacetime distortion, called warp bubble, where a massive particle inside it
acquires global superluminal velocities, or warp speeds. This work presents
solutions of the Einstein equations for the Alcubierre metric having fluid
matter as gravity source. The energy-momentum tensor considered two fluid
contents, the perfect fluid and the parametrized perfect fluid (PPF), a
tentative more flexible model whose aim is to explore the possibilities of warp
drive solutions with positive matter density content. Santos-Pereira et al.
(2020; arXiv:2008.06560) have already showed that the Alcubierre metric having
dust as source connects this geometry to the Burgers equation, which describes
shock waves moving through an inviscid fluid, but led the solutions back to
vacuum. The same happened for two out of four solutions subcases for the
perfect fluid. Other solutions for the perfect fluid indicate the possibility
of warp drive with positive matter density, but at the cost of a complex
solution for the warp drive regulating function. Regarding the PPF, solutions
were also obtained indicating that warp speeds could be created with positive
matter density. Weak, dominant, strong and null energy conditions were
calculated for all studied subcases, being satisfied for the perfect fluid and
creating constraints in the PPF quantities such that positive matter density is
also possible for creating a warp bubble. Summing up all
results,energy-momentum tensors describing more complex forms of matter, or
field, distributions generate solutions for the Einstein equations with the
warp drive metric where negative matter density might not be a strict
precondition for attaining warp speeds.


Abstract:
  We present two fiberized vector magnetic-field sensors, based on
nitrogen-vacancy (NV) centers in diamond. The sensors feature
sub-nT/$\sqrt{\textrm{Hz}}$ magnetic sensitivity. We use commercially available
components to construct sensors with a small sensor size, high photon
collection, and minimal sensor-sample distance. Both sensors are located at the
end of optical fibres with the sensor-head freely accessible and robust under
movement. These features make them ideal for mapping magnetic fields with high
sensitivity and spatial resolution ($\leq$\,mm). As a demonstration we use one
of the sensors to map the vector magnetic field inside the bore of a $\geq$
100\,mT Halbach array. The vector field sensing protocol translates microwave
spectroscopy data addressing all diamonds axes and including double quantum
transitions to a 3D magnetic field vector.


Abstract:
  Robot apps are becoming more automated, complex and diverse. An app usually
consists of many functions, interacting with each other and the environment.
This allows robots to conduct various tasks. However, it also opens a new door
for cyber attacks: adversaries can leverage these interactions to threaten the
safety of robot operations. Unfortunately, this issue is rarely explored in
past works.
  We present the first systematic investigation about the function interactions
in common robot apps. First, we disclose the potential risks and damages caused
by malicious interactions. We introduce a comprehensive graph to model the
function interactions in robot apps by analyzing 3,100 packages from the Robot
Operating System (ROS) platform. From this graph, we identify and categorize
three types of interaction risks. Second, we propose RTron, a novel system to
detect and mitigate these risks and protect the operations of robot apps. We
introduce security policies for each type of risks, and design coordination
nodes to enforce the policies and regulate the interactions. We conduct
extensive experiments on 110 robot apps from the ROS platform and two complex
apps (Baidu Apollo and Autoware) widely adopted in industry. Evaluation results
indicated RTron can correctly identify and mitigate all potential risks with
negligible performance cost. To validate the practicality of the risks and
solutions, we implement and evaluate RTron on a physical UGV (Turtlebot) with
real-word apps and environments.


Abstract:
  We consider supersymmetric surface defects in compactifications of the $6d$
minimal $(D_{N+3},D_{N+3})$ conformal matter theories on a punctured Riemann
surface. For the case of $N=1$ such defects are introduced into the
supersymmetric index computations by an action of the $BC_1\,(\sim A_1\sim
C_1)$ van Diejen model. We (re)derive this fact using three different field
theoretic descriptions of the four dimensional models. The three field
theoretic descriptions are naturally associated with algebras $A_{N=1}$,
$C_{N=1}$, and $(A_1)^{N=1}$. The indices of these $4d$ theories give rise to
three different Kernel functions for the $BC_1$ van Diejen model. We then
consider the generalizations with $N>1$. The operators introducing defects into
the index computations are certain $A_{N}$, $C_N$, and $(A_1)^{N}$
generalizations of the van Diejen model. The three different generalizations
are directly related to three different effective gauge theory descriptions one
can obtain by compactifying the minimal $(D_{N+3},D_{N+3})$ conformal matter
theories on a circle to five dimensions. We explicitly compute the operators
for the $A_N$ case, and derive various properties these operators have to
satisfy as a consequence of $4d$ dualities following from the geometric setup.
In some cases we are able to verify these properties which in turn serve as
checks of said dualities. As a by-product of our constructions we also discuss
a simple Lagrangian description of a theory corresponding to compactification
on a sphere with three maximal punctures of the minimal $(D_5,D_5)$ conformal
matter and as consequence give explicit Lagrangian constructions of
compactifications of this 6d SCFT on arbitrary Riemann surfaces.


Abstract:
  Adapter modules were recently introduced as an efficient alternative to
fine-tuning in NLP. Adapter tuning consists in freezing pretrained parameters
of a model and injecting lightweight modules between layers, resulting in the
addition of only a small number of task-specific trainable parameters. While
adapter tuning was investigated for multilingual neural machine translation,
this paper proposes a comprehensive analysis of adapters for multilingual
speech translation (ST). Starting from different pre-trained models (a
multilingual ST trained on parallel data or a multilingual BART (mBART) trained
on non-parallel multilingual data), we show that adapters can be used to: (a)
efficiently specialize ST to specific language pairs with a low extra cost in
terms of parameters, and (b) transfer from an automatic speech recognition
(ASR) task and an mBART pre-trained model to a multilingual ST task.
Experiments show that adapter tuning offer competitive results to full
fine-tuning, while being much more parameter-efficient.


Abstract:
  Entanglement measures quantify nonclassical correlations present in a quantum
system, but can be extremely difficult to calculate, even more so, when
information on its state is limited. Here, we consider broad families of
entanglement criteria that are based on variances of arbitrary operators and
analytically derive the lower bounds these criteria provide for two relevant
entanglement measures: the best separable approximation (BSA) and the
generalized robustness (GR). This yields a practical method for quantifying
entanglement in realistic experimental situations, in particular, when only few
measurements of simple observables are available. As a concrete application of
this method, we quantify bipartite and multipartite entanglement in
spin-squeezed Bose-Einstein condensates of $\sim 500$ atoms, by lower bounding
the BSA and the GR only from measurements of first and second moments of the
collective spin operator.


Abstract:
  Federated Learning allows training machine learning models by using the
computation and private data resources of many distributed clients such as
smartphones and IoT devices. Most existing works on Federated Learning (FL)
assume the clients have ground-truth labels. However, in many practical
scenarios, clients may be unable to label task-specific data, e.g., due to a
lack of expertise. This work considers a server that hosts a labeled dataset
and wishes to leverage clients with unlabeled data for supervised learning. We
propose a new Federated Learning framework referred to as SemiFL to address
Semi-Supervised Federated Learning (SSFL). In SemiFL, clients have completely
unlabeled data, while the server has a small amount of labeled data. SemiFL is
communication efficient since it separates the training of server-side
supervised data and client-side unsupervised data. We demonstrate several
strategies of SemiFL that enhance efficiency and prediction and develop
intuitions of why they work. In particular, we provide a theoretical
understanding of the use of strong data augmentation for Semi-Supervised
Learning (SSL), which can be interesting in its own right. Extensive empirical
evaluations demonstrate that our communication efficient method can
significantly improve the performance of a labeled server with unlabeled
clients. Moreover, we demonstrate that SemiFL can outperform many existing FL
results trained with fully supervised data, and perform competitively with the
state-of-the-art centralized SSL methods. For instance, in standard
communication efficient scenarios, our method can perform $93\%$ accuracy on
the CIFAR10 dataset with only $4000$ labeled samples at the server. Such
accuracy is only $2\%$ away from the result trained from $50000$ fully labeled
data, and it improves about $30\%$ upon existing SSFL methods in the
communication efficient setting.


Abstract:
  We consider the diffusive limit of a typical pure-jump Markovian control
problem as the intensity of the driving Poisson process tends to infinity. We
show that the convergence speed is provided by the H\"older constant of the
Hessian of the limit problem, and explain how correction terms can be
constructed. This provides an alternative efficient method for the numerical
approximation of the optimal control of a pure-jump problem in situations with
very high intensity of jump. We illustrate this approach in the context of a
display advertising auction problem.


Abstract:
  We give a formula for the bivariate generating function of a stationary
1-dependent counting process in terms of its run probability generating
function, with a probabilistic proof. The formula reduces to the well known
bivariate generating function of the Eulerian distribution in the case of
descents of a sequence of indepependent and identically distributed random
variables. The formula is compared with alternative expressions from the theory
of determinantal point processes and the combinatorics of sequences.


Abstract:
  The purpose of this study is to examine the impact of board member
composition and board members' social media presence on the performance of
startups. Using multiple sources, we compile a unique dataset of about 500
US-based technology startups. We find that startups with more venture
capitalists on the board and whose board members are active on Twitter attract
additional funding over the years, though they do not generate additional
sales. By contrast, startups which have no venture capitalists on the board and
whose board members are not on Twitter show an increased ability to translate
assets into sales. Consistent with other research, our results indicate that
startups potentially benefit from working with VCs because of the opportunity
to access additional funding, although their presence does not necessarily
translate into sales growth and operational efficiency. We use a number of
control variables, including board gender representation and board members'
position in the interlocking directorates' network.


Abstract:
  Entity Linking (EL) seeks to align entity mentions in text to entries in a
knowledge-base and is usually comprised of two phases: candidate generation and
candidate ranking. While most methods focus on the latter, it is the candidate
generation phase that sets an upper bound to both time and accuracy performance
of the overall EL system. This work's contribution is a significant improvement
in candidate generation which thus raises the performance threshold for EL, by
generating candidates that include the gold entity in the least candidate set
(top-K). We propose a simple approach that efficiently embeds mention-entity
pairs in dense space through a BERT-based bi-encoder. Specifically, we extend
(Wu et al., 2020) by introducing a new pooling function and incorporating
entity type side-information. We achieve a new state-of-the-art 84.28% accuracy
on top-50 candidates on the Zeshel dataset, compared to the previous 82.06% on
the top-64 of (Wu et al., 2020). We report the results from extensive
experimentation using our proposed model on both seen and unseen entity
datasets. Our results suggest that our method could be a useful complement to
existing EL approaches.


Abstract:
  We have conducted an Arecibo 327 MHz search of two dwarf irregular galaxies
in the Local Group, Leo A and T, for radio pulsars and single pulses from fast
radio bursts and other giant pulse emitters. We detected no astrophysical
signals in this search, and we estimate flux density limits on both periodic
and burst emission. Our derived luminosity limits indicate that only the most
luminous radio pulsars known in our Galaxy and in the Magellanic Clouds (MCs)
would have been detectable in our search if they were at the distances of Leo A
and T. Given the much smaller stellar mass content and star formation rates of
Leo A and T compared to the Milky Way and the MCs, there are likely to be few
(if any) extremely luminous pulsars in these galaxies. It is therefore not
surprising that we detected no pulsars in our search.


Abstract:
  We derive low-order, inf-sup stable and divergence-free finite element
approximations for the Stokes problem using Worsey-Farin splits in three
dimensions and Powell-Sabin splits in two dimensions. The velocity space simply
consists of continuous, piecewise linear polynomials, where as the pressure
space is a subspace of piecewise constants with weak continuity properties at
singular edges (3D) and singular vertices (2D). We discuss implementation
aspects that arise when coding the pressure space, and in particular, show that
the pressure constraints can be enforced at an algebraic level.


Abstract:
  The system size dependence of baryon-strangeness (BS) correlations ($C_{BS}$)
are investigated with a multiphase transport (AMPT) model for various collision
systems from $\mathrm{^{10}B+^{10}B}$, $\mathrm{^{12}C+^{12}C}$,
$\mathrm{^{16}O+^{16}O}$, $\mathrm{^{20}Ne+^{20}Ne}$,
$\mathrm{^{40}Ca+^{40}Ca}$, $\mathrm{^{96}Zr+^{96}Zr}$, and
$\mathrm{^{197}Au+^{197}Au}$ at RHIC energies $\sqrt{s_{NN}}$ of 200, 39, 27,
20, and 7.7 GeV. Both effects of hadron rescattering and a combination of
different hadrons play a leading role for baryon-strangeness correlations. When
the kinetic window is limited to absolute rapidity $|y|>3$, these correlations
tend to be constant after the final-state interaction whatever kind of hadrons
subset we chose based on the AMPT framework. The correlation is found to
smoothly increase with baryon chemical potential $\mu_B$, corresponding to the
collision system or energy from the quark-gluon-plasma-like phase to the
hadron-gas-like phase. Besides, the influence of initial nuclear geometrical
structures of $\alpha$-clustered nuclear collision systems of
$\mathrm{^{12}C+^{12}C}$ as well as $\mathrm{^{16}O+^{16}O}$ collisions is
discussed but the effect is found negligible. The current model studies provide
baselines for searching for the signals of Quantum Chromodynamics (QCD) phase
transition and critical point in heavy-ion collisions through the BS
correlation.


Abstract:
  Scalar Field Dark Matter (SFDM) comprised of ultralight ($\gtrsim 10^{-22}$
eV) bosons is an alternative to standard, collisionless Cold Dark Matter (CDM)
that is CDM-like on large scales but inhibits small-scale structure formation.
As a Bose-Einstein condensate, its free-field ("fuzzy") limit (FDM) suppresses
structure below the de Broglie wavelength, $\lambda_\text{deB}$, creating
virialized haloes with central cores of radius $\sim\lambda_\text{deB}$,
surrounded by CDM-like envelopes, and a halo mass function (HMF) with a sharp
cut-off on small scales. With a strong enough repulsive self-interaction (SI),
structure is inhibited, instead, below the Thomas-Fermi (TF) radius,
$R_\text{TF}$ (the size of an SI-pressure-supported ($n=1$)-polytrope), when
$R_\text{TF} > \lambda_\text{deB}$. Previously, we developed tools to describe
SFDM dynamics on scales above $\lambda_\text{deB}$ and showed that SFDM-TF
haloes formed by Jeans-unstable collapse from non-cosmological initial
conditions have $R_\text{TF}$-sized cores, surrounded by CDM-like envelopes.
Revisiting SFDM-TF in the cosmological context, we simulate halo formation by
cosmological infall and collapse, and derive its transfer function from linear
perturbation theory to produce cosmological initial conditions and predict
statistical measures of structure formation, such as the HMF. Since FDM and
SFDM-TF transfer functions both have small-scale cut-offs, we can align them to
let observational constraints on FDM proxy for SFDM-TF, finding FDM with
particle masses $1 \lesssim m/(10^{-22} \text{ eV}/c^2) \lesssim 30$
corresponds to SFDM-TF with $10 \gtrsim R_\text{TF}/(1 \text{ pc}) \gtrsim 1$,
favoring sub-galactic (sub-kpc) core-size. The SFDM-TF HMF cuts off gradually,
however, leaving more small-mass haloes: its Jeans mass shrinks so fast, scales
filtered early can still recover and grow!


Abstract:
  This study applies convolutional neural network (CNN)-based automatic
segmentation and distensibility measurement of the ascending and descending
aorta from 2D phase-contrast cine magnetic resonance imaging (PC-cine MRI)
within the large MESA cohort with subsequent assessment on an external cohort
of thoracic aortic aneurysm (TAA) patients. 2D PC-cine MRI images of the
ascending and descending aorta at the pulmonary artery bifurcation from the
MESA study were included. Train, validation, and internal test sets consisted
of 1123 studies (24282 images), 374 studies (8067 images), and 375 studies
(8069 images), respectively. An external test set of TAAs consisted of 37
studies (3224 images). A U-Net based CNN was constructed, and performance was
evaluated utilizing dice coefficient (for segmentation) and concordance
correlation coefficients (CCC) of aortic geometric parameters by comparing to
manual segmentation and parameter estimation. Dice coefficients for aorta
segmentation were 97.6% (CI: 97.5%-97.6%) and 93.6% (84.6%-96.7%) on the
internal and external test of TAAs, respectively. CCC for comparison of manual
and CNN maximum and minimum ascending aortic areas were 0.97 and 0.95,
respectively, on the internal test set and 0.997 and 0.995, respectively, for
the external test. CCCs for maximum and minimum descending aortic areas were
0.96 and 0. 98, respectively, on the internal test set and 0.93 and 0.93,
respectively, on the external test set. We successfully developed and validated
a U-Net based ascending and descending aortic segmentation and distensibility
quantification model in a large multi-ethnic database and in an external cohort
of TAA patients.


Abstract:
  The paper describes an algorithm to compute a consensus sequence from a set
of DNA sequences of approximatively identical length generated by 3rd
sequencing generation technologies. Its purpose targets DNA storage and is
guided by specific features that cannot be exhibited from biological data such
as the exact length of the consensus sequences, the precise location of known
patterns, the kmer composition, etc.


Abstract:
  This paper studies the complexity for finding approximate stationary points
of nonconvex-strongly-concave (NC-SC) smooth minimax problems, in both general
and averaged smooth finite-sum settings. We establish nontrivial lower
complexity bounds of $\Omega(\sqrt{\kappa}\Delta L\epsilon^{-2})$ and
$\Omega(n+\sqrt{n\kappa}\Delta L\epsilon^{-2})$ for the two settings,
respectively, where $\kappa$ is the condition number, $L$ is the smoothness
constant, and $\Delta$ is the initial gap. Our result reveals substantial gaps
between these limits and best-known upper bounds in the literature. To close
these gaps, we introduce a generic acceleration scheme that deploys existing
gradient-based methods to solve a sequence of crafted
strongly-convex-strongly-concave subproblems. In the general setting, the
complexity of our proposed algorithm nearly matches the lower bound; in
particular, it removes an additional poly-logarithmic dependence on accuracy
present in previous works. In the averaged smooth finite-sum setting, our
proposed algorithm improves over previous algorithms by providing a
nearly-tight dependence on the condition number.


Abstract:
  We perform a finite-size Kirkwood--Buff analysis on model supercooled
liquids, with and without attractive interactions, to compare thermodynamic
quantities affected by long-range fluctuations in two-body correlation
functions. The attractive potential induces anomalous structural behaviour,
apparent in the $k\to 0$ limit of density and composition structure factors,
analogous to the nucleation of Fischer clusters. By contrast, the purely
repulsive system remains perfectly miscible. This difference contradicts the
widespread belief that the two systems exhibit similar two-body structures.
Moreover, the anomaly disappears at high density, where both systems display
nearly identical dynamical properties. This result suggests that long-range
structures might contribute to the dynamic slowing down of glass-forming
liquids.


Abstract:
  Is a geometric model required to synthesize novel views from a single image?
Being bound to local convolutions, CNNs need explicit 3D biases to model
geometric transformations. In contrast, we demonstrate that a transformer-based
model can synthesize entirely novel views without any hand-engineered 3D
biases. This is achieved by (i) a global attention mechanism for implicitly
learning long-range 3D correspondences between source and target views, and
(ii) a probabilistic formulation necessary to capture the ambiguity inherent in
predicting novel views from a single image, thereby overcoming the limitations
of previous approaches that are restricted to relatively small viewpoint
changes. We evaluate various ways to integrate 3D priors into a transformer
architecture. However, our experiments show that no such geometric priors are
required and that the transformer is capable of implicitly learning 3D
relationships between images. Furthermore, this approach outperforms the state
of the art in terms of visual quality while covering the full distribution of
possible realizations. Code is available at https://git.io/JOnwn


Abstract:
  Reconfigurable intelligent surfaces (RISs) have recently received widespread
attention in the field of wireless communication. An RIS can be controlled to
reflect incident waves from the transmitter towards the receiver; a feature
that is believed to fundamentally contribute to beyond 5G wireless technology.
The typical RIS consists of entirely passive elements, which requires the
high-dimensional channel estimation to be done elsewhere. Therefore, in this
paper, we present a semi-passive large-scale RIS architecture equipped with
only a small fraction of simplified receiver units with only 1-bit
quantization. Based on this architecture, we first propose an alternating
direction method of multipliers (ADMM)-based approach to recover the training
signals at the passive RIS elements, We then obtain the global channel by
combining a channel sparsification step with the generalized approximate
message passing (GAMP) algorithm. Our proposed scheme exploits both the
sparsity and low-rankness properties of the channel in the joint
spatial-frequency domain of a wideband mmWave multiple-input-multiple-output
(MIMO) communication system. Simulation results show that the proposed
algorithm can significantly reduce the pilot signaling needed for accurate
channel estimation and outperform previous methods, even with fewer receiver
units.


Abstract:
  In this paper, we show that replacing the annealed disorder by the quenched
one can result in the switch from the continuous to discontinuous phase
transition. This is an opposite phenomenon to the one, which is usually seen.
So far, it was indicated by numerous studies that the quenched disorder can
round or completely destroy existence of discontinuous phase transition. To
show the possibility of such a behavior, we study a generalized multi-state
$q$-voter model with two types of disorder, annealed and quenched, that
represents so-called anticonformity. We analyze the model on the complete graph
analytically and via Monte Carlo simulations and show that in such a model the
quenched disorder promotes discontinuous phase transitions.


Abstract:
  Background subtraction (BGS) is a fundamental video processing task which is
a key component of many applications. Deep learning-based supervised algorithms
achieve very good perforamnce in BGS, however, most of these algorithms are
optimized for either a specific video or a group of videos, and their
performance decreases dramatically when applied to unseen videos. Recently,
several papers addressed this problem and proposed video-agnostic supervised
BGS algorithms. However, nearly all of the data augmentations used in these
algorithms are limited to the spatial domain and do not account for temporal
variations that naturally occur in video data. In this work, we introduce
spatio-temporal data augmentations and apply them to one of the leading
video-agnostic BGS algorithms, BSUV-Net. We also introduce a new
cross-validation training and evaluation strategy for the CDNet-2014 dataset
that makes it possible to fairly and easily compare the performance of various
video-agnostic supervised BGS algorithms. Our new model trained using the
proposed data augmentations, named BSUV-Net 2.0, significantly outperforms
state-of-the-art algorithms evaluated on unseen videos of CDNet-2014. We also
evaluate the cross-dataset generalization capacity of BSUV-Net 2.0 by training
it solely on CDNet-2014 videos and evaluating its performance on LASIESTA
dataset. Overall, BSUV-Net 2.0 provides a ~5% improvement in the F-score over
state-of-the-art methods on unseen videos of CDNet-2014 and LASIESTA datasets.
Furthermore, we develop a real-time variant of our model, that we call Fast
BSUV-Net 2.0, whose performance is close to the state of the art.


Abstract:
  We study charged massless fermionic perturbations in the background of
$4$-dimensional linear dilaton black holes in Einstein-Maxwell-dilaton theory
with double Liouville-type potentials. We present the analytical fermionic
quasinormal modes, whose Dirac equations are solved in terms of hypergeometric
functions. We also discuss the stability of these black holes under the charged
fermionic perturbations.


Abstract:
  Both linear complementary dual (LCD) codes and maximum distance separable
(MDS) codes have good algebraic structures, and they have interesting practical
applications such as communication systems, data storage, quantum codes, and so
on. So far, most of LCD MDS codes have been constructed by employing
generalized Reed-Solomon codes. In this paper we construct some classes of new
Euclidean LCD MDS codes and Hermitian LCD MDS codes which are not monomially
equivalent to Reed-Solomon codes, called LCD MDS codes of non-Reed- Solomon
type. Our method is based on the constructions of Beelen et al. (2017) and Roth
and Lempel (1989). To the best of our knowledge, this is the first paper on the
construction of LCD MDS codes of non-Reed-Solomon type; any LCD MDS code of
non- Reed-Solomon type constructed by our method is not monomially equivalent
to any LCD code constructed by the method of Carlet et al. (2018).


Abstract:
  We show that a positive proportion of quartic fields are not monogenic,
despite having no local obstruction to being monogenic. Our proof builds on the
corresponding result for cubic fields that we obtained in a previous work.
Along the way, we also prove that a positive proportion of quartic rings of
integers do not arise as the invariant order of an integral binary quartic form
despite having no local obstruction.


Abstract:
  In real acoustic environment, speech enhancement is an arduous task to
improve the quality and intelligibility of speech interfered by background
noise and reverberation. Over the past years, deep learning has shown great
potential on speech enhancement. In this paper, we propose a novel real-time
framework called DBNet which is a dual-branch structure with alternate
interconnection. Each branch incorporates an encoder-decoder architecture with
skip connections. The two branches are responsible for spectrum and waveform
modeling, respectively. A bridge layer is adopted to exchange information
between the two branches. Systematic evaluation and comparison show that the
proposed system substantially outperforms related algorithms under very
challenging environments. And in INTERSPEECH 2021 Deep Noise Suppression (DNS)
challenge, the proposed system ranks the top 8 in real-time track 1 in terms of
the Mean Opinion Score (MOS) of the ITU-T P.835 framework.


Abstract:
  This paper introduces the F3ORNITS non-iterative co-simulation algorithm in
which F3 stands for the 3 flexible aspects of the method: flexible polynomial
order representation of coupling variables, flexible time-stepper applying
variable co-simulation step size rules on subsystems allowing it and flexible
scheduler orchestrating the meeting times among the subsystems and capable of
asynchronousness when subsystems constraints requires it. The motivation of the
F3ORNITS method is to accept any kind of co-simulation model, including any
kind of subsystem, regardless on their available capabilities. Indeed, one the
major problems in industry is that the subsystems usually have constraints or
lack of advanced capabilities making it impossible to implement most of the
advanced co-simulation algorithms on them. The method makes it possible to
preserve the dynamics of the coupling constraints when necessary as well as to
avoid breaking C1 smoothness at communication times, and also to adapt the
co-simulation step size in a way that is robust both to zero-crossing variables
(contrary to classical relative error-based criteria) and to jumps. Two test
cases are presented to illustrate the robustness of the F3ORNITS method as well
as its higher accuracy than the non-iterative Jacobi coupling algorithm (the
most commonly used method in industry) for a smaller number of co-simulation
steps.


Abstract:
  The Hall effects comprise one of the oldest but most vital fields in
condensed matter physics, and they persistently inspire new findings, such as
quantum Hall effects and topological phases of matter. The recently discovered
nonlinear Hall effect is a new member of the family of Hall effects. It is
characterized as a transverse Hall voltage in response to two longitudinal
currents in the Hall measurement, but it does not require time-reversal
symmetry to be broken. It has deep connections to symmetry and topology and,
thus, opens new avenues by which to probe the spectral, symmetry and
topological properties of emergent quantum materials and phases of matter. In
this Perspective, we present an overview of the recent progress regarding the
nonlinear Hall effect. We discuss the open problems, the prospects of the use
of the nonlinear Hall effect in spectroscopic and device applications, and
generalizations to other nonlinear transport effects.


Abstract:
  Accessible machine learning algorithms, software, and diagnostic tools for
energy-efficient devices and systems are extremely valuable across a broad
range of application domains. In scientific domains, real-time near-sensor
processing can drastically improve experimental design and accelerate
scientific discoveries. To support domain scientists, we have developed hls4ml,
an open-source software-hardware codesign workflow to interpret and translate
machine learning algorithms for implementation with both FPGA and ASIC
technologies. We expand on previous hls4ml work by extending capabilities and
techniques towards low-power implementations and increased usability: new
Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long
pipeline kernels for low power, and new device backends include an ASIC
workflow. Taken together, these and continued efforts in hls4ml will arm a new
generation of domain scientists with accessible, efficient, and powerful tools
for machine-learning-accelerated discovery.


Abstract:
  In the last few years, image denoising has benefited a lot from the fast
development of neural networks. However, the requirement of large amounts of
noisy-clean image pairs for supervision limits the wide use of these models.
Although there have been a few attempts in training an image denoising model
with only single noisy images, existing self-supervised denoising approaches
suffer from inefficient network training, loss of useful information, or
dependence on noise modeling. In this paper, we present a very simple yet
effective method named Neighbor2Neighbor to train an effective image denoising
model with only noisy images. Firstly, a random neighbor sub-sampler is
proposed for the generation of training image pairs. In detail, input and
target used to train a network are images sub-sampled from the same noisy
image, satisfying the requirement that paired pixels of paired images are
neighbors and have very similar appearance with each other. Secondly, a
denoising network is trained on sub-sampled training pairs generated in the
first stage, with a proposed regularizer as additional loss for better
performance. The proposed Neighbor2Neighbor framework is able to enjoy the
progress of state-of-the-art supervised denoising networks in network
architecture design. Moreover, it avoids heavy dependence on the assumption of
the noise distribution. We explain our approach from a theoretical perspective
and further validate it through extensive experiments, including synthetic
experiments with different noise distributions in sRGB space and real-world
experiments on a denoising benchmark dataset in raw-RGB space.


Abstract:
  In this work we aim to characterise the dust motion in the inner coma of
comet 67P/Churyumov- Gerasimenko to provide constraints for theoretical 3D coma
models. The OSIRIS camera onboard the Rosetta mission was able for the first
time to acquire images of single dust particles from inside the cometary coma,
very close to the nucleus. We analyse a large number of particles, performing a
significant statistic of their behaviour during the post perihelion period,
when the spacecraft covered distances from the nucleus ranging between 80 and
400 km. We describe the particle trajectories, investigating their orientation
and finding highly radial motion with respect to the nucleus. Then, from the
particle brightness profiles, we derive a particle rotational frequency of v <
3.6 Hz, revealing that they are slow rotators and do not undergo fragmentation.
We use scattering models to compare the observed spectral radiance of the
particles with the simulated ones in order to estimate their size, finding
values that range from millimetres up to centimetres. The statistics performed
in this paper provide useful parameters to constrain the cometary coma
dynamical models.


Abstract:
  We present RAWLSNET, a system for altering Bayesian Network (BN) models to
satisfy the Rawlsian principle of fair equality of opportunity (FEO).
RAWLSNET's BN models generate aspirational data distributions: data generated
to reflect an ideally fair, FEO-satisfying society. FEO states that everyone
with the same talent and willingness to use it should have the same chance of
achieving advantageous social positions (e.g., employment), regardless of their
background circumstances (e.g., socioeconomic status). Satisfying FEO requires
alterations to social structures such as school assignments. Our paper
describes RAWLSNET, a method which takes as input a BN representation of an FEO
application and alters the BN's parameters so as to satisfy FEO when possible,
and minimize deviation from FEO otherwise. We also offer guidance for applying
RAWLSNET, including on recognizing proper applications of FEO. We demonstrate
the use of our system with publicly available data sets. RAWLSNET's altered BNs
offer the novel capability of generating aspirational data for FEO-relevant
tasks. Aspirational data are free from the biases of real-world data, and thus
are useful for recognizing and detecting sources of unfairness in machine
learning algorithms besides biased data.