Abstract:
  Semantic segmentation of nighttime images plays an equally important role as
that of daytime images in autonomous driving, but the former is much more
challenging due to poor illuminations and arduous human annotations. In this
paper, we propose a novel domain adaptation network (DANNet) for nighttime
semantic segmentation without using labeled nighttime image data. It employs an
adversarial training with a labeled daytime dataset and an unlabeled dataset
that contains coarsely aligned day-night image pairs. Specifically, for the
unlabeled day-night image pairs, we use the pixel-level predictions of static
object categories on a daytime image as a pseudo supervision to segment its
counterpart nighttime image. We further design a re-weighting strategy to
handle the inaccuracy caused by misalignment between day-night image pairs and
wrong predictions of daytime images, as well as boost the prediction accuracy
of small objects. The proposed DANNet is the first one stage adaptation
framework for nighttime semantic segmentation, which does not train additional
day-night image transfer models as a separate pre-processing stage. Extensive
experiments on Dark Zurich and Nighttime Driving datasets show that our method
achieves state-of-the-art performance for nighttime semantic segmentation.


Abstract:
  This is the second paper in the series to study the initial perturbation of
mean curvature flow. We study the initial perturbation of mean curvature flow,
whose first singularity is modeled by an asymptotic conical shrinker. The
noncompactness of the limiting shrinker creates essential difficulties.
  We introduce the Feynman-Kac formula to get precise asymptotic behaviour of
the linearized rescaled mean curvature equation along an orbit. We also develop
the invariant cone method to the non-compact setting for the local dynamics
near the shrinker. As a consequence, we prove that after a generic initial
perturbation, the perturbed rescaled mean curvature flow avoids the conical
singularity.


Abstract:
  We analyse a manuscript star catalogue by Wilhem IV, Landgraf von
Hessen-Kassel, from 1586. From measurements of altitudes and of angles between
stars, given in the catalogue, we find that the measurement accuracy averages
26 arcsec for eight fundamental stars, compared to 49 arcsec of the
measurements by Brahe. The computation in converting altitudes to declinations
and angles between stars to celestial position is very accurate, with errors
negligible with respect to the measurement errors. Due to an offset in the
position of the vernal equinox the positional error of the catalogue is
slightly worse than that of Brahe's catalogue, but when correction is made for
the offset -- which was known to 17th century astronomers -- the catalogue is
more accurate than that of Brahe by a factor two. We provide machine-readable
Tables of the catalogue.


Abstract:
  High-fidelity two-qubit entangling gates are essential building blocks for
fault-tolerant quantum computers. Over the past decade, tremendous efforts have
been made to develop scalable high-fidelity two-qubit gates with
superconducting quantum circuits. Recently, an easy-to-scale controlled-phase
gate scheme that utilizes the tunable-coupling architecture with
fixed-frequency qubits [Phys. Rev. Lett. 125, 240502; Phys. Rev. Lett. 125,
240503] has been demonstrated with high fidelity and attracted broad interest.
However, in-depth understanding of the underlying mechanism is still missing,
preventing us from fully exploiting its potential. Here we present a
comprehensive theoretical study, explaining the origin of the high-contrast ZZ
interaction. Based on improved understanding, we develop a general yet
convenient method for shaping an adiabatic pulse in a multilevel system, and
identify how to optimize the gate performance from design. Given
state-of-the-art coherence properties, we expect the scheme to potentially
achieve a two-qubit gate error rate near $10^{-5}$, which would drastically
speed up the progress towards fault-tolerant quantum computation.


Abstract:
  We present optical photometry and spectroscopy of SN\,2019stc
(=ZTF19acbonaa), an unusual Type Ic supernova (SN Ic) at a redshift of
$z=0.117$. SN\,2019stc exhibits a broad double-peaked light curve, with the
first peak having an absolute magnitude of $M_r=-20.0$ mag, and the second
peak, about 80 rest-frame days later, $M_r=-19.2$ mag. The total radiated
energy is large, $E_{\rm rad}\approx 2.5\times 10^{50}$ erg. Despite its large
luminosity, approaching those of Type I superluminous supernovae (SLSNe),
SN\,2019stc exhibits a typical SN Ic spectrum, bridging the gap between SLSNe
and SNe Ic. The spectra indicate the presence of Fe-peak elements, but modeling
of the first light curve peak with radioactive heating alone leads to an
unusually high nickel mass fraction of $f_{\rm Ni}\approx 31\%$ ($M_{\rm
Ni}\approx 3.2$ M$_\odot$). Instead, if we model the first peak with a combined
magnetar spin-down and radioactive heating model we find a better match with
$M_{\rm ej}\approx 4$ M$_\odot$, a magnetar spin period of $P_{\rm spin}\approx
7.2$ ms and magnetic field of $B\approx 10^{14}$ G, and $f_{\rm Ni}\lesssim
0.2$ (consistent with SNe Ic). The prominent second peak cannot be naturally
accommodated with radioactive heating or magnetar spin-down, but instead can be
explained as circumstellar interaction with $\approx 0.7$ $M_\odot$ of
hydrogen-free material located $\approx 400$ AU from the progenitor. Including
the remnant mass leads to a CO core mass prior to explosion of $\approx 6.5$
M$_\odot$. The host galaxy has a metallicity of $\approx 0.26$ Z$_\odot$, low
for SNe Ic but consistent with SLSNe. Overall, we find that SN\,2019stc is a
transition object between normal SNe Ic and SLSNe.


Abstract:
  Text-to-SQL aims to map natural language questions to SQL queries. The
sketch-based method combined with execution-guided (EG) decoding strategy has
shown a strong performance on the WikiSQL benchmark. However, execution-guided
decoding relies on database execution, which significantly slows down the
inference process and is hence unsatisfactory for many real-world applications.
In this paper, we present the Schema Dependency guided multi-task Text-to-SQL
model (SDSQL) to guide the network to effectively capture the interactions
between questions and schemas. The proposed model outperforms all existing
methods in both the settings with or without EG. We show the schema dependency
learning partially cover the benefit from EG and alleviates the need for it.
SDSQL without EG significantly reduces time consumption during inference,
sacrificing only a small amount of performance and provides more flexibility
for downstream applications.


Abstract:
  Designing a static state-feedback controller subject to structural constraint
achieving asymptotic stability is a relevant problem with many applications,
including network decentralized control, coordinated control, and sparse
feedback design. Leveraging on the Projection Lemma, this work presents a new
solution to a class of state-feedback control problems, in which the controller
is constrained to belong to a given linear space. We show through extensive
discussion and numerical examples that our approach leads to several advantages
with respect to existing methods: first, it is computationally efficient;
second, it is less conservative than previous methods, since it relaxes the
requirement of restricting the Lyapunov matrix to a block-diagonal form.


Abstract:
  We report on the first complete computation of the mixed QCD$-$electroweak
(EW) corrections to the neutral-current Drell$-$Yan process. Superseding
previously applied approximations, our calculation provides the first result at
this order that is valid in the entire range of dilepton invariant masses. The
two-loop virtual contribution is computed by using semi-analytical techniques,
overcoming the technical problems in the evaluation of the relevant master
integrals. The cancellation of soft and collinear singularities is achieved by
a formulation of the $q_T$ subtraction formalism valid in presence of charged
massive particles in the final state. We present numerical results for the
fiducial cross section and selected kinematical distributions. At large values
of the lepton $p_T$ the mixed QCD$-$EW corrections are negative and increase in
size, to about $-15\%$ with respect to the next-to-leading-order QCD result at
$p_T=500\,$GeV. Up to dilepton invariant masses of 1 TeV the computed
corrections amount to about $-1.5\%$ with respect to the next-to-leading-order
QCD result.


Abstract:
  We implement the PaperRank and AuthorRank indices introduced in [Amodio &
Brugnano, 2014] in the Scopus database, in order to highlight quantitative and
qualitative information that the bare number of citations and/or the h-index of
an author are unable to provide. In addition to this, the new indices can be
cheaply updated in Scopus, since this has a cost comparable to that of updating
the number of citations. Some examples are reported to provide insight in their
potentialities, as well as possible extensions.


Abstract:
  Network intrusion is a well-studied area of cyber security. Current machine
learning-based network intrusion detection systems (NIDSs) monitor network data
and the patterns within those data but at the cost of presenting significant
issues in terms of privacy violations which may threaten end-user privacy.
Therefore, to mitigate risk and preserve a balance between security and
privacy, it is imperative to protect user privacy with respect to intrusion
data. Moreover, cost is a driver of a machine learning-based NIDS because such
systems are increasingly being deployed on resource-limited edge devices. To
solve these issues, in this paper we propose a NIDS called PCC-LSM-NIDS that is
composed of a Pearson Correlation Coefficient (PCC) based feature selection
algorithm and a Least Square Method (LSM) based privacy-preserving algorithm to
achieve low-cost intrusion detection while providing privacy preservation for
sensitive data. The proposed PCC-LSM-NIDS is tested on the benchmark intrusion
database UNSW-NB15, using five popular classifiers. The experimental results
show that the proposed PCC-LSM-NIDS offers advantages in terms of less
computational time, while offering an appropriate degree of privacy protection.


Abstract:
  In this article, we study the componentwise linear ideals in the Veronese
subrings of $R=K[x_1,\ldots,x_n]$. If char$(K)=0$, then we give a
characterization for graded ideals in the $c^{th}$ Veronese ring $R^{(c)}$ to
be componentwise linear. This characterization is an analogue of that over $R$
due to Aramova, Herzog and Hibi in \cite{ah00} and Conca, Herzog and Hibi in
\cite{chh04}.


Abstract:
  Emergent electromagnetism in magnets originates from the strong coupling
between conduction electron spins and those of noncollinear ordered moments and
the consequent Berry phase. This offers possibilities to develop new functions
of quantum transport and optical responses. The emergent inductance in spiral
magnets is an example recently proposed and experimentally demonstrated, used
the emergent electric field induced by alternating currents. However, the
microscopic theory of this phenomenon is missing, which should reveal the
factors to determine the magnitude, sign, frequency dependence, and
nonlinearity of the inductance L. Here we theoretically study electromagnetic
responses of spiral magnets taking into account their collective modes. In
sharp contrast to the collinear spin-density wave, the system remains metallic
even in one-dimension, and the canonical conjugate relation of uniform
magnetization and phason coordinate plays an essential role, determining the
properties of L. This result opens a way to design the emergent inductance of
desired properties.


Abstract:
  We study the entanglement properties of non-Hermitian free fermionic models
with translation symmetry using the correlation matrix technique. Our results
show that the entanglement entropy has a logarithmic correction to the area law
in both one-dimensional and two-dimensional systems. For any one-dimensional
one-band system, we prove that each Fermi point of the system contributes
exactly 1/2 to the coefficient c of the logarithmic correction. Moreover, this
relation between c and Fermi point is verified for more general one-dimensional
and two-dimensional cases by numerical calculations and finite-size scaling
analysis. In addition, we also study the single-particle and density-density
correlation functions.


Abstract:
  Here are two problems. First, understand the dynamics of a tiling billiard in
a cyclic quadrilateral periodic tiling. Second, describe the topology of
connected components of plane sections of a centrally symmetric subsurface $S
\subset \mathbb{T}^3$ of genus $3$. In this note we show that these two
problems are related via a helicoidal construction proposed recently by Ivan
Dynnikov. The second problem is a particular case of a classical question
formulated by Sergei Novikov. The exploration of the relationship between a
large class of tiling billiards (periodic locally foldable tiling billiards)
and Novikov's problem in higher genus seems promising, as we show in the end of
this note.


Abstract:
  Polls are a common way of collecting data, including product reviews and
feedback forms. However, few data collectors give upfront privacy guarantees.
Additionally, when privacy guarantees are given upfront, they are often vague
claims about 'anonymity'. Instead, we propose giving quantifiable privacy
guarantees through the statistical notion of differential privacy.
Nevertheless, privacy does not come for free. At the heart of differential
privacy lies an inherent trade-off between accuracy and privacy that needs to
be balanced. Thus, it is vital to properly adjust the accuracy-privacy
trade-off before setting out to collect data. Altogether, getting started with
differentially private data collection can be challenging. Ideally, a data
analyst should not have to be concerned about all the details of differential
privacy, but rather get differential privacy by design. Still, to the best of
our knowledge, no tools for gathering poll data under differential privacy
exists.
  Motivated by the lack of tools to gather poll data under differential
privacy, we set out to engineer our own tool. Specifically, to make local
differential privacy accessible for all, in this systems paper we present
Randori, a set of novel open source tools for differentially private poll data
collection. Randori is intended to help data analysts keep their focus on what
data their poll is collecting, as opposed to how they should collect it. Our
tools also allow the data analysts to analytically predict the accuracy of
their poll. Furthermore, we show that differential privacy alone is not enough
to achieve end-to-end privacy in a server-client setting. Consequently, we also
investigate and mitigate implicit data leaks in Randori.


Abstract:
  Taking Shapiro's cyclic sums $\sum_{i=1}^n x_i/(x_{i+1}+x_{i+2})$ (assuming
index addition mod $n$) as a starting point, we introduce a broader class of
cyclic sums, called generalized Shapiro-Diananda sums, where the denominators
are $p$-th order power means of the sets $\{x_{i+j_1},\dots,x_{i+j_k}\}$ with
fixed distinct integers $j_1,\dots,j_k$ and $1\leq i\leq n$.
  Generalizing further, we replace the set of arguments of the power mean in
the $i$-th denominator by an arbitrary nonempty subset of $\{1,\dots,n\}$
interpreted as the set of out-neighbors of the node number $i$ in a directed
graph with $n$ nodes. We call such sums graphic power sums since their
structure is controlled by directed graphs.
  The inquiry, as in the well-researched case of Shapiro's sums, concerns the
greatest lower bound of the given ``sum'' as a function of positive variables
$x_1,\dots,x_n$. We show that the cases of $p=+\infty$ (max-sums) and
$p=-\infty$ (min-sums) are tractable.
  For the max-sum associated with a given graph the g.l.b. is always an
integer; for a strongly connected graph it equals to graph's girth.
  For the similar min-sum, we could not relate the g.l.b. to a known
combinatorial invariant; we only give some estimates and describe a method for
finding the g.l.b., which has factorial complexity in $n$.
  A satisfactory analytical treatment is available for the secondary
minimization -- when the g.l.b.'s of min-sums for individual graphs are
mininized over the class of strongly connected graphs with $n$ nodes. The
result (depending only on $n$) is found to be asymptotic to $e\ln n$.


Abstract:
  In this paper, we present a video-based learning framework for animating
personalized 3D talking faces from audio. We introduce two training-time data
normalizations that significantly improve data sample efficiency. First, we
isolate and represent faces in a normalized space that decouples 3D geometry,
head pose, and texture. This decomposes the prediction problem into regressions
over the 3D face shape and the corresponding 2D texture atlas. Second, we
leverage facial symmetry and approximate albedo constancy of skin to isolate
and remove spatio-temporal lighting variations. Together, these normalizations
allow simple networks to generate high fidelity lip-sync videos under novel
ambient illumination while training with just a single speaker-specific video.
Further, to stabilize temporal dynamics, we introduce an auto-regressive
approach that conditions the model on its previous visual state. Human ratings
and objective metrics demonstrate that our method outperforms contemporary
state-of-the-art audio-driven video reenactment benchmarks in terms of realism,
lip-sync and visual quality scores. We illustrate several applications enabled
by our framework.


Abstract:
  This paper concerned with the effect of generalized uncertainty principle
(GUP) on the stochastic gravitational wave (SGW) background signal that
produced during first order cosmological QCD phase transition in early
universe. A modified formula of entropy is used to calculate the temporal
evolution of temperature of the universe as a function of the Hubble parameter.
The pressure that results from the recent lattice calculations, which provides
parameterizations of the pressure due to $u,~d,~s$ quarks and gluons, with
trace anomaly is used to describe the equation of state around QCD epoch. A
redshift in the peak frequency of SGW at current epoch is calculated. The
results indicate an increase in the frequency peak due to GUP effect, which
improves the ability to detect it. Taking into account bubble wall collisions
(BWC) and turbulent magnetohydrodynamics (MHD) as a source of SGW, a fractional
energy density is investigated. It is found that the GUP effect weakens the SGW
signal generated during QCD phase transition in comparison to its counterpart
in the absence of GUP. These results support understanding the cosmological QCD
phase transition and test the effectiveness of the GUP theory.


Abstract:
  Molecular machine learning bears promise for efficient molecule property
prediction and drug discovery. However, due to the limited labeled data and the
giant chemical space, machine learning models trained via supervised learning
perform poorly in generalization. This greatly limits the applications of
machine learning methods for molecular design and discovery. In this work, we
present MolCLR: Molecular Contrastive Learning of Representations via Graph
Neural Networks (GNNs), a self-supervised learning framework for large
unlabeled molecule datasets. Specifically, we first build a molecular graph,
where each node represents an atom and each edge represents a chemical bond. A
GNN is then used to encode the molecule graph. We propose three novel molecule
graph augmentations: atom masking, bond deletion, and subgraph removal. A
contrastive estimator is utilized to maximize the agreement of different graph
augmentations from the same molecule. Experiments show that molecule
representations learned by MolCLR can be transferred to multiple downstream
molecular property prediction tasks. Our method thus achieves state-of-the-art
performance on many challenging datasets. We also prove the efficiency of our
proposed molecule graph augmentations on supervised molecular classification
tasks.


Abstract:
  Developing deep learning models to analyze histology images has been
computationally challenging, as the massive size of the images causes excessive
strain on all parts of the computing pipeline. This paper proposes a novel deep
learning-based methodology for improving the computational efficiency of
histology image classification. The proposed approach is robust when used with
images that have reduced input resolution and can be trained effectively with
limited labeled data. Pre-trained on the original high-resolution (HR) images,
our method uses knowledge distillation (KD) to transfer learned knowledge from
a teacher model to a student model trained on the same images at a much lower
resolution. To address the lack of large-scale labeled histology image
datasets, we perform KD in a self-supervised manner. We evaluate our approach
on two histology image datasets associated with celiac disease (CD) and lung
adenocarcinoma (LUAD). Our results show that a combination of KD and
self-supervision allows the student model to approach, and in some cases,
surpass the classification accuracy of the teacher, while being much more
efficient. Additionally, we observe an increase in student classification
performance as the size of the unlabeled dataset increases, indicating that
there is potential to scale further. For the CD data, our model outperforms the
HR teacher model, while needing 4 times fewer computations. For the LUAD data,
our student model results at 1.25x magnification are within 3% of the teacher
model at 10x magnification, with a 64 times computational cost reduction.
Moreover, our CD outcomes benefit from performance scaling with the use of more
unlabeled data. For 0.625x magnification, using unlabeled data improves
accuracy by 4% over the baseline. Thus, our method can improve the feasibility
of deep learning solutions for digital pathology with standard computational
hardware.


Abstract:
  The digital media, identified as computational propaganda provides a pathway
for propaganda to expand its reach without limit. State-backed propaganda aims
to shape the audiences' cognition toward entities in favor of a certain
political party or authority. Furthermore, it has become part of modern
information warfare used in order to gain an advantage over opponents. Most of
the current studies focus on using machine learning, quantitative, and
qualitative methods to distinguish if a certain piece of information on social
media is propaganda. Mainly conducted on English content, but very little
research addresses Chinese Mandarin content. From propaganda detection, we want
to go one step further to provide more fine-grained information on propaganda
techniques that are applied. In this research, we aim to bridge the information
gap by providing a multi-labeled propaganda techniques dataset in Mandarin
based on a state-backed information operation dataset provided by Twitter. In
addition to presenting the dataset, we apply a multi-label text classification
using fine-tuned BERT. Potentially this could help future research in detecting
state-backed propaganda online especially in a cross-lingual context and cross
platforms identity consolidation.


Abstract:
  The creativity and emergence of biological and psychological behavior are
nonlinear. However, that does not necessarily mean only that the measurements
of the behaviors are curvilinear. Furthermore, the linear model might fail to
reduce these measurements to a sum of independent random factors, implying
nonlinear changes over time. The present work reviews some of the concepts
implicated in linear changes over time and details the mathematical steps
involved. It introduces multifractality as a mathematical framework helpful in
determining whether and to what degree the measured time series exhibits
nonlinear changes over time. The mathematical steps include multifractal
analysis and surrogate data production for resolving when multifractality
entails nonlinear changes over time. Ultimately, when measurements fail to fit
the structures of the traditional linear model, multifractal modeling gives us
the means to make those nonlinear excursions explicit and perhaps permit the
development of theory that draws on both linear and nonlinear processes.


Abstract:
  This paper discusses the problem of weakly supervised classification, in
which instances are given weak labels that are produced by some
label-corruption process. The goal is to derive conditions under which loss
functions for weak-label learning are proper and lower-bounded -- two essential
requirements for the losses used in class-probability estimation. To this end,
we derive a representation theorem for proper losses in supervised learning,
which dualizes the Savage representation. We use this theorem to characterize
proper weak-label losses and find a condition for them to be lower-bounded.
From these theoretical findings, we derive a novel regularization scheme called
generalized logit squeezing, which makes any proper weak-label loss bounded
from below, without losing properness. Furthermore, we experimentally
demonstrate the effectiveness of our proposed approach, as compared to improper
or unbounded losses. The results highlight the importance of properness and
lower-boundedness.


Abstract:
  Recent years have witnessed significant progress in person re-identification
(ReID). However, current ReID approaches still suffer from considerable
performance degradation when unseen testing domains exhibit different
characteristics from the source training ones, known as the domain
generalization problem. Given multiple source training domains, previous Domain
Generalizable ReID (DG-ReID) methods usually learn all domains together using a
shared network, which can't learn sufficient knowledge from each domain. In
this paper, we propose a novel Multiple Domain Experts Collaborative Learning
(MECL) framework for better exploiting all training domains, which benefits
from the proposed Domain-Domain Collaborative Learning (DDCL) and
Universal-Domain Collaborative Learning (UDCL). DDCL utilizes domain-specific
experts for fully exploiting each domain, and prevents experts from
over-fitting the corresponding domain using a meta-learning strategy. In UDCL,
a universal expert supervises the learning of domain experts and continuously
gathers knowledge from all domain experts. Note, only the universal expert will
be used for inference. Extensive experiments on DG-ReID benchmarks demonstrate
the effectiveness of DDCL and UDCL, and show that the whole MECL framework
significantly outperforms state-of-the-arts. Experimental results on
DG-classification benchmarks also reveal the great potential of applying MECL
to other DG tasks.


Abstract:
  Transformers have become the powerhouse of natural language processing and
recently found use in computer vision tasks. Their effective use of attention
can be used in other contexts as well, and in this paper, we propose a
transformer-based approach for efficiently solving the complex motion planning
problems. Traditional neural network-based motion planning uses convolutional
networks to encode the planning space, but these methods are limited to fixed
map sizes, which is often not realistic in the real-world. Our approach first
identifies regions on the map using transformers to provide attention to map
areas likely to include the best path, and then applies local planners to
generate the final collision-free path. We validate our method on a variety of
randomly generated environments with different map sizes, demonstrating
reduction in planning complexity and achieving comparable accuracy to
traditional planners.


Abstract:
  Consider the short-time probability distribution $\mathcal{P}(H,t)$ of the
one-point interface height difference $h(x=0,\tau=t)-h(x=0,\tau=0)=H$ of the
stationary interface $h(x,\tau)$ described by the Kardar-Parisi-Zhang equation.
It was previously shown that the optimal path -- the most probable history of
the interface $h(x,\tau)$ which dominates the upper tail of $\mathcal{P}(H,t)$
-- is described by any of \emph{two} ramp-like structures of $h(x,\tau)$
traveling either to the left, or to the right. These two solutions emerge, at a
critical value of $H$, via a spontaneous breaking of the mirror symmetry
$x\leftrightarrow -x$ of the optimal path, and this symmetry breaking is
responsible for a second-order dynamical phase transition in the system. We
simulate the interface configurations numerically by employing a
large-deviation Monte Carlo sampling algorithm in conjunction with the mapping
between the KPZ interface and the directed polymer in a random potential at
high temperature. This allows us to observe the optimal paths, which determine
each of the two tails of $\mathcal{P}(H,t)$, down to probability densities as
small as $10^{-500}$. At short times we observe mirror-symmetry-broken
traveling optimal paths for the upper tail, and a single mirror-symmetric path
for the lower tail, in good quantitative agreement with analytical predictions.
At long times, even at moderate values of $H$, where the optimal fluctuation
method is \emph{not} supposed to apply, we still observe two well-defined
dominating paths. Each of them violates the mirror symmetry $x\leftrightarrow
-x$ and is a mirror image of the other.


Abstract:
  To explore the vulnerability of deep neural networks (DNNs), many attack
paradigms have been well studied, such as the poisoning-based backdoor attack
in the training stage and the adversarial attack in the inference stage. In
this paper, we study a novel attack paradigm, which modifies model parameters
in the deployment stage for malicious purposes. Specifically, our goal is to
misclassify a specific sample into a target class without any sample
modification, while not significantly reduce the prediction accuracy of other
samples to ensure the stealthiness. To this end, we formulate this problem as a
binary integer programming (BIP), since the parameters are stored as binary
bits ($i.e.$, 0 and 1) in the memory. By utilizing the latest technique in
integer programming, we equivalently reformulate this BIP problem as a
continuous optimization problem, which can be effectively and efficiently
solved using the alternating direction method of multipliers (ADMM) method.
Consequently, the flipped critical bits can be easily determined through
optimization, rather than using a heuristic strategy. Extensive experiments
demonstrate the superiority of our method in attacking DNNs.


Abstract:
  The reliable operation of power grid is supported by energy management
systems (EMS) that provide monitoring and control functionalities. Contingency
analysis is a critical application of EMS to evaluate the impacts of outages
and prepare for system failures. However, false data injection attacks (FDIAs)
have demonstrated the possibility of compromising sensor measurements and
falsifying the estimated power system states. As a result, FDIAs may mislead
system operations and other EMS applications including contingency analysis and
optimal power flow. In this paper, we assess the effect of FDIAs and
demonstrate that such attacks can affect the resulted number of contingencies.
In order to mitigate the FDIA impact, we propose CHIMERA, a hybrid
attack-resilient state estimation approach that integrates model-based and
data-driven methods. CHIMERA combines the physical grid information with a Long
Short Term Memory (LSTM)-based deep learning model by considering a static loss
of weighted least square errors and a dynamic loss of the difference between
the temporal variations of the actual and the estimated active power. Our
simulation experiments based on the load data from New York state demonstrate
that CHIMERA can effectively mitigate 91.74% of the cases in which FDIAs can
maliciously modify the contingencies.


Abstract:
  One objective of a lander mission to Jupiter's icy moon Europa is to detect
liquid water within 30 km as well as characterizing the subsurface ocean. In
order to satisfy this objective, water within the ice shell must also be
identified. Inductive electromagnetic (EM) methods are optimal for water
detection on Europa because even a small fraction of dissolved salts will make
water orders of magnitude more electrically conductive than the ice shell.
Compared to induction studies by the Galileo spacecraft, measurements of
higher-frequency ambient EM fields are necessary to resolve the shallower
depths of intrashell water. Although these fields have been mostly
characterized by prior missions, their unknown source structures and plasma
properties do not allow EM sounding using a single surface magnetometer or the
orbit-to-surface magnetic transfer function, respectively. Instead, broadband
EM sounding can be accomplished from a single surface station using the
magnetotelluric (MT) method, which measures horizontal electric fields as well
as the three-component magnetic field. We have developed a prototype Europa
Magnetotelluric Sounder (EMS) to meet the measurement requirements in the
relevant thermal, vacuum, and radiation environment. EMS comprises central
electronics, a fluxgate magnetometer on a mast, and three ballistically
deployed electrodes to measure differences in surface electric potential. In
this paper, we describe EMS development and testing as well as providing
supporting information on the concept of operations and calculations on water
detectability. EMS can uniquely determine the occurrence of intrashell water on
Europa, providing important constraints on habitability.


Abstract:
  Laboratory experiments play a key role in deciphering the chemistry of the
interstellar medium (ISM) and the role that product complex organic molecules
(COMs) may play in the origins of life. However, to date, most studies in
experimental astrochemistry have made use of reductionist approaches to
experimental design in which chemical responses to variations in a single
parameter are investigated while all other parameters are held constant.
Although such work does afford insight into the chemistry of the ISM, it is
likely that several important points, such as the relative importance of an
experimental parameter in determining the chemical outcome of a reaction and
the interaction between parameters, remain ambiguous. In light of this, we
propose adopting a new systems astrochemistry framework for experimental
studies which draws on current work performed in the field of prebiotic
chemistry, and present the basic tenants of such an approach in this article.
This systems approach would focus on the emergent properties of the chemical
system by performing the simultaneous variation of multiple experimental
parameters and would allow for the effect of each parameter, as well as their
interactions, to be quantified. We anticipate that the application of systems
science to laboratory astrochemistry, coupled with developments in hyphenated
analytical techniques and data analytics, will uncover significant new data
hitherto unknown, and will aid in better linking laboratory experiments to
observations and modelling work.


Abstract:
  This volume contains a selection of papers presented at LFMTP 2020, the 15th
International Workshop on Logical Frameworks and Meta-Languages: Theory and
Practice (LFMTP), held the 29-30th of June, 2019, using the Zoom video
conferencing tool due to COVID restrictions. Officially the workshop was held
in Paris, France, and it was affiliated with IJCAR 2020, FSCD 2020 and many
other satellite events.
  Logical frameworks and meta-languages form a common substrate for
representing, implementing and reasoning about a wide variety of deductive
systems of interest in logic and computer science. Their design, implementation
and their use in reasoning tasks, ranging from the correctness of software to
the properties of formal systems, have been the focus of considerable research
over the last two decades. This workshop will bring together designers,
implementors and practitioners to discuss various aspects impinging on the
structure and utility of logical frameworks, including the treatment of
variable binding, inductive and co-inductive reasoning techniques and the
expressiveness and lucidity of the reasoning process.


Abstract:
  We propose a novel way of studying the gluon number density (the so-called
Weizs\"acker-Williams gluon distribution) using the planned Electron Ion
Collider. Namely, with the help of the azimuthal correlations between the total
transverse momentum of the dijet system and the scattered electron, we examine
an interplay between the effect of the soft gluon emissions (the Sudakov form
factor) and the gluon saturation effects. The kinematic cuts are chosen such
that the dijet system is produced in the forward direction in the laboratory
frame, which provides an upper bound on the probed longitudinal fractions of
the hadron momentum carried by scattered gluons. Further cuts enable us to use
the factorization formalism that directly involves the unpolarized
Weizs\"acker-Williams gluon distribution. We find this observable to be very
sensitive to the soft gluon emission and moderately sensitive to the gluon
saturation.


Abstract:
  The behaviour of partially ionized hot compressed matter is critical to the
study of planetary interiors as well as for nuclear fusion studies. A recent
quantum study of carbon in the 10-70 Gbar range and at a temperature of 100 eV
used $N$-atom density functional theory (DFT) with $N\sim 32$-64 and molecular
dynamics (MD). This involves band-structure type electronic calculations and
averaging over many MD generated ion configurations. The calculated average
number of free electrons per ion, viz., $\bar{Z}$, was systematically higher
than from a standard average atom (AA) quantum calculation. To clarify this
offset, we examine (a) the effect of the self-interaction (SI) error in such
estimates (b) the possibility of carbon being a granular plasma containing
Coulomb crystals. The possibility of `magic-number' bound states is considered.
The electrical conductivity, pressure, and the compressibility of the carbon
system are examined. The very low conductivity and the high $\bar{Z}$ results
of DFT-MD point to the existence of carbon in a complex non-uniform
low-conducting dispersed phase, possibly containing magic-number Coulomb
crystals. The NPA estimate of $\bar{Z}$, conductivity, compressibility, and
pressure reported here pertain to the {\it uniform liquid}.


Abstract:
  We study the birational geometry of the moduli spaces of hyperelliptic curves
with marked points. We complete the Kodaira classification proving that these
spaces are of Calabi-Yau type when the number of markings is $4g+6$. Further,
we provide a full classification of the structure of the pseudo-effective cone
of divisors, showing the cone is non-polyhedral when the number of markings is
at least two and polyhedral in the remaining cases.


Abstract:
  In this work, we study the outage probability (OP) at the destination of an
intelligent reflecting surface (IRS) assisted communication system in a
$\kappa-\mu$ fading environment. A practical system model that takes into
account the presence of phase error due to quantization at the IRS when a)
source-destination (SD) link is present and b) SD link is absent is considered.
First, an exact expression is derived, and then we derive three simple
approximations for the OP using the following approaches: (i) uni-variate
dimension reduction, (ii) moment matching and, (iii) Kullback-Leibler
divergence minimization. The resulting expressions for OP are simple to
evaluate and quite tight even in the tail region. The validity of these
approximations is demonstrated using extensive Monte Carlo simulations. We also
study the impact of the number of bits available for quantization, the position
of IRS with respect to the source and destination and the number of IRS
elements on the OP for systems with and without an SD link.


Abstract:
  Robot interfaces often only use the visual channel. Inspired by Wickens'
Multiple Resource Theory, we investigated if the addition of audio elements
would reduce cognitive workload and improve performance. Specifically, we
designed a search and threat-defusal task (primary) with a memory test task
(secondary). Eleven participants - predominantly first responders - were
recruited to control a robot to clear all threats in a combination of four
conditions of primary and secondary tasks in visual and auditory channels. We
did not find any statistically significant differences in performance or
workload across subjects, making it questionable that Multiple Resource Theory
could shorten longer-term task completion time and reduce workload. Our results
suggest that considering individual differences for splitting interface
modalities across multiple channels requires further investigation.


Abstract:
  The latest video coding standard, called versatile video coding (VVC),
includes several novel and refined coding tools at different levels of the
coding chain. These tools bring significant coding gains with respect to the
previous standard, high efficiency video coding (HEVC). However, the encoder
may still introduce visible coding artifacts, mainly caused by coding decisions
applied to adjust the bitrate to the available bandwidth. Hence, pre and
post-processing techniques are generally added to the coding pipeline to
improve the quality of the decoded video. These methods have recently shown
outstanding results compared to traditional approaches, thanks to the recent
advances in deep learning. Generally, multiple neural networks are trained
independently to perform different tasks, thus omitting to benefit from the
redundancy that exists between the models. In this paper, we investigate a
learning-based solution as a post-processing step to enhance the decoded VVC
video quality. Our method relies on multitask learning to perform both quality
enhancement and super-resolution using a single shared network optimized for
multiple degradation levels. The proposed solution enables a good performance
in both mitigating coding artifacts and super-resolution with fewer network
parameters compared to traditional specialized architectures.


Abstract:
  Context: Software engineering has a problem in that when we empirically
evaluate competing prediction systems we obtain conflicting results. Objective:
To reduce the inconsistency amongst validation study results and provide a more
formal foundation to interpret results with a particular focus on continuous
prediction systems. Method: A new framework is proposed for evaluating
competing prediction systems based upon (1) an unbiased statistic, Standardised
Accuracy, (2) testing the result likelihood relative to the baseline technique
of random 'predictions', that is guessing, and (3) calculation of effect sizes.
Results: Previously published empirical evaluations of prediction systems are
re-examined and the original conclusions shown to be unsafe. Additionally, even
the strongest results are shown to have no more than a medium effect size
relative to random guessing. Conclusions: Biased accuracy statistics such as
MMRE are deprecated. By contrast this new empirical validation framework leads
to meaningful results. Such steps will assist in performing future
meta-analyses and in providing more robust and usable recommendations to
practitioners.


Abstract:
  Chatbots are often designed to mimic social roles attributed to humans.
However, little is known about the impact on user's perceptions of using
language that fails to conform to the associated social role. Our research
draws on sociolinguistic theory to investigate how a chatbot's language choices
can adhere to the expected social role the agent performs within a given
context. In doing so, we seek to understand whether chatbots design should
account for linguistic register. This research analyzes how register
differences play a role in shaping the user's perception of the human-chatbot
interaction. Ultimately, we want to determine whether register-specific
language influences users' perceptions and experiences with chatbots. We
produced parallel corpora of conversations in the tourism domain with similar
content and varying register characteristics and evaluated users' preferences
of chatbot's linguistic choices in terms of appropriateness, credibility, and
user experience. Our results show that register characteristics are strong
predictors of user's preferences, which points to the needs of designing
chatbots with register-appropriate language to improve acceptance and users'
perceptions of chatbot interactions.


Abstract:
  As software systems evolve, their architecture is meant to adapt accordingly
by following the changes in requirements, the environment, and the
implementation. However, in practice, the evolving system often deviates from
the architecture, causing severe consequences to system maintenance and
evolution. This phenomenon of architecture erosion has been studied extensively
in research, but not yet been examined from the point of view of developers. In
this exploratory study, we look into how developers perceive the notion of
architecture erosion, its causes and consequences, as well as tools and
practices to identify and control architecture erosion. To this end, we
searched through several popular online developer communities for collecting
data of discussions related to architecture erosion. Besides, we identified
developers involved in these discussions and conducted a survey with 10
participants and held interviews with 4 participants. Our findings show that:
(1) developers either focus on the structural manifestation of architecture
erosion or on its effect on run-time qualities, maintenance and evolution; (2)
alongside technical factors, architecture erosion is caused to a large extent
by non-technical factors; (3) despite the lack of dedicated tools for detecting
architecture erosion, developers usually identify erosion through a number of
symptoms; and (4) there are effective measures that can help to alleviate the
impact of architecture erosion.


Abstract:
  For a point of the projective space $\PG(n,q)$, its R\'edei factor is the
linear polynomial in $n+1$ variables, whose coefficients are the point
coordinates. The power sum polynomial of a subset $S$ of $\PG(n,q)$ is the sum
of the $(q-1)$-th powers of the R\'edei factors of the points of $S$. The fact
that many subsets may share the same power sum polynomial offers a natural
connection to discrete tomography. In this paper we deal with the
two-dimensional case and show that the notion of ghost, whose employment
enables to find all solutions of the tomographic problem, can be rephrased in
the finite geometry context, where subsets with null power sum polynomial are
called ghosts as well. In the latter case, one can add ghosts still preserving
the power sum polynomial by means of the multiset sum (modulo the field
characteristic). We prove some general results on ghosts in $\PG(2,q)$ and
compute their number in case $q$ is a prime.


Abstract:
  Imaging dynamical processes at interfaces and on the nanoscale is of great
importance throughout science and technology. While light-optical imaging
techniques often cannot provide the necessary spatial resolution,
electron-optical techniques damage the specimen and cause dose-induced
artefacts. Here, Optical Near-field Electron Microscopy (ONEM) is proposed, an
imaging technique that combines non-invasive probing with light, with a high
spatial resolution read-out via electron optics. Close to the specimen, the
optical near-fields are converted into a spatially varying electron flux using
a planar photocathode. The electron flux is imaged using low energy electron
microscopy, enabling label-free nanometric resolution without the need to scan
a probe across the sample. The specimen is never exposed to damaging electrons.


Abstract:
  With the growth in automated data collection of construction projects, the
need for semantic navigation of mobile robots is increasing. In this paper, we
propose an infrastructure to leverage building-related information for smarter,
safer and more precise robot navigation during construction phase. Our use of
Building Information Models (BIM) in robot navigation is twofold: (1) the
intuitive semantic information enables non-experts to deploy robots and (2) the
semantic data exposed to the navigation system allows optimal path planning
(not necessarily the shortest one). Our Building Information Robotic System
(BIRS) uses Industry Foundation Classes (IFC) as the interoperable data format
between BIM and the Robotic Operating System (ROS). BIRS generates topological
and metric maps from BIM for ROS usage. An optimal path planer, integrating
critical components for construction assessment is proposed using a cascade
strategy (global versus local). The results are validated through series of
experiments in construction sites.


Abstract:
  Methods for solving PDEs using neural networks have recently become a very
important topic. We provide an a priori error analysis for such methods which
is based on the $\mathcal{K}_1(\mathbb{D})$-norm of the solution. We show that
the resulting constrained optimization problem can be efficiently solved using
a greedy algorithm, which replaces stochastic gradient descent. Following this,
we show that the error arising from discretizing the energy integrals is
bounded both in the deterministic case, i.e. when using numerical quadrature,
and also in the stochastic case, i.e. when sampling points to approximate the
integrals. In the later case, we use a Rademacher complexity analysis, and in
the former we use standard numerical quadrature bounds. This extends existing
results to methods which use a general dictionary of functions to learn
solutions to PDEs and importantly gives a consistent analysis which
incorporates the optimization, approximation, and generalization aspects of the
problem. In addition, the Rademacher complexity analysis is simplified and
generalized, which enables application to a wide range of problems.


Abstract:
  Two prominent methods for integer factorization are those based on general
integer sieve and elliptic curve. The general integer sieve method can be
specialized to quadratic integer sieve method. In this paper, a probability
analysis for the success of these methods is described, under some reasonable
conditions. The estimates presented are specialized for the elliptic curve
factorization. These methods are compared through heuristic estimates. It is
shown that the elliptic curve method is a probabilistic polynomial time
algorithm under the assumption of uniform probability distribution for the
arising group orders and clearly more likely to succeed, faster asymptotically.


Abstract:
  Mapping a search query to a set of relevant categories in the product
taxonomy is a significant challenge in e-commerce search for two reasons: 1)
Training data exhibits severe class imbalance problem due to biased click
behavior, and 2) queries with little customer feedback (e.g., tail queries) are
not well-represented in the training set, and cause difficulties for query
understanding. To address these problems, we propose a deep learning model,
DeepCAT, which learns joint word-category representations to enhance the query
understanding process. We believe learning category interactions helps to
improve the performance of category mapping on minority classes, tail and torso
queries. DeepCAT contains a novel word-category representation model that
trains the category representations based on word-category co-occurrences in
the training set. The category representation is then leveraged to introduce a
new loss function to estimate the category-category co-occurrences for refining
joint word-category embeddings. To demonstrate our model's effectiveness on
minority categories and tail queries, we conduct two sets of experiments. The
results show that DeepCAT reaches a 10% improvement on minority classes and a
7.1% improvement on tail queries over a state-of-the-art label embedding model.
Our findings suggest a promising direction for improving e-commerce search by
semantic modeling of taxonomy hierarchies.


Abstract:
  Eccentric compact binaries pose not only a challenge for gravitational wave
detectors, but also provide a probe into the nuclear equation of state if one
of the objects is a neutron star. At the short pericenter passage, tidal
interactions excite f-modes on the star, which in turn emit their own
gravitational waves. We derive an analytic waveform for these stellar
oscillations within the effective fly-by framework, modeling the emission to
leading post-Newtonian order. At this order, the f-mode response can be written
in a Fourier decomposition in terms of orbital harmonics, with the amplitudes
of each harmonic depending on Hansen coefficients. Re-summing the harmonics of
the f-mode results in a simple decaying harmonic oscillator, with the amplitude
now determined by a Hansen coefficient of complex harmonic number. We compute
the match between the re-summed f-mode and numerical integrations of the tidal
response, and find ${\cal{M}} > 0.98$ for systems with high orbital
eccentricity $(e > 0.8)$ and low semi-latus rectum $(p < 15M)$ for three
equations of state. We further compare our model to modes generated from
subsequent pericenter passages under the effect of radiation reaction, and
develop an accurate model to time pericenter passages. We show how the timing
model can be used to specify initial conditions to accurately track the f-mode
excitation across multiple pericenter passages.


Abstract:
  Rather than obtaining cosmic acceleration with a scalar field potential
(quintessence) or noncanonical kinetic term (k-essence), we can do it purely
through a modified gravity braiding of the scalar and metric, i.e. the $G_3$
Horndeski action term. Such "Horndessence" allows an exact $\Lambda$CDM
cosmological expansion without any cosmological constant, and by requiring
shift symmetry we can derive the exact form of $G_3$. We find that this route
of deriving $G_3(X)$ leads to a functional form far from the usual simple
assumptions such as a power law. Horndessence without any kinetic term or
potential has the same number of parameters as $\Lambda$CDM and makes an exact
prediction for the expansion history ($\Lambda$CDM) and modified gravity cosmic
growth history; we show the viable gravitational strength $G_{\rm eff}(a)$ and
growth rate $f\sigma_8(a)$. The simplest versions of the theory fail soundness
criteria, but we learn interesting lessons along the way, in particular about
robust parametrization, and indicate possible sound extensions.


Abstract:
  As the field of connectomics has matured, it has expanded from mapping the
existence of connections between brain components to measuring the strength of
connections. This information is increasingly accessible via methodologies such
as pairing functional magnetic resonance (MR) imaging and MR tractography in
the same human subject, as well as novel methods in non-human animals using
optogenetics. Systems and network neuroscience have in recent years focused
extensively on explaining correlation patterns of functional activity in the
brain in terms of the degree of connectedness of brain components, the
so-called functional connectivity-structural connectivity relationship (SC-FC).
What has been surprising has been how low the SC-FC correlations are. Why is it
that brain parts that are more well-connected appear not to engender more
correlated activity between them? Several explanations have been proposed but
one possibility has not been considered: perhaps more neural activity doesn't
imply more functional involvement. This article examines this possibility and
proposes a new general framework for understanding brain network dynamics based
on the design constraints of large-scale network communication systems. With
this new perspective, we may start to answer the article's main question, and
perhaps others.


Abstract:
  The article introduces an elementary cost and storage reduction method for
spectral clustering and principal component analysis. The method consists in
randomly "puncturing" both the data matrix $X\in\mathbb{C}^{p\times n}$ (or
$\mathbb{R}^{p\times n}$) and its corresponding kernel (Gram) matrix $K$
through Bernoulli masks: $S\in\{0,1\}^{p\times n}$ for $X$ and
$B\in\{0,1\}^{n\times n}$ for $K$. The resulting "two-way punctured" kernel is
thus given by $K=\frac{1}{p}[(X \odot S)^{\sf H} (X \odot S)] \odot B$. We
demonstrate that, for $X$ composed of independent columns drawn from a Gaussian
mixture model, as $n,p\to\infty$ with $p/n\to c_0\in(0,\infty)$, the spectral
behavior of $K$ -- its limiting eigenvalue distribution, as well as its
isolated eigenvalues and eigenvectors -- is fully tractable and exhibits a
series of counter-intuitive phenomena. We notably prove, and empirically
confirm on GAN-generated image databases, that it is possible to drastically
puncture the data, thereby providing possibly huge computational and storage
gains, for a virtually constant (clustering of PCA) performance. This
preliminary study opens as such the path towards rethinking, from a large
dimensional standpoint, computational and storage costs in elementary machine
learning models.


Abstract:
  Recently, few certified defense methods have been developed to provably
guarantee the robustness of a text classifier to adversarial synonym
substitutions. However, all existing certified defense methods assume that the
defenders are informed of how the adversaries generate synonyms, which is not a
realistic scenario. In this paper, we propose a certifiably robust defense
method by randomly masking a certain proportion of the words in an input text,
in which the above unrealistic assumption is no longer necessary. The proposed
method can defend against not only word substitution-based attacks, but also
character-level perturbations. We can certify the classifications of over 50%
texts to be robust to any perturbation of 5 words on AGNEWS, and 2 words on
SST2 dataset. The experimental results show that our randomized smoothing
method significantly outperforms recently proposed defense methods across
multiple datasets.


Abstract:
  We study fair classification in the presence of an omniscient adversary that,
given an $\eta$, is allowed to choose an arbitrary $\eta$-fraction of the
training samples and arbitrarily perturb their protected attributes. The
motivation comes from settings in which protected attributes can be incorrect
due to strategic misreporting, malicious actors, or errors in imputation; and
prior approaches that make stochastic or independence assumptions on errors may
not satisfy their guarantees in this adversarial setting. Our main contribution
is an optimization framework to learn fair classifiers in this adversarial
setting that comes with provable guarantees on accuracy and fairness. Our
framework works with multiple and non-binary protected attributes, is designed
for the large class of linear-fractional fairness metrics, and can also handle
perturbations besides protected attributes. We prove near-tightness of our
framework's guarantees for natural hypothesis classes: no algorithm can have
significantly better accuracy and any algorithm with better fairness must have
lower accuracy. Empirically, we evaluate the classifiers produced by our
framework for statistical rate on real-world and synthetic datasets for a
family of adversaries.


Abstract:
  In this paper, we provide a framework of designing the local discontinuous
Galerkin scheme for integral fractional Laplacian $(-\Delta)^{s}$ with
$s\in(0,1)$ in two dimensions. We theoretically prove and numerically verify
the numerical stability and convergence of the scheme with the convergence rate
no worse than $\mathcal{O}(h^{k+\frac{1}{2}})$.


Abstract:
  We aim at estimating the invariant density associated to a stochastic
differential equation with jumps in low dimension, which is for $d=1$ and
$d=2$. We consider a class of jump diffusion processes whose invariant density
belongs to some H\"older space. Firstly, in dimension one, we show that the
kernel density estimator achieves the convergence rate $\frac{1}{T}$, which is
the optimal rate in the absence of jumps. This improves the convergence rate
obtained in [Amorino, Gloter (2021)], which depends on the Blumenthal-Getoor
index for $d=1$ and is equal to $\frac{\log T}{T}$ for $d=2$. Secondly, we show
that is not possible to find an estimator with faster rates of estimation.
Indeed, we get some lower bounds with the same rates $\{\frac{1}{T},\frac{\log
T}{T}\}$ in the mono and bi-dimensional cases, respectively. Finally, we obtain
the asymptotic normality of the estimator in the one-dimensional case.


Abstract:
  Text recognition is a popular research subject with many associated
challenges. Despite the considerable progress made in recent years, the text
recognition task itself is still constrained to solve the problem of reading
cropped line text images and serves as a subtask of optical character
recognition (OCR) systems. As a result, the final text recognition result is
limited by the performance of the text detector. In this paper, we propose a
simple, elegant and effective paradigm called Implicit Feature Alignment (IFA),
which can be easily integrated into current text recognizers, resulting in a
novel inference mechanism called IFAinference. This enables an ordinary text
recognizer to process multi-line text such that text detection can be
completely freed. Specifically, we integrate IFA into the two most prevailing
text recognition streams (attention-based and CTC-based) and propose
attention-guided dense prediction (ADP) and Extended CTC (ExCTC). Furthermore,
the Wasserstein-based Hollow Aggregation Cross-Entropy (WH-ACE) is proposed to
suppress negative predictions to assist in training ADP and ExCTC. We
experimentally demonstrate that IFA achieves state-of-the-art performance on
end-to-end document recognition tasks while maintaining the fastest speed, and
ADP and ExCTC complement each other on the perspective of different application
scenarios. Code will be available at
https://github.com/WangTianwei/Implicit-feature-alignment.


Abstract:
  Linear modal instabilities of flow over finite-span untapered wings have been
investigated numerically at Reynolds number 400, at a range of angles of attack
and sweep on two wings having aspect ratios 4 and 8. Base flows have been
generated by direct numerical simulation, marching the unsteady incompressible
three-dimensional Navier-Stokes equations to a steady state, or using selective
frequency damping to obtain stationary linearly unstable flows. Unstable
three-dimensional linear global modes of swept wings have been identified for
the first time using spectral-element time-stepping solvers. The effect of the
wing geometry and flow parameters on these modes has been examined in detail.
An increase of the angle of attack was found to destabilize the flow, while an
increase of the sweep angle had the opposite effect. On unswept wings,
TriGlobal analysis revealed that the most unstable global mode peaks in the
midspan region of the wake; the peak of the mode structure moves towards the
tip as sweep is increased. Data-driven analysis was then employed to study the
effects of wing geometry and flow conditions on the nonlinear wake. On unswept
wings, the dominant mode at low angles of attack is a Kelvin-Helmholtz-like
instability, qualitatively analogous with global modes of infinite-span wings
under same conditions. At higher angles of attack and moderate sweep angles,
the dominant mode is a structure denominated the interaction mode. At high
sweep angles, this mode evolves into elongated streamwise vortices on higher
aspect ratio wings, while on shorter wings it becomes indistinguishable from
tip-vortex instability.


Abstract:
  This paper recalls the main achievements of the nTRV experiment which
measured two components of the transverse polarization ($\sigma_{T_{1}}$,
$\sigma_{T_{2}}$) of electrons emitted in the $\beta$-decay of polarized, free
neutrons and deduced two correlation coefficients, $R$ and $N$, that are
sensitive to physics beyond the Standard Model. The value of time-reversal odd
coefficient $R$, 0.004$\pm$0.012$\pm$0.005, significantly improved limits on
the relative strength of imaginary scalar coupling constant in the weak
interaction. The value obtained for the time-reversal even correlation
coefficient $N$, 0.067$\pm$0.011$\pm$0.004, agrees with the Standard Model
expectation, providing an important sensitivity test of the electron
polarimeter. One of the conclusions of this pioneering experiment was that the
transverse electron polarization in the neutron $\beta$-decay is worth more
systematic exploring by measurements of yet experimentally not attempted
correlation coefficients such as $H$, $L$, $S$, $U$ and $V$. This article
presents a brief outlook on that questions.


Abstract:
  For a fixed set $X$ containing $n$ taxon labels, an ordered pair consisting
of a gene tree topology $G$ and a species tree $S$ bijectively labeled with the
labels of $X$ possesses a set of coalescent histories -- mappings from the set
of internal nodes of $G$ to the set of edges of $S$ describing possible lists
of edges in $S$ on which the coalescences in $G$ take place. Enumerations of
coalescent histories for gene trees and species trees have produced suggestive
results regarding the pairs $(G,S)$ that, for a fixed $n$, have the largest
number of coalescent histories. We define a class of 2-cherry binary tree
topologies that we term $p$-pseudocaterpillars, examining coalescent histories
for non-matching pairs $(G,S)$, in the case in which $S$ has a caterpillar
shape and $G$ has a $p$-pseudocaterpillar shape. Using a construction that
associates coalescent histories for $(G,S)$ with a class of "roadblocked"
monotonic paths, we identify the $p$-pseudocaterpillar labeled gene tree
topology that, for a fixed caterpillar labeled species tree topology, gives
rise to the largest number of coalescent histories. The shape that maximizes
the number of coalescent histories places the "second" cherry of the
$p$-pseudocaterpillar equidistantly from the root of the "first" cherry and
from the tree root. A symmetry in the numbers of coalescent histories for
$p$-pseudocaterpillar gene trees and caterpillar species trees is seen to exist
around the maximizing value of the parameter $p$. The results provide insight
into the factors that influence the number of coalescent histories possible for
a given gene tree and species tree.


Abstract:
  Mesoporous silica nanoparticles have been reported as suitable drug carriers,
but their successful delivery to target tissues following systemic
administration remains a challenge. In the present work, ultrasound-induced
inertial cavitation was evaluated as a mechanism to promote their extravasation
in a flow-through tissue-mimicking agarose phantom. Two different ultrasound
frequencies, 0.5 or 1.6 MHz, with pressures in the range 0.5-4 MPa were used to
drive cavitation activity which was detected in real time. The optimal
ultrasound conditions identified were employed to deliver dye-loaded
nanoparticles as a model for drug-loaded nanocarriers, with the level of
extravasation evaluated by fluorescence microscopy. The same nanoparticles were
then co-injected with submicrometric polymeric cavitation nuclei as a means to
promote cavitation activity and decrease the required in-situ acoustic pressure
required to attain extravasation. The overall cavitation energy and penetration
of the combination was compared to mesoporous silica nanoparticles alone. The
results of the present work suggest that combining mesoporous silica
nanocarriers and submcrometric cavitation nuclei may help enhance the
extravasation of the nanocarrier, thus enabling subsequent sustained drug
release to happen from those particles already embedded in the tumour tissue.


Abstract:
  Metal additive manufacturing (AM) by laser powder bed fusion (L-PBF) builds
upon fundamentals established in the field of laser welding which include the
influence of gas and plume dynamics on weld depth and quality. L-PBF demands a
thorough investigation of the complex thermophysical phenomena that occur where
the laser interacts with the metal powder bed. In particular, melt pool
turbulence and evaporation are influenced by the ambient gas chemistry and
pressure. This paper presents the design and validation of high pressure laser
melting (HPLM) testbed; this accommodates bare metal plate samples as well as
manually-coated single powder layers, and operates at up to 300 psig. The open
architecture of this testbed allows for full control of all relevant laser
parameters in addition to ambient gas pressure and gas flow over the build
area. Representative melt tracks and rasters on bare plate and powder are
examined in order to validate system performance, and preliminary analysis
concludes that pressure has a significant impact on melt pool aspect ratio. The
HPLM system thus enables careful study pressure effects on processing of common
L-PBF materials, and can be applied in the future to materials that are
challenging to process under ambient pressure, such as those with high vapor
pressures.


Abstract:
  Pilot studies are often the first step of experimental research. It is
usually on a smaller scale and the results can inform intervention development,
study feasibility and how the study implementation will play out, if such a
larger main study is undertaken. This paper illustrates the relationship
between pilot study sample size and the performance study design of main
studies. We present two simple sample size calculation methods to ensure
adequate study planning for main studies. We use numerical examples and
simulations to demonstrate the use and performance of proposed methods.
Practical heuristic guidelines are provided based on the results.


Abstract:
  This paper proposes a general framework for constructing feedback controllers
that drive complex dynamical systems to "efficient" steady-state (or slowly
varying) operating points. Efficiency is encoded using generalized equations
which can model a broad spectrum of useful objectives, such as optimality or
equilibria (e.g. Nash, Wardrop, etc.) in noncooperative games. The core idea of
the proposed approach is to directly implement iterative solution (or
equilibrium seeking) algorithms in closed loop with physical systems.
Sufficient conditions for closed-loop stability and robustness are derived;
these also serve as the first closed-loop stability results for sampled-data
feedback-based optimization. Numerical simulations of smart building automation
and game-theoretic robotic swarm coordination support the theoretical results.


Abstract:
  Digital twins are meant to bridge the gap between real-world physical systems
and virtual representations. Both stand-alone and descriptive digital twins
incorporate 3D geometric models, which are the physical representations of
objects in the digital replica. Digital twin applications are required to
rapidly update internal parameters with the evolution of their physical
counterpart. Due to an essential need for having high-quality geometric models
for accurate physical representations, the storage and bandwidth requirements
for storing 3D model information can quickly exceed the available storage and
bandwidth capacity. In this work, we demonstrate a novel approach to geometric
change detection in the context of a digital twin. We address the issue through
a combined solution of Dynamic Mode Decomposition (DMD) for motion detection,
YOLOv5 for object detection, and 3D machine learning for pose estimation. DMD
is applied for background subtraction, enabling detection of moving foreground
objects in real-time. The video frames containing detected motion are extracted
and used as input to the change detection network. The object detection
algorithm YOLOv5 is applied to extract the bounding boxes of detected objects
in the video frames. Furthermore, the rotational pose of each object is
estimated in a 3D pose estimation network. A series of convolutional neural
networks conducts feature extraction from images and 3D model shapes. Then, the
network outputs the estimated Euler angles of the camera orientation with
respect to the object in the input image. By only storing data associated with
a detected change in pose, we minimize necessary storage and bandwidth
requirements while still being able to recreate the 3D scene on demand.


Abstract:
  Thermal gas in the center of galaxy clusters can show substantial motions
that generate surface-brightness and temperature discontinuities known as cold
fronts. The motions may be triggered by minor or off-axis mergers that preserve
the cool core of the system. The dynamics of the thermal gas can also generate
radio emission from the intra-cluster medium (ICM) and impact the evolution of
clusters' radio sources. We aim to study the central region of Abell 1775, a
system in an ambiguous dynamical state at $z=0.072$ which is known to host an
extended head-tail radio galaxy, with the goal of investigating the connection
between thermal and nonthermal components in its center. We made use of a deep
(100 ks) Chandra observation accompanied by LOFAR 144 MHz, GMRT 235 MHz and 610
MHz, and VLA 1.4 GHz radio data. We find a spiral-like pattern in the X-ray
surface brightness that is mirrored in the temperature and pseudo-entropy maps.
Additionally, we characterize an arc-shaped cold front in the ICM. We interpret
these features in the context of a slingshot gas tail scenario. The structure
of the head-tail radio galaxy "breaks" at the position of the cold front,
showing an extension that is detected only at low frequencies, likely due to
its steep and curved spectrum. We speculate that particle reacceleration is
occurring in the outer region of this tail, which in total covers a projected
size of $\sim800$ kpc. We also report the discovery of revived fossil plasma
with ultra-steep spectrum radio emission in the cluster core together with a
central diffuse radio source that is bounded by the arc-shaped cold front. The
results reported in this work demonstrate the interplay between thermal and
nonthermal components in the cluster center and the presence of ongoing
particle reacceleration in the ICM on different scales.


Abstract:
  The most successful tech companies of the world release new software versions
to production multiple times a day. Thereby, they are able to quickly fix
emerging bugs and rapidly deliver new features to their customers. This leads
to short development cycles, minimal lead times and a high customer-centricity.
Short development cycles are easy to achieve if you start a software project on
a green field. Nevertheless, this does not apply to brown field environments
which are usually found in big corporates such as traditional car
manufacturers. For instance, if you want to integrate with real cars you have
to interface legacy systems with development cycles of up to several months. We
present a solution, which worked for one of the world's largest car
manufacturer, leveraging in-house core development teams, dynamic stages and
feature-toggles to overcome a brown field environment, allow for short
development cycles and minimize the lead time.


Abstract:
  Complex oxide functionality, such as ferroelectricity, magnetism or
superconductivity is often achieved in epitaxial thin-film geometries. Oxygen
vacancies tend to be the dominant type of defect in these materials but a
fundamental understanding of their stability and electronic structure has so
far mostly been established in the bulk or strained bulk, neglecting interfaces
and surfaces present in a thin-film geometry. We investigate here, via density
functional theory calculations, oxygen vacancies in the model system of a
SrMnO$_3$ (SMO) thin film grown on a SrTiO$_3$ (STO) (001) substrate.
Structural and electronic differences compared to bulk SMO result mainly from
undercoordination at the film surface. The changed crystal field leads to a
depletion of subsurface valence-band states and transfer of this charge to
surface Mn atoms, both of which strongly affect the defect chemistry in the
film. The result is a strong preference of oxygen vacancies in the surface
region compared to deeper layers. Finally, for metastable oxygen vacancies in
the substrate, we predict a spatial separation of the defect from its excess
charge, the latter being accommodated in the film but close to the substrate
boundary. These results show that surface and interface effects lead to
significant differences in stability and electronic structure of oxygen
vacancies in thin-film geometries compared to the (strained) bulk.


Abstract:
  Reliable and secure communication is an important aspect of modern fiber
optic communication. In this work we consider a multi-mode fiber (MMF) channel
wiretapped by an eavesdropper. We assume the transmitter knows the legitimate
channel, but statistical knowledge of the eavesdropper's channel only. We
propose a transmission scheme with artificial noise (AN) for such a channel. In
particular, we formulate the corresponding optimization problem which aims to
maximize the average secrecy rate and develop an algorithm to solve it. We
apply this algorithm to actual measured MMF channels. As real fiber
measurements show, for a 55 mode MMF we can achieve positive average secrecy
rates with the proper use of AN. Furthermore, the gain compared to standard
precoding and power allocation schemes is illustrated.


Abstract:
  Our project task was to create a model that, given a speaker ID, chat
history, and an utterance query, can predict the response utterance in a
conversation. The model is personalized for each speaker. This task can be a
useful tool for building speech bots that talk in a human-like manner in a live
conversation. Further, we succeeded at using dense-vector encoding clustering
to be able to retrieve relevant historical dialogue context, a useful strategy
for overcoming the input limitations of neural-based models when predictions
require longer-term references from the dialogue history. In this paper, we
have implemented a state-of-the-art model using pre-training and fine-tuning
techniques built on transformer architecture and multi-headed attention blocks
for the Switchboard corpus. We also show how efficient vector clustering
algorithms can be used for real-time utterance predictions that require no
training and therefore work on offline and encrypted message histories.


Abstract:
  Robustness against adversarial attack in neural networks is an important
research topic in the machine learning community. We observe one major source
of vulnerability of neural nets is from overparameterized fully-connected
layers. In this paper, we propose a new neighborhood preserving layer which can
replace these fully connected layers to improve the network robustness. We
demonstrate a novel neural network architecture which can incorporate such
layers and also can be trained efficiently. We theoretically prove that our
models are more robust against distortion because they effectively control the
magnitude of gradients. Finally, we empirically show that our designed network
architecture is more robust against state-of-art gradient descent based
attacks, such as a PGD attack on the benchmark datasets MNIST and CIFAR10.


Abstract:
  Deep metric learning is a technique used in a variety of discriminative tasks
to achieve zero-shot, one-shot or few-shot learning. When applied, the system
learns an embedding space where a non-parametric approach, such as \gls{knn},
can be used to discriminate features during test time. This work focuses on
investigating to what extent feature information contained within this
embedding space can be used to carry out sub-discrimination in the feature
space. The study shows that within a discrimination embedding, the information
on the salient attributes needed to solve the problem of sub-discrimination is
saved within the embedding and that this inherent information can be used to
carry out sub-discriminative tasks. To demonstrate this, an embedding designed
initially to discriminate faces is used to differentiate several attributes
such as gender, age and skin tone, without any additional training. The study
is split into two study cases: intra class discrimination where all the
embeddings took into consideration are from the same identity; and extra class
discrimination where the embeddings represent different identities. After the
study, it is shown that it is possible to infer common attributes to different
identities. The system can also perform extra class sub-discrimination with a
high accuracy rate, notably 99.3\%, 99.3\% and 94.1\% for gender, skin tone,
and age, respectively. Intra class tests show more mixed results with more
nuanced attributes like emotions not being reliably classified, while more
distinct attributes such as thick-framed glasses and beards, achieving 97.2\%
and 95.8\% accuracy, respectively.


Abstract:
  Time-Delay Interferometry (TDI) is the data processing technique that cancels
the large laser phase fluctuations affecting the one-way Doppler measurements
made by unequal-arm space-based gravitational wave interferometers. By taking
finite linear combinations of properly time-shifted Doppler measurements, laser
phase fluctuations are removed at any time $t$ and gravitational wave signals
can be studied at a requisite level of precision. In this article we show the
delay operators used in TDI can be represented as matrices acting on arrays
associated with the laser noises and Doppler measurements. The matrix
formulation is nothing but the group theoretic representation (ring
homomorphism) of the earlier approach involving time-delay operators and so in
principle is the same. It is shown that the homomorphism is valid generally and
we cover all situations of interest. To understand the potential advantages the
matrix representation brings, care must be taken by the data analyst to account
for the light travel times when linearly relating the one-way Doppler
measurements to the laser noises. This is especially important in view of the
future gravitational wave projects envisaged. We show that the matrix
formulation of TDI results in the cancellation of the laser noises at an
arbitrary time $t$ by only linearly combining a finite number of samples of the
one-way Doppler data measured at and around time $t$.


Abstract:
  Recent advances in techniques for generating quantum light have stimulated
research on novel spectroscopic measurements using quantum entangled photons.
One such spectroscopy technique utilizes non-classical correlations among
entangled photons to enable measurements with enhanced sensitivity and
selectivity. Here, we investigate spectroscopic measurement utilizing entangled
three photons. In this measurement, time-resolved entangled photon spectroscopy
with monochromatic pumping [J. Chem. Phys. 153, 051102 (2020).] is integrated
with the frequency-dispersed two-photon counting technique, which suppresses
undesired accidental photon counts in the detector and thus allows one to
separate the weak desired signal. This time-resolved frequency-dispersed
two-photon counting signal, which is a function of two frequencies, is shown to
provide the same information as that of coherent two-dimensional optical
spectra. The spectral distribution of the phase-matching function works as a
frequency filter to selectively resolve a specific region of the
two-dimensional spectra, whereas the excited-state dynamics under investigation
are temporally resolved in the time region longer than the entanglement time.
The signal is not subject to Fourier limitations on the joint temporal and
spectral resolution, and therefore, it is expected to be useful for
investigating complex molecular systems in which multiple electronic states are
present within a narrow energy range.


Abstract:
  Stock market returns are typically analyzed using standard regression, yet
they reside on irregular domains which is a natural scenario for graph signal
processing. To this end, we consider a market graph as an intuitive way to
represent the relationships between financial assets. Traditional methods for
estimating asset-return covariance operate under the assumption of statistical
time-invariance, and are thus unable to appropriately infer the underlying true
structure of the market graph. This work introduces a class of graph spectral
estimators which cater for the nonstationarity inherent to asset price
movements, and serve as a basis to represent the time-varying interactions
between assets through a dynamic spectral market graph. Such an account of the
time-varying nature of the asset-return covariance allows us to introduce the
notion of dynamic spectral portfolio cuts, whereby the graph is partitioned
into time-evolving clusters, allowing for online and robust asset allocation.
The advantages of the proposed framework over traditional methods are
demonstrated through numerical case studies using real-world price data.


Abstract:
  The formation of drops of a complex fluid, for instance including dissolved
polymers and/or solid particles, has practical implications in several
industrial and biophysical processes. In this Letter, we experimentally
investigate the generation of drops of a viscoelastic suspension, made of
non-Brownian spherical particles dispersed in a dilute polymer solution. Using
high-speed imaging, we characterize the different stages of the detachment. Our
experiments show that the particles primarily affect the initial Newtonian
necking by increasing the fluid viscosity. In the viscoelastic regime,
particles do not affect the thinning until the onset of the blistering
instability, which they accelerate. We find that the transition from one regime
to another, which corresponds to the coil-stretch transition of the polymer
chains, strongly depends on the particle content. Considering that the presence
of rigid particles increase the deformation of the liquid phase, we propose an
expression for the local strain rate in the suspension, which rationalizes our
experimental results. This method could enable the precise measurement of local
stresses in particulate suspensions.


Abstract:
  Transition metal dichalcogenides have emerged as promising materials for
nano-photonic resonators due to their large refractive index, low absorption
within a large portion of the visible spectrum and compatibility with a wide
range of substrates. Here we use these properties to fabricate WS$_2$
double-pillar nano-antennas in a variety of geometries enabled by the
anisotropy in the crystal structure. Using dark field spectroscopy, we reveal
multiple Mie resonances, to which we couple WSe$_2$ monolayer photoluminescence
and achieve Purcell enhancement and an increased fluorescence by factors up to
240. We introduce post-fabrication atomic force microscope repositioning and
rotation of dimer nano-antennas, achieving gaps as small as 10$\pm$5 nm,
opening the possibility to a host of potential applications including strong
Purcell enhancement of single photon emitters and optical trapping, which we
study in simulations. Our findings highlight the advantages of using transition
metal dichalcogenides for nano-photonics by exploring new applications enabled
by their unique properties.


Abstract:
  Variational methods have proven to be excellent tools to approximate ground
states of complex many body Hamiltonians. Generic tools like neural networks
are extremely powerful, but their parameters are not necessarily physically
motivated. Thus, an efficient parametrization of the wave-function can become
challenging. In this letter we introduce a neural-network based variational
ansatz that retains the flexibility of these generic methods while allowing for
a tunability with respect to the relevant correlations governing the physics of
the system. We illustrate the success of this approach on topological,
long-range correlated and frustrated models. Additionally, we introduce
compatible variational optimization methods for exploration of low-lying
excited states without symmetries that preserve the interpretability of the
ansatz.


Abstract:
  Unmanned aerial vehicles (UAVs), as aerial base stations, are a promising
solution for providing wireless communications, thanks to their high
flexibility and autonomy. Moreover, emerging services, such as extended
reality, require high-capacity communications. To achieve this, millimeter wave
(mmWave), and recently, terahertz bands have been considered for UAV
communications. However, communication at these high frequencies requires a
line-of-sight (LoS) to the terminals, which may be located in 3D space and may
have extremely limited direct-line-of-view (LoV) due to blocking objects, like
buildings and trees. In this paper, we investigate the problem of determining
3D placement and orientation of UAVs such that users have guaranteed LoS
coverage by at least one UAV and the signal-to-noise ratio (SNR) between the
UAV-user pairs are maximized. We formulate the problem as an integer linear
programming(ILP) problem and prove its NP-hardness. Next, we propose a
low-complexity geometry-based greedy algorithm to solve the problem
efficiently. Our simulation results show that the proposed algorithm (almost)
always guarantees LoS coverage to all users in all considered simulation
settings.


Abstract:
  This paper studies chemical kinetic systems which decompose into weakly
reversible complex factorizable (CF) systems. Among power law kinetic systems,
CF systems (denoted as PL-RDK systems) are those where branching reactions of a
reactant complex have identical rows in the kinetic order matrix. Mass action
and generalized mass action systems (GMAS) are well-known examples. Schmitz's
global carbon cycle model is a previously studied non-complex factorizable (NF)
power law system (denoted as PL-NDK). We derive novel conditions for the
existence of weakly reversible CF-decompositions and present an algorithm for
verifying these conditions. We discuss methods for identifying independent
decompositions, i.e., those where the stoichiometric subspaces of the
subnetworks form a direct sum, as such decompositions relate positive
equilibria sets of the subnetworks to that of the whole network. We then use
the results to determine the positive equilibria sets of PL-NDK systems which
admit an independent weakly reversible decomposition into PL-RDK systems of PLP
type, i.e., the positive equilibria are log-parametrized, which is a broad
generalization of a Deficiency Zero Theorem of Fortun et al. (2019).


Abstract:
  The creation of plausible and controllable 3D human motion animations is a
long-standing problem that requires a manual intervention of skilled artists.
Current machine learning approaches can semi-automate the process, however,
they are limited in a significant way: they can handle only a single trajectory
of the expected motion that precludes fine-grained control over the output. To
mitigate that issue, we reformulate the problem of future pose prediction into
pose completion in space and time where multiple trajectories are represented
as poses with missing joints. We show that such a framework can generalize to
other neural networks designed for future pose prediction. Once trained in this
framework, a model is capable of predicting sequences from any number of
trajectories. We propose a novel transformer-like architecture, TrajeVAE, that
builds on this idea and provides a versatile framework for 3D human animation.
We demonstrate that TrajeVAE offers better accuracy than the trajectory-based
reference approaches and methods that base their predictions on past poses. We
also show that it can predict reasonable future poses even if provided only
with an initial pose.


Abstract:
  Non-orthogonal multiple access (NOMA) is a technology enabler for the fifth
generation and beyond networks, which has shown a great flexibility such that
it can be readily integrated with other wireless technologies. In this paper,
we investigate the interplay between NOMA and generalized space shift keying
(GSSK) in a hybrid NOMA-GSSK (N-GSSK) network. Specifically, we provide a
comprehensive analytical framework and propose a novel suboptimal energy-based
maximum likelihood (ML) detector for the N-GSSK scheme. The proposed ML decoder
exploits the energy of the received signals in order to estimate the active
antenna indices. Its performance is investigated in terms of pairwise error
probability, bit error rate union bound, and achievable rate. Finally, we
establish the validity of our analysis through Monte-Carlo simulations and
demonstrate that N-GSSK outperforms conventional NOMA and GSSK, particularly in
terms of spectral efficiency.


Abstract:
  This paper considers wave-based imaging through a heterogeneous (random)
scattering medium. The goal is to estimate the support of the reflectivity
function of a remote scene from measurements of the backscattered wave field.
The proposed imaging methodology is based on the coherent interferometric
(CINT) approach that exploits the local empirical cross correlations of the
measurements of the wave field. The standard CINT images are known to be robust
(statistically stable) with respect to the random medium, but the stability
comes at the expense of a loss of resolution. This paper shows that a two-point
CINT function contains the information needed to obtain statistically stable
and high-resolution images. Different methods to build such images are
presented, theoretically analyzed and compared with the standard imaging
approaches using numerical simulations. The first method involves a
phase-retrieval step to extract the reflectivity function from the modulus of
its Fourier transform. The second method involves the evaluation of the leading
eigenvector of the two-point CINT imaging function seen as the kernel of a
linear operator. The third method uses an optimization step to extract the
reflectivity function from some cross products of its Fourier transform. The
presentation is for the synthetic aperture radar data acquisition setup, where
a moving sensor probes the scene with signals emitted periodically and records
the resulting backscattered wave. The generalization to other imaging setups,
with passive or active arrays of sensors, is discussed briefly.


Abstract:
  We analyse a possible connection between CP violations in the quark and
lepton sectors, parametrised by the CKM and PMNS phases. If one assumes that CP
breaking arises from complex Yukawa couplings, both in the quark and lepton
sectors, the above connection is not possible in general, since Yukawa
couplings in the two sectors have independent flavour structures. We show that
both the CKM and PMNS phases can instead be generated by a vacuum phase in a
class of two Higgs doublet models, and in this case a connection may be
established. This scenario requires the presence of scalar FCNC at tree level,
both in the quark and lepton sectors. The appearance of these FCNC is an
obstacle and a blessing. An obstacle since one has to analyse which models are
able to conform to the strict experimental limits on FCNC, both in the quark
and lepton sectors. A blessing, because this class of models is falsifiable
since FCNC arise at a level which can be probed experimentally in the near
future, specially in the processes $h\to e^\pm\tau^\mp$ and $t\to h c$. The
connection between CP violations in CKM and PMNS is explicitely illustrated in
models with Minimal Flavour Violation.


Abstract:
  We study the first chiral homology group of elliptic curves with coefficients
in vacuum insertions of a conformal vertex algebra V. We find finiteness
conditions on V guaranteeing that these homologies are finite dimensional,
generalizing the $C_2$-cofinite, or quasi-lisse condition in the degree 0 case.
We determine explicitly the flat connections that these homologies acquire
under smooth variation of the elliptic curve, as insertions of the conformal
vector and the Weierstrass $\zeta$ function. We construct linear functionals
associated to self-extensions of V-modules and prove their convergence under
said finiteness conditions. These linear functionals turn out to be degree 1
analogs of the n-point functions in the degree 0 case. As a corollary we prove
the vanishing of the first chiral homology group of an elliptic curve with
values in several rational vertex algebras, including affine $sl_2$ at
non-negative integral level, the (2,2k+1)-minimal models and arbitrary simple
affine vertex algebras at level 1. Of independent interest, we prove a Fourier
space version of the Borcherds formula.


Abstract:
  For $m\in\mathbb{R}$ we introduce the symbol classes $S^m$, $m\in\mathbb{R}$,
consisting of smooth functions $\sigma$ on $\mathbb{R}^{2d}$ such that
$|\partial^\alpha \sigma(z)|\leq C_\alpha (1+|z|^2)^{m/2}$,
$z\in\mathbb{R}^{2d}$, and we show that can be characterized by an intersection
of different types of modulation spaces. In the case $m=0$ we recapture the
H\"{o}rmander class $S^0_{0,0}$ that can be obtained by intersection of
suitable Besov spaces as well. Such spaces contain the Shubin classes
$\Gamma^m_\rho$, $0<\rho\leq1$, and can be viewed as their limit case $\rho=0$.
We exhibit almost diagonalization properties for the Gabor matrix of
$\tau$-pseudodifferential operators with symbols in such classes, extending the
characterization proved by Gr\"{o}chenig and Rzeszotnik. Finally, we compute
the Gabor matrix of a Born-Jordan operator, which allows to prove new
boundedness results for such operators.