Abstract:
  Less than one percent of the discovered small solar system objects have
highly inclined orbits ($i>60^{\circ}$), and revolve around the Sun on
near-polar or retrograde orbits. The origin and evolutionary history of these
objects are not yet clear. In this work we study the surface properties and
orbital dynamics of selected high-inclination objects. BVRI photometric
observations were performed in 2019-2020 using the 2.0m telescope at the
Terskol Observatory and the 2.6m telescope at the Crimean Astrophysical
Observatory. Additionally, we searched for high-inclination objects in the
Sloan Digital Sky Survey and Pan-STARRS. The dynamics of the selected objects
was studied using numerical simulations. We obtained new photometric
observations of six high-inclination objects. All of the objects have similar
$B-V$, $V-R$, $R-I$ colours, which are close to those of moderately red TNOs
and grey Centaurs. The photometric data that were extracted from the all-sky
surveys also correspond to moderately red surfaces of high-inclination objects.
No signs of ultra-red material on the surface of high-inclination asteroids
were found, which supports the results of previous works. The comet C/2018 DO4
(Lemmon) revealed some complex morphology with structures that could be
associated with particles that were ejected from the cometary nucleus. For
objects 2013 LU28, 2015 KZ120, and 2020 EP we estimated future and past
lifetimes on their orbits. It appears that the orbits of considered objects are
strongly chaotic, and with the available accuracy of the orbital elements no
reliable predictions can be made about their distant past or future. The
lifetimes of high-inclination objects turned out to be highly non-sensitive to
the precision of the orbital elements, and to the Yarkovsky orbital drift.


Abstract:
  This paper presents a layer-2 micropayment pool design which supports
high-throughput blockchain off-chain payment, designed specifically for the use
case of rewarding users who share redundant bandwidth to assist video stream
delivery. We analyze the validity and effectiveness of the proposed
micropayment pool. Our analysis results demonstrate that the proposed
micropayment pool design is better suited for the bandwidth reward use case
compared to existing off-chain payment channel solution.


Abstract:
  We give infinitely many knots in $S^3$ that are not smoothly H-slice (that
is, bounding a null-homologous disk) in many $4$-manifolds but they are
topologically H-slice. In particular, we give such knots in punctured elliptic
surfaces $E(2n)$. In addition, we give obstructions to codimension-0 embedding
of weak symplectic fillings with $b_3=0$ into closed symplectic 4-manifolds
with $b_1=0$ and $b_2^+\equiv 3 \operatorname{mod} 4$. We also show that any
weakly symplectically fillable $3$-manifold bounds a $4$-manifold with at least
two smooth structures. All of these results follow from our main result that
gives an adjunction inequality for embedded surfaces in certain $4$-manifolds
with contact boundary under a non-vanishing assumption on Bauer-Furuta type
invariants.


Abstract:
  Green and Tao famously proved in a 2008 paper that there are arithmetic
progressions of prime numbers of arbitrary lengths. Soon after, analogous
statements were proved by Tao for the ring of Gaussian integers and by L\^e for
the polynomial rings over finite fields. In 2020 this was extented to orders of
arbitrary number fields by Kai-Mimura-Munemasa-Seki-Yoshino. We settle the case
of the coordinate rings of affine curves over finite fields. The main
contribution of this paper is subtle choice of a polynomial subring of the
given ring which plays the role of $\mathbb Z$ in the number field case. This
is enabled by the Riemann-Roch formula.


Abstract:
  Facial action unit recognition has many applications from market research to
psychotherapy and from image captioning to entertainment. Despite its recent
progress, deployment of these models has been impeded due to their limited
generalization to unseen people and demographics. This work conducts an
in-depth analysis of performance across several dimensions: individuals(40
subjects), genders (male and female), skin types (darker and lighter), and
databases (BP4D and DISFA). To help suppress the variance in data, we use the
notion of self-supervised denoising autoencoders to design a method for deep
face normalization(DeepFN) that transfers facial expressions of different
people onto a common facial template which is then used to train and evaluate
facial action recognition models. We show that person-independent models yield
significantly lower performance (55% average F1 and accuracy across 40
subjects) than person-dependent models (60.3%), leading to a generalization gap
of 5.3%. However, normalizing the data with the newly introduced DeepFN
significantly increased the performance of person-independent models (59.6%),
effectively reducing the gap. Similarly, we observed generalization gaps when
considering gender (2.4%), skin type (5.3%), and dataset (9.4%), which were
significantly reduced with the use of DeepFN. These findings represent an
important step towards the creation of more generalizable facial action unit
recognition systems.


Abstract:
  To avoid serious diabetic complications, people with type 1 diabetes must
keep their blood glucose levels (BGLs) as close to normal as possible. Insulin
dosages and carbohydrate consumption are important considerations in managing
BGLs. Since the 1960s, models have been developed to forecast blood glucose
levels based on the history of BGLs, insulin dosages, carbohydrate intake, and
other physiological and lifestyle factors. Such predictions can be used to
alert people of impending unsafe BGLs or to control insulin flow in an
artificial pancreas. In past work, we have introduced an LSTM-based approach to
blood glucose level prediction aimed at "what if" scenarios, in which people
could enter foods they might eat or insulin amounts they might take and then
see the effect on future BGLs. In this work, we invert the "what-if" scenario
and introduce a similar architecture based on chaining two LSTMs that can be
trained to make either insulin or carbohydrate recommendations aimed at
reaching a desired BG level in the future. Leveraging a recent state-of-the-art
model for time series forecasting, we then derive a novel architecture for the
same recommendation task, in which the two LSTM chain is used as a repeating
block inside a deep residual architecture. Experimental evaluations using real
patient data from the OhioT1DM dataset show that the new integrated
architecture compares favorably with the previous LSTM-based approach,
substantially outperforming the baselines. The promising results suggest that
this novel approach could potentially be of practical use to people with type 1
diabetes for self-management of BGLs.


Abstract:
  In this work we discuss extensions of the pioneering analysis by
Dzyaloshinskii and Larkin of correlation functions for one-dimensional Fermi
systems, focusing on the effects of quasiparticle relaxation enabled by a
nonlinear dispersion. Throughout the work we employ both, the weakly
interacting Fermi gas picture and nonlinear Luttinger liquid theory to describe
attenuation of excitations and explore the fermion-boson duality between both
approaches. Special attention is devoted to the role of spin-exchange
processes, effects of interaction screening, and integrability. Thermalization
rates for electron- and hole-like quasiparticles, as well as the decay rate of
collective plasmon excitations and the momentum space mobility of spin
excitations are calculated for various temperature regimes. The phenomenon of
spin-charge drag is considered and the corresponding momentum transfer rate is
determined. We further discuss how momentum relaxation due to several competing
mechanisms, viz. triple electron collisions, electron-phonon scattering, and
long-range inhomogeneities affect transport properties, and highlight energy
transfer facilitated by plasmons from the perspective of the inhomogeneous
Luttinger liquid model. Finally, we derive the full matrix of thermoelectric
coefficients at the quantum critical point of the first conductance plateau
transition, and address magnetoconductance in ballistic semiconductor nanowires
with strong Rashba spin-orbit coupling.


Abstract:
  We derive equivalent linear and dynamic programs for infinite horizon
risk-sensitive control for minimization of the asymptotic growth rate of the
cumulative cost.


Abstract:
  Breiman organizes "Statistical modeling: The two cultures" around a simple
visual. Data, to the far right, are compelled into a "black box" with an arrow
and then catapulted left by a second arrow, having been transformed into an
output. Breiman then posits two interpretations of this visual as encapsulating
a distinction between two cultures in statistics. The divide, he argues is
about what happens in the "black box." In this comment, I argue for a broader
perspective on statistics and, in doing so, elevate questions from "before" and
"after" the box as fruitful areas for statistical innovation and practice.


Abstract:
  The presence of metamorphism in the protein's native state is not yet fully
understood. In an attempt to throw light on this issue here we present an
assessment, in terms of the amide hydrogen exchange protection factor, that
aims to determine the likely existence of structural fluctuations in the
native-state consistent with both the upper bound marginal stability of
proteins and the metamorphism presence. The preliminary results enable us to
conclude that the native-state metamorphism is, indeed, more probable than
thought.


Abstract:
  Quantum metrology is the use of genuinely quantum properties such as
entanglement as a resource to outperform classical sensing strategies.
Typically, entanglement is created by implementing gate operations or inducing
many-body interactions. However, existing sensing schemes with these approaches
require accurate control of the probe system such as switching on and off the
interaction among qubits, which can be challenging for practical applications.
Here, we propose an entanglement-enhanced sensing scheme with an always-on
nearest-neighbor interaction between qubits. We adopt the transverse field
Ising chain as the probe system, making use of the so-called quantum domino
dynamics for the generation of the entangled states. In addition to the
advantage that our scheme can be implemented without controlling the
interactions, it only requires initialization of the system, projective
measurements on a single qubit, and control of the uniform magnetic fields. We
can achieve an improved sensitivity beyond the standard quantum limit even
under the effect of realistic decoherence.


Abstract:
  Medical imaging is widely used in cancer diagnosis and treatment, and
artificial intelligence (AI) has achieved tremendous success in various tasks
of medical image analysis. This paper reviews AI-based tumor subregion analysis
in medical imaging. We summarize the latest AI-based methods for tumor
subregion analysis and their applications. Specifically, we categorize the
AI-based methods by training strategy: supervised and unsupervised. A detailed
review of each category is presented, highlighting important contributions and
achievements. Specific challenges and potential AI applications in tumor
subregion analysis are discussed.


Abstract:
  The possible conditions for hadron-quark phase transition in hybrid star
cores are investigated in the present work. For the hadronic matter part the
effective chiral model is adopted. Exotic baryonic degrees like hyperons and
the delta baryons are also taken into account. As $\Delta$s posses Breit-Wigner
mass distribution ($1232 \pm 120$ MeV), the hadronic equation of state is
obtained by varying the mass of the delta baryons in this range. For the quark
phase the MIT bag model is chosen with repulsive effects of the unpaired
quarks. Phase transition is achieved using Gibbs construction and the gross
properties of the resultant hybrid star are calculated in both static and
rotating conditions and compared with the various constraints on them from
different observational and empirical perspectives. The work presents a
thorough study of the phase transition properties like the critical density of
appearance of quarks, the density range for the persistence of the mixed phase
and the population of different hadrons and quarks in hybrid star matter. The
hybrid star properties, calculated in both static and rotating conditions, are
found to be consistent with the bounds on them from different perspectives.


Abstract:
  Intrusive uncertainty quantification methods for hyperbolic problems exhibit
spurious oscillations at shocks, which leads to a significant reduction of the
overall approximation quality. Furthermore, a challenging task is to preserve
hyperbolicity of the gPC moment system. An intrusive method which guarantees
hyperbolicity is the intrusive polynomial moment (IPM) method, which performs
the gPC expansion on the entropy variables. The method, while still being
subject to oscillations, requires solving a convex optimization problem in
every spatial cell and every time step. The aim of this work is to mitigate
oscillations in the IPM solution by applying filters. Filters reduce
oscillations by damping high order gPC coefficients. Naive filtering, however,
may lead to unrealizable moments, which means that the IPM optimization problem
does not have a solution and the method breaks down. In this paper, we propose
and analyze two separate strategies to guarantee the existence of a solution to
the IPM problem. First, we propose a filter which maintains realizability by
being constructed from an underlying Fokker-Planck equation. Second, we
regularize the IPM optimization problem to be able to cope with non-realizable
gPC coefficients. Consequently, standard filters can be applied to the
regularized IPM method. We demonstrate numerical results for the two strategies
by investigating the Euler equations with uncertain shock structures in one-
and two-dimensional spatial settings. We are able to show a significant
reduction of spurious oscillations by the proposed filters.


Abstract:
  In the present paper we show that it is possible to obtain the well known
Pauli group $P=\langle X,Y,Z \ | \ X^2=Y^2=Z^2=1, (YZ)^4=(ZX)^4=(XY)^4=1
\rangle $ of order $16$ as an appropriate quotient group of two distinct spaces
of orbits of the three dimensional sphere $S^3$. The first of these spaces of
orbits is realized via an action of the quaternion group $Q_8$ on $S^3$; the
second one via an action of the cyclic group of order four $\mathbb{Z}(4)$ on
$S^3$. We deduce a result of decomposition of $P$ of topological nature and
then we find, in connection with the theory of pseudo-fermions, a possible
physical interpretation of this decomposition.


Abstract:
  Combined diverse two-dimensional (2D) materials for semiconductor interfaces
are attractive for electrically controllable carrier confinement to enable
excellent electrostatic control. We investigated the transport characteristic
in heterointerface of multilayer molybdenum disulfide and hexagonal boron
nitride (MoS$_2$/h-BN) to reveal that the charge transfer switching (CTS) is
highly dependent on both the local gate constriction and bias in the channel.
Notably, the CTS is shown to be controlled at a molecular level through
electrotunable gated constriction. The resulting significant change in
conductance due to exposing 100 parts-per-billion of nitrogen dioxide gas led
to a high on/off ratio of 10 2 for completely switching off the channel thus,
acting as a molecular switch. First-principle calculations further explained
the mechanism of molecular CTS in the device. The molecular tunability of CTS
has not been previously reported in any of the van der Waals semiconductor
interfaces. Our finding opens avenues to exploit various atomically thin
heterostructures for the mesoscopic transport phenomena towards molecular
switching operation at room temperature.


Abstract:
  In stochastic optimal control, change of measure arguments have been crucial
for stochastic analysis. Such an approach is often called static reduction in
dynamic team theory and has been an effective method for establishing existence
and approximation results for optimal policies. These arguments have also been
applied to the study of dynamic games. In this paper, we demonstrate the
limitations of such an approach for a wide class of stochastic dynamic games,
where additionally, unlike the team setting considered in Part I, informational
dependence of equilibrium behavior is significantly more complicated. We
identify three types of static reductions: (i) those that are
policy-independent (as those introduced by Witsenhausen for teams), (ii) those
that are policy-dependent (as those introduced by Ho and Chu for partially
nested dynamic teams), and (iii) a third type that we will refer to as static
measurements with control-sharing reduction as in Part I (where the
measurements are static although control actions are shared according to the
partially nested information structure). For the first type, we show that there
is a bijection between Nash equilibrium policies under the original information
structure and their policy-independent static reductions. However, for the
second type, we show that there is generally no isomorphism between Nash
equilibrium solutions under the original information structure and their
policy-dependent static reductions. Sufficient conditions are presented to
establish such an isomorphism relationship between Nash equilibria of dynamic
non-zero-sum games and their policy-dependent static reductions. For zero-sum
games, these sufficient conditions are relaxed and stronger results are
established. We also study three classes of multi-stage games, where we
establish connections between closed-loop, open-loop, and control-sharing Nash
and saddle point equilibria.


Abstract:
  The discovery of ferroelectric HfO2 in thin films and more recently in bulk
is an important breakthrough because of its silicon-compatibility and
unexpectedly persistent polarization at low dimensions, but the origin of its
ferroelectricity is still under debate. The stabilization of the metastable
polar orthorhombic phase was often considered as the cumulative result of
various extrinsic factors such as stress, grain boundary, and oxygen vacancies
as well as phase transition kinetics during the annealing process. We propose a
novel mechanism to stabilize the polar orthorhombic phase over the nonpolar
monoclinic phase that is the bulk ground state. Our first-principles
calculations demonstrate that the doubly positively charged oxygen vacancy, an
overlooked defect but commonly presented in binary oxides, is critical for the
stabilization of ferroelectric phase. The charge state of oxygen vacancy serves
as a new degree of freedom to control the thermodynamic stability of competing
phases of wide-band-gap oxides.


Abstract:
  Let T d (n) be the set of d-ary rooted trees with n internal nodes. We give a
method to construct a sequence (t n , n $\ge$ 0) where, for any n $\ge$ 1, t n
has the uniform distribution in T d (n), and t n is constructed from t n--1 by
the addition of a new node, and a rearrangement of the structure of t n--1.
This method is inspired by R{\'e}my's algorithm which does this job in the
binary case, but it is different from it. This provides a method for the random
generation of a uniform d-ary tree in T d (n) with a cost linear in n.


Abstract:
  Vaccine hesitancy and misinformation on social media has increased concerns
about COVID-19 vaccine uptake required to achieve herd immunity and overcome
the pandemic. However anti-science and political misinformation and
conspiracies have been rampant throughout the pandemic. For COVID-19 vaccines,
we investigate misinformation and conspiracy campaigns and their characteristic
behaviours. We identify whether coordinated efforts are used to promote
misinformation in vaccine related discussions, and find accounts coordinately
promoting a `Great Reset' conspiracy group promoting vaccine related
misinformation and strong anti-vaccine and anti-social messages such as boycott
vaccine passports, no lock-downs and masks. We characterize other
misinformation communities from the information diffusion structure, and study
the large anti-vaccine misinformation community and smaller anti-vaccine
communities, including a far-right anti-vaccine conspiracy group. In comparison
with the mainstream and health news, left-leaning group, which are more
pro-vaccine, the right-leaning group is influenced more by the anti-vaccine and
far-right misinformation/conspiracy communities. The misinformation communities
are more vocal either specific to the vaccine discussion or political
discussion, and we find other differences in the characteristic behaviours of
different communities. Lastly, we investigate misinformation narratives and
tactics of information distortion that can increase vaccine hesitancy, using
topic modeling and comparison with reported vaccine side-effects (VAERS)
finding rarer side-effects are more frequently discussed on social media.


Abstract:
  Revealing the mechanisms shaping the architecture of planetary systems is
crucial for our understanding of their formation and evolution. In this
context, it has been recently proposed that stellar clustering might be the key
in shaping the orbital architecture of exoplanets. The main goal of this work
is to explore the factors that shape the orbits of planets. We used a
homogeneous sample of relatively young FGK dwarf stars with RV detected planets
and tested the hypothesis that their association to phase space
(position-velocity) over-densities ('cluster' stars) and under-densities
('field' stars) impacts the orbital periods of planets. When controlling for
the host star properties, on a sample of 52 planets orbiting around 'cluster'
stars and 15 planets orbiting around 'field' star, we found no significant
difference in the period distribution of planets orbiting these two populations
of stars. By considering an extended sample of 73 planets orbiting around
'cluster' stars and 25 planets orbiting 'field' stars, a significant different
in the planetary period distributions emerged. However, the hosts associated to
stellar under-densities appeared to be significantly older than their 'cluster'
counterparts. This did not allow us to conclude whether the planetary
architecture is related to age, environment, or both. We further studied a
sample of planets orbiting 'cluster' stars to study the mechanism responsible
for the shaping of orbits of planets in similar environments. We could not
identify a parameter that can unambiguously be responsible for the orbital
architecture of massive planets, perhaps, indicating the complexity of the
issue. Conclusions. Increased number of planets in clusters and in over-density
environments will help to build large and unbiased samples which will then
allow to better understand the dominant processes shaping the orbits of
planets.


Abstract:
  Determining physically admissible boundary conditions for higher moments in
an extended continuum model is recognised as a major obstacle. Boundary
conditions for the regularised 26-moment (R26) equations obtained using
Maxwell's accommodation model do exist in the literature; however, we show in
this article that these boundary conditions violate the second law of
thermodynamics and the Onsager reciprocity relations for certain boundary value
problems, and, hence, are not physically admissible. We further prove that the
linearised R26 (LR26) equations possess a proper $H$-theorem (second-law
inequality) by determining a quadratic form without cross-product terms for the
entropy density. The establishment of the $H$-theorem for the LR26 equations in
turn leads to a complete set of boundary conditions that are physically
admissible for all processes and comply with the Onsager reciprocity relations.
As an application, the problem of a slow rarefied gas flow past a spherical
droplet with and without evaporation is considered and solved analytically. The
results are compared with the numerical solution of the linearised Boltzmann
equation, experimental results from the literature and/or other macroscopic
theories to show that the LR26 theory with the physically admissible boundary
conditions provides an excellent prediction up to Knudsen number $\,\lesssim 1$
and, consequently, provides transpicuous insights into intriguing effects, such
as thermal polarisation. In particular, the analytic results for the drag force
obtained in the present work are in an excellent agreement with experimental
results even for very large values of the Knudsen number.


Abstract:
  Versatile video coding (VVC) is the next generation video coding standard
developed by the joint video experts team (JVET) and released in July 2020. VVC
introduces several new coding tools providing a significant coding gain over
the high efficiency video coding (HEVC) standard. It is well known that
increasing the coding efficiency adds more dependencies in the video bitstream
making format-compliant encryption with the standard more challenging. In this
paper we tackle the problem of selective encryption of the VVC standard in
format-compliant and constant bitrate. These two constraints ensure that the
encrypted bitstream can be decoded by any VVC decoder while the bitrate remains
unchanged by the encryption. The selective encryption of all possible VVC
syntax elements is investigated. A new algorithm is proposed to encrypt in
format-compliant and constant bitrate the transform coefficients (TCs) together
with other syntax elements at the level of the entropy encoder. The proposed
solution was integrated and assessed under the VVC reference software model
version 6.0. Experimental results showed that the encryption drastically
decreases the video quality while the encryption is robust against several
types of attacks. The encryption space is estimated in the range of 15% to 26%
of the bitstream size resulting in a lightweight encryption process. The web
page of this work is available at https://gugautie.github.io/sevvc/.


Abstract:
  Age of Information (AoI) has emerged as an important quality-of-service
measure for applications that prioritize delivery of the freshest information,
e.g., virtual or augmented reality over mobile devices and wireless sensor
networks used in the control of cyber-physical systems. We derive the Laplace
transform of the stationary AoI for the M/GI/1/2 system with a "dynamic"
service preemption and pushout policy depending on the existing service time of
the in-service message. Thus, our system generalizes both the static M/GI/1/2
queue-pushout system without service preemption and the M/GI/1/1 bufferless
system with service preemption - two systems considered to provide very good
AoI performance. Based on our analysis, for a service-time distribution that is
a mixture of deterministic and exponential, we numerically show that the
dynamic policy has lower mean AoI than that of these two static policies and
also that of the well studied M/GI/1/1 blocking system.


Abstract:
  We report on recent results that show that the pair correlation function of
systems with exponentially decaying interactions can fail to exhibit
Ornstein-Zernike asymptotics at all sufficiently high temperatures and all
sufficiently small densities. This turns out to be related to a lack of
analyticity of the correlation length as a function of temperature and/or
density and even occurs for one-dimensional systems.


Abstract:
  Let $X$ be a complex smooth quasi-projective surface acted upon by a finite
group $G$ such that the quotient $X/G$ has singularities only of ADE type. We
obtain an explicit expression for the generating series of the Euler
characteristics of the zero-dimensional components in the moduli space of
zero-dimensional subschemes on $X$ invariant under the action of $G$. We show
that this generating series (up to a suitable rational power of the formal
variable) is a holomorphic modular form.


Abstract:
  We analyse high-frequency realised volatility dynamics and spillovers in the
bitcoin market, focusing on two pairs: bitcoin against the US dollar (the main
fiat-crypto pair) and trading bitcoin against tether (the main crypto-crypto
pair). We find that the tether-margined perpetual contract on Binance is
clearly the main source of volatility, continuously transmitting strong flows
to all other instruments and receiving only a little volatility. Moreover, we
find that (i) during US trading hours, traders pay more attention and are more
reactive to prevailing market conditions when updating their expectations and
(ii) the crypto market exhibits a higher interconnectedness when traditional
Western stock markets are open. Our results highlight that regulators should
not only consider spot exchanges offering bitcoin-fiat trading but also the
tether-margined derivatives products available on most unregulated exchanges,
most importantly Binance.


Abstract:
  Microgrids (MG) are anticipated to be important players in the future smart
grid. For proper operation of MGs an Energy Management System (EMS) is
essential. The EMS of an MG could be rather complicated when renewable energy
resources (RER), energy storage system (ESS) and demand side management (DSM)
need to be orchestrated. Furthermore, these systems may belong to different
entities and competition may exist between them. Nash equilibrium is most
commonly used for coordination of such entities however the convergence and
existence of Nash equilibrium can not always be guaranteed. To this end, we use
the correlated equilibrium to coordinate agents, whose convergence can be
guaranteed. In this paper, we build an energy trading model based on mid-market
rate, and propose a correlated Q-learning (CEQ) algorithm to maximize the
revenue of each agent. Our results show that CEQ is able to balance the revenue
of agents without harming total benefit. In addition, compared with Q-learning
without correlation, CEQ could save 19.3% cost for the DSM agent and 44.2% more
benefits for the ESS agent.


Abstract:
  Uncertainty quantification in forecasting represents a topic of great
importance in statistics, especially when dealing with complex data
characterized by non-trivial dependence structure. Pushed by novel works
concerning distribution-free prediction, we propose a scalable procedure that
outputs closed-form simultaneous prediction bands for multivariate functional
response variables in a time series setting, which is able to guarantee
performance bounds in terms of unconditional coverage and asymptotic exactness,
both under some conditions. After evaluating its performance on synthetic data,
the method is used to build multivariate prediction bands for daily demand and
offer curves in the Italian gas market. The prediction framework thus obtained
allows traders to directly evaluate the impact of their own offers/bids on the
market, providing an intriguing tool for the business practice.


Abstract:
  Multiplex tissue immunostaining is a technology of growing relevance as it
can capture in situ the complex interactions existing between the elements of
the tumor microenvironment. The existence and availability of large, annotated
image datasets is key for the objective development and benchmarking of
bioimage analysis algorithms. Manual annotation of multiplex images, is
however, laborious, often impracticable. In this paper, we present Synplex, a
simulation system able to generate multiplex immunostained in situ tissue
images based on user-defined parameters. This includes the specification of
structural attributes, such as the number of cell phenotypes, the number and
level of expression of cellular markers, or the cell morphology. Synplex
consists of three sequential modules, each being responsible for a separate
task: modeling of cellular neighborhoods, modeling of cell phenotypes, and
synthesis of realistic cell/tissue textures. Synplex flexibility and accuracy
are demonstrated qualitatively and quantitatively by generating synthetic
tissues that simulate disease paradigms found in the real scenarios. Synplex is
publicly available for scientific purposes, and we believe it will become a
valuable tool for the training and/or validation of multiplex image analysis
algorithms.


Abstract:
  Accurate channel parameter estimation is challenging for wideband
millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much
fewer radio frequency (RF) chains than antennas. This paper presents a novel
joint delay and angle estimation approach for wideband mmWave fully-connected
hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to
reduce the dimension of received signals on the horizontal plane by exploiting
the convergence of the Bessel function, and to reduce the active beams in the
vertical direction through preselection. The important recurrence relationship
of the received signals needed for subspace-based angle and delay estimation is
preserved, even with substantially fewer RF chains than antennas. Then, linear
interpolation is generalized to reconstruct the received signals of the hybrid
beamformer, so that the signals can be coherently combined across the whole
band to suppress the beam squint. As a result, efficient subspace-based
algorithm algorithms can be developed to estimate the angles and delays of
multipath components. The estimated delays and angles are further matched and
correctly associated with different paths in the presence of non-negligible
noises, by putting forth perturbation operations. Simulations show that the
proposed approach can approach the Cram\'{e}r-Rao lower bound (CRLB) of the
estimation with a significantly lower computational complexity than existing
techniques.


Abstract:
  Invariance and equivariance to the rotation group have been widely discussed
in the 3D deep learning community for pointclouds. Yet most proposed methods
either use complex mathematical tools that may limit their accessibility, or
are tied to specific input data types and network architectures. In this paper,
we introduce a general framework built on top of what we call Vector Neuron
representations for creating SO(3)-equivariant neural networks for pointcloud
processing. Extending neurons from 1D scalars to 3D vectors, our vector neurons
enable a simple mapping of SO(3) actions to latent spaces thereby providing a
framework for building equivariance in common neural operations -- including
linear layers, non-linearities, pooling, and normalizations. Due to their
simplicity, vector neurons are versatile and, as we demonstrate, can be
incorporated into diverse network architecture backbones, allowing them to
process geometry inputs in arbitrary poses. Despite its simplicity, our method
performs comparably well in accuracy and generalization with other more complex
and specialized state-of-the-art methods on classification and segmentation
tasks. We also show for the first time a rotation equivariant reconstruction
network.


Abstract:
  The nature of circumstellar envelopes (CSE) around Cepheids is still a matter
of debate. The physical origin of their infrared (IR) excess could be either a
shell of ionized gas, or a dust envelope, or both. This study aims at
constraining the geometry and the IR excess of the environment of the
long-period Cepheid $\ell$ Car (P=35.5 days) at mid-IR wavelengths to
understand its physical nature. We first use photometric observations in
various bands and Spitzer Space Telescope spectroscopy to constrain the IR
excess of $\ell$ Car. Then, we analyze the VLTI/MATISSE measurements at a
specific phase of observation, in order to determine the flux contribution, the
size and shape of the environment of the star in the L band. We finally test
the hypothesis of a shell of ionized gas in order to model the IR excess. We
report the first detection in the L band of a centro-symmetric extended
emission around l Car, of about 1.7$R_\star$ in FWHM, producing an excess of
about 7.0\% in this band. In the N band, there is no clear evidence for dust
emission from VLTI/MATISSE correlated flux and Spitzer data. On the other side,
the modeled shell of ionized gas implies a more compact CSE
($1.13\pm0.02\,R_\star$) and fainter (IR excess of 1\% in the L band). We
provide new evidences for a compact CSE of $\ell$ Car and we demonstrate the
capabilities of VLTI/MATISSE for determining common properties of CSEs. While
the compact CSE of $\ell$ Car is probably of gaseous nature, the tested model
of a shell of ionized gas is not able to simultaneously reproduce the IR excess
and the interferometric observations. Further Galactic Cepheids observations
with VLTI/MATISSE are necessary for determining the properties of CSEs, which
may also depend on both the pulsation period and the evolutionary state of the
stars.


Abstract:
  Detecting out-of-distribution (OOD) samples plays a key role in open-world
and safety-critical applications such as autonomous systems and healthcare.
Recently, self-supervised representation learning techniques (via contrastive
learning and pretext learning) have shown effective in improving OOD detection.
However, one major issue with such approaches is the choice of shifting
transformations and pretext tasks which depends on the in-domain distribution.
In this paper, we propose a simple framework that leverages a shifting
transformation learning setting for learning multiple shifted representations
of the training set for improved OOD detection. To address the problem of
selecting optimal shifting transformation and pretext tasks, we propose a
simple mechanism for automatically selecting the transformations and modulating
their effect on representation learning without requiring any OOD training
samples. In extensive experiments, we show that our simple framework
outperforms state-of-the-art OOD detection models on several image datasets. We
also characterize the criteria for a desirable OOD detector for real-world
applications and demonstrate the efficacy of our proposed technique against
state-of-the-art OOD detection techniques.


Abstract:
  The recent many-fold increase in the size of deep neural networks makes
efficient distributed training challenging. Many proposals exploit the
compressibility of the gradients and propose lossy compression techniques to
speed up the communication stage of distributed training. Nevertheless,
compression comes at the cost of reduced model quality and extra computation
overhead. In this work, we design an efficient compressor with minimal
overhead. Noting the sparsity of the gradients, we propose to model the
gradients as random variables distributed according to some sparsity-inducing
distributions (SIDs). We empirically validate our assumption by studying the
statistical characteristics of the evolution of gradient vectors over the
training process. We then propose Sparsity-Inducing Distribution-based
Compression (SIDCo), a threshold-based sparsification scheme that enjoys
similar threshold estimation quality to deep gradient compression (DGC) while
being faster by imposing lower compression overhead. Our extensive evaluation
of popular machine learning benchmarks involving both recurrent neural network
(RNN) and convolution neural network (CNN) models shows that SIDCo speeds up
training by up to 41:7%, 7:6%, and 1:9% compared to the no-compression
baseline, Topk, and DGC compressors, respectively.


Abstract:
  Structured illumination microscopy (SIM) has emerged as an essential
technique for 3D and live-cell super-resolution imaging. However, to date,
there has not been a dedicated workshop or journal issue covering the various
aspects of SIM, from bespoke hardware and software development and the use of
commercial instruments to biological applications. This special issue aims to
recap recent developments as well as outline future trends. In addition to SIM,
we cover related topics such as complementary super-resolution microscopy
techniques, computational imaging, visualisation and image processing methods.


Abstract:
  We investigate the equilibrium and radial stability of spherically symmetric
relativistic stars, considering a polytropic equation of state (EoS), within
the framework of $f(R,T)$ gravity with a conservative energy-momentum tensor.
Both modified stellar structure equations and Chandrasekhar's pulsation
equations are derived for the $f(R,T)= R+ h(T)$ gravity model, where the
function $h(T)$ assumes a specific form in order to safeguard the conservation
equation for the energy-momentum tensor. The neutron star properties, such as
radius, mass, binding energy and oscillation spectrum are studied in detail.
Our results show that a cusp -- which signals the appearance of instability --
is formed when the binding energy is plotted as a function of the compact star
proper mass. We find that the squared frequency of the fundamental vibration
mode passes through zero at the central-density value corresponding to such a
cusp where the binding energy is a minimum.


Abstract:
  Customer service is a setting that calls for empathy in live human agent
responses. Recent advances have demonstrated how open-domain chatbots can be
trained to demonstrate empathy when responding to live human utterances. We
show that a blended skills chatbot model that responds to customer queries is
more likely to resemble actual human agent response if it is trained to
recognize emotion and exhibit appropriate empathy, than a model without such
training. For our analysis, we leverage a Twitter customer service dataset
containing several million customer<->agent dialog examples in customer service
contexts from 20 well-known brands.


Abstract:
  The Fermi constant ($G_F$) is extremely well measured through the muon
lifetime, defining one of the key fundamental parameters in the Standard Model
(SM). Therefore, to search for physics beyond the SM (BSM) via $G_F$, the
constraining power is determined by the precision of the second-best
independent determination of $G_F$. The best alternative extractions of $G_F$
proceed either via the global electroweak (EW) fit or from superallowed $\beta$
decays in combination with the Cabibbo angle measured in kaon, $\tau$, or $D$
decays. Both variants display some tension with $G_F$ from muon decay, albeit
in opposite directions, reflecting the known tensions within the EW fit and
hints for the apparent violation of CKM unitarity, respectively. We investigate
how BSM physics could bring the three determinations of $G_F$ into agreement
using SM effective field theory and comment on future perspectives.


Abstract:
  Supervised neural network training has led to significant progress on
single-channel sound separation. This approach relies on ground truth isolated
sources, which precludes scaling to widely available mixture data and limits
progress on open-domain tasks. The recent mixture invariant training (MixIT)
method enables training on in-the-wild data; however, it suffers from two
outstanding problems. First, it produces models which tend to over-separate,
producing more output sources than are present in the input. Second, the
exponential computational complexity of the MixIT loss limits the number of
feasible output sources. In this paper we address both issues. To combat
over-separation we introduce new losses: sparsity losses that favor fewer
output sources and a covariance loss that discourages correlated outputs. We
also experiment with a semantic classification loss by predicting weak class
labels for each mixture. To handle larger numbers of sources, we introduce an
efficient approximation using a fast least-squares solution, projected onto the
MixIT constraint set. Our experiments show that the proposed losses curtail
over-separation and improve overall performance. The best performance is
achieved using larger numbers of output sources, enabled by our efficient MixIT
loss, combined with sparsity losses to prevent over-separation. On the FUSS
test set, we achieve over 13 dB in multi-source SI-SNR improvement, while
boosting single-source reconstruction SI-SNR by over 17 dB.


Abstract:
  We perform a systematic study on the interactions of the
$\Sigma_c^{(*)}D^{(*)}$ systems within the framework of chiral effective field
theory. We introduce the contact term, one-pion-exchange and two-pion-exchange
contributions to describe the short-, long-, and intermediate-range
interactions. The low energy constants of the $\Sigma_c^{(*)}D^{(*)}$ systems
are estimated from the $N\bar{N}$ scattering data by introducing a quark level
Lagrangian. With three solutions of LECs, all the $\Sigma_c^{(*)}D^{(*)}$
systems with isospin $I=1/2$ can form bound states, in which different inputs
of LECs may lead to distinguishable mass spectra. In addition, we also
investigate the interactions of the charmed-bottom
$\Sigma_c^{(*)}\bar{B}^{(*)}$, $\Sigma_b^{(*)}D^{(*)}$, and
$\Sigma_b^{(*)}\bar{B}^{(*)}$ systems. Among the obtained bound states, the
bindings become deeper when the reduced masses of the corresponding systems are
heavier.


Abstract:
  Computer systems are so complex, so they are usually designed and analyzed in
terms of layers of abstraction. Complexity is still a challenge facing logical
reasoning tools that are used to find software design flaws and implementation
bugs. Abstraction is also a common technique for scaling those tools to more
complex systems. However, the abstractions used in the design phase of systems
are in many cases different from those used for assurance. In this paper we
argue that different software quality assurance techniques operate on different
aspects of software systems. To facilitate assurance, and for a smooth
integration of assurance tools into the Software Development Lifecycle (SDLC),
we present a 4-dimensional meta-architecture that separates computational,
coordination, and stateful software artifacts early on in the design stage. We
enumerate some of the design and assurance challenges that can be addressed by
this meta-architecture, and demonstrate it on the high-level design of a simple
file system.


Abstract:
  The FE$^2$ method is a very flexible but computationally expensive tool for
multiscale simulations. In conventional implementations, the microscopic
displacements are iteratively solved for within each macroscopic iteration
loop, although the macroscopic strains imposed as boundary conditions at the
micro-scale only represent estimates. In order to reduce the number of
expensive micro-scale iterations, the present contribution presents a
monolithic FE$^2$ scheme, for which the displacements at the micro-scale and at
the macro-scale are solved for in a common Newton-Raphson loop. In this case,
the linear system of equations within each iteration is solved by static
condensation, so that only very limited modifications to the conventional,
staggered scheme are necessary. The proposed monolithic FE$^2$ algorithm is
implemented into the commercial FE code Abaqus. Benchmark examples demonstrate
that the monolithic scheme saves up to ~60% of computational costs.


Abstract:
  Khovanov and Sazdanovic recently introduced symmetric monoidal categories
parameterized by rational functions and given by quotients of categories of
two-dimensional cobordisms. These categories generalize Deligne's interpolation
categories of representations of symmetric groups. In this paper, we classify
indecomposable objects and identify the associated graded Grothendieck rings of
Khovanov-Sazdanovic's categories through sums of representation categories over
crossed products of polynomial rings over a general field. To obtain these
results, we introduce associated graded categories for Krull-Schmidt categories
with filtrations as a categorification of the associated graded Grothendieck
ring, and study field extensions and Galois descent for Krull-Schmidt
categories.


Abstract:
  Predicting 3D human pose from a single monoscopic video can be highly
challenging due to factors such as low resolution, motion blur and occlusion,
in addition to the fundamental ambiguity in estimating 3D from 2D. Approaches
that directly regress the 3D pose from independent images can be particularly
susceptible to these factors and result in jitter, noise and/or inconsistencies
in skeletal estimation. Much of which can be overcome if the temporal evolution
of the scene and skeleton are taken into account. However, rather than tracking
body parts and trying to temporally smooth them, we propose a novel transformer
based network that can learn a distribution over both pose and motion in an
unsupervised fashion. We call our approach Skeletor. Skeletor overcomes
inaccuracies in detection and corrects partial or entire skeleton corruption.
Skeletor uses strong priors learn from on 25 million frames to correct skeleton
sequences smoothly and consistently. Skeletor can achieve this as it implicitly
learns the spatio-temporal context of human motion via a transformer based
neural network. Extensive experiments show that Skeletor achieves improved
performance on 3D human pose estimation and further provides benefits for
downstream tasks such as sign language translation.


Abstract:
  We report a systematic magnetotransport study of superconducting
infinite-layer nickelate thin films Nd$_{1-x}$Sr$_x$NiO$_2$ with $0.15 \leq x
\leq 0.225$. By suppressing superconductivity with out-of-plane magnetic fields
up to 37.5 T, we find that the normal state resistivity of
Nd$_{1-x}$Sr$_x$NiO$_2$ is characterized by a crossover from a metallic
$T^2$-behavior to an insulating log(1/$T$)-behavior for all $x$ except $x =
0.225$, at which the resistivity is predominantly metallic. The
log(1/$T$)-behavior is found to be robust against magnetic fields, inconsistent
with scenarios involving localization or Kondo scattering, and points to an
anomalous insulating state possibly driven by strong correlations. In the
metallic state, we find no evidence for non-Fermi-liquid behavior arising from
proximity to a putative quantum critical point located inside the
superconducting dome.


Abstract:
  Time series exploration and mining has many applications across several
industrial and scientific domains. In this paper, we consider the problem of
detecting locally similar pairs and groups, called bundles, over co-evolving
time series. These are pairs or groups of subsequences whose values do not
differ by more than {\epsilon} for at least delta consecutive timestamps, thus
indicating common local patterns and trends. We first present a baseline
algorithm that performs a sweep line scan across all timestamps to identify
matches. Then, we propose a filter-verification technique that only examines
candidate matches at judiciously chosen checkpoints across time. Specifically,
we introduce two block scanning algorithms for discovering local pairs and
bundles respectively, which leverage the potential of checkpoints to
aggressively prune the search space. We experimentally evaluate our methods
against real-world and synthetic datasets, demonstrating a speed-up in
execution time by an order of magnitude over the baseline. This paper has been
published in the 16th International Symposium on Spatial and Temporal Databases
(SSTD19).


Abstract:
  In a stochastic process, where noise is always present, the
fluctuation-dissipation theorem (FDT) becomes one of the most important tools
in statistical mechanics and, consequently, it appears everywhere. Its major
utility is to provide a simple response to study certain processes in solids
and fluids. However, in many situations we are not talking about a FDT, but
about the noise intensity. For example, noise has enormous importance in
diffusion and growth phenomena. Although we have an explicit FDT for diffusion
phenomena, we do not have one for growth processes where we have a noise
intensity. We show that there is a hidden FDT for the growth phenomenon,
similar to the diffusive one. Moreover, we show that growth with correlated
noise presents as well a similar form of FDT. We also call attention to the
hierarchy within the theorems of statistical mechanics and how this explains
the violation of the FDT in some phenomena.


Abstract:
  In this paper a hypergraph will be identified with the family of its edges. A
hypergraph $\mathcal E$ possesses property $C(k,{\rho})$ iff $|\bigcap \mathcal
E'|<{\rho}$ for each $\mathcal E'\in {[\mathcal E]}^{k}$. A vertex set
$Y\subset \bigcup\mathcal E$ is a "vertex cover" of $\mathcal E$ iff $E\cap
Y\ne \emptyset$ for each $E\in \mathcal E$. A vertex cover $Y$ is "minimal" iff
no proper subset of $Y$ is vertex cover. If $A$ is a set and $S$ is a set of
cardinals, write $$ {[A]}^{S}=\{B\subset A: |B|\in S\}. $$ If ${\lambda}$ and
${\rho}$ are cardinals, $S$ is a set of cardinals, $k\in {\omega}$, then we
write $$\mathbf M({{\lambda}},{S},{k},{{\mu}})\to \mathbf{MinVC} $$ iff every
hypergraph $\mathcal E\subset {[{\lambda}]}^{S}$ possessing property
$C({k},{{\rho}})$ has a minimal vertex cover. If $S=\{\kappa\}$, then we simply
write $\mathbf M({{\lambda}},{\kappa},{k},{{\mu}})\to \mathbf{MinVC}$ for
$\mathbf M({{\lambda}},\{{\kappa}\},{k},{{\mu}})\to \mathbf{MinVC}$ A set $S$
of cardinals is "nowhere stationary" iff $S\cap {\alpha}$ is not stationary in
${\alpha}$ for any ordinal ${\alpha}$ with $cf({\alpha})>{\omega}$. Countable
sets of cardinals, and sets of successor cardinals are nowhere stationary. In
this paper we prove:
  (1) $\mathbf M({{\lambda}},{S},{2},{k})\to \mathbf{MinVC}$ for each nowhere
stationary set $S$ of cardinals and ${\omega}\le {\lambda}$,
  (2) $\mathbf M({{\lambda}},{{\kappa}} ,{2},{{\rho}})\to \mathbf{MinVC}$
provided ${\rho}<\beth_{\omega}\le {\kappa}\le {\lambda}$,
  (3) $\mathbf M({{\lambda}},{{\omega}},{r},{k})\to \mathbf{MinVC}$ provided
${\omega}\le {\lambda}$ and $k,r\in {\omega}$,
  (4) $\mathbf M({{\lambda}},{{\omega}_1},{3},{k})\to \mathbf{MinVC}$ provided
${\omega}_1\le {\lambda}$ and $k\in {\omega}$.


Abstract:
  The paper proposes and develops new globally convergent algorithms of the
generalized damped Newton type for solving important classes of nonsmooth
optimization problems. These algorithms are based on the theory and
calculations of second-order subdifferentials of nonsmooth functions with
employing the machinery of second-order variational analysis and generalized
differentiation. First we develop a globally superlinearly convergent damped
Newton-type algorithm for the class of continuously differentiable functions
with Lipschitzian gradients, which are nonsmooth of second order. Then we
design such a globally convergent algorithm to solve a class of nonsmooth
convex composite problems with extended-real-valued cost functions, which
typically arise in machine learning and statistics. Finally, the obtained
algorithmic developments and justifications are applied to solving a major
class of Lasso problems with detailed numerical implementations. We present the
results of numerical experiments and compare the performance of our main
algorithm applied to Lasso problems with those achieved by other first-order
and second-order methods.


Abstract:
  We employ a typical genetic circuit model to explore how noise can influence
the dynamic structure. With the increase of a key interactive parameter, the
model will deterministically go through two bifurcations and three dynamic
structure regions. We find that a new quasi-periodic component, which is not
allowed by deterministic dynamics, will be generated by noise inducing in the
first two regions, and this quasi-period will be more and more stable along
with the noise increasing. Especially, in the second region, the quasi-period
will compete with a stable limit cycle and perform a new transient rhythm.
Furthermore, we ascertain the entropy production rate (EPR) and the heat
dissipation rate (HDR), and discover a minimal value with elucidating
theoretically. In the end, we unveil the mechanism of the quasi-periods
forming, and show a practical biological instance. We expect that this work is
helpful to solve some biological or ecological problems, such as genetic origin
of periodical cicadas and population dynamics with fluctuation.


Abstract:
  We study a stochastic behavior of aerosols by the non-equilibrium statistical
mechanics approach using the analytical approach of the Langevin equation. We
firstly show that superdiffusion can possibly occur right after the emission,
which may be attributed to the physical mechanism of the outbreak of the
COVID-19 pandemic. We also provide clear evidence of the least required
distance to prevent infections occurred by aerosols. In particular, the
required distance to prevent aerosol infections is derived to be about 42 m
when we assume Cauchy distribution as an initial velocity distribution. This
fact implies that due to superdiffusion the aerosol infection can occur even
far away from a long distance as compared to the previous considerations.


Abstract:
  Chemical transformations, such as ion exchange, are commonly employed to
modify nanocrystal compositions. Yet the mechanisms of these transformations,
which often operate far from equilibrium and entail mixing diverse chemical
species, remain poorly understood. Here, we explore an idealized model for ion
exchange in which a chemical potential drives compositional defects to
accumulate at a crystal's surface. These impurities subsequently diffuse
inward. We find that the nature of interactions between sites in a
compositionally impure crystal strongly impacts exchange trajectories. In
particular, elastic deformations which accompany lattice-mismatched species
promote spatially modulated patterns in the composition. These same patterns
can be produced at equilibrium in core/shell nanocrystals, whose structure
mimics transient motifs observed in nonequilibrium trajectories. Moreover, the
core of such nanocrystals undergoes a phase transition - from modulated to
unstructured - as the thickness or stiffness of the shell is decreased. Our
results help explain the varied patterns observed in heterostructured
nanocrystals produced by ion exchange and suggest principles for the rational
design of compositionally-patterned nanomaterials.


Abstract:
  Novel sparse reconstruction algorithms are proposed for beamspace channel
estimation in massive multiple-input multiple-output systems. The proposed
algorithms minimize a least-squares objective having a nonconvex regularizer.
This regularizer removes the penalties on a few large-magnitude elements from
the conventional l1-norm regularizer, and thus it only forces penalties on the
remaining elements that are expected to be zeros. Accurate and fast
reconstructions can be achieved by performing gradient projection updates
within the framework of difference of convex functions (DC) programming. A
double-loop algorithm and a single-loop algorithm are proposed via different DC
decompositions, and these two algorithms have distinct computation complexities
and convergence rates. Then, an extension algorithm is further proposed by
designing the step sizes of the single-loop algorithm. The extension algorithm
has a faster convergence rate and can achieve approximately the same level of
accuracy as the proposed double-loop algorithm. Numerical results show
significant advantages of the proposed algorithms over existing reconstruction
algorithms in terms of reconstruction accuracies and runtimes. Compared to the
benchmark channel estimation techniques, the proposed algorithms also achieve
smaller mean squared error and higher achievable spectral efficiency.


Abstract:
  We present ObSuRF, a method which turns a single image of a scene into a 3D
model represented as a set of Neural Radiance Fields (NeRFs), with each NeRF
corresponding to a different object. A single forward pass of an encoder
network outputs a set of latent vectors describing the objects in the scene.
These vectors are used independently to condition a NeRF decoder, defining the
geometry and appearance of each object. We make learning more computationally
efficient by deriving a novel loss, which allows training NeRFs on RGB-D inputs
without explicit ray marching. After confirming that the model performs equal
or better than state of the art on three 2D image segmentation benchmarks, we
apply it to two multi-object 3D datasets: A multiview version of CLEVR, and a
novel dataset in which scenes are populated by ShapeNet models. We find that
after training ObSuRF on RGB-D views of training scenes, it is capable of not
only recovering the 3D geometry of a scene depicted in a single input image,
but also to segment it into objects, despite receiving no supervision in that
regard.


Abstract:
  Non-orthogonal multiple access (NOMA) and massive multiple-input
multiple-output (MIMO) systems are highly efficient. Massive MIMO systems are
inherently resistant to passive attackers (eavesdroppers), thanks to
transmissions directed to the desired users. However, active attackers can
transmit a combination of legitimate user pilot signals during the channel
estimation phase. This way they can mislead the base station (BS) to rotate the
transmission in their direction, and allow them to eavesdrop during the
downlink data transmission phase. In this paper, we analyse this vulnerability
in an improved system model and stronger adversary assumptions, and investigate
how physical layer security can mitigate such attacks and ensure secure
(confidential) communication. We derive the secrecy outage probability (SOP)
and a lower bound on the ergodic secrecy capacity, using stochastic geometry
tools when the number of antennas in the BSs tends to infinity. We adapt the
result to evaluate the secrecy performance in massive orthogonal multiple
access (OMA). We find that appropriate power allocation allows NOMA to
outperform OMA in terms of ergodic secrecy rate and SOP.


Abstract:
  Coupled quantum Hall edge channels show intriguing non-trivial modes, for
example, charge and neutral modes at Landau level filling factors 2 and 2/3. We
propose an appropriate and effective model with Coulomb interaction and
disorder-induced tunneling characterized by coupling capacitances and tunneling
conductances, respectively. This model explains how the transport eigenmodes,
within the interaction- and disorder-dominated regimes, change with the
coupling capacitance, tunneling conductance, and measurement frequency. We
propose frequency- and time-domain transport experiments, from which eigenmodes
can be determined using this model.


Abstract:
  Communities of practice in science communication can make important
contributions to public engagement with science but are under-researched. In
this article, we look at the perspectives of a community of practice in
astronomy communication regarding (relations with) their public(s). Most
participants in this study consider that public(s) have several deficits and
vulnerabilities. Moreover, practitioners have little to no contact with (and
therefore make no use of) academic research on science communication. We argue
that collaboration between science communication researchers and practitioners
could benefit the science-public relationship and that communities of practice
may be critical to that purpose.


Abstract:
  In this paper we study CP violation in photon self-interactions at low
energy. These interactions, mediated by the effective operator $FFF\tilde{F}$,
where ($\tilde F$) $F$ is the (dual) electromagnetic field strength, have yet
to be directly probed experimentally. Possible sources for such interactions
are weakly coupled light scalars with both scalar and pseudoscalar couplings to
photons (for instance, complex Higgs-portal scalars or the relaxion), or new
light fermions coupled to photons via dipole operators. We propose a method to
isolate the CP-violating contribution to the photon self-interactions using
Superconducting Radio-Frequency cavities and vacuum birefringence experiments.
In addition, we consider several theoretical and experimental indirect bounds
on the scale of new physics associated with the above effective operator, and
present projections for the sensitivity of the proposed experiments to this
scale. We also discuss the implications of these bounds on the CP-violating
couplings of new light particles coupled to photons.


Abstract:
  Flaming phenomena represent the divergence in the strength of user dynamics
as created by user interactions in online social networks (OSNs). Although it
has been known that flaming phenomena occur when the Laplacian matrix of the
OSN has non-real eigenvalues, it was recently shown that flaming phenomena may
occur even if all the eigenvalues are real numbers. This effect appears only in
the situation that some eigenvalues are degenerate, and a special unitary
transformation is applied to the equations representing user dynamics; whether
actual OSNs satisfy this condition has not been fully discussed. In this paper,
we clarify that the user dynamics caused by the degeneration of eigenvalue 0 is
one specific example of the above condition. We also investigate the mechanism
and characteristics of flaming phenomena generated by degenerated eigenvalues.
Furthermore, we demonstrate through numerical simulations that the degeneration
of eigenvalues can cause divergence.


Abstract:
  Spatio-temporal forecasting is challenging attributing to the high
nonlinearity in temporal dynamics as well as complex location-characterized
patterns in spatial domains, especially in fields like weather forecasting.
Graph convolutions are usually used for modeling the spatial dependency in
meteorology to handle the irregular distribution of sensors' spatial location.
In this work, a novel graph-based convolution for imitating the meteorological
flows is proposed to capture the local spatial patterns. Based on the
assumption of smoothness of location-characterized patterns, we propose
conditional local convolution whose shared kernel on nodes' local space is
approximated by feedforward networks, with local representations of coordinate
obtained by horizon maps into cylindrical-tangent space as its input. The
established united standard of local coordinate system preserves the
orientation on geography. We further propose the distance and orientation
scaling terms to reduce the impacts of irregular spatial distribution. The
convolution is embedded in a Recurrent Neural Network architecture to model the
temporal dynamics, leading to the Conditional Local Convolution Recurrent
Network (CLCRN). Our model is evaluated on real-world weather benchmark
datasets, achieving state-of-the-art performance with obvious improvements. We
conduct further analysis on local pattern visualization, model's framework
choice, advantages of horizon maps and etc.


Abstract:
  We develop a version of the Bass-Serre theory for Lie algebras (over a field
$k$) via a homological approach. We define the notion of fundamental Lie
algebra of a graph of Lie algebras and show that this construction yields
Mayer-Vietoris sequences. We extend some well known results in group theory to
$\mathbb{N}$-graded Lie algebras: for example, we show that one relator
$\mathbb{N}$-graded Lie algebras are iterated HNN extensions with free bases
which can be used for cohomology computations and apply the Mayer-Vietoris
sequence to give some results about coherence of Lie algebras.


Abstract:
  We consider the problem of secure distributed matrix multiplication. Coded
computation has been shown to be an effective solution in distributed matrix
multiplication, both providing privacy against workers and boosting the
computation speed by efficiently mitigating stragglers. In this work, we
present a non-direct secure extension of the recently introduced bivariate
polynomial codes. Bivariate polynomial codes have been shown to be able to
further speed up distributed matrix multiplication by exploiting the partial
work done by the stragglers rather than completely ignoring them while reducing
the upload communication cost and/or the workers' storage's capacity needs. We
show that, especially for upload communication or storage constrained settings,
the proposed approach reduces the average computation time of secure
distributed matrix multiplication compared to its competitors in the
literature.


Abstract:
  Autonomous vehicles rely heavily upon their perception subsystems to see the
environment in which they operate. Unfortunately, the effect of variable
weather conditions presents a significant challenge to object detection
algorithms, and thus it is imperative to test the vehicle extensively in all
conditions which it may experience. However, development of robust autonomous
vehicle subsystems requires repeatable, controlled testing - while real weather
is unpredictable and cannot be scheduled. Real-world testing in adverse
conditions is an expensive and time-consuming task, often requiring access to
specialist facilities. Simulation is commonly relied upon as a substitute, with
increasingly visually realistic representations of the real-world being
developed. In the context of the complete autonomous vehicle control pipeline,
subsystems downstream of perception need to be tested with accurate recreations
of the perception system output, rather than focusing on subjective visual
realism of the input - whether in simulation or the real world. This study
develops the untapped potential of a lightweight weather augmentation method in
an autonomous racing vehicle - focusing not on visual accuracy, but rather the
effect upon perception subsystem performance in real time. With minimal
adjustment, the prototype developed in this study can replicate the effects of
water droplets on the camera lens, and fading light conditions. This approach
introduces a latency of less than 8 ms using compute hardware well suited to
being carried in the vehicle - rendering it ideal for real-time implementation
that can be run during experiments in simulation, and augmented reality testing
in the real world.


Abstract:
  The incoherent approximation (IA) is often used for calculating the
one-phonon inelastic neutron scattering cross section for arbitrary solids. It
is valid for thermal neutrons but for slow neutrons it requires a correction,
which is significant for isotopes that are strong coherent scatterers. In this
article, we present the extension of the Placzek--Van Hove corrections for slow
neutrons in the limit of low temperatures using the example of solid
\emph{ortho}-deuterium (sD$_2$). Our approach yields realistic one-phonon
up-scattering cross sections for sD$_2$ and shows the IA to be a factor of 2 to
5 too high for ultracold neutron (UCN) up-scattering in sD$_2$. Our
calculations are compared with previously published Monte Carlo calculations of
the one-phonon cross section based on the dynamic structure function
$S(q,\omega)$ of polycrystalline \emph{ortho}-deuterium and are found to be
consistent with them. Furthermore, we provide the means for easily replicable
calculations of the one-phonon up-scattering cross sections of solid
\emph{ortho}-deuterium for slow neutrons. These should from now on be used in
calculations and simulations of UCN scattering in sD$_2$.


Abstract:
  In this paper, we show that the bulk reconstruction in the AdS/CFT
correspondence is rather simple and has an intuitive picture, by showing that
the HKLL bulk reconstruction formula can be simplified. We also reconstruct the
wave packets in the bulk theory from the CFT primary operators. With these wave
packets, we discuss the causality and duality constraints and find our picture
is only the consistent one. Our picture of the bulk reconstruction can be
applied to the asymptotic AdS spacetime.


Abstract:
  To perform precision medicine in realtime at home, a device capable of long
distance continuously monitoring target biomolecules in unprocessed blood under
dynamic situations is essential. In this study, an integrative buffer free
wireless device is developed to measure drug concentrations in patients blood
in real time for remote clinical healthcare. To demonstrate its capability, the
drug molecules (i.e., small molecule drug doxorubicin, DOX) are continuously
measured in the unprocessed whole blood of live animals (e.g., rats). The
dynamic changes of drug concentrations with sub minute temporal resolution are
recorded for an extended period of time (8 hours). As an advance in remote
diagnosis, this device would benefit the public by enabling long-distance
precision medicine to prevent pandemics in advance.


Abstract:
  We analyze the modulation characteristics of the uncooled terahertz (THz) and
infrared (IR) detectors using the variation of the density and effective
temperature of the two-dimensional electron-hole plasma in uniform graphene
layers (GLs) and perforated graphene layers (PGLs) due to the absorption of THz
and IR radiation. The performance of the photodetectors (both the
GL-photoresistor and the PGL-based barrier photodiodes) are compared. Their
characteristics are also compared with the GL reverse-biased photodiodes. The
obtained results allow to evaluate the ultimate modulation frequencies of these
photodetectors and can be used for their optimization.


Abstract:
  The cross sections of the process $e^{+}e^{-} \to K_{S}^{0}K_{L}^{0}$ are
measured at fifteen center-of-mass energies $\sqrt{s}$ from $2.00$ to
$3.08~{\rm GeV}$ with the BESIII detector at the Beijing Electron Positron
Collider (BEPCII). The results are found to be consistent with those obtained
by BaBar. A resonant structure around $2.2~{\rm GeV}$ is observed, with a mass
and width of $2273.7 \pm 5.7 \pm 19.3~{\rm MeV}/c^2$ and $86 \pm 44 \pm 51~{\rm
MeV}$, respectively, where the first uncertainties are statistical and the
second ones are systematic. The product of its radiative width
($\Gamma_{e^+e^-}$) with its branching fraction to $K_{S}^{0}K_{L}^{0}$
($Br_{K_{S}^{0}K_{L}^{0}}$) is $0.9 \pm 0.6 \pm 0.7~{\rm eV}$.


Abstract:
  The pion electromagnetic form factor has been calculated for the first time
from the solutions of the Bethe-Salpeter equation, obtained directly in
Minkowski space by using the Nakanishi integral representation of the
Bethe-Salpeter amplitude. The one-gluon exchange kernel contains as inputs the
quark and gluon masses, as well as a scale parameter featuring the extended
quark-gluon vertex. The range of variability of these parameters is suggested
by lattice calculations. After presenting a very successful comparison with the
existing data in the whole range of the momentum transfer, we show how
inserting the light-front (LF) formalism in our approach allows to achieve
further interesting results, like the evaluation of the LF valence form factor
and a first investigation of the end-points effects.


Abstract:
  Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term,
sentiment and opinion term triplets from sentences and tries to provide a
complete solution for aspect-based sentiment analysis (ABSA). However, some
triplets extracted by ASTE are confusing, since the sentiment in a triplet
extracted by ASTE is the sentiment that the sentence expresses toward the
aspect term rather than the sentiment of the aspect term and opinion term pair.
In this paper, we introduce a more fine-grained Aspect-Sentiment-Opinion
Triplet Extraction (ASOTE) Task. ASOTE also extracts aspect term, sentiment and
opinion term triplets. However, the sentiment in a triplet extracted by ASOTE
is the sentiment of the aspect term and opinion term pair. We build four
datasets for ASOTE based on several popular ABSA benchmarks. We propose a
Position-aware BERT-based Framework (PBF) to address this task. PBF first
extracts aspect terms from sentences. For each extracted aspect term, PBF first
generates aspect term-specific sentence representations considering both the
meaning and the position of the aspect term, then extracts associated opinion
terms and predicts the sentiments of the aspect term and opinion term pairs
based on the sentence representations. Experimental results on the four
datasets show the effectiveness of PBF.


Abstract:
  A new method for the classification of respiratory diseases is presented. The
method is based on a novel class of features, extracted from pulmonary sounds,
by parameterizing their spectrograms that are represented as surfaces, and by
utilizing geometrical distortions defined with reference to these surfaces.
This method yields a set of highly descriptive features for the analysis of
pulmonary sound recordings. Furthermore, by combining these features with
Mel-frequency cepstral coefficients, we introduce a powerful model for the
automatic diagnosis of common respiratory pathologies. Compared with baseline
methods, our model achieves superior results for binary and multi-class
classifications of common respiratory diseases. Our new approach to the
classification of one-dimensional signals is applicable to other signals in the
context of their representations in combined spaces or manifolds.


Abstract:
  We establish upper bounds for the joint moments of the $2k^{\text{th}}$ power
of the Riemann zeta function with the $2h^{\text{th}}$ power of its derivative
for $0 \leq h \leq 1$ and $1 \leq k \leq 2$. These bounds are expected to be
sharp based upon predictions from random matrix theory.


Abstract:
  Depending on the application, radiological diagnoses can be associated with
high inter- and intra-rater variabilities. Most computer-aided diagnosis (CAD)
solutions treat such data as incontrovertible, exposing learning algorithms to
considerable and possibly contradictory label noise and biases. Thus, managing
subjectivity in labels is a fundamental problem in medical imaging analysis. To
address this challenge, we introduce auto-decoded deep latent embeddings
(ADDLE), which explicitly models the tendencies of each rater using an
auto-decoder framework. After a simple linear transformation, the latent
variables can be injected into any backbone at any and multiple points,
allowing the model to account for rater-specific effects on the diagnosis.
Importantly, ADDLE does not expect multiple raters per image in training,
meaning it can readily learn from data mined from hospital archives. Moreover,
the complexity of training ADDLE does not increase as more raters are added.
During inference each rater can be simulated and a 'mean' or 'greedy' virtual
rating can be produced. We test ADDLE on the problem of liver steatosis
diagnosis from 2D ultrasound (US) by collecting 46 084 studies along with
clinical US diagnoses originating from 65 different raters. We evaluated
diagnostic performance using a separate dataset with gold-standard biopsy
diagnoses. ADDLE can improve the partial areas under the curve (AUCs) for
diagnosing severe steatosis by 10.5% over standard classifiers while
outperforming other annotator-noise approaches, including those requiring 65
times the parameters.


Abstract:
  Dynamical backaction resulting from radiation pressure forces in
optomechanical systems has proven to be a versatile tool for manipulating
mechanical vibrations. Notably, dynamical backaction has resulted in the
cooling of a mechanical resonator to its ground-state, driving phonon lasing,
the generation of entangled states, and observation of the optical-spring
effect. In certain magnetic materials, mechanical vibrations can interact with
magnetic excitations (magnons) via the magnetostrictive interaction, resulting
in an analogous magnon-induced dynamical backaction. In this article, we
directly observe the impact of magnon-induced dynamical backaction on a
spherical magnetic sample's mechanical vibrations. Moreover, dynamical
backaction effects play a crucial role in many recent theoretical proposals;
thus, our work provides the foundation for future experimental work pursuing
many of these theoretical proposals.


Abstract:
  We study a function $\mathcal{L}_{\Gamma}$ which quantifies the LEF (local
embeddability into finite groups) property for a finitely generated group
$\Gamma$. We compute this "LEF growth" function in some examples, including
certain wreath products. We compare LEF growth with the analogous quantitative
version of residual finiteness, and exhibit a family of finitely generated
residually finite groups which nevertheless admit many more local embeddings
into finite groups than they do finite quotients. Along the way, we give a new
proof that B.H. Neumann's continuous family of $2$-generated groups contains no
finitely presented group, a result originally due to Baumslag and Miller. We
compare $\mathcal{L}_{\Gamma}$ with quantitative versions of soficity and other
metric approximation properties of groups. Finally, we show that there exists a
"universal" function which is an upper bound on the LEF growth of any group on
a given number of generators, and that (for non-cyclic groups) any such
function is non-computable.


Abstract:
  In this research, we have two serum SELDI (surface-enhanced laser desorption
and ionization) mass spectra (MS) datasets to be used to select features
amongst them to identify proteomic cancerous serums from normal serums.
Features selection techniques have been applied and classification techniques
have been applied as well. Amongst the features selection techniques we have
chosen to evaluate the performance of PCA (Principal Component Analysis ) and
GA (Genetic algorithm), and amongst the classification techniques we have
chosen the LDA (Linear Discriminant Analysis) and Neural networks so as to
evaluate the ability of the selected features in identifying the cancerous
patterns. Results were obtained for two combinations of features selection
techniques and classification techniques, the first one was PCA+(t-test)
technique for features selection and LDA for accuracy tracking yielded an
accuracy of 93.0233 % , the other one was genetic algorithm and neural network
yielded an accuracy of 100%. So, we conclude that GA is more efficient for
features selection and hence for cancerous patterns detection than PCA
technique.


Abstract:
  We calculate the fully differential medium-induced radiative spectrum at
next-to-leading order (NLO) accuracy within the Improved Opacity Expansion
(IOE) framework. This scheme allows us to gain analytical control of the
radiative spectrum at low and high gluon frequencies simultaneously. The high
frequency regime can be obtained in the standard opacity expansion framework in
which the resulting power series diverges at the characteristic frequency
$\omega_c\sim \hat q L^2$. In the IOE, all orders in opacity are resumed
systematically below $\omega_c$ yielding an asymptotic series controlled by
logarithmically suppressed remainders down to the thermal scale $T \ll
\omega_c$, while matching the opacity expansion at high frequency. Furthermore,
we demonstrate that the IOE at NLO accuracy reproduces the characteristic
Coulomb tail of the single hard scattering contribution as well as the Gaussian
distribution resulting from multiple soft momentum exchanges. Finally, we
compare our analytic scheme with a recent numerical solution, that includes a
full resummation of multiple scatterings, for LHC-inspired medium parameters.
We find a very good agreement both at low and high frequencies showcasing the
performance of the IOE which provides for the first time accurate analytic
formulas for radiative energy loss in the relevant perturbative kinematic
regimes for dense media.


Abstract:
  We propose and investigate a scheme based on Markovian feedback control that
allows for the preparation of single targeted eigenstates of a system of
bosonic atoms in a one-dimensional optical lattice with high fidelity. It can
be used for in-situ cooling the interacting system without particle loss, both
for weak and strong interactions, and for experimentally preparing and probing
individual excited eigenstates. For that purpose the system is assumed to be
probed weakly via homodyne detection of photons that are scattered
off-resonantly by the atoms from a structured probe beam into a cavity mode. By
applying an inertial force to the system that is proportional to the measured
signal, the system is then guided into a pure target state. The scheme is found
to be robust against reduced measurement efficiencies.


Abstract:
  We prove concentration inequalities for functions of independent random
variables {under} sub-gaussian and sub-exponential conditions. The utility of
the inequalities is demonstrated by an extension of the now classical method of
Rademacher complexities to Lipschitz function classes and unbounded
sub-exponential distribution.


Abstract:
  We investigate feedback forms for linear time-invariant systems described by
differential-algebraic equations. Feedback forms are representatives of certain
equivalence classes. For example state space transformations, invertible
transformations from the left, and proportional state feedback constitute an
equivalence relation. The representative of such an equivalence class, which we
call proportional feedback form for the above example, allows to read off
relevant system theoretic properties. Our main contribution is to derive a
quasi proportional feedback form. This form is advantageous since it provides
some geometric insight and is simple to compute, but still allows to read off
the relevant structural properties of the control system. We also derive a
quasi proportional and derivative feedback form. Similar advantages hold.


Abstract:
  We present a novel stochastic approach to binary optimization for optimal
experimental design (OED) for Bayesian inverse problems governed by
mathematical models such as partial differential equations. The OED utility
function, namely, the regularized optimality criterion, is cast into a
stochastic objective function in the form of an expectation over a multivariate
Bernoulli distribution. The probabilistic objective is then solved by using a
stochastic optimization routine to find an optimal observational policy. The
proposed approach is analyzed from an optimization perspective and also from a
machine learning perspective with correspondence to policy gradient
reinforcement learning. The approach is demonstrated numerically by using an
idealized two-dimensional Bayesian linear inverse problem, and validated by
extensive numerical experiments carried out for sensor placement in a parameter
identification setup.


Abstract:
  The trend of deploying digital systems in numerous industries has induced a
hike in recording digital information. The health sector has observed an
extensive adoption of digital devices and systems that generate large volumes
of personal medical records. Electronic health records contain valuable
information for retrospective and prospective analysis that is often not
entirely exploited because of the dense information storage. The crude purpose
of condensing health records is to select the information that holds most
characteristics of the original documents based on reported disease. These
summaries may boost diagnosis and extend a doctor's time with the patient
during a high workload situation like the COVID-19 pandemic. In this paper, we
propose applying a multi-head attention-based mechanism to perform extractive
summarization of meaningful phrases in clinical notes. This method finds major
sentences for a summary by correlating tokens, segments, and positional
embeddings. The model outputs attention scores that are statistically
transformed to extract key phrases and can be used to projection on the
heat-mapping tool for visual and human use.


Abstract:
  This paper proposes a method for representation learning of multimodal data
using contrastive losses. A traditional approach is to contrast different
modalities to learn the information shared between them. However, that approach
could fail to learn the complementary synergies between modalities that might
be useful for downstream tasks. Another approach is to concatenate all the
modalities into a tuple and then contrast positive and negative tuple
correspondences. However, that approach could consider only the stronger
modalities while ignoring the weaker ones. To address these issues, we propose
a novel contrastive learning objective, TupleInfoNCE. It contrasts tuples based
not only on positive and negative correspondences but also by composing new
negative tuples using modalities describing different scenes. Training with
these additional negatives encourages the learning model to examine the
correspondences among modalities in the same tuple, ensuring that weak
modalities are not ignored. We provide a theoretical justification based on
mutual information for why this approach works, and we propose a sample
optimization algorithm to generate positive and negative samples to maximize
training efficacy. We find that TupleInfoNCE significantly outperforms the
previous state of the arts on three different downstream tasks.


Abstract:
  Topological Coding consists of two different kinds of mathematics:
topological structure and mathematical relation. The colorings and labelings of
graph theory are main techniques in topological coding applied in asymmetric
encryption system. Topsnut-gpws (also, colored graphs) have the following
advantages: (1) Run fast in communication networks because they are saved in
computer by popular matrices rather than pictures. (2) Produce easily
text-based (number-based) strings for encrypt files. (3) Diversity of
asymmetric ciphers, one public-key corresponds to more private-keys, or more
public-keys correspond more private-keys. (4) Irreversibility, Topsnut-gpws can
generate quickly text-based (number-based) strings with bytes as long as
desired, but these strings can not reconstruct the original Topsnut-gpws. (5)
Computational security, since there are many non-polynomial (NP-complete,
NP-hard) algorithms in creating Topsnut-gpws. (6) Provable security, since
there are many mathematical conjectures (open problems) in graph labelings and
graph colorings. We are committed to create more kinds of new Topsnut-gpws to
approximate practical applications and antiquantum computation, and try to use
algebraic method and Topsnut-gpws to establish graphic group, graphic lattice,
graph homomorphism etc.


Abstract:
  Adversarial attacks have shown the vulnerability of machine learning models,
however, it is non-trivial to conduct textual adversarial attacks on natural
language processing tasks due to the discreteness of data. Most previous
approaches conduct attacks with the atomic \textit{replacement} operation,
which usually leads to fixed-length adversarial examples and therefore limits
the exploration on the decision space. In this paper, we propose
variable-length textual adversarial attacks~(VL-Attack) and integrate three
atomic operations, namely \textit{insertion}, \textit{deletion} and
\textit{replacement}, into a unified framework, by introducing and manipulating
a special \textit{blank} token while attacking. In this way, our approach is
able to more comprehensively find adversarial examples around the decision
boundary and effectively conduct adversarial attacks. Specifically, our method
drops the accuracy of IMDB classification by $96\%$ with only editing $1.3\%$
tokens while attacking a pre-trained BERT model. In addition, fine-tuning the
victim model with generated adversarial samples can improve the robustness of
the model without hurting the performance, especially for length-sensitive
models. On the task of non-autoregressive machine translation, our method can
achieve $33.18$ BLEU score on IWSLT14 German-English translation, achieving an
improvement of $1.47$ over the baseline model.


Abstract:
  Separating raypaths in a multipath shallow-water environment is a challenge
problem due to the interferences between them and colored noise existing in
ocean environment, especially for two raypaths arrive close to each other.
Thus, in this paper, a three dimensional (3D) higher-order raypath separation
in an array to array configuration is proposed. Performance tests using
simulation data in a multipath environment, real data obtained in an ultrasonic
waveguide and ocean shallow-water data, respectively, illustrate that the
proposed algorithm achieves a higher resolution and a stronger robustness
comparing to the existing algorithms.


Abstract:
  Machine unlearning refers to mechanisms that can remove the influence of a
subset of training data upon request from a trained model without incurring the
cost of re-training from scratch. This paper develops a unified PAC-Bayesian
framework for machine unlearning that recovers the two recent design principles
- variational unlearning (Nguyen et.al., 2020) and forgetting Lagrangian
(Golatkar et.al., 2020) - as information risk minimization problems
(Zhang,2006). Accordingly, both criteria can be interpreted as PAC-Bayesian
upper bounds on the test loss of the unlearned model that take the form of free
energy metrics.


Abstract:
  Dimension reduction techniques are often used when the high-dimensional
tensor has relatively low intrinsic rank compared to the ambient dimension of
the tensor. The CANDECOMP/PARAFAC (CP) tensor completion is a widely used
approach to find a low-rank approximation for a given tensor. In the tensor
model, an $\ell_1$ regularized optimization problem was formulated with an
appropriate choice of the regularization parameter. The choice of the
regularization parameter is important in the approximation accuracy. However,
the emergence of the large amount of data poses onerous computational burden
for computing the regularization parameter via classical approaches such as the
weighted generalized cross validation (WGCV), the unbiased predictive risk
estimator, and the discrepancy principle. In order to improve the efficiency of
choosing the regularization parameter and leverage the accuracy of the CP
tensor, we propose a new algorithm for tensor completion by embedding the
flexible hybrid method into the framework of the CP tensor. The main benefits
of this method include incorporating regularization automatically and
efficiently, improved reconstruction and algorithmic robustness. Numerical
examples from image reconstruction and model order reduction demonstrate the
performance of the propose algorithm.


Abstract:
  Attention-based end-to-end automatic speech recognition (ASR) systems have
recently demonstrated state-of-the-art results for numerous tasks. However, the
application of self-attention and attention-based encoder-decoder models
remains challenging for streaming ASR, where each word must be recognized
shortly after it was spoken. In this work, we present the dual
causal/non-causal self-attention (DCN) architecture, which in contrast to
restricted self-attention prevents the overall context to grow beyond the
look-ahead of a single layer when used in a deep architecture. DCN is compared
to chunk-based and restricted self-attention using streaming transformer and
conformer architectures, showing improved ASR performance over restricted
self-attention and competitive ASR results compared to chunk-based
self-attention, while providing the advantage of frame-synchronous processing.
Combined with triggered attention, the proposed streaming end-to-end ASR
systems obtained state-of-the-art results on the LibriSpeech, HKUST, and
Switchboard ASR tasks.


Abstract:
  Data-driven optimization has found many successful applications in the real
world and received increased attention in the field of evolutionary
optimization. Most existing algorithms assume that the data used for
optimization is always available on a central server for construction of
surrogates. This assumption, however, may fail to hold when the data must be
collected in a distributed way and is subject to privacy restrictions. This
paper aims to propose a federated data-driven evolutionary
multi-/many-objective optimization algorithm. To this end, we leverage
federated learning for surrogate construction so that multiple clients
collaboratively train a radial-basis-function-network as the global surrogate.
Then a new federated acquisition function is proposed for the central server to
approximate the objective values using the global surrogate and estimate the
uncertainty level of the approximated objective values based on the local
models. The performance of the proposed algorithm is verified on a series of
multi/many-objective benchmark problems by comparing it with two
state-of-the-art surrogate-assisted multi-objective evolutionary algorithms.


Abstract:
  Shocks form the basis of our understanding for the density and velocity
statistics of supersonic turbulent flows, such as those found in the cool
interstellar medium (ISM). The variance of the density field,
$\sigma^2_{\rho/\rho_0}$, is of particular interest for molecular clouds (MCs),
the birthplaces of stars in the Universe. The density variance may be used to
infer underlying physical processes in an MC, and parameterises the star
formation (SF) rate of a cloud. However, models for $\sigma^2_{\rho/\rho_0}$
all share a common feature -- the variance is assumed to be isotropic. This
assumption does not hold when a trans/sub-Alfv\'enic mean magnetic field,
$\vec{B}_0$, is present in the cloud, which observations suggest is relevant
for some MCs. We develop an anisotropic model for $\sigma_{\rho/\rho_0}^2$,
using contributions from hydrodynamical and fast magnetosonic shocks that
propagate orthogonal to each other. Our model predicts an upper bound for
$\sigma_{\rho/\rho_0}^2$ in the high Mach number $(\mathcal{M})$ limit as
small-scale density fluctuations become suppressed by the strong $\vec{B}_0$.
The model reduces to the isotropic $\sigma_{\rho/\rho_0}^2-\mathcal{M}$
relation in the hydrodynamical limit. To validate our model, we calculate
$\sigma_{\rho/\rho_0}^2$ from 12~high-resolution, three-dimensional,
supersonic, sub-Alfv\'enic magnetohydrodynamical (MHD) turbulence simulations
and find good agreement with our theory. We discuss how the two MHD shocks may
be the bimodally oriented over-densities observed in some MCs and the
implications for SF theory in the presence of a sub-Alfv\'enic $\vec{B}_0$. By
creating an anisotropic, supersonic density fluctuation model, this study paves
the way for SF theory in the highly anisotropic regime of interstellar
turbulence.