Abstract:
  As the "Mobile AI" revolution continues to grow, so does the need to
understand the behaviour of edge-deployed deep neural networks. In particular,
MobileNets are the go-to family of deep convolutional neural networks (CNN) for
mobile. However, they often have significant accuracy degradation under
post-training quantization. While studies have introduced quantization-aware
training and other methods to tackle this challenge, there is limited
understanding into why MobileNets (and potentially depthwise-separable CNNs
(DWSCNN) in general) quantize so poorly compared to other CNN architectures.
Motivated to gain deeper insights into this phenomenon, we take a different
strategy and study the multi-scale distributional dynamics of MobileNet-V1, a
set of smaller DWSCNNs, and regular CNNs. Specifically, we investigate the
impact of quantization on the weight and activation distributional dynamics as
information propagates from layer to layer, as well as overall changes in
distributional dynamics at the network level. This fine-grained analysis
revealed significant dynamic range fluctuations and a "distributional mismatch"
between channelwise and layerwise distributions in DWSCNNs that lead to
increasing quantized degradation and distributional shift during information
propagation. Furthermore, analysis of the activation quantization errors show
that there is greater quantization error accumulation in DWSCNN compared to
regular CNNs. The hope is that such insights can lead to innovative strategies
for reducing such distributional dynamics changes and improve post-training
quantization for mobile.


Abstract:
  Modeling user preference from his historical sequences is one of the core
problems of sequential recommendation. Existing methods in this field are
widely distributed from conventional methods to deep learning methods. However,
most of them only model users' interests within their own sequences and ignore
the dynamic collaborative signals among different user sequences, making it
insufficient to explore users' preferences. We take inspiration from dynamic
graph neural networks to cope with this challenge, modeling the user sequence
and dynamic collaborative signals into one framework. We propose a new method
named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which
connects different user sequences through a dynamic graph structure, exploring
the interactive behavior of users and items with time and order information.
Furthermore, we design a Dynamic Graph Recommendation Network to extract user's
preferences from the dynamic graph. Consequently, the next-item prediction task
in sequential recommendation is converted into a link prediction between the
user node and the item node in a dynamic graph. Extensive experiments on three
public benchmarks show that DGSR outperforms several state-of-the-art methods.
Further studies demonstrate the rationality and effectiveness of modeling user
sequences through a dynamic graph.


Abstract:
  Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective
for privacy protection; nevertheless, object recognition research typically
assumes access to complete, unobfuscated images. In this paper, we explore the
effects of face obfuscation on the popular ImageNet challenge visual
recognition benchmark. Most categories in the ImageNet challenge are not people
categories; however, many incidental people appear in the images, and their
privacy is a concern. We first annotate faces in the dataset. Then we
demonstrate that face blurring -- a typical obfuscation technique -- has
minimal impact on the accuracy of recognition models. Concretely, we benchmark
multiple deep neural networks on face-blurred images and observe that the
overall recognition accuracy drops only slightly (no more than 0.68%). Further,
we experiment with transfer learning to 4 downstream tasks (object recognition,
scene recognition, face attribute classification, and object detection) and
show that features learned on face-blurred images are equally transferable. Our
work demonstrates the feasibility of privacy-aware visual recognition, improves
the highly-used ImageNet challenge benchmark, and suggests an important path
for future visual datasets. Data and code are available at
https://github.com/princetonvisualai/imagenet-face-obfuscation.


Abstract:
  In this paper, we survey our recent results on the Benjamin-Ono equation on
the torus. As an application of the methods developed we construct large
families of periodic or quasiperiodic solutions, which are not
$C^\infty$-smooth.


Abstract:
  In this paper, we first provide a brief review of the effective dynamics of
two recently well-studied models of modified loop quantum cosmologies (mLQCs),
which arise from different regularizations of the Hamiltonian constraint and
show the robustness of a generic resolution of the big bang singularity,
replaced by a quantum bounce due to non-perturbative Planck scale effects. As
in loop quantum cosmology (LQC), in these modified models the slow-roll
inflation happens generically. We consider the cosmological perturbations
following the dressed and hybrid approaches and clarify some subtle issues
regarding the ambiguity of the extension of the effective potential of the
scalar perturbations across the quantum bounce, and the choice of initial
conditions. Both of the modified regularizations yield primordial power spectra
that are consistent with current observations for the Starobinsky potential
within the framework of either the dressed or the hybrid approach. But
differences in primordial power spectra are identified among the mLQCs and LQC.
In addition, for mLQC-I, striking differences arise between the dressed and
hybrid approaches in the infrared and oscillatory regimes. While the
differences between the two modified models can be attributed to differences in
the Planck scale physics, the permissible choices of the initial conditions and
the differences between the two perturbation approaches have been reported for
the first time. All these differences, due to either the different
regularizations or the different perturbation approaches in principle can be
observed in terms of non-Gaussianities.


Abstract:
  Among various recommender techniques, collaborative filtering (CF) is the
most successful one. And a key problem in CF is how to represent users and
items. Previous works usually represent a user (an item) as a vector of latent
factors (aka. \textit{embedding}) and then model the interactions between users
and items based on the representations. Despite its effectiveness, we argue
that it's insufficient to yield satisfactory embeddings for collaborative
filtering. Inspired by the idea of SVD++ that represents users based on
themselves and their interacted items, we propose a general collaborative
filtering framework named DNCF, short for Dual-embedding based Neural
Collaborative Filtering, to utilize historical interactions to enhance the
representation. In addition to learning the primitive embedding for a user (an
item), we introduce an additional embedding from the perspective of the
interacted items (users) to augment the user (item) representation. Extensive
experiments on four publicly datasets demonstrated the effectiveness of our
proposed DNCF framework by comparing its performance with several traditional
matrix factorization models and other state-of-the-art deep learning based
recommender models.


Abstract:
  In celestial conformal field theory, gluons are represented by primary fields
with dimensions $\Delta=1+i\lambda$, $\lambda\in\mathbb{R}$ and spin $J=\pm 1$,
in the adjoint representation of the gauge group. All two- and three-point
correlation functions of these fields are zero as a consequence of
four-dimensional kinematic constraints. Four-point correlation functions
contain delta-function singularities enforcing planarity of four-particle
scattering events. We relax these constraints by taking a shadow transform of
one field and perform conformal block decomposition of the corresponding
correlators. We compute the conformal block coefficients. When decomposed in
channels that are "compatible" in two and four dimensions, such four-point
correlators contain conformal blocks of primary fields with dimensions
$\Delta=2+M+i\lambda$, where $M\ge 0$ is an integer, with integer spin
$J=-M,-M+2,\dots,M-2,M$. They appear in all gauge group representations
obtained from a tensor product of two adjoint representations. When decomposed
in incompatible channels, they also contain primary fields with continuous
complex spin, but with positive integer dimensions.


Abstract:
  Prior work has shown Convolutional Neural Networks (CNNs) trained on
surrogate Computer Aided Design (CAD) models are able to detect and classify
real-world artefacts from photographs. The applications of which support
twinning of digital and physical assets in design, including rapid extraction
of part geometry from model repositories, information search \& retrieval and
identifying components in the field for maintenance, repair, and recording. The
performance of CNNs in classification tasks have been shown dependent on
training data set size and number of classes. Where prior works have used
relatively small surrogate model data sets ($<100$ models), the question
remains as to the ability of a CNN to differentiate between models in
increasingly large model repositories. This paper presents a method for
generating synthetic image data sets from online CAD model repositories, and
further investigates the capacity of an off-the-shelf CNN architecture trained
on synthetic data to classify models as class size increases. 1,000 CAD models
were curated and processed to generate large scale surrogate data sets,
featuring model coverage at steps of 10$^{\circ}$, 30$^{\circ}$, 60$^{\circ}$,
and 120$^{\circ}$ degrees. The findings demonstrate the capability of computer
vision algorithms to classify artefacts in model repositories of up to 200,
beyond this point the CNN's performance is observed to deteriorate
significantly, limiting its present ability for automated twinning of physical
to digital artefacts. Although, a match is more often found in the top-5
results showing potential for information search and retrieval on large
repositories of surrogate models.


Abstract:
  We present the results of ALMA band 3 observations of a nearby type Ic
supernova (SN) 2020oi. Under the standard assumptions on the SN-circumstellar
medium (CSM) interaction and the synchrotron emission, the data indicate that
the CSM structure deviates from a smooth distribution expected from the
steady-state mass loss in the very vicinity of the SN (~10^{15} cm), which is
then connected to the outer smooth distribution (~10^{16} cm). This structure
is further confirmed through the light curve modeling of the whole radio data
set as combined with data at lower frequency previously reported. Being an
explosion of a bare carbon-oxygen (C+O) star having a fast wind, we can trace
the mass-loss history of the progenitor of SN 2020oi in the final year. The
inferred non-smooth CSM distribution corresponds to fluctuations on the
sub-year time scale in the mass-loss history toward the SN explosion. Our
finding suggests that the pre-SN activity is likely driven by the accelerated
change in the nuclear burning stage in the last moments just before the massive
star's demise. The structure of the CSM derived in this study is beyond the
applicability of the other methods at optical wavelengths, highlighting an
importance and uniqueness of quick follow-up observations of SNe by ALMA and
other radio facilities.


Abstract:
  We investigate the asymmetry properties of quantum states in relation to the
Hamiltonian responsible for the magnetic dipolar interaction (MDI) dynamics,
and we evaluate its relationship to entanglement production. We consider some
classes of pure and mixed quantum states of two qubits evolved under MDI and,
using the asymmetry measure defined via the Wigner-Yanase skew information, we
describe the asymmetry dependence on the Hamiltonian parameters and initial
conditions of the system. In addition, we define and calculate the dynamics of
the asymmetry of local states, characterizing their temporal and interaction
parameters dependence. Finally, because the MDI Hamiltonian has a null
eigenvalue, the group generator-based asymmetry measure does not adequately
quantify the state susceptibility with respect to the action of the subspace
generated by the eigenvectors associated with this eigenvalue. For this reason,
we also define and study the group element-based asymmetry measure with
relation to the unitary operator associated with the MDI Hamiltonian.


Abstract:
  We report exact black hole solutions in asymptotically flat or (A)dS
four-dimensional spacetime with a conformally coupled self-interacting scalar
field in $f(R)$ gravity. We first consider the asymptotically flat model $f(R)
= R -2\alpha \sqrt{R}$ and derive an exact black hole solution. Then, we
consider the asymptotically (A)dS model $f(R) =R -2 \Lambda -2 \alpha \sqrt{R-4
\Lambda }$ and derive an exact black hole solution. In both cases the modified
gravity parameter $\alpha$, which has the dimension of the inverse mass, cannot
be set to zero and the self-interacting potential is determined from the
Klein-Gordon equation, preserving the conformal invariance. The thermodynamics
of the solutions is also studied.


Abstract:
  We experimentally demonstrate the generation of spin-wave frequency combs
based on the nonlinear interaction of propagating spin waves in a
microstructured waveguide. By means of time and space-resolved Brillouin light
scattering spectroscopy, we show that the simultaneous excitation of spin waves
with different frequencies leads to a cascade of four-magnon scattering events
which ultimately results in well-defined frequency combs. Their spectral weight
can be tuned by the choice of amplitude and frequency of the input signals.
Furthermore, we introduce a model for stimulated four-magnon scattering which
describes the formation of spin-wave frequency combs in the frequency and time
domain.


Abstract:
  Light-manipulation of correlated electronic phases in solids offers the
tantalizing prospect of realizing electronic devices operating at the ultrafast
time-scale. In this context, the experimental realization of non-equilibrium
transitions from a metal to a band or Mott insulator has shown to be
particularly elusive. Using dynamical mean-field theory, we study a simple
model representing the main physical properties of the oxygen-enriched compound
LaTiO$_{3+x}$. By properly optimizing the photo-doping of electrons from a
low-energy band into the valence states of the system, we show it is possible
to induce a valence transition from a correlated metallic state to a Mott
insulator at ultrashort time scales and to contain the heating during this
process, with the final non-thermal valence insulator having almost the same
effective temperature of the starting metal.


Abstract:
  We show that the dynamics of an epidemic are governed by exponential time
relaxation since the start of the outbreak, and explain this counterintuitive
result using an analogy with chemical reactions where an exponential relaxation
of the thermodynamic driving force, or affinity, occurs. The theoretical
reaction rate takes a form similar to the infection rate equation of
mathematical epidemiology, while the affinity measured in epidemic data relaxes
exponentially. The rise and fall of an epidemic wave directly follows from this
exponential relaxation, but change in public health policies, major travels or
new variants induce visible perturbations on the relaxation dynamics.


Abstract:
  Capturing interactions among event arguments is an essential step towards
robust event argument extraction (EAE). However, existing efforts in this
direction suffer from two limitations: 1) The argument role type information of
contextual entities is mainly utilized as training signals, ignoring the
potential merits of directly adopting it as semantically rich input features;
2) The argument-level sequential semantics, which implies the overall
distribution pattern of argument roles over an event mention, is not well
characterized. To tackle the above two bottlenecks, we formalize EAE as a
Seq2Seq-like learning problem for the first time, where a sentence with a
specific event trigger is mapped to a sequence of event argument roles. A
neural architecture with a novel Bi-directional Entity-level Recurrent Decoder
(BERD) is proposed to generate argument roles by incorporating contextual
entities' argument role predictions, like a word-by-word text generation
process, thereby distinguishing implicit argument distribution patterns within
an event more accurately.


Abstract:
  Motivated by the need for compact descriptions of the evolution of
non-classical wakes behind yawed wind turbines, we develop an analytical model
to predict the shape of curled wakes. Interest in such modelling arises due to
the potential of wake steering as a strategy for mitigating power reduction and
unsteady loading of downstream turbines in wind farms. We first estimate the
distribution of the shed vorticity at the wake edge due to both yaw offset and
rotating blades. By considering the wake edge as an ideally thin vortex sheet,
we describe its evolution in time moving with the flow. Vortex sheet equations
are solved using a power series expansion method, and an approximate solution
for the wake shape is obtained. The vortex sheet time evolution is then mapped
into a spatial evolution by using a convection velocity. Apart from the wake
shape, the lateral deflection of the wake including ground effects is modelled.
Our results show that there exists a universal solution for the shape of curled
wakes if suitable dimensionless variables are employed. For the case of
turbulent boundary layer inflow, the decay of vortex sheet circulation due to
turbulent diffusion is included. Finally, we modify the Gaussian wake model by
incorporating the predicted shape and deflection of the curled wake, so that we
can calculate the wake profiles behind yawed turbines. Model predictions are
validated against large-eddy simulations and laboratory experiments for
turbines with various operating conditions.


Abstract:
  The uniform field assumption used to derive semi-classical cutoff energies of
$10U_p$ for electron emission and $3.17U_p$ for high harmonic generation is
applicable for ponderomotive amplitudes ($\propto E\lambda^2$) much smaller
than the field drop-off scale. For large wavelength and high field experiments
at nanoscale structures this assumption may break down by predicting energies
beyond the true classical energy limits. Here we provide generalized
calculations for these cutoff energies by taking into account the spatial field
drop-off. The modified cutoff energies vary significantly from the uniform
field results even with ponderomotive amplitudes still an order of magnitude
below the field drop-off scale. Electron emission and scattering energy as a
function of the time-of-ionization is considered for the nanotip ($\sim1/r^2$)
field profile. The cutoff energies as a function of the adiabaticity parameter
$\delta$, which may be easily calculated for given wavelength, apex field
strength, and nanostructure scale, are then determined through maximization for
nanotip, nanoblade ($\sim1/r$), and exponential field profiles. These profiles
deviate from each other in electron emission energy by up to nearly a factor of
the ponderomotive energy, indicating the importance of mid-field profile
behavior. The electron emission energy cutoff also attains an additional factor
of $U_p$ due to the smooth integrated ponderomotive force in the adiabatic
drop-off and very long pulse regime. These results also provided as
double-exponential fits for ease of use. We then compare the nanoblade electron
emission cutoffs with a quantum simulation of the electron rescattering
process. We also consider a short (few-cycle) pulsed field, focusing on a
cosine-like pulse and overviewing the general carrier-envelope phase
dependencies.


Abstract:
  Analogous to conventional charge-based electronics, valleytronics aims at
encoding data via the valley degree of freedom, enabling new routes for
information processing. Long-lived interlayer excitons (IXs) in van der Waals
heterostructures (HSs) stacked by transition metal dichalcogenides (TMDs) carry
valley-polarized information and thus could find promising applications in
valleytronic devices. Although great progress of studies on valleytronic
devices has been achieved, nonvolatile valleytronic memory, an indispensable
device in valleytronics, is still lacking up to date. Here, we demonstrate an
IX-based nonvolatile valleytronic memory in a WS2/WSe2 HS. In this device, the
emission characteristics of IXs exhibit a large excitonic/valleytronic
hysteresis upon cyclic-voltage sweeping, which is ascribed to the
chemical-doping of O2/H2O redox couple trapped between the TMDs and substrate.
Taking advantage of the large hysteresis, the first nonvolatile valleytronic
memory has been successfully made, which shows a good performance with
retention time exceeding 60 minutes. These findings open up an avenue for
nonvolatile valleytronic memory and could stimulate more investigations on
valleytronic devices.


Abstract:
  Communication-avoiding algorithms for Linear Algebra have become increasingly
popular, in particular for distributed memory architectures. In practice, these
algorithms assume that the data is already distributed in a specific way, thus
making data reshuffling a key to use them. For performance reasons, a
straightforward all-to-all exchange must be avoided.
  Here, we show that process relabeling (i.e. permuting processes in the final
layout) can be used to obtain communication optimality for data reshuffling,
and that it can be efficiently found by solving a Linear Assignment Problem
(Maximum Weight Bipartite Perfect Matching). Based on this, we have developed a
Communication-Optimal Shuffle and Transpose Algorithm (COSTA): this
highly-optimised algorithm implements $A=\alpha\cdot \operatorname{op}(B) +
\beta \cdot A,\ \operatorname{op} \in \{\operatorname{transpose},
\operatorname{conjugate-transpose}, \operatorname{identity}\}$ on distributed
systems, where $A, B$ are matrices with potentially different (distributed)
layouts and $\alpha, \beta$ are scalars. COSTA can take advantage of the
communication-optimal process relabeling even for heterogeneous network
topologies, where latency and bandwidth differ among nodes. The implementation
not only outperforms the best available ScaLAPACK redistribute and transpose
routines multiple times, but is also able to deal with more general matrix
layouts, in particular it is not limited to block-cyclic layouts. Finally, we
use COSTA to integrate a communication-optimal matrix multiplication algorithm
into the CP2K quantum chemistry simulation package. This way, we show that
COSTA can be used to unlock the full potential of recent Linear Algebra
algorithms in applications by facilitating interoperability between algorithms
with a wide range of data layouts, in addition to bringing significant
redistribution speedups.


Abstract:
  Food waste is a major challenge for the present world. It is the precursor to
several socioeconomic problems that are plaguing the modern society. To counter
the same and to, simultaneously, stand by the undernourished, surplus food
redistribution has surfaced as a viable solution. Information and
Communications Technology (ICT)-mediated food redistribution is a highly
scalable approach and it percolates into the masses far better. Even if ICT is
not brought into the picture, the presence of food surplus redistribution in
developing countries like India is scarce and is limited to only a few of the
major cities. The discussion of a surplus food redistribution framework under
strategic settings is a less discussed topic around the globe. This paper aims
at addressing a surplus food redistribution framework under strategic settings,
thereby facilitating a smoother exchange of surplus food in the smart cities of
developing countries, and beyond. As ICT is seamlessly available in smart
cities, the paper aims to focus the framework in these cities. However, this
can be extended beyond the smart cities to places with greater human
involvement.


Abstract:
  Power system transient stability has been translated into a Lyapunov
stability problem of the post-disturbance equilibrium for decades. Despite
substantial results, conventional theories suffer from the stringent
requirement of knowing the post-disturbance equilibrium a priori. In contrast,
the wisdom from practice, which certificates stability by only the observation
of converging frequencies and voltages, seems to provide an
equilibrium-independent approach. Here, we formulate the empirical wisdom by
the concept of augmented synchronization and aim to bridge such a
theory-practice gap. First, we derive conditions under which the convergence to
augmented synchronization implies the convergence to the equilibrium set,
laying the first theoretical foundation for the empirical wisdom. Then, we
reveal from what initial values the power system can achieve augmented
synchronization. Our results open the possibility of an equilibrium-independent
power system stability analytic that re-defines the nominal motion as augmented
synchronization rather than certain equilibrium. Single-machine examples and
the IEEE 9-bus system well verify our results and illustrate promising
implications.


Abstract:
  We compare two properties for a CW-space $X$: (1) being homotopy equivalent
to a CW-complex without $j$-cells for $k\leq j\leq \ell$ (($k,\ell$)-cellfree)
and (2) $H^j(X;R)=0$ for any $\mathbb Z\pi_1(X)$-module $R$ when $k\leq j\leq
\ell$ (cohomogy ($k,\ell$)-silent). Using the technique of Wall's finiteness
obstruction, we show that a connected CW-space $X$ of finite type which is
cohomogy ($k,\ell$)-silent determines a "cell-dispensability obstruction''
$w_k(X)\in\tilde K_0(\mathbb Z\pi_1(X))$ which vanishes if and only if $X$ is
($k,\ell$)-cellfree ($k\geq 4$). Any class in $\tilde K_0(\mathbb Z\pi)$ may
occur as the cell-dispensability obstruction $w_k(X)$ for a CW-space $X$ with
$\pi_1(X)$ identified with $\pi$. Using projective surgery, a similar theory is
obtained for manifolds, replacing "cells" by "handles" (antisimple manifolds).


Abstract:
  This paper presents a real-time Acoustic Echo Cancellation (AEC) algorithm
submitted to the AEC-Challenge. The algorithm consists of three modules:
Generalized Cross-Correlation with PHAse Transform (GCC-PHAT) based time delay
compensation, weighted Recursive Least Square (wRLS) based linear adaptive
filtering and neural network based residual echo suppression. The wRLS filter
is derived from a novel semi-blind source separation perspective. The neural
network model predicts a Phase-Sensitive Mask (PSM) based on the aligned
reference and the linear filter output. The algorithm achieved a mean
subjective score of 4.00 and ranked 2nd in the AEC-Challenge.


Abstract:
  This work is to tackle the problem of point cloud semantic segmentation for
3D hybrid scenes under the framework of zero-shot learning. Here by hybrid, we
mean the scene consists of both seen-class and unseen-class 3D objects, a more
general and realistic setting in application. To our knowledge, this problem
has not been explored in the literature. To this end, we propose a network to
synthesize point features for various classes of objects by leveraging the
semantic features of both seen and unseen object classes, called PFNet. The
proposed PFNet employs a GAN architecture to synthesize point features, where
the semantic relationship between seen-class and unseen-class features is
consolidated by adapting a new semantic regularizer, and the synthesized
features are used to train a classifier for predicting the labels of the
testing 3D scene points. Besides we also introduce two benchmarks for
algorithmic evaluation by re-organizing the public S3DIS and ScanNet datasets
under six different data splits. Experimental results on the two benchmarks
validate our proposed method, and we hope our introduced two benchmarks and
methodology could be of help for more research on this new direction.


Abstract:
  Analyzing multi-source data, which are multiple views of data on the same
subjects, has become increasingly common in molecular biomedical research.
Recent methods have sought to uncover underlying structure and relationships
within and/or between the data sources, and other methods have sought to build
a predictive model for an outcome using all sources. However, existing methods
that do both are presently limited because they either (1) only consider data
structure shared by all datasets while ignoring structures unique to each
source, or (2) they extract underlying structures first without consideration
to the outcome. We propose a method called supervised joint and individual
variation explained (sJIVE) that can simultaneously (1) identify shared (joint)
and source-specific (individual) underlying structure and (2) build a linear
prediction model for an outcome using these structures. These two components
are weighted to compromise between explaining variation in the multi-source
data and in the outcome. Simulations show sJIVE to outperform existing methods
when large amounts of noise are present in the multi-source data. An
application to data from the COPDGene study reveals gene expression and
proteomic patterns that are predictive of lung function. Functions to perform
sJIVE are included in the R.JIVE package, available online at
http://github.com/lockEF/r.jive .


Abstract:
  The A-polynomial is a knot invariant related to the space of
$SL_2(\mathbb{C})$ representations of the knot group. In this paper our
interests lies in the logarithmic Gauss map of the A-polynomial. We develop a
homological point of view on this slope by extending the constructions of
Degtyarev, the second author and Lecuona to the setting of non-abelian
representations. It defines a rational function on the character variety, which
unifies various known invariants such as the change of curves in the
Reidemeister function, the modulus of boundary-parabolic representations, the
boundary slope of some incompressible surfaces embedded in the exterior of the
knot $K$ or equivalently the slopes of the sides of the Newton polygon of the
A-polynomial $A_K$. We also present a method to compute $s_K$ in terms of
Alexander matrices and Fox calculus.


Abstract:
  Time evolution of quantum many-body systems typically leads to a state with
maximal entanglement allowed by symmetries. Two distinct routes to impede
entanglement growth are inducing localization via spatial disorder, or
subjecting the system to non-unitary evolution, e.g., via projective
measurements. Here we employ the idea of space-time rotation of a circuit to
explore the relation between systems that fall into these two classes. In
particular, by space-time rotating unitary Floquet circuits that display a
localization transition, we construct non-unitary circuits that display a rich
variety of entanglement scaling and phase transitions. One outcome of our
approach is a non-unitary circuit for free fermions in 1d that exhibits an
entanglement transition from logarithmic scaling to volume-law scaling. This
transition is accompanied by a 'purification transition' analogous to that seen
in hybrid projective-unitary circuits. We follow a similar strategy to
construct a non-unitary 2d Clifford circuit that shows a transition from area
to volume-law entanglement scaling. Similarly, we space-time rotate a 1d spin
chain that hosts many-body localization to obtain a non-unitary circuit that
exhibits an entanglement transition. Finally, we introduce an unconventional
correlator and argue that if a unitary circuit hosts a many-body localization
transition, then the correlator is expected to be singular in its non-unitary
counterpart as well.


Abstract:
  We show that a curve is a soliton solution to the curve shortening flow if
and only if its geodesic curvature can be written as the inner product between
its tangent vector field and a fixed vector of the 3-dimensional Minkowski
space. We use this characterization to provide a qualitative study of the
solitons. We show that for each fixed vector there is a 2-parameter family of
soliton solutions to the curve shortening flow on the 2-dimensional hyperbolic
space. Moreover, we prove that each soliton is defined on the entire real line,
it is embedded and its geodesic curvature converges to a constant at each end.


Abstract:
  The outstanding performance of transformer-based language models on a great
variety of NLP and NLU tasks has stimulated interest in exploring their inner
workings. Recent research has focused primarily on higher-level and complex
linguistic phenomena such as syntax, semantics, world knowledge, and common
sense. The majority of the studies are anglocentric, and little remains known
regarding other languages, precisely their morphosyntactic properties. To this
end, our work presents Morph Call, a suite of 46 probing tasks for four
Indo-European languages of different morphology: English, French, German and
Russian. We propose a new type of probing task based on the detection of guided
sentence perturbations. We use a combination of neuron-, layer- and
representation-level introspection techniques to analyze the morphosyntactic
content of four multilingual transformers, including their less explored
distilled versions. Besides, we examine how fine-tuning for POS-tagging affects
the model knowledge. The results show that fine-tuning can improve and decrease
the probing performance and change how morphosyntactic knowledge is distributed
across the model. The code and data are publicly available, and we hope to fill
the gaps in the less studied aspect of transformers.


Abstract:
  In this study we present a mixture of deep experts (MoDE) neural-network
architecture for single microphone speech enhancement. Our architecture
comprises a set of deep neural networks (DNNs), each of which is an 'expert' in
a different speech spectral pattern such as phoneme. A gating DNN is
responsible for the latent variables which are the weights assigned to each
expert's output given a speech segment. The experts estimate a mask from the
noisy input and the final mask is then obtained as a weighted average of the
experts' estimates, with the weights determined by the gating DNN. A soft
spectral attenuation, based on the estimated mask, is then applied to enhance
the noisy speech signal. As a byproduct, we gain reduction at the complexity in
test time. We show that the experts specialization allows better robustness to
unfamiliar noise types.


Abstract:
  Data augmentation has recently seen increased interest in NLP due to more
work in low-resource domains, new tasks, and the popularity of large-scale
neural networks that require large amounts of training data. Despite this
recent upsurge, this area is still relatively underexplored, perhaps due to the
challenges posed by the discrete nature of language data. In this paper, we
present a comprehensive and unifying survey of data augmentation for NLP by
summarizing the literature in a structured manner. We first introduce and
motivate data augmentation for NLP, and then discuss major methodologically
representative approaches. Next, we highlight techniques that are used for
popular NLP applications and tasks. We conclude by outlining current challenges
and directions for future research. Overall, our paper aims to clarify the
landscape of existing literature in data augmentation for NLP and motivate
additional work in this area. We also present a GitHub repository with a paper
list that will be continuously updated at
https://github.com/styfeng/DataAug4NLP


Abstract:
  Different types of graphene-related materials (GRM) are industrially
available and have been exploited for thermal conductivity enhancement in
polymers. These include materials with very different features, in terms of
thickness, lateral size and composition, especially concerning the oxygen to
carbon ratio and the possible presence of surface functionalization. Due to the
variability of GRM properties, the differences in polymer nanocomposites
preparation methods and the microstructures obtained, a large scatter of
thermal conductivity performance is found in literature. However, detailed
correlations between GRM-based nanocomposites features, including nanoplatelets
thickness and size, defectiveness, composition and dispersion, with their
thermal conductivity remain mostly undefined. In the present paper, the thermal
conductivity of GRM-based polymer nanocomposites, prepared by melt
polymerization of cyclic polybutylene terephtalate oligomers, exploiting 13
different GRM grades, was investigated. The selected GRM, covering a wide range
of specific surface area, size and defectiveness, secure a sound basis for the
understanding of the effect of GRM properties on the thermal conductivity of
their relevant polymer nanocomposites. Indeed, the thermal conductivity
obtained appeared to depend on the interplay between the above GRM feature. In
particular, the combination of low GRM defectiveness and high filler
percolation density was found to maximize the nanocomposites thermal
conductivity.


Abstract:
  We propose a novel method for protecting trained models with a secret key so
that unauthorized users without the correct key cannot get the correct
inference. By taking advantage of transfer learning, the proposed method
enables us to train a large protected model like a model trained with ImageNet
by using a small subset of a training dataset. It utilizes a learnable
encryption step with a secret key to generate learnable transformed images.
Models with pre-trained weights are fine-tuned by using such transformed
images. In experiments with the ImageNet dataset, it is shown that the
performance of a protected model was close to that of a non-protected model
when the correct key was given, while the accuracy tremendously dropped when an
incorrect key was used. The protected model was also demonstrated to be robust
against key estimation attacks.


Abstract:
  Recent research on the time-domain audio separation networks (TasNets) has
brought great success to speech separation. Nevertheless, conventional TasNets
struggle to satisfy the memory and latency constraints in industrial
applications. In this regard, we design a low-cost high-performance
architecture, namely, globally attentive locally recurrent (GALR) network.
Alike the dual-path RNN (DPRNN), we first split a feature sequence into 2D
segments and then process the sequence along both the intra- and inter-segment
dimensions. Our main innovation lies in that, on top of features recurrently
processed along the inter-segment dimensions, GALR applies a self-attention
mechanism to the sequence along the inter-segment dimension, which aggregates
context-aware information and also enables parallelization. Our experiments
suggest that GALR is a notably more effective network than the prior work. On
one hand, with only 1.5M parameters, it has achieved comparable separation
performance at a much lower cost with 36.1% less runtime memory and 49.4% fewer
computational operations, relative to the DPRNN. On the other hand, in a
comparable model size with DPRNN, GALR has consistently outperformed DPRNN in
three datasets, in particular, with a substantial margin of 2.4dB absolute
improvement of SI-SNRi in the benchmark WSJ0-2mix task.


Abstract:
  We investigated Wosret, a region located on the small lobe of the
67P/Churyumov-Gerasimenko comet subject to strong heating during the perihelion
passage. This region includes Abydos, the final landing site of the Philae
lander. We analyzed high-resolution images of the Wosret region acquired
between 2015 and 2016 by the OSIRIS instrument on board the Rosetta spacecraft.
We observed a few morphological changes in Wosret, related to local dust
coating removal with an estimated depth of $\sim$ 1 m, along with the formation
of a cavity measuring 30 m in length and 6.5 m in depth, for a total estimated
mass loss of 1.2 $\times$ 10$^6$ kg. The spectrophotometry of the region is
typical of medium-red regions of comet 67P, with spectral slope values of 15-16
\%/(100 nm) in pre-perihelion data acquired at phase angle 60$^o$. Wosret has a
spectral phase reddening of 0.0546 $\times 10^{-4}$ nm$^{-1} deg^{-1}$, which
is about a factor of 2 lower than what was determined for the nucleus northern
hemisphere regions, possibly indicating a reduced surface micro-roughness due
to the lack of widespread dust coating. A few tiny bright spots are observed.
Morphological features such as "goosebumps" or clods are widely present and
larger in size than similar features located in the big lobe. Compared to Anhur
and Khonsu, two southern hemisphere regions in the big lobe which are also
exposed to high insolation during perihelion, Wosret exhibits fewer exposed
volatiles and less morphological variations due to activity events. Our
analysis indicates that the small lobe has different physical and mechanical
properties than the big one and a lower volatile content, at least in its
uppermost layers. These results support the hypothesis that comet 67P
originated from the merging of two distinct bodies in the early Solar System.


Abstract:
  Muscle fatigue is usually defined as a decrease in the ability to produce
force. The surface electromyography (sEMG) signals have been widely used to
provide information about muscle activities including detecting muscle fatigue
by various data-driven techniques such as machine learning and statistical
approaches. However, it is well-known that sEMG signals are weak signals (low
amplitude of the signals) with a low signal-to-noise ratio, data-driven
techniques cannot work well when the quality of the data is poor. In
particular, the existing methods are unable to detect muscle fatigue coming
from static poses. This work exploits the concept of weak monotonicity, which
has been observed in the process of fatigue, to robustly detect muscle fatigue
in the presence of measurement noises and human variations. Such a population
trend methodology has shown its potential in muscle fatigue detection as
demonstrated by the experiment of a static pose.


Abstract:
  We have previously shown for powders that Mo substitution into the CuO chains
of YBa2Cu3O7 can create effective pinning centres which significantly increase
the critical current density (j_c) in 7 T field by a factor of 4 and 10 at 50
and 60 K, respectively. The present work reports on the influence of the Mo
substitution and high-pressure oxygen annealing on the pinning properties and
critical currents of YBa_2Cu_{3-x}Mo_xO_{7-d} by comparing pure (x = 0, d > 0)
and substituted (x = 0.03, d < 0) single crystals. Pinning properties have been
investigated by measurements of magnetization loops and calculations of j_c in
the ab-plane, in the temperature range from 2 to 90 K and in fields up to 14 T.
Depending on the Mo substitution and the oxygen treatment, several types of
pinning centres increasing j_c have been revealed and analysed in the frame of
Dew-Hughes' and Kramer's models.


Abstract:
  Significant efforts are being invested to bring state-of-the-art
classification and recognition to edge devices with extreme resource
constraints (memory, speed and lack of GPU support). Here, we demonstrate the
first deep network for acoustic recognition that is small enough for an
off-the-shelf microcrocontroller, yet achieves state-of-the-art performance on
standard benchmarks. Rather than handcrafting a once-off solution, we present a
universal pipeline that converts a large deep convolutional network
automatically via compression and quantization into a network for
resource-impoverished edge devices. After introducing ACDNet, which produces
above state-of-the-art accuracy on ESC-10 (96.65%) and ESC-50 (87.1%), we
describe the compression pipeline and show that it allows us to achieve 97.22%
size reduction and 97.28% FLOP reduction while maintaining close to
state-of-the-art accuracy (83.65% on ESC-50). We describe a successful
implementation on a standard off-the-shelf microcontroller and, beyond
laboratory benchmarks, report successful tests on real-world data sets.


Abstract:
  Despite coverage enhancement in rural areas is one of the main requirements
in next generations of wireless networks (i.e., 5G and 6G), the low expected
profit prevents telecommunication providers from investing in such sparsely
populated areas. Hence, it is required to design and deploy cost efficient
alternatives for extending the cellular infrastructure to these regions. A
concrete mathematical model that characterizes and clearly captures the
aforementioned problem might be a key-enabler for studying the efficiency of
any potential solution. Unfortunately, the commonly used mathematical tools
that model large scale wireless networks are not designed to capture the
unfairness, in terms of cellular coverage, suffered by exurban and rural areas.
In big cities, in fact, cellular deployment is essentially capacity driven and
thus cellular base station densities are maximum in the town centers and
decline when getting far from them. In this paper, a new stochastic
geometry-based model is implemented in order to show the coverage spatial
variation among urban, suburban, and exurban settlements. Indeed, by
implementing inhomogeneous Poisson point processes (PPPs) it is possible to
study the performance metrics in a realistic scenario where terrestrial base
stations (TBSs) are clustered around the urban center while outer aerial base
stations (ABSs) are uniformly distributed outside an urban exclusion zone.
Based on this, our simulation results can quantify the improvement, in terms of
coverage probability, that even a surprisingly low density of ABSs can bring to
peripheral regions depending on the extension of the exclusion zone, enabling
us to draw insightful considerations.


Abstract:
  The El Ni\~no Southern Oscillation (ENSO) is the most important driver of
interannual climate variability and can trigger extreme weather events and
disasters in various parts of the globe. Recently, we have developed two
approaches for the early forecasting of El Ni\~no. The climate network-based
approach allows forecasting the onset of an El Ni\~no event about 1 year ahead.
The complexity-based approach allows additionally to forecast the magnitude of
an upcoming El Ni\~no event in the calendar year before. Here we communicate
the forecasts of both methods for 2021. The methods indicate a high probability
(about 89% and 90%, respectively) for the absence of an El Ni\~no in 2021.


Abstract:
  Land cover mapping is essential for monitoring global environmental change
and managing natural resources. Unfortunately, traditional classification
models are plagued by limited training data available in existing land cover
products and data heterogeneity over space and time. In this survey, we provide
a structured and comprehensive overview of challenges in land cover mapping and
machine learning methods used to address these problems. We also discuss the
gaps and opportunities that exist for advancing research in this promising
direction.


Abstract:
  Video affective understanding, which aims to predict the evoked expressions
by the video content, is desired for video creation and recommendation. In the
recent EEV challenge, a dense affective understanding task is proposed and
requires frame-level affective prediction. In this paper, we propose a
multi-granularity network with modal attention (MGN-MA), which employs
multi-granularity features for better description of the target frame.
Specifically, the multi-granularity features could be divided into frame-level,
clips-level and video-level features, which corresponds to visual-salient
content, semantic-context and video theme information. Then the modal attention
fusion module is designed to fuse the multi-granularity features and emphasize
more affection-relevant modals. Finally, the fused feature is fed into a
Mixtures Of Experts (MOE) classifier to predict the expressions. Further
employing model-ensemble post-processing, the proposed method achieves the
correlation score of 0.02292 in the EEV challenge.


Abstract:
  5G networks are going to support a variety of vertical services, with a
diverse set of key performance indicators (KPIs), by using enabling
technologies such as software-defined networking and network function
virtualization. It is the responsibility of the network operator to efficiently
allocate the available resources to the service requests in such a way to honor
KPI requirements, while accounting for the limited quantity of available
resources and their cost. A critical challenge is that requests may be highly
varying over time, requiring a solution that accounts for their dynamic
generation and termination. With this motivation, we seek to make joint
decisions for request admission, resource activation, VNF placement, resource
allocation, and traffic routing. We do so by considering real-world aspects
such as the setup times of virtual machines, with the goal of maximizing the
mobile network operator profit. To this end, first, we formulate a one-shot
optimization problem which can attain the optimum solution for small size
problems given the complete knowledge of arrival and departure times of
requests over the entire system lifespan. We then propose an efficient and
practical heuristic solution that only requires this knowledge for the next
time period and works for realistically-sized scenarios. Finally, we evaluate
the performance of these solutions using real-world services and large-scale
network topologies. {Results demonstrate that our heuristic solution performs
better than a state-of-the-art online approach and close to the optimum.


Abstract:
  This paper studies bilinear saddle point problems $\min_{\bf{x}}
\max_{\bf{y}} g(\bf{x}) + \bf{x}^{\top} \bf{A} \bf{y} - h(\bf{y})$, where the
functions $g, h$ are smooth and strongly-convex. When the gradient and proximal
oracle related to $g$ and $h$ are accessible, optimal algorithms have already
been developed in the literature \cite{chambolle2011first,
palaniappan2016stochastic}. However, the proximal operator is not always easy
to compute, especially in constraint zero-sum matrix games
\cite{zhang2020sparsified}. This work proposes a new algorithm which only
requires the access to the gradients of $g, h$. Our algorithm achieves a
complexity upper bound $\tilde{\mathcal{O}}\left(
\frac{\|\bf{A}\|_2}{\sqrt{\mu_x \mu_y}} + \sqrt[4]{\kappa_x \kappa_y (\kappa_x
+ \kappa_y)} \right)$ which has optimal dependency on the coupling condition
number $\frac{\|\bf{A}\|_2}{\sqrt{\mu_x \mu_y}}$ up to logarithmic factors.


Abstract:
  While convolutional neural networks have shown a tremendous impact on various
computer vision tasks, they generally demonstrate limitations in explicitly
modeling long-range dependencies due to the intrinsic locality of the
convolution operation. Initially designed for natural language processing
tasks, Transformers have emerged as alternative architectures with innate
global self-attention mechanisms to capture long-range dependencies. In this
paper, we propose TransDepth, an architecture that benefits from both
convolutional neural networks and transformers. To avoid the network losing its
ability to capture local-level details due to the adoption of transformers, we
propose a novel decoder that employs attention mechanisms based on gates.
Notably, this is the first paper that applies transformers to pixel-wise
prediction problems involving continuous labels (i.e., monocular depth
prediction and surface normal estimation). Extensive experiments demonstrate
that the proposed TransDepth achieves state-of-the-art performance on three
challenging datasets. Our code is available at:
https://github.com/ygjwd12345/TransDepth.


Abstract:
  This report presents design considerations for automatically generating
satellite imagery datasets for training machine learning models with emphasis
placed on dense classification tasks, e.g. semantic segmentation. The
implementation presented makes use of freely available Sentinel-2 data which
allows generation of large scale datasets required for training deep neural
networks. We discuss issues faced from the point of view of deep neural network
training and evaluation such as checking the quality of ground truth data and
comment on the scalability of the approach. Accompanying code is provided in
https://github.com/michaeltrs/DeepSatData.


Abstract:
  The sparse LiDAR point clouds become more and more popular in various
applications, e.g., the autonomous driving. However, for this type of data,
there exists much under-explored space in the corresponding compression
framework proposed by MPEG, i.e., geometry-based point cloud compression
(G-PCC). In G-PCC, only the distance-based similarity is considered in the
intra prediction for the attribute compression. In this paper, we propose a
normal-based intra prediction scheme, which provides a more efficient lossless
attribute compression by introducing the normals of point clouds. The angle
between normals is used to further explore accurate local similarity, which
optimizes the selection of predictors. We implement our method into the G-PCC
reference software. Experimental results over LiDAR acquired datasets
demonstrate that our proposed method is able to deliver better compression
performance than the G-PCC anchor, with $2.1\%$ gains on average for lossless
attribute coding.


Abstract:
  The numerical analysis of time fractional evolution equations with the
second-order elliptic operator including general time-space dependent variable
coefficients is challenging, especially when the classical weak initial
singularities are taken into account. In this paper, we introduce a concise
technique to construct efficient time-stepping schemes with variable time step
sizes for two-dimensional time fractional sub-diffusion and diffusion-wave
equations with general time-space dependent variable coefficients. By means of
the novel technique, the nonuniform Alikhanov type schemes are constructed and
analyzed for the sub-diffusion and diffusion-wave problems. For the
diffusion-wave problem, our scheme is constructed by employing the recently
established symmetric fractional-order reduction (SFOR) method. The
unconditional stability of proposed schemes is rigorously discussed under mild
assumptions on variable coefficients and, based on reasonable regularity
assumptions and weak time mesh restrictions, the second-order convergence is
obtained with respect to discrete $H^1$-norm. Numerical experiments are given
to demonstrate the theoretical statements.


Abstract:
  This work investigates the problem of learning temporal interaction networks.
A temporal interaction network consists of a series of chronological
interactions between users and items. Previous methods tackle this problem by
using different variants of recurrent neural networks to model sequential
interactions, which fail to consider the structural information of temporal
interaction networks and inevitably lead to sub-optimal results. To this end,
we propose a novel Deep Structural Point Process termed as DSPP for learning
temporal interaction networks. DSPP simultaneously incorporates the topological
structure and long-range dependency structure into our intensity function to
enhance model expressiveness. To be specific, by using the topological
structure as a strong prior, we first design a topological fusion encoder to
obtain node embeddings. An attentive shift encoder is then developed to learn
the long-range dependency structure between users and items in continuous time.
The proposed two modules enable our model to capture the user-item correlation
and dynamic influence in temporal interaction networks. DSPP is evaluated on
three real-world datasets for both tasks of item prediction and time
prediction. Extensive experiments demonstrate that our model achieves
consistent and significant improvements over state-of-the-art baselines.


Abstract:
  We study decomposition of $SU(2)$ gauge field into monopole and monopoleless
components. After fixing the Maximal Abelian gauge in $SU(2)$ lattice gauge
theory we decompose the nonabelian gauge field into the Abelian field created
by monopoles and the modified nonabelian field with monopoles removed. We then
calculate respective static potentialis and show that the potential due to the
modified nonabelian field is nonconfining while, as is well known, the Abelian
field produces linear potential. We further find that the sum of these
potentials approximates the nonabelian static potential with good precision at
all distances considered. We conclude that at large distances the monopole
field potential describes the classical energy of the hadronic string while the
static potential due to the modified nonabelian field describes the string
fluctuations energy.


Abstract:
  We show that the Mabuchi energy of any polarized manifold (X,L) is (bounded
below) proper on the full space of Kahler metrics in the first Chern class of L
if and only if (X,L) is asymptotically (semi)stable. In particular it now
follows from work of Xiuxiong Chen and Jinguri Cheng that there exists a cscK
metric in the first Chern class of L if and only if (X,L) is asymptotically
stable, provided the reduced automorphism group of (X,L) is finite.


Abstract:
  In the infinite dimensional Heisenberg group, we construct a left invariant
weak Riemannian metric that gives a degenerate geodesic distance. The same
construction yields a degenerate sub-Riemannian distance. We show how the
standard notion of sectional curvature adapts to our framework, but it cannot
be defined everywhere and it is unbounded on suitable sequences of planes. The
vanishing of the distance precisely occurs along this sequence of planes, so
that the degenerate Riemannian distance appears in connection with an unbounded
sectional curvature. In the 2005 paper by Michor and Mumford, this phenomenon
was first observed in some specific Fr\'echet manifolds.


Abstract:
  We report the ground-level detection of a Galactic Cosmic-Ray (GCR) flux
enhancement lasting $\sim$ 17 hr and associated with the passage of a magnetic
flux rope (MFR) over the Earth. The MFR was associated with a slow Coronal Mass
Ejection (CME) caused by the eruption of a filament on 2016 October 9. Due to
the quiet conditions during the eruption and the lack of interactions during
the interplanetary CME transport to the Earth, the associated MFR preserved its
configuration and reached the Earth with a strong magnetic field, low density,
and a very low turbulence level compared to the local background, thus
generating the ideal conditions to redirect and guide GCRs (in the $\sim$ 8 to
60 GV rigidity range) along the magnetic field of the MFR. An important
negative $B_Z$ component inside the MFR caused large disturbances in the
geomagnetic field and a relatively strong geomagnetic storm. However, these
disturbances are not the main factors behind the GCR enhancement. Instead, we
found that the major factor was the alignment between the MFR axis and the
asymptotic direction of the observer.


Abstract:
  By using the relativistic precession model, we have studied frequencies of
quasi-periodic oscillations in the spacetime of a disformal Kerr black hole.
This black hole owns an extra disformal parameter and belongs to a class of
non-stealth solutions in quadratic degenerate higher-order scalar-tensor
(DHOST) theories. Our result shows that only the periastron precession
frequency is related to the disformal parameter, while the azimuthal frequency
and the nodal precession frequency are identical with those in the usual Kerr
black hole in general relativity. Combing with the observation data of GRO
J1655-40, we fit parameters of the disformal Kerr black hole, and find that the
disformal parameter $\alpha$ is almost negative in the range of $1 \sigma$,
which implies the negative disformal parameter $\alpha$ is favored by the
observation data of GRO J1655-40.


Abstract:
  Migration of halogen vacancies is one of the primary sources of phase
segregation and material degradation in lead-halide perovskites. Here we use
first principles density functional theory to compare migration energy barriers
and paths of bromine vacancies in the bulk and at a (001) surface of cubic
CsPbBr$_3$. Our calculations indicate that surfaces might facilitate bromine
vacancy migration in these perovskites, due to their soft structure that allows
for bond lengths variations larger than in the bulk. We calculate the migration
energy for axial-to-axial bromine vacancy migration at the surface to be only
half of the value in the bulk. Furthermore, we study the effect of modifying
the surface with four different alkali halide monolayers, finding an increase
of the migration barrier to almost the bulk value for the NaCl-passivated
system. Migration barriers are found to be correlated to the lattice mismatch
between the CsPbBr$_3$ surface and the alkali halide monolayer. Our
calculations suggest that surfaces might play a significant role in mediating
vacancy migration in halide perovskites, a result with relevance for perovskite
nanocrystals with large surface-to-volume ratios. Moreover, we propose viable
ways for suppressing this undesirable process through passivation with alkali
halide salts.


Abstract:
  Best match graphs (BMGs) are a class of colored digraphs that naturally
appear in mathematical phylogenetics and can be approximated with the help of
similarity measures between gene sequences, albeit not without errors. The
corresponding graph editing problem can be used as a means of error correction.
Since the arc set modification problems for BMGs are NP-complete, efficient
heuristics are needed if BMGs are to be used for the practical analysis of
biological sequence data. Since BMGs have a characterization in terms of
consistency of a certain set of rooted triples, we consider heuristics that
operate on triple sets. As an alternative, we show that there is a close
connection to a set partitioning problem that leads to a class of top-down
recursive algorithms that are similar to Aho's supertree algorithm and give
rise to BMG editing algorithms that are consistent in the sense that they leave
BMGs invariant. Extensive benchmarking shows that community detection
algorithms for the partitioning steps perform best for BMG editing.


Abstract:
  The present status of the field theoretical model studies of the deep
inelastic scattering induced by (anti)neutrino on the nuclear targets in a wide
range of Bjorken variable $x$ and four momentum transfer square $Q^2$, has been
reviewed~\cite{Haider:2011qs,Haider:2012nf,Haider:2016zrk,Zaidi:2019mfd,Zaidi:2019asc,Ansari:2020xne}.
  The effect of the nonperturbative corrections such as target mass correction
and higher twist effects, perturbative evolution of the parton densities,
nuclear medium modifications in the nucleon structure functions, nuclear
isoscalarity corrections on the weak nuclear structure functions have been
discussed. These structure functions have been used to obtain the differential
scattering cross sections. The various nuclear medium effects like the Fermi
motion, binding energy, nucleon correlations, mesonic contributions, shadowing
and antishadowing corrections relevant in the different regions of $x$ and
$Q^2$ have been discussed. The numerical results for the structure functions
and the cross sections are compared with some of the available experimental
data including the recent results from MINERvA. The predictions are made in
argon nuclear target which is planned to be used as a target material in DUNE
at the Fermilab.


Abstract:
  We present a test platform for the Athena X-IFU detection chain, which will
serve as the first demonstration of the representative end-to-end detection and
readout chain for the X-IFU, using prototypes of the future flight electronics
and currently available subsystems. This test bench, housed in a commercial
two-stage ADR cryostat, includes a focal plane array placed at the 50 mK cold
stage of the ADR with a kilopixel array of transition-edge sensor
microcalorimeter spectrometers and associated cold readout electronics.
Prototype room temperature electronics for the X-IFU provide the readout, and
will evolve over time to become more representative of the X-IFU mission
baseline. The test bench yields critical feedback on subsystem designs and
interfaces, in particular the warm readout electronics, and will provide an
in-house detection system for continued testing and development of the warm
readout electronics and for the validation of X-ray calibration sources. In
this paper, we describe the test bench subsystems and design, characterization
of the cryostat, and current status of the project.


Abstract:
  Controlling network systems has become a problem of paramount importance.
Optimally controlling a network system with linear dynamics and minimizing a
quadratic cost is a particular case of the well-studied linear-quadratic
problem. When the specific topology of the network system is ignored, the
optimal controller is readily available. However, this results in a
\emph{centralized} controller, facing limitations in terms of implementation
and scalability. Finding the optimal \emph{distributed} controller, on the
other hand, is intractable in the general case. In this paper, we propose the
use of graph neural networks (GNNs) to parametrize and design a distributed
controller. GNNs exhibit many desirable properties, such as being naturally
distributed and scalable. We cast the distributed linear-quadratic problem as a
self-supervised learning problem, which is then used to train the GNN-based
controllers. We also obtain sufficient conditions for the resulting closed-loop
system to be input-state stable, and derive an upper bound on the trajectory
deviation when the system is not accurately known. We run extensive simulations
to study the performance of GNN-based distributed controllers and show that
they are computationally efficient and scalable.


Abstract:
  The major part of the vanilla vision transformer (ViT) is the attention block
that brings the power of mimicking the global context of the input image. For
better performance, ViT needs large-scale training data. To overcome this data
hunger limitation, many ViT-based networks, or hybrid-ViT, have been proposed
to include local context during the training. The robustness of ViTs and its
variants against adversarial attacks has not been widely investigated in the
literature like CNNs. This work studies the robustness of ViT variants 1)
against different Lp-based adversarial attacks in comparison with CNNs, 2)
under adversarial examples (AEs) after applying preprocessing defense methods
and 3) under the adaptive attacks using expectation over transformation (EOT)
framework. To that end, we run a set of experiments on 1000 images from
ImageNet-1k and then provide an analysis that reveals that vanilla ViT or
hybrid-ViT are more robust than CNNs. For instance, we found that 1) Vanilla
ViTs or hybrid-ViTs are more robust than CNNs under Lp-based attacks and under
adaptive attacks. 2) Unlike hybrid-ViTs, Vanilla ViTs are not responding to
preprocessing defenses that mainly reduce the high frequency components.
Furthermore, feature maps, attention maps, and Grad-CAM visualization jointly
with image quality measures, and perturbations' energy spectrum are provided
for an insight understanding of attention-based models.


Abstract:
  The theory for multiplier empirical processes has been one of the central
topics in the development of the classical theory of empirical processes, due
to its wide applicability to various statistical problems. In this paper, we
develop theory and tools for studying multiplier $U$-processes, a natural
higher-order generalization of the multiplier empirical processes. To this end,
we develop a multiplier inequality that quantifies the moduli of continuity of
the multiplier $U$-process in terms of that of the (decoupled) symmetrized
$U$-process. The new inequality finds a variety of applications including (i)
multiplier and bootstrap central limit theorems for $U$-processes, (ii) general
theory for bootstrap $M$-estimators based on $U$-statistics, and (iii) theory
for $M$-estimation under general complex sampling designs, again based on
$U$-statistics.


Abstract:
  Motivated by recent data analyses in biomedical imaging studies, we consider
a class of image-on-scalar regression models for imaging responses and scalar
predictors. We propose using flexible multivariate splines over triangulations
to handle the irregular domain of the objects of interest on the images, as
well as other characteristics of images. The proposed estimators of the
coefficient functions are proved to be root-n consistent and asymptotically
normal under some regularity conditions. We also provide a consistent and
computationally efficient estimator of the covariance function. Asymptotic
pointwise confidence intervals and data-driven simultaneous confidence
corridors for the coefficient functions are constructed. Our method can
simultaneously estimate and make inferences on the coefficient functions while
incorporating spatial heterogeneity and spatial correlation. A highly efficient
and scalable estimation algorithm is developed. Monte Carlo simulation studies
are conducted to examine the finite-sample performance of the proposed method,
which is then applied to the spatially normalized positron emission tomography
data of the Alzheimer's Disease Neuroimaging Initiative.


Abstract:
  Given a spectral triple on a $C^*$-algebra $\mathcal A$ together with a
unital injective endomorphism $\alpha$, the problem of defining a suitable
crossed product $C^*$-algebra endowed with a spectral triple is addressed. The
proposed construction is mainly based on the works of Cuntz and of Hawkins,
Skalski, White and Zacharias, and on our previous papers. The embedding of
$\alpha(\mathcal A)$ in $\mathcal A$ can be considered as the dual form of a
covering projection between noncommutative spaces. A main assumption is the
expansiveness of the endomorphism, which takes the form of the local
isometricity of the covering projection and is expressed via the compatibility
of the Lip-norms on $\mathcal A$ and $\alpha(\mathcal A)$.


Abstract:
  Neural ODE Processes approach the problem of meta-learning for dynamics using
a latent variable model, which permits a flexible aggregation of contextual
information. This flexibility is inherited from the Neural Process framework
and allows the model to aggregate sets of context observations of arbitrary
size into a fixed-length representation. In the physical sciences, we often
have access to structured knowledge in addition to raw observations of a
system, such as the value of a conserved quantity or a description of an
understood component. Taking advantage of the aggregation flexibility, we
extend the Neural ODE Process model to use additional information within the
Learning Using Privileged Information setting, and we validate our extension
with experiments showing improved accuracy and calibration on simulated
dynamics tasks.


Abstract:
  There are two elementary superconducting qubit types that derive directly
from the quantum harmonic oscillator. In one the inductor is replaced by a
nonlinear Josephson junction to realize the widely used charge qubits with a
compact phase variable and a discrete charge wavefunction. In the other the
junction is added in parallel, which gives rise to an extended phase variable,
continuous wavefunctions and a rich energy level structure due to the loop
topology. While the corresponding rf-SQUID Hamiltonian was introduced as a
quadratic, quasi-1D potential approximation to describe the fluxonium qubit
implemented with long Josephson junction arrays, in this work we implement it
directly using a linear superinductor formed by a single uninterrupted aluminum
wire. We present a large variety of qubits all stemming from the same circuit
but with drastically different characteristic energy scales. This includes flux
and fluxonium qubits but also the recently introduced quasi-charge qubit with
strongly enhanced zero point phase fluctuations and a heavily suppressed flux
dispersion. The use of a geometric inductor results in high precision of the
inductive and capacitive energy as guaranteed by top-down lithography - a key
ingredient for intrinsically protected superconducting qubits. The geometric
fluxonium also exhibits a large magnetic dipole, which renders it an
interesting new candidate for quantum sensing applications.


Abstract:
  The development of pathological speech systems is currently hindered by the
lack of a standardised objective evaluation framework. In this work, (1) we
utilise existing detection and analysis techniques to propose a general
framework for the consistent evaluation of synthetic pathological speech. This
framework evaluates the voice quality and the intelligibility aspects of speech
and is shown to be complementary using our experiments. (2) Using our proposed
evaluation framework, we develop and test a dysarthric voice conversion system
(VC) using CycleGAN-VC and a PSOLA-based speech rate modification technique. We
show that the developed system is able to synthesise dysarthric speech with
different levels of speech intelligibility.


Abstract:
  Using the BRST approach to higher spin field theories we develop a generic
technique for constructing the cubic interaction vertices for N=1
supersymmetric massless higher spin fields on four, six and ten dimensional
flat backgrounds. Such an approach allows formulation of the equations for
cubic vertices including bosonic and fermionic higher spin fields, and the
problem of finding the vertices is reduced to finding the consistent solutions
to these equations. As a realization of this procedure, we present the
particular solutions for the vertices where the fields obey some off-shell
constraints. It is shown that the supersymmetry imposes additional constraints
on the vertices and singles out a particular subclass of the solutions. As a
concrete application of the generic scheme, we consider supersymmetric
Yang-Mills-like systems in four, six and ten dimensions where the higher spin
fields transform under some internal symmetry group, as well as
supergravity-like systems in the same dimensions.


Abstract:
  Reliable platforms for data collation during airline schedule operations have
significantly increased the quality and quantity of available information for
effectively managing airline schedule disruptions. To that effect, this paper
applies macroscopic and microscopic techniques by way of basic statistics and
machine learning, respectively, to analyze historical scheduling and operations
data from a major airline in the United States. Macroscopic results reveal that
majority of irregular operations in airline schedule that occurred over a
one-year period stemmed from disruptions due to flight delays, while
microscopic results validate different modeling assumptions about key drivers
for airline disruption management like turnaround as a Gaussian process.


Abstract:
  Logistic regression model is widely used in many studies to investigate the
relationship between a binary response variable $Y$ and a set of potential
predictors $\mathbf X$. The binary response may represent, for example, the
occurrence of some outcome of interest ($Y=1$ if the outcome occurred and $Y=0$
otherwise). When the dependent variable $Y$ represents a rare event, the
logistic regression model shows relevant drawbacks. In order to overcome these
drawbacks we propose the Generalized Extreme Value (GEV) regression model. In
particular, we suggest the quantile function of the GEV distribution as link
function, so our attention is focused on the tail of the response curve for
values close to one. A sample of observations is said to contain a cure
fraction when a proportion of the study subjects (the so-called cured
individuals, as opposed to the susceptibles) cannot experience the outcome of
interest. One problem arising then is that it is usually unknown who are the
cured and the susceptible subjects, unless the outcome of interest has been
observed. In these settings, a logistic regression analysis of the relationship
between $\mathbf X$ and $Y$ among the susceptibles is no more straightforward.
We develop a maximum likelihood estimation procedure for this problem, based on
the joint modeling of the binary response of interest and the cure status. We
investigate the identifiability of the resulting model. Then, we conduct a
simulation study to investigate its finite-sample behavior, and application to
real data.


Abstract:
  This paper describes a bounded generation result concerning the minimal
natural number $K$ such that for
$Q(C_2,2R):=\{A\varepsilon_{\phi}(2x)A^{-1}|x\in R,A\in{\rm Sp}_4(R),\phi\in
C_2\}$, one has $N_{C_2,2R}=\{X_1\cdots X_K|\forall 1\leq i\leq K:X_i\in
Q(C_2,2R)\}$ for rings of algebraic integers $R$ and the principal congruence
subgroup $N_{C_2,2R}$ in ${\rm Sp}_4(R).$ This gives an explicit version of an
abstract bounded generation result of a similar type as presented by Morris.
Furthermore, the result presented does not depend on several number-theoretic
quantities unlike Morris' result. Using this bounded generation result, we
further give explicit bounds for the strong boundedness of ${\rm Sp}_4(R)$ for
certain examples of rings $R,$ thereby giving explicit versions of results in
an earlier paper. We further give a classification of normally generating
subsets of ${\rm Sp}_4(R)$ for $R$ a ring of algebraic integers.


Abstract:
  A connected, locally finite graph $\Gamma$ is a Cayley--Abels graph for a
totally disconnected, locally compact group $G$ if $G$ acts vertex-transitively
with compact, open vertex stabilizers on $\Gamma$. Define the minimal degree of
$G$ as the minimal degree of a Cayley--Abels graph of $G$. We relate the
minimal degree in various ways to the modular function, the scale function and
the structure of compact open subgroups. As an application, we prove that if
$T_d$ denotes the $d$-regular tree, then the minimal degree of ${\rm Aut}(T_d)$
is $d$ for all $d\geq 2$.


Abstract:
  Via a unified geometric approach, a class of generalized trigonometric
functions with two parameters are analytically extended to maximal domains on
which they are univalent. Some consequences are deduced concerning radius of
convergence for the Maclaurin series, commutation with rotation, continuation
beyond the domain of univalence, and periodicity.


Abstract:
  Modelling joint dynamics of liquid vanilla options is crucial for
arbitrage-free pricing of illiquid derivatives and managing risks of option
trade books. This paper develops a nonparametric model for the European options
book respecting underlying financial constraints and while being practically
implementable. We derive a state space for prices which are free from static
(or model-independent) arbitrage and study the inference problem where a model
is learnt from discrete time series data of stock and option prices. We use
neural networks as function approximators for the drift and diffusion of the
modelled SDE system, and impose constraints on the neural nets such that
no-arbitrage conditions are preserved. In particular, we give methods to
calibrate \textit{neural SDE} models which are guaranteed to satisfy a set of
linear inequalities. We validate our approach with numerical experiments using
data generated from a Heston stochastic local volatility model.


Abstract:
  Multi-task learning is a framework that enforces different learning tasks to
share their knowledge to improve their generalization performance. It is a hot
and active domain that strives to handle several core issues; particularly,
which tasks are correlated and similar, and how to share the knowledge among
correlated tasks. Existing works usually do not distinguish the polarity and
magnitude of feature weights and commonly rely on linear correlation, due to
three major technical challenges in: 1) optimizing the models that regularize
feature weight polarity, 2) deciding whether to regularize sign or magnitude,
3) identifying which tasks should share their sign and/or magnitude patterns.
To address them, this paper proposes a new multi-task learning framework that
can regularize feature weight signs across tasks. We innovatively formulate it
as a biconvex inequality constrained optimization with slacks and propose a new
efficient algorithm for the optimization with theoretical guarantees on
generalization performance and convergence. Extensive experiments on multiple
datasets demonstrate the proposed methods' effectiveness, efficiency, and
reasonableness of the regularized feature weighted patterns.


Abstract:
  We investigate the performance of Gaussianmodulated coherent-state QKD
protocols in the presence of canonical attacks, which are collective Gaussian
attacks resulting in Gaussian channels described by one of the possible
canonical forms. We present asymptotic key rates and then we extend the results
to the finite-size regime using a recently-developed toolbox for composable
security.


Abstract:
  The defect corrections to polarization and dielectric functions of Bloch
electrons in quantum wells are first calculated. Following this, we derive the
first two moment equations from Boltzmann transport theory and apply them to
explore defect effects on magneto-transport of Bloch electrons. Meanwhile, we
obtain analytically the momentum-relaxation time and mobility tensor for Bloch
electrons making use of the screened defect-corrected polarization function.
Based on quantum-statistical theory, we further investigate the defect capture
and charging dynamics by employing a parameterized physics model for defects to
obtain defect wave functions. After this, both capture and relaxation rates, as
well as density for captured Bloch electrons, are calculated self-consistently
as functions of temperature, doping density and different defect types. By
applying the energy-balance equation, the number of occupied energy levels and
chemical potential of defects are determined, with which the transition rate
for defect capturing is obtained. By using these results, defect
energy-relaxation, capture and escape rates, and Bloch-electron chemical
potential are obtained self-consistently. At the same time, the Bloch-electron
energy- and momentum-relaxation rates, as well as the current suppression
factor, are also investigated quantitatively. Finally, by combining all these
studies together, the temperature dependence of the Hall and longitudinal
mobilities is demonstrated for Bloch electrons in either single- or
multi-quantum wells, which can be utilized for quantifying burst noise in
transistors and blinking noise in photo-detectors.


Abstract:
  Earlier there was no mode of online communication between users. In big or
small organizations communication between users posed a challenge. There was a
requirement to record these communications and store the data for further
evaluation. The idea is to automate the existing Simple Chat Room system and
make the users utilize the software so that their valuable information is
stored digitally and can be retrieved for further management purposes. There
was no online method of communicating with different users. There were many
different interfaces available in the market but this method of using windows
sockets to communicate between nodes would be fast and reliable. The main
objective of our Simple Chat Room project is to create a chat application that
helps different users to communicate with each other through a server
connected. This is a simple chat program with a server and can have many
clients. The server needs to be started first and clients can be connected
later. Simple Chat Room provides bidirectional communication between client and
server. It enables users to seamlessly communicate with each other. The user
can chat using this chat application. If the user at the other end is active
then they can start a chat session. The chat is recorded in this application.


Abstract:
  We have designed, optimized, fabricated and characterized highly reflective
quasi-omnidirectional (angular range of $0-60^\circ$) multilayered structures
with a wide spectral range. Two techniques, chirping (a continuous change in
thicknesses) and stacking of Bragg-type sub-structures, have been used to
enhance the reflectance with minimum thickness for a given pair of refractive
indices. Numerical calculations were carried out employing the transfer matrix
method and we optimized the design parameters to obtain maximal reflectance
averaged over different spectral ranges for all angles. We fabricated some of
the optimized structures with porous silicon dielectric multilayers with low
refractive contrast and compared their measured optical properties with the
calculations. Two chirped structures with thicknesses 21.6 $\mu$m and 60.4
$\mu$m, resulting in quasi omnidirectional mirrors with bandwidths of 360 nm
and 1800 nm, centered at 1160 nm and 1925 nm respectively have been shown. In
addition, we fabricated a stacked sub-Bragg mirror structure with a quasi
omnidirectional bandwidth of 1800 nm (centered at 1850 nm) and a thickness of
41.5 $\mu$m, which is almost two third (in thickness) of the chirped structure.
Thus, our techniques allowed us to obtain relatively thin quasi-omnidirectional
mirrors with wide bands over different wavelength ranges. Our analysis
techniques can be used for the optimization of the reflectance not only in
multilayered PS systems with different refractive index contrasts but also for
systems with other types of materials with low refractive index contrast. The
present study could also be useful for obtaining omnidirectional dielectric
mirrors in large spectral regions using different materials, flat focusing
reflectors, thermal regulators, or, if defects are included, as filters or
remote chemical/biosensors with a wide angular independent response.


Abstract:
  In this paper, we investigate the question: Given a small number of
datapoints, for example N = 30, how tight can PAC-Bayes and test set bounds be
made? For such small datasets, test set bounds adversely affect generalisation
performance by withholding data from the training procedure. In this setting,
PAC-Bayes bounds are especially attractive, due to their ability to use all the
data to simultaneously learn a posterior and bound its generalisation risk. We
focus on the case of i.i.d. data with a bounded loss and consider the generic
PAC-Bayes theorem of Germain et al. While their theorem is known to recover
many existing PAC-Bayes bounds, it is unclear what the tightest bound derivable
from their framework is. For a fixed learning algorithm and dataset, we show
that the tightest possible bound coincides with a bound considered by Catoni;
and, in the more natural case of distributions over datasets, we establish a
lower bound on the best bound achievable in expectation. Interestingly, this
lower bound recovers the Chernoff test set bound if the posterior is equal to
the prior. Moreover, to illustrate how tight these bounds can be, we study
synthetic one-dimensional classification tasks in which it is feasible to
meta-learn both the prior and the form of the bound to numerically optimise for
the tightest bounds possible. We find that in this simple, controlled scenario,
PAC-Bayes bounds are competitive with comparable, commonly used Chernoff test
set bounds. However, the sharpest test set bounds still lead to better
guarantees on the generalisation error than the PAC-Bayes bounds we consider.


Abstract:
  Co-part segmentation is an important problem in computer vision for its rich
applications. We propose an unsupervised learning approach for co-part
segmentation from images. For the training stage, we leverage motion
information embedded in videos and explicitly extract latent representations to
segment meaningful object parts. More importantly, we introduce a dual
procedure of part-assembly to form a closed loop with part-segmentation,
enabling an effective self-supervision. We demonstrate the effectiveness of our
approach with a host of extensive experiments, ranging from human bodies,
hands, quadruped, and robot arms. We show that our approach can achieve
meaningful and compact part segmentation, outperforming state-of-the-art
approaches on diverse benchmarks.


Abstract:
  String theory on ${\rm AdS}_3\times {\rm S}^3 \times\mathbb{T}^4$ with one
unit ($k=1$) of NS-NS flux is considered in the hybrid formalism of Berkovits,
Vafa & Witten (BVW). Using the free field realisation of the world-sheet theory
at $k=1$, we identify explicitly the BRST cohomology classes corresponding to
some of the low-lying states of the dual CFT. In particular, we do this for the
${\cal N}=4$ superconformal generators of the symmetric orbifold theory, and we
confirm these identifications by showing that the worldsheet correlators
reproduce the expected dual CFT answer. Along the way we note that the physical
vertex operators on the worldsheet have a simpler form if one works with a
different, but equivalent, choice for the BRST operators relative to BVW.


Abstract:
  The reliability of Deep Learning systems depends on their accuracy but also
on their robustness against adversarial perturbations to the input data.
Several attacks and defenses have been proposed to improve the performance of
Deep Neural Networks under the presence of adversarial noise in the natural
image domain. However, robustness in computer-aided diagnosis for volumetric
data has only been explored for specific tasks and with limited attacks. We
propose a new framework to assess the robustness of general medical image
segmentation systems. Our contributions are two-fold: (i) we propose a new
benchmark to evaluate robustness in the context of the Medical Segmentation
Decathlon (MSD) by extending the recent AutoAttack natural image classification
framework to the domain of volumetric data segmentation, and (ii) we present a
novel lattice architecture for RObust Generic medical image segmentation (ROG).
Our results show that ROG is capable of generalizing across different tasks of
the MSD and largely surpasses the state-of-the-art under sophisticated
adversarial attacks.


Abstract:
  We prove that the topological recursion formalism can be used to quantize any
generic classical spectral curve with smooth ramification points and simply
ramified away from poles. For this purpose, we build both the associated
quantum curve, i.e. the differential operator quantizing the algebraic equation
defining the classical spectral curve considered, and a basis of wave
functions, that is to say a basis of solutions of the corresponding
differential equation. We further build a Lax pair representing the resulting
quantum curve and thus present it as a point in an associated space of
meromorphic connections on the Riemann sphere, a first step towards
isomonodromic deformations. We finally propose two examples: the derivation of
a 2-parameter family of formal trans-series solutions to Painlev\'e 2 equation
and the quantization of a degree three spectral curve with pole only at
infinity.


Abstract:
  In this paper, approximate outage probability (OP) expressions are derived
for uplink cell-free massive multiple-input-multiple-output (CF-mMIMO) system.
The access points (APs) of the system considered have imperfect channel state
information (CSI). The approximate expressions are derived first using
conditional expectations and then using a novel dimension reduction method that
approximates higher-order integration by several single order integrations.
Using the same approach, closed-form approximations are also derived for
conventional massive MIMO (mMIMO) systems. The OP approximations are then used
to characterize the performance of cell-edge users of CF-mMIMO systems and
compare the designs of CF-mMIMO and mMIMO systems. The derived expressions have
a close match with the simulated expression for OP.


Abstract:
  In this paper, we propose an interesting semi-sparsity smoothing algorithm
based on a novel sparsity-inducing optimization framework. This method is
derived from the multiple observations, that is, semi-sparsity prior knowledge
is more universally applicable, especially in areas where sparsity is not fully
admitted, such as polynomial-smoothing surfaces. We illustrate that this
semi-sparsity can be identified into a generalized $L_0$-norm minimization in
higher-order gradient domains, thereby giving rise to a new "feature-aware"
filtering method with a powerful simultaneous-fitting ability in both sparse
features (singularities and sharpening edges) and non-sparse regions
(polynomial-smoothing surfaces). Notice that a direct solver is always
unavailable due to the non-convexity and combinatorial nature of $L_0$-norm
minimization. Instead, we solve the model based on an efficient half-quadratic
splitting minimization with fast Fourier transforms (FFTs) for acceleration. We
finally demonstrate its versatility and many benefits to a series of
signal/image processing and computer vision applications.


Abstract:
  Multicore fibers are expected to be a game-changer in the coming decades
thanks to their intrinsic properties, allowing a larger transmission bandwidth
and a lower footprint in optical communications. In addition, multicore fibers
have recently been explored for quantum communication, attesting their
uniqueness in transporting high-dimensional quantum states. However,
investigations and experiments reported in literature have been carried out in
research laboratories, typically making use of short fiber links in controlled
environments. Thus, the possibility of using long distance multicore fibers for
quantum applications is still to be proven. We here characterize for the first
time, in terms of phase stability, multiple strands of a 4-core multicore fiber
installed underground in the city of L'Aquila, with an overall fiber length up
to about 25 km. In this preliminary study, we investigate the possibility of
using such an infrastructure to implement quantum-enhanced schemes, such as
high-dimensional quantum key distribution, quantum-based environmental sensors,
and more in general quantum communication protocols.


Abstract:
  The standard classic special relativistic transformation of the
electromagnetic (EM) field under proper Lorentz transformations is revisited.
As to the pure Lorentz-boosts, popular treatments on EM transformation
contemplate ideal geometries generating special static charge and steady
current distributions and in conjunction, invoke parallel and perpendicular (to
the boost-velocity) components of the fields so engendered; the outcomes
subsequently being suitably generalized. We demonstrate ab initio, the EM field
transformation under arbitrary oblique uniform relative boost velocities from a
broader, rigorous yet remarkably lucid perspective, exploiting the antisymmetry
of the EM-field tensor and symmetry of the general Lorentz-boost transformation
preemptively eliminating the labor of performing complicated matrix
multiplications. Gratifyingly, this instructive exercise manifestly encompasses
time varying fields and yields the transformation relations in a
coordinate-free form. Further, the EM field transformation under proper,
instantaneous 3D rotations is demonstrated to transform as a passive rotation
of the coordinate axes, where the electric and magnetic fields transform
independently as purely electric and magnetic fields, respectively. The present
didactical exercise, amenable to be incorporated in graduate courses on
relativistic electrodynamics, should serve as an excellent illustration on how
powerful symmetry arguments simplify the otherwise tedious calculations while
endowing complete generality to the connections obtained.


Abstract:
  We consider the Navier--Stokes--Fourier system describing the motion of a
compressible, viscous, and heat conducting fluid in a bounded domain with
general non-homogeneous Dirichlet boundary conditions for the velocity and the
absolute temperature, with the associated boundary conditions for the density
on the inflow part. We introduce a new concept of weak solution based on the
satisfaction of the entropy inequality together with a balance law for the
ballistic energy. We show the weak-strong uniqueness principle as well as the
existence of global-in-time solutions.


Abstract:
  In this paper, we propose a known-plaintext attack (KPA) method based on deep
learning for traditional chaotic encryption scheme. We employ the convolutional
neural network to learn the operation mechanism of chaotic cryptosystem, and
accept the trained network as the final decryption system. To evaluate the
attack performance of different networks on different chaotic cryptosystem, we
adopt two neural networks to perform known-plaintext attacks on two distinct
chaotic encryption schemes. The experimental results demonstrate the potential
of deep learning-based method for known-plaintext attack against chaotic
cryptosystem. Different from the previous known-plaintext attack methods, which
were usually limited to a specific chaotic cryptosystem, a neural network can
be applied to the cryptanalysis of various chaotic cryptosystems with deep
learning-based approach, while several different networks can be designed for
the cryptanalysis of chaotic cryptosystems. This paper provides a new idea for
the cryptanalysis of chaotic image encryption algorithm.


Abstract:
  Let $R$ be the power series ring or the polynomial ring over a field $k$ and
let $I $ be an ideal of $R.$ Macaulay proved that the Artinian Gorenstein
$k$-algebras $R/I$ are in one-to-one correspondence with the cyclic
$R$-submodules of the divided power series ring $\Gamma. $ The result is
effective in the sense that any polynomial of degree $s$ produces an Artinian
Gorenstein $k$-algebra of socle degree $s.$ In a recent paper, the authors
extended Macaulay's correspondence characterizing the $R$-submodules of $\Gamma
$ in one-to-one correspondence with Gorenstein d-dimensional $k$-algebras.
However, these submodules in positive dimension are not finitely generated. Our
goal is to give constructive and finite procedures for the construction of
Gorenstein $k$-algebras of dimension one and any codimension. This has been
achieved through a deep analysis of the $G$-admissible submodules of $\Gamma. $
Applications to the Gorenstein linkage of zero-dimensional schemes and to
Gorenstein affine semigroup rings are discussed.