Abstract:
  A near-infrared (NIR) enhanced silicon single-photon avalanche diode (SPAD)
fabricated in a customized 0.13 $\mu$m CMOS technology is presented. The SPAD
has a depleted absorption volume of approximately 15 $\mu$m x 15 $\mu$m x 18
$\mu$m. Electrons generated in the absorption region are efficiently
transported by drift to a central active avalanche region with a diameter of 2
$\mu$m. At the operating voltage, the active region contains a spherically
uniform field peak, enabling the multiplication of electrons originating from
all corners of the device. The advantages of the SPAD architecture include high
NIR photon detection efficiency (PDE), drift-based transport, low afterpulsing,
and compatibility with an integrated CMOS readout. A front-side illuminated
device is fabricated and characterized. The SPAD has a PDE of 13% at wavelength
905 nm, an afterpulsing probability < 0.1% for a dead time of 13 ns, and a
median dark count rate (DCR) of 840 Hz at room temperature. The device shows
promising performance for time-of-flight applications that benefit from uniform
NIR-sensitive SPAD arrays.


Abstract:
  The fluidic pinball is a geometrically simple flow configuration with three
rotating cylinders on the vertex of an equilateral triangle. Yet, it remains
physically rich enough to host a range of interacting frequencies and to allow
testing of control laws within minutes on a laptop. The system has multiple
inputs (the three cylinders can independently rotate around their axis) and
multiple outputs (downstream velocity sensors). Investigating the natural flow
dynamics, we found that the first unsteady transition undergone by the wake
flow, when increasing the Reynolds number, is a Hopf bifurcation leading to the
usual time-periodic vortex shedding phenomenon, typical of cylinder wake flows,
in which the mean flow field preserves axial symmetry. We extract dynamically
consistent modes from the flow data in order to built a reduced-order model
(ROM) of this flow regime. We show that the main dynamical features of the
primary Hopf bifurcation can be described by a non-trivial lowest-order model
made of three degrees of freedom.


Abstract:
  In this paper, we propose a novel reference based image super-resolution
approach via Variational AutoEncoder (RefVAE). Existing state-of-the-art
methods mainly focus on single image super-resolution which cannot perform well
on large upsampling factors, e.g., 8$\times$. We propose a reference based
image super-resolution, for which any arbitrary image can act as a reference
for super-resolution. Even using random map or low-resolution image itself, the
proposed RefVAE can transfer the knowledge from the reference to the
super-resolved images. Depending upon different references, the proposed method
can generate different versions of super-resolved images from a hidden
super-resolution space. Besides using different datasets for some standard
evaluations with PSNR and SSIM, we also took part in the NTIRE2021 SR Space
challenge and have provided results of the randomness evaluation of our
approach. Compared to other state-of-the-art methods, our approach achieves
higher diverse scores.


Abstract:
  We consider zero sets of entire functions belonging to the Schwartz algebra.
This algebra is defined as the Fourier-Laplace transform image of the space of
all distributions compactly supported on the real line. We study the conditions
under which given complex sequence forms zero set of some invertible in the
sense of Ehrenpreis element of the Schwartz algebra (slowly decreasing
function).


Abstract:
  Smart cities will be characterized by a variety of intelligent and networked
services, each with specific requirements for the underlying network
infrastructure. While smart city architectures and services have been studied
extensively, little attention has been paid to the network technology. The
KIGLIS research project, consisting of a consortium of companies, universities
and research institutions, focuses on artificial intelligence for optimizing
fiber-optic networks of a smart city, with a special focus on future mobility
applications, such as automated driving. In this paper, we present early
results on our process of collecting smart city requirements for communication
networks, which will lead towards reference infrastructure and architecture
solutions. Finally, we suggest directions in which artificial intelligence will
improve smart city networks.


Abstract:
  Terahertz (THz) imaging has recently attracted significant attention thanks
to its non-invasive, non-destructive, non-ionizing, material-classification,
and ultra-fast nature for object exploration and inspection. However, its
strong water absorption nature and low noise tolerance lead to undesired blurs
and distortions of reconstructed THz images. The performances of existing
restoration methods are highly constrained by the diffraction-limited THz
signals. To address the problem, we propose a novel
Subspace-and-Attention-guided Restoration Network (SARNet) that fuses
multi-spectral features of a THz image for effective restoration. To this end,
SARNet uses multi-scale branches to extract spatio-spectral features of
amplitude and phase which are then fused via shared subspace projection and
attention guidance. Here, we experimentally construct ultra-fast THz
time-domain spectroscopy system covering a broad frequency range from 0.1 THz
to 4 THz for building up temporal/spectral/spatial/phase/material THz database
of hidden 3D objects. Complementary to a quantitative evaluation, we
demonstrate the effectiveness of our SARNet model on 3D THz tomographic
reconstruction


Abstract:
  The solar energy is clean and future energy for electricity generation. Its
energy has enormous potential but do not optimal to utilized caused by
intermittent energy. The intermittent radiance and ambient temperature
influence the energy produced fluctuates and unstable. These power fluctuations
affect system stability and frequency. To overcome this problem, several
methods for PV power stabilization have been developed. One of them is the
Stabilized Power Generation where PV output power is to a certain power value.
The result of SPG method is reducing in PV power fluctuations but still
unstable. For reaching the stable condition of PV Power output, the Dynamic
Power Insertion method is proposed which is a modification of the SPG method
with energy storage batteries. DPI improve the SPG method and make PV power
stable with active power management with charge and discharge action. The
battery is using as energy storage when PV power is larger than Power limit. On
the other hand battery as an energy source for active power insertion at PV
power is smaller than Power limit. Thus the output power in each condition can
be maintained as Power PV equals to P limit. For test this method, DPI modules
are built on ARM lpc1768 NXP and monitored with the Thing speak webserver
(simulated with Simulink MatLab Software). The experimental results of DPI can
stabilize PV power fluctuation at its setting power with an error of 5 percent.


Abstract:
  Image relighting is attracting increasing interest due to its various
applications. From a research perspective, image relighting can be exploited to
conduct both image normalization for domain adaptation, and also for data
augmentation. It also has multiple direct uses for photo montage and aesthetic
enhancement. In this paper, we review the NTIRE 2021 depth guided image
relighting challenge.
  We rely on the VIDIT dataset for each of our two challenge tracks, including
depth information. The first track is on one-to-one relighting where the goal
is to transform the illumination setup of an input image (color temperature and
light source position) to the target illumination setup. In the second track,
the any-to-any relighting challenge, the objective is to transform the
illumination settings of the input image to match those of another guide image,
similar to style transfer. In both tracks, participants were given depth
information about the captured scenes. We had nearly 250 registered
participants, leading to 18 confirmed team submissions in the final competition
stage. The competitions, methods, and final results are presented in this
paper.


Abstract:
  The synthesis problem asks to automatically generate, if it exists, an
algorithm from a specification of correct input-output pairs. In this paper, we
consider the synthesis of computable functions of infinite words, for a
classical Turing computability notion over infinite inputs. We consider
specifications which are rational relations of infinite words, i.e.,
specifications defined non-deterministic parity transducers. We prove that the
synthesis problem of computable functions from rational specifications is
undecidable. We provide an incomplete but sound reduction to some parity game,
such that if Eve wins the game, then the rational specification is realizable
by a computable function. We prove that this function is even computable by a
deterministic two-way transducer. We provide a sufficient condition under which
the latter game reduction is complete. This entails the decidability of the
synthesis problem of computable functions, which we prove to be
ExpTime-complete, for a large subclass of rational specifications, namely
deterministic rational specifications. This subclass contains the class of
automatic relations over infinite words, a yardstick in reactive synthesis.


Abstract:
  Great progress has been made in unsupervised bilingual lexicon induction
(UBLI) by aligning the source and target word embeddings independently trained
on monolingual corpora. The common assumption of most UBLI models is that the
embedding spaces of two languages are approximately isomorphic. Therefore the
performance is bound by the degree of isomorphism, especially on etymologically
and typologically distant languages. To address this problem, we propose a
transformation-based method to increase the isomorphism. Embeddings of two
languages are made to match with each other by rotating and scaling. The method
does not require any form of supervision and can be applied to any language
pair. On a benchmark data set of bilingual lexicon induction, our approach can
achieve competitive or superior performance compared to state-of-the-art
methods, with particularly strong results being found on distant languages.


Abstract:
  We present the results of photometric and spectroscopic monitoring campaigns
of the changing look AGN NGC 3516 carried out in 2018 to 2020 covering the
wavelength range from the X-ray to the optical. The facilities included the
telescopes of the CMO SAI MSU, the 2.3-m WIRO telescope, and the XRT and UVOT
of Swift. We found that NGC 3516 brightened to a high state and could be
classified as Sy1.5 during the late spring of 2020. We have measured time
delays in the responses of the Balmer and He II 4686 lines to continuum
variations. In the case of the best-characterized broad H-beta line, the delay
to continuum variability is about 17 days in the blue wing and is clearly
shorter, 9 days, in the red, which is suggestive of inflow. As the broad lines
strengthened, the blue side came to dominate the Balmer lines, resulting in
very asymmetric profiles with blueshifted peaks during this high state. During
the outburst the X-ray flux reached its maximum on 1 April 2020 and it was the
highest value ever observed for NGC 3516 by the Swift observatory. The X-ray
hard photon index became softer, about 1.8 in the maximum on 21 Apr 2020
compared to the mean about 0.7 during earlier epochs before 2020. We have found
that the UV and optical variations correlated well (with a small time delay of
1-2 days) with the X-ray until the beginning of April 2020, but later, until
the end of Jun. 2020, these variations were not correlated. We suggest that
this fact may be a consequence of partial obscuration by Compton-thick clouds
crossing the line of sight.


Abstract:
  Image augmentation techniques apply transformation functions such as
rotation, shearing, or color distortion on an input image. These augmentations
were proven useful in improving neural networks' generalization ability. In
this paper, we present a novel augmentation operation, InAugment, that exploits
image internal statistics. The key idea is to copy patches from the image
itself, apply augmentation operations on them, and paste them back at random
positions on the same image. This method is simple and easy to implement and
can be incorporated with existing augmentation techniques. We test InAugment on
two popular datasets -- CIFAR and ImageNet. We show improvement over
state-of-the-art augmentation techniques. Incorporating InAugment with Auto
Augment yields a significant improvement over other augmentation techniques
(e.g., +1% improvement over multiple architectures trained on the CIFAR
dataset). We also demonstrate an increase for ResNet50 and EfficientNet-B3
top-1's accuracy on the ImageNet dataset compared to prior augmentation
methods. Finally, our experiments suggest that training convolutional neural
network using InAugment not only improves the model's accuracy and confidence
but its performance on out-of-distribution images.


Abstract:
  This study presents experimental and computational analyses of the phase
composition and mechanical behavior of a functionally graded material (FGM)
grading from 100 vol% 304L stainless steel (SS304L) to 100 vol% Ni-20Cr (NiCr)
in 10 vol% increments. The mechanical behavior of the FGM was evaluated by
extracting tensile specimens from SS304L-rich and NiCr-rich regions of the FGM,
with the former behaving in a ductile manner and the latter being relatively
brittle. The brittle response of the NiCr-rich sample was explained by the
presence of the eutectic FCC+BCC phase in this sample, as identified by
Scheil-Gulliver simulations along with experimental phase and composition
analyses. The spatially varying yield and flow behavior of the FGM was
determined through comparison of finite element analysis simulations with
experimentally measured stress-strain curves and surface strain contours. The
results highlight that the overall ductile behavior of SS304L and brittle
behavior of NiCr are preserved in the SS304L/NiCr FGM but the formation of the
brittle eutectic phase further reduced the ductility within the regions where
it formed (90 vol% NiCr/10 vol% SS304L).


Abstract:
  The generalized fractional Brownian motion (GFBM) $X:=\{X(t)\}_{t\ge0}$ with
parameters $\gamma \in [0, 1)$ and $\alpha\in \left(-\frac12+\frac{\gamma}{2},
\, \frac12+\frac{\gamma}{2} \right)$ is a centered Gaussian $H$-self-similar
process introduced by Pang and Taqqu (2019) as the scaling limit of power-law
shot noise processes, where $H = \alpha-\frac{\gamma}{2}+\frac12 \in(0,1)$.
When $\gamma = 0$, $X$ is the ordinary fractional Brownian motion. For $\gamma
\in (0, 1)$, GFBM $X$ does not have stationary increments, and its sample path
properties such as H\"older continuity, path
differentiability/non-differentiability, and the functional law of the iterated
logarithm have been investigated recently by Ichiba, Pang and Taqqu (2020).
They mainly focused on sample path properties that are described in terms of
the self-similarity index $H$ (e.g., LILs at the origin and at infinity).
  In this paper, we further study the sample path properties of GFBM $X$ and
establish the exact uniform modulus of continuity, small ball probabilities,
and Chung's law of iterated logarithm. Our results show that the local
regularity properties of GFBM $X$ away from the origin are determined by the
index $\alpha +\frac1 2$, instead of the self-similarity index $H$. This is in
contrast with the properties of ordinary fractional Brownian motion whose local
and asymptotic properties are determined by the single index $H$.


Abstract:
  This paper solves the rational noncommutative analog of Hilbert's 17th
problem: if a noncommutative rational function is positive semidefinite on all
tuples of hermitian matrices in its domain, then it is a sum of hermitian
squares of noncommutative rational functions. This result is a generalization
and culmination of earlier positivity certificates for noncommutative
polynomials or rational functions without hermitian singularities. More
generally, a rational Positivstellensatz for free spectrahedra is given: a
noncommutative rational function is positive semidefinite or undefined at every
matricial solution of a linear matrix inequality $L\succeq0$ if and only if it
belongs to the rational quadratic module generated by $L$. The essential
intermediate step towards this Positivstellensatz for functions with
singularities is an extension theorem for invertible evaluations of linear
matrix pencils.


Abstract:
  The DerSimonian-Laird (DL) weighted average method has been widely used for
estimation of a pooled effect size from an aggregated data meta-analysis study.
It is mainly criticized for its underestimation of the standard error of the
pooled effect size in the presence of heterogeneous study effect sizes. The
uncertainty in the estimation of the between-study variance is not accounted
for in the calculation of this standard error. Due to this negative property,
many alternative estimation approaches have been proposed in literature. One
approach was developed by Hardy and Thompson (HT), who implemented a profile
likelihood approach instead of the moment-based approach of DL. Others have
further extended the likelihood approach and proposed higher-order likelihood
inferences (e.g., Bartlett-type corrections). Likelihood-based methods better
address the uncertainty in estimating the between-study variance than the DL
method, but all these methods assume that the within-study standard deviations
are known and equal to the observed standard error of the study effect sizes.
Here we treat the observed standard errors as estimators for the within-study
variability and we propose a bivariate likelihood approach that jointly
estimates the pooled effect size, the between-study variance, and the
potentially heteroscedastic within-study variances. We study the performance of
the proposed method by means of simulation, and compare it to DL, HT, and the
higher-order likelihood methods. Our proposed approach appears to be less
sensitive to the number of studies, and less biased in case of
heteroscedasticity.


Abstract:
  For an algebraic number $\alpha$ we consider the orders of the reductions of
$\alpha$ in finite fields. In the case where $\alpha$ is an integer, it is
known by the work on Artin's primitive root conjecture that the order is
"almost always almost maximal" assuming the generalized Riemann hypothesis, but
unconditional results remain modest. We consider higher degree variants under
GRH. First, we modify an argument of Roskam to settle the case where $\alpha$
and the reduction have degree two. Second, we give a positive lower density
result when $\alpha$ is of degree three and the reduction is of degree two.
Third, we give higher rank results in situations where the reductions are of
degree two, three, four or six. As an application we give an almost
equidistribution result for linear recurrences modulo primes. Finally, we
present a general result conditional to GRH and a hypothesis on smooth values
of polynomials at prime arguments.


Abstract:
  This is an article or technical note which is intended to provides an insight
journey of Machine Learning Systems (MLS) testing, its evolution, current
paradigm and future work. Machine Learning Models, used in critical
applications such as healthcare industry, Automobile, and Air Traffic control,
Share Trading etc., and failure of ML Model can lead to severe consequences in
terms of loss of life or property. To remediate this, developers, scientists,
and ML community around the world, must build a highly reliable test
architecture for critical ML application. At the very foundation layer, any
test model must satisfy the core testing attributes such as test properties and
its components. This attribute comes from the software engineering, but the
same cannot be applied in as-is form to the ML testing and we will tell you
why.


Abstract:
  Facial expressions play a fundamental role in human communication. Indeed,
they typically reveal the real emotional status of people beyond the spoken
language. Moreover, the comprehension of human affect based on visual patterns
is a key ingredient for any human-machine interaction system and, for such
reasons, the task of Facial Expression Recognition (FER) draws both scientific
and industrial interest. In the recent years, Deep Learning techniques reached
very high performance on FER by exploiting different architectures and learning
paradigms. In such a context, we propose a multi-resolution approach to solve
the FER task. We ground our intuition on the observation that often faces
images are acquired at different resolutions. Thus, directly considering such
property while training a model can help achieve higher performance on
recognizing facial expressions. To our aim, we use a ResNet-like architecture,
equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild
2 dataset. Not being available a test set, we conduct tests and models
selection by employing the validation set only on which we achieve more than
90\% accuracy on classifying the seven expressions that the dataset comprises.


Abstract:
  This paper studies average-cost Markov decision processes with semi-uniform
Feller transition probabilities. This class of MDPs was recently introduced by
the authors to study MDPs with incomplete information. This paper studies the
validity of optimality inequalities, the existence of optimal policies, and the
approximations of optimal policies by policies optimizing total discounted
costs.


Abstract:
  Voluntary shelter-in-place directives and lockdowns are the main
non-pharmaceutical interventions that governments around the globe have used to
contain the Covid-19 pandemic. In this paper we study the impact of such
interventions in the capital of a developing country, Santiago, Chile, that
exhibits large socioeconomic inequality. A distinctive feature of our study is
that we use granular geolocated mobile phone data to construct mobility
measures that capture (1) shelter-in-place behavior, and (2) trips within the
city to destinations with potentially different risk profiles. Using panel data
linear regression models we first show that the impact of social distancing
measures and lockdowns on mobility is highly heterogeneous and dependent on
socioeconomic levels. More specifically, our estimates indicate that while
zones of high socioeconomic levels can exhibit reductions in mobility of around
50\% to 90\% depending on the specific mobility metric used, these reductions
are only 20\% to 50\% for lower-income communities. The large reductions in
higher-income communities are significantly driven by voluntary
shelter-in-place behavior. Second, also using panel data methods we show that
our mobility measures are important predictors of infections: roughly, a 10\%
increase in mobility correlates with a 5\% increase in the rate of infection.
Our results suggest that mobility is an important factor explaining differences
in infections rates between high and low incomes areas within the city.
Further, they confirm the challenges of reducing mobility in lower-income
communities, where people generate their income from their daily work. To be
effective, shelter-in-place restrictions in municipalities of low socioeconomic
levels may need to be complemented by other supporting measures that enable
their inhabitants to increase compliance.


Abstract:
  Robotic assembly planning has the potential to profoundly change how
buildings can be designed and created. It enables architects to explicitly
account for the assembly process already during the design phase, and enables
efficient building methods that profit from the robots' different capabilities.
Previous work has addressed planning of robot assembly sequences and
identifying the feasibility of architectural designs. This paper extends
previous work by enabling assembly planning with large, heterogeneous teams of
robots. We present a scalable planning system which enables parallelization of
complex task and motion planning problems by iteratively solving smaller
sub-problems. Combining optimization methods to solve for manipulation
constraints with a sampling-based bi-directional space-time path planner
enables us to plan cooperative multi-robot manipulation with unknown
arrival-times. Thus, our solver allows for completing sub-problems and tasks
with differing timescales and synchronizes them effectively. We demonstrate the
approach on multiple case-studies and on two long-horizon building assembly
scenarios to show the robustness and scalability of our algorithm.


Abstract:
  To cater the rapidly growing demand for electricity leading to the
integration of renewable energy sources in power system. Due to intermittent
nature of renewables, it also brings challenges for research community during
the planning and operation stage in power system. Therefore it is primary
necessity of the community to develop an accurate forecasting technique to
solve the intermittency problem. In this report, A forecasting technique is
proposed based on ensemble of state of the art forecasting techniques. For
performance comparison among the techniques, GEFCom2014 meteorological data are
used to predict the photovoltaic power, and the obtained results are included
in this report.


Abstract:
  Compliant mechanisms are alternatives to conventional mechanisms which
exploit elastic strain to produce desired deformations instead of using
moveable parts. They are designed for a kinematic task (providing desired
deformations) but do not possess a kinematics in the strict sense. This leads
to difficulties while assessing the quality of a compliant mechanism's design.
The kinematics of a compliant mechanism can be seen as a fuzzy property. There
is no unique kinematics, since every deformation need a particular force system
to act; however, certain deformations are easier to obtain than others. A
parallel can be made with measurement theory: the measured value of a quantity
is not unique, but exists as statistic distribution of measures. A
representative measure of this distribution can be chosen to evaluate how far
the measures divert from a reference value. Based on this analogy, the concept
of accuracy and precision of compliant systems are introduced and discussed in
this paper. A quantitative determination of these qualities based on the
eigenvalue analysis of the hinge's stiffness is proposed. This new approach is
capable of removing most of the ambiguities included in the state-of-the-art
assessment criteria (usually based on the concepts of path deviation and
parasitic motion).


Abstract:
  The paper describes the usage of intelligent approaches for field development
tasks that may assist a decision-making process. We focused on the problem of
wells location optimization and two tasks within it: improving the quality of
oil production estimation and estimation of reservoir characteristics for
appropriate wells allocation and parametrization, using machine learning
methods. For oil production estimation, we implemented and investigated the
quality of forecasting models: physics-based, pure data-driven, and hybrid one.
The CRMIP model was chosen as a physics-based approach. We compare it with the
machine learning and hybrid methods in a frame of oil production forecasting
task. In the investigation of reservoir characteristics for wells location
choice, we automated the seismic analysis using evolutionary identification of
convolutional neural network for the reservoir detection. The Volve oil field
dataset was used as a case study to conduct the experiments. The implemented
approaches can be used to analyze different oil fields or adapted to similar
physics-related problems.


Abstract:
  Binomial coefficients have been used for centuries in a variety of fields and
have accumulated numerous definitions. In this paper, we introduce a new way of
defining binomial coefficients as repeated sums of ones. A multitude of
binomial coefficient identities will be shown in order to prove this
definition. Using this new definition, we simplify some particular sums such as
the repeated Harmonic sum and the repeated Binomial-Harmonic sum. We derive
formulae for simplifying general repeated sums as well as a variant containing
binomial coefficients. Additionally, we study the $m$-th difference of a
sequence and show how sequences whose $m$-th difference is constant can be
related to binomial coefficients.


Abstract:
  A crucial subroutine in quantum computing is to load the classical data of
$N$ complex numbers into the amplitude of a superposed $n=\lceil
\log_2N\rceil$-qubit state. It has been proven that any algorithm universally
implementing this subroutine would need at least $\mathcal O(N)$ constant
weight operations. However, the proof assumes that only $n$ qubits are used,
whereas the circuit depth could be reduced by extending the space and allowing
ancillary qubits. Here we investigate this space-time tradeoff in quantum state
preparation with classical data. We propose quantum algorithms with $\mathcal
O(n^2)$ circuit depth to encode any $N$ complex numbers using only single-,
two-qubit gates and local measurements with ancillary qubits. Different
variances of the algorithm are proposed with different space and runtime. In
particular, we present a scheme with $\mathcal O(N^2)$ ancillary qubits,
$\mathcal O(n^2)$ circuit depth, and $\mathcal O(n^2)$ average runtime, which
exponentially improves the conventional bound. While the algorithm requires
more ancillary qubits, it consists of quantum circuit blocks that only
simultaneously act on a constant number of qubits and at most $\mathcal O(n)$
qubits are entangled. We also prove a fundamental lower bound $\mit\Omega(n)$
for the minimum circuit depth and runtime with arbitrary number of ancillary
qubits, aligning with our scheme with $\mathcal O(n^2)$. The algorithms are
expected to have wide applications in both near-term and universal quantum
computing.


Abstract:
  In this note, we show that one can use average embeddings, introduced
recently in [Naor'20, arXiv:1905.01280], to obtain efficient algorithms for
approximate nearest neighbor search. In particular, a metric $X$ embeds into
$\ell_2$ on average, with distortion $D$, if, for any distribution $\mu$ on
$X$, the embedding is $D$ Lipschitz and the (square of) distance does not
decrease on average (wrt $\mu$). In particular existence of such an embedding
(assuming it is efficient) implies a $O(D^3)$ approximate nearest neighbor
search under $X$. This can be seen as a strengthening of the classic
(bi-Lipschitz) embedding approach to nearest neighbor search, and is another
application of data-dependent hashing paradigm.


Abstract:
  In this study, we propose a novel approach to predict the distances of the
detected objects in an observed scene. The proposed approach modifies the
recently proposed Convolutional Support Estimator Networks (CSENs). CSENs are
designed to compute a direct mapping for the Support Estimation (SE) task in a
representation-based classification problem. We further propose and demonstrate
that representation-based methods (sparse or collaborative representation) can
be used in well-designed regression problems. To the best of our knowledge,
this is the first representation-based method proposed for performing a
regression task by utilizing the modified CSENs; and hence, we name this novel
approach as Representation-based Regression (RbR). The initial version of CSENs
has a proxy mapping stage (i.e., a coarse estimation for the support set) that
is required for the input. In this study, we improve the CSEN model by
proposing Compressive Learning CSEN (CL-CSEN) that has the ability to jointly
optimize the so-called proxy mapping stage along with convolutional layers. The
experimental evaluations using the KITTI 3D Object Detection distance
estimation dataset show that the proposed method can achieve a significantly
improved distance estimation performance over all competing methods. Finally,
the software implementations of the methods are publicly shared at
https://github.com/meteahishali/CSENDistance.


Abstract:
  3D convolution is powerful for video classification but often computationally
expensive, recent studies mainly focus on decomposing it on spatial-temporal
and/or channel dimensions. Unfortunately, most approaches fail to achieve a
preferable balance between convolutional efficiency and feature-interaction
sufficiency. For this reason, we propose a concise and novel Channel
Tensorization Network (CT-Net), by treating the channel dimension of input
feature as a multiplication of K sub-dimensions. On one hand, it naturally
factorizes convolution in a multiple dimension way, leading to a light
computation burden. On the other hand, it can effectively enhance feature
interaction from different channels, and progressively enlarge the 3D receptive
field of such interaction to boost classification accuracy. Furthermore, we
equip our CT-Module with a Tensor Excitation (TE) mechanism. It can learn to
exploit spatial, temporal and channel attention in a high-dimensional manner,
to improve the cooperative power of all the feature dimensions in our
CT-Module. Finally, we flexibly adapt ResNet as our CT-Net. Extensive
experiments are conducted on several challenging video benchmarks, e.g.,
Kinetics-400, Something-Something V1 and V2. Our CT-Net outperforms a number of
recent SOTA approaches, in terms of accuracy and/or efficiency. The codes and
models will be available on https://github.com/Andy1621/CT-Net.


Abstract:
  In today's business marketplace, many high-tech Internet enterprises
constantly explore innovative ways to provide optimal online user experiences
for gaining competitive advantages. The great needs of developing intelligent
interactive recommendation systems are indicated, which could sequentially
suggest users the most proper items by accurately predicting their preferences,
while receiving the up-to-date feedback to refine the recommendation results,
continuously. Multi-armed bandit algorithms, which have been widely applied
into various online systems, are quite capable of delivering such efficient
recommendation services. However, few existing bandit models are able to adapt
to new changes introduced by the modern recommender systems.


Abstract:
  During the pandemic of COVID-19, the demand of the transportation systems are
drastically changed both qualitatively and quantitatively and the network has
become obsolete. In this article, we study the problem of finding an optimal
subnetwork that guarantee that (i) the minimal access time from any node of the
urban network to the new network is not {\em too large} compared to the
original transportation network; (ii) for any itinerary, the delay caused by
the deletion of nodes of the transportation network is not {\em too big}; and
(iii) the number of nodes of the transportation network has been reduced at
least by a known factor. A solution is optimal if it induces a minimal global
delay. We model this problem as a Mixed Integer Linear Program before applying
the model on a real-case application on the Lyon's buses transportation
network.


Abstract:
  In recent years, ransomware has been one of the most notorious malware
targeting end users, governments, and business organizations. It has become a
very profitable business for cybercriminals with revenues of millions of
dollars, and a very serious threat to organizations with financial loss of
billions of dollars. Numerous studies were proposed to address the ransomware
threat, including surveys that cover certain aspects of ransomware research.
However, no study exists in the literature that gives the complete picture on
ransomware and ransomware defense research with respect to the diversity of
targeted platforms. Since ransomware is already prevalent in
PCs/workstations/desktops/laptops, is becoming more prevalent in mobile
devices, and has already hit IoT/CPS recently, and will likely grow further in
the IoT/CPS domain very soon, understanding ransomware and analyzing defense
mechanisms with respect to target platforms is becoming more imperative. In
order to fill this gap and motivate further research, in this paper, we present
a comprehensive survey on ransomware and ransomware defense research with
respect to PCs/workstations, mobile devices, and IoT/CPS platforms.
Specifically, covering 137 studies over the period of 1990-2020, we give a
detailed overview of ransomware evolution, comprehensively analyze the key
building blocks of ransomware, present a taxonomy of notable ransomware
families, and provide an extensive overview of ransomware defense research
(i.e., analysis, detection, and recovery) with respect to platforms of
PCs/workstations, mobile devices, and IoT/CPS. Moreover, we derive an extensive
list of open issues for future ransomware research. We believe this survey will
motivate further research by giving a complete picture on state-of-the-art
ransomware research.


Abstract:
  Deeply learned representations are the state-of-the-art descriptors for face
recognition methods. These representations encode latent features that are
difficult to explain, compromising the confidence and interpretability of their
predictions. Most attempts to explain deep features are visualization
techniques that are often open to interpretation. Instead of relying only on
visualizations, we use the outputs of hidden layers to predict face attributes.
The obtained performance is an indicator of how well the attribute is
implicitly learned in that layer of the network. Using a variable selection
technique, we also analyze how these semantic concepts are distributed inside
each layer, establishing the precise location of relevant neurons for each
attribute. According to our experiments, gender, eyeglasses and hat usage can
be predicted with over 96% accuracy even when only a single neural output is
used to predict each attribute. These performances are less than 3 percentage
points lower than the ones achieved by deep supervised face attribute networks.
In summary, our experiments show that, inside DCNNs optimized for face
identification, there exists latent neurons encoding face attributes almost as
accurately as DCNNs optimized for these attributes.


Abstract:
  The growing popularity of wearable sensors has generated large quantities of
temporal physiological and activity data. Ability to analyze this data offers
new opportunities for real-time health monitoring and forecasting. However,
temporal physiological data presents many analytic challenges: the data is
noisy, contains many missing values, and each series has a different length.
Most methods proposed for time series analysis and classification do not handle
datasets with these characteristics nor do they offer interpretability and
explainability, a critical requirement in the health domain. We propose an
unsupervised method for learning representations of time series based on common
patterns identified within them. The patterns are, interpretable, variable in
length, and extracted using Byte Pair Encoding compression technique. In this
way the method can capture both long-term and short-term dependencies present
in the data. We show that this method applies to both univariate and
multivariate time series and beats state-of-the-art approaches on a real world
dataset collected from wearable sensors.


Abstract:
  We consider the problem of computing the topology and describing the geometry
of a parametric curve in $\mathbb{R}^n$. We present an algorithm, PTOPO, that
constructs an abstract graph that is isotopic to the curve in the embedding
space. Our method exploits the benefits of the parametric representation and
does not resort to implicitization.
  Most importantly, we perform all computations in the parameter space and not
in the implicit space. When the parametrization involves polynomials of degree
at most $d$ and maximum bitsize of coefficients $\tau$, then the worst case bit
complexity of PTOPO is $
\widetilde{\mathcal{O}}_B(nd^6+nd^5\tau+d^4(n^2+n\tau)+d^3(n^2\tau+
n^3)+n^3d^2\tau)$. This bound matches the current record bound
$\widetilde{\mathcal{O}}_B(d^6+d^5\tau)$ for the problem of computing the
topology of a plane algebraic curve given in implicit form. For plane and space
curves, if $N = \max\{d, \tau \}$, the complexity of PTOPO becomes
$\widetilde{\mathcal{O}}_B(N^6)$, which improves the state-of-the-art result,
due to Alc\'azar and D\'iaz-Toca [CAGD'10], by a factor of $N^{10}$. In the
same time complexity, we obtain a graph whose straight-line embedding is
isotopic to the curve. However, visualizing the curve on top of the abstract
graph construction, increases the bound to $\widetilde{\mathcal{O}}_B(N^7)$.
For curves of general dimension, we can also distinguish between ordinary and
non-ordinary real singularities and determine their multiplicities in the same
expected complexity of PTOPO by employing the algorithm of Blasco and
P\'erez-D\'iaz [CAGD'19]. We have implemented PTOPO in Maple for the case of
plane and space curves. Our experiments illustrate its practical nature.


Abstract:
  Let $f$ and $g$ be two circle endomorphisms of degree $d\geq 2$ such that
each has bounded geometry, preserves the Lebesgue measure, and fixes $1$. Let
$h$ fixing $1$ be the topological conjugacy from $f$ to $g$. That is, $h\circ
f=g\circ h$. We prove that $h$ is a symmetric circle homeomorphism if and only
if $h=Id$. Many other rigidity results in circle dynamics follow from this very
general symmetric rigidity result.


Abstract:
  To describe the double-charge-exchange (DCE) processes, we have designed
recently the $(pn,2p2n)$-QTDA model which fully includes the pairing
correlations and four quasiparticle excitations. It has been applied in $2\nu$
double beta decays (DBDs), and the double charge-exchange resonances (DCERs).
Here we extend it to $ 0\nu $ DBD and discuss the relationship between the
nuclear matrix elements (NMEs), and the DCE reaction matrix elements (RMEs)
with the same spin-isospin structure. We do it for all final $0^+$ states, even
in the region of DCERs, where the DBD is energetically forbidden.
  As an example, we evaluate the DBD $^{76}$Ge $\rightarrow ^{76}$Se, both for
$2\nu$ and $0\nu$ modes, as well as the associated DCE sum rules, excitation
energies within the $Q$-value window for DBD, and the $Q$-value itself. We find
that the $0\nu$ NMEs are correlated with the RMEs, both at low energy, and in
the DCER region where most of the transition strength is concentrated. These
findings occur in other nuclei as well and suggest that measurements of $0^+$
DCERs could provide useful information regarding the $ 0\nu $ DBD. An analogous
comparison and conclusion cannot be made for the $2^+$ states, since the $0\nu$
NMEs and RMEs transition operators are not similar to each other in this case.


Abstract:
  Let $S_{g,n}$ be an oriented surface of genus $g$ with $n$ punctures, where
$2g-2+n>0$ and $n>0$. Any ideal triangulation of $S_{g,n}$ induces a global
parametrization of the Teichm\"uller space $\mathcal{T}_{g,n}$ called the
shearing coordinates. We study the asymptotics of the number of the mapping
class group orbits with respect to the standard Euclidean norm of the shearing
coordinates. The result is based on the works of Mirzakhani.


Abstract:
  We obtain high energy asymptotics of Titchmarsh-Weyl functions of the
generalised canonical systems generalising in this way a seminal Gesztesy-Simon
result. The matrix valued analog of the amplitude function satisfies in this
case an interesting new identity. The corresponding structured operators are
studied as well.


Abstract:
  Traditional video summarization methods generate fixed video representations
regardless of user interest. Therefore such methods limit users' expectations
in content search and exploration scenarios. Multi-modal video summarization is
one of the methods utilized to address this problem. When multi-modal video
summarization is used to help video exploration, a text-based query is
considered as one of the main drivers of video summary generation, as it is
user-defined. Thus, encoding the text-based query and the video effectively are
both important for the task of multi-modal video summarization. In this work, a
new method is proposed that uses a specialized attention network and
contextualized word representations to tackle this task. The proposed model
consists of a contextualized video summary controller, multi-modal attention
mechanisms, an interactive attention network, and a video summary generator.
Based on the evaluation of the existing multi-modal video summarization
benchmark, experimental results show that the proposed model is effective with
the increase of +5.88% in accuracy and +4.06% increase of F1-score, compared
with the state-of-the-art method.


Abstract:
  The nucleation of crystals from the liquid melt is often characterized by a
competition between different crystalline structures or polymorphs, and can
result in nuclei with heterogeneous compositions. These mixed-phase nuclei can
display nontrivial spatial arrangements, such as layered and onion-like
structures, whose composition varies according to the radial distance, and
which so far have been explained on the basis of bulk and surface free-energy
differences between the competing phases. Here we extend the generality of
these non-classical nucleation processes, showing that layered and onion-like
structures can emerge solely based on structural fluctuations even in absence
of free-energy differences. We consider two examples of competing crystalline
structures, hcp and fcc forming in hard spheres, relevant for repulsive
colloids and dense liquids, and the cubic and hexagonal diamond forming in
water, relevant also for other group 14 elements such as carbon and silicon. We
introduce a novel structural order parameter that combined with a neural
network classification scheme allows us to study the properties of the growing
nucleus from the early stages of nucleation. We find that small nuclei have
distinct size fluctuations and compositions from the nuclei that emerge from
the growth stage. The transition between these two regimes is characterized by
the formation of onion-like structures, in which the composition changes with
the distance from the center of the nucleus, similarly to what seen in two-step
nucleation process.


Abstract:
  Natural and resonant oscillations of suspended circular graphene and
hexagonal boron nitride (h-BN) membranes (single-layer sheets lying on a flat
substrate having a circular hole of radius $R$) have been simulated using
full-atomic models. Substrates formed by flat surfaces of graphite and h-BN
crystal, hexagonal ice, silicon carbide 6H-SiC and nickel surface (111) have
been used. The presence of the substrate leads to the forming of a gap at the
bottom of the frequency spectrum of transversal vibrations of the sheet. The
frequencies of natural oscillations of the membrane (oscillations localized on
the suspended section of the sheet) always lie in this gap, and the frequencies
of oscillations decrease by increasing radius of the membrane as $(R+R_i)^{-2}$
with nonezero effective increase of radius $R_i>0$. The modeling of the sheet
dynamics has shown that small periodic transversal displacements of the
substrate lead to resonant vibrations of the membranes at frequencies close to
eigenfrequencies of nodeless vibrations of membranes with a circular symmetry.
The energy distribution of resonant vibrations of the membrane has a circular
symmetry and several nodal circles, whose number $i$ coincides with the number
of the resonant frequency. The frequencies of the resonances decrease by
increasing the radius of the membrane as $(R+R_i)^{\alpha_i}$ with exponent
$\alpha_i<2$. The lower rate of resonance frequency decrease is caused by the
anharmonicity of membrane vibrations.


Abstract:
  Nowadays, users are encouraged to activate across multiple online social
networks simultaneously. Anchor link prediction, which aims to reveal the
correspondence among different accounts of the same user across networks, has
been regarded as a fundamental problem for user profiling, marketing,
cybersecurity, and recommendation. Existing methods mainly address the
prediction problem by utilizing profile, content, or structural features of
users in symmetric ways. However, encouraged by online services, users would
also post asymmetric information across networks, such as geo-locations and
texts. It leads to an emerged challenge in aligning users with asymmetric
information across networks. Instead of similarity evaluation applied in
previous works, we formalize correlation between geo-locations and texts and
propose a novel anchor link prediction framework for matching users across
networks. Moreover, our model can alleviate the label scarcity problem by
introducing external data. Experimental results on real-world datasets show
that our approach outperforms existing methods and achieves state-of-the-art
results.


Abstract:
  We consider a system of two singularly perturbed Boundary Value Problems
(BVPs) of convection-diffusion type with discontinuous source terms and a small
positive parameter multiplying the highest derivatives. Then their solutions
exhibit boundary layers as well as weak interior layers. A numerical method
based on finite element method (Shishkin and Bakhvalov-Shishkin meshes) is
presented. We derive an error estimate of order $O(N^{-1}\ln^{3/2}{N})$ in the
energy norm with respect to the perturbation parameter. Numerical experiments
are also presented to support our theoretical results.


Abstract:
  Vulnerable individuals have a limited ability to make reasonable financial
decisions and choices and, thus, the level of care that is appropriate to be
provided to them by financial institutions may be different from that required
for other consumers. Therefore, identifying vulnerability is of central
importance for the design and effective provision of financial services and
products. However, validating the information that customers share and
respecting their privacy are both particularly important in finance and this
poses a challenge for identifying and caring for vulnerable populations. This
position paper examines the potential of the combination of two emerging
technologies, Decentralized Identifiers (DIDs) and Verifiable Credentials
(VCs), for the identification of vulnerable consumers in finance in an
efficient and privacy-preserving manner.


Abstract:
  Different from general photo retouching tasks, portrait photo retouching
(PPR), which aims to enhance the visual quality of a collection of flat-looking
portrait photos, has its special and practical requirements such as
human-region priority (HRP) and group-level consistency (GLC). HRP requires
that more attention should be paid to human regions, while GLC requires that a
group of portrait photos should be retouched to a consistent tone. Models
trained on existing general photo retouching datasets, however, can hardly meet
these requirements of PPR. To facilitate the research on this high-frequency
task, we construct a large-scale PPR dataset, namely PPR10K, which is the first
of its kind to our best knowledge. PPR10K contains $1, 681$ groups and $11,
161$ high-quality raw portrait photos in total. High-resolution segmentation
masks of human regions are provided. Each raw photo is retouched by three
experts, while they elaborately adjust each group of photos to have consistent
tones. We define a set of objective measures to evaluate the performance of PPR
and propose strategies to learn PPR models with good HRP and GLC performance.
The constructed PPR10K dataset provides a good benchmark for studying automatic
PPR methods, and experiments demonstrate that the proposed learning strategies
are effective to improve the retouching performance. Datasets and codes are
available: https://github.com/csjliang/PPR10K.


Abstract:
  Gioseffo Zarlino reintroduced the Pythagorean paradigm into Renaissance
musical theory. In a similar fashion, Nicolaus Copernicus, Galileo Galilei,
Johannes Kepler, and Isaac Newton reinvigorated Pythagorean ideas in celestial
mechanics; Kepler and Newton explicitly invoked musical principles. Today, the
theory of dynamical systems allows us to describe very different applications
of physics, from the orbits of asteroids in the Solar System to the pitch of
complex sounds. Our aim in this text is to review the overarching aims of our
research in this field over the past quarter of a century. We demonstrate with
a combination of dynamical systems theory and music theory the thread running
from Pythagoras to Zarlino that allowed the latter to construct musical scales
using the ideas of proportion known to the former, and we discuss how the
modern theory of dynamical systems, with the study of resonances in nonlinear
systems, returns to Pythagorean ideas of a Musica Universalis.


Abstract:
  We study nonparametric dependence detection with the proposed binary
expansion approximation of uniformity (BEAUTY) approach, which generalizes the
celebrated Euler's formula, and approximates the characteristic function of any
copula with a linear combination of expectations of binary interactions from
marginal binary expansions. This novel theory enables a unification of many
important tests through approximations from some quadratic forms of symmetry
statistics, where the deterministic weight matrix characterizes the power
properties of each test. To achieve a robust power, we study test statistics
with data-adaptive weights, referred to as the binary expansion adaptive
symmetry test (BEAST). By utilizing the properties of the binary expansion
filtration, we show that the Neyman-Pearson test of uniformity can be
approximated by an oracle weighted sum of symmetry statistics. The BEAST with
this oracle provides a benchmark of feasible power against any alternative by
leading all existing tests with a substantial margin. To approach this oracle
power, we develop the BEAST through a regularized resampling approximation of
the oracle test. The BEAST improves the empirical power of many existing tests
against a wide spectrum of common alternatives and provides clear
interpretation of the form of dependency when significant.


Abstract:
  Precise real time estimates of earthquake magnitude and location are
essential for early warning and rapid response. While recently multiple deep
learning approaches for fast assessment of earthquakes have been proposed, they
usually rely on either seismic records from a single station or from a fixed
set of seismic stations. Here we introduce a new model for real-time magnitude
and location estimation using the attention based transformer networks. Our
approach incorporates waveforms from a dynamically varying set of stations and
outperforms deep learning baselines in both magnitude and location estimation
performance. Furthermore, it outperforms a classical magnitude estimation
algorithm considerably and shows promising performance in comparison to a
classical localization algorithm. In this work, we furthermore conduct a
comprehensive study of the requirements on training data, the training
procedures and the typical failure modes using three diverse and large scale
data sets. Our analysis gives several key insights. First, we can precisely
pinpoint the effect of large training data; for example, a four times larger
training set reduces the required time for real time assessment by a factor of
four. Second, the basic model systematically underestimates large magnitude
events. This issue can be mitigated by incorporating events from other regions
into the training through transfer learning. Third, location estimation is
highly precise in areas with sufficient training data, but is strongly degraded
for events outside the training distribution. Our analysis suggests that these
characteristics are not only present for our model, but for most deep learning
models for fast assessment published so far. They result from the black box
modeling and their mitigation will likely require imposing physics derived
constraints on the neural network.


Abstract:
  The recent TASK meta-GGA density functional [Phys. Rev. Research, 1, 033082
(2019)] is constructed with an enhanced nonlocality in the generalized
Kohn-Sham scheme, and therefore harbors great opportunities for band gap
prediction. Although this approximation was found to yield excellent band gaps
of bulk solids, this accuracy cannot be straightforwardly transferred to
low-dimensional materials. The reduced screening of these materials results in
larger band gaps compared to their bulk counterparts, as an additional barrier
to overcome. In this work, we demonstrate how the alteration of exact physical
constraints in this functional affects the band gaps of monolayers and
nanoribbons, and present accurate band gaps competing with the HSE06
approximation. In order to achieve this goal, we have modified the TASK
functional (a) by changing the tight upper-bound for one or two-electron
systems ($h_X^0$) from 1.174 to 1.29 (b) by changing the limit of interpolation
function $f_X (\alpha \rightarrow \infty$) of the TASK functional that
interpolates the exchange enhancement factor $F_X (s,\alpha)$ from $\alpha=$ 0
to 1. The resulting modified TASK (mTASK) was tested for various materials from
3D to 2D to 1D (nanoribbons), and was compared with the results of the
higher-level hybrid functional HSE06 or with the G$_0$W$_0$ approximation
within many-body perturbation theory. We find that mTASK greatly improves the
band gaps and band structures of 2D and 1D systems, without significantly
affecting the accuracy of the original TASK for the bulk 3D materials, when
compared to the PBE-GGA and SCAN meta-GGA. We further demonstrate the
applicability of mTASK by assessing the band structures of TMD nanoribbons with
respect to various bending curvatures.


Abstract:
  The problem of measuring sentence similarity is an essential issue in the
natural language processing (NLP) area. It is necessary to measure the
similarity between sentences accurately. There are many approaches to measuring
sentence similarity. Deep learning methodology shows a state-of-the-art
performance in many natural language processing fields and is used a lot in
sentence similarity measurement methods. However, in the natural language
processing field, considering the structure of the sentence or the word
structure that makes up the sentence is also important. In this study, we
propose a methodology combined with both deep learning methodology and a method
considering lexical relationships. Our evaluation metric is the Pearson
correlation coefficient and Spearman correlation coefficient. As a result, the
proposed method outperforms the current approaches on a KorSTS standard
benchmark Korean dataset. Moreover, it performs a maximum of 65% increase than
only using deep learning methodology. Experiments show that our proposed method
generally results in better performance than those with only a deep learning
model.


Abstract:
  The 1-2-3 Conjecture asks whether almost all graphs can be (edge-)labelled
with $1,2,3$ so that no two adjacent vertices are incident to the same sum of
labels. In the last decades, several aspects of this problem have been studied
in literature, including more general versions and slight variations. Notable
such variations include the List 1-2-3 Conjecture variant, in which edges must
be assigned labels from dedicated lists of three labels, and the Multiplicative
1-2-3 Conjecture variant, in which labels~$1,2,3$ must be assigned to the edges
so that adjacent vertices are incident to different products of labels. Several
results obtained towards these two variants led to observe some behaviours that
are distant from those of the original conjecture.
  In this work, we consider the list version of the Multiplicative 1-2-3
Conjecture, proposing the first study dedicated to this very problem. In
particular, given any graph $G$, we wonder about the minimum~$k$ such that $G$
can be labelled as desired when its edges must be assigned labels from
dedicated lists of size~$k$. Exploiting a relationship between our problem and
the List 1-2-3 Conjecture, we provide upper bounds on~$k$ when $G$ belongs to
particular classes of graphs. We further improve some of these bounds through
dedicated arguments.


Abstract:
  This paper establishes asymptotic results for the maximum likelihood and
restricted maximum likelihood (REML) estimators of the parameters in the nested
error regression model for clustered data when both of the number of
independent clusters and the cluster sizes (the number of observations in each
cluster) go to infinity. Under very mild conditions, the estimators are shown
to be asymptotically normal with an elegantly structured covariance matrix.
There are no restrictions on the rate at which the cluster size tends to
infinity but it turns out that we need to treat within cluster parameters (i.e.
coefficients of unit-level covariates that vary within clusters and the within
cluster variance) differently from between cluster parameters (i.e.
coefficients of cluster-level covariates that are constant within clusters and
the between cluster variance) because they require different normalisations and
are asymptotically independent.


Abstract:
  We study a one-dimensional array of bosons with infinite-range interactions
mediated by a laser-driven dissipative optical cavity. The cavity-mediated
infinite-range interactions open up a new pathway to fermionization, hitherto
only known for dipolar bosons due to their long-range interactions. In
parameter ranges attainable in state-of-the-art experiments, we systematically
compare observables for bosons and fermions with infinite-range interactions.
At large enough laser pump power, many observables, including density
distributions in real and momentum space, correlation functions, eigenvalues of
the one-body density matrix, and superradiance order parameter, become
identical for bosons and fermions. We map out the emergence of this
cavity-induced fermionization as a function of pump power and contact
interactions. We discover that cavity-mediated interactions can compensate a
reduction by several orders of magnitude in the strength of the contact
interactions needed to trigger fermionization.


Abstract:
  Structural health monitoring (SHM) using self-sensing cement-based materials
has been reported before, where nano-fillers have been incorporated in
cementitious matrices as functional sensing elements. A percolation threshold
is always required in order for conductive nano-fillers modified concrete to be
useful for SHM. Nonetheless, the best pressure/strain sensitivity results
achieved for any self-sensing cementitious matrix are <0.01 MPa-1. In this
work, we introduce novel reduced graphene oxide (RGO) based electronic textile
(e-textile) embedded in plain and polymer-binder-modified cementitious matrix
for SHM applications. As a proof of concept, it was demonstrated that these
coated fabric-based sensors can be successfully embedded within the
cement-based structures, which are independent of any percolation threshold due
to the interconnected fabric inside the host matrix. The piezo-resistive
response was measured by applying direct and cyclic compressive loads (0.1 to
3.9 MPa). A pressure sensitivity of 1.5 MPa-1 and an ultra-high gauge factor of
2000 was obtained for the system of the self-sensing cementitious structure
with embedded e-textiles. The sensitivity of this new system with embedded
e-textile is many orders of magnitude higher than nanoparticle based
self-sensing of cementitious composites. The manufactured e-textile sensors
showed mechanical stability and functional durability over long-term cyclic
compression tests of 1000 cycles.


Abstract:
  X-ray bursts have recently been discovered in the Cepheids $\delta$ Cep and
$\beta$ Dor modulated by the pulsation cycle. We have obtained an observation
of the Cepheid $\eta$ Aql with the XMM-Newton satellite at the phase of maximum
radius, the phase at which there is a burst of X-rays in $\delta$ Cep. No
X-rays were seen from the Cepheid $\eta$ Aql at this phase, and the
implications for Cepheid upper atmospheres are discussed. We have also used the
combination of X-ray sources and Gaia and 2MASS data to search for a possible
grouping around the young intermediate mass Cepheid. No indication of such a
group was found.


Abstract:
  Source-free so-called ModMax theories of nonlinear electrodynamics in the
four dimensional Minkowski spacetime vacuum are the only possible continuous
deformations -- and as a function of a single real and positive parameter -- of
source-free Maxwell linear electrodynamics in the same vacuum, which preserve
all the same Poincar\'e and conformal spacetime symmetries as well as the
continuous duality invariance of Maxwell's theory. Null field configurations of
the latter however, including null electromagnetic knots, are singular for the
Lagrangian formulation of any spacetime Poincar\'e and conformal invariant
theory of nonlinear electrodynamics. In particular null hopfion-Ra\~nada knots
are a distinguished and fascinating class on their own of topologically
nontrivial solutions to Maxwell's equations. This work addresses the fate of
these configurations within ModMax theories. A doubled class of ModMax deformed
hopfion-Ra\~nada knots is thereby identified, each of which coalescing back in
a continuous fashion to the original hopfion-Ra\~nada knot when the nonlinear
deformation parameter is turned off.


Abstract:
  Consider a channel ${\bf Y}={\bf X}+ {\bf N}$ where ${\bf X}$ is an
$n$-dimensional random vector, and ${\bf N}$ is a Gaussian vector with a
covariance matrix ${\bf \mathsf{K}}_{\bf N}$. The object under consideration in
this paper is the conditional mean of ${\bf X}$ given ${\bf Y}={\bf y}$, that
is ${\bf y} \to E[{\bf X}|{\bf Y}={\bf y}]$. Several identities in the
literature connect $E[{\bf X}|{\bf Y}={\bf y}]$ to other quantities such as the
conditional variance, score functions, and higher-order conditional moments.
The objective of this paper is to provide a unifying view of these identities.
  In the first part of the paper, a general derivative identity for the
conditional mean is derived. Specifically, for the Markov chain ${\bf U}
\leftrightarrow {\bf X} \leftrightarrow {\bf Y}$, it is shown that the Jacobian
of $E[{\bf U}|{\bf Y}={\bf y}]$ is given by ${\bf \mathsf{K}}_{{\bf N}}^{-1}
{\bf Cov} ( {\bf X}, {\bf U} | {\bf Y}={\bf y})$.
  In the second part of the paper, via various choices of ${\bf U}$, the new
identity is used to generalize many of the known identities and derive some new
ones. First, a simple proof of the Hatsel and Nolte identity for the
conditional variance is shown. Second, a simple proof of the recursive identity
due to Jaffer is provided. Third, a new connection between the conditional
cumulants and the conditional expectation is shown. In particular, it is shown
that the $k$-th derivative of $E[X|Y=y]$ is the $(k+1)$-th conditional
cumulant.
  The third part of the paper considers some applications. In a first
application, the power series and the compositional inverse of $E[X|Y=y]$ are
derived. In a second application, the distribution of the estimator error
$(X-E[X|Y])$ is derived. In a third application, we construct consistent
estimators (empirical Bayes estimators) of the conditional cumulants from an
i.i.d. sequence $Y_1,...,Y_n$.


Abstract:
  Based on PixelHop and PixelHop++, which are recently developed using the
successive subspace learning (SSL) framework, we propose an enhanced solution
for object classification, called E-PixelHop, in this work. E-PixelHop consists
of the following steps. First, to decouple the color channels for a color
image, we apply principle component analysis and project RGB three color
channels onto two principle subspaces which are processed separately for
classification. Second, to address the importance of multi-scale features, we
conduct pixel-level classification at each hop with various receptive fields.
Third, to further improve pixel-level classification accuracy, we develop a
supervised label smoothing (SLS) scheme to ensure prediction consistency.
Forth, pixel-level decisions from each hop and from each color subspace are
fused together for image-level decision. Fifth, to resolve confusing classes
for further performance boosting, we formulate E-PixelHop as a two-stage
pipeline. In the first stage, multi-class classification is performed to get a
soft decision for each class, where the top 2 classes with the highest
probabilities are called confusing classes. Then,we conduct a binary
classification in the second stage. The main contributions lie in Steps 1, 3
and 5.We use the classification of the CIFAR-10 dataset as an example to
demonstrate the effectiveness of the above-mentioned key components of
E-PixelHop.


Abstract:
  A popular way to accelerate the sampling of rare events in molecular dynamics
simulations is to introduce a potential that increases the fluctuations of
selected collective variables. For this strategy to be successful, it is
critical to choose appropriate variables. Here we review some recent
developments in the data-driven design of collective variables, with a focus on
the combination of Fisher's discriminant analysis and neural networks. This
approach allows to compress the fluctuations of metastable states into a
low-dimensional representation. We illustrate through several examples the
effectiveness of this method in accelerating the sampling, while also
identifying the physical descriptors that undergo the most significant changes
in the process.


Abstract:
  Motivated by the need for fair algorithmic decision making in the age of
automation and artificially-intelligent technology, this technical report
provides a theoretical insight into adversarial training for fairness in deep
learning. We build upon previous work in adversarial fairness, show the
persistent tradeoff between fair predictions and model performance, and explore
further mechanisms that help in offsetting this tradeoff.


Abstract:
  Reconfigurable intelligent surface (RIS) technology has recently emerged as a
spectral- and cost-efficient approach for wireless communications systems.
However, existing hand-engineered schemes for passive beamforming design and
optimization of RIS, such as the alternating optimization (AO) approaches,
require a high computational complexity, especially for
multiple-input-multiple-output (MIMO) systems. To overcome this challenge, we
propose a low-complexity unsupervised learning scheme, referred to as
learning-phase-shift neural network (LPSNet), to efficiently find the solution
to the spectral efficiency maximization problem in RIS-aided MIMO systems. In
particular, the proposed LPSNet has an optimized input structure and requires a
small number of layers and nodes to produce efficient phase shifts for the RIS.
Simulation results for a 16x2 MIMO system assisted by an RIS with 40 elements
show that the LPSNet achieves 97.25% of the SE provided by the AO counterpart
with more than a 95% reduction in complexity.


Abstract:
  Industrial Internet of Things (IIoT) revolutionizes the future manufacturing
facilities by integrating the Internet of Things technologies into industrial
settings. With the deployment of massive IIoT devices, it is difficult for the
wireless network to support the ubiquitous connections with diverse
quality-of-service (QoS) requirements. Although machine learning is regarded as
a powerful data-driven tool to optimize wireless network, how to apply machine
learning to deal with the massive IIoT problems with unique characteristics
remains unsolved. In this paper, we first summarize the QoS requirements of the
typical massive non-critical and critical IIoT use cases. We then identify
unique characteristics in the massive IIoT scenario, and the corresponding
machine learning solutions with its limitations and potential research
directions. We further present the existing machine learning solutions for
individual layer and cross-layer problems in massive IIoT. Last but not the
least, we present a case study of massive access problem based on deep neural
network and deep reinforcement learning techniques, respectively, to validate
the effectiveness of machine learning in massive IIoT scenario.


Abstract:
  The appearance of a light composite $0^+$ scalar resonance in nearly
conformal gauge-fermion theories motivates further study of the low energy
structure of these theories. To this end, we present a nonperturbative lattice
calculation of s-wave scattering of Goldstone bosons in the maximal-isospin
channel in SU(3) gauge theory with $N_f=8$ light, degenerate flavors. The
scattering phase shift is measured both for different values of the underlying
fermion mass and for different values of the scattering momentum. We examine
the effect of a light flavor-singlet scalar (reported in earlier studies) on
Goldstone boson scattering, employing a dilaton effective field theory (EFT) at
the tree level. The EFT gives a good description of the scattering data,
insofar as the magnitude of deviations between EFT and lattice data are no
larger than the expected size of next-to-leading order corrections in the EFT.


Abstract:
  Determining whether nodes can be uniquely localized, called localizability
detection, is a concomitant problem of network localization. Localizability
under traditional Non-Linear Localization (NLL) schema has been well explored,
whereas localizability under the emerging Barycentric coordinate-based Linear
Localization (BLL) schema has not been well touched. In this paper, we
investigate the deficiency of existing localizability theories and algorithms
in BLL, and then propose a necessary condition and a sufficient condition for
BLL node localizability. Based on these two conditions, an efficient iterative
maximum flow (IMF) algorithm is designed to identify BLL localizable nodes.
Finally, our algorithms are validated by both theoretical analysis and
experimental evaluations.


Abstract:
  Most commercially available optical see-through head-mounted displays
(OST-HMDs) utilize optical combiners to simultaneously visualize the physical
background and virtual objects. The displayed images perceived by users are a
blend of rendered pixels and background colors. Enabling high fidelity color
perception in mixed reality (MR) scenarios using OST-HMDs is an important but
challenging task. We propose a real-time rendering scheme to enhance the color
contrast between virtual objects and the surrounding background for OST-HMDs.
Inspired by the discovery of color perception in psychophysics, we first
formulate the color contrast enhancement as a constrained optimization problem.
We then design an end-to-end algorithm to search the optimal complementary
shift in both chromaticity and luminance of the displayed color. This aims at
enhancing the contrast between virtual objects and the real background as well
as keeping the consistency with the original color. We assess the performance
of our approach using a simulated OST-HMD environment and an off-the-shelf
OST-HMD. Experimental results from objective evaluations and subjective user
studies demonstrate that the proposed approach makes rendered virtual objects
more distinguishable from the surrounding background, thereby bringing a better
visual experience.


Abstract:
  Massive Klein-Gordon theory is quantized on the timelike hypercylinder in
Minkowski space. Crucially, not only the propagating, but also the evanescent
sector of phase space is included, laying in this way foundations for a quantum
scattering theory of fields at finite distance. To achieve this, the novel
$\alpha$-K\"ahler quantization scheme is employed in the framework of general
boundary quantum field theory. A potential quantization ambiguity is fixed by
stringent requirements, leading to a unitary radial evolution. Formulas for
building scattering amplitudes and correlation functions are exhibited.


Abstract:
  Digital twins are emerging in many industries, typically consisting of
simulation models and data associated with a specific physical system. One of
the main reasons for developing a digital twin, is to enable the simulation of
possible consequences of a given action, without the need to interfere with the
physical system itself. Physical systems of interest, and the environments they
operate in, do not always behave deterministically. Moreover, information about
the system and its environment is typically incomplete or imperfect.
Probabilistic representations of systems and environments may therefore be
called for, especially to support decisions in application areas where actions
may have severe consequences.
  In this paper we introduce the probabilistic digital twin (PDT). We will
start by discussing how epistemic uncertainty can be treated using measure
theory, by modelling epistemic information via $\sigma$-algebras. Based on
this, we give a formal definition of how epistemic uncertainty can be updated
in a PDT. We then study the problem of optimal sequential decision making. That
is, we consider the case where the outcome of each decision may inform the
next. Within the PDT framework, we formulate this optimization problem. We
discuss how this problem may be solved (at least in theory) via the maximum
principle method or the dynamic programming principle. However, due to the
curse of dimensionality, these methods are often not tractable in practice. To
mend this, we propose a generic approximate solution using deep reinforcement
learning together with neural networks defined on sets. We illustrate the
method on a practical problem, considering optimal information gathering for
the estimation of a failure probability.


Abstract:
  Route controlled autonomous vehicles could have a significant impact in
reducing congestion in the future. Before applying multi-agent reinforcement
learning algorithms to route control, we can model the system using a
congestion game to predict and mitigate potential issues. We consider the
problem of distributed operating systems in a transportation network that
control the routing choices of their assigned vehicles. We formulate an
associated network control game, consisting of multiple actors seeking to
optimise the social welfare of their assigned subpopulations in an underlying
nonatomic congestion game. Then we find the inefficiency of the routing
equilibria by calculating the Price of Anarchy for polynomial cost functions.
Finally, we extend the analysis to allow vehicles to choose their operating
system.


Abstract:
  Semiconducting transition-metal dichalcogenides (TMDCs) provide a fascinating
discovery platform for strong light-matter interaction effects in the visible
spectrum at ambient conditions. While most of the work has focused on
hybridizing excitons with resonant photonic modes of external mirrors,
cavities, or nanostructures, intriguingly, TMDC flakes of sub-wavelength
thickness can themselves act as nanocavities. Here, we determine the optical
response of such freestanding planar waveguides of WSe$_2$, by means of
cathodoluminescence spectroscopy. We reveal strong exciton-photon interaction
effects that foster long-range propagating exciton-polaritons and enable direct
imaging of the energy transfer dynamics originating from cavity-like
Fabry-Perot resonances. Furthermore, confinement effects due to discontinuities
in the flakes are demonstrated as an efficient means to tailor the
exciton-photon coupling strength, along the edges of natural flakes. Our
combined experimental and theoretical results provide a deeper understanding of
exciton-photon self-hybridization in semiconducting TMDCs and may pave the way
to optoelectronic nanocircuits exploiting exciton-photon interaction.


Abstract:
  Bose-Einstein condensates (BECs) in free fall constitute a promising source
for space-borne matter-wave interferometry. Indeed, BECs enjoy a slowly
expanding wave function, display a large spatial coherence and can be
engineered and probed by optical techniques. On a sounding rocket, we explore
matter-wave fringes of multiple spinor components of a BEC released in free
fall employing light-pulses to drive Bragg processes and induce phase
imprinting. The prevailing microgravity played a crucial role in the
observation of these interferences which not only reveal the spatial coherence
of the condensates but also allow us to measure differential forces. Our work
establishes matter-wave interferometry in space with future applications in
fundamental physics, navigation and Earth observation.


Abstract:
  In this paper we present an experience report for the RMQFMU, a plug and play
tool, that enables feeding data to/from an FMI2-based co-simulation environment
based on the AMQP protocol. Bridging the co-simulation to an external
environment allows on one side to feed historical data to the co-simulation,
serving different purposes, such as visualisation and/or data analysis. On the
other side, such a tool facilitates the realisation of the digital twin concept
by coupling co-simulation and hardware/robots close to real-time. In the paper
we present limitations of the initial version of the RMQFMU with respect to the
capability of bridging co-simulation with the real world. To provide the
desired functionality of the tool, we present in a step-by-step fashion how
these limitations, and subsequent limitations, are alleviated. We perform
various experiments in order to give reason to the modifications carried out.
Finally, we report on two case-studies where we have adopted the RMQFMU, and
provide guidelines meant to aid practitioners in its use.


Abstract:
  We consider Markov Decision Processes (MDPs) where distributional parameters,
such as transition probabilities, are unknown and estimated from data. The
popular distributionally robust approach to addressing the parameter
uncertainty can sometimes be overly conservative. In this paper, we propose a
Bayesian risk approach to MDPs with parameter uncertainty, where a risk
functional is applied in nested form to the expected discounted total cost with
respect to the Bayesian posterior distributions of the unknown parameters in
each time stage. The proposed approach provides more flexibility of risk
attitudes towards parameter uncertainty and takes into account the availability
of data in future time stages. For the finite-horizon MDPs, we show the dynamic
programming equations can be solved efficiently with an upper confidence bound
(UCB) based adaptive sampling algorithm. For the infinite-horizon MDPs, we
propose a risk-adjusted Bellman operator and show the proposed operator is a
contraction mapping that leads to the optimal value function to the Bayesian
risk formulation. We demonstrate the empirical performance of our proposed
algorithms in the finite-horizon case on an inventory control problem and a
path planning problem.


Abstract:
  Information technology demands continuous increase of data-storage density.
In high-density magnetic recording media, the large magneto-crystalline
anisotropy (MCA) stabilizes the stored information against decay through
thermal fluctuations. In the latest generation storage media, MCA is so large
that magnetic order needs to be transiently destroyed by heat to enable bit
writing. Here we show an alternative approach to control high-anisotropy
magnets: With ultrashort laser pulses the anisotropy itself can be manipulated
via electronic state excitations. In rare-earth materials like terbium metal,
magnetic moment and high MCA both originate from the 4f electronic state.
Following infrared laser excitation 5d-4f electron-electron scattering
processes lead to selective orbital excitations that change the 4f orbital
occupation and significantly alter the MCA. Besides these excitations within
the 4f multiplet, 5d-4f electron transfer causes a transient change of the 4f
occupation number, which, too, strongly alters the MCA. Such MCA change cannot
be achieved by heating: The material would rather be damaged than the 4f
configuration modified. Our results show a way to overcome this limitation for
a new type of efficient magnetic storage medium. Besides potential
technological relevance, the observation of MCA-changing excitations also has
implications for a general understanding of magnetic dynamics processes on
ultrashort time scales, where the 4f electronic state affects the angular
momentum transfer between spin system and lattice.


Abstract:
  The concept of zero forcing was introduced in the context of linear algebra,
and was further studied by both graph theorists and linear algebraists. It is
based on the process of activating vertices of a graph $G$ starting from a set
of vertices that are already active, and applying the rule that an active
vertex with exactly one non-active neighbor forces that neighbor to become
active. A set $S\subset V(G)$ is called a zero forcing set of $G$ if initially
only vertices of $S$ are active and the described process enforces all vertices
of $G$ to become active. The size of a minimum zero forcing set in $G$ is
called the zero forcing number of $G$. While a minimum zero forcing set can
only be unique in edgeless graphs, we consider the weaker uniqueness condition,
notably that for every two minimum zero forcing sets in a graph $G$ there is an
automorphism that maps one to the other. We characterize the class of trees
that enjoy this condition by using properties of minimum path covers of trees.
In addition, we investigate both variations of uniqueness for several concepts
of Grundy domination, which first appeared in the context of domination games,
yet they are also closely related to zero forcing. For each of the four
variations of Grundy domination we characterize the graphs that have only one
Grundy dominating set of the given type, and characterize those forests that
enjoy the weaker (isomorphism based) condition of uniqueness. The latter
characterizations lead to efficient algorithms for recognizing the
corresponding classes of forests.


Abstract:
  A new protomodular analog of the classical criterion for the existence of a
group term in the algebraic theory of a variety of universal algebras is given.
To this end, the notion of a right-cancellable protomodular algebra is
introduced. It is proved that the algebraic theory of a variety of universal
algebras contains a group term if and only if it contains protomodular terms
with respect to which all algebras from the variety are right-cancellable.
This, in particular, gives a partial answer to the extended version of an open
problem from loop theory whether any Hausdorff topological (semi-)loop is
completely regular. Moreover, the right-cancellable algebras from the simplest
protomodular varieties are characterized as sets with principal group actions
as well as groups with simple additional structures.


Abstract:
  Till now geometric structures don't play a major role in cryptography.
Gilbert, MacWilliams and Sloane introduced in 1974 an authentication scheme in
the projective plane and showed its perfectness in the sense of the definition
of Shannon. In this paper we will show that this authentication scheme also
fulfills the requirement of completeness according to Kam and Davida and we
will extend the application of geometric structures in cryptography by
introducing an encryption scheme in the M\"obius plane. We will further examine
its properties, showing that it also fulfills the requirement of completeness
and Shannon's requirement of perfectness in first approximation. The results of
this paper can be used to define similar encryption schemes in the circle
geometries of Laguerre and Minkowski.


Abstract:
  For an arranged subset $Q = \{q_1, q_2, ..., q_k\}$ of vertices in a
connected graph $G$ the metric representation of a vertex $v$ in $G$, is the
$k$-vector $r(v | Q) = (d(v, q_1), d(v, q_2), ..., d(v, q_k ))$ relative to
$Q$. Also, the subset $Q$ is considered as resolving set for $G$ if any pair of
vertices of $G$ is distinguished by some vertices of $Q$. In the present
article, we study the minimum size of resolving set, and doubly resolving set
for the graph $H(n)$, and the line graph of the graph $H(n)$ is denoted by
$L(n)$. It is well known that these problems are NP hard.


Abstract:
  In this article, we study geometric properties of nilpotent groups. We find a
geometric criterion for the word problem for the finitely generated free
nilpotent groups. By geometric criterion, we mean a way to determine whether
two words represent the same element in a free nilpotent group of rank $r$ and
class $k$ by analyzing their behavior on the Cayley graph of the free nilpotent
group of rank $r$ and class $k-1$.


Abstract:
  An approach is proposed for inferring Granger causality between jointly
stationary, Gaussian signals from quantized data. First, a necessary and
sufficient rank criterion for the equality of two conditional Gaussian
distributions is proved. Assuming a partial finite-order Markov property,
conditions are then derived under which Granger causality between them can be
reliably inferred from the second order moments of the quantized processes. A
necessary and sufficient condition is proposed for Granger causality inference
under binary quantization. Furthermore, sufficient conditions are introduced to
infer Granger causality between jointly Gaussian signals through measurements
quantized via non-uniform, uniform or high resolution quantizers. This approach
does not require the statistics of the underlying Gaussian signals to be
estimated, or a system model to be identified. No assumptions are made on the
identifiability of the jointly Gaussian random processes through the quantized
observations. The effectiveness of the proposed method is illustrated by
simulation results.


Abstract:
  We consider online convex optimization when a number k of data points are
outliers that may be corrupted. We model this by introducing the notion of
robust regret, which measures the regret only on rounds that are not outliers.
The aim for the learner is to achieve small robust regret, without knowing
where the outliers are. If the outliers are chosen adversarially, we show that
a simple filtering strategy on extreme gradients incurs O(k) additive overhead
compared to the usual regret bounds, and that this is unimprovable, which means
that k needs to be sublinear in the number of rounds. We further ask which
additional assumptions would allow for a linear number of outliers. It turns
out that the usual benign cases of independently, identically distributed
(i.i.d.) observations or strongly convex losses are not sufficient. However,
combining i.i.d. observations with the assumption that outliers are those
observations that are in an extreme quantile of the distribution, does lead to
sublinear robust regret, even though the expected number of outliers is linear.


Abstract:
  High-order perturbative calculations for thermodynamic quantities in QCD are
complicated by the physics of dynamical screening that affects the soft,
long-wavelength modes of the system. Here, we provide details for the
evaluation of this soft contribution to the next-to-next-to-next-to-leading
order (NNNLO) pressure of high-density, zero-temperature quark matter (QM),
complementing our accompanying paper in arXiv:2103.05658. Our calculation
requires the determination of the pressure of the hard-thermal-loop (HTL)
effective theory to full two-loop order at zero temperature, which we go
through in considerable detail. In addition to this, we comprehensively discuss
the structure of the weak-coupling expansion of the QM pressure, and lay out a
roadmap towards the evaluation of the contributions missing from a full NNNLO
result for this quantity.


Abstract:
  Tidal disruption events are an excellent probe for supermassive black holes
in distant inactive galaxies because they show bright multi-wavelength flares
lasting several months to years. AT2019dsg presents the first potential
association with neutrino emission from such an explosive event.


Abstract:
  We design a low complexity distributed compression scheme for computing
arbitrary functions of sources with discrete alphabets. We use a helper-based
method that extends the definition of the G{\'a}cs-K{\"o}rner-Witsenhausen
(GKW) common information to functional common information. The helper relaxes
the combinatorial structure of GKW by partitioning the joint source
distribution into nests imposed by the function, which ensures hierarchical
cooperation between the sources for effectively distributed computing. By
contrasting our approach's performance with existing efficient techniques, we
demonstrate the rate savings in recovering function and source data.
  Permutation invariant functions are prevalent in learning and combinatorial
optimization fields and most recently applied to graph neural networks. We
consider the efficient compression for computing permutation invariant
functions in a network with two sources and one decoder. We use a bipartite
graph representation, where two disjoint sets of vertices (parts) denote the
individual source graphs and the edge weights capture the joint source
distribution. We compress bipartite graphs by creating connected components
determined by the function's distribution, accounting for the edge symmetries,
and eliminating the low probability edges. We separately encode those edges and
send them as refinements. Our approach can substitute high complexity joint
decoding techniques and inform neural networks to reduce the computing time and
reduce complexity.


Abstract:
  In this work, we investigate a novel simultaneous transmission and reflection
reconfigurable intelligent surface (RIS)-assisted multiple-input
multiple-output downlink system, where three practical transmission protocols,
namely, energy splitting (ES), mode selection (MS), and time splitting (TS),
are studied. For the system under consideration, we maximize the weighted sum
rate with multiple coupled variables. To solve this optimization problem, a
block coordinate descent algorithm is proposed to reformulate this problem and
design the precoding matrices and the transmitting and reflecting coefficients
(TARCs) in an alternate manner. Specifically, for the ES scheme, the precoding
matrices are solved using the Lagrange dual method, while the TARCs are
obtained using the penalty concave-convex method. Additionally, the proposed
method is extended to the MS scheme by solving a mixed-integer problem.
Moreover, we solve the formulated problem for the TS scheme using a
one-dimensional search and the Majorization-Minimization technique. Our
simulation results reveal that: 1) Simultaneous transmission and reflection RIS
(STAR-RIS) can achieve better performance than reflecting-only RIS; 2) In
unicast communication, TS scheme outperforms the ES and MS schemes, while in
broadcast communication, ES scheme outperforms the TS and MS schemes.


Abstract:
  The smoothing procedure known as the gradient flow that suppresses
ultraviolet fluctuations of gauge fields plays an important role in lattice
gauge theory calculations. In particular, this procedure is often used for
high-precision scale setting and renormalization of operators. The gradient
flow equation is defined on the SU(3) manifold and therefore requires
geometric, or structure-preserving, integration methods to obtain its numerical
solutions. We examine the properties and origins of the three-stage third-order
explicit Runge-Kutta Lie group integrator commonly used in the lattice gauge
theory community, demonstrate its relation to 2N-storage classical Runge-Kutta
methods and explore how its coefficients can be tuned for optimal performance
in integrating the gradient flow. We also compare the performance of the tuned
method with two third-order variable step size methods. Next, based on the
recently established connection between low-storage Lie group integrators and
classical 2N-storage Runge-Kutta methods, we study two fourth-order low-storage
methods that provide a computationally efficient alternative to the commonly
used third-order method while retaining the convenient iterative property of
the latter. Finally, we demonstrate that almost no coding effort is needed to
implement the low-storage Lie group methods into existing gradient flow codes.


Abstract:
  Ultralight dark photons predicted in several Standard Model extensions can
trigger the superradiant instability around rotating black holes if their
Compton wavelength is comparable to the Blackhole radius. Consequently, the
angular momentum of the black hole is reduced to a value which depends upon the
mass and spin of the black hole as well as the mass of the dark photon. We use
the mass and spin measurements of the primary black holes in two recently
observed binary black hole systems: GW190517 and GW190426_152155 to constrain
dark photon mass in the ranges $1.7\times 10^{-14}{\rm\ eV}<m_{A'}<7.6\times
10^{-13}{\rm\ eV}$ and $1.3\times 10^{-13}{\rm\ eV}<m_{A'}<4.2\times
10^{-12}{\rm\ eV}$ respectively, assuming a timescale of a few million years
from the time of formation of the binary black hole system to the time of their
merger. We also discuss an interesting X-ray binary system, MAXI J1820_070,
albeit with a relatively small value of the spin parameter.


Abstract:
  Distributed Feedback Laser plays a key role as a light source component in
optical fiber communication systems ranging from metro, long-haul to submarine
one thanks to its competitive features of superior narrow spectral width and
wavelength cohesion. Characterizing such lasers via obtaining their electrical
and spectral data and extracting their internal parameters therefore remains a
critical task in designing and troubleshooting optical fiber systems. This
paper presents first an agile framework for a rapid collection of laser data
via automatic measurement and second an efficient approach for extracting laser
internal parameters.


Abstract:
  In this paper we study test time decoding; an ubiquitous step in almost all
sequential text generation task spanning across a wide array of natural
language processing (NLP) problems. Our main contribution is to develop a
continuous relaxation framework for the combinatorial NP-hard decoding problem
and propose Disco - an efficient algorithm based on standard first order
gradient based. We provide tight analysis and show that our proposed algorithm
linearly converges to within $\epsilon$ neighborhood of the optima. Finally, we
perform preliminary experiments on the task of adversarial text generation and
show superior performance of Disco over several popular decoding approaches.