Abstract:
  This paper introduces a martingale that characterizes two properties of
evolving forecast distributions. Ideal forecasts of a future event behave as
martingales, sequen- tially updating the forecast to leverage the available
information as the future event approaches. The threshold martingale introduced
here measures the proportion of the forecast distribution lying below a
threshold. In addition to being calibrated, a threshold martingale has
quadratic variation that accumulates to a total determined by a quantile of the
initial forecast distribution. Deviations from calibration or to- tal
volatility signal problems in the underlying model. Calibration adjustments are
well-known, and we augment these by introducing a martingale filter that
improves volatility while guaranteeing smaller mean squared error. Thus,
post-processing can rectify problems with calibration and volatility without
revisiting the original forecast- ing model. We apply threshold martingales
first to forecasts from simulated models and then to models that predict the
winner in professional basketball games.


Abstract:
  Floquet-Bloch lattices are systems in which wave packets are subjet to
periodic modulations both in time and space, showing rich dynamics. While this
type of lattices is difficult to implement in solid-state physics, optical
systems have provided excellent platforms to probe their physics: among other
effects, they have revealed genuine phenomena such as the anomalous Floquet
topological insulator and the funnelling of light into localised interface
modes. Despite the crucial importance of the band dispersion in the photon
dynamics and the topological properties of the lattice, the direct experimental
measurement of the Floquet-Bloch bands has remained elusive. Here we report the
direct measurement of the Floquet-Bloch bands of a photonic lattice with a
single shot method. We use a system of two coupled fibre rings that implements
a time-multiplexed Floquet-Bloch lattice. By Fourier transforming the impulse
response of the lattice we obtain the band structure together with an accurate
characterization of the lattice eigenmodes, i. e. the amplitudes and the phases
of the Floquet-Bloch eigenvectors over the entire Brillouin zone. Our results
open promising perspectives for the observation of topological effects in the
linear and nonlinear regime in Floquet systems.


Abstract:
  Many neuroscientific applications require robust and accurate localization of
neurons. It is still an unsolved problem because of the enormous variation in
intensity, texture, spatial overlap, morphology and background artifacts. In
addition, curation of a large dataset containing complete manual annotation of
neurons from high-resolution images to train a classifier requires significant
time and effort. We present Cellcounter, a deep learning-based model trained on
images containing incompletely-annotated neurons with highly-varied morphology
and control images containing artifacts and background structures. Leveraging
the striking self-learning ability, Cellcounter gradually labels neurons,
obviating the need for time-intensive complete annotation. Cellcounter shows
its efficacy over the state of the arts in the accurate localization of neurons
while significantly reducing false-positive detection in several protocols.


Abstract:
  Indoor positioning is currently recognized as one of the important features
in emergency, commercial and industrial applications. The 5G network enhances
mobility, flexibility, reliability, and security to new higher levels which
greatly benefit the IoT and industrial applications. Industrial IoT (IIoT)
use-cases are characterized by ambitious system requirements for positioning
accuracy in many verticals. For example, on the factory floor, it is important
to locate assets and moving objects such as forklifts. The deployment design
for different IIoT environments has a significant impact on the positioning
per-performance in terms of both accuracy and availability of the service.
Indoor factory (InF) and indoor open office (IOO) are two available and
standardized Third Generation Partnership Project (3GPP) scenarios for
evaluation of indoor channel models and positioning performance in IIoT use
cases. This paper aims to evaluate the positioning performance in terms of
accuracy and availability while considering different deployment strategies.
Our simulation-based evaluation shows that deployment plays a vital role when
it comes to achieving high accuracy positioning performance. It is for example
favorable to deploy the 5G Transmission and Reception Points (TRPs) on the
walls of the factory halls than deploying them attached to the ceiling.


Abstract:
  Deployment, operation and maintenance of large IT systems becomes
increasingly complex and puts human experts under extreme stress when problems
occur. Therefore, utilization of machine learning (ML) and artificial
intelligence (AI) is applied on IT system operation and maintenance -
summarized in the term AIOps. One specific direction aims at the recognition of
re-occurring anomaly types to enable remediation automation. However, due to IT
system specific properties, especially their frequent changes (e.g. software
updates, reconfiguration or hardware modernization), recognition of reoccurring
anomaly types is challenging. Current methods mainly assume a static
dimensionality of provided data. We propose a method that is invariant to
dimensionality changes of given data. Resource metric data such as CPU
utilization, allocated memory and others are modelled as multivariate time
series. The extraction of temporal and spatial features together with the
subsequent anomaly classification is realized by utilizing TELESTO, our novel
graph convolutional neural network (GCNN) architecture. The experimental
evaluation is conducted in a real-world cloud testbed deployment that is
hosting two applications. Classification results of injected anomalies on a
cassandra database node show that TELESTO outperforms the alternative GCNNs and
achieves an overall classification accuracy of 85.1%. Classification results
for the other nodes show accuracy values between 85% and 60%.


Abstract:
  In this paper, a novel Byzantine consensus protocol among $n$ players is
proposed for the partially synchronous model. In particular, by assuming that
standard cryptography is unbreakable, and that
$n>\max\bigl(\frac{3}{2}k+3t,2(k+t)\bigr)$, this protocol is an equilibrium
where no coalition of $k$ rational players can coordinate to increase their
expected utility regardless of the arbitrary behavior of up to $t$ Byzantine
players. We show that a baiting strategy is necessary and sufficient to solve
this, so-called rational agreement problem. First, we show that it is
impossible to solve this rational agreement problem without implementing a
baiting strategy, a strategy that rewards rational players for betraying its
coalition, by exposing undeniable proofs of fraud. Second, we propose the
Huntsman protocol that solves the rational agreement problem by building recent
advances in the context of accountable Byzantine agreement in partial
synchrony. This protocol finds applications in distributed ledgers where
players are incentivized to steal assets by leading other players to a
disagreement on two distinct decisions where they ``double spend''.


Abstract:
  The weight distribution of the cosets of maximum distance separable (MDS)
codes is considered. In 1990, P.G. Bonneau proposed a relation to obtain the
full weight distribution of a coset of an MDS code with minimum distance $d$
using the known numbers of vectors of weights $\le d-2$ in this coset. In this
paper, the Bonneau formula is transformed into a more structured and convenient
form. The new version of the formula allows to consider effectively cosets of
distinct weights $W$. (The weight $W$ of a coset is the smallest Hamming weight
of any vector in the coset.) For each of the considered $W$ or regions of $W$,
special relations more simple than the general ones are obtained. For the MDS
code cosets of weight $W=1$ and weight $W=d-1$ we obtain formulas of the weight
distributions depending only on the code parameters. This proves that all the
cosets of weight $W=1$ (as well as $W=d-1$) have the same weight distribution.
The cosets of weight $W=2$ or $W=d-2$ may have different weight distributions;
in this case, we proved that the distributions are symmetrical in some sense.
The weight distributions of the cosets of MDS codes corresponding to arcs in
the projective plane $\mathrm{PG}(2,q)$ are also considered. For MDS codes of
covering radius $R=d-1$ we obtain the number of the weight $W=d-1$ cosets and
their weight distribution that gives rise to a certain classification of the
so-called deep holes. We show that any MDS code of covering radius $R=d-1$ is
an almost perfect multiple covering of the farthest-off points (deep holes);
moreover, it corresponds to an optimal multiple saturating set in the
projective space $\mathrm{PG}(N,q)$.


Abstract:
  LiteBIRD is a JAXA-led strategic Large-Class satellite mission designed to
measure the polarization of the cosmic microwave background and cosmic
foregrounds from 34 to 448 GHz across the entire sky from L2 in the late
2020's. The primary focus of the mission is to measure primordially generated
B-mode polarization at large angular scales. Beyond its primary scientific
objective LiteBIRD will generate a data-set capable of probing a number of
scientific inquiries including the sum of neutrino masses. The primary
responsibility of United States will be to fabricate the three flight model
focal plane units for the mission. The design and fabrication of these focal
plane units is driven by heritage from ground based experiments and will
include both lenslet-coupled sinuous antenna pixels and horn-coupled orthomode
transducer pixels. The experiment will have three optical telescopes called the
low frequency telescope, mid frequency telescope, and high frequency telescope
each of which covers a portion of the mission's frequency range. JAXA is
responsible for the construction of the low frequency telescope and the
European Consortium is responsible for the mid- and high- frequency telescopes.
The broad frequency coverage and low optical loading conditions, made possible
by the space environment, require development and adaptation of detector
technology recently deployed by other cosmic microwave background experiments.
This design, fabrication, and characterization will take place at UC Berkeley,
NIST, Stanford, and Colorado University, Boulder. We present the current status
of the US deliverables to the LiteBIRD mission.


Abstract:
  We study the influence of anisotropy, treated as a dimensional crossover
between 1D and 3D system, on the topological instability induced by a
(self-consistent) uniaxial periodic potential. The mechanism on which the
instability is based involves the topological reconstruction of the Fermi
surface, from initially closed pockets to the surface with open Fermi sheets,
creating two peculiar points in the band dispersion - the saddle point and
elliptical point, between which the pseudogap in electron density of states
develops. The self-consistent periodic potential appears as a result of
interactions, either electron-phonon, or electron electron, which, linked with
the topological instability of the system, results in formation of a new ground
state of the system - the density wave provided that the relevant coupling
constant is larger than critical. Our analysis shows that the phase transition
takes place along the whole continuous interval of a dimensional crossover
between 1D and 3D, but that the critical coupling strength significantly
increases with the dimensionality of the system. It is our intention to give an
initial framework for understanding the nature of charge density waves
experimentally observed in a number of materials, like high$-T_c$ cuprates or
graphite intercalates, both being materials with a closed, rather isotropic
Fermi surface far from the nesting regime.


Abstract:
  We describe and make available the game-based material developed for a
laboratory run at several Italian science festivals to popularize NLP among
young students.


Abstract:
  We present variability analyses of twenty pointed XMM-Newton observations of
the high energy peaked TeV blazar PG 1553+113 taken during 2010 to 2018. We
found intraday variability in the total X-ray energy range (0.3 -- 10 keV) in
16 out of 19 light curves or a duty cycle of ~84%. A discrete correlation
function analysis of the intraday light curves in the soft and hard X-ray bands
peaks on zero lag, showing that the emission in hard and soft bands are
co-spatial and emitted from the same population of leptons. Red-noise dominates
the power spectral density (PSD) of all the LCs although the PSDs have a range
of spectral slopes from -2.36 to -0.14. On longer timescales, the optical and
UV variability patterns look almost identical and well correlated, as are the
soft and hard X-ray bands, but the optical/UV variations are not correlated to
those in the X-ray band, indicating that the optical/UV and X-ray emissions are
emitted by two different populations of leptons. We briefly discuss physical
mechanisms which may be capable of explaining the observed flux and spectral
variability of PG 1553+113 on these diverse timescales.


Abstract:
  We demonstrate a method of micromotion minimization of a trapped ion in a
linear Paul trap based on the precision measurement of the ion trapping
position displacement due to a stray electric field in the radial plane by ion
fluorescence imaging. The amount of displacement in the radial plane is
proportional to the strength of a stray electric field. Therefore, we evaluated
the micromotion compensation condition by measuring the ion displacements from
the ion equilibrium position using two different radial trap frequencies with
various combinations of the compensation voltage. The residual electric field
uncertainty of this technique reached a few volts per meter. This compensation
technique does not depend on the orientation of the incident cooling laser or
the detuning and imaging direction. Therefore, this method is suitable for a
planar ion trap, a stylus ion trap, which limits the propagation angle of
lasers, or miniaturized ion trap systems for sensing and metrological
applications.


Abstract:
  This paper concerns the local study of analytic constrained differential
systems (or impasse systems) of the form $A(x)\dot{x} = F(x)$,
$x\in\mathbb{R}^{2}$, where $F$ is a vector field and $A$ is a matrix valued
function. Using techniques of resolution of singularities with weighted blow
ups, we extend well-known results in the literature on topological
classification of phase portraits of planar vector fields to the context of
such class of systems. We also study and classify phase portraits of
constrained systems near singular points of the so called impasse set
$\Delta=\{x:\det A (x) = 0\}$.


Abstract:
  A cubic polynomial $P$ with a non-repelling fixed point $b$ is said to be
\emph{immediately renormalizable} if there exists a (connected) quadratic-like
invariant filled Julia set $K^*$ such that $b\in K^*$. In that case exactly one
critical point of $P$ does not belong to $K^*$. We show that if, in addition,
the Julia set of $P$ has no (pre)periodic cutpoints then this critical point is
recurrent.


Abstract:
  Vanadium disulfide (VS_{2}) attracts elevated interests for its
charge-density wave (CDW) phase transition, ferromagnetism, and catalytic
reactivity, but the electronic structure of monolayer has not been well
understood yet. Here we report synthesis of epitaxial 1T VS_{2} monolayer on
bilayer graphene grown by molecular-beam epitaxy (MBE). Angle-resolved
photoemission spectroscopy (ARPES) measurements reveal that Fermi surface with
six elliptical pockets centered at the M points shows gap opening at low
temperature. Temperature-dependence of the gap size suggests existence of CDW
phase transition above room temperature. Our observations provide important
evidence to understand the strongly correlated electron physics and the related
surface catalytic properties in two-dimensional transition-metal
dichalcogenides (TMDCs).


Abstract:
  Due to the wide use of highly-valuable and large-scale deep neural networks
(DNNs), it becomes crucial to protect the intellectual property of DNNs so that
the ownership of disputed or stolen DNNs can be verified. Most existing
solutions embed backdoors in DNN model training such that DNN ownership can be
verified by triggering distinguishable model behaviors with a set of secret
inputs. However, such solutions are vulnerable to model fine-tuning and
pruning. They also suffer from fraudulent ownership claim as attackers can
discover adversarial samples and use them as secret inputs to trigger
distinguishable behaviors from stolen models. To address these problems, we
propose a novel DNN watermarking solution, named HufuNet, for protecting the
ownership of DNN models. We evaluate HufuNet rigorously on four benchmark
datasets with five popular DNN models, including convolutional neural network
(CNN) and recurrent neural network (RNN). The experiments demonstrate HufuNet
is highly robust against model fine-tuning/pruning, kernels cutoff/supplement,
functionality-equivalent attack, and fraudulent ownership claims, thus highly
promising to protect large-scale DNN models in the real-world.


Abstract:
  A discussion about conditions on the profiles of voters in order to avoid
contradictory decisions that have been designed by Sen and others.


Abstract:
  We explore thermodynamic contributions to the three-dimensional de Sitter
horizon originating from metric and Chern-Simons gauge field fluctuations. In
Euclidean signature these are computed by the partition function of gravity
coupled to matter semi-classically expanded about the round three-sphere
saddle. We investigate a corresponding Lorentzian picture - drawing inspiration
from the topological entanglement entropy literature - in the form of an
edge-mode theory residing at the de Sitter horizon. We extend the discussion to
three-dimensional gravity with positive cosmological constant, viewed
(semi-classically) as a complexified Chern-Simons theory. The putative
gravitational edge-mode theory is a complexified version of the chiral
Wess-Zumino-Witten model associated to the edge-modes of ordinary Chern-Simons
theory. We introduce and solve a family of complexified Abelian Chern-Simons
theories as a way to elucidate some of the more salient features of the
gravitational edge-mode theories. We comment on the relation to the
AdS$_4$/CFT$_3$ correspondence.


Abstract:
  The emergence of spatial and temporal coherence of light emitted from
solid-state systems is a fundamental phenomenon, rooting in a plethora of
microscopic processes. It is intrinsically aligned with the control of
light-matter coupling, and canonical for laser oscillation. However, it also
emerges in the superradiance of multiple, phase-locked emitters, and more
recently, coherence and long-range order have been investigated in bosonic
condensates of thermalized light, as well as in exciton-polaritons driven to a
ground state via stimulated scattering. Here, we experimentally show that the
interaction between photons in a Fabry-Perot microcavity and excitons in an
atomically thin WSe$_2$ layer is sufficient such that the system enters the
hybridized regime of strong light-matter coupling at ambient conditions. Via
Michelson interferometry, we capture clear evidence of increased spatial and
temporal coherence of the emitted light from the spatially confined system
ground-state. The coherence build-up is accompanied by a threshold-like
behaviour of the emitted light intensity, which is a fingerprint of a polariton
laser effect. Valley-physics is manifested in the presence of an external
magnetic field, which allows us to manipulate K and K' polaritons via the
Valley-Zeeman-effect. Our findings are of high application relevance, as they
confirm the possibility to use atomically thin crystals as simple and versatile
components of coherent light-sources, and in valleytronic applications at room
temperature.


Abstract:
  Intelligent reflecting surface (IRS) has emerged as a promising paradigm to
improve the capacity and reliability of a wireless communication system by
smartly reconfiguring the wireless propagation environment. To achieve the
promising gains of IRS, the acquisition of the channel state information (CSI)
is essential, which however is practically difficult since the IRS does not
employ any transmit/receive radio frequency (RF) chains in general and it has
limited signal processing capability. In this paper, we study the uplink
channel estimation problem for an IRS-aided multiuser single-input multi-output
(SIMO) system, and propose a novel two-phase channel estimation (2PCE) strategy
which can alleviate the negative effects caused by error propagation in the
existing three-phase channel estimation approach, i.e., the channel estimation
errors in previous phases will deteriorate the estimation performance in later
phases, and enhance the channel estimation performance with the same amount of
channel training overhead as in the existing approach. Moreover, the asymptotic
mean squared error (MSE) of the 2PCE strategy is analyzed when the least-square
(LS) channel estimation method is employed, and we show that the 2PCE strategy
can outperform the existing approach. Finally, extensive simulation results are
presented to validate the effectiveness of the 2PCE strategy.


Abstract:
  Artificial Intelligence (AI), and in particular, the explainability thereof,
has gained phenomenal attention over the last few years. Whilst we usually do
not question the decision-making process of these systems in situations where
only the outcome is of interest, we do however pay close attention when these
systems are applied in areas where the decisions directly influence the lives
of humans. It is especially noisy and uncertain observations close to the
decision boundary which results in predictions which cannot necessarily be
explained that may foster mistrust among end-users. This drew attention to AI
methods for which the outcomes can be explained. Bayesian networks are
probabilistic graphical models that can be used as a tool to manage
uncertainty. The probabilistic framework of a Bayesian network allows for
explainability in the model, reasoning and evidence. The use of these methods
is mostly ad hoc and not as well organised as explainability methods in the
wider AI research field. As such, we introduce a taxonomy of explainability in
Bayesian networks. We extend the existing categorisation of explainability in
the model, reasoning or evidence to include explanation of decisions. The
explanations obtained from the explainability methods are illustrated by means
of a simple medical diagnostic scenario. The taxonomy introduced in this paper
has the potential not only to encourage end-users to efficiently communicate
outcomes obtained, but also support their understanding of how and, more
importantly, why certain predictions were made.


Abstract:
  We present a calculation of the pion and kaon Mellin moment $\langle x^3
\rangle$ extracted directly in lattice QCD using a three-derivative local
operator. We use one ensemble of gauge configurations with two degenerate
light, a strange and a charm quark ($N_f=2+1+1$) of maximally twisted mass
fermions with clover improvement. The ensemble reproduces a pion mass $\sim260$
MeV, and a kaon mass $\sim530$ MeV. Excited-states contamination is evaluated
using four values of the source-sink time separation within the range of
$1.12-1.67$ fm. We use an operator that is free of mixing, and apply a
multiplicative renormalization function calculated non-perturbatively. Our
results are converted to the $\overline{\rm MS}$ scheme and evolved at a scale
of 2 GeV, using three-loop expressions in perturbation theory. The final values
are $\langle x^3 \rangle_\pi^{u^+}=0.024(18)_{\rm stat}(2)_{\rm syst}$,
$\langle x^3 \rangle_K^{u^+}=0.035(6)_{\rm stat}(3)_{\rm syst}$, and $\langle
x^3 \rangle_K^{s^+}=0.075(5)_{\rm stat}(1)_{\rm syst}$, where the systematic
error is the uncertainty due to excited state contamination.
  We combine $\langle x^3 \rangle$ with the two lower moments to obtain the
ratios $\langle x^3 \rangle/\langle x \rangle$ and $\langle x^3 \rangle/\langle
x^2 \rangle$, as well as ratios between the pion and kaon moments. In addition,
we reconstruct the $x$-dependence of the pion and kaon PDFs via 2- and
3-parameter fits to our results. We find that the reconstruction is feasible
and that our lattice data favor a large $x$-dependence that falls as $(1-x)^2$
for both the pion and kaon PDFs. We integrate the reconstructed PDFs to extract
the higher moments with $4\leq n\leq 6$. Finally, we compare the pion and kaon
PDFs, as well as the ratios of their moments, to address the effect of SU(3)
flavor symmetry breaking.


Abstract:
  With the advent of 2-dimensional Convolution Neural Networks (2D CNNs), the
face recognition accuracy has reached above 99%. However, face recognition is
still a challenge in real world conditions. A video, instead of an image, as an
input can be more useful to solve the challenges of face recognition in real
world conditions. This is because a video provides more features than an image.
However, 2D CNNs cannot take advantage of the temporal features present in the
video. We therefore, propose a framework called $Sf_{3}CNN$ for face
recognition in videos. The $Sf_{3}CNN$ framework uses 3-dimensional Residual
Network (3D Resnet) and A-Softmax loss for face recognition in videos. The use
of 3D ResNet helps to capture both spatial and temporal features into one
compact feature map. However, the 3D CNN features must be highly discriminative
for efficient face recognition. The use of A-Softmax loss helps to extract
highly discriminative features from the video for face recognition. $Sf_{3}CNN$
framework gives an increased accuracy of 99.10% on CVBL video database in
comparison to the previous 97% on the same database using 3D ResNets.


Abstract:
  In this work, we propose an economical model to address some open
cosmological problems such as the absence of the initial cosmological
singularity, an early acceleration of the Universe and the generation of
matter-antimatter asymmetry. The model is based on a scenario in which the
early Universe consists of a non-linear electrodynamics fields. It is found
that the non-linear electrodynamics model has an equation of state
$p=\frac{1}{3} \rho - \frac{4}{3} \beta \rho^{1+\alpha}$ which shows that the
Universe undergoes an early epoch acceleration to a radiation era given by $p
=\frac{1}{3} \rho$. We show that the singularities in the energy density,
pressure and curvature are absent at early stages. In our scenario, the baryon
asymmetry is generated by the non-linearity parameter $\beta$. Additionally, we
calculate the resulting baryon asymmetry and discuss how a successful
gravitational baryogenesis is obtained for different values of the model's
parameter space.


Abstract:
  This paper considers the massive connectivity problem in an asynchronous
grant-free random access system, where a huge number of devices sporadically
transmit data to a base station (BS) with imperfect synchronization. The goal
is to design algorithms for joint user activity detection, delay detection, and
channel estimation. By exploiting the sparsity on both user activity and
delays, we formulate a hierarchical sparse signal recovery problem in both the
single-antenna and the multiple-antenna scenarios. While traditional compressed
sensing algorithms can be applied to these problems, they suffer high
computational complexity and often require the perfect statistical information
of channel and devices. This paper solves these problems by designing the
Learned Approximate Message Passing (LAMP) network, which belongs to
model-driven deep learning approaches and ensures efficient performance without
tremendous training data. Particularly, in the multiple-antenna scenario, we
design three different LAMP structures, namely, distributed, centralized and
hybrid ones, to balance the performance and complexity. Simulation results
demonstrate that the proposed LAMP networks can significantly outperform the
conventional AMP method thanks to their ability of parameter learning. It is
also shown that LAMP has robust performance to the maximal delay spread of the
asynchronous users.


Abstract:
  We investigate the Yukawa coupling unification for the third generation in a
class of $\mathsf{SO(10)}$ unified models which are consistent with the 4.2
$\sigma$ deviation from the standard model of the muon $g-2$ seen by the
Fermilab experiment E989. A recent analysis in supergravity grand unified
models shows that such an effect can arise from supersymmetric loops
correction. Using a neural network, we further analyze regions of the parameter
space where Yukawa coupling unification consistent with the Fermilab result can
appear. In the analysis we take into account the contributions to Yukawas from
the cubic and the quartic interactions. We test the model at the high
luminosity and high energy LHC and estimate the integrated luminosities needed
to discover sparticles predicted by the model.


Abstract:
  For general relativistic, inviscid, axisymmetric flow around Kerr black hole
one may choose different flow thickness. The stationary flow equations can be
solved using methods of dynamical system to get transonic accretion flows ,
i.e, flow infalling in the blackhole that turns supersonic from subsonic with
decreasing radial distance, or vice versa. This transonic flows are obtained by
choosing the particular flow passing through critical points of phase portrait.
For certain flow thickness like the one maintaining conical shape, the sonic
point coincide with the critical point. But there are certain flows maintaining
hydrostatic equilibrium, such as the one described by Novikov-Thorne, where the
sonic point is not same as the critical point. We perturb the flow for both
kind of flow and study the behaviour of linear perturbation which behaves like
massless scalar field in some curved spacetime, known as, analogue space time.
We draw the compactified causal structure, i.e, Penrose Carter diagram for both
kind of analogue metric and prove that for both cases critical points are the
acoustic horizons, whereas in the case where sonic points do not coincide with
critical points, the sonic points are not the acoustic horizon, as one may
expect from the definition of sound speed.


Abstract:
  The relativistic theory of the time- and position-dependent energy and
momentum densities of light in glasses and other low-loss dispersive media,
where different wavelengths of light propagate at different phase velocities,
has remained a largely unsolved challenge until now. This is astonishing in
view of the excellent theoretical understanding of Maxwell's equations and the
abundant experimental measurements of optical phenomena in dispersive media.
The challenge is related to the complexity of the interference patterns of
partial waves and to the coupling of the field and medium dynamics by the
optical force. In this work, we use the mass-polariton theory of light [Phys.
Rev. A 96, 063834 (2017)] to derive the stress-energy-momentum (SEM) tensors of
the field and the dispersive medium. Our starting point, the fundamental local
conservation laws of energy and momentum, is close to that of a recent
theoretical work on light in dispersive media by Philbin [Phys. Rev. A 83,
013823 (2011)], which however, excludes the power-conversion and force density
source terms describing the coupling between the field and the medium. In the
general inertial frame, we present the SEM tensors in terms of Lorentz scalars,
four-vectors, and field tensors that reflect in a transparent way the Lorentz
covariance of the theory. The SEM tensors of the field and the medium are
symmetric, form-invariant, and in full accordance with the covariance principle
of the special theory of relativity. The SEM tensor of the coupled field-medium
state of light also has zero four-divergence. Therefore, light in a dispersive
medium has well-defined four-momentum and rest frame. The volume integrals of
the total energy and momentum densities of light agree with the model of
mass-polariton quasiparticles having a nonzero rest mass. The predictions of
our work are directly accessible to experiments.


Abstract:
  We present the Time Lag/Delay Reconstructor (TLDR), an algorithm for
reconstructing velocity delay maps in the Maximum A Posteriori framework for
reverberation mapping. Reverberation mapping is a tomographical method for
studying the kinematics and geometry of the broad-line region of active
galactic nuclei at high spatial resolution. Leveraging modern image
reconstruction techniques, including Total Variation and Compressed Sensing,
TLDR applies multiple regularization schemes to re-construct velocity delay
maps using the Alternating Direction Method of Multipliers. Along with the
detailed description of the TLDR algorithm we present test reconstructions from
TLDR applied to synthetic reverberation mapping spectra as well as a
preliminary reconstruction of the H\b{eta}feature of Arp 151 from the 2008 Lick
Active Galactic Nuclei Monitoring Project.


Abstract:
  Does the proof-of-work protocol serve its intended purpose of supporting
decentralized cryptocurrency mining? To address this question, we develop a
game-theoretical model where miners first invest in hardware to improve the
efficiency of their operations, and then compete for mining rewards in a
rent-seeking game. We argue that because of capacity constraints faced by
miners, centralization in mining is lower than indicated by both public
discourse and recent academic work. We show that advancements in hardware
efficiency do not necessarily lead to larger miners increasing their advantage,
but rather allow smaller miners to expand and new miners to enter the
competition. Our calibrated model illustrates that hardware efficiency has a
small impact on the cost of attacking a network, while the mining reward has a
significant impact. This highlights the vulnerability of smaller and emerging
cryptocurrencies, as well as of established cryptocurrencies transitioning to a
fee-based mining reward scheme.


Abstract:
  2D image representations are in regular grids and can be processed
efficiently, whereas 3D point clouds are unordered and scattered in 3D space.
The information inside these two visual domains is well complementary, e.g., 2D
images have fine-grained texture while 3D point clouds contain plentiful
geometry information. However, most current visual recognition systems process
them individually. In this paper, we present a \emph{bidirectional projection
network (BPNet)} for joint 2D and 3D reasoning in an end-to-end manner. It
contains 2D and 3D sub-networks with symmetric architectures, that are
connected by our proposed \emph{bidirectional projection module (BPM)}. Via the
\emph{BPM}, complementary 2D and 3D information can interact with each other in
multiple architectural levels, such that advantages in these two visual domains
can be combined for better scene recognition. Extensive quantitative and
qualitative experimental evaluations show that joint reasoning over 2D and 3D
visual domains can benefit both 2D and 3D scene understanding simultaneously.
Our \emph{BPNet} achieves top performance on the ScanNetV2 benchmark for both
2D and 3D semantic segmentation. Code is available at
\url{https://github.com/wbhu/BPNet}.


Abstract:
  We focus on the secular changes of the orbital elements of a planet in the
solar system, determined by the magnetic-like part of a gravitational wave
field. Using Fermi coordinates we show that the total force acting on a test
particle is made of two contributions: a gravito-electric one and a
gravito-magnetic one. While the electric-like force has been thoroughly
discussed in the past, the effect of the gravito-magnetic force, which depends
on the velocity of the test particle, has not been considered yet. We obtain
approximated results to some orders in the orbital eccentricity and show that
these effects are much smaller than the corresponding gravito-electric ones.


Abstract:
  Measurement-device-independent quantum key distribution (MDI-QKD) is a
technique for quantum-secured communication that eliminates all detector
side-channels, although is currently limited by implementation complexity and
low secure key rates. Here, we introduce a simple and compact MDI-QKD system
design at gigahertz clock rates with enhanced resilience to laser fluctuations
- thus enabling free-running semiconductor laser sources to be employed without
spectral or phase feedback. This is achieved using direct laser modulation,
carefully exploiting gain-switching and injection-locking laser dynamics to
encode phase-modulated time-bin bits. Our design enables secure key rates that
improve upon the state of the art by an order of magnitude, up to 8 bps at 54
dB channel loss and 2 kbps in the finite-size regime for 30 dB channel loss.
This greatly simplified MDI-QKD system design and proof-of-principle
demonstration shows that MDI-QKD is a practical, high-performance solution for
future quantum communication networks.


Abstract:
  Amorphous solids appear to react elastically to small external strains, but
in contrast to ideal elastic media, plastic responses abound immediately, at
any value of the strain. Such plastic responses are quasi-localized in nature,
with the ``cheapest" one being a quadrupolar source. The existence of such
plastic responses results in {\em screened elasticity} in which strains and
stresses can either quantitatively or qualitatively differ from the un-screened
theory, depending on the specific screening mechanism. Here we offer a theory
of such screening effects by plastic quadrupoles, dipoles and monopoles,
explain their natural appearance, and point out the analogy to electrostatic
screening by electric charges and dipoles. For low density of quadrupoles the
effect is to normalize the elastic moduli without a qualitative change compared
to pure elasticity theory; for higher density of quadrupoles the screening
effects result in qualitative changes. Predictions for the spatial dependence
of displacement fields caused by local sources of strains are provided and
compared to numerical simulations. We find that anomalous elasticity is richer
than electrostatics in having a screening mode that does not appear in the
electrostatic analog


Abstract:
  We analyze the extracellular stimulation current and the charge injection
capacitity (CIC) of microelectrodes coated with an insulating layer to prevent
toxic electrochemical effects in bioelectronic applications. We show for a
microelectrode coated with an insulating ferroelectric layer, that the
ferroelectric polarization current contributes to the extracellular stimulation
current. Depending on the remanent polarization $P_{\rm r}$ of the
ferroelectric, the polarization current in the switching regime can increase
the CIC by up to two orders of magnitude as compared to the commonly used
extracellular capacitive stimulation with microelectrodes that are coated with
a dielectric layer.


Abstract:
  We investigated the suitability of the astronomical 15 GHz VLBA observing
program MOJAVE-5 for estimation of geodetic parameters, such as station
coordinates and Earth orientation parameters. We processed contemporary
geodetic dual-band RV and CN experiments observed at 2.3 GHz and 8.6 GHz
starting on September 2016 through July 2020 as reference dataset. We showed
that the baseline length repeatability from MOJAVE-5 experiments is only a
factor of 1.5 greater than from the dedicated geodetic dataset and still below
1~ppb. The wrms of the difference of estimated EOP with respect to the
reference IERS C04 time series are a factor of 1.3 to 1.8 worse. We isolated
three major differences between the datasets in terms their possible impact on
the geodetic results, i.e. the scheduling approach, treatment of the
ionospheric delay, and selection of target radio sources. We showed that the
major factor causing discrepancies in the estimated geodetic parameters is the
different scheduling approach of the datasets. We conclude that systematic
errors in MOJAVE-5 dataset are low enough for these data to be used as an
excellent testbed for further investigations on the radio source structure
effects in geodesy and astrometry.


Abstract:
  Supervised deep learning has swiftly become a workhorse for accelerated MRI
in recent years, offering state-of-the-art performance in image reconstruction
from undersampled acquisitions. Training deep supervised models requires large
datasets of undersampled and fully-sampled acquisitions typically from a
matching set of subjects. Given scarce access to large medical datasets, this
limitation has sparked interest in unsupervised methods that reduce reliance on
fully-sampled ground-truth data. A common framework is based on the deep image
prior, where network-driven regularization is enforced directly during
inference on undersampled acquisitions. Yet, canonical convolutional
architectures are suboptimal in capturing long-range relationships, and
randomly initialized networks may hamper convergence. To address these
limitations, here we introduce a novel unsupervised MRI reconstruction method
based on zero-Shot Learned Adversarial TransformERs (SLATER). SLATER embodies a
deep adversarial network with cross-attention transformer blocks to map noise
and latent variables onto MR images. This unconditional network learns a
high-quality MRI prior in a self-supervised encoding task. A zero-shot
reconstruction is performed on undersampled test data, where inference is
performed by optimizing network parameters, latent and noise variables to
ensure maximal consistency to multi-coil MRI data. Comprehensive experiments on
brain MRI datasets clearly demonstrate the superior performance of SLATER
against several state-of-the-art unsupervised methods.


Abstract:
  Quantitative susceptibility mapping (QSM) has demonstrated great potential in
quantifying tissue susceptibility in various brain diseases. However, the
intrinsic ill-posed inverse problem relating the tissue phase to the underlying
susceptibility distribution affects the accuracy for quantifying tissue
susceptibility. Recently, deep learning has shown promising results to improve
accuracy by reducing the streaking artifacts. However, there exists a mismatch
between the observed phase and the theoretical forward phase estimated by the
susceptibility label. In this study, we proposed a model-based deep learning
architecture that followed the STI (susceptibility tensor imaging) physical
model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the
relationship between STI-derived phase contrast induced by the susceptibility
tensor terms (ki13,ki23,ki33) and the acquired single-orientation phase. The
convolution neural networks are embedded into the physical model to learn a
regularization term containing prior information. ki33 and phase induced by
ki13 and ki23 terms were used as the labels for network training. Quantitative
evaluation metrics (RSME, SSIM, and HFEN) were compared with recently developed
deep learning QSM methods. The results showed that MoDL-QSM achieved superior
performance, demonstrating its potential for future applications.


Abstract:
  Automatic verification of array manipulating programs is a challenging
problem because it often amounts to the inference of in ductive quantified loop
invariants which, in some cases, may not even be firstorder expressible. In
this paper, we suggest a novel verification tech nique that is based on
induction on userdefined rank of program states as an alternative to
loopinvariants. Our technique, dubbed inductive rank reduction, works in two
steps. Firstly, we simplify the verification problem and prove that the program
is correct when the input state con tains an input array of length B or less,
using the length of the array as the rank of the state. Secondly, we employ a
squeezing function g which converts a program state sigma with an array of
length > B to a state g(sigma) containing an array of length minus 1 or less.
We prove that when g satisfies certain natural conditions then if the program
violates its specification on sigma then it does so also on g(sigma). The
correctness of the program on inputs with arrays of arbitrary lengths follows
by induction. We make our technique automatic for array programs whose length
of execution is proportional to the length of the input arrays by (i) perform
ing the first step using symbolic execution, (ii) verifying the conditions
required of g using Z3, and (iii) providing a heuristic procedure for syn
thesizing g. We implemented our technique and applied it successfully to
several interesting arraymanipulating programs, including a bidirec tional
summation program whose loop invariant cannot be expressed in firstorder logic
while its specification is quantifier free.


Abstract:
  In current seismic acquisition practice, there is an increasing drive for
sparsely (in space) acquired data, often in irregular geometry. These surveys
can trade off subsurface information for efficiency/cost - creating a problem
of "missing seismic data" that can greatly hinder subsequent processing and
interpretation. Reconstruction of regularly sampled dense data from highly
sparse, irregular data can therefore aid in processing and interpretation of
these far sparser, more efficient seismic surveys. Here, two methods are
compared to solve the reconstruction problem in both space-time and
wavenumber-frequency domain. This requires an operator that maps sparse to
dense data: the operator is generally unknown, being the inverse of a known
data sampling operator. As such, here the deterministic inversion is
efficiently solved by least squares optimisation using a numerically efficient
Python-based linear operator representation. An alternative approach is
probabilistic and uses deep learning. Here, two deep learning architectures are
benchmarked against each other and the deterministic approach; a Recurrent
Inference Machine (RIM), which is designed specifically to solve inverse
problems given known forward operators, and the well-known U-Net. The trained
deep learning networks are capable of successfully mapping sparse to dense
seismic data for a range of different datasets and decimation percentages,
thereby significantly reducing spatial aliasing in the wavenumber-frequency
domain. The deterministic inversion on the contrary, could not reconstruct the
missing data and thus did not reduce the undesired spatial aliasing. Results
show that the application of Deep Learning for seismic reconstruction is
promising, but the treatment of large-volume, multi-component seismic datasets
will require dedicated learning architectures not yet realisable with existing
tools.


Abstract:
  A capacitive displacement system was constructed to measure the
electric-field-induced piezoelectric strain in the simple form of either a bulk
or thin film. The system can determine an AC displacement of 2 pm precisely by
using a lock-in detection, and can measure the large displacement within a
range of $\pm$ 25 $\mu$m with a sub-nanometer resolution. The system can also
be used to measure the variation in strain within a temperature range of 210 -
450 K, allowing the evaluation of the temperature coefficient of a
piezoelectric constant and the studies on the effects of a phase transition on
the piezoelectric response. Experimental results on quartz, PZT ceramics and
thin films, and BaTiO$_3$ confirm the capabilities of the developed system.


Abstract:
  Intensity modulators are an essential component in optics for controlling
free-space beams. Many applications require the intensity of a free-space beam
to be modulated at a single frequency, including wide-field lock-in detection
for sensitive measurements, mode-locking in lasers, and phase-shift
time-of-flight imaging (LiDAR). Here, we report a new type of single frequency
intensity modulator that we refer to as a longitudinal piezoelectric resonant
photoelastic modulator. The modulator consists of a thin lithium niobate wafer
coated with transparent surface electrodes. One of the fundamental acoustic
modes of the modulator is excited through the surface electrodes, confining an
acoustic standing wave to the electrode region. The modulator is placed between
optical polarizers; light propagating through the modulator and polarizers is
intensity modulated with a wide acceptance angle and record breaking modulation
efficiency in the megahertz frequency regime. As an illustration of the
potential of our approach, we show that the proposed modulator can be
integrated with a standard image sensor to effectively convert it into a
time-of-flight imaging system.


Abstract:
  An $n$-vertex graph is said to to be $(p,\beta)$-bijumbled if for any vertex
sets $A,B\subseteq V(G)$, we have \[e(A,B)=p|A||B|\pm \beta \sqrt{|A||B|}.\]
  We prove that for any $3\leq r\in \mathbb{N}$ and $c>0$ there exists an
$\varepsilon>0$ such that any $n$-vertex $(p,\beta)$-bijumbled graph with $n\in
r \mathbb{N}$, $\delta(G)\geq cpn$ and $\beta \leq \varepsilon p^{r-1}n$,
contains a $K_r$-factor. This implies a corresponding result for the stronger
pseudorandom notion of $(n,d,\lambda)$-graphs.
  For the case of triangle factors, that is when $r=3$, this result resolves a
conjecture of Krivelevich, Sudakov and Szab\'o from 2004 and it is tight due to
a pseudorandom triangle-free construction of Alon. In fact, in this case even
more is true: as a corollary to this result and a result of Han, Kohayakawa,
Person and the author, we can conclude that the same condition of
$\beta=o(p^2n)$ actually guarantees that a $(p,\beta)$-bijumbled graph $G$
contains every graph on $n$ vertices with maximum degree at most 2.


Abstract:
  This paper is devoted to providing quantitative bounds on the location of
eigenvalues, both discrete and embedded, of non self-adjoint Lam\'e operators
of elasticity $-\Delta^\ast + V$ in terms of suitable norms of the potential
$V$. In particular, this allows to get sufficient conditions on the size of the
potential such that the point spectrum of the perturbed operator remains empty.
In three dimensions we show full spectral stability under suitable
form-subordinated perturbations: we prove that the spectrum is purely
continuous and coincides with the non negative semi-axis as in the free case.


Abstract:
  Reasonable setting of traffic signals can be very helpful in alleviating
congestion in urban traffic networks. Meta-heuristic optimization algorithms
have proved themselves to be able to find high-quality signal timing plans.
However, they generally suffer from performance deterioration when solving
large-scale traffic signal optimization problems due to the huge search space
and limited computational budget. Directing against this issue, this study
proposes a surrogate-assisted cooperative signal optimization (SCSO) method.
Different from existing methods that directly deal with the entire traffic
network, SCSO first decomposes it into a set of tractable sub-networks, and
then achieves signal setting by cooperatively optimizing these sub-networks
with a surrogate-assisted optimizer. The decomposition operation significantly
narrows the search space of the whole traffic network, and the
surrogate-assisted optimizer greatly lowers the computational burden by
reducing the number of expensive traffic simulations. By taking Newman fast
algorithm, radial basis function and a modified estimation of distribution
algorithm as decomposer, surrogate model and optimizer, respectively, this
study develops a concrete SCSO algorithm. To evaluate its effectiveness and
efficiency, a large-scale traffic network involving crossroads and T-junctions
is generated based on a real traffic network. Comparison with several existing
meta-heuristic algorithms specially designed for traffic signal optimization
demonstrates the superiority of SCSO in reducing the average delay time of
vehicles.


Abstract:
  We prove that a probability solution of the stationary Kolmogorov equation
generated by a first order perturbation $v$ of the Ornstein--Uhlenbeck operator
$L$ possesses a highly integrable density with respect to the Gaussian measure
satisfying the non-perturbed equation provided that $v$ is sufficiently
integrable. More generally, a similar estimate is proved for solutions to
inequalities connected with Markov semigroup generators under the curvature
condition $CD(\theta,\infty)$. For perturbations from $L^p$ an analog of the
Log-Sobolev inequality is obtained. It is also proved in the Gaussian case that
the gradient of the density is integrable to all powers. We obtain
dimension-free bounds on the density and its gradient, which also covers the
infinite-dimensional case.


Abstract:
  A repeating source of fast radio bursts (FRBs) is recently discovered from a
globular cluster of M81. Association to a globular cluster (or other old
stellar systems) suggests that strongly magnetized neutron stars, which are the
most likely objects responsible for FRBs, are born not only when young massive
stars undergo core-collapse, but also by mergers of old white dwarfs. We find
that the fractional contribution to the total FRB rate by old stellar
populations is at least a few percent, and the precise fraction can be
constrained by FRB searches in the directions of nearby galaxies, both
star-forming and elliptical ones. Using very general arguments, we show that
the activity time of the M81-FRB object is between 10^4 and 10^6 years, and
more likely of order 10^5 years. The energetics of radio outbursts puts a lower
limit on the magnetic field strength of 10^{13} G, and the spin period > 0.2
sec, thereby ruling out the source being a milli-second pulsar. The upper limit
on the persistent X-ray luminosity (provided by Chandra), together with the
high FRB luminosity and frequent repetitions, severely constrains (or rules
out) the possibility that the M81-FRB is a scaled-up version of giant pulses
from Galactic pulsars. Finally, the 50 ns variability time of the FRB
lightcurve suggests that the emission is produced in a compact region inside
the neutron star magnetosphere, as it cannot be accounted for when the emission
is at distances > 10^{10} cm.


Abstract:
  We consider the task of retrieving audio using free-form natural language
queries. To study this problem, which has received limited attention in the
existing literature, we introduce challenging new benchmarks for text-based
audio retrieval using text annotations sourced from the Audiocaps and Clotho
datasets. We then employ these benchmarks to establish baselines for
cross-modal audio retrieval, where we demonstrate the benefits of pre-training
on diverse audio tasks. We hope that our benchmarks will inspire further
research into cross-modal text-based audio retrieval with free-form text
queries.


Abstract:
  Direct numerical simulation has been carried out on one attached and two
separated boundary layer flows (involving small and large separation) under the
influence of an adverse pressure gradient. A unified picture of the
pre-transitional boundary layer for the three cases has been provided that
reveals a "mixed-mode" instability, involving contribution from instability
waves and streamwise streaks. A time-frequency analysis of the transitional
velocity signals has been performed which shows that as the Reynolds number
decreases, the character of the time traces evolves continuously from a
"spotty" behaviour (exhibiting distinct turbulent spots) for the attached case
to a "non-spotty" behaviour (involving more "uniform" distribution of turbulent
fluctuations in time) for the large separation case, encompassing the entire
spectrum of transition scenarios. The variation of the intermittency factor
within the transition zone is seen to compare well with the Narasimha universal
intermittency distribution. We find that although the time variation of
velocity for large separation is non-spotty (or more "uniform"), the spanwise
variation of velocity is spotty, showing a clear clustering of high-wavenumber
fluctuations separated by quasi-laminar regions. Thus, all the cases exhibit
spottiness in the transition zone with different manifestations. We present a
physical cartoon for the transition scenarios for the attached and separated
cases, using the ideas of vortex-wall interaction and instability of spanwise
vortical structures. We find that concentrated breakdown is exhibited by all
the three cases near the transition onset and the spot breakdown processes are
broadly consistent with the postulates underlying the universal intermittency
distribution.


Abstract:
  This paper brings together the concepts of navigation transformation and
harmonic functions to form navigation functions that are
correct-by-construction in the sense that no tuning is required. The form of
the navigation function is explicitly related to the number of obstacles in the
environment. This enables application of navigation functions for autonomous
robot navigation in partially or fully unknown environments, with the
capability of on-the-fly adjustment of the navigation function when new
obstacles are discovered by the robot. Appropriate navigation controllers,
applicable to robots with local, sector bounded sensing, are presented and
analyzed for a~kinematic point-mass robot and then for the dynamic point-mass
robot system. The closed form nature of the proposed navigation scheme provides
for online, fast-feedback based navigation. In addition to the analytic
guarantees, simulation studies are presented to verify the effectiveness of the
methodology.


Abstract:
  Observations of hot-Jupiter atmospheres show large variations in the location
of the hot spot and the amplitude of spectral features. Atmospheric flow
simulations using the commonly-employed forcing and initialization have
generally produced a large, monolithic patch of stationary hot area located
eastward of the substellar point at $\sim 3\!\times\! 10^{-3}$ MPa pressure
level. Here we perform high-resolution (up to T682) pseudospectral simulations
that accurately capture small-scale eddies and waves, inherent in hot-Jupiter
atmospheres due to ageostrophy. The atmospheres contain a large number of
intense storms over a wide range of scales, including the planetary-scale. The
latter sized storms dictate the large-scale spatial distribution and temporal
variability of hot, as well as cold, regions over the planet. In addition, the
large storms exhibit quasi-periodic life cycles within multiple equilibrium
states -- all identifiable in the disk-integrated time series of the
temperature flux.


Abstract:
  Collisionless shocks are ubiquitous in the Universe and are held responsible
for the production of non-thermal particles and high-energy radiation. In the
absence of particle collisions in the system, theoretical works show that the
interaction of an expanding plasma with a pre-existing electromagnetic
structure (as in our case) is able to induce energy dissipation and allow for
shock formation. Shock formation can alternatively take place when two plasmas
interact, through microscopic instabilities inducing electromagnetic fields
which are able in turn to mediate energy dissipation and shock formation. Using
our platform where we couple a fast-expanding plasma induced by high-power
lasers (JLF/Titan at LLNL and LULI2000) with high-strength magnetic fields, we
have investigated the generation of magnetized collisionless shock and the
associated particle energization. We have characterized the shock to be
collisionless and super-critical. We report here on measurements of the plasma
density, temperature, the electromagnetic field structures, and particle
energization in the experiments, under various conditions of ambient plasma and
B-field. We have also modeled the formation of the shocks using macroscopic
hydrodynamic simulations and the associated particle acceleration using kinetic
particle-in-cell simulations. As a companion paper of
\citet{yao2020laboratory}, here we show additional results of the experiments
and simulations, providing more information to reproduce them and demonstrating
the robustness of our interpreted proton energization mechanism to be shock
surfing acceleration.


Abstract:
  Nonholonomic mechanical systems encompass a large class of practically
interesting robotic structures, such as wheeled mobile robots, space
manipulators, and multi-fingered robot hands. However, few results exist on the
cooperative control of such systems in a generic, distributed approach. In this
work we extend a recently developed distributed Interconnection and Damping
Assignment Passivity-Based Control (IDA-PBC) method to such systems. More
specifically, relying on port-Hamiltonian system modelling for networks of
mechanical systems, we propose a full-state stabilization control law for a
class of nonholonomic systems within the framework of distributed IDA-PBC. This
enables the cooperative control of heterogeneous, underactuated and
nonholonomic systems with a unified control law. This control law primarily
relies on the notion of Passive Configuration Decomposition (PCD) and a novel,
non-smooth desired potential energy function proposed here. A low-level
collision avoidance protocol is also implemented in order to achieve dynamic
inter-agent collision avoidance, enhancing the practical relevance of this
work. Theoretical results are tested in different simulation scenarios in order
to highlight the applicability of the derived method.


Abstract:
  Online continual learning for image classification studies the problem of
learning to classify images from an online stream of data and tasks, where
tasks may include new classes (class incremental) or data nonstationarity
(domain incremental). One of the key challenges of continual learning is to
avoid catastrophic forgetting (CF), i.e., forgetting old tasks in the presence
of more recent tasks. Over the past few years, many methods and tricks have
been introduced to address this problem, but many have not been fairly and
systematically compared under a variety of realistic and practical settings. To
better understand the relative advantages of various approaches and the
settings where they work best, this survey aims to (1) compare state-of-the-art
methods such as MIR, iCARL, and GDumb and determine which works best at
different experimental settings; (2) determine if the best class incremental
methods are also competitive in domain incremental setting; (3) evaluate the
performance of 7 simple but effective trick such as "review" trick and nearest
class mean (NCM) classifier to assess their relative impact. Regarding (1), we
observe iCaRL remains competitive when the memory buffer is small; GDumb
outperforms many recently proposed methods in medium-size datasets and MIR
performs the best in larger-scale datasets. For (2), we note that GDumb
performs quite poorly while MIR -- already competitive for (1) -- is also
strongly competitive in this very different but important setting. Overall,
this allows us to conclude that MIR is overall a strong and versatile method
across a wide variety of settings. For (3), we find that all 7 tricks are
beneficial, and when augmented with the "review" trick and NCM classifier, MIR
produces performance levels that bring online continual learning much closer to
its ultimate goal of matching offline training.


Abstract:
  The record-breaking achievements of deep neural networks (DNNs) in image
classification and detection tasks resulted in a surge of new computer vision
applications during the past years. However, their computational complexity is
restricting their deployment to powerful stationary or complex dedicated
processing hardware, limiting their use in smart edge processing applications.
We propose an automated deployment framework for DNN acceleration at the edge
on field-programmable gate array (FPGA)-based cameras. The framework
automatically converts an arbitrary-sized and quantized trained network into an
efficient streaming-processing IP block that is instantiated within a generic
adapter block in the FPGA. In contrast to prior work, the accelerator is purely
logic and thus supports end-to-end processing on FPGAs without on-chip
microprocessors. Our mapping tool features automatic translation from a trained
Caffe network, arbitrary layer-wise fixed-point precision for both weights and
activations, an efficient XNOR implementation for fully binary layers as well
as a balancing mechanism for effective allocation of computational resources in
the streaming dataflow. To present the performance of the system we employ this
tool to implement two CNN edge processing networks on an FPGA-based high-speed
camera with various precision settings showing computational throughputs of up
to 337GOPS in low-latency streaming mode (no batching), running entirely on the
camera.


Abstract:
  In spite that Federated Learning (FL) is well known for its privacy
protection when training machine learning models among distributed clients
collaboratively, recent studies have pointed out that the naive FL is
susceptible to gradient leakage attacks. In the meanwhile, Differential Privacy
(DP) emerges as a promising countermeasure to defend against gradient leakage
attacks. However, the adoption of DP by clients in FL may significantly
jeopardize the model accuracy. It is still an open problem to understand the
practicality of DP from a theoretic perspective. In this paper, we make the
first attempt to understand the practicality of DP in FL through tuning the
number of conducted iterations. Based on the FedAvg algorithm, we formally
derive the convergence rate with DP noises in FL. Then, we theoretically
derive: 1) the conditions for the DP based FedAvg to converge as the number of
global iterations (GI) approaches infinity; 2) the method to set the number of
local iterations (LI) to minimize the negative influence of DP noises. By
further substituting the Laplace and Gaussian mechanisms into the derived
convergence rate respectively, we show that: 3) The DP based FedAvg with the
Laplace mechanism cannot converge, but the divergence rate can be effectively
prohibited by setting the number of LIs with our method; 4) The learning error
of the DP based FedAvg with the Gaussian mechanism can converge to a constant
number finally if we use a fixed number of LIs per GI. To verify our
theoretical findings, we conduct extensive experiments using two real-world
datasets. The results not only validate our analysis results, but also provide
useful guidelines on how to optimize model accuracy when incorporating DP into
FL


Abstract:
  High-power tunable lasers with good longitudinal and transverse modes are
fundamental tools for exploring quantum physics. Here we report a high-power
continuous-wave injection-locked titanium:sapphire laser with a low-loss cavity
configuration, where only a laser crystal was installed in the laser cavity.
Although the transverse mode was affected by a thermal lens formed in the laser
crystal, the focal length of the thermal lens could be shifted via the
temperature of the laser crystal holder or the pump power. As a result, we
found a condition that 10 W single-frequency oscillation with a good transverse
mode and a slope efficiency of 51% were achieved.


Abstract:
  In Online Social Networks (OSN) numerous are the cases in which users create
multiple accounts that publicly seem to belong to different people but are
actually fake identities of the same person. These fictitious characters can be
exploited to carry out abusive behaviors such as manipulating opinions,
spreading fake news and disturbing other users. In literature this problem is
known as the Sockpuppet problem. In our work we focus on Telegram, a
wide-spread instant messaging application, often known for its exploitation by
members of organized crime and terrorism, and more in general for its high
presence of people who have offensive behaviors.


Abstract:
  In this paper, we consider a class of optimal control problems governed by
state-equations of Kobayashi--Warren--Carter type. The control is given by
physical temperature. The focus is on problems in dimensions less than equal to
4. The results are divided in four Main Theorems, concerned with: solvability
and parameter-dependence of state-equations and optimal control problems; the
first order necessary optimality conditions for these regularized optimal
control problems. Subsequently, we derive the limiting systems and optimality
conditions and study their well-posedness.


Abstract:
  Quantum error correction and symmetries play central roles in quantum
information science and physics. It is known that quantum error-correcting
codes covariant with respect to continuous symmetries cannot correct erasure
errors perfectly (an important case being the Eastin-Knill theorem), in
contrast to the case without symmetry constraints. Furthermore, there are
fundamental limits on the accuracy of such covariant codes for approximate
quantum error correction. Here, we consider the quantum error correction
capability of random covariant codes. In particular, we show that
$U(1)$-covariant codes generated by Haar random $U(1)$-symmetric unitaries,
i.e. unitaries that commute with the charge operator (or conserve the charge),
typically saturate the fundamental limits to leading order in terms of both the
average- and worst-case purified distances against erasure noise. We note that
the results hold for symmetric variants of unitary 2-designs, and comment on
the convergence problem of charge-conserving random circuits. Our results not
only indicate (potentially efficient) randomized constructions of optimal
$U(1)$-covariant codes, but also reveal fundamental properties of random
charge-conserving unitaries, which may underlie important models of complex
quantum systems in wide-ranging physical scenarios where conservation laws are
present, such as black holes and many-body spin systems.


Abstract:
  While relation extraction is an essential task in knowledge acquisition and
representation, and new-generated relations are common in the real world, less
effort is made to predict unseen relations that cannot be observed at the
training stage. In this paper, we formulate the zero-shot relation extraction
problem by incorporating the text description of seen and unseen relations. We
propose a novel multi-task learning model, zero-shot BERT (ZS-BERT), to
directly predict unseen relations without hand-crafted attribute labeling and
multiple pairwise classifications. Given training instances consisting of input
sentences and the descriptions of their relations, ZS-BERT learns two functions
that project sentences and relation descriptions into an embedding space by
jointly minimizing the distances between them and classifying seen relations.
By generating the embeddings of unseen relations and new-coming sentences based
on such two functions, we use nearest neighbor search to obtain the prediction
of unseen relations. Experiments conducted on two well-known datasets exhibit
that ZS-BERT can outperform existing methods by at least 13.54\% improvement on
F1 score.


Abstract:
  The performance of Non-orthogonal Multiple Access (NOMA) system dramatically
decreases in the presence of inter-cell interference. This condition gets more
challenging if a smart jammer is interacting in a network. In this paper, the
NOMA power allocation of two independent Base Stations (BSs) against a smart
jammer is, modeled as a sequential game. In this game, at first, each BS as a
leader independently chooses its power allocation strategy. Then, the smart
jammer as the follower selects its optimal strategy based on the strategies of
the BSs. The solutions of this game are, derived under different conditions.
Based on the game-theoretical analysis, three new schemes are proposed for
anti-jamming NOMA power allocation in a two-cell scenario called a) Q-Learning
based Unselfish (QLU) NOMA power allocation scheme, b) Deep Q-Learning based
Unselfish (DQLU) NOMA power allocation scheme, and c) Hot Booting Deep
Q-Learning based Unselfish (HBDQLU) NOMA power allocation scheme. In these
methods the BSs do not coordinate with each other. But our analysis
theoretically predicts that with high probability, the proposed methods will
converge to the optimal strategy from the total network point of view.
Simulation results show the convergence of the proposed schemes and also their
outperformance with respect to the Q-Learning-based Selfish (QLS) NOMA power
allocation method.


Abstract:
  Square-root topological phases have been discussed mainly for systems with
chiral symmetry. In this paper, we analyze the topology of the squared
Hamiltonian for systems preserving the time-reversal and particle-hole
symmetry. Our analysis elucidates that two-dimensional systems of class CII
host helical edge states due to the nontrivial topology of the squared
Hamiltonian in contrast to the absence of ordinary topological phases. The
emergence of the helical edge modes is demonstrated by analyzing a toy model.
We also show the emergence of surface states induced by the non-trivial
topology of the squared Hamiltonian in three dimensions.


Abstract:
  Many scientific datasets are compositional in nature. Important examples
include species abundances in ecology, rock compositions in geology, topic
compositions in large-scale text corpora, and sequencing count data in
molecular biology. Here, we provide a causal view on compositional data in an
instrumental variable setting where the composition acts as the cause.
Throughout, we pay particular attention to the interpretation of compositional
causes from the viewpoint of interventions and crisply articulate potential
pitfalls for practitioners. Focusing on modern high-dimensional microbiome
sequencing data as a timely illustrative use case, our analysis first reveals
that popular one-dimensional information-theoretic summary statistics, such as
diversity and richness, may be insufficient for drawing causal conclusions from
ecological data. Instead, we advocate for multivariate alternatives using
statistical data transformations and regression techniques that take the
special structure of the compositional sample space into account. In a
comparative analysis on synthetic and semi-synthetic data we show the
advantages and limitations of our proposal. We posit that our framework may
provide a useful starting point for cause-effect estimation in the context of
compositional data.


Abstract:
  This paper present a construction and the analysis of a class of non-Gaussian
positive-definite matrix-valued homogeneous random fields with uncertain
spectral measure for stochastic elliptic operators. Then the stochastic
elliptic boundary value problem in a bounded domain of the 3D-space is
introduced and analyzed for stochastic homogenization.


Abstract:
  We study an equation governed by a discontinuous fully nonlinear operator.
Such discontinuities are solution-dependent, which introduces a free boundary.
Working under natural assumptions, we prove the existence of $L^p$-viscosity
and strong solutions to the problem. The operator does not satisfy the usual
structure conditions and to obtain the existence of solutions we resort to
solving approximate problems, combined with an iterative scheme. We believe our
strategy can be applied to other classes of non-variational free boundary
problems.


Abstract:
  The in vitro clonogenic assay is a technique to study the ability of a cell
to form a colony in a culture dish. By optical imaging, dishes with stained
colonies can be scanned and assessed digitally. Identification, segmentation
and counting of stained colonies play a vital part in high-throughput screening
and quantitative assessment of biological assays. Image processing of such
pictured/scanned assays can be affected by image/scan acquisition artifacts
like background noise and spatially varying illumination, and contaminants in
the suspension medium. Although existing approaches tackle these issues, the
segmentation quality requires further improvement, particularly on noisy and
low contrast images. In this work, we present an objective and versatile
machine learning procedure to amend these issues by characterizing, extracting
and segmenting inquired colonies using principal component analysis, k-means
clustering and a modified watershed segmentation algorithm. The intention is to
automatically identify visible colonies through spatial texture assessment and
accordingly discriminate them from background in preparation for successive
segmentation. The proposed segmentation algorithm yielded a similar quality as
manual counting by human observers. High F1 scores (>0.9) and low
root-mean-square errors (around 14%) underlined good agreement with ground
truth data. Moreover, it outperformed a recent state-of-the-art method. The
methodology will be an important tool in future cancer research applications.


Abstract:
  Strong Coulomb correlations together with multi-valley electronic bands in
the presence of spin-orbit interaction and possible new optoelectronic
applications are at the heart of studies of the rich physics of excitons in
semiconductor structures made of monolayers of transition metal dichalcogenides
(TMD). In intrinsic TMD monolayers the basic, intravalley excitons are formed
by a hole from the top of the valence band and an electron either from the
lower or upper spin-orbit-split conduction band subbands: one of these excitons
is optically active, the second one is "dark", although possibly observed under
special conditions. Here we demonstrate the s-series of Rydberg dark exciton
states in monolayer WSe$_2$, which appears in addition to a conventional bright
exciton series in photoluminescence spectra measured in high in-plane magnetic
fields. The comparison of energy ladders of bright and dark Rydberg excitons is
shown to be a method to experimentally evaluate one of the missing band
parameters in TMD monolayers: the amplitude of the spin-orbit splitting of the
conduction band.


Abstract:
  Multi-task learning (MTL) and attention mechanism have been proven to
effectively extract robust acoustic features for various speech-related tasks
in noisy environments. In this study, we propose an attention-based MTL (ATM)
approach that integrates MTL and the attention-weighting mechanism to
simultaneously realize a multi-model learning structure that performs speech
enhancement (SE) and speaker identification (SI). The proposed ATM system
consists of three parts: SE, SI, and attention-Net (AttNet). The SE part is
composed of a long-short-term memory (LSTM) model, and a deep neural network
(DNN) model is used to develop the SI and AttNet parts. The overall ATM system
first extracts the representative features and then enhances the speech signals
in LSTM-SE and specifies speaker identity in DNN-SI. The AttNet computes
weights based on DNN-SI to prepare better representative features for LSTM-SE.
We tested the proposed ATM system on Taiwan Mandarin hearing in noise test
sentences. The evaluation results confirmed that the proposed system can
effectively enhance speech quality and intelligibility of a given noisy input.
Moreover, the accuracy of the SI can also be notably improved by using the
proposed ATM system.


Abstract:
  Given the algebra of observables of a quantum system subject to selection
rules, a state can be represented by different density matrices. As a result,
different von Neumann entropies can be associated with the same state.
Motivated by a minimality property of the von Neumann entropy of a density
matrix with respect to its possible decompositions into pure states, we give a
purely algebraic definition of entropy for states of an algebra of observables,
thus solving the above ambiguity. The entropy so defined satisfies all the
desirable thermodynamic properties, and reduces to the von Neumann entropy in
the quantum mechanical case. Moreover, it can be shown to be equal to the von
Neumann entropy of the unique representative density matrix belonging to the
operator algebra of a multiplicity-free Hilbert-space representation.


Abstract:
  In prescriptive analytics, the decision-maker observes historical samples of
$(X, Y)$, where $Y$ is the uncertain problem parameter and $X$ is the
concurrent covariate, without knowing the joint distribution. Given an
additional covariate observation $x$, the goal is to choose a decision $z$
conditional on this observation to minimize the cost $\mathbb{E}[c(z,Y)|X=x]$.
This paper proposes a new distributionally robust approach under Wasserstein
ambiguity sets, in which the nominal distribution of $Y|X=x$ is constructed
based on the Nadaraya-Watson kernel estimator concerning the historical data.
We show that the nominal distribution converges to the actual conditional
distribution under the Wasserstein distance. We establish the out-of-sample
guarantees and the computational tractability of the framework. Through
synthetic and empirical experiments about the newsvendor problem and portfolio
optimization, we demonstrate the strong performance and practical value of the
proposed framework.


Abstract:
  In this paper, a practical fractional-order variable-gain super-twisting
algorithm (PFVSTA) is proposed to improve the tracking performance of wafer
stages for semiconductor manufacturing. Based on the sliding mode control
(SMC), the proposed PFVSTA enhances the tracking performance from three
aspects: 1) alleviating the chattering phenomenon via super-twisting algorithm
and a novel fractional-order sliding surface~(FSS) design, 2) improving the
dynamics of states on the sliding surface with fast response and small
overshoots via the designed novel FSS and 3) compensating for disturbances via
variable-gain control law. Based on practical conditions, this paper analyzes
the stability of the controller and illustrates the theoretical principle to
compensate for the uncertainties caused by accelerations. Moreover, numerical
simulations prove the effectiveness of the proposed sliding surface and control
scheme, and they are in agreement with the theoretical analysis. Finally,
practice-based comparative experiments are conducted. The results show that the
proposed PFVSTA can achieve much better tracking performance than the
conventional methods from various perspectives.


Abstract:
  The input-output behaviour of the Wiener neuronal model subject to
alternating input is studied under the assumption that the effect of such an
input is to make the drift itself of an alternating type. Firing densities and
related statistics are obtained via simulations of the sample-paths of the
process in the following three cases: the drift changes occur during random
periods characterized by (i) exponential distribution, (ii) Erlang distribution
with a preassigned shape parameter, and (iii) deterministic distribution. The
obtained results are compared with those holding for the Wiener neuronal model
subject to sinusoidal input


Abstract:
  This paper introduces SuSketch, a design tool for first person shooter
levels. SuSketch provides the designer with gameplay predictions for two
competing players of specific character classes. The interface allows the
designer to work side-by-side with an artificially intelligent creator and to
receive varied types of feedback such as path information, predicted balance
between players in a complete playthrough, or a predicted heatmap of the
locations of player deaths. The system also proactively designs alternatives to
the level and class pairing, and presents them to the designer as suggestions
that improve the predicted balance of the game. SuSketch offers a new way of
integrating machine learning into mixed-initiative co-creation tools, as a
surrogate of human play trained on a large corpus of artificial playtraces. A
user study with 16 game developers indicated that the tool was easy to use, but
also highlighted a need to make SuSketch more accessible and more explainable.


Abstract:
  We consider a setting in which agents are allocated land plots and they have
additive preferences over which plot they get and who their neighbor is.
Strategyproofness, Pareto optimality, and individual rationality are three
fundamental properties in economic design. We present two impossibility results
showing that the three properties are incompatible in this context.


Abstract:
  Modifying gravity at large distances by means of a massive graviton may
explain the observed acceleration of the Universe without Dark Energy. The
standard paradigm for Massive Gravity is the Fierz-Pauli theory, which,
nonetheless, displays well known flaws in its massless limit. The most serious
one is represented by the vDVZ discontinuity, which consists in a disagreement
between the massless limit of the Fierz-Pauli theory and General Relativity.
Our approach is based on a field theoretical treatment of Massive Gravity:
General Relativity, in the weak field approximation, is treated as a gauge
theory of a symmetric rank-2 tensor field. This leads us to propose an
alternative theory of linearized Massive Gravity, describing five degrees of
freedom of the graviton, with a good massless limit, without vDVZ
discontinuity, and depending on one mass parameter only, in agreement with the
Fierz-Pauli theory.


Abstract:
  One of the most appealing aspects of photonic crystal structures is the
photonic bandgap created in structures with sufficiently high dielectric
contrasts between constituent materials. Understanding how specific photonic
crystal structures and their associated stopband positions can selectively
interfere with incoming light is vital for implementing these structured
dielectrics in a range of optical applications. Metallo-dielectric photonic
crystals act to incorporate metal particles into the ordered arrangement of
these structures. We examined copper, nickel and gold metal infiltration into
polystyrene opals and TiO2 inverse opals. We report a consistent optical
phenomena directly associated with the creation of metallo-dielectric photonic
crystal structures. More pronounced and numerous diffraction resonances emerge
in opal photonic crystals with a metal deposited across the top layer. Common
to both opal and inverse opal structures, was a blue-shift in the position of
the (111) photonic stopband which increased in magnitude with greater metal
content in the structure. We investigate the origin of the photonic stopband
blue-shift by variation of the metal content and the placement of metal in the
photonic crystal structure. Metal introduced to structured dielectric media
tunes the photonic stopband by altering the effective dielectric constant of
the photonic crystal.


Abstract:
  The ability to deterministically generate genuine multi-partite entanglement
is fundamental for the advancement of quantum information science. We show that
the interaction between entangled twin beams of light and an atomic ensemble
under conditions for electromagnetically induced transparency leads to the
generation of genuine hybrid tri-partite entanglement between the two input
fields and the atomic ensemble. In such a configuration, the system is driven
through dissipation to a steady state given by the hybrid entangled state. To
show the presence of the genuine hybrid entanglement, we introduce a new
approach to treat the atomic operators that makes it possible to show a
violation of a tri-partite entanglement criterion based on the properties of
the two optical fields and collective properties of the atomic ensemble.
Additionally, we show that while each of the input optical fields does not
exhibit single beam quadrature squeezing, as the fields propagate through the
atomic medium their individual quadratures can become squeezed and in some
cases oscillate between the presence and absence of squeezing. Finally, we
propose a technique to characterize the tri-partite entanglement through joint
measurements of the fields leaving the atomic medium, making such an approach
experimentally accessible.


Abstract:
  We present a conceptually simple framework for object instance segmentation
called Contour Proposal Network (CPN), which detects possibly overlapping
objects in an image while simultaneously fitting closed object contours using
an interpretable, fixed-sized representation based on Fourier Descriptors. The
CPN can incorporate state of the art object detection architectures as backbone
networks into a single-stage instance segmentation model that can be trained
end-to-end. We construct CPN models with different backbone networks, and apply
them to instance segmentation of cells in datasets from different modalities.
In our experiments, we show CPNs that outperform U-Nets and Mask R-CNNs in
instance segmentation accuracy, and present variants with execution times
suitable for real-time applications. The trained models generalize well across
different domains of cell types. Since the main assumption of the framework are
closed object contours, it is applicable to a wide range of detection problems
also outside the biomedical domain. An implementation of the model architecture
in PyTorch is freely available.


Abstract:
  In this paper, we propose a phase attention residual network (PA-ResSeg) to
model multi-phase features for accurate liver tumor segmentation, in which a
phase attention (PA) is newly proposed to additionally exploit the images of
arterial (ART) phase to facilitate the segmentation of portal venous (PV)
phase. The PA block consists of an intra-phase attention (Intra-PA) module and
an inter-phase attention (Inter-PA) module to capture channel-wise
self-dependencies and cross-phase interdependencies, respectively. Thus it
enables the network to learn more representative multi-phase features by
refining the PV features according to the channel dependencies and
recalibrating the ART features based on the learned interdependencies between
phases. We propose a PA-based multi-scale fusion (MSF) architecture to embed
the PA blocks in the network at multiple levels along the encoding path to fuse
multi-scale features from multi-phase images. Moreover, a 3D boundary-enhanced
loss (BE-loss) is proposed for training to make the network more sensitive to
boundaries. To evaluate the performance of our proposed PA-ResSeg, we conducted
experiments on a multi-phase CT dataset of focal liver lesions (MPCT-FLLs).
Experimental results show the effectiveness of the proposed method by achieving
a dice per case (DPC) of 0.77.87, a dice global (DG) of 0.8682, a volumetric
overlap error (VOE) of 0.3328 and a relative volume difference (RVD) of 0.0443
on the MPCT-FLLs. Furthermore, to validate the effectiveness and robustness of
PA-ResSeg, we conducted extra experiments on another multi-phase liver tumor
dataset and obtained a DPC of 0.8290, a DG of 0.9132, a VOE of 0.2637 and a RVD
of 0.0163. The proposed method shows its robustness and generalization
capability in different datasets and different backbones.


Abstract:
  In unmanned aerial vehicle (UAV)-assisted data collection system, UAVs can be
deployed to charge ground terminals (GTs) via wireless power transfer (WPT) and
collect data from them via wireless information transmission (WIT). In this
paper, we aim to minimize the time required by a UAV via jointly optimizing the
trajectory of the UAV and the transmission scheduling for all the GTs. This
problem is formulated as a mixed integer nonlinear programming (MINLP) which
are difficult to address in general. To this end, we develop an iterative
algorithm based on binary search and successive convex optimization (SCO) to
solve it. The simulation shows that our proposed solution outperforms the
benchmark algorithms.


Abstract:
  We construct Barrow holographic dark energy in the case of non-flat universe.
In particular, considering closed and open spatial geometry we extract the
differential equations that determine the evolution of the dark-energy density
parameter, and we provide the analytical expression for the corresponding dark
energy equation-of-state parameter. We show that the scenario can describe the
thermal history of the universe, with the sequence of matter and dark energy
epochs. Comparing to the flat case, where the phantom regime is obtained for
relative large Barrow exponents, the incorporation of positive curvature leads
the universe into the phantom regime for significantly smaller values.
Additionally, in the case of negative curvature we find a reversed behavior,
namely for increased Barrow exponent we acquire algebraically higher
dark-energy equation-of-state parameters. Furthermore, we confront the scenario
with Hubble parameter measurements and supernova type Ia data. Hence, the
incorporation of slightly non-flat spatial geometry to Barrow holographic dark
energy improves the phenomenology while keeping the new Barrow exponent to
smaller values.


Abstract:
  The Convolutional Neural Network has amazed us with its usage on several
applications. Age range estimation using CNN is emerging due to its application
in myriad of areas which makes it a state-of-the-art area for research and
improve the estimation accuracy. A deep CNN model is used for identification of
people's age range in our proposed work. At first, we extracted only face
images from image dataset using MTCNN to remove unnecessary features other than
face from the image. Secondly, we used random crop technique for data
augmentation to improve the model performance. We have used the concept of
transfer learning in our research. A pretrained face recognition model i.e
VGG-Face is used to build our model for identification of age range whose
performance is evaluated on Adience Benchmark for confirming the efficacy of
our work. The performance in test set outperformed existing state-of-the-art by
substantial margins.


Abstract:
  Accurate layout estimation is crucial for planning and navigation in robotics
applications, such as self-driving. In this paper, we introduce the Stereo
Bird's Eye ViewNetwork (SBEVNet), a novel supervised end-to-end framework for
estimation of bird's eye view layout from a pair of stereo images. Although our
network reuses some of the building blocks from the state-of-the-art deep
learning networks for disparity estimation, we show that explicit depth
estimation is neither sufficient nor necessary. Instead, the learning of a good
internal bird's eye view feature representation is effective for layout
estimation. Specifically, we first generate a disparity feature volume using
the features of the stereo images and then project it to the bird's eye view
coordinates. This gives us coarse-grained information about the scene
structure. We also apply inverse perspective mapping (IPM) to map the input
images and their features to the bird's eye view. This gives us fine-grained
texture information. Concatenating IPM features with the projected feature
volume creates a rich bird's eye view representation which is useful for
spatial reasoning. We use this representation to estimate the BEV semantic map.
Additionally, we show that using the IPM features as a supervisory signal for
stereo features can give an improvement in performance. We demonstrate our
approach on two datasets:the KITTI dataset and a synthetically generated
dataset from the CARLA simulator. For both of these datasets, we establish
state-of-the-art performance compared to baseline techniques.


Abstract:
  Fractured metal fragments with rough and irregular surfaces are often found
at crime scenes. Current forensic practice visually inspects the complex jagged
trajectory of fractured surfaces to recognize a ``match'' using comparative
microscopy and physical pattern analysis. We developed a novel computational
framework, utilizing the basic concepts of fracture mechanics and statistical
analysis to provide quantitative match analysis for match probability and error
rates. The framework employs the statistics of fracture surfaces to become
non-self-affine with unique roughness characteristics at relevant microscopic
length scale, dictated by the intrinsic material resistance to fracture and its
microstructure. At such a scale, which was found to be greater than two
grain-size or micro-feature-size, we establish that the material intrinsic
properties, microstructure, and exposure history to external forces on an
evidence fragment have the premise of uniqueness, which quantitatively
describes the microscopic features on the fracture surface for forensic
comparisons. The methodology utilizes 3D spectral analysis of overlapping
topological images of the fracture surface and classifies specimens with very
high accuracy using statistical learning. Cross correlations of image-pairs in
two frequency ranges are used to develop matrix variate statistical models for
the distributions among matching and non-matching pairs of images, and provides
a decision rule for identifying matches and determining error rates. A set of
thirty eight different fracture surfaces of steel articles were correctly
classified. The framework lays the foundations for forensic applications with
quantitative statistical comparison across a broad range of fractured materials
with diverse textures and mechanical properties.


Abstract:
  Entangled two-photon spectroscopy is expected to provide advantages compared
with classical protocols. It is achieved by coherently controlling the spectral
properties of energy-entangled photons. We present here an experimental setup
that allows the spectral shaping of entangled photons with high resolution. We
evaluate its performances by detecting sum frequency generation in a non-linear
crystal. The efficiency of the process is compared when performed with
classical or entangled light.


Abstract:
  Understanding interactions between objects in an image is an important
element for generating captions. In this paper, we propose a relationship-based
neural baby talk (R-NBT) model to comprehensively investigate several types of
pairwise object interactions by encoding each image via three different
relationship-based graph attention networks (GATs). We study three main
relationships: \textit{spatial relationships} to explore geometric
interactions, \textit{semantic relationships} to extract semantic interactions,
and \textit{implicit relationships} to capture hidden information that could
not be modelled explicitly as above. We construct three relationship graphs
with the objects in an image as nodes, and the mutual relationships of pairwise
objects as edges. By exploring features of neighbouring regions individually
via GATs, we integrate different types of relationships into visual features of
each node. Experiments on COCO dataset show that our proposed R-NBT model
outperforms state-of-the-art models trained on COCO dataset in three image
caption generation tasks.