Abstract:
  We present a novel remote gas detection and identification technique based on
correlation spectroscopy with a piezoelectric tunable fibre-optic Fabry-P\'erot
filter. We show that the spectral correlation amplitude between the filter
transmission window and gas absorption features is related to the gas
absorption optical depth, and that different gases can be distinguished from
one another using their correlation signal phase. Using an observed
telluric-corrected, high-resolution near-infrared spectrum of Venus, we show
via simulation that the Doppler shift of gases lines can be extracted from the
phase of the lock-in signal using low-cost, compact, and lightweight
fibre-optic components with lock-in amplification to improve the
signal-to-noise ratio. This correlation spectroscopy technique has applications
in the detection and radial velocity determination of faint spectral features
in astronomy and remote sensing. We experimentally demonstrate remote CO2
detection system using a lock-in amplifier, fibre-optic Fabry-P\'erot filter,
and single channel photodiode.


Abstract:
  We prove a metastability result for finitary microstates which are good
models for a Gibbs measure for a nearest-neighbor interaction on a
finitely-generated group. This is used to show that any maximal-entropy joining
of two such Gibbs states is a relative product over the tail $\sigma$-algebra,
except in degenerate cases.
  We also use results on extremal cuts of random graphs to further investigate
optimal self-joinings of the Ising model on a free group.


Abstract:
  We present new $AdS_5$ solutions in 11d supergravity and identify them as the
gravity duals of a class of 4d $\mathcal N = 2$ SCFTs of Argyres-Douglas type,
engineered by a stack of M5-branes wrapped on a sphere with one irregular
puncture and one regular puncture. The gravity solutions feature an internal
M5-brane source, which is dual to the irregular puncture. Thanks to a novel
St\"uckelberg mechanism involving an axion originating from the expansion of
the M-theory 3-form, one of the isometry generators on the gravity side is not
mapped to a continuous symmetry on the SCFT side.


Abstract:
  This paper presents the development of a control system for vision-guided
pick-and-place tasks using a robot arm equipped with a 3D camera. The main
steps include camera intrinsic and extrinsic calibration, hand-eye calibration,
initial object pose registration, objects pose alignment algorithm, and
pick-and-place execution. The proposed system allows the robot be able to to
pick and place object with limited times of registering a new object and the
developed software can be applied for new object scenario quickly. The
integrated system was tested using the hardware combination of kuka iiwa,
Robotiq grippers (two finger gripper and three finger gripper) and 3D cameras
(Intel realsense D415 camera, Intel realsense D435 camera, Microsoft Kinect
V2). The whole system can also be modified for the combination of other robotic
arm, gripper and 3D camera.


Abstract:
  In this paper, we make a substantial step towards an encoding of Cubical Type
Theory (CTT) in the Dedukti logical framework. Type-checking CTT expressions
features a decision procedure in a de Morgan algebra that so far could not be
expressed by the rewrite rules of Dedukti. As an alternative, 2 Layer Type
Theories are variants of Martin-L\"of Type Theory where all or part of the
definitional equality can be represented in terms of a so-called external
equality. We propose to split the encoding by giving an encoding of 2 Layer
Type Theories (2LTT) in Dedukti, and a partial encoding of CTT in 2LTT.


Abstract:
  By means of the extended Gould-Hsu inverse series relations, we find that the
dual relation of Dougall's summation theorem for the well--poised
$_7F_6$-series can be utilized to construct numerous interesting
Ramanujan--like infinite series expressions for $\pi^{\pm1}$ and $\pi^{\pm2}$,
including an elegant formula of $\pi^{-2}$ due to Guillera.


Abstract:
  This work proposes a novel framework for visual tracking based on the
integration of an iterative particle filter, a deep convolutional neural
network, and a correlation filter. The iterative particle filter enables the
particles to correct themselves and converge to the correct target position. We
employ a novel strategy to assess the likelihood of the particles after the
iterations by applying K-means clustering. Our approach ensures a consistent
support for the posterior distribution. Thus, we do not need to perform
resampling at every video frame, improving the utilization of prior
distribution information. Experimental results on two different benchmark
datasets show that our tracker performs favorably against state-of-the-art
methods.


Abstract:
  In this paper, we study a new micro-macro model for a reactive polymeric
fluid, which is derived recently in [Y. Wang, T.-F. Zhang, and C. Liu, \emph{J.
Non-Newton. Fluid Mech.} 293 (2021), 104559, 13 pp], by using the energetic
variational approach. The model couples the breaking/reforming reaction scheme
of the microscopic polymers with other mechanical effects in usual viscoelastic
complex fluids. We establish the global existence of classical solutions near
the global equilibrium, in which the treatment on the chemo-mechanical coupling
effect is the most crucial part. In particular, a weighted Poincar\'e
inequality with a mean value is employed to overcome the difficulty that arises
from the non-conservative number density distribution of each species.


Abstract:
  The recent development of electron sensitive and pixelated detectors has
attracted the use of four-dimensional scanning transmission electron microscopy
(4D-STEM). Here, we present a precession electron diffraction assisted 4D-STEM
technique for automated orientation mapping using diffraction spot patterns
directly captured by an in-column scintillator based complementary
metal-oxide-semiconductor (CMOS) detector. We compare the results to a
conventional approach, which utilizes a fluorescent screen filmed by an
external CCD camera. The high dynamic range and signal-to-noise characteristics
of the detector largely improve the image quality of the diffraction patterns,
especially the visibility of diffraction spots at high scattering angles. In
the orientation maps reconstructed via the template matching process, the CMOS
data yields a significant reduction of false indexing and higher reliability
compared to the conventional approach. The angular resolution of misorientation
measurement could also be improved by masking reflections close to the direct
beam. This is because the orientation sensitive, weak and small diffraction
spots at high scattering angle are more significant. The results show that fine
details such as nanograins, nanotwins and sub-grain boundaries can be resolved
with a sub-degree angular resolution which is comparable to orientation mapping
using Kikuchi diffraction patterns.


Abstract:
  We explore properties of the doubly charged vector tetraquark $Z_{\mathrm{V}
}^{++}=[cu][\overline{s}\overline{d}]$ built of four quarks of different
flavors using the QCD sum rule methods. The mass and current coupling of $Z_{
\mathrm{V}}^{++}$ are computed in the framework of the QCD two-point sum rule
approach by taking into account quark, gluon and mixed vacuum condensates up to
dimension $10$. The full width of this tetraquark is saturated by $S$-wave
$Z_{\mathrm{V}}^{++} \to \pi ^{+}D_{s1}(2460)^{+},\ \rho^{+}D_{s0}^{\ast
}(2317)^{+}$, and $P$-wave $Z_{\mathrm{V}}^{++} \to \pi ^{+}D_{s}^{+},\
K^{+}D^{+}$ decays. Strong couplings required to find partial widths of
aforementioned decays are calculated in the context of the QCD light-cone sum
rule method and a soft-meson approximation. Our predictions for the mass
$m=(3515 \pm 125)~\mathrm{MeV}$ and full width $ \Gamma
_{\mathrm{full}}=156_{-30}^{+56}~\mathrm{MeV}$ of $Z_{\mathrm{V} }^{++} $ are
useful to search for this exotic meson in various processes. Recently, the LHCb
collaboration discovered neutral states $X_{0(1)}(2900)$ as resonance-like
peaks in $D^{-}K^{+}$ invariant mass distribution in the decay $B^{+} \to
D^{+}D^{-}K^{+}$. We argue that mass distribution of $ D^{+}K^{+}$ mesons in
the same $B$ decay can be used to observe the doubly charged scalar
$Z_{\mathrm{S}}^{++}$ and vector $Z_{\mathrm{V}}^{++}$ tetraquarks.


Abstract:
  Novel effects induced by nonmagnetic impurities in frustrated magnets and
quantum spin liquid represent a highly nontrivial and interesting problem. A
theoretical proposal of extended modulated spin structures induced by doping of
such magnets, distinct from the well-known skyrmions has attracted significant
interest. Here, we demonstrate that nonmagnetic impurities can produce such
extended spin structures in h-YMnO3, a triangular antiferromagnet with
noncollinear magnetic order. Using inelastic neutron scattering (INS), we
measured the full dynamical structure factor in Al-doped h-YMnO3 and confirmed
the presence of magnon damping with a clear momentum dependence. Our
theoretical calculations can reproduce the key features of the INS data,
supporting the formation of the proposed spin textures. As such, our study
provides the first experimental confirmation of the impurity-induced spin
textures. It offers new insights and understanding of the impurity effects in a
broad class of noncollinear magnetic systems.


Abstract:
  The abundant production of beauty and charm hadrons in the $5 \times 10^{12}$
$Z^0$ decays expected at FCC-ee offers outstanding opportunities in flavour
physics that in general exceed those available at Belle II, and are
complementary to the heavy-flavour programme of the LHC. A wide range of
measurements will be possible in heavy-flavour spectroscopy, rare decays of
heavy-flavoured particles and $C\!P$-violation studies, which will benefit from
the low-background experimental environment, the high Lorentz boost, and the
availability of the full spectrum of hadron species. This essay first surveys
the important questions in heavy-flavour physics, and assesses the likely
theoretical and experimental landscape at the turn-on of FCC-ee. From this
certain measurements are identified where the impact of FCC-ee will be
particularly important. A full exploitation of the heavy-flavour potential of
FCC-ee places specific constraints and challenges on detector design, which in
some cases are in tension with those imposed by the other physics goals of the
facility. These requirements and conflicts are discussed.


Abstract:
  In the past decade, model-free reinforcement learning (RL) has provided
solutions to challenging domains such as robotics. Model-based RL shows the
prospect of being more sample-efficient than model-free methods in terms of
agent-environment interactions, because the model enables to extrapolate to
unseen situations. In the more recent past, model-based methods have shown
superior results compared to model-free methods in some challenging domains
with non-linear state transitions. At the same time, it has become apparent
that RL is not market-ready yet and that many real-world applications are going
to require model-based approaches, because model-free methods are too
sample-inefficient and show poor performance in early stages of training. The
latter is particularly important in industry, e.g. in production systems that
directly impact a company's revenue. This demonstrates the necessity for a
toolbox to push the boundaries for model-based RL. While there is a plethora of
toolboxes for model-free RL, model-based RL has received little attention in
terms of toolbox development. Bellman aims to fill this gap and introduces the
first thoroughly designed and tested model-based RL toolbox using
state-of-the-art software engineering practices. Our modular approach enables
to combine a wide range of environment models with generic model-based agent
classes that recover state-of-the-art algorithms. We also provide an experiment
harness to compare both model-free and model-based agents in a systematic
fashion w.r.t. user-defined evaluation metrics (e.g. cumulative reward). This
paves the way for new research directions, e.g. investigating uncertainty-aware
environment models that are not necessarily neural-network-based, or developing
algorithms to solve industrially-motivated benchmarks that share
characteristics with real-world problems.


Abstract:
  The present study investigates the passive flow control phenomena over a
two-dimensional circular cylinder using numerical simulations in the laminar
regime. The aim is to explore one of the passive control techniques, which
involves the introduction of a slit to the geometry of the cylinder. The two
parameters, slit width ratio S/D (slit width/diameter) and slit angle (measured
with respect to the incoming flow direction), play an essential role in
determining the trend of critical Reynolds number (Rec). Most of the analysis
invokes flow visualization and saturation amplitude methods to obtain the
critical Reynolds number (indicative of the onset of vortex shedding) for
different cases. Further, Hopf bifurcation analysis using Stuart-Landau
equation and global stability analysis confirm the accuracy and consistency in
the predicted solutions. The additional amount of flow through slit increases
the pressure downstream of the cylinder, which consequently leads to an
increase in Rec of the modified cylinder. The critical Reynolds number
increases with S/D of the modified cylinder at 0 deg slit angle (as an
additional amount of flow grows with S/D). The critical Reynolds number shows
an increasing trend with the slit angle in the range of S/D (0.05-0.15) as the
fluctuation intensity reduces with slit angle in this range. For S/D=
0.15-0.25, the extra amount of flow through slit induces the instability in
wake which causes a decrease in Rec with slit angle. A correlation is obtained
that estimates the critical Reynolds number for a given S/D and slit angle.


Abstract:
  We propose an experimental architecture where an array of optical tweezers
affords site-dependent control over the confining potential of a conventional
radio-frequency ion trap. The site-dependent control enables programmable
manipulation of phonon modes of ions, with many potential applications in
quantum information processing (QIP) and thermodynamics. We describe protocols
for programming the array of optical tweezers to attain a set of target phonon
modes with high accuracy. We propose applications of such controls in
simulating quantum thermodynamics of a particle of programmable effective mass
via Jarzynski's equality and improving the efficiency of sympathetic cooling
and quantum logic gates in a multi-species ion system of disparate masses. We
discuss the required optical parameters in a realistic ion trap system and
potential adverse effects of optical tweezers in QIP. Our scheme extends the
utility of trapped-ions as a platform for quantum computation and simulation.


Abstract:
  In this PhD thesis a wide variety of cosmological models beyond the
$\Lambda$CDM are studied in detail. Great emphasis is put on the running vacuum
models (RVM's), which can be motivated in the context of Quantum Field Theory
in curved spacetime. They consider the possibility of a smoothly evolving
vacuum energy density that inherits its time-dependence from cosmological
variables, such as the Hubble rate and its time derivative. The analysis
presented, however, is not limited only to the dynamical vacuum models but
considers also models of dark energy such as the Peebles \& Ratra scalar field
model or simple parameterizations like the XCDM or the CPL. Their theoretical
predictions are tested against the wealth of cosmological data and a fitting
procedure is carried out in order to obtain constraints over the free
parameters that characterize the models under study. Finally, an extended
discussion about the $\sigma_8$ and $H_0$ tensions is provided and a possible
solution for both of them is described within the framework of Brans and Dicke
gravity with a cosmological constant, which mimics the RVM with a mild
time-evolving gravitational coupling $G$. The main conclusion of this study is
that appreciable signals in favour of a time-evolving dark energy density are
found in the current data.


Abstract:
  Consideration of slamming loads within the structural design of planning
hulls is of critical importance in ensuring adequate structural performance in
order to avoid potential catastrophic consequences. However, because of the
intricacy in the interplay between complex fluid flows and nonlinear structural
deformations that accompany the phenomenology of slamming, a general
engineering theory in slamming has yet to be uncovered, and so design relies on
specialized theories. In this paper, we propose one such theory for a design
case that has, until now, eluded a proper description. In pursuit of this
theory, we employ a specialized implicit, partitioned fluid-structural
interaction (FSI) simulation approach, in order to study the underlying
physical mechanisms accompanying the oblique impact of a flexible plate during
water entry. In the present work, we first present validation results from
flexible plate water entry experiments, to confirm the veracity of the
developed FSI solver. Subsequent to validation, we carry out a series of
numerical analyses, in an effort to characterize the regimes in impact force
and plate out-of-plane deformations, as a function of impact velocities and
plate flexural rigidity. Finally, we use our FSI solver, as a kind of
"microscope", to study the mechanistic evolution of fluid flows and elastic
plate deformations that occur during slamming. Based on these observations, we
propose a novel, but simple engineering theory for flexible plates obliquely
impacting the water free surface (e.g. high speed porpoising water craft
reentry).


Abstract:
  We present results from a suite of binary merging cluster simulations. The
hydrodynamical cluster simulations are performed employing a smoothed particle
hydrodynamics (SPH) formulation in which gradient errors are strongly reduced
by means of an integral approach. We consider adiabatic as well as radiative
simulations, in which we include gas cooling, star formation and energy
feedback from supernovae. We explore the effects of merging on the
thermodynamic structure of the intracluster gas of the final merger remnant. In
particular, we study how core entropy is generated during the merging and the
stability properties of the initial cool-core profile against disruption. To
this end, we consider a range of initial mass ratio and impact parameters.
Final entropy profiles of our adiabatic merging simulations are in good accord
with previous findings (ZuHone 2011), with cool-cores being disrupted for all
of the initial merging setups. For equal-mass off-axis mergers, we find that a
significant contribution to the final primary core entropy is due to
hydrodynamic instabilities generated by rotational motions, which are induced
by tidal torques during the first pericenter passage. In radiative simulations,
cool-cores are more resilient against heating processes; nonetheless, they are
able to maintain their integrity only in the case of off-axis mergers with very
unequal masses. We suggest that these results are robust against changes in the
gas physical modeling, in particular to the inclusion of AGN thermal feedback.
  Our findings support the view that the observed core cluster morphology
emerges naturally in a merging cluster context, and conclude that the merging
angular momentum is a key parameter in shaping the thermodynamical properties
of the final merger remnant.


Abstract:
  To extend several known centered Gaussian processes, we introduce a new
centered mixed self-similar Gaussian process called the mixed generalized
fractional Brownian motion, which could serve as a good model for a larger
class of natural phenomena. This process generalizes both the well known mixed
fractional Brownian motion introduced by Cheridito [10] and the generalized
fractional Brownian motion introduced by Zili [31]. We study its main
stochastic properties, its non-Markovian and non-stationarity characteristics
and the conditions under which it is not a semimartingale. We prove the long
range dependence properties of this process.


Abstract:
  In this paper, we provide a non-homogeneous $T(1)$ theorem on product spaces
$(X_1 \times X_2, \rho_1 \times \rho_2, \mu_1 \times \mu_2)$ equipped with a
quasimetric $\rho_1 \times \rho_2$ and a Borel measure $\mu_1 \times \mu_2$,
which, need not be doubling but satisfies an upper control on the size of
quasiballs.


Abstract:
  We elucidate the fate of neighboring two and three-$\alpha$ particles in cold
neutron matter by focusing on an analogy between such $\alpha$ systems and
Fermi polarons realized in ultracold atoms. We describe in-medium excitation
properties of an $\alpha$ particle and neutron-mediated two- and three-$\alpha$
interactions using theoretical approaches developed for studies of cold atomic
systems. We numerically solve the few-body Schr\"odinger equation of $\alpha$
particles within standard $\alpha$ cluster models combined with in-medium
properties of $\alpha$ particles. We point out that the resultant two-$\alpha$
ground state and three-$\alpha$ first excited state, which correspond to $^8$Be
and the Hoyle state, respectively, known as main components in the
triple-$\alpha$ reaction, can become bound states in such a many-neutron
background although these states are unstable in vacuum. Our results suggest a
significance of these in-medium cluster states not only in astrophysical
environments such as core-collapsed supernova explosions and neutron star
mergers but also in neutron-rich nuclei.


Abstract:
  MIMO transmit arrays allow for flexible design of the transmit beampattern.
However, the large number of elements required to achieve certain performance
using uniform linear arrays (ULA) maybe be too costly. This motivated the need
for thinned arrays by appropriately selecting a small number of elements so
that the full array beampattern is preserved. In this paper, we propose
Learn-to-Select (L2S), a novel machine learning model for selecting antennas
from a dense ULA employing a combination of multiple Softmax layers constrained
by an orthogonalization criterion. The proposed approach can be efficiently
scaled for larger problems as it avoids the combinatorial explosion of the
selection problem. It also offers a flexible array design framework as the
selection problem can be easily formulated for any metric.


Abstract:
  Exploiting multi-scale features has shown great potential in tackling
semantic segmentation problems. The aggregation is commonly done with sum or
concatenation (concat) followed by convolutional (conv) layers. However, it
fully passes down the high-level context to the following hierarchy without
considering their interrelation. In this work, we aim to enable the low-level
feature to aggregate the complementary context from adjacent high-level feature
maps by a cross-scale pixel-to-region relation operation. We leverage
cross-scale context propagation to make the long-range dependency capturable
even by the high-resolution low-level features. To this end, we employ an
efficient feature pyramid network to obtain multi-scale features. We propose a
Relational Semantics Extractor (RSE) and Relational Semantics Propagator (RSP)
for context extraction and propagation respectively. Then we stack several RSP
into an RSP head to achieve the progressive top-down distribution of the
context. Experiment results on two challenging datasets Cityscapes and COCO
demonstrate that the RSP head performs competitively on both semantic
segmentation and panoptic segmentation with high efficiency. It outperforms
DeeplabV3 [1] by 0.7% with 75% fewer FLOPs (multiply-adds) in the semantic
segmentation task.


Abstract:
  Metal Artifacts creates often difficulties for a high quality visual
assessment of post-operative imaging in {c}omputed {t}omography (CT). A vast
body of methods have been proposed to tackle this issue, but {these} methods
were designed for regular CT scans and their performance is usually
insufficient when imaging tiny implants. In the context of post-operative
high-resolution {CT} imaging, we propose a 3D metal {artifact} reduction
algorithm based on a generative adversarial neural network. It is based on the
simulation of physically realistic CT metal artifacts created by cochlea
implant electrodes on preoperative images. The generated images serve to train
a 3D generative adversarial networks for artifacts reduction. The proposed
approach was assessed qualitatively and quantitatively on clinical conventional
and cone-beam CT of cochlear implant postoperative images. These experiments
show that the proposed method {outperforms other} general metal artifact
reduction approaches.


Abstract:
  Federated learning (FL) utilizes edge computing devices to collaboratively
train a shared model while each device can fully control its local data access.
Generally, FL techniques focus on learning model on independent and identically
distributed (iid) dataset and cannot achieve satisfiable performance on non-iid
datasets (e.g. learning a multi-class classifier but each client only has a
single class dataset). Some personalized approaches have been proposed to
mitigate non-iid issues. However, such approaches cannot handle underlying data
distribution shift, namely data distribution skew, which is quite common in
real scenarios (e.g. recommendation systems learn user behaviors which change
over time). In this work, we provide a solution to the challenge by leveraging
smart-contract with federated learning to build optimized, personalized deep
learning models. Specifically, our approach utilizes smart contract to reach
consensus among distributed trainers on the optimal weights of personalized
models. We conduct experiments across multiple models (CNN and MLP) and
multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate
that our personalized learning models can achieve better accuracy and faster
convergence compared to classic federated and personalized learning. Compared
with the model given by baseline FedAvg algorithm, the average accuracy of our
personalized learning models is improved by 2% to 20%, and the convergence rate
is about 2$\times$ faster. Moreover, we also illustrate that our approach is
secure against recent attack on distributed learning.


Abstract:
  We report on the strong coupling between a metallic ferromagnetic Fe75Co25
thin film patterned element and a range of superconducting Nb half-wavelength
co-planar waveguide (CPW) resonators. By varying the volume of the ferromagnet
we demonstrate that the coupling rate scales linearly with the square root of
the number of spins and achieve a coupling rate over 700 MHz, approaching the
ultrastrong coupling regime. Experiments varying the center conductor width
while maintaining constant magnetic volume verify that decreasing the center
conductor width increases coupling and cooperativity. Our results show that the
frequency dependence of the coupling rate is linear with the fundamental and
higher order odd harmonics of the CPW, but with differing efficiencies. The
results show promise for scaling planar superconducting resonator/magnetic
hybrid systems to smaller dimensions.


Abstract:
  In this work we study the impact of noise on the training of object detection
networks for the medical domain, and how it can be mitigated by improving the
training procedure. Annotating large medical datasets for training data-hungry
deep learning models is expensive and time consuming. Leveraging information
that is already collected in clinical practice, in the form of text reports,
bookmarks or lesion measurements would substantially reduce this cost.
Obtaining precise lesion bounding boxes through automatic mining procedures,
however, is difficult. We provide here a quantitative evaluation of the effect
of bounding box coordinate noise on the performance of Faster R-CNN object
detection networks for breast mass detection. Varying degrees of noise are
simulated by randomly modifying the bounding boxes: in our experiments,
bounding boxes could be enlarged up to six times the original size. The noise
is injected in the CBIS-DDSM collection, a well curated public mammography
dataset for which accurate lesion location is available. We show how, due to an
imperfect matching between the ground truth and the network bounding box
proposals, the noise is propagated during training and reduces the ability of
the network to correctly classify lesions from background. When using the
standard Intersection over Union criterion, the area under the FROC curve
decreases by up to 9%. A novel matching criterion is proposed to improve
tolerance to noise.


Abstract:
  In this paper, we introduce a novel semi-supervised learning framework for
end-to-end speech separation. The proposed method first uses mixtures of
unseparated sources and the mixture invariant training (MixIT) criterion to
train a teacher model. The teacher model then estimates separated sources that
are used to train a student model with standard permutation invariant training
(PIT). The student model can be fine-tuned with supervised data, i.e., paired
artificial mixtures and clean speech sources, and further improved via model
distillation. Experiments with single and multi channel mixtures show that the
teacher-student training resolves the over-separation problem observed in the
original MixIT method. Further, the semisupervised performance is comparable to
a fully-supervised separation system trained using ten times the amount of
supervised data.


Abstract:
  Reconfigurable Intelligent Surface (RIS) has becoming a useful tool in future
wireless communication systems for close-distance communication network. This
paper we use Reconfigurable Intelligent Surface (RIS) for downlink multi-user
communication designed to improve energy collection performance while
satisfying wireless information and Power Transfer (WIPT). The designed system
consists of an IRS-assisted system consists of a multi-antenna assisted base
station (BS) and two opposite multi-antenna assisted information receiver
cooperated (RIS) as energy receiver (ERs) that meets energy collection
requirements. Based on the electromagnetic property of Reconfigurable
Intelligent Surface (RIS), like two mirrors that are opposite each other,
setting two Reconfigurable Intelligent Surface (RIS) attached to the city
buildings to reflect the sending signals. The transmitting precoding of the
Multi-antenna Auxiliary Base Station (BS) and the angular phase transfer matrix
of the multi-antenna Auxiliary Information Receiver (IRs) need to be optimized
together to maximize the energy harvesting of IoT devices for energy efficiency
(EE) of the IRs system and to provide users with the efficiency of the received
signal. In order to solve the joint optimization problem effectively, we turn
the non-convex maximize problem into the equivalent formal error method based
on the mean square, and finally use the iterative algorithm for optimization.
As for algorithm, we respectively use MSE method, semidefinite relaxation
techniques to simplify transmitting beamforming matrix and the matrix phase
shift. Through the observation of simulation data, it can be concluded that the
performance optimization method of SDR based on RIS is effective.


Abstract:
  Variational quantum algorithms (VQAs) have been considered to be useful
applications of noisy intermediate-scale quantum (NISQ) devices. Typically, in
the VQAs, a parametrized ansatz circuit is used to generate a trial wave
function, and the parameters are optimized to minimize a cost function. On the
other hand, blind quantum computing (BQC) has been studied in order to provide
the quantum algorithm with security by using cloud networks. A client with a
limited ability to perform quantum operations hopes to have access to a quantum
computer of a server, and BQC allows the client to use the server's computer
without leakage of the client's information (such as input, running quantum
algorithms, and output) to the server. However, BQC is designed for
fault-tolerant quantum computing, and this requires many ancillary qubits,
which may not be suitable for NISQ devices. Here, we propose an efficient way
to implement the NISQ computing with guaranteed security for the client. In our
architecture, only N+ 1 qubits are required, under an assumption that the form
of ansatzes is known to the server, where N denotes the necessary number of the
qubits in the original NISQ algorithms. The client only performs single-qubit
measurements on an ancillary qubit sent from the server, and the measurement
angles can specify the parameters for the ansatzes of the NISQ algorithms.
No-signaling principle guarantees that neither parameters chosen by the client
nor the outputs of the algorithm are leaked to the server. This work paves the
way for new applications of NISQ devices.


Abstract:
  In this paper, we discuss complete minimal immersions in
$\mathbb{R}^N$($N\geq4$) with finite total curvature and embedded planar ends.
First, we prove nonexistence for the following cases: (1) genus 1 with 2
embedded planar ends, (2) genus $\neq4$, hyperelliptic with 2 embedded planar
ends like the Lagrangian catenoid. Then we show the existence of embedded
minimal spheres in $\mathbb{R}^4$ with 3 embedded planar ends. Moreover, we
construct genus $g$ examples in $\mathbb{R}^4$ with $d$ embedded planar ends
such that $g\geq 1$ and $g+2\leq d\leq 2g+1$. These examples include a family
of embedded minimal tori with 3 embedded planar ends.


Abstract:
  In this report we present a systematic study of the magnonic modes in the
disordered Fe$_{0.5}$Co$_{0.5}$ alloy based on the Heisenberg Hamiltonian using
two complementary approaches. In order to account for substitutional disorder,
on the one hand we directly average the transverse magnetic susceptibility in
real space over different disorder configurations and on the other hand we use
the coherent potential approximation (CPA). While the method of direct
averaging is numerically exact, it is computationally expensive and limited by
the maximal size of the supercell which can be simulated on a computer. On the
contrary the CPA does not suffer from this drawback and yields a cheap
numerical scheme. Therefore, we additionally compare the results of these two
approaches and show that the CPA gives very good results for most of the
magnetic properties, including the magnon energies and the spatial shape of the
eigenmodes. However, it turns out that while reproducing the general trend, the
CPA systematically underestimates the disorder induced damping of the magnons.
This provides evidence that the physics of impurity scattering in this system
is governed by non-local effects missing in the CPA. Finally, we study the real
space eigenmodes of the system, including their spatial shapes, and analyze
their temperature dependence within the random phase approximation.


Abstract:
  This paper deals with design of an integrated secure Blockchain network
framework to prevent damages from attackers. The multi-layer concept which
could handle multiple number of networks is adapted on the top of Blockchain
Governance Game frameworks. This new integrated theoretical model is designed
to find the best strategies toward preparation for preventing whole network
systems malfunction from attackers and it is developed based on the combination
of the Blockchain Governance Game and the Strategic Alliance for Blockchain
Governance Game. Analytically tractable results for executing a safety mode are
fully obtained and simulated results are demonstrated to obtain the optimal
values of hyper parameters of a Blockchain based security network. This
research helps those whom are constructing a multiple layer network by
enhancing security features through multi-layer framework in a decentralized
network.


Abstract:
  Motivated by the recent experiment demonstration of stacking dependent
interlayer magnetic interaction [T. Song et al., Nat. Mater. 18, 1298 (2019);
T. Li et al., Nat. Mater. 18, 1303 (2019); W. Chen et al., Science 366, 983
(2019)], we investigate the magnetization textures and the control
possibilities in the moir\'{e} pattern formed of twisted bilayer
two-dimensional (2D) magnets CrX$_3$ (X=Br, I). We find that the stacking
dependent interlayer magnetic interaction results in the formation of periodic
magnetization domains in a long-period moir\'{e} pattern. Magnetization
textures with various topological numbers can be constructed, depending on the
winding of the textures around the domain walls. A uniform external magnetic
field competes with the lateral modulated interlayer magnetic interaction and
can be utilized to tune the magnetization textures.


Abstract:
  We consider a class of multiplicative processes which, added with stochastic
reset events, give origin to stationary distributions with power-law tails --
ubiquitous in the statistics of social, economic, and ecological systems. Our
main goal is to provide a series of exact results on the dynamics and
asymptotic behaviour of increasingly complex versions of a basic multiplicative
process with resets, including discrete and continuous-time variants and
several degrees of randomness in the parameters that control the process. In
particular, we show how the power-law distributions are built up as time
elapses, how their moments behave with time, and how their stationary profiles
become quantitatively determined by those parameters. Our discussion emphasizes
the connection with financial systems, but these stochastic processes are also
expected to be fruitful in modeling a wide variety of social and biological
phenomena.


Abstract:
  We prove the consistency of the theory ZFC + there is a strongly compact
cardinal from the existence of a cardinal preserving embedding from the
universe into an inner model. The proof almost shows that under SCH, every
cardinal preserving embedding preserves the cofinality and continuum functions.


Abstract:
  Recently, deep learning (DL) methods such as convolutional neural networks
(CNNs) have gained prominence in the area of image denoising. This is owing to
their proven ability to surpass state-of-the-art classical image denoising
algorithms such as BM3D. Deep denoising CNNs (DnCNNs) use many feedforward
convolution layers with added regularization methods of batch normalization and
residual learning to improve denoising performance significantly. However, this
comes at the expense of a huge number of trainable parameters. In this paper,
we address this issue by reducing the number of parameters while achieving a
comparable level of performance. We derive motivation from the improved
performance obtained by training networks using the dense-sparse-dense (DSD)
training approach. We extend this training approach to a reduced DnCNN (RDnCNN)
network resulting in a faster denoising network with significantly reduced
parameters and comparable performance to the DnCNN.


Abstract:
  Second harmonic generation (SHG) is a non-linear optical process, where two
photons coherently combine into one photon of twice their energy. Efficient SHG
occurs for crystals with broken inversion symmetry, such as transition metal
dichalcogenide monolayers. Here we show tuning of non-linear optical processes
in an inversion symmetric crystal. This tunability is based on the unique
properties of bilayer MoS2, that shows strong optical oscillator strength for
the intra- but also inter-layer exciton resonances. As we tune the SHG signal
onto these resonances by varying the laser energy, the SHG amplitude is
enhanced by several orders of magnitude. In the resonant case the bilayer SHG
signal reaches amplitudes comparable to the off-resonant signal from a
monolayer. In applied electric fields the interlayer exciton energies can be
tuned due to their in-built electric dipole via the Stark effect. As a result
the interlayer exciton degeneracy is lifted and the bilayer SHG response is
further enhanced by an additional two orders of magnitude, well reproduced by
our model calculations.


Abstract:
  We present the results of ALMA band-5 (~170 GHz) observations of the merging
ultraluminous infrared galaxy, the "Superantennae" (IRAS 19254-7245) at
z=0.0617, which has been diagnosed as containing a luminous obscured active
galactic nucleus (AGN). In addition to dense molecular line emission (HCN,
HCO+, and HNC J = 2-1), we detect a highly luminous (~6e4Lsun) 183 GHz H2O
3(1,3)-2(2,0) emission line. We interpret the strong H2O emission as largely
originating in maser amplification in AGN-illuminated dense and warm molecular
gas, based on (1) the spatially compact (<220 pc) nature of the H2O emission,
unlike spatially resolved (>500 pc) dense molecular emission, and (2) a
strikingly different velocity profile from, and (3) significantly elevated flux
ratio relative to, dense molecular emission lines. H2O maser emission, other
than the widely studied 22 GHz 6(1,6)-5(2,3) line, has been expected to provide
important information on the physical properties of gas in the vicinity of a
central mass-accreting supermassive black hole (SMBH), because of different
excitation energy. We here demonstrate that with highly sensitive ALMA,
millimetre 183 GHz H2O maser detection is feasible out to >270 Mpc, opening a
new window to scrutinize molecular gas properties around a mass-accreting SMBH
far beyond the immediately local universe.


Abstract:
  For a Hilbert space valued martingale $(f_n)$ and an adapted sequence of
positive random variables $(w_n)$, we show the weighted Davis type inequality
\[ \mathbb{E} \Bigl( |f_0| w_0 + \frac{1}{4} \sum_{n=1}^{N}
\frac{|df_n|^2}{f^*_n} w_n \Bigr) \leq \mathbb{E} ( f^*_N w^*_N). \] This
inequality is sharp and implies several results about the martingale square
function. We also obtain a variant of this inequality for martingales with
values in uniformly convex Banach spaces.


Abstract:
  Asteroseismic observations are crucial to constrain stellar models with
precision. Bayesian Estimation of STellar Parameters (BESTP) is a tool that
utilizes Bayesian statistics and nested sampling Monte Carlo algorithm to
search for the stellar models that best match a given set of classical and
asteroseismic constraints from observations. The computation and evaluation of
models are efficiently performed in an automated and a multi-threaded way. To
illustrate the capabilities of BESTP, we estimate fundamental stellar
properties for the Sun and the red-giant star HD 222076. In both cases, we find
models that are consistent with the observations. We also evaluate the
improvement in the precision of stellar parameters when the oscillation
frequencies of individual modes are included as constraints, compared to the
case when only the the large frequency separation is included. For the solar
case, the uncertainties of estimated masses, radii and ages are reduced by
0.7%, 0.3% and 8% respectively. For HD 222076, they are reduced even more
noticeably by 2%, 0.5% and 4.7%. We also note an improvement of 10% for the age
of HD 222076 when the Gaia parallax is included as a constraint compared to the
case when only the large separation is included as constraint.


Abstract:
  The direct detection of gravitational waves by ground-based interferometers
opened an unprecedented channel to probe alternative theories of gravitation.
Several theories predict a dispersion of the gravitational waves during their
propagation, distorting the signals observed by LIGO and Virgo compared to
their predictions from general relativity. Such dispersion could induce a
modification of the luminosity distance inferred with gravitational radiation
with regards to electromagnetic radiation. By analysing two multimessenger
events, we set constraints on a large class of proposed theories, including
extra-dimensional and scalar-tensor theories. The multimessenger events are the
binary neutron star merger GW170817 associated to GRB170817A, and the binary
black hole merger GW190521 with postulated candidate electromagnetic
counterpart ZTF19abanrhr. Without relying on multimessenger emission, a class
of proposed theories predict a frequency-dependent dispersion of the
gravitational waves breaking Lorentz invariance. By analysing 31 GW events from
binary-black holes coalescence, we constrain several coefficients
parameterising Lorentz violation, including the best constraint on the graviton
mass.


Abstract:
  We give debiased machine learners of parameters of interest that depend on
generalized linear regressions, which regressions make a residual orthogonal to
regressors. The parameters of interest include many causal and policy effects.
We give neural net learners of the bias correction that are automatic in only
depending on the object of interest and the regression residual. Convergence
rates are given for these neural nets and for more general learners of the bias
correction. We also give conditions for asymptotic normality and consistent
asymptotic variance estimation of the learner of the object of interest.


Abstract:
  The present theory considers the transient evolution of the temperature and
fuel mass fraction distributions, and it can determine the critical heating
power and minimum ignition energy for successful ignition. The flame initiation
process subject to central heating can be approximately decomposed into four
stages, i.e., the fast establishment of the ignition kernel, the
ignition-energy-supported flame kernel propagation, unsteady transition of the
flame kernel, and quasi-steady spherical flame propagation. Comparison between
present transient theory and previous quasi-steady theory indicates that the
unsteady effects directly lead to the appearance of flame kernel establishing
stage and considerably affect the subsequent flame kernel development by
lowering the flame propagation speed. Time scale analysis is conducted, and it
is found that the transient formulation completely degenerates to the
quasi-steady theory either in the neighborhood of stationary flame ball or as
approaching the planar flame. Previous quasi-steady theory shows that the
critical heating power for successful ignition is proportional to the cube of
the critical flame radius. However, the critical heating power predicted by the
present transient formulation deviates this scaling law, which can be
attributed to the unsteady thermal conduction from heating center to the flame
front. Furthermore, in the transient formulation the central heating with
finite duration is considered and the minimum ignition energy is obtained. For
the first time, the memory effect that persistently drives flame propagation
subsequent to central heating switching-off is examined. It is found that as
the heating power grows, the memory effect becomes increasingly important and
it can greatly reduce the minimum ignition energy.


Abstract:
  This paper proposes a mesh-free computational framework and machine learning
theory for solving elliptic PDEs on unknown manifolds, identified with point
clouds, based on diffusion maps (DM) and deep learning. The PDE solver is
formulated as a supervised learning task to solve a least-squares regression
problem that imposes an algebraic equation approximating a PDE (and boundary
conditions if applicable). This algebraic equation involves a graph-Laplacian
type matrix obtained via DM asymptotic expansion, which is a consistent
estimator of second-order elliptic differential operators. The resulting
numerical method is to solve a highly non-convex empirical risk minimization
problem subjected to a solution from a hypothesis space of neural-network type
functions. In a well-posed elliptic PDE setting, when the hypothesis space
consists of feedforward neural networks with either infinite width or depth, we
show that the global minimizer of the empirical loss function is a consistent
solution in the limit of large training data. When the hypothesis space is a
two-layer neural network, we show that for a sufficiently large width, the
gradient descent method can identify a global minimizer of the empirical loss
function. Supporting numerical examples demonstrate the convergence of the
solutions and the effectiveness of the proposed solver in avoiding numerical
issues that hampers the traditional approach when a large data set becomes
available, e.g., large matrix inversion.


Abstract:
  For a local complete intersection morphism, we establish fiberwise denseness
in the $n$-dimensional irreducible components of the compactification Nisnevich
locally.


Abstract:
  The Soft X-ray Telescope (SXT) aboard the $AstroSat$ satellite is the first
Indian X-ray telescope in space. It is a modest size X-ray telescope with a
charge coupled device (CCD) camera in the focal plane, which provides X-ray
images in the $\sim 0.3-8.0$ keV band. A forte of SXT is in providing
undistorted spectra of relatively bright X-ray sources, in which it excels some
current large CCD-based X-ray telescopes. Here, we highlight some of the
published spectral and timing results obtained using the SXT data to
demonstrate the capabilities and overall performance of this telescope.


Abstract:
  Large Eddy Simulation (LES) with dynamic Smagorinsky model has been applied
to numerically investigate the complicated flow structures that evolve in the
near wake of a cylindrical after body aligned with a uniform Mach 2.46 flow.
Mean flow field properties obtained from numerical simulations, such as axial
velocity, pressure on base surface, have been compared with the experimental
measurements as well as with other published results. It has been found that
standard k-epsilon model fails to predict the flow properties in the
recirculation region where better agreement has been observed between the data
obtained from LES and experimental measurements. Flow Statistics like turbulent
kinetic energy and primary Reynolds stress have also been calculated and
compared with the results obtained from experiments in order to quantitatively
assess the ability of LES technique to predict the turbulence properties of
flow field in the highly compressible shear layer region. The data obtained
from LES has been further analyzed to understand the evolution of coherent
structures in the flow field. Proper Orthogonal Decomposition (POD) of the data
obtained from central plane in the wake region has been performed in order to
reveal the most energetic structures present in the flow field.


Abstract:
  We propose a general construction of commuting projector lattice models for
2D and 3D topological phases enriched by U(1) symmetry, with finite-dimensional
Hilbert space per site. The construction starts from a commuting projector
model of the topological phase and decorates U(1) charges to the state space in
a consistent manner. We show that all 2D U(1) symmetry-enriched topological
phases which allow gapped boundary without breaking symmetry, can be realized
through our construction. We also construct a large class of 3D topological
phases with U(1) symmetry fractionalized on particles or loop excitations.


Abstract:
  Existing pre-trained models for knowledge-graph-to-text (KG-to-text)
generation simply fine-tune text-to-text pre-trained models such as BART or T5
on KG-to-text datasets, which largely ignore the graph structure during
encoding and lack elaborate pre-training tasks to explicitly model graph-text
alignments. To tackle these problems, we propose a graph-text joint
representation learning model called JointGT. During encoding, we devise a
structure-aware semantic aggregation module which is plugged into each
Transformer layer to preserve the graph structure. Furthermore, we propose
three new pre-training tasks to explicitly enhance the graph-text alignment
including respective text / graph reconstruction, and graph-text alignment in
the embedding space via Optimal Transport. Experiments show that JointGT
obtains new state-of-the-art performance on various KG-to-text datasets.


Abstract:
  The search for extraterrestrial intelligence (SETI) has been conducted for
nearly 60 years. A Dyson Sphere, a spherical structure that surrounds a star
and transports its radiative energy outward as an energy source for an advanced
civilisation, is one of the main targets of SETI. In this study, we discuss
whether building a Dyson Sphere around a black hole is effective. We consider
six energy sources: (i) the cosmic microwave background, (ii) the Hawking
radiation, (iii) an accretion disk, (iv) Bondi accretion, (v) a corona, and
(vi) relativistic jets. To develop future civilisations (for example, a Type II
civilisation), $4\times10^{26}\,{\rm W}$($1\,{\rm L_{\odot}}$) is expected to
be needed. Among (iii) to (vi), the largest luminosity can be collected from an
accretion disk, reaching $10^{5}\,{\rm L_{\odot}}$, enough to maintain a Type
II civilisation. Moreover, if a Dyson Sphere collects not only the
electromagnetic radiation but also other types of energy (e.g., kinetic energy)
from the jets, the total collected energy would be approximately 5 times
larger. Considering the emission from a Dyson Sphere, our results show that the
Dyson Sphere around a stellar-mass black hole in the Milky Way ($10\,\rm kpc$
away from us) is detectable in the ultraviolet$(\rm 10-400\,{\rm nm)}$,
optical$(\rm 400-760\,{\rm nm)}$, near-infrared($\rm 760\,{\rm nm}-5\,{\rm \mu
m}$), and mid-infrared($\rm 5-40\,{\rm \mu m}$) wavelengths via the waste heat
radiation using current telescopes such as Galaxy Evolution Explorer
Ultraviolet Sky Surveys. Performing model fitting to observed spectral energy
distributions and measuring the variability of radial velocity may help us to
identify these possible artificial structures.


Abstract:
  We propose a general purpose approach to detect landmarks with improved
temporal consistency, and personalization. Most sparse landmark detection
methods rely on laborious, manually labelled landmarks, where inconsistency in
annotations over a temporal volume leads to sub-optimal landmark learning.
Further, high-quality landmarks with personalization is often hard to achieve.
We pose landmark detection as an image translation problem. We capture two sets
of unpaired marked (with paint) and unmarked videos. We then use a generative
adversarial network and cyclic consistency to predict deformations of landmark
templates that simulate markers on unmarked images until these images are
indistinguishable from ground-truth marked images. Our novel method does not
rely on manually labelled priors, is temporally consistent, and image class
agnostic -- face, and hand landmarks detection examples are shown.


Abstract:
  We show that two-mode squeezed vacuum-like states may be engineered in the
Bohm-Madelung formalism by adequately choosing the phase of the wavefunction.
The difference between our wavefunction and the one of the squeezed vacuum
states is given precisely by the phase we choose.


Abstract:
  We generalize the concept of local states (LS) for the prediction of
high-dimensional, potentially mixed chaotic systems. The construction of
generalized local states (GLS) relies on defining distances between time series
on the basis of their (non-)linear correlations. We demonstrate the prediction
capabilities of our approach based on the reservoir computing (RC) paradigm
using the Kuramoto-Sivashinsky (KS), the Lorenz-96 (L96) and a combination of
both systems. In the mixed system a separation of the time series belonging to
the two different systems is made possible with GLS. More importantly,
prediction remains possible with GLS, where the LS approach must naturally
fail. Applications for the prediction of very heterogeneous time series with
GLSs are briefly outlined.


Abstract:
  We study neutrino and antineutrino emission from the direct Urca process in
neutron-star matter in the presence of strong magnetic fields. We calculate the
neutrino emissivity of the direct Urca process, whereby a neutron converts to a
proton, an electron and an antineutrino, or a proton-electron pair converts to
a neutron-neutrino pair. We solve exact wave functions for protons and
electrons in the states described with Landau levels. We find that the direct
Urca process can satisfy the kinematic constraints even in density regions
where this process could not normally occur in the absence of a magnetic field.


Abstract:
  It is well known that adversarial attacks can fool deep neural networks with
imperceptible perturbations. Although adversarial training significantly
improves model robustness, failure cases of defense still broadly exist. In
this work, we find that the adversarial attacks can also be vulnerable to small
perturbations. Namely, on adversarially-trained models, perturbing adversarial
examples with a small random noise may invalidate their misled predictions.
After carefully examining state-of-the-art attacks of various kinds, we find
that all these attacks have this deficiency to different extents. Enlightened
by this finding, we propose to counter attacks by crafting more effective
defensive perturbations. Our defensive perturbations leverage the advantage
that adversarial training endows the ground-truth class with smaller local
Lipschitzness. By simultaneously attacking all the classes, the misled
predictions with larger Lipschitzness can be flipped into correct ones. We
verify our defensive perturbation with both empirical experiments and
theoretical analyses on a linear model. On CIFAR10, it boosts the
state-of-the-art model from 66.16% to 72.66% against the four attacks of
AutoAttack, including 71.76% to 83.30% against the Square attack. On ImageNet,
the top-1 robust accuracy of FastAT is improved from 33.18% to 38.54% under the
100-step PGD attack.


Abstract:
  The Gromov-Wasserstein (GW) framework adapts ideas from optimal transport to
allow for the comparison of probability distributions defined on different
metric spaces. Scalable computation of GW distances and associated matchings on
graphs and point clouds have recently been made possible by state-of-the-art
algorithms such as S-GWL and MREC. Each of these algorithmic breakthroughs
relies on decomposing the underlying spaces into parts and performing matchings
on these parts, adding recursion as needed. While very successful in practice,
theoretical guarantees on such methods are limited. Inspired by recent advances
in the theory of quantization for metric measure spaces, we define Quantized
Gromov Wasserstein (qGW): a metric that treats parts as fundamental objects and
fits into a hierarchy of theoretical upper bounds for the GW problem. This
formulation motivates a new algorithm for approximating optimal GW matchings
which yields algorithmic speedups and reductions in memory complexity.
Consequently, we are able to go beyond outperforming state-of-the-art and apply
GW matching at scales that are an order of magnitude larger than in the
existing literature, including datasets containing over 1M points.


Abstract:
  Newspapers are trustworthy media where people get the most reliable and
credible information compared with other sources. On the other hand, social
media often spread rumors and misleading news to get more traffic and
attention. Careful characterization, evaluation, and interpretation of
newspaper data can provide insight into intrigue and passionate social issues
to monitor any big social incidence. This study analyzed a large set of
spatio-temporal Bangladeshi newspaper data related to the COVID-19 pandemic.
The methodology included volume analysis, topic analysis, automated
classification, and sentiment analysis of news articles to get insight into the
COVID-19 pandemic in different sectors and regions in Bangladesh over a period
of time. This analysis will help the government and other organizations to
figure out the challenges that have arisen in society due to this pandemic,
what steps should be taken immediately and in the post-pandemic period, how the
government and its allies can come together to address the crisis in the
future, keeping these problems in mind.


Abstract:
  In 2017, Lienert and Tumulka proved Born's rule on arbitrary Cauchy surfaces
in Minkowski space-time assuming Born's rule and a corresponding collapse rule
on horizontal surfaces relative to a fixed Lorentz frame, as well as a given
unitary time evolution between any two Cauchy surfaces, satisfying that there
is no interaction faster than light and no propagation faster than light. Here,
we prove Born's rule on arbitrary Cauchy surfaces from a different, but equally
reasonable, set of assumptions. The conclusion is that if detectors are placed
along any Cauchy surface $\Sigma$, then the observed particle configuration on
$\Sigma$ is a random variable with distribution density $|\Psi_\Sigma|^2$,
suitably understood. The main different assumption is that the Born and
collapse rules hold on any spacelike hyperplane, i.e., at any time coordinate
in any Lorentz frame. Heuristically, this follows if the dynamics of the
detectors is Lorentz invariant.


Abstract:
  Motivated by the development of high-dispersion spectrographs in the
mid-infrared (MIR) range, we study their application to the atmospheric
characterization of nearby non-transiting temperate terrestrial planets around
M-type stars. We examine the detectability of CO$_2$, H$_2$O, N$_2$O, and O$_3$
in high-resolution planetary thermal emission spectra at 12-18 $\mu $m assuming
an Earth-like profile and a simplified thermal structure. The molecular line
width of such planets can be comparable to or broader than the Doppler shift
due to the planetary orbital motion. Given the likely difficulty in knowing the
high-resolution MIR spectrum of the host star with sufficient accuracy, we
propose to observe the target system at two quadrature phases and extract the
differential spectra as the planetary signal. In this case, the signals can be
substantially suppressed compared with the case where the host star spectrum is
perfectly known, as some parts of the spectral features do not remain in the
differential spectra. Despite this self-subtraction, the CO$_2$ and H$_2$O
features of nearby ($\lesssim $ 5~pc) systems with mid-/late-M host stars would
be practical with a 6.5-meter-class cryogenic telescope, and orbital
inclination could also be constrained for some of them. For CO$_2$ and N$_2$O
in a 1~bar Earth-like atmosphere, this method would be sensitive when the
mixing ratio is 1-10$^3$ ppm. The detectability of molecules except O$_3$ is
not significantly improved when the spectral resolution is higher than
$\mathcal{R}\gtrsim 10,000$, although the constraint on the orbital inclination
is improved. This study provides some benchmark cases useful for assessing the
value of MIR high-resolution spectroscopy in terms of characterization of
potentially habitable planets.


Abstract:
  Let $A$ be a bounded linear operator on a complex Hilbert space and $\Re(A)$
( $\Im(A)$ ) denote the real part (imaginary part) of A. Among other
refinements of the lower bounds for the numerical radius of $A$, we prove that
\begin{eqnarray*} w(A)&\geq &\frac{1}{2} \left \|A \right\| + \frac{ 1}{2} \mid
\|\Re(A)\|-\|\Im(A)\|\mid,\,\,\mbox{and}\\ w^2(A)&\geq& \frac{1}{4} \left
\|A^*A+AA^* \right\| + \frac{1}{2}\mid \|\Re(A)\|^2-\|\Im(A)\|^2 \mid,
\end{eqnarray*} where $w(A)$ is the numerical radius of the operator $A$. We
study the equality conditions for $w(A)=\frac{1}{2}\sqrt{\|A^*A+AA^*\|}$ and
prove that $w(A)=\frac{1}{2}\sqrt{\|A^*A+AA^*\|} $ if and only if the numerical
range of $A$ is a circular disk with center at the origin and radius
$\frac{1}{2}\sqrt{\|A^*A+AA^*\|} $.
  We also obtain upper bounds for the numerical radius of commutators of
operators which improve on the existing ones.


Abstract:
  As an attempt to bridge between numerical analysis and algebraic geometry,
this paper formulates the multiplicity for the general nonlinear system at an
isolated zero, presents an algorithm for computing the multiplicity structure,
proposes a depth-deflation method for accurate computation of multiple zeros,
and introduces the basic algebraic theory of the multiplicity. Furthermore,
this paper elaborates and proves some fundamental properties of the
multiplicity, including local finiteness, consistency, perturbation invariance,
and depth-deflatability. As a justification of this formulation, the
multiplicity is proved to be consistent with the multiplicity defined in
algebraic geometry for the special case of polynomial systems. The proposed
algorithms can accurately compute the multiplicity and the multiple zeros using
floating point arithmetic even if the nonlinear system is perturbed.


Abstract:
  The ribbon cocycle invariant is defined by means of a partition function
using ternary cohomology of self-distributive structures (TSD) and colorings of
ribbon diagrams of a framed link, following the same paradigm introduced by
Carter, Jelsovsky, Kamada, Langfor and Saito in Transactions of the American
Mathematical Society 2003;355(10):3947-89, for the quandle cocycle invariant.
In this article we show that the ribbon cocycle invariant is a quantum
invariant. We do so by constructing a ribbon category from a TSD set whose
twisting and braiding morphisms entail a given TSD $2$-cocycle. Then we show
that the quantum invariant naturally associated to this braided category
coincides with the cocycle invariant. We generalize this construction to
symmetric monoidal categories and provide classes of examples obtained from
Hopf monoids and Lie algebras. We further introduce examples from
Hopf-Frobenius algebras, objects studied in quantum computing.


Abstract:
  We present two methods to algorithmically compute both least and greatest
solutions of polynomial equation systems over absorptive semirings (with
certain completeness and continuity assumptions), such as the tropical
semiring. Both methods require a polynomial number of semiring operations,
including semiring addition, multiplication and an infinitary power operation.
Our main result is a closed-form solution for least and greatest fixed points
based on the fixed-point iteration. The proof builds on the notion of (possibly
infinite) derivation trees; a careful analysis of the shape of these trees
allows us to collapse the fixed-point iteration to a linear number of steps.
The second method is an iterative symbolic computation in the semiring of
generalized absorptive polynomials, largely based on results on Kleene
algebras.


Abstract:
  The future interior of black holes in AdS/CFT can be described in terms of a
quantum circuit. We investigate boundary quantities detecting properties of
this quantum circuit. We discuss relations between operator size, quantum
complexity, and the momentum of an infalling particle in the black hole
interior. We argue that the trajectory of the infalling particle in the
interior close to the horizon is related to the growth of operator size. The
notion of size here differs slightly from the size which has previously been
related to momentum of exterior particles and provides an interesting
generalization. The fact that both exterior and interior momentum are related
to operator size growth is a manifestation of complementarity.


Abstract:
  The increased mobile connectivity, the range and number of services available
in various computing environments in the network, demand mobile applications to
be highly dynamic to be able to efficiently incorporate those services into
applications, along with other local capabilities on mobile devices. However,
the monolithic structure and mostly static configuration of mobile application
components today limit application's ability to dynamically manage internal
components, to be able to adapt to the user and the environment, and utilize
various services in the network for improving the application experience.
  In this paper, we present REACT, a new Android-based framework that enables
apps to be developed as a collection of loosely coupled microservices (MS). It
allows individual distribution, dynamic management and offloading of MS to be
executed by services in the network, based on contextual changes. REACT aims to
provide i) a framework as an Android Library for creating MS-based apps that
adapt to contextual changes ii) a unified HTTP-based communication mechanism,
using Android Inter-Process Communication (IPC) for transporting requests
between locally running MS, while allowing flexible and transparent switching
between network and IPC requests, when offloading. We evaluate REACT by
implementing a video streaming app that dynamically offloads MS to web services
in the network, adapting to contextual changes. The evaluation shows the
adaptability to contextual changes and reductions in power consumption when
offloading, while our communication mechanism overcomes performance limitations
of Android IPC by enabling efficient transferring of large payloads between
mobile MS.


Abstract:
  The natural association between visual observations and their corresponding
sound provides powerful self-supervisory signals for learning video
representations, which makes the ever-growing amount of online videos an
attractive source of training data. However, large portions of online videos
contain irrelevant audio-visual signals because of edited/overdubbed audio, and
models trained on such uncurated videos have shown to learn suboptimal
representations. Therefore, existing approaches rely almost exclusively on
datasets with predetermined taxonomies of semantic concepts, where there is a
high chance of audio-visual correspondence. Unfortunately, constructing such
datasets require labor intensive manual annotation and/or verification, which
severely limits the utility of online videos for large-scale learning. In this
work, we present an automatic dataset curation approach based on subset
optimization where the objective is to maximize the mutual information between
audio and visual channels in videos. We demonstrate that our approach finds
videos with high audio-visual correspondence and show that self-supervised
models trained on our data achieve competitive performances compared to models
trained on existing manually curated datasets. The most significant benefit of
our approach is scalability: We release ACAV100M that contains 100 million
videos with high audio-visual correspondence, ideal for self-supervised video
representation learning.


Abstract:
  In this work the experimental realization of a tunable high photon flux
extreme ultraviolet light source is presented. This is enabled by high harmonic
generation of two temporally delayed driving pulses, resulting in a tuning
range of 0.8 eV at the 19th harmonic at 22.8 eV. The implemented approach
allows for fast tuning of the spectrum, is highly flexible and is scalable
towards full spectral coverage at higher photon energies.


Abstract:
  One of the most significant current discussions in the field of data mining
is classifying imbalanced data. In recent years, several ways are proposed such
as algorithm level (internal) approaches, data level (external) techniques, and
cost-sensitive methods. Although extensive research has been carried out on
imbalanced data classification, however, several unsolved challenges remain
such as no attention to the importance of samples to balance, determine the
appropriate number of classifiers, and no optimization of classifiers in the
combination of classifiers. The purpose of this paper is to improve the
efficiency of the ensemble method in the sampling of training data sets,
especially in the minority class, and to determine better basic classifiers for
combining classifiers than existing methods. We proposed a hybrid ensemble
algorithm based on Genetic Programming (GP) for two classes of imbalanced data
classification. In this study uses historical data from UCI Machine Learning
Repository to assess minority classes in imbalanced datasets. The performance
of our proposed algorithm is evaluated by Rapid-miner studio v.7.5.
Experimental results show the performance of the proposed method on the
specified data sets in the size of the training set shows 40% and 50% better
accuracy than other dimensions of the minority class prediction.


Abstract:
  For positive integers $s,t,u,v$, we define a bipartite graph
$\Gamma_{\mathbb{R}}(X^s Y^t,X^u Y^v)$ where each partite set is a copy of
$\mathbb{R}^3$, and a vertex $(a_1,a_2,a_3)$ in the first partite set is
adjacent to a vertex $[x_1,x_2,x_3]$ in the second partite set if and only if
  \[
  a_2 + x_2 = a_1^s x_1^t
  \quad
  \text{and}
  \quad
  a_3+x_3=a_1^ux_1^v.
  \] In this paper, we classify all such graphs according to girth.


Abstract:
  One of the main requirements of tumor extraction is the annotation and
segmentation of tumor boundaries correctly. For this purpose, we present a
threefold deep learning architecture. First classifiers are implemented with a
deep convolutional neural network(CNN) andsecond a region-based convolutional
neural network (R-CNN) is performed on the classified images to localize the
tumor regions of interest. As the third and final stage, the concentratedtumor
boundary is contoured for the segmentation process by using the
Chan-Vesesegmentation algorithm. As the typical edge detection algorithms based
on gradients of pixel intensity tend to fail in the medical image segmentation
process, an active contour algorithm defined with the level set function is
proposed. Specifically, Chan- Vese algorithm was applied to detect the tumor
boundaries for the segmentation process. To evaluate the performance of the
overall system, Dice Score,Rand Index (RI), Variation of Information (VOI),
Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean
absolute error (MAE), and Peak Signal to Noise Ratio (PSNR) werecalculated by
comparing the segmented boundary area which is the final output of the
proposed, against the demarcations of the subject specialists which is the gold
standard. Overall performance of the proposed architecture for both glioma and
meningioma segmentation is with average dice score of 0.92, (also, with RI of
0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076 and MAE of
52.946), pointing to high reliability of the proposed architecture.


Abstract:
  We show that closed subsets with vanishing first homology in two-dimensional
spaces inherit the upper curvature bound from their ambient spaces and discuss
topological applications.


Abstract:
  This paper exhibits two families of unravelled abstract regular polytopes in
Coxeter groups of type Bn. For one family they have rank 4 while the other
family has arbitrarily large rank.


Abstract:
  Manipulating the spin degrees of freedom of electrons affords an excellent
platform for exploring novel quantum states in condensed-matter physics and
material science. Based on first-principles calculations and analysis of
crystal symmetries, we propose a fully spin-polarized composite semimetal
state, which is combined with the one-dimensional nodal lines and
two-dimensional nodal surfaces, in the half-metal material CaFeO$_3$. In the
nodal line-surface states, the Baguenaudier-like nodal lines feature six rings
linked together, which are protected by the three independent symmetry
operations:$\mathcal{PT}$, $\mathcal{M}_{y}$, and
$\mathcal{\widetilde{M}}_{z}$. Near the Fermi level, the spin-polarized nodal
surface states are guaranteed by the joint operation
$\mathcal{T}\mathcal{S}_{2i}$ in the $k_{i(i=x,y,z)}=\pi$ plane. Furthermore,
high-quality CaFeO$_3$ harbors ultra-clean energy dispersion, which is rather
robust against strong triaxial compressional strain and correlation effect. The
realization of the Weyl nodal line-surface half-metal presents great potential
for spintronics applications with high speed and low power consumption.


Abstract:
  Experimental demonstrations of quantum annealing with native implementation
of Boolean logic Hamiltonians are reported. As a superconducting integrated
circuit, a problem Hamiltonian whose set of ground states is consistent with a
given truth table is implemented for quantum annealing with no redundant
qubits. As examples of the truth table, NAND and NOR are successfully
fabricated as an identical circuit. Similarly, a native implementation of a
multiplier comprising six superconducting flux qubits is also demonstrated.
These native implementations of Hamiltonians consistent with Boolean logic
provide an efficient and scalable way of applying annealing computation to
so-called circuit satisfiability problems that aim to find a set of inputs
consistent with a given output over any Boolean logic functions, especially
those like factorization through a multiplier Hamiltonian. A proof-of-concept
demonstration of a hybrid computing architecture for domain-specific quantum
computing is described.


Abstract:
  Spin-polarized samples and spin mixtures of quantum degenerate fermionic
atoms are prepared in selected excited Bloch bands of an optical chequerboard
square lattice. For the spin-polarized case, extreme band lifetimes above
$10\,$s are observed, reflecting the suppression of collisions by Pauli's
exclusion principle. For spin mixtures, lifetimes are reduced by an order of
magnitude by two-body collisions between different spin components, but still
remarkably large values of about one second are found. By analyzing momentum
spectra, we can directly observe the orbital character of the optical lattice.
The observations demonstrated here form the basis for exploring the physics of
Fermi gases with two paired spin components in orbital optical lattices,
including the regime of unitarity.


Abstract:
  Direction-of-arrival (DOA) estimation is one of the most demanding tasks for
the millimeter wave (mmWave) communication of massive multiple-input
multiple-output (MIMO) systems with the hybrid beamforming (HBF) architecture.
In this paper, we focus on the optimization of the HBF matrix for receiving
pilots to enhance the DOA estimation performance. Motivated by the fact that
many existing DOA estimation algorithms can achieve the Cram\'{e}r-Rao bound
(CRB), we formulate the HBF optimization problem aiming at minimizing the CRB
with the prior knowledge of the rough DOA range. Then, to tackle the problem
with intractable non-convex constraint introduced by the analog beamformers, we
propose an efficient manifold optimization (MO) based algorithm. Simulation
results demonstrate the significant improvement of the proposed CRB-MO
algorithm over the conventional random HBF algorithm, and provide insights for
the HBF design in the beam training stage for practical applications.


Abstract:
  The control of structured waves has recently opened innovative scenarios in
the perspective of radiation propagation and light-matter interaction. In
particular, the transmission of customized electromagnetic fields is
investigated for telecommunications, with the aim of exploring new modulation
formats besides the traditional, almost saturated, division multiplexing
techniques. Beams carrying twisted wavefronts have long been recognized as the
promising candidates, however their phase singularities and efficient
multiplexing still raise open issues. In a more general insight into
structured-phase beams, we introduce and develop here a new and unique paradigm
based on the transmission of beams with harmonic phases having a multipole
structure. The outlined framework encompasses multiplexing, transmission, and
demultiplexing as a whole for the first time, describing wavefields evolution
in terms of conformal mappings, and solving straightforwardly the critical
issues of previous solutions. Because of its potentialities, versatility, and
ease of implementation, we expect this completely new paradigm to find
widespread applications for space division multiplexing especially in free
space, from the optical to the microwave and radio regimes.


Abstract:
  We study the Yamabe flow starting from an asymptotically flat manifold
$(M^n,g_0)$. We show that the flow converges to an asymptotically flat, scalar
flat metric in a weighted global sense if $Y(M,[g_0])>0$, and show that the
flow does not converge otherwise. If the scalar curvature is nonnegative and
integrable, then the ADM mass at time infinity drops by the limit of the total
scalar curvature along the flow.


Abstract:
  In this paper, an approach for hate speech detection against women in Arabic
community on social media (e.g. Youtube) is proposed. In the literature,
similar works have been presented for other languages such as English. However,
to the best of our knowledge, not much work has been conducted in the Arabic
language. A new hate speech corpus (Arabic\_fr\_en) is developed using three
different annotators. For corpus validation, three different machine learning
algorithms are used, including deep Convolutional Neural Network (CNN), long
short-term memory (LSTM) network and Bi-directional LSTM (Bi-LSTM) network.
Simulation results demonstrate the best performance of the CNN model, which
achieved F1-score up to 86\% for the unbalanced corpus as compared to LSTM and
Bi-LSTM.


Abstract:
  This paper reviews recent literature results on the mechanics of structures
formed by layers of pentamode lattices alternating with stiffening plates,
which can be effectively employed for the development of seismic isolation
devices and vibration attenuation tools, with nearly complete band gaps for
shear waves. It is shown that such structures, named pentamode bearings, can
respond either in the stretching-dominated regime, or in the bending-dominated
regime, depending on the nature of the joints connecting the different members.
Their response is characterized by high vertical stiffness and theoretically
zero shear stiffness in the stretching dominated regime, or considerably low
values of such a quantity in the bending dominated regime. Available results on
the experimental response of 3d printed models to combined compression and
shear loading highlight that the examined structures are able to exhibit energy
dissipation capacity and effective damping that are suitable for seismic
isolation devices. Their fabrication does not necessarily require heavy
industry, and expensive materials, being possible with ordinary 3-D printers.


Abstract:
  The same-sign tetralepton signature via mixing of neutral Higgs bosons and
their cascade decays to charged Higgs bosons is a unique signal in the type-II
seesaw model. In this paper, we study this signature at future lepton
colliders, such as ILC, CLIC, and MuC. Constrained by direct search,
$H^{\pm\pm}\to W^\pm W^\pm$ is the only viable decay mode for $M_{A^0}=400$ GeV
at $\sqrt{s}=1$ TeV ILC. With an integrated luminosity of $\mathcal{L}=8~
\mathrm{ab}^{-1}$, the promising region with about 150 signal events
corresponds to a narrow band in the range of $10^{-4}~\text{GeV}\lesssim
v_\Delta \lesssim10^{-2}$ GeV. For heavier triplet scalars $M_{A^0}\gtrsim 900$
GeV, although the $H^{\pm\pm}\to \ell^\pm \ell^\pm$ decay mode is allowed, the
cascade decays are suppressed. A maximum event number $\sim 16$ can be obtained
around $v_\Delta\sim4\times10^{-4}$ GeV and $\lambda_4\sim0.26$ for
$M_{A^0}=1000$ GeV with $\mathcal{L}=5~ \mathrm{ab}^{-1}$ at $\sqrt{s}=3$ TeV
CLIC. Meanwhile, we find that this signature is not promising for
$M_{A^0}=1500$ GeV at $\sqrt{s}=6$ TeV MuC.


Abstract:
  Microstructure reconstruction is a key enabler of process-structure-property
linkages, a central topic in materials engineering. Revisiting classical
optimization-based reconstruction techniques,they are recognized as a powerful
framework to reconstruct random heterogeneous media, especially due to their
generality and controllability. The stochasticity of the available approaches
is, however, identified as a performance bottleneck. In this work,
reconstruction is approached as a differentiable optimization problem, where
the error of a generic prescribed descriptor is minimized under consideration
of its derivative. As an exemplary descriptor, a suitable differentiable
version of spatial correlations is formulated, along with a multigrid scheme to
ensure scalability. The applicability of differentiable optimization realized
through this descriptor is demonstrated using a wide variety of heterogeneous
media, achieving exact statistical equivalence with errors as low as 0 in a
short time. We conclude that, while still in an early stage of development,
this approach has the potential to significantly alleviate the computational
effort currently associated with reconstructing general random heterogeneous
media.


Abstract:
  Translating text into a language unknown to the text's author, dubbed
outbound translation, is a modern need for which the user experience has
significant room for improvement, beyond the basic machine translation
facility. We demonstrate this by showing three ways in which user confidence in
the outbound translation, as well as its overall final quality, can be
affected: backward translation, quality estimation (with alignment) and source
paraphrasing. In this paper, we describe an experiment on outbound translation
from English to Czech and Estonian. We examine the effects of each proposed
feedback module and further focus on how the quality of machine translation
systems influence these findings and the user perception of success. We show
that backward translation feedback has a mixed effect on the whole process: it
increases user confidence in the produced translation, but not the objective
quality.


Abstract:
  The analysis of contingency tables is a powerful statistical tool used in
experiments with categorical variables. This study improves parts of the theory
underlying the use of contingency tables. Specifically, the linkage
disequilibrium parameter as a measure of two-way interactions applied to
three-way tables makes it possible to quantify Simpsons paradox by a simple
formula. With tests on three-way interactions, there is only one that
determines whether the partial interactions of all variables agree or whether
there is at least one variable whose partial interactions disagree. To date,
there has been no test available that determines whether the partial
interactions of a certain variable agree or disagree, and the presented work
closes this gap. This work reveals the relation of the multiplicative and the
additive measure of a three-way interaction. Another contribution addresses the
question of which cells in a contingency table are fixed when the first- and
second-order marginal totals are given. The proposed procedure not only detects
fixed zero counts but also fixed positive counts. This impacts the
determination of the degrees of freedom. Furthermore, limitations of methods
that simulate contingency tables with given pairwise associations are
addressed.


Abstract:
  The alternating directions method of multipliers (ADMM) is a powerful
splitting algorithm for linearly constrained convex optimization problems. In
view of its popularity and applicability, a growing attention is drawn towards
the ADMM in nonconvex settings. Recent studies of minimization problems for
noncovex functions include various combinations of assumptions on the objective
function including, in particular, a Lipschitz gradient assumption. We consider
the case where the objective is the sum of a strongly convex function and a
weakly convex function. To this end we present and study an adaptive version of
the ADMM which incorporates generalized notions of convexity and varying
penalty parameters adapted to the convexity constants of the functions. We
prove convergence of the scheme under natural assumptions. To this end we
employ the recent adaptive Douglas--Rachford algorithm by revisiting the well
known duality relation between the classical ADMM and the Douglas--Rachford
splitting algorithm, generalizing this connection to our setting. We illustrate
our approach by relating and comparing to alternatives, and by numerical
experiments on a signal denoising problem.


Abstract:
  A novel matrix pencil-based interference mitigation approach for FMCW radars
is proposed in this paper. The interference-contaminated segment of the beat
signal is firstly cut out and then the signal samples in the cut-out region are
reconstructed by modeling the beat signal as a sum of complex exponentials and
using the matrix pencil method to estimate their parameters. The efficiency of
the proposed approach for the interference with different parameters (i.e.
interference duration, signal-to-noise ratio (SNR), and different target
scenarios) is investigated by means of numerical simulations. The proposed
interference mitigation approach is intensively verified on experimental data.
Comparisons of the proposed approach with the zeroing and other beat-frequency
interpolation techniques are presented. The results indicate the broad
applicability and superiority of the proposed approach, especially in low SNR
and long interference duration situations.


Abstract:
  Human motion prediction is an essential component for enabling closer
human-robot collaboration. The task of accurately predicting human motion is
non-trivial. It is compounded by the variability of human motion, both at a
skeletal level due to the varying size of humans and at a motion level due to
individual movement's idiosyncrasies. These variables make it challenging for
learning algorithms to obtain a general representation that is robust to the
diverse spatio-temporal patterns of human motion. In this work, we propose a
modular sequence learning approach that allows end-to-end training while also
having the flexibility of being fine-tuned. Our approach relies on the
diversity of training samples to first learn a robust representation, which can
then be fine-tuned in a continual learning setup to predict the motion of new
subjects. We evaluated the proposed approach by comparing its performance
against state-of-the-art baselines. The results suggest that our approach
outperforms other methods over all the evaluated temporal horizons, using a
small amount of data for fine-tuning. The improved performance of our approach
opens up the possibility of using continual learning for personalized and
reliable motion prediction.


Abstract:
  Day after day, the number of mobile applications deployed on cloud computing
continues in increasing because o f smartphone capabilities improvement. Cloud
computing has already succeeded in the web based application, for that reason,
the demand for context aware services provided by cloud computing increases. To
customize a cloud service that takes into account th e consumer requirements,
which depend on information change, it brings to light many recent challenges
to cloud computing about environment aware, location aware, time aware. The
cloud provider, moreover, has to manage personalized applications and the con
straints of mobile devices in matters of interaction abilities and
communication restrictions. This paper proposes a strategy for selecting
automatically an appropriate cloud environment that runs out whole
requirements, defines a configuration for the ass ociated cloud environment and
able to easily adapt to the change of the environment on either the user or the
cloud side or both. This process builds on the principles of dynamic software
product lines, Agent oriented software engineering, and the MAPE k m odel to
select and configure cloud environments according to the consumer needs and the
context change.


Abstract:
  Two papers recently reported the detection of gaseous nickel and iron in the
comae of over 20 comets from observations collected over two decades, including
interstellar comet 2I/Borisov. To evaluate the state of the laboratory data in
support of these identifications, we re-analyzed archived spectra of comet
C/1996 B2 (Hyakutake), one of the nearest and brightest comets of the last
century, using a combined experimental and computational approach. We developed
a new, many-level fluorescence model that indicates that the fluorescence
emission of Fe I and Ni I vary greatly with heliocentric velocity. Combining
this model with laboratory spectra of an Fe-Ni plasma, we identified 22 lines
of Fe I and 14 lines of Ni I in the spectrum of Hyakutake. Using Haser models,
we estimate the nickel and iron production rates as Q(Ni) = 2.6 - 4.1 x 10^22
s^-1 and Q(Fe) = 0.4 - 2.8 x 10^23 s^-1. From derived column densities, the
Ni/Fe abundance ratio log10[Ni/Fe] = -0.15 +/- 0.07 deviates significantly from
solar abundance ratios, and it is consistent with the ratios observed in solar
system comets. Possible production and emission mechanisms are analyzed in
context of existing laboratory measurements. Based on the observed spatial
distributions, excellent fluorescence model agreement, and Ni/Fe ratio, our
findings support an origin consisting of a short-lived unknown parent followed
by fluorescence emission. Our models suggest that the strong heliocentric
velocity dependence of the fluorescence efficiencies can provide a meaningful
test of the physical process responsible for the Fe I and Ni I emission.


Abstract:
  Applications towards 6G have brought a huge interest towards arrays with a
high number of antennas and operating within the millimeter and sub-THz
bandwidths for joint communication and localization. With such large arrays,
the plane wave approximation is often not accurate because the system may
operate in the near-field propagation region (Fresnel region) where the
electromagnetic field wavefront is spherical. In this case, the curvature of
arrival (CoA) is a measure of the spherical wavefront that can be used to infer
the source position using only a single large array. In this paper, we study a
near-field tracking problem for inferring the state (i.e., the position and
velocity) of a moving source with an ad-hoc observation model that accounts for
the phase profile of a large receiving array. For this tracking problem, we
derive the posterior Cram\'er-Rao Lower Bound (P-CRLB) and show the effects
when the source moves inside and outside the Fresnel region. We provide
insights on how the loss of positioning information outside Fresnel comes from
an increase of the ranging error rather than from inaccuracies of angular
estimation. Then, we investigate the performance of different Bayesian tracking
algorithms in the presence of model mismatches and abrupt trajectory changes.
Our results demonstrate the feasibility and high accuracy for most of the
tracking approaches without the need of wideband signals and of any
synchronization scheme. signals and of any synchronization scheme.


Abstract:
  In magnetic confinement fusion devices, the equilibrium configuration of a
plasma is determined by the balance between the hydrostatic pressure in the
fluid and the magnetic forces generated by an array of external coils and the
plasma itself. The location of the plasma is not known a priori and must be
obtained as the solution to a free boundary problem. The partial differential
equation that determines the behavior of the combined magnetic field depends on
a set of physical parameters (location of the coils, intensity of the electric
currents going through them, magnetic permeability, etc.) that are subject to
uncertainty and variability. The confinement region is in turn a function of
these stochastic parameters as well. In this work, we consider variations on
the current intensities running through the external coils as the dominant
source of uncertainty. This leads to a parameter space of dimension equal to
the number of coils in the reactor. With the aid of a surrogate function built
on a sparse grid in parameter space, a Monte Carlo strategy is used to explore
the effect that stochasticity in the parameters has on important features of
the plasma boundary such as the location of the x-point, the strike points, and
shaping attributes such as triangularity and elongation. The use of the
surrogate function reduces the time required for the Monte Carlo simulations by
factors that range between 7 and over 30.


Abstract:
  Understanding and predicting lattice dynamics in strongly anharmonic crystals
is one of the long-standing challenges in condensed matter physics. Here we
propose a first-principles method that gives accurate quasiparticle (QP) peaks
of the phonon spectrum with strong anharmonic broadening. On top of the
conventional first-order self-consistent phonon (SC1) dynamical matrix, the
proposed method incorporates frequency renormalization effects by the bubble
self-energy within the QP approximation. We apply the developed methodology to
the strongly anharmonic $\alpha$-CsPbBr$_3$ that displays phonon instability
within the harmonic approximation in the whole Brillouin zone. While the SC1
theory significantly underestimates the cubic-to-tetragonal phase transition
temperature (\tc) by more than 50\%, we show that our approach yields \tc =
404--423~K, in excellent agreement with the experimental value of 403~K. We
also demonstrate that an accurate determination of QP peaks is paramount for
quantitative prediction and elucidation of lattice thermal conductivity.


Abstract:
  The Hard Lefschetz Property (HLP) is an important property which has been
studied in several categories of the symplectic world. For Sasakian manifolds,
this duality is satisfied by the basic cohomology (so, it is a transverse
property), but a new version of the HLP has been recently given in terms of
duality of the cohomology of the manifold itself in arXiv:1306.2896. Both
properties were proved to be equivalent (see arXiv:1311.1431) in the case of
K-contact flows. In this paper we extend both versions of the HLP (transverse
and not) to the more general category of isometric flows, and show that they
are equivalent. We also give some explicit examples which illustrate the
categories where the HLP could be considered.


Abstract:
  On-demand shared mobility is a promising and sustainable transportation
approach that can mitigate vehicle externalities, such as traffic congestion
and emission. On-demand shared mobility systems require matching of one
(one-to-one) or multiple riders (many-to-one) to a vehicle based on real-time
information. We propose a novel Graph-based Many-to-One ride-Matching
(GMOMatch) algorithm for the dynamic many-to-one matching problem in the
presence of traffic congestion. GMOMatch, which is an iterative two-step
method, provides high service quality and is efficient in terms of
computational complexity. It starts with a one-to-one matching in Step 1 and is
followed by solving a maximum weight matching problem in Step 2 to combine the
travel requests. To evaluate the performance, it is compared with a
ride-matching algorithm developed by Simonetto et al. (2019). Both algorithms
are implemented in a micro-traffic simulator to assess their performance and
their impact on traffic congestion in Downtown, Toronto road network. In
comparison to the Simonetto, GMOMatch improved the service rate, vehicle
kilometer traveled and traffic travel time by 32%, 16.07%, and 4%,
respectively. The sensitivity analysis indicated that utilizing vehicles with a
capacity of 10 can achieve 25% service rate improvement compared to a capacity
of 4.