Abstract:
  Automatic breast lesion segmentation in ultrasound helps to diagnose breast
cancer, which is one of the dreadful diseases that affect women globally.
Segmenting breast regions accurately from ultrasound image is a challenging
task due to the inherent speckle artifacts, blurry breast lesion boundaries,
and inhomogeneous intensity distributions inside the breast lesion regions.
Recently, convolutional neural networks (CNNs) have demonstrated remarkable
results in medical image segmentation tasks. However, the convolutional
operations in a CNN often focus on local regions, which suffer from limited
capabilities in capturing long-range dependencies of the input ultrasound
image, resulting in degraded breast lesion segmentation accuracy. In this
paper, we develop a deep convolutional neural network equipped with a global
guidance block (GGB) and breast lesion boundary detection (BD) modules for
boosting the breast ultrasound lesion segmentation. The GGB utilizes the
multi-layer integrated feature map as a guidance information to learn the
long-range non-local dependencies from both spatial and channel domains. The BD
modules learn additional breast lesion boundary map to enhance the boundary
quality of a segmentation result refinement. Experimental results on a public
dataset and a collected dataset show that our network outperforms other medical
image segmentation methods and the recent semantic segmentation methods on
breast ultrasound lesion segmentation. Moreover, we also show the application
of our network on the ultrasound prostate segmentation, in which our method
better identifies prostate regions than state-of-the-art networks.


Abstract:
  Because of their capacity-approaching performance, graph-based codes have a
wide range of applications, including communications and storage. In these
codes, unequal error protection (UEP) can offer performance gains with limited
rate loss. Recent empirical results in magnetic recording (MR) systems show
that extra protection for the parity bits of a low-density parity-check (LDPC)
code via constrained coding results in significant density gains. In
particular, when UEP is applied via more reliable parity bits, higher fidelity
messages of parity bits are spread to all bits by message passing algorithm,
enabling performance gains. Threshold analysis is a tool to measure the
effectiveness of a graph-based code or coding scheme. In this paper, we provide
a theoretical analysis of this UEP idea using extrinsic information transfer
(EXIT) charts in the binary erasure channel (BEC) and the binary symmetric
channel (BSC). We use EXIT functions to investigate the effect of change in
mutual information of parity bits on the overall coding scheme. We propose a
setup in which parity bits of a repeat-accumulate (RA) LDPC code have lower
erasure or crossover probabilities than input information bits. We derive the
a-priori and extrinsic mutual information functions for check nodes and
variable nodes of the code. After applying our UEP setup to the information
functions, we formulate a linear programming problem to find the optimal degree
distribution that maximizes the code rate under the decoding convergence
constraint. Results show that UEP via higher fidelity parity bits achieves up
to about $17\%$ and $28\%$ threshold gains on BEC and BSC, respectively.


Abstract:
  Underground physics experiments such as dark matter direct detection need to
keep control of the background contribution. Hosting these experiments in
underground facilities helps to minimize certain background sources such as the
cosmic rays. One of the largest remaining background sources is the radon
emanated from the rocks enclosing the research facility. The radon particles
could be deposited inside the detectors when they are opened to perform the
maintenance operations. Therefore, forecasting the radon levels is a crucial
task in an attempt to schedule the maintenance operations when radon level is
minimum. In the past, deep learning models have been implemented to forecast
the radon time series at the Canfranc Underground Laboratory (LSC), in Spain,
with satisfactory results. When forecasting time series, the past values of the
time series are taken as input variables. The present work focuses on
understanding the relative contribution of these input variables to the
predictions generated by neural networks. The results allow us to understand
how the predictions of the time series depend on the input variables. These
results may be used to build better predictors in the future.


Abstract:
  Developing specialized cloud-based and open-source testbeds is a practical
approach to investigate network slicing functionalities in the fifth-generation
(5G) mobile networks. This paper provides a comprehensive review of most of the
existing cost-efficient and small-scale testbeds that partially or fully deploy
network slicing. First, we present relevant software packages for the three
main functional blocks of the ETSI NFV MANO framework and for emulating the
access and core network domains. Second, we define primary and secondary design
criteria for deploying network slicing testbeds. These design criteria are
later used for comparison between the testbeds. Third, we present the
state-of-the-art testbeds, including their design objectives, key technologies,
network slicing deployment, and experiments. Next, we evaluate the testbeds
according to the defined design criteria and present an in-depth summary table.
This assessment concludes with the superiority of some of them over the rest
and the most dominant software packages satisfying the ETSI NFV MANO framework.
Finally, challenges, potential solutions, and future works of network slicing
testbeds are discussed.


Abstract:
  This paper is devoted to the study of the expected-integral multifunctions
given in the form \begin{equation*}
\operatorname{E}_\Phi(x):=\int_T\Phi_t(x)d\mu, \end{equation*} where
$\Phi\colon T\times\mathbb{R}^n \rightrightarrows \mathbb{R}^m$ is a set-valued
mapping on a measure space $(T,\mathcal{A},\mu)$. Such multifunctions appear in
applications to stochastic programming, which require developing efficient
calculus rules of generalized differentiation. Major calculus rules are
developed in this paper for coderivatives of multifunctions
$\operatorname{E}_\Phi$ and second-order subdifferentials of the corresponding
expected-integral functionals with applications to constraint systems arising
in stochastic programming. The paper is self-contained with presenting in the
preliminaries some needed results on sequential first-order subdifferential
calculus of expected-integral functionals taken from the first paper of this
series.


Abstract:
  Recurrent transducer models have emerged as a promising solution for speech
recognition on the current and next generation smart devices. The transducer
models provide competitive accuracy within a reasonable memory footprint
alleviating the memory capacity constraints in these devices. However, these
models access parameters from off-chip memory for every input time step which
adversely effects device battery life and limits their usability on low-power
devices.
  We address transducer model's memory access concerns by optimizing their
model architecture and designing novel recurrent cell designs. We demonstrate
that i) model's energy cost is dominated by accessing model weights from
off-chip memory, ii) transducer model architecture is pivotal in determining
the number of accesses to off-chip memory and just model size is not a good
proxy, iii) our transducer model optimizations and novel recurrent cell reduces
off-chip memory accesses by 4.5x and model size by 2x with minimal accuracy
impact.


Abstract:
  Demonstrated for a digital image sensor based camera is a calibration target
optimized method for finding the Camera Response Function (CRF). The proposed
method uses localized known target zone pixel outputs spatial averaging and
histogram analysis for saturated pixel detection. Using the proposed CRF
generation method with a 87 dB High Dynamic Range (HDR) silicon CMOS image
sensor camera viewing a 90 dB HDR calibration target, experimentally produced
is a non-linear CRF with a limited 40 dB linear CRF zone. Next, a 78 dB test
target is deployed to test the camera with this measured CRF and its restricted
40 dB zone. By engaging the proposed minimal exposures, weighting free,
multi-exposure imaging method with 2 images, demonstrated is a highly robust
recovery of the test target. In addition, the 78 dB test target recovery with
16 individual DR value patches stays robust over a factor of 20 change in test
target illumination lighting. In comparison, a non-robust test target image
recovery is produced by 5 leading prior-art multi-exposure HDR recovery
algorithms using 16 images having 16 different exposure times, with each
consecutive image having a sensor dwell time increasing by a factor of 2.
Further validation of the proposed HDR image recovery method is provided using
two additional experiments, the first using a 78 dB calibrated target combined
with a natural indoor scene to form a hybrid design target and a second
experiment using an uncalibrated indoor natural scene. The proposed technique
applies to all digital image sensor based cameras having exposure time and
illumination controls. In addition, the proposed methods apply to various
sensor technologies, spectral bands, and imaging applications.


Abstract:
  Motivated by recent work involving the analysis of biomedical imaging data,
we present a novel procedure for constructing simultaneous confidence corridors
for the mean of imaging data. We propose to use flexible bivariate splines over
triangulations to handle irregular domain of the images that is common in brain
imaging studies and in other biomedical imaging applications. The proposed
spline estimators of the mean functions are shown to be consistent and
asymptotically normal under some regularity conditions. We also provide a
computationally efficient estimator of the covariance function and derive its
uniform consistency. The procedure is also extended to the two-sample case in
which we focus on comparing the mean functions from two populations of imaging
data. Through Monte Carlo simulation studies we examine the finite-sample
performance of the proposed method. Finally, the proposed method is applied to
analyze brain Positron Emission Tomography (PET) data in two different studies.
One dataset used in preparation of this article was obtained from the
Alzheimer's Disease Neuroimaging Initiative (ADNI) database.


Abstract:
  The paper deals with group dualities. A group duality is simply a pair $(G,
H)$ where $G$ is an abstract abelian group and $H$ a subgroup of characters
defined on $G$. A group topology $\tau$ defined on $G$ is {\it compatible} with
the group duality (also called dual pair) $(G, H)$ if $G$ equipped with $\tau$
has dual group $H$.
  A topological group $(G, \tau)$ gives rise to the natural duality $(G,
G^\wedge)$, where $G^\wedge$ stands for the group of continuous characters on
$G$. We prove that the existence of a $g$-barrelled topology on $G$ compatible
with the dual pair $(G, G^\wedge)$ is equivalent to the semireflexivity in
Pontryagin's sense of the group $G^\wedge$ endowed with the pointwise
convergence topology $\sigma(G^\wedge, G)$. We also deal with $k$-group
topologies. We prove that the existence of $k$-group topologies on $G$
compatible with the duality $(G, G^\wedge)$ is determined by a sort of
completeness property of its Bohr topology $\sigma (G, G^\wedge)$ (Theorem
3.3).


Abstract:
  Enhancing the spatio-temporal observability of distributed energy resources
(DERs) is crucial for achieving secure and efficient operations in distribution
grids. This paper puts forth a joint recovery framework for residential loads
by leveraging the complimentary strengths of heterogeneous measurements in real
time. The proposed framework integrates low-resolution smart meter data
collected at every load node with fast-sampled feeder-level measurements from
limited number of distribution phasor measurement units. To address the lack of
data, we exploit two key characteristics for the loads and DERs, namely the
sparse changes due to infrequent activities of appliances and electric vehicles
(EVs) and the locational dependence of solar photovoltaic (PV) generation.
Accordingly, meaningful regularization terms are introduced to cast a convex
load recovery problem, which will be further simplified to reduce the
computational complexity. The load recovery solutions can be utilized to
identify the EV charging events at each load node and to infer the total
behind-the-meter PV output. Numerical tests using real-world data have
demonstrated the effectiveness of the proposed approaches in enhancing the
visibility of grid-edge DERs.


Abstract:
  Magnetic flux rope, formed by the helical magnetic field lines, can sometimes
remain its shape while carrying significant plasma flow that is aligned with
the local magnetic field. We report the existence of such structures and static
flux ropes by applying the Grad-Shafranov-based algorithm to the Parker Solar
Probe (PSP) in-situ measurements in the first five encounters. These structures
are detected at heliocentric distances, ranging from 0.13 to 0.66 au, in a
total of 4-month time period. We find that flux ropes with field-aligned flows,
although occur more frequently, have certain properties similar to those of
static flux ropes, such as the decaying relations of the magnetic fields within
structures with respect to heliocentric distances. Moreover, these events are
more likely with magnetic pressure dominating over the thermal pressure. About
one-third of events are detected in the relatively fast-speed solar wind.
Taking into account the high Alfvenicity, we also compare with switchback
spikes identified during three encounters and interpret their inter-relations.
We find that some switchbacks can be detected when the spacecraft traverses
flux rope-like structures. The cross-section maps for selected events are
presented via the new Grad-Shafranov type reconstruction. Finally, the possible
evolution of the magnetic flux rope structures in the inner heliosphere is
discussed.


Abstract:
  In the last 50 years, equilibrium density functional theory (DFT) has been
proven to be a powerful, versatile and predictive approach for the statics and
structure of classical particles. This theory can be extended to the
nonequilibrium dynamics of completely overdamped Brownian colloidal particles
towards so-called dynamical density functional theory (DDFT). The success of
DDFT makes it a promising candidate for a first-principle description of active
matter. In this lecture, we shall first recapitulate classical DDFT for passive
colloidal particles typically described by Smoluchowski equation. After a basic
derivation of DDFT from the Smoluchowski equation, we discuss orientational
degrees of freedom and the effect of hydrodynamic interactions for passive
particles. This brings us into an ideal position to generalize DDFT towards
active matter. In particular we distinguish between "dry active matter" which
is composed of self-propelled particles that contain no hydrodynamic flow
effects of a surrounding solvent and "wet active matter" where the hydrodynamic
flow fields generated by the microswimmers are taken into account. For the
latter, DDFT is a tool which unifies thermal fluctuations, direct particle
interactions, external driving fields and hydrodynamic effects arising from
internal self-propulsion discriminating between "pushers" and "pullers". A
number of recent applications is discussed including transient clustering of
self-propelled rods and the spontaneous formation of a hydrodynamic pump in
confined microswimmers.


Abstract:
  In this paper, a new key-agreement scheme is proposed and analyzed. In
addition to being provably secure in shared secret key indistinguishability
model, the scheme has an interesting feature: while using exponentiation over a
cyclic subgroup to establish the key-agreement, the generator of that subgroup
is hidden to secure the scheme against adversaries that are capable of solving
the Discrete Logarithm Problem, which means that the scheme might be candidate
as a post-quantum key exchange scheme.


Abstract:
  Fano resonances and Rabi splittings are routinely reported in the scientific
literature. Asymmetric resonance lineshapes are usually associated with Fano
resonances, and two split peaks in the spectrum are often attributed to a Rabi
splitting. True Fano resonances and Rabi splittings are unequivocal signatures
of coherent coupling between subsystems. However, can the same spectral
lineshapes characterizing Fano resonances and Rabi splittings arise from a
purely incoherent sum of intensities? Here we answer this question through
experiments with a tunable Fabry-P\'erot cavity containing a
CsPbBr\textsubscript{3} perovskite crystal. By measuring the transmission and
photoluminescence of this system using microscope objectives with different
numerical aperture ($NA$), we find that even a modest $NA=0.4$ can artificially
generate Fano resonances and Rabi splittings. We furthermore show that this
modest $NA$ can obscure the anti-crossing of a bona fide strongly coupled
light-matter system. Through transfer matrix calculations we confirm that these
spectral artefacts are due to the incoherent sum of transmitted intensities at
different angles captured by the $NA$. Our results are relevant to the wide
nanophotonics community, characterizing dispersive optical systems with high
numerical aperture microscope objectives. We conclude with general guidelines
to avoid pitfalls in the characterization of such optical systems.


Abstract:
  The integrated nested Laplace approximation (INLA) is a well-known and
popular technique for spatial modeling with a user-friendly interface in the
R-INLA package. Unfortunately, only a certain class of latent Gaussian models
are amenable to fitting with INLA. In this paper we describe Template Model
Builder (TMB), an existing technique which is well-suited to fitting complex
spatio-temporal models. TMB is relatively unknown to the spatial statistics
community, but is a highly flexible random effects modeling tool which allows
users to define complex random effects models through simple C++ templates.
After contrasting the methodology behind TMB with INLA, we provide a
large-scale simulation study assessing and comparing R-INLA and TMB for
continuous spatial models, fitted via the Stochastic Partial Differential
Equations (SPDE) approximation. The results show that the predictive fields
from both methods are comparable in most situations even though TMB estimates
for fixed or random effects may have slightly larger bias than R-INLA. We also
present a smaller discrete spatial simulation study, in which both approaches
perform well. We conclude with an analysis of breast cancer incidence and
mortality data using a joint model which cannot be fit with INLA.


Abstract:
  We perform a detailed analysis of the asymptotic behavior of a multifield
cosmological model with phantom terms. Specifically, we consider the
Chiral-Phantom model consisting of two scalar fields with a mixed kinetic term,
while one scalar field has negative kinetic energy, that is, it has phantom
properties. We show that the Hubble function can change sign, and we study the
global evolution of the field equation in the finite and infinity regions with
the use of Poincar\'{e} variables. We find that in the Chiral-Phantom model the
limit of the quintessence scalar field is recovered while the cosmological
evolution differs from the standard hyperbolic theory. Finally, the linear
cosmological perturbations are studied.


Abstract:
  A model of the Galaxy with the outer ring R1R2 can explain the observed
distribution of the radial, VR, and azimuthal, VT, velocity components along
the Galactocentric distance, R, derived from the Gaia EDR3 data. We selected
stars from the Gaia EDR3 catalogue with reliable parallaxes, proper motions and
line-of-sight velocities lying near the Galactic plane, |z|<200 pc, and in the
sector of the Galactocentic angles |theta|<15 degrees and calculated the median
velocities VR and VT in small bins along the distance R. The distribution of
observed velocities appears to have some specific features: the radial velocity
VR demonstrates a smooth fall from +5 km s-1 at the distance of R=R0-1.5 kpc to
-3 km s-1 at R=R0+1.0 kpc while the azimuthal velocity VT shows a sharp drop by
7 km s-1 in the distance interval R0<R<R0+1.0 kpc, where R0 is the solar
Galactocentric distance. We build a model of the Galaxy including bulge, bar,
disc and halo components, which reproduces the observed specific features of
the velocity distribution in the Galactocentric distance interval |R-R0|< 1.5
kpc. The best agreement corresponds to the time 1.8+/-0.5 Gyr after the start
of the simulation. A model of the Galaxy with the bar rotating at the angular
velocity of Omega_b=55+/-3 km s-1 kpc-1, which sets the OLR of the bar at the
distance of R0-0.5+/-0.4 kpc, provides the best agreement between the model and
observed velocities. The position angle of the bar, theta_b, corresponding to
the best agreement between the model and observed velocities is theta_b=45+/-15
degrees.


Abstract:
  In recent years, Evolutionary Algorithms (EAs) have frequently been adopted
to evolve instances for optimization problems that pose difficulties for one
algorithm while being rather easy for a competitor and vice versa. Typically,
this is achieved by either minimizing or maximizing the performance difference
or ratio which serves as the fitness function. Repeating this process is useful
to gain insights into strengths/weaknesses of certain algorithms or to build a
set of instances with strong performance differences as a foundation for
automatic per-instance algorithm selection or configuration. We contribute to
this branch of research by proposing fitness-functions to evolve instances that
show large performance differences for more than just two algorithms
simultaneously. As a proof-of-principle, we evolve instances of the
multi-component Traveling Thief Problem~(TTP) for three incomplete TTP-solvers.
Our results point out that our strategies are promising, but unsurprisingly
their success strongly relies on the algorithms' performance complementarity.


Abstract:
  Grasp detection in clutter requires the robot to reason about the 3D scene
from incomplete and noisy perception. In this work, we draw insight that 3D
reconstruction and grasp learning are two intimately connected tasks, both of
which require a fine-grained understanding of local geometry details. We thus
propose to utilize the synergies between grasp affordance and 3D reconstruction
through multi-task learning of a shared representation. Our model takes
advantage of deep implicit functions, a continuous and memory-efficient
representation, to enable differentiable training of both tasks. We train the
model on self-supervised grasp trials data in simulation. Evaluation is
conducted on a clutter removal task, where the robot clears cluttered objects
by grasping them one at a time. The experimental results in simulation and on
the real robot have demonstrated that the use of implicit neural
representations and joint learning of grasp affordance and 3D reconstruction
have led to state-of-the-art grasping results. Our method outperforms baselines
by over 10% in terms of grasp success rate. Additional results and videos can
be found at https://sites.google.com/view/rpl-giga2021


Abstract:
  After major hurricanes, electric production is significantly reduced owing to
not only electric infrastructure destruction, but also the economic crisis
associated with damage to private and public activities. The full restoration
of the electric infrastructure is not always simultaneously performed with the
full restoration of electric production. Here, we describe the electric
production curves for the islands of Saint-Martin, Saint-Barthelemy, and Puerto
Rico in the Caribbean, where two major hurricanes occurred in 2017. After the
major hurricanes, the electric energy production was characterised by a slow
recovery followed by a stable phase during several months, corresponding to
approximately $75\%$ of the initial electric production. A resilience time of
several months (1 month $< t_s <$ 5 months) is necessary to attain the new
electric energy production equilibrium $E_{is}$, which is lower than the
initial electric energy production $E_i$. The reduction in electric energy
consumption per capita is of $20-25\%$. In Saint-Martin, during the
post-hurricane stable phase, the electric production was only approximately
$60\%$ of the initial electric energy production instead of $75\%$. The
reduction of 15 percent in the electric production of Saint-Martin after
Hurricane Irma could be attributed to the migration of approximately 8 000
inhabitants (approximately $23\%$) outside the island. This approach makes it
possible to anticipate the production of electricity during several months
after a major hurricane in the Caribbean islands.


Abstract:
  A heuristic description of the spin-rotation-gravity coupling is presented
and the implications of the corresponding gravitomagnetic Stern--Gerlach force
are briefly mentioned. It is shown, within the framework of linearized general
relativity, that the gravitomagnetic Stern--Gerlach force reduces in the
appropriate correspondence limit to the classical Mathisson spin-curvature
force.


Abstract:
  The spectral density of bound pairs in ideal 1D, 2D and Bethe lattices is
computed for weak and strong interactions. The computations are performed with
Green's functions by an efficient recursion method in real space. For the range
of interaction strengths within which bound states are predominantly single
pairs, the spectral profiles guide to the energy bandwidths where the bound
pairs can be maximized.


Abstract:
  We theoretically study electric quadrupole orders in f-electron systems on
the fcc lattice. Qudrupole degrees of freedom $O_{20}$ and $O_{22}$ originate
in the non-Kramers doublet ground state $\Gamma_3$ of ion with $f^2$ electron
configuration. For discussing quadrupole orders, we use a minimal model with
isotropic ($J$) and anisotropic ($K$) nearest-neighbor interactions, and
determine the phase diagram using a four-site mean-field approximation at zero
and finite temperatures. Quadrupoles couple the $\Gamma_3$ doublet to the
singlet excited state $\Gamma_1$, and its effects on canting antiferro orders
are examined in detail. We found that this coupling leads to a rich phase
diagram including two- and four-sublattice antiferro phases, and that two
phases show a partial order of quadrupoles at finite temperatures.


Abstract:
  Van der Waals (vdW) heterostructures consisting of bilayer graphene (BLG)
encapsulated within monolayers of strong spin-orbit semiconductor WS$_2$ or
ferromagnetic semiconductor Cr$_2$Ge$_2$Te$_6$ (CGT), are investigated. By
performing realistic first-principles calculations we capture the essential BLG
band structure features, including layer- and sublattice-resolved proximity
spin-orbit or exchange couplings. For different relative twist angles (0 or
60$^{\circ}$) of the WS$_2$ layers, and the magnetizations (parallel or
antiparallel) of the CGT layers, with respect to BLG, the low energy bands are
found and characterized by a series of fit parameters of model Hamiltonians.
These effective models are then employed to investigate the tunability of the
relevant energy dispersions by a gate field. For WS$_2$/BLG/WS$_2$
encapsulation we find that twisting allows to turn off the spin splittings away
from the $K$ points, due to opposite proximity-induced valley-Zeeman couplings
in the two sheets of BLG. Close to the $K$ points the electron spins are
polarized out of the plane. This polarization can be flipped by applying a gate
field. As for magnetic CGT/BLG/CGT structures, we realize the recently proposed
spin-valve effect, whereby a gap opens for antiparallel magnetizations of the
CGT layers. Furthermore, we find that for the antiferromagnetic orientation the
electron states away from $K$ have vanishingly weak proximity exchange, while
the states close to $K$ remain spin polarized in the presence of an electric
field. The induced magnetization can be flipped by changing the gate field.
These findings should be useful for spin transport, spin filtering, and spin
relaxation anisotropy studies of BLG-based vdW heterostructures.


Abstract:
  In this work, we study the computational complexity of reducing the squared
gradient magnitude for smooth minimax optimization problems. First, we present
algorithms with accelerated $\mathcal{O}(1/k^2)$ last-iterate rates, faster
than the existing $\mathcal{O}(1/k)$ or slower rates for extragradient, Popov,
and gradient descent with anchoring. The acceleration mechanism combines
extragradient steps with anchoring and is distinct from Nesterov's
acceleration. We then establish optimality of the $ \mathcal{O}(1/k^2)$ rate
through a matching lower bound.


Abstract:
  Terahertz (THz) communications are regarded as a pillar technology for the
sixth generation (6G) wireless systems, by offering multi-ten-GHz bandwidth. To
overcome the short transmission distance and huge propagation loss,
ultra-massive (UM) MIMO systems that employ sub-millimeter wavelength antennas
array are proposed to enable an enticingly high array gain. In the UM-MIMO
systems, hybrid beamforming stands out for its great potential in promisingly
high data rate and reduced power consumption. In this paper, challenges and
features of the THz hybrid beamforming design are investigated, in light of the
distinctive THz peculiarities. Specifically, we demonstrate that the spatial
degree-of-freedom (SDoF) is less than 5, which is caused by the extreme
sparsity of the THz channel. The blockage problem caused by the huge reflection
and scattering losses, as high as 15 dB or over, is studied. Moreover, we
analyze the challenges led by the array containing 1024 or more antennas,
including the requirement for intelligent subarray architecture, strict energy
efficiency, and propagation characterization based on spherical-wave
propagation mechanisms. Owning up to hundreds of GHz bandwidth, beam squint
effect could cause over 5~dB array gain loss, when the fractional bandwidth
exceeds 10%. Inspired by these facts, three novel THz-specific hybrid
beamforming architectures are presented, including widely-spaced
multi-subarray, dynamic array-of-subarrays, and true-time-delay-based
architectures. We also demonstrate the potential data rate, power consumption,
and array gain capabilities for THz communications. As a roadmap of THz hybrid
beamforming design, multiple open problems and potential research directions
are elaborated.


Abstract:
  In this paper, we introduce a reproducible cleaning process for the text
extracted from PDFs using n-gram models. Our approach compares the originally
extracted text with the text generated from, or expected by, these models using
earlier text as stimulus. To guide this process, we introduce the notion of a
consistency score, which refers to the proportion of text that is expected by
the model. This is used to monitor changes during the cleaning process, and
across different corpuses. We illustrate our process on text from the book Jane
Eyre and introduce both a Shiny application and an R package to make our
process easier for others to adopt.


Abstract:
  Discovering the physical requirements for meeting the indefinite permittivity
in natural material as well as proposing a new natural hyperbolic media offer a
possible route to significantly improve our knowledge and ability to confine
and controlling light in optoelectronic devices. We demonstrate the
hyperbolicity in a class of materials with hexagonal P6/mmm and P6$_{3}$/mmc
layered crystal structures and its physical origin is thoroughly investigated.
By utilizing density functional theory and solving the Bethe-Salpeter equation
(BSE), we find that the layered crystal structure and symmetry imposed
constraints in Li$_{3}$N gives rise to an exceedingly strong anisotropy in
optical responses along in- and out-of-plane directions of the crystals making
it a natural hyperbolic in a broad spectral range from the visible spectrum to
the ultraviolet. More excitingly, the hyperbolicity relation to anisotropic
interband absorption in addition to the impressive dependency of the conduction
band to the lattice constant along the out-of-plane direction provide the
hyperbolicity tunability in these hexagonal structures under strain, doping,
and alloying. Our findings not only suggest a large family of real hexagonal
compounds as a unique class of materials for realization of the highly tunable
broad band hyperbolicity but also offers an approach to search for new
hyperbolic


Abstract:
  The production of heavy-mass elements due to the rapid neutron-capture
mechanism (r-process) is associated with astrophysical scenarios, such as
supernovae and neutron-star mergers. In the r-process the capture of neutrons
is followed by $\beta$-decays until nuclear stability is reached. A key element
in the chain of nuclear weak-decays leading to the production of isotopes may
be the change of the parameters controlling the neutrino sector, due to the
mixing of active and sterile species. In this work we have addressed this
question and calculated $\beta$-decay rates for the nuclei involved in the
r-process chains as a function of the neutrino mixing parameters. These rates
were then used in the calculation of the abundance of the heavy elements
produced in core-collapse supernova and in neutron-star mergers, starting from
different initial mass-fraction distributions. The analysis shows that the
core-collapse supernova environment contributes with approximately $30\%$ of
the total heavy nuclei abundance while the neutron-star merger contributes with
about $70\%$ of it. Using available experimental data we have performed a
statistical analysis to set limits on the active-sterile neutrino mixing angle
and found a best-fit value $\sin^2 2\theta_{14}=0.22$, a value comparable with
those found in other studies reported in the literature.


Abstract:
  Change point detection becomes more and more important as datasets increase
in size, where unsupervised detection algorithms can help users process data.
To detect change points, a number of unsupervised algorithms have been
developed which are based on different principles. One approach is to define an
optimisation problem and minimise a cost function along with a penalty
function. In the optimisation approach, the choice of the cost function affects
the predictions made by the algorithm. In extension to the existing studies, a
new type of cost function using Tikhonov regularisation is introduced. Another
approach uses Bayesian statistics to calculate the posterior probability
distribution of a specific point being a change point. It uses a priori
knowledge on the distance between consecutive change points and a likelihood
function with information about the segments. The optimisation and Bayesian
approaches for offline change point detection are studied and applied to
simulated datasets as well as a real world multi-phase dataset. The approaches
have previously been studied separately and a novelty lies in comparing the
predictions made by the two approaches in a specific setting, consisting of
simulated datasets and a real world example. The study has found that the
performance of the change point detection algorithms are affected by the
features in the data.


Abstract:
  Hexagonal boron nitride (hBN)-long-known as a thermally stable ceramic-is now
available as atomically smooth, single-crystalline flakes, revolutionizing its
use in optoelectronics. For nanophotonics, these flakes offer strong
nonlinearities, hyperbolic dispersion, and single-photon emission, providing
unique properties for optical and quantum-optical applications. For
nanoelectronics, their pristine surfaces, chemical stability, and wide bandgap
have made them the key substrate, encapsulant, and gate dielectric for
two-dimensional electronic devices. However, while exploring these advantages,
researchers have been restricted to flat flakes or those patterned with basic
slits and holes, severely limiting advanced architectures. If freely varying
flake profiles were possible, the hBN structure would present a powerful design
parameter to further manipulate the flow of photons, electrons, and excitons in
next-generation devices. Here, we demonstrate freeform nanostructuring of hBN
by combining thermal scanning-probe lithography and reactive-ion etching to
shape flakes with surprising fidelity. We leverage sub-nanometer height control
and high spatial resolution to produce previously unattainable flake structures
for a broad range of optoelectronic applications. For photonics, we fabricate
microelements and show the straightforward transfer and integration of such
elements by placing a spherical hBN microlens between two planar mirrors to
obtain a stable, high-quality optical microcavity. We then decrease the
patterning length scale to introduce Fourier surfaces for electrons, creating
sophisticated, high-resolution landscapes in hBN, offering new possibilities
for strain and band-structure engineering. These capabilities can advance the
discovery and exploitation of emerging phenomena in hyperbolic metamaterials,
polaritonics, twistronics, quantum materials, and 2D optoelectronic devices.


Abstract:
  In this article, we study algorithms for nonnegative matrix factorization
(NMF) in various applications involving streaming data. Utilizing the continual
nature of the data, we develop a fast two-stage algorithm for highly efficient
and accurate NMF. In the first stage, an alternating non-negative least squares
(ANLS) framework is used, in combination with active set method with warm-start
strategy for the solution of subproblems. In the second stage, an interior
point method is adopted to accelerate the local convergence. The convergence of
the proposed algorithm is proved. The new algorithm is compared with some
existing algorithms in benchmark tests using both real-world data and synthetic
data. The results demonstrate the advantage of our algorithm in finding
high-precision solutions.


Abstract:
  Counterfactual quantum communication is unique in its own way that allows
remote parties to transfer information without sending any message carrier in
the channel. Although no message carrier travels in the channel at the time of
successful information transmission, it is impossible to transmit information
faster than the speed of light, thus without an information carrier. In this
paper, we address an important question What carries the information in
counterfactual quantum communication? and optimize the resource efficiency of
the counterfactual quantum communication in terms of the number of channels
used, time consumed to transmit 1-bit classical information between two remote
parties, and the number of qubits required to accomplish the counterfactual
quantum communication.


Abstract:
  GJ9827 is a bright star hosting a planetary system with three transiting
planets. As a multi-planet system with planets that sprawl within the
boundaries of the radius gap between terrestrial and gaseous planets, GJ9827 is
an optimal target to study the evolution of the atmospheres of close-in planets
with a common evolutionary history and their dependence from stellar
irradiation. Here, we report on the Hubble Space Telescope (HST) and CARMENES
transit observations of GJ9827 planets b and d. We performed a stellar and
interstellar medium characterization from the ultraviolet HST spectra,
obtaining fluxes for Ly-alpha and MgII of F(Ly-alpha) = (5.42+0.96-0.75) X
10^{-13} erg cm^{-2} s^{-1} and F(MgII) = (5.64 +- 0.24) X 10^{-14} erg cm^{-2}
s^{-1}. We also investigated a possible absorption signature in Ly-alpha in the
atmosphere of GJ9827b during a transit event from HST spectra, as well as
H-alpha and HeI signature for the atmosphere of GJ9827b and d from CARMENES
spectra. We found no evidence of an extended atmosphere in either of the
planets. This result is also supported by our analytical estimations of
mass-loss based on the measured radiation fields for all the three planets of
this system, which led to a mass-loss rate of 0.4, 0.3, and 0.1 planetary
masses per Gyr, for GJ9827b, c, and d respectively. These values indicate that
the planets could have lost their volatiles quickly in their evolution and
probably do not retain an atmosphere at the current stage.


Abstract:
  We consider an initial-boundary value problem for the $n$-dimensional wave
equation with the variable sound speed, $n\geq 1$. We construct three-level
implicit in time and compact in space (three-point in each space direction) 4th
order finite-difference schemes on the uniform rectangular meshes including
their one-parameter (for $n=2$) and three-parameter (for $n=3$) families. We
also show that some already known methods can be converted into such schemes.
In a unified manner, we prove the conditional stability of schemes in the
strong and weak energy norms together with the 4th order error estimate under
natural conditions on the time step. We also transform an unconditionally
stable 4th order two-level scheme suggested for $n=2$ to the three-level form,
extend it for any $n\geq 1$ and prove its stability. We also give an example of
a compact scheme for non-uniform in space and time rectangular meshes. We
suggest simple fast iterative methods based on FFT to implement the schemes. A
new effective initial guess to start iterations is given too. We also present
promising results of numerical experiments.


Abstract:
  This is the second article in a series about Hurwitz spaces. We associate,
with each nice couple $(\mathcal{X},\mathcal{Y})$ of subspaces of $\mathbb{C}$
and with each PMQ-group pair $(\mathcal{Q},G)$, a coordinate-free Hurwitz space
$\mathrm{Hur}(\mathcal{X},\mathcal{Y};\mathcal{Q},G)$. We define maps of
Hurwitz spaces induced functorially by morphisms of nice couples and of
PMQ-group pairs. For a locally finite PMQ $\mathcal{Q}$ we prove a
homeomorphism between $\mathrm{Hur}((0,1)^2;\mathcal{Q}_+)$ and the simplicial
Hurwitz space $\mathrm{Hur}^{\Delta}(\mathcal{Q})$.


Abstract:
  Laser based ranging (LiDAR) - already ubiquitously used in industrial
monitoring, atmospheric dynamics, or geodesy - is key sensor technology.
Coherent laser ranging, in contrast to time-of-flight approaches, is immune to
ambient light, operates continuous wave allowing higher average powers, and
yields simultaneous velocity and distance information. State-of-the-art
coherent single laser-detector architectures reach hundreds of kilopixel per
second rates. While emerging applications such as autonomous driving, robotics,
and augmented reality mandate megapixel per second point sampling to support
real-time video-rate imaging. Yet, such rates of coherent LiDAR have not been
demonstrated. Here we report a swept dual-soliton microcomb technique enabling
coherent ranging and velocimetry at megapixel per second line scan measurement
rates with up to 64 spectrally dispersed optical channels. It is based on
recent advances in photonic chip-based microcombs that offer a solution to
reduce complexity both on the transmitter and receiver sides.
Multi-heterodyning two synchronously frequency-modulated microcombs yields
distance and velocity information of all individual ranging channels on a
single receiver alleviating the need for individual separation, detection, and
digitization. The reported LiDAR implementation is hardware-efficient,
compatible with photonic integration, and demonstrates the significant
advantages of acquisition speed afforded by the convergence of optical
telecommunication and metrology technologies. We anticipate our research will
motivate further investigation of frequency swept microresonator dual-comb
approach in the neighboring fields of linear and nonlinear spectroscopy,
optical coherence tomography.


Abstract:
  We report a non-blocking high-resolution digital delay line based on an
asynchronous circuit design. Field programmable gate array logic primitives
were used as a source of delay and optimally arranged using combinatorial
optimization. This approach allows for an efficient trade-off of the resolution
and a delay range together with minimized dead time operation. We demonstrate
the method by implementing the delay line adjustable from 23 ns up to 1635 ns
with a resolution of 10 ps. We present a detailed experimental characterization
of the device focusing on thermal instability, timing jitter, and pulse
spreading, which represent three main issues of the asynchronous design. We
found a linear dependence of the delay on the temperature with the slope of 0.2
ps/K per a logic primitive. We measured the timing jitter of the delay to be in
the range of 7 ps - 165 ps, linearly increasing over the dynamic range of the
delay. We reduced the effect of pulse spreading by introducing pulse shrinking
circuits, and reached the overall dead time of 4 ns - 22.5 ns within the
dynamic range of the delay. The presented non-blocking delay line finds usage
in applications where the dead time minimization is crucial, and tens of
picoseconds excess jitter is acceptable, such as in many advanced photonic
networks.


Abstract:
  Recent developments in deep learning have significantly improved the quality
of synthesized singing voice audio. However, prominent neural singing voice
synthesis systems suffer from slow inference speed due to their autoregressive
design. Inspired by MLP-Mixer, a novel architecture introduced in the vision
literature for attention-free image classification, we propose MLP Singer, a
parallel Korean singing voice synthesis system. To the best of our knowledge,
this is the first work that uses an entirely MLP-based architecture for voice
synthesis. Listening tests demonstrate that MLP Singer outperforms a larger
autoregressive GAN-based system, both in terms of audio quality and synthesis
speed. In particular, MLP Singer achieves a real-time factor of up to 200 and
3400 on CPUs and GPUs respectively, enabling order of magnitude faster
generation on both environments.


Abstract:
  We present results on the nature of extreme ejective feedback episodes and
the physical conditions of a population of massive ($\rm M_* \sim 10^{11}
M_{\odot}$), compact starburst galaxies at z = 0.4-0.7. We use data from
Keck/NIRSPEC, SDSS, Gemini/GMOS, MMT, and Magellan/MagE to measure rest-frame
optical and near-IR spectra of 14 starburst galaxies with extremely high star
formation rate surface densities (mean $\rm \Sigma_{SFR} \sim 3000 \,M_{\odot}
yr^{-1} kpc^{-2}$) and powerful galactic outflows (maximum speeds v$_{98} \sim$
1000-3000 km s$^{-1}$). Our unique data set includes an ensemble of both
emission [OII]$\lambda\lambda$3726,3729, H$\beta$,
[OIII]$\lambda\lambda$4959,5007, H$\alpha$, [NII]$\lambda\lambda$6548,6583, and
[SII]$\lambda\lambda$6716,6731) and absorption MgII$\lambda\lambda$2796,2803,
and FeII$\lambda$2586) lines that allow us to investigate the kinematics of the
cool gas phase (T$\sim$10$^4$ K) in the outflows. Employing a suite of line
ratio diagnostic diagrams, we find that the central starbursts are
characterized by high electron densities (median n$_e \sim$ 530 cm$^{-3}$), and
high metallicity (solar or super-solar). We show that the outflows are most
likely driven by stellar feedback emerging from the extreme central starburst,
rather than by an AGN. We also present multiple intriguing observational
signatures suggesting that these galaxies may have substantial Lyman continuum
(LyC) photon leakage, including weak [SII] nebular emission lines. Our results
imply that these galaxies may be captured in a short-lived phase of extreme
star formation and feedback where much of their gas is violently blown out by
powerful outflows that open up channels for LyC photons to escape.


Abstract:
  By incorporating aspects of coordination and collaboration, workflow
implementations of information systems require a sound conceptualisation of
\EM{business processing} semantics. Traditionally, the success of conceptual
modelling techniques has depended largely on the adequacy of conceptualisation,
expressive power, comprehensibility and formal foundation. An equally important
requirement, particularly with the increased conceptualisation of business
aspects, is \EM{business suitability}. In this paper, the focus is on the
business suitability of workflow modelling for a commonly encountered class of
(operational) business processing, e.g. those of insurance claims, bank loans
and land conveyancing. A general assessment is first conducted on some
\EM{integrated} techniques characterising well-known paradigms - structured
process modelling, object-oriented modelling, behavioural process modelling and
business-oriented modelling. Through this, an insight into business suitability
within the broader perspective of technique adequacy, is gained. A specific
business suitability diagnosis then follows using a particular characterisation
of business processing, i.e.\ one where the intuitive semantics and
inter-relationship of business services and business processes are nuanced. As
a result, five business suitability principles are elicited. These are proposed
for a more detailed understanding and (synthetic) development of workflow
modelling techniques. Accordingly, further insight into workflow specification
languages and workflow globalisation in open distributed architectures may also
be gained.


Abstract:
  The recent development of high-performance computing enables us to generate
spatio-temporal high-resolution data of nonlinear dynamical systems and to
analyze them for deeper understanding of their complex nature. This trend can
be found in a wide range of science and engineering communities, which suggests
that detailed investigations on efficient data handling in physical science
must be required in future. To this end, we introduce the use of convolutional
neural networks (CNNs) to achieve an efficient data storage and estimation of
scientific big data derived from nonlinear dynamical systems. The CNN is
utilized to reconstruct three-dimensional data from a few numbers of
two-dimensional sections in a computationally friendly manner. The present
model is a combination of two- and three-dimensional CNNs, which allows users
to save only some of the two-dimensional sections to reconstruct the volumetric
data. As an example of three-dimensional data, we consider a fluid flow around
a square cylinder at the diameter-based Reynolds number $Re_D$ of 300, and show
that volumetric fluid flow data can successfully be reconstructed with the
present method from as few as five sections. Furthermore, we also propose a
combination of the present CNN-based reconstruction with an adaptive
sampling-based super-resolution analysis to augment the data compression
capability of the present methods. Our report can be a significant bridge
toward practical data handling for not only the fluid mechanics field but also
a vast range of physical sciences.


Abstract:
  Primordial black holes (PBHs) from the early Universe have been connected
with the nature of dark matter and can significantly affect cosmological
history. We show that coincidence dark radiation and density fluctuation
gravitational wave signatures associated with evaporation of $\lesssim10^9$ g
PBHs can be used to explore and obtain important hints about the formation
mechanisms of spinning and non-spinning PBHs spanning orders of magnitude in
mass-range, which is challenging to do otherwise.


Abstract:
  Many reinforcement learning (RL) agents require a large amount of experience
to solve tasks. We propose Contrastive BERT for RL (CoBERL), an agent that
combines a new contrastive loss and a hybrid LSTM-transformer architecture to
tackle the challenge of improving data efficiency. CoBERL enables efficient,
robust learning from pixels across a wide range of domains. We use
bidirectional masked prediction in combination with a generalization of recent
contrastive methods to learn better representations for transformers in RL,
without the need of hand engineered data augmentations. We find that CoBERL
consistently improves performance across the full Atari suite, a set of control
tasks and a challenging 3D environment.


Abstract:
  The coexistence of metallicity and ferroelectricity has been an intriguing
and controversial phenomenon as these two material properties are considered
incompatible in bulk. We clarify the concept of ferroelectric metal by
revisiting the original definitions for ferroelectric and metal.
Two-dimensional (2D) ferroelectrics with out-of-plane polarization can be
engineered via layer stacking to a genuine ferroelectric metal characterized by
switchable polarization and non-zero density of states at the Fermi level. We
demonstrate that 2D ferroelectric metals can serve as electrically-tunable,
high-quality electrocatalysts.


Abstract:
  An in-depth understanding of the particular environment is crucial in
reinforcement learning (RL). To address this challenge, the decision-making
process of a mobile collaborative robotic assistant modeled by the Markov
decision process (MDP) framework is studied in this paper. The optimal
state-action combinations of the MDP are calculated with the non-linear Bellman
optimality equations. This system of equations can be solved with relative ease
by the computational power of Wolfram Mathematica, where the obtained optimal
action-values point to the optimal policy. Unlike other RL algorithms, this
methodology does not approximate the optimal behavior, it gives the exact,
explicit solution, which provides a strong foundation for our study. With this,
we offer new insights into understanding the action selection mechanisms in RL
by presenting various small modifications on the very same schema that lead to
different optimal policies.


Abstract:
  There is an escalating need for methods to identify latent patterns in text
data from many domains. We introduce a new method to identify topics in a
corpus and represent documents as topic sequences. Discourse Atom Topic
Modeling draws on advances in theoretical machine learning to integrate topic
modeling and word embedding, capitalizing on the distinct capabilities of each.
We first identify a set of vectors ("discourse atoms") that provide a sparse
representation of an embedding space. Atom vectors can be interpreted as latent
topics: Through a generative model, atoms map onto distributions over words;
one can also infer the topic that generated a sequence of words. We illustrate
our method with a prominent example of underutilized text: the U.S. National
Violent Death Reporting System (NVDRS). The NVDRS summarizes violent death
incidents with structured variables and unstructured narratives. We identify
225 latent topics in the narratives (e.g., preparation for death and physical
aggression); many of these topics are not captured by existing structured
variables. Motivated by known patterns in suicide and homicide by gender, and
recent research on gender biases in semantic space, we identify the gender bias
of our topics (e.g., a topic about pain medication is feminine). We then
compare the gender bias of topics to their prevalence in narratives of female
versus male victims. Results provide a detailed quantitative picture of
reporting about lethal violence and its gendered nature. Our method offers a
flexible and broadly applicable approach to model topics in text data.


Abstract:
  In graphene, where the electron-electron scattering is dominant, electrons
collectively act as a fluid. This hydrodynamic behaviour of charge carriers
leads to exciting nonlinear phenomena such as solitary waves and shocks, among
others. In the future, such waves might be exploited on plasmonic devices,
either for modulation or signal propagation along graphene waveguides. We study
the nature of nonlinear perturbations by performing the reductive perturbation
method on the hydrodynamic description of graphene electrons, taking into
consideration the effect of Bohm quantum potential and odd viscosity. Thus,
deriving a dissipative Kadomtsev-Petviashvili equation for the bidimensional
flow as well as its unidimensional limit in the form of Korteweg-de
Vries-Burgers. The stability analysis of these equations unveils the existence
of unstable modes that can be excited and launched through graphene plasmonic
devices.


Abstract:
  An infinitesimal deformation of $C^{\infty}(\mathbb R^d)$ has a vanishing
primary obstruction if and only if its skew form is Poisson, and therefore is
integrable to a full deformation of $C^{\infty}(\mathbb R^d)$ by the work of
Kontsevich. This is further equivalent to having its coefficients satisfy a set
of $\binom{d}{3}$ nonlinear partial differential equations. The single such
equation in the case $d=3$ suggests that a big bang might be localized to an
instant in time and point in space. This note also reexamines the Basic
Universal Deformation Formula (UDF) which asserts that if $D_1, D_2$ are
commuting derivations of an associative algebra $\mathcal{A}$ over the
rationals then exponentiation of $D_1\smile D_2$ provides a full deformation of
$\mathcal{A}$. It gives further applications to quantization and to UDFs with
non-commuting derivations.


Abstract:
  Global existence is established for classical solutions to a chemotaxis model
with signal-dependent motility for a general class of motility functions
$\gamma$ which may in particular decay in an arbitrary way at infinity.
Assuming further that $\gamma$ is non-increasing and decays sufficiently slowly
at infinity, in the sense that $\gamma(s)\sim s^{-k}$ as $s\to\infty$ for some
$k\in (0,N/(N-2)_+)$, it is also shown that global solutions are uniformly
bounded with respect to time. The admissible decay of $\gamma$ at infinity here
is higher than in previous works.


Abstract:
  As the proportion of variable inverter-based renewable energy generation in
electricity systems increases from a minority to the majority of total supply,
the complexity and cost of providing ancillary system services increases in
parallel. Australia is experiencing this shift now - from having the third most
carbon-intensive electricity sector in the world in 2010, to now having
penetrations of variable renewable energy (VRE) regularly reaching 100 percent
in some regions, with world-leading uptake of distributed energy resources
(DER). This paper presents pioneering work exploring new technical, economic
and regulatory frameworks for the provision of Essential System Services (ESS),
also known as ancillary services, in power systems dominated by variable
inverter-based renewable energy resources. We explore the recent application of
the concept of demand curves and nomograms to the procurement of ESS, and
emerging design principles for frameworks in facilitating the evolution from
default provision of system services by synchronous generation, to
co-optimisation of services through unit commitment, to independent provision
of services through inverter based resources. The paper also analyses the
recent technical and financial success of the world's largest battery, the
Hornsdale Power Reserve in South Australia, and its ability to inform how
future electricity market frameworks may incentivize and accommodate new
technological capability, both first and Nth of a kind systems. Finally, the
paper reviews emerging energy system technological capability, including the
provision of synthetic inertia and RoCoF control, grid-forming inverters, and
advanced DER aggregation in providing ESS and system restart capability for
secure, resilient and island-able grids.


Abstract:
  In this paper, we contribute a new million-scale face benchmark containing
noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M
faces (WebFace42M) training data, as well as an elaborately designed
time-constrained evaluation protocol. Firstly, we collect 4M name list and
download 260M faces from the Internet. Then, a Cleaning Automatically utilizing
Self-Training (CAST) pipeline is devised to purify the tremendous WebFace260M,
which is efficient and scalable. To the best of our knowledge, the cleaned
WebFace42M is the largest public face recognition training set and we expect to
close the data gap between academia and industry. Referring to practical
scenarios, Face Recognition Under Inference Time conStraint (FRUITS) protocol
and a test set are constructed to comprehensively evaluate face matchers.
  Equipped with this benchmark, we delve into million-scale face recognition
problems. A distributed framework is developed to train face recognition models
efficiently without tampering with the performance. Empowered by WebFace42M, we
reduce relative 40% failure rate on the challenging IJB-C set, and ranks the
3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior
performance compared with public training set. Furthermore, comprehensive
baselines are established on our rich-attribute test set under
FRUITS-100ms/500ms/1000ms protocol, including MobileNet, EfficientNet,
AttentionNet, ResNet, SENet, ResNeXt and RegNet families. Benchmark website is
https://www.face-benchmark.org.


Abstract:
  Pulsed nuclear magnetic resonance (NMR) is widely used in high-precision
magnetic field measurements. The absolute value of the magnetic field is
determined from the precession frequency of nuclear magnetic moments. The
Hilbert transform is widely used to extract the phase function from the
observed free induction decay (FID) signal and then its frequency. In this
paper, a detailed implementation of a Hilbert-transform based FID frequency
extraction method is described. How artifacts and noise level in the FID signal
affect the extracted phase function are derived analytically. A method of
mitigating the artifacts in the extracted phase function of an FID is
discussed. Correlations between noises of the phase function samples are
studied for different noise spectra. We discovered that the error covariance
matrix for the extracted phase function is nearly singular and improper for
constructing the $\chi^2$ used in the fitting routine. A down-sampling method
for fixing the singular covariance matrix has been developed, so that the
minimum $\chi^2$-fit yields properly the statistical uncertainty of the
extracted frequency. Other practical methods of obtaining the statistical
uncertainty are also discussed.


Abstract:
  In this paper, we investigate the problem of reasoning over natural language
statements. Prior neural based approaches do not explicitly consider the
inter-dependency among answers and their proofs. In this paper, we propose
PRobr, a novel approach for joint answer prediction and proof generation. PRobr
defines a joint probabilistic distribution over all possible proof graphs and
answers via an induced graphical model. We then optimize the model using
variational approximation on top of neural textual representation. Experiments
on multiple datasets under diverse settings (fully supervised, few-shot and
zero-shot evaluation) verify the effectiveness of PRobr, e.g., achieving
10%-30% improvement on QA accuracy in few/zero-shot evaluation. Our codes and
models can be found at https://github.com/changzhisun/PRobr/.


Abstract:
  Instance segmentation models today are very accurate when trained on large
annotated datasets, but collecting mask annotations at scale is prohibitively
expensive. We address the partially supervised instance segmentation problem in
which one can train on (significantly cheaper) bounding boxes for all
categories but use masks only for a subset of categories. In this work, we
focus on a popular family of models which apply differentiable cropping to a
feature map and predict a mask based on the resulting crop. Under this family,
we study Mask R-CNN and discover that instead of its default strategy of
training the mask-head with a combination of proposals and groundtruth boxes,
training the mask-head with only groundtruth boxes dramatically improves its
performance on novel classes. This training strategy also allows us to take
advantage of alternative mask-head architectures, which we exploit by replacing
the typical mask-head of 2-4 layers with significantly deeper off-the-shelf
architectures (e.g. ResNet, Hourglass models). While many of these
architectures perform similarly when trained in fully supervised mode, our main
finding is that they can generalize to novel classes in dramatically different
ways. We call this ability of mask-heads to generalize to unseen classes the
strong mask generalization effect and show that without any specialty modules
or losses, we can achieve state-of-the-art results in the partially supervised
COCO instance segmentation benchmark. Finally, we demonstrate that our effect
is general, holding across underlying detection methodologies (including
anchor-based, anchor-free or no detector at all) and across different backbone
networks. Code and pre-trained models are available at https://git.io/deepmac.


Abstract:
  We prove that the bi-invariant Einstein metric on $SU_{2n+1}$ is isolated in
the moduli space of Einstein metrics, even though it admits infinitesimal
deformations. This gives a non-K\"ahler, non-product example of this phenomenon
adding to the famous example of $\mathbb{CP}^{2n}\times\mathbb{CP}^{1}$ found
by Koiso. We apply our methods to derive similar solitonic rigidity results for
the K\"ahler--Einstein metrics on `odd' Grassmannians. We also make explicit a
connection between non-integrable deformations and the dynamical instability of
metrics under Ricci flow.


Abstract:
  We introduce an algorithm to solve linear inverse problems regularized with
the total (gradient) variation in a gridless manner. Contrary to most existing
methods, that produce an approximate solution which is piecewise constant on a
fixed mesh, our approach exploits the structure of the solutions and consists
in iteratively constructing a linear combination of indicator functions of
simple polygons.


Abstract:
  Since the introduction of electronic cigarettes to the United States market
in 2007, vaping prevalence has surged in both adult and adolescent populations.
E-cigarettes are advertised as a safer alternative to traditional cigarettes
and as a method of smoking cessation, but the U.S. government and health
professionals are concerned that e-cigarettes attract young non-smokers. Here,
we develop and analyze a dynamical systems model of competition between
traditional and electronic cigarettes for users. With this model, we predict
the change in smoking prevalence due to the introduction of vaping, and we
determine the conditions under which e-cigarettes present a net public health
benefit or harm to society.


Abstract:
  A "scheduled" arrival process is one in which the n th arrival is scheduled
for time n, but instead occurs at a different time. The difference between the
scheduled time and the arrival time is called the perturbation. The sequence of
perturbations is assumed to be iid. We describe here the behavior of a single
server queue fed by such traffic in which the processing times are
deterministic. A particular focus is on perturbation with Pareto-like tails but
with finite mean. We obtain tail approximations for the steady-state workload
in both cases where the queue is critically loaded and under a heavy-traffic
regime. A key to our approach is our analysis of the tail behavior of a sum of
independent Bernoulli random variables with success probability following a
power law with parameter strictly larger than 1.


Abstract:
  Outlier ensemble methods have shown outstanding performance on the discovery
of instances that are significantly different from the majority of the data.
However, without the awareness of fairness, their applicability in the ethical
scenarios, such as fraud detection and judiciary judgement system, could be
degraded. In this paper, we propose to reduce the bias of the outlier ensemble
results through a fairness-aware ensemble framework. Due to the lack of ground
truth in the outlier detection task, the key challenge is how to mitigate the
degradation in the detection performance with the improvement of fairness. To
address this challenge, we define a distance measure based on the output of
conventional outlier ensemble techniques to estimate the possible cost
associated with detection performance degradation. Meanwhile, we propose a
post-processing framework to tune the original ensemble results through a
stacking process so that we can achieve a trade off between fairness and
detection performance. Detection performance is measured by the area under ROC
curve (AUC) while fairness is measured at both group and individual level.
Experiments on eight public datasets are conducted. Results demonstrate the
effectiveness of the proposed framework in improving fairness of outlier
ensemble results. We also analyze the trade-off between AUC and fairness.


Abstract:
  Double holography plays a crucial role in recent studies of Hawking radiation
and information paradox by relating an intermediate picture, in which a
dynamical gravity living on an end-of-the-world brane is coupled to a
non-gravitational heat bath, to a much better-understood BCFT picture as well
as a bulk picture. In this paper, causal structures in generic double
holographic setups are studied. We find that the causal structure in the bulk
picture is compatible with causality in the BCFT picture, thanks to a
generalization of the Gao-Wald theorem. On the other hand, consistency with the
bulk causal structure requires the effective theory in the intermediate picture
to contain a special type of superluminal and nonlocal effect which is
significant at long range or IR. These are confirmed by both geometrical
analysis and commutators of microscopic fields. Subregion correspondences in
double holography are discussed with the knowledge of this nonlocality.
Possible fundamental origins of this nonlocality and its difference with other
types of nonlocality will also be discussed.


Abstract:
  This article reviews deep learning applications in biomedical optics with a
particular emphasis on image formation. The review is organized by imaging
domains within biomedical optics and includes microscopy, fluorescence lifetime
imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography,
photoacoustic imaging, diffuse tomography, and functional optical brain
imaging. For each of these domains, we summarize how deep learning has been
applied and highlight methods by which deep learning can enable new
capabilities for optics in medicine. Challenges and opportunities to improve
translation and adoption of deep learning in biomedical optics are also
summarized.


Abstract:
  The level of experimental precision that will be achieved with LHC Run-III
data and in the forthcoming High Luminosity stage calls for equally accurate
theoretical predictions to compare with. Here we present updated cross section
calculations for the electroweak production of SUSY particles at the LHC with
aNNLO+NNLL (approximate next-to-next-to-leading-order plus
next-to-next-to-leading-logarithmic) accuracy. Results are shown for slepton
pair production and for electroweakinos pair production in their mostly
higgsino or gaugino configuration, finding a further significant reduction of
the factorisation and renormalisation scale dependence that stabilises the
predictions to the permil level.


Abstract:
  The study of topology protected electronic properties is a fascinating topic
in present day condensed matter physics research. New topological materials are
frequently being proposed and explored through various experimental techniques.
Ta$_{3}$SiTe$_{6}$ is a newly predicted topological semimetal with fourfold
degenerate nodal-line crossing in absence of spin-orbit coupling (SOC) and an
hourglass Dirac loop, when SOC is included. Recent angle-resolved photoemission
spectroscopy study in this material, has also confirmed Dirac like dispersions
and two nodal-lines near the Fermi energy, protected by nonsymmorphic glide
mirror symmetry. In this work, we present the detailed magnetotransport
properties of single crystalline Ta$_{3}$SiTe$_{6}$. A nonsaturating
magnetoresistance has been observed. Hall measurements reveal hole type charge
carriers with high carrier density and a moderate value of carrier mobility.
Furthermore, we report a robust planar Hall effect, which persists up to high
temperatures. These results validate the nontrivial nature of the electronic
band structure.


Abstract:
  Quantification of uncertainty in point cloud matching is critical in many
tasks such as pose estimation, sensor fusion, and grasping. Iterative closest
point (ICP) is a commonly used pose estimation algorithm which provides a point
estimate of the transformation between two point clouds. There are many sources
of uncertainty in this process that may arise due to sensor noise, ambiguous
environment, and occlusion. However, for safety critical problems such as
autonomous driving, a point estimate of the pose transformation is not
sufficient as it does not provide information about the multiple solutions.
Current probabilistic ICP methods usually do not capture all sources of
uncertainty and may provide unreliable transformation estimates which can have
a detrimental effect in state estimation or decision making tasks that use this
information. In this work we propose a new algorithm to align two point clouds
that can precisely estimate the uncertainty of ICP's transformation parameters.
We develop a Stein variational inference framework with gradient based
optimization of ICP's cost function. The method provides a non-parametric
estimate of the transformation, can model complex multi-modal distributions,
and can be effectively parallelized on a GPU. Experiments using 3D kinect data
as well as sparse indoor/outdoor LiDAR data show that our method is capable of
efficiently producing accurate pose uncertainty estimates.


Abstract:
  Scalable quantum computing can become a reality with error correction,
provided coherent qubits can be constructed in large arrays. The key premise is
that physical errors can remain both small and sufficiently uncorrelated as
devices scale, so that logical error rates can be exponentially suppressed.
However, energetic impacts from cosmic rays and latent radioactivity violate
both of these assumptions. An impinging particle ionizes the substrate,
radiating high energy phonons that induce a burst of quasiparticles, destroying
qubit coherence throughout the device. High-energy radiation has been
identified as a source of error in pilot superconducting quantum devices, but
lacking a measurement technique able to resolve a single event in detail, the
effect on large scale algorithms and error correction in particular remains an
open question. Elucidating the physics involved requires operating large
numbers of qubits at the same rapid timescales as in error correction, exposing
the event's evolution in time and spread in space. Here, we directly observe
high-energy rays impacting a large-scale quantum processor. We introduce a
rapid space and time-multiplexed measurement method and identify large bursts
of quasiparticles that simultaneously and severely limit the energy coherence
of all qubits, causing chip-wide failure. We track the events from their
initial localised impact to high error rates across the chip. Our results
provide direct insights into the scale and dynamics of these damaging error
bursts in large-scale devices, and highlight the necessity of mitigation to
enable quantum computing to scale.


Abstract:
  Longitudinal magnetoresistance (LMR) refers to the change in resistance due
to a magnetic field when the current and the magnetic field are parallel to
each other. For this to be nonzero in the semiclassical regime, kinetic theory
stipulates that the electronic dispersion must satisfy certain conditions: it
should either be sufficiently anisotropic or have topological features. The
former results in a positive LMR while the latter results in a negative LMR.
Here, I propose a different mechanism that leads to LMR in any dispersion
without a need to satisfy the above requirements. The mechanism is quantum in
origin but is applicable in the semiclassical regime. It arises due to the
change in the density of states with the magnetic field and is not kinetic in
origin. Remarkably, LMR is found to be negative even if the dispersion is
nontopological, provided it is nonparabolic. An analytical expression is
derived for this novel contribution to LMR. It is found to depend on the
orbital magnetic susceptibility. The analytical findings are confirmed by
numerical calculations.


Abstract:
  Demand forecasting is extremely important in revenue management. After all,
it is one of the inputs to an optimisation method which aim is to maximize
revenue. Most, if not all, forecasting methods use historical data to forecast
the future, disregarding the "why". In this paper, we combine data from
multiple sources, including competitor data, pricing, social media, safety and
airline reviews. Next, we study five competitor pricing movements that, we
hypothesize, affect customer behavior when presented a set of itineraries.
Using real airline data for ten different OD-pairs and by means of Extreme
Gradient Boosting, we show that customer behavior can be categorized into
price-sensitive, schedule-sensitive and comfort ODs. Through a simulation
study, we show that this model produces forecasts that result in higher revenue
than traditional, time series forecasts.


Abstract:
  Nasal adhesions are a known postoperative complication following surgical
procedures for nasal airway obstruction (NAO); and are a common cause of
surgical failure, with patients often reporting significant NAO, despite
relatively minor adhesion size. Division of such nasal adhesions often provides
much greater relief than anticipated, based on the minimal reduction in
cross-sectional area associated with the adhesion. The available literature
regarding nasal adhesions provides little evidence examining their quantitative
and qualitative effects on nasal airflow using objective measures. This study
examined the impact of nasal adhesions at various anatomical sites on nasal
airflow and mucosal cooling using computational fluid dynamics (CFD). A
high-resolution CT scan of the paranasal sinuses of a 25-year-old, healthy
female patient was segmented to create a three-dimensional nasal airway model.
Virtual nasal adhesions of 2.5~mm diameter were added to various locations
within the nasal cavity, representing common sites seen following NAO surgery.
A series of models with single adhesions were created. CFD analysis was
performed on each model and compared with a baseline no-adhesion model,
comparing airflow and heat and mass transfer. The nasal adhesions resulted in
no significant change in bulk airflow patterns through the nasal cavity.
However, significant changes were observed in local airflow and mucosal cooling
around and immediately downstream to the nasal adhesions. These were most
evident with anterior nasal adhesions at the internal valve and anterior
inferior turbinate.


Abstract:
  The axion, a hypothetical elementary pseudoscalar, is expected to solve the
strong CP problem of QCD and is also a promising candidate for dark matter. The
most sensitive axion search experiments operate at millikelvin temperatures and
hence rely on instrumentation that carries signals from a system at cryogenic
temperatures to room temperature instrumentation. One of the biggest limiting
factors affecting the parameter scanning speed of these detectors is the noise
added by the components in the signal detection chain. Since the first
amplifier in the chain limits the minimum noise, low-noise amplification is of
paramount importance. This paper reports on the operation of a flux-driven
Josephson parametric amplifier (JPA) operating at around 2.3 GHz with added
noise approaching the quantum limit. The JPA was employed as a first stage
amplifier in an experimental setting similar to the ones used in haloscope
axion detectors. By operating the JPA at a gain of 19 dB and cascading it with
two cryogenic amplifiers operating at 4 K, noise temperatures as low as 120 mK
were achieved for the whole signal detection chain.


Abstract:
  Thanks to the great progress of machine learning in the last years, several
Artificial Intelligence (AI) techniques have been increasingly moving from the
controlled research laboratory settings to our everyday life. AI is clearly
supportive in many decision-making scenarios, but when it comes to sensitive
areas such as health care, hiring policies, education, banking or justice, with
major impact on individuals and society, it becomes crucial to establish
guidelines on how to design, develop, deploy and monitor this technology.
Indeed the decision rules elaborated by machine learning models are data-driven
and there are multiple ways in which discriminatory biases can seep into data.
Algorithms trained on those data incur the risk of amplifying prejudices and
societal stereotypes by over associating protected attributes such as gender,
ethnicity or disabilities with the prediction task. Starting from the extensive
experience of the National Metrology Institute on measurement standards and
certification roadmaps, and of Politecnico di Torino on machine learning as
well as methods for domain bias evaluation and mastering, we propose a first
joint effort to define the operational steps needed for AI fairness
certification. Specifically we will overview the criteria that should be met by
an AI system before coming into official service and the conformity assessment
procedures useful to monitor its functioning for fair decisions.


Abstract:
  This paper studies the canonical symmetric connection $\nabla$ associated to
any Lie group $G$. The salient properties of $\nabla$ are stated and proved.
The Lie symmetries of the geodesic system of a general linear connection are
formulated. The results are then applied to $\nabla$ in the special case where
the Lie algebra $\g$ of $G$, has a codimension one abelian nilradical. The
conditions that determine a Lie symmetry in such a case are completely
integrated. Finally the results obtained are compared with some
four-dimensional Lie groups whose Lie algebras have three-dimensional abelian
nilradicals, for which the calculations were performed by MAPLE.


Abstract:
  Presolar silicon carbide (SiC) grains in meteoritic samples can help
constrain circumstellar condensation processes and conditions in C-rich stars
and core-collapse supernovae. This study presents our findings on eight
presolar SiC grains from AGB stars (four mainstream and one Y grain) and
core-collapse supernovae (three X grains), chosen on the basis of {\mu}-Raman
spectral features that were indicative of their having unusual non-3C polytypes
and/or high degrees of crystal disorder. Analytical transmission electron
microscopy (TEM), which provides elemental compositional and structural
information, shows evidence for complex histories for the grains. Our TEM
results confirm the presence of non-3C,2H crystal domains. Minor element
heterogeneities and/or subgrains were observed in all grains analyzed for their
compositions. The C/O ratios inferred for the parent stars varied from 0.98 to
greater than or equal to 1.03. Our data show that SiC condensation can occur
under a wide range of conditions, in which environmental factors other than
temperature (e.g., pressure, gas composition, heterogeneous nucleation on
pre-condensed phases) play a significant role. Based on previous {\mu}-Raman
studies, about 10% of SiC grains may have infrared (IR) spectral features that
are influenced by crystal defects, porosity, and/or subgrains. Future
sub-diffraction limited IR measurements of complex SiC grains might shed
further light on the relative contributions of each of these features to the
shape and position of the characteristic IR 11-{\mu}m SiC feature and thus
improve the interpretation of IR spectra of AGB stars like those that produced
the presolar SiC grains.


Abstract:
  The search for an ideal single-photon source has generated significant
interest in discovering novel emitters in materials as well as developing new
manipulation techniques to gain better control over the emitters' properties.
Quantum emitters in atomically thin two-dimensional (2D) materials have proven
very attractive with high brightness, operation under ambient conditions, and
the ability to be integrated with a wide range of electronic and photonic
platforms. This perspective highlights some of the recent advances in quantum
light generation from 2D materials, focusing on hexagonal boron nitride and
transition metal dichalcogenides (TMDs). Efforts in engineering and
deterministically creating arrays of quantum emitters in 2D materials, their
electrical excitation, and their integration with photonic devices are
discussed. Lastly, we address some of the challenges the field is facing and
the near-term efforts to tackle them. We provide an outlook towards efficient
and scalable quantum light generation from 2D materials towards controllable
and addressable on-chip quantum sources.


Abstract:
  $J/\psi$ production in p-p ultra-peripheral collisions through the elastic
and inelastic photoproduction processes, where the virtual photons emitted from
the projectile interact with the target, are studied. The comparisions between
the exact treatment results and the ones of equivalent photon approximation are
expressed as $Q^{2}$ (virtuality of photon), $z$ and $p_{T}$ distributions, and
the total cross sections are also estimated. The method developed by Martin and
Ryskin is employed to avoid double counting when the different production
mechanism are considered simultaneously. The numerical results indicate that,
the equivalent photon approximation can be only applied to the coherent or
elastic electromagnetic process, the improper choice of $Q^{2}_{\mathrm{max}}$
and $y_{\mathrm{max}}$ will cause obvious errors. And the exact treatment is
needed to deal accurately with the $J/\psi$ photoproduction.


Abstract:
  When a finite order vector autoregressive model is fitted to VAR($\infty$)
data the asymptotic distribution of statistics obtained via smooth functions of
least-squares estimates requires care. L\"utkepohl and Poskitt (1991) provide a
closed-form expression for the limiting distribution of (structural) impulse
responses for sieve VAR models based on the Delta method. Yet, numerical
simulations have shown that confidence intervals built in such way appear
overly conservative. In this note I argue that these results stem naturally
from the limit arguments used in L\"utkepohl and Poskitt (1991), that they
manifest when sieve inference is improperly applied, and that they can be
"remedied" by either using bootstrap resampling or, simply, by using standard
(non-sieve) asymptotics.


Abstract:
  Prior research on self-supervised learning has led to considerable progress
on image classification, but often with degraded transfer performance on object
detection. The objective of this paper is to advance self-supervised pretrained
models specifically for object detection. Based on the inherent difference
between classification and detection, we propose a new self-supervised pretext
task, called instance localization. Image instances are pasted at various
locations and scales onto background images. The pretext task is to predict the
instance category given the composited images as well as the foreground
bounding boxes. We show that integration of bounding boxes into pretraining
promotes better task alignment and architecture alignment for transfer
learning. In addition, we propose an augmentation method on the bounding boxes
to further enhance the feature alignment. As a result, our model becomes weaker
at Imagenet semantic classification but stronger at image patch localization,
with an overall stronger pretrained model for object detection. Experimental
results demonstrate that our approach yields state-of-the-art transfer learning
results for object detection on PASCAL VOC and MSCOCO.


Abstract:
  Powered lower-limb exoskeletons provide assistive torques to coordinate limb
motion during walking in individuals with movement disorders. Advances in
sensing and actuation have improved the wearability and portability of
state-of-the-art exoskeletons for walking. Cable-driven exoskeletons offload
the actuators away from the user, thus rendering light-weight devices to
facilitate locomotion training. However, cable-driven mechanisms experience a
slacking behavior if tension is not accurately controlled. Moreover,
counteracting forces can arise between the agonist and antagonist motors
yielding undesired joint motion. In this paper, the strategy is to develop two
control layers to improve the performance of a cable-driven exoskeleton. First,
a joint tracking controller is designed using a high-gain robust approach to
track desired knee and hip trajectories. Second, a motor synchronization
objective is developed to mitigate the effects of cable slacking for a pair of
electric motors that actuate each joint. A sliding-mode robust controller is
designed for the motor synchronization objective. A Lyapunov-based stability
analysis is developed to guarantee a uniformly ultimately bounded result for
joint tracking and exponential tracking for the motor synchronization
objective. Moreover, an average dwell time analysis provides a bound on the
number of motor switches when allocating the control between motors that
actuate each joint. An experimental result with an able-bodied individual
illustrates the feasibility of the developed control methods.


Abstract:
  Hot Jupiters are predicted to have hot, clear daysides and cooler, cloudy
nightsides. Recently, an asymmetric signature of iron absorption has been
resolved in the transmission spectrum of WASP-76b using ESPRESSO on ESO's Very
large Telescope. This feature is interpreted as being due to condensation of
iron on the nightside, resulting in a different absorption signature from the
evening than from the morning limb of the planet. It represents the first time
that a chemical gradient has been observed across the surface of a single
exoplanet. In this work, we confirm the presence of the asymmetric iron feature
using archival HARPS data of four transits. The detection shows that such
features can also be resolved by observing multiple transits on smaller
telescopes. By increasing the number of planets where these condensation
features are detected, we can make chemical comparisons between exoplanets and
map condensation across a range of parameters for the first time.


Abstract:
  We propose a primal-dual interior-point method (IPM) with convergence to
second-order stationary points (SOSPs) of nonlinear semidefinite optimization
problems, abbreviated as NSDPs. As far as we know, the current algorithms for
NSDPs only ensure convergence to first-order stationary points such as
Karush-Kuhn-Tucker points. The proposed method generates a sequence
approximating SOSPs while minimizing a primal-dual merit function for NSDPs by
using scaled gradient directions and directions of negative curvature. Under
some assumptions, the generated sequence accumulates at an SOSP with a
worst-case iteration complexity. This result is also obtained for a primal IPM
with slight modification. Finally, our numerical experiments show the benefits
of using directions of negative curvature in the proposed method.


Abstract:
  We present a control design for semilinear and quasilinear 2x2 hyperbolic
partial differential equations with the control input at one boundary and a
nonlinear ordinary differential equation coupled to the other. The controller
can be designed to asymptotically stabilize the system at an equilibrium or
relative to a reference signal. Two related but different controllers for
semilinear and general quasilinear systems are presented and the additional
challenges in quasilinear systems are discussed. Moreover, we present an
observer that estimates the distributed PDE state and the unmeasured ODE state
from measurements at the actuated boundary only, which can be used to also
solve the output feedback control problem.


Abstract:
  Sentiment analysis is an important task in natural language processing (NLP).
Most of existing state-of-the-art methods are under the supervised learning
paradigm. However, human annotations can be scarce. Thus, we should leverage
more weak supervision for sentiment analysis. In this paper, we propose a
posterior regularization framework for the variational approach to the weakly
supervised sentiment analysis to better control the posterior distribution of
the label assignment. The intuition behind the posterior regularization is that
if extracted opinion words from two documents are semantically similar, the
posterior distributions of two documents should be similar. Our experimental
results show that the posterior regularization can improve the original
variational approach to the weakly supervised sentiment analysis and the
performance is more stable with smaller prediction variance.


Abstract:
  Locality Sensitive Hashing (LSH) is an effective method of indexing a set of
items to support efficient nearest neighbors queries in high-dimensional
spaces. The basic idea of LSH is that similar items should produce hash
collisions with higher probability than dissimilar items.
  We study LSH for (not necessarily convex) polygons, and use it to give
efficient data structures for similar shape retrieval. Arkin et al. represent
polygons by their "turning function" - a function which follows the angle
between the polygon's tangent and the $ x $-axis while traversing the perimeter
of the polygon. They define the distance between polygons to be variations of
the $ L_p $ (for $p=1,2$) distance between their turning functions. This metric
is invariant under translation, rotation and scaling (and the selection of the
initial point on the perimeter) and therefore models well the intuitive notion
of shape resemblance.
  We develop and analyze LSH near neighbor data structures for several
variations of the $ L_p $ distance for functions (for $p=1,2$). By applying our
schemes to the turning functions of a collection of polygons we obtain
efficient near neighbor LSH-based structures for polygons. To tune our
structures to turning functions of polygons, we prove some new properties of
these turning functions that may be of independent interest.
  As part of our analysis, we address the following problem which is of
independent interest. Find the vertical translation of a function $ f $ that is
closest in $ L_1 $ distance to a function $ g $. We prove tight bounds on the
approximation guarantee obtained by the translation which is equal to the
difference between the averages of $ g $ and $ f $.


Abstract:
  The weak-field Schwarzschild and NUT solutions of general relativity are
gravitoelectromagnetically dual to each other, except on the positive $z$-axis.
The presence of non-locality weakens this duality and violates it within a
smeared region around the positive $z$-axis, whose typical transverse size is
given by the scale of non-locality. We restore an exact non-local
gravitoelectromagnetic duality everywhere via a manifestly dual modification of
the linearized non-local field equations. In the limit of vanishing
non-locality we recover the well-known results from weak-field general
relativity.


Abstract:
  In this paper, we determine the 4-adic complexity of the balanced quaternary
sequences of period $2p$ and $2(2^n-1)$ with ideal autocorrelation defined by
Kim et al. (ISIT, pp. 282-285, 2009) and Jang et al. (ISIT, pp. 278-281, 2009),
respectively. Our results show that the 4-adic complexity of the quaternary
sequences defined in these two papers is large enough to resist the attack of
the rational approximation algorithm.


Abstract:
  GPUs are now used for a wide range of problems within HPC. However, making
efficient use of the computational power available with multiple GPUs is
challenging. The main challenges in achieving good performance are memory
layout, affecting memory bandwidth, effective use of the memory spaces with a
GPU, inter-GPU communication, and synchronization. We address these problems
with the Ripple library, which provides a unified view of the computational
space across multiple dimensions and multiple GPUs, allows polymorphic data
layout, and provides a simple graph interface to describe an algorithm from
which inter-GPU data transfers can be optimally scheduled. We describe the
abstractions provided by Ripple to allow complex computations to be described
simply, and to execute efficiently across many GPUs with minimal overhead. We
show performance results for a number of examples, from particle motion to
finite-volume methods and the eikonal equation, as well as showing good strong
and weak scaling results across multiple GPUs.


Abstract:
  We construct a structure-preserving finite element method and time-stepping
scheme for compressible barotropic magnetohydrodynamics (MHD) both in the ideal
and resistive cases, and in the presence of viscosity. The method is deduced
from the geometric variational formulation of the equations. It preserves the
balance laws governing the evolution of total energy and magnetic helicity, and
preserves mass and the constraint $ \operatorname{div}B = 0$ to machine
precision, both at the spatially and temporally discrete levels. In particular,
conservation of energy and magnetic helicity hold at the discrete levels in the
ideal case. It is observed that cross helicity is well conserved in our
simulation in the ideal case.


Abstract:
  The main result of this paper is to study the local deformations of
Calabi-Yau $\partial\bar{\partial}$-manifold that are co-polarised by the
Gauduchon metric by considering the subfamily of co-polarised fibres by the
class of Aeppli/De Rham-Gauduchon cohomology of Gauduchon metric given at the
beginning on the central fibre. In the latter part, we prove that the $p$-SKT
$h$-$\partial\bar{\partial}$-property is deformation open by constructing and
studying a new notion called $hp$-Hermitian symplectic ($hp$-HS) form.