Abstract:
  Resting state functional magnetic resonance images (fMRI) are commonly used
for classification of patients as having Alzheimer's disease (AD), mild
cognitive impairment (MCI), or being cognitive normal (CN). Most methods use
time-series correlation of voxels signals during the observation period as a
basis for the classification. In this paper we show that Convolutional Neural
Network (CNN) classification based on spatial correlation of time-averaged
signals yield a classification accuracy of up to 82% (sensitivity 86%,
specificity 80%)for a data set with 429 subjects (246 cognitive normal and 183
Alzheimer patients). For the spatial correlation of time-averaged signal values
we use voxel subdomains around center points of the 90 regions AAL atlas. We
form the subdomains as sets of voxels along a Hilbert curve of a bounding box
in which the brain is embedded with the AAL regions center points serving as
subdomain seeds. The matrix resulting from the spatial correlation of the 90
arrays formed by the subdomain segments of the Hilbert curve yields a symmetric
90x90 matrix that is used for the classification based on two different CNN
networks, a 4-layer CNN network with 3x3 filters and with 4, 8, 16, and 32
output channels respectively, and a 2-layer CNN network with 3x3 filters and
with 4 and 8 output channels respectively. The results of the two networks are
reported and compared.


Abstract:
  We consider the calculation of the thermal self-energy of a neutrino that
propagates in a medium composed of fermions and scalars interacting via a
Yukawa-type coupling, in the case that the neutri no energy is much larger than
the fermion and scalar masses, as well as the temperature and chemical
potentials of the background. In this kinematic regime the one-loop
contribution to the imaginary part of the self-energy is negligible. We
consider the two-loop contribution and we encounter the so-called pinch
singularities which are known to arise in higher loop self-energy calculations
in Thermal Field Theory. With a judicious use of the properties and
parametrizations of the thermal propagators the singularities are treated
effectively and actually disappear. From the imaginary part of the self-energy,
we obtain a precise formula for the damping matrix expressed in terms of
integrals over the background particle distributions. The formulas predict a
specific dependence of the damping terms on the neutrino energy, depending on
the background conditions. For guidance to estimating the effects in specific
contexts, we compute the damping terms for several limiting cases of the
momentum distribution functions of the background particles. We discuss briefly
the connection between the results of our calculations for the damping matrix
and the decoherence effects described in terms of the Lindblad equation.


Abstract:
  Name disambiguation is a complex but highly relevant challenge whenever
analysing real-world user data, such as data from version control systems. We
propose gambit, a rule-based disambiguation tool that only relies on name and
email information. We evaluate its performance against two commonly used
algorithms with similar characteristics on manually disambiguated ground-truth
data from the Gnome GTK project. Our results show that gambit significantly
outperforms both algorithms, achieving an F1 score of 0.985.


Abstract:
  Complicated assembly processes can be described as a sequence of two main
activities: grasping and insertion. While general grasping solutions are common
in industry, insertion is still only applicable to small subsets of problems,
mainly ones involving simple shapes in fixed locations and in which the
variations are not taken into consideration. Recently, RL approaches with prior
knowledge (e.g., LfD or residual policy) have been adopted. However, these
approaches might be problematic in contact-rich tasks since interaction might
endanger the robot and its equipment. In this paper, we tackled this challenge
by formulating the problem as a regression problem. By combining visual and
force inputs, we demonstrate that our method can scale to 16 different
insertion tasks in less than 10 minutes. The resulting policies are robust to
changes in the socket position, orientation or peg color, as well as to small
differences in peg shape. Finally, we demonstrate an end-to-end solution for 2
complex assembly tasks with multi-insertion objectives when the assembly board
is randomly placed on a table.


Abstract:
  On the basis of density functional theory and semi-classical Boltzmann
theory, we have investigated the structural, elastic, electronic, optical and
thermoelectric properties of 18--valence electron count rhodium based
half-Heusler alloys focusing on RhTiP, RhTiAs, RhTiSb, and RhTiBi. The absence
of imaginary frequencies in the phonon dispersion curve for these system
verifies that they are structurally stable. RhTiP is ductile in nature, while
others are brittle. The alloys are found to be semiconducting with indirect
band gaps ranging from 0.94 to 1.01 eV. Our calculations suggest these
materials to have high absorption coefficient and optical conductivity in the
ultraviolet as well as visible region. While considering thermoelectricity, we
found that $p$--type doping is more favorable in improving the thermoelectric
properties. The calculated values of power factor with $p$-type doping are
comparable to some of the reported half-Heusler materials. The optimum figure
of merit \zt\ is $\sim1$ for RhTiBi suggesting it as a promising candidate for
thermoelectric applications while RhTiP, RhTiAs, and RhTiSb with optimum \zt \
values between 0.38 to 0.67 are possible candidates for use in thermoelectric
devices.


Abstract:
  Modeling fuzziness and imprecision in human rating data is a crucial problem
in many research areas, including applied statistics, behavioral, social, and
health sciences. Because of the interplay between cognitive, affective, and
contextual factors, the process of answering survey questions is a complex
task, which can barely be captured by standard (crisp) rating responses. Fuzzy
rating scales have progressively been adopted to overcome some of the
limitations of standard rating scales, including their inability to disentangle
decision uncertainty from individual responses. The aim of this article is to
provide a novel fuzzy scaling procedure which uses Item Response Theory trees
(IRTrees) as a psychometric model for the stage-wise latent response process.
In so doing, fuzziness of rating data is modeled using the overall rater's
pattern of responses instead of being computed using a single-item based
approach. This offers a consistent system for interpreting fuzziness in terms
of individual-based decision uncertainty. A simulation study and two empirical
applications are adopted to assess the characteristics of the proposed model
and provide converging results about its effectiveness in modeling fuzziness
and imprecision in rating data.


Abstract:
  We consider the singularly perturbed problem $F_\varepsilon
(u,\Omega):=\int_\Omega \varepsilon |\nabla^2u| + \varepsilon^{-1}|1-|\nabla
u|^2|^2$ on bounded domains $\Omega \subset\mathbb{R}^2$. Under appropriate
boundary conditions, we prove that if $\Omega$ is an ellipse then the
minimizers of $F_\varepsilon(\cdot,\Omega)$ converge to the viscosity solution
of the eikonal equation $|\nabla u|=1$ as $\varepsilon \to 0$.


Abstract:
  Assume that each species $l$ has its own jump rate $b_l$ in the multi-species
totally asymmetric simple exclusion process. We show that this model is
integrable in the sense that the Bethe Ansatz method is applicable to obtain
the transition probabilities for all possible $N$-particle systems with up to
$N$ different species.


Abstract:
  In this work, we propose an approach for extrinsic sensor calibration from
per-sensor ego-motion estimates. Our problem formulation is based on dual
quaternions, enabling two different online capable solving approaches. We
provide a certifiable globally optimal and a fast local approach along with a
method to verify the globality of the local approach. Additionally, means for
integrating previous knowledge, for example, a common ground plane for planar
sensor motion, are described. Our algorithms are evaluated on simulated data
and on a publicly available dataset containing RGB-D camera images. Further,
our online calibration approach is tested on the KITTI odometry dataset, which
provides data of a lidar and two stereo camera systems mounted on a vehicle.
Our evaluation confirms the short run time, state-of-the-art accuracy, as well
as online capability of our approach while retaining the global optimality of
the solution at any time.


Abstract:
  We show that for every finite set of prime numbers $S$, there are at most
finitely many singular moduli that are $S$-units. The key new ingredient is
that for every prime number $p$, singular moduli are $p$-adically disperse. We
prove analogous results for the Weber modular functions, the
$\lambda$-invariants and the McKay-Thompson series associated to the elements
of the monster group. Finally, we also obtain that a modular function that
specializes to infinitely many algebraic units at quadratic imaginary numbers
must be a modular unit.


Abstract:
  Circuit quantum electrodynamics is one of the most promising platforms for
efficient quantum simulation and computation. In recent groundbreaking
experiments, the immense flexibility of superconducting microwave resonators
was utilized to realize hyperbolic lattices that emulate quantum physics in
negatively curved space. Here we investigate experimentally feasible settings
in which a few superconducting qubits are coupled to a bath of photons evolving
on the hyperbolic lattice. We compare our numerical results for finite lattices
with analytical results for continuous hyperbolic space on the Poincar\'{e}
disk. We find good agreement between the two descriptions in the
long-wavelength regime. We show that photon-qubit bound states have a
curvature-limited size. We propose to use a qubit as a local probe of the
hyperbolic bath, for example by measuring the relaxation dynamics of the qubit.
We find that, although the boundary effects strongly impact the photonic
density of states, the spectral density is well described by the continuum
theory. We show that interactions between qubits are mediated by photons
propagating along geodesics. We demonstrate that the photonic bath can give
rise to geometrically-frustrated hyperbolic quantum spin models with
finite-range or exponentially-decaying interaction.


Abstract:
  A high-tech manufacturer often produces products that consist of many
modules. These modules are either sourced from one of its suppliers or produced
in-house. In this paper we study an assembly system in which one module is
sourced from a supplier with a fixed lead-time, while the other module is
produced by the manufacturer itself in a make-to-order production system. Since
unavailability of one of the modules has costly consequences for the production
of the end-product, it is important to coordinate between the ordering policy
for one module and the production of the other. We propose an order policy for
the lead-time module with base-stock levels depending on the number of
outstanding orders in the production system of the in-house produced module. We
prove monotonicity properties of this policy and show optimality. Furthermore,
we conduct a computational experiment to evaluate how the costs of this policy
compare to those of a policy with fixed base-stock levels and show that average
savings of 17% are attained.


Abstract:
  Given a sequence of Marcinkiewicz-Zygmund inequalities in $L_2$ on a usual
compact space $\mathcal M$, Gr\"ochenig introduced the weighted least squares
polynomials and the least squares quadrature from pointwise samples of a
function, and obtained approximation theorems and quadrature errors. In this
paper we obtain approximation theorems and quadrature errors on the sphere
which are optimal. We also give upper bounds of the operator norms of the
weighted least squares operators.


Abstract:
  We study finite-time horizon continuous-time linear-convex reinforcement
learning problems in an episodic setting. In this problem, the unknown linear
jump-diffusion process is controlled subject to nonsmooth convex costs. We show
that the associated linear-convex control problems admit Lipchitz continuous
optimal feedback controls and further prove the Lipschitz stability of the
feedback controls, i.e., the performance gap between applying feedback controls
for an incorrect model and for the true model depends Lipschitz-continuously on
the magnitude of perturbations in the model coefficients; the proof relies on a
stability analysis of the associated forward-backward stochastic differential
equation. We then propose a novel least-squares algorithm which achieves a
regret of the order $O(\sqrt{N\ln N})$ on linear-convex learning problems with
jumps, where $N$ is the number of learning episodes; the analysis leverages the
Lipschitz stability of feedback controls and concentration properties of
sub-Weibull random variables.


Abstract:
  In the compact variable-gap undulator with Hydraulic-Assist Driver, miniature
hydraulic cylinders compensate for ~90% of magnetic forces and mechanical
actuators are used to accurately control the undulator gap. This method of
hydraulic assist enables design simplicity and compactness of the undulator.
Proof-of-concept experiments and test results of the undulator prototype in
step-by-step motion mode indicated excellent accuracy and stability properties
of the driver. In this note we present test results of hydraulic-assist driver
in continuous motion mode. Results indicate that hydraulic-assist driver can
provide smooth continuous undulator gap change, keeping the undulator harmonic
energy variation within the required 1eV/sec, and speed and RMS normalized
energy errors at 1.8e-4 level (or less). That satisfies any application planned
at CHESS.


Abstract:
  The rate and location of Binary Neutron Star (BNS) mergers are determined by
a combination of the star formation history and the Delay Time Distribution
(DTD) function. In this paper, we couple the star formation rate histories
(SFRHs) from the IllustrisTNG model to a series of varied assumptions for the
BNS DTD to make predictions for the BNS merger host galaxy mass function. These
predictions offer two outcomes: (i) in the near term: influence BNS merger
event follow-up strategy by scrutinizing where most BNS merger events are
expected to occur and (ii) in the long term: constrain the DTD for BNS merger
events once the host galaxy mass function is observationally well determined.
From our fiducial model analysis, we predict that 50% of BNS mergers will occur
in host galaxies with stellar mass between $10^{10} - 10^{11}$ $M_\odot$, 68%
between $4 \times 10^{9} - 3\times 10^{11}$ $M_\odot$, and 95% between $4
\times 10^8 - 2 \times 10^{12}$ $M_\odot$. We find that the details of the DTD
employed does not have a strong effect on the peak of the host mass function.
However, varying the DTD provides enough spread that the true DTD can be
determined from enough electromagnetic observations of BNS mergers. Knowing the
true DTD can help us determine the prevalence of BNS systems formed through
highly eccentric and short separation fast-merging channels and can constrain
the dominant source of r-process material.


Abstract:
  The HYDRO mini-application has been successfully used as a research vehicle
in previous PRACE projects [6]. In this paper, we evaluate the benefits of the
tasking model introduced in recent OpenMP standards [9]. We have developed a
new version of HYDRO using the concept of OpenMP tasks and this implementation
is compared to already existing and optimized OpenMP versions of HYDRO.


Abstract:
  The non-collinear antiferromagnetic (AFM) structure makes Mn3Sn exhibit
exotic properties. At present, it has been found that both the hydrostatic
pressure and the strain introduced by interstitial N atoms have a great
influence on this magnetic structure. Here, the effect of the residual strain
(RS) on it is investigated. AC and DC magnetic measurement results suggest that
Mn3Sn without RS has the non-collinear AFM structure only in the temperature
range of 285 K to 400 K; while Mn3Sn with RS has a non-coplanar AFM structure
in the entire temperature range from 5 K to 400 K. Both anomalous Hall effect
and topological Hall effect appears in Mn3Sn with RS, supporting the
anticipated non-coplanar AFM structure. Our findings point out a method to
realize the chiral non-coplanar AFM structure through the engineering, thereby
providing a path for the construction of topological antiferromagnets.


Abstract:
  A flat solvmanifold is a compact quotient $\Gamma\backslash G$ where $G$ is a
simply-connected solvable Lie group endowed with a flat left invariant metric
and $\Gamma$ is a lattice of $G$. Any such Lie group can be written as
$G=\mathbb{R}^k\ltimes_{\phi} \mathbb{R}^m$ with $\mathbb{R}^m$ the nilradical.
In this article we focus on 6-dimensional splittable flat solvmanifolds, which
are obtained quotienting $G$ by a lattice $\Gamma$ that can be decomposed as
$\Gamma=\Gamma_1\ltimes_{\phi}\Gamma_2$, where $\Gamma_1$ and $\Gamma_2$ are
lattices of $\mathbb{R}^k$ and $\mathbb{R}^m$, respectively. We obtain their
classification by analyzing the conjugacy classes of integer matrices of finite
order in dimensions 4 and 5.


Abstract:
  Recently, language-guided global image editing draws increasing attention
with growing application potentials. However, previous GAN-based methods are
not only confined to domain-specific, low-resolution data but also lacking in
interpretability. To overcome the collective difficulties, we develop a
text-to-operation model to map the vague editing language request into a series
of editing operations, e.g., change contrast, brightness, and saturation. Each
operation is interpretable and differentiable. Furthermore, the only
supervision in the task is the target image, which is insufficient for a stable
training of sequential decisions. Hence, we propose a novel operation planning
algorithm to generate possible editing sequences from the target image as
pseudo ground truth. Comparison experiments on the newly collected MA5k-Req
dataset and GIER dataset show the advantages of our methods. Code is available
at https://jshi31.github.io/T2ONet.


Abstract:
  The central (`bulge') region of the Milky Way is teeming with a significant
fraction of mildly metal-deficient stars with atmospheres that are strongly
enriched in cyanogen ($^{12}$C$^{14}$N). Some of these objects, which are also
known as nitrogen-enhanced stars, are hypothesised to be relics of the ancient
assembly history of the Milky Way. Although the chemical similarity of
nitrogen-enhanced stars to the unique chemical patterns observed in globular
clusters has been observed, a direct connection between field stars and
globular clusters has not yet been proven. In this work, we report on
high-resolution, near-infrared spectroscopic observations of the bulge globular
cluster NGC 6723, and the serendipitous discovery of a star,
2M18594405$-$3651518, located outside the cluster (near the tidal radius) but
moving on a similar orbit, providing the first clear piece of evidence of a
star that was very likely once a cluster member and has recently been ejected.
Its nitrogen abundance ratio ([N/Fe]$\gtrsim + 0.94$) is well above the typical
Galactic field-star levels, and it exhibits noticeable enrichment in the heavy
$s$-process elements (Ce, Nd, and Yb), along with moderate carbon enrichment;
all characteristics are known examples in globular clusters. This result
suggests that some of the nitrogen-enhanced stars in the bulge likely
originated from the tidal disruption of globular clusters.


Abstract:
  Various methods for the recursive evaluation of scattering amplitudes in
quantum field theory and string theory have been put forward during the last
couple of years. In these proceedings we describe a geometrical framework,
which is believed to be capable of treating many of these recursions in a
unified way. Our recursive framework is based on manipulating iterated
integrals on Riemann surfaces with boundaries. A geometric parameter appears as
variable of a differential equation of KZ or KZB type. The parameter
interpolates between two associated regularized boundary values, which contain
iterated integrals closely related to scattering amplitudes defined on two
different geometries.


Abstract:
  In Geometric function theory, occasionally attempts have been made to solve a
particular problem for the Ma-Minda classes, $\mathcal{S}^*(\psi)$ and
$\mathcal{C}(\psi)$ of univalent starlike and convex functions, respectively.
Recently, a popular radius problem generally known as Bohr's phenomenon has
been studied in various settings, however little is known about Rogosinski
radius. In this article, for a fixed $f\in \mathcal{S}^*(\psi)$ or
$\mathcal{C}(\psi),$ the class of analytic subordinants $S_{f}(\psi):= \{g :
g\prec f \} $ is studied for the Bohr-Rogosinski phenomenon in a general
setting. It's applications to the classes $\mathcal{S}^*(\psi)$ and
$\mathcal{C}(\psi)$ are also shown.


Abstract:
  A 2-uniform tiling is an edge-to-edge tiling by regular polygons having $2$
distinct transitivity classes of vertices. There are 20 distinct 2-uniform
tilings (these are of $14$ different types) on the plane, and since the plane
is the universal cover of the torus, it is natural to explore maps on the torus
that correspond to the 2-uniform tilings. In this article, we discuss that if a
map is the quotient of a plane's $2$-uniform lattice then what would be the
bounds of the number of vertex orbits.
  A 3-uniform tiling is an edge-to-edge tiling by regular polygons having $3$
distinct transitivity classes of vertices. There are $61$ distinct $3$-uniform
tilings on the plane. In this article, we discuss that if a map is the quotient
of a plane's $3$-uniform lattice then what would be the bounds of the number of
vertex orbits.


Abstract:
  Turbulent plane Poiseuille and Couette flows share the same geometry, but
produce their flow rate owing to different external drivers, pressure gradient
and shear respectively. By looking at integral energy fluxes, we pose and
answer the question of which flow performs better at creating flow rate. We
define a flow {\em efficiency}, that quantifies the fraction of power used to
produce flow rate instead of being wasted as a turbulent overhead; {\em
effectiveness}, instead, describes the amount of flow rate produced by a given
power. The work by Gatti \emph{et al.} (\emph{J. Fluid Mech.} vol.857, 2018,
pp. 345--373), where the constant power input (CPI) concept was developed to
compare turbulent Poiseuille flows with drag reduction, is here extended to
compare different flows. By decomposing the mean velocity field into a laminar
contribution and a deviation, analytical expressions are derived which are the
energy-flux equivalents of the FIK identity. These concepts are applied to
literature data supplemented by a new set of direct numerical simulations, to
find that Couette flows are less efficient but more effective than Poiseuille
ones. The reason is traced to the more effective laminar component of Couette
flows, which compensates for their higher turbulent activity. It is also
observed that, when the fluctuating fields of the two flows are fed with the
same total power fraction, Couette flows dissipate a smaller percentage of it
via turbulent dissipation. A decomposition of the fluctuating field into large
and small scales explains this feature: Couette flows develop stronger
large-scale structures, which alter the mean flow while contributing less
significantly to dissipation.


Abstract:
  Propagation of ultrashort pulses at least a few tens of optical cycles in
duration through a negative index material is investigated theoretically based
on the generalized nonlinear Schr\"{o}dinger equation with pseudo-quintic
nonlinearity and self-steepening effect. Novel periodic waves of different
forms are shown to exist in the system in the presence of higher-order effects.
It is found that such periodic structures exhibit an interesting chirping
property which depends on the light field intensity. The nonlinearity in pulse
chirp is found to be caused by the presence of self-steepening effect in the
negative index medium. The solutions also comprise dark, bright, and kink
solitary waves. Stability of the solitary wave solutions are proved
analytically using the theory of nonlinear dispersive waves. The stability of
the solutions is numerically studied under finite initial perturbations.


Abstract:
  Imagine, you enter a grocery store to buy food. How many peopledo you overlap
with in this store? How much time do you overlap witheach person in the store?
In this paper, we answer these questions bystudying the overlap times between
customers in the infinite serverqueue. We compute in closed form the steady
state distribution ofthe overlap time between a pair of customers and the
distribution ofthe number of customers that an arriving customer will overlap
with.Finally, we define a residual process that counts the number of
over-lapping customers that overlap in the queue for at least{\delta}time
unitsand compute its mean, variance, and distribution in the exponentialservice
setting


Abstract:
  In arXiv:1201.4067 and arXiv:1611.08030, Eyink and Shi and Chibbaro et al.,
respectively, formally derived an infinite, coupled hierarchy of equations for
the spectral correlation functions of a system of weakly interacting nonlinear
dispersive waves with random phases in the standard kinetic limit. Analogously
to the relationship between the Boltzmann hierarchy and Boltzmann equation,
this spectral hierarchy admits a special class of factorized solutions, where
each factor is a solution to the wave kinetic equation (WKE). A question left
open by these works and highly relevant for the mathematical derivation of the
WKE is whether solutions of the spectral hierarchy are unique, in particular
whether factorized initial data necessarily lead to factorized solutions. In
this article, we affirmatively answer this question in the case of 4-wave
interactions by showing, for the first time, that this spectral hierarchy is
well-posed in an appropriate function space. Our proof draws on work of Chen
and Pavlovi\'{c} for the Gross-Pitaevskii hierarchy in quantum many-body theory
and of Germain et al. for the well-posedness of the WKE.


Abstract:
  Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of
the leading causes of death among men, with almost 1.41 million new cases and
around 375,000 deaths in 2020. Artificial Intelligence algorithms have had a
huge impact in medical image analysis, including digital histopathology, where
Convolutional Neural Networks (CNNs) are used to provide a fast and accurate
diagnosis, supporting experts in this task. To perform an automatic diagnosis,
prostate tissue samples are first digitized into gigapixel-resolution
whole-slide images. Due to the size of these images, neural networks cannot use
them as input and, therefore, small subimages called patches are extracted and
predicted, obtaining a patch-level classification. In this work, a novel patch
aggregation method based on a custom Wide & Deep neural network model is
presented, which performs a slide-level classification using the patch-level
classes obtained from a CNN. The malignant tissue ratio, a 10-bin malignant
probability histogram, the least squares regression line of the histogram, and
the number of malignant connected components are used by the proposed model to
perform the classification. An accuracy of 94.24% and a sensitivity of 98.87%
were achieved, proving that the proposed system could aid pathologists by
speeding up the screening process and, thus, contribute to the fight against
PCa.


Abstract:
  In this paper, we explore and evaluate the use of ranking-based objective
functions for learning simultaneously a word string and a word image encoder.
We consider retrieval frameworks in which the user expects a retrieval list
ranked according to a defined relevance score. In the context of a word
spotting problem, the relevance score has been set according to the string edit
distance from the query string. We experimentally demonstrate the competitive
performance of the proposed model on query-by-string word spotting for both,
handwritten and real scene word images. We also provide the results for
query-by-example word spotting, although it is not the main focus of this work.


Abstract:
  We obtain an improvement of the John-Nirenberg inequality for the series of
the form $\sum_{n=1}^{\infty}n^{-1}e^{2\pi i n^k x},\;k>2,$ on intervals
consisting of points of a same convergent of their continued fractions. We also
establish a convergence criterion for these series.


Abstract:
  The amount of data collected from patients involved in clinical trials is
continuously growing. All patient characteristics are potential covariates that
could be used to improve clinical trial analysis and power. However, the
restricted number of patients in phases I and II studies limits the possible
number of covariates included in the analyses. In this paper, we investigate
the cost/benefit ratio of including covariates in the analysis of clinical
trials. Within this context, we address the long-running question "What is the
optimum number of covariates to include in a clinical trial?" To further
improve the cost/benefit ratio of covariates, historical data can be leveraged
to pre-specify the covariate weights, which can be viewed as the definition of
a new composite covariate. We analyze the use of a composite covariate while
estimating the treatment effect in small clinical trials. A composite covariate
limits the loss of degrees of freedom and the risk of overfitting.


Abstract:
  The coupling between the angular momentum of a compact object and an external
tidal field gives rise to the "rotational" tidal Love numbers, which affect the
tidal deformability of a spinning self-gravitating body and enter the
gravitational waveform of a binary inspiral at high post-Newtonian order. We
provide numerical evidence for a surprising "hidden" symmetry among the
rotational tidal Love numbers with opposite parities, which are associated to
perturbations belonging to separate sectors. This symmetry, whose existence had
been suggested on the basis of a Lagrangian description of the tidal
interaction in a binary system, holds independently of the equation of state of
the star.


Abstract:
  Recent studies have highlighted the potential significance of intracluster
medium (ICM) clumping and its important implications for cluster cosmology and
baryon physics. Many of the ICM clumps can originate from infalling galaxies,
as stripped interstellar medium (ISM) mixing into the hot ICM. However, a
direct connection between ICM clumping and stripped ISM has not been
unambiguously established before. Here we present the discovery of the first
and still the only known isolated cloud (or orphan cloud, OC) detected in both
X-rays and H$\alpha$ in the nearby cluster Abell 1367. With an effective radius
of 30 kpc, this cloud has an average X-ray temperature of 1.6 keV, a bolometric
X-ray luminosity of $\sim 3.1\times 10^{41}$ erg s$^{-1}$ and a hot gas mass of
$\sim 10^{10}\ {\rm M}_\odot$. From the MUSE data, the OC shows an interesting
velocity gradient nearly along the east-west direction with a low level of
velocity dispersion of $\sim 80$ km/s, which may suggest a low level of the ICM
turbulence. The emission line diagnostics suggest little star formation in the
main H$\alpha$ cloud and a LI(N)ER-like spectrum, but the excitation mechanism
remain unclear. This example shows that the stripped ISM, even long time after
the initial removal from the galaxy, can still induce the ICM inhomogeneities.
We suggest that magnetic field can stabilize the OC by suppressing hydrodynamic
instabilities and thermal conduction. This example also suggests that at least
some ICM clumps are multi-phase in nature and implies that the ICM clumps can
also be traced in H$\alpha$. Thus, future deep and wide-field H$\alpha$ survey
can be used to probe the ICM clumping and turbulence.


Abstract:
  Following N.Kozyrev's idea about the influence of the gravitational fields of
the Sun and the Moon on the Earth's crust, we consider a low-frequency
resonance of the Earth's crust blocks is happening before the occurrence of the
earthquake. The resonance affects several geophysical parameters and is
associated with many short-term precursory phenomena; the most notable are the
release of underground gases (radon), the air temperature rises, change of
thermal radiation in the troposphere, and an increase in the electron density
in the ionosphere. During the final stage of the earthquake genesis,
long-period gravity-seismic waves called the Kozyrev-Yagodin ( KaY-) wave are
formed and moves from the periphery to the epicenter of the future earthquake.
We discuss that by integrating information from different stages of earthquake
genesis: the network observations of KaY waves and satellite thermal radiation,
we significantly could advance the accuracy and reliability of short-term
earthquake forecasting, previously unachievable.


Abstract:
  Node clustering is a powerful tool in the analysis of networks. We introduce
a graph neural network framework to obtain node embeddings for directed
networks in a self-supervised manner, including a novel probabilistic imbalance
loss, which can be used for network clustering. Here, we propose directed flow
imbalance measures, which are tightly related to directionality, to reveal
clusters in the network even when there is no density difference between
clusters. In contrast to standard approaches in the literature, in this paper,
directionality is not treated as a nuisance, but rather contains the main
signal. DIGRAC optimizes directed flow imbalance for clustering without
requiring label supervision, unlike existing GNN methods, and can naturally
incorporate node features, unlike existing spectral methods. Experimental
results on synthetic data, in the form of directed stochastic block models, and
real-world data at different scales, demonstrate that our method, based on flow
imbalance, attains state-of-the-art results on directed graph clustering, for a
wide range of noise and sparsity levels and graph structures and topologies.


Abstract:
  We consider the fractional mean curvature flow of entire Lipschitz graphs. We
provide regularity results, and we study the long time asymptotics of the flow.
In particular we show that in a suitable rescaled framework, if the initial
graph is a sublinear perturbation of a cone, the evolution asymptotically
approaches an expanding self-similar solution. We also prove stability of
hyperplanes and of convex cones in the unrescaled setting.


Abstract:
  In this paper, we quantitatively consider the enhance-dissipation effect of
the advection term to the $p$-Laplacian equations. More precisely, we show the
mixing property of flow for the passive scalar enhances the dissipation process
of the $p$-Laplacian in the sense of $L^2$ decay, that is, the $L^2$ decay can
be arbitrarily fast. The main ingredient of our argument is to understand the
underlying iteration structure inherited from the evolution $p$-Laplacian
advection equations. This extends the well-known results of the dissipation
enhancement result of the linear Laplacian by Constantin, Kiselev, Ryzhik, and
Zlato\v{s} into a non-linear setting.


Abstract:
  We settle the case of equality for the relative isoperimetric inequality
outside any arbitrary convex set with not empty interior.


Abstract:
  Data scientists face a steep learning curve in understanding a new domain for
which they want to build machine learning (ML) models. While input from domain
experts could offer valuable help, such input is often limited, expensive, and
generally not in a form readily consumable by a model development pipeline. In
this paper, we propose Ziva, a framework to guide domain experts in sharing
essential domain knowledge to data scientists for building NLP models. With
Ziva, experts are able to distill and share their domain knowledge using domain
concept extractors and five types of label justification over a representative
data sample. The design of Ziva is informed by preliminary interviews with data
scientists, in order to understand current practices of domain knowledge
acquisition process for ML development projects. To assess our design, we run a
mix-method case-study to evaluate how Ziva can facilitate interaction of domain
experts and data scientists. Our results highlight that (1) domain experts are
able to use Ziva to provide rich domain knowledge, while maintaining low mental
load and stress levels; and (2) data scientists find Ziva's output helpful for
learning essential information about the domain, offering scalability of
information, and lowering the burden on domain experts to share knowledge. We
conclude this work by experimenting with building NLP models using the Ziva
output by our case study.


Abstract:
  This paper develops and evaluates the performance of an allocation agent to
be potentially integrated into the onboard Detect and Avoid (DAA) computer of
an Unmanned Aircraft System (UAS). We consider a UAS that can be fully
controlled by the onboard DAA system and by a remote human pilot. With a
communication channel prone to latency, we consider a mixed initiative
interaction environment, where the control authority of the UAS is dynamically
allocated by the allocation agent. In an encounter with a dynamic intruder, the
probability of collision may increase in the absence of pilot commands in the
presence of latency. Moreover, a delayed pilot command may not result in safe
resolution of the current scenario and need to be improvised. We design an
optimization algorithm to reduce collision risk and refine delayed pilot
commands. Towards this end, a Markov Decision Process (MDP)and its solution are
employed to create a wait time map. The map consists of estimated times that
the UAS can wait for the remote pilot commands at each state. A command
blending algorithm is designed to select an avoidance maneuver that prioritizes
the pilot intention extracted from the pilot commands. The wait time map and
the command blending algorithm are implemented and integrated into a
closed-loop simulator. We conduct ten thousands fast-time Monte Carlo
simulations and compare the performance of the integrated setup with a
standalone DAA setup. The simulation results show that the allocation agent
enables the UAS to wait without inducing any near mid air collision (NMAC) and
severe loss of well clear (LoWC) while positively improve pilot involvement in
the encounter resolution.


Abstract:
  We prove that if ${(P_x)}_{x\in \mathscr X}$ is a family of probability
measures which satisfy the log-Sobolev inequality and whose pairwise
chi-squared divergences are uniformly bounded, and $\mu$ is any mixing
distribution on $\mathscr X$, then the mixture $\int P_x \, \mathrm{d} \mu(x)$
satisfies a log-Sobolev inequality. In various settings of interest, the
resulting log-Sobolev constant is dimension-free. In particular, our result
implies a conjecture of Zimmermann and Bardet et al. that Gaussian convolutions
of measures with bounded support enjoy dimension-free log-Sobolev inequalities.


Abstract:
  Science Information Network (SINET) is a Japanese academic backbone network
for more than 800 research institutions and universities. In this paper, we
present a multi-GPU-driven pipeline for handling huge session data of SINET.
Our pipeline consists of ELK stack, multi-GPU server, and Splunk. A multi-GPU
server is responsible for two procedures: discrimination and histogramming.
Discrimination is dividing session data into ingoing/outgoing with subnet mask
calculation and network address matching. Histogramming is grouping
ingoing/outgoing session data into bins with map-reduce. In our architecture,
we use GPU for the acceleration of ingress/egress discrimination of session
data. Also, we use a tiling design pattern for building a two-stage map-reduce
of CPU and GPU. Our multi-GPU-driven pipeline has succeeded in processing huge
workloads of about 1.2 to 1.6 billion session streams (500GB-650GB) within 24
hours.


Abstract:
  For each degree p, we construct on any closed manifold a family of Riemannian
metrics, with fixed volume such that any positive eigenvalues of the rough and
Hodge Laplacians acting on differential p-forms converge to zero. In
particular, on the sphere, we can choose these Riemannian metrics as those of
non-negative sectional curvature. This is a generalization of the results by
Colbois and Maerten in 2010 to the case of higher degree forms.


Abstract:
  Stream fusion, also known as system combination, is a common technique in
automatic speech recognition for traditional hybrid hidden Markov model
approaches, yet mostly unexplored for modern deep neural network end-to-end
model architectures. Here, we investigate various fusion techniques for the
all-attention-based encoder-decoder architecture known as the transformer,
striving to achieve optimal fusion by investigating different fusion levels in
an example single-microphone setting with fusion of standard magnitude and
phase features. We introduce a novel multi-encoder learning method that
performs a weighted combination of two encoder-decoder multi-head attention
outputs only during training. Employing then only the magnitude feature encoder
in inference, we are able to show consistent improvement on Wall Street Journal
(WSJ) with language model and on Librispeech, without increase in runtime or
parameters. Combining two such multi-encoder trained models by a simple late
fusion in inference, we achieve state-of-the-art performance for
transformer-based models on WSJ with a significant WER reduction of 19%
relative compared to the current benchmark approach.


Abstract:
  The multicolor Ramsey number problem asks, for each pair of natural numbers
$\ell$ and $t$, for the largest $\ell$-coloring of a complete graph with no
monochromatic clique of size $t$. Recent works of Conlon-Ferber and Wigderson
have improved the longstanding lower bound for this problem. We make a further
improvement by replacing an explicit graph appearing in their constructions by
a random graph. Graphs useful for this construction are exactly those relevant
for a problem of Erd\H{o}s on graphs with no large cliques and few large
independent sets. We also make some basic observations about this problem.


Abstract:
  In this paper, a stage structured predator-prey model with general nonlinear
type of functional response is established and analyzed. The state-dependent
time delay (hereafter SDTD) is the time taken from predator's birth to its
maturity, formatted as a monotonical (ly) increasing, continuous(ly)
differentiable and bounded function on the number of mature predator. The model
is quite different from many previous models with SDTD, in the sense that the
derivative of delay on the time is involved in the model. First, we have shown
that for a large class of commonly used types of functional responses,
including Holling types I, II and III, Beddington-DeAngelis-type (hereafter
BD-type), etc, the predator coexists with the prey permanently if and only if
the predator's net reproduction number is larger than one unit; Secondly, we
have discussed the local stability of the equilibria of the model; Finally, for
the special case of BD-type functional response, we claim that if the system is
permanent, that is, the derivative of SDTD on the state is small enough and the
predator interference is large enough, then the coexistence equilibrium is
globally asymptotically stable.


Abstract:
  Let $\Lambda$ be an Artin algebra. In 2014, T. Adachi, O. Iyama and I. Reiten
proved that the torsion funtorially finite classes in $\mathrm{mod}\,(\Lambda)$
can be described by the $\tau$-tilting theory. The aim of this paper is to
introduce the notion of $F$-torsion class in $\mathrm{mod}\,(\Lambda)$, where
$F$ is an additive subfunctor of $\mathrm{Ext}^1_\Lambda,$ and to characterize
when these clases are preenveloping and $F$-preenveloping. In order to do that,
we introduce the notion of $F$-presilting $\Lambda$-module. The latter is both
a generalization of $\tau$-rigid and $F$-tilting in $\mathrm{mod}(\Lambda).$


Abstract:
  Time-dependent rotational electric polarizations have been proposed to
generate temporally varying magnetic moments, for example, through a
combination of ferroelectric polarization and optical phonons. This phenomenon
has been called dynamical multiferroicity, but explicit experimental
demonstrations have been elusive to date. Here, we report the detection of a
temporal magnetic moment as high as 1.2 mu_B/atom in charge-doped thin film of
silicon under flexural strain. We demonstrate that the magnetic moment is
generated by a combination of electric polarization arising from a
flexoelectronic charge separation along the strain gradient and the deformation
potential of phonons. The effect can be controlled by adjusting the external
strain gradient, doping concentration and dopant, and can be regarded as a
dynamical multiferroic effect involving flexoelectronics polarization instead
of ferroelectricity. The discovery of a large magnetic moment in silicon may
enable the use of non-magnetic and non-ferroelectric semiconductors in various
multiferroic and spintronic applications.


Abstract:
  A mathematical model of COVID-19 with minimal compartments is developed. The
model is simple enough to fit data on confirmed cases, estimate the hidden
infection figure and incorporate the effect of vaccination. With the effect of
the new variant being considered, the model fits well with Philippine data of
confirmed cases. With the fitted parameters, results show that a second peak,
strikingly similar in magnitude to the first, is to be expected if the current
transmission rate is increased by about 5\% and already a complete disaster if
it will go up to 20\%. The model is then used to estimate time to achieve herd
immunity considering natural immunity from being infected and vaccination plan
of the country. Results show that if the plan of 200,000 vaccinated people per
day is achieved then herd immunity can be attained by March 2022. But if only
half of that is achieved the time could go up to February 2023.


Abstract:
  A key challenge in wide adoption of sophisticated context-aware applications
is the requirement of continuous sensing and context computing. This paper
presents Panorama, a middleware that identifies collaboration opportunities to
offload context computing tasks to nearby mobile devices as well as
cloudlets/cloud. At the heart of Panorama is a multi-objective optimizer that
takes into account different constraints such as access cost, computation
capability, access latency, energy consumption and data privacy, and
efficiently computes a collaboration plan optimized simultaneously for
different objectives such as minimizing cost, energy and/or execution time.
Panorama provides support for discovering nearby devices and cloudlets/cloud,
computing an optimal collaboration plan, distributing computation to
participating devices, and getting the results back. The paper provides an
extensive evaluation of Panorama via two representative context monitoring
applications over a set of Android devices and a cloudlet/cloud under different
constraints.


Abstract:
  When developing reinforcement learning agents, the standard approach is to
train an agent to converge to a fixed policy that is as close to optimal as
possible for a single fixed reward function. If different agent behaviour is
required in the future, an agent trained in this way must normally be either
fully or partially retrained, wasting valuable time and resources. In this
study, we leverage multi-objective reinforcement learning to create tunable
agents, i.e. agents that can adopt a range of different behaviours according to
the designer's preferences, without the need for retraining. We apply this
technique to sequential social dilemmas, settings where there is inherent
tension between individual and collective rationality. Learning a single fixed
policy in such settings leaves one at a significant disadvantage if the
opponents' strategies change after learning is complete. In our work, we
demonstrate empirically that the tunable agents framework allows easy adaption
between cooperative and competitive behaviours in sequential social dilemmas
without the need for retraining, allowing a single trained agent model to be
adjusted to cater for a wide range of behaviours and opponent strategies.


Abstract:
  The Renormalization Group encodes three concepts that could be key to
accelerate progress in quantum gravity. First, it provides a micro-macro
connection that could connect microscopic spacetime physics to phenomenology at
observationally accessible scales. Second, it enables a search for universality
classes that could link diverse quantum-gravity approaches and allow us to
discover that distinct approaches could encode the same physics in
mathematically distinct structures. Third, it enables the emergence of
symmetries at fixed points of the Renormalization Group flow, providing a way
for spacetime symmetries to emerge from settings in which these are broken at
intermediate steps of the construction. These three concepts make the
Renormalization Group an attractive method and conceptual underpinning of
quantum gravity. Yet, in its traditional setup as a local coarse-graining, it
could appear at odds with concepts like background independence that are
expected of quantum gravity. Within the last years, several approaches to
quantum gravity have found ways how these seeming contradictions could be
reconciled and the power of the Renormalization Group approach unleashed in
quantum gravity. This special issue brings together research papers and reviews
from a broad range of quantum gravity approaches, providing a partial snapshot
of this evolving field.


Abstract:
  Resonant metasurfaces are an attractive platform for enhancing the non-linear
optical processes, such as second harmonic generation (SHG), since they can
generate very large local electromagnetic fields while relaxing the
phase-matching requirements. Here, we take this platform a step closer to the
practical applications by demonstrating visible range, continuous wave (CW)
SHG. We do so by combining the attractive material properties of gallium
phosphide with engineered, high quality-factor photonic modes enabled by bound
states in the continuum. For the optimum case, we obtain efficiencies around
5e-5 % W$^{-1}$ when the system is pumped at 1200 nm wavelength with CW
intensities of 1 kW/cm$^2$. Moreover, we measure external efficiencies as high
as 0.1 % W$^{-1}$ with pump intensities of only 10 MW/cm$^2$ for pulsed
irradiation. This efficiency is higher than the values previously reported for
dielectric metasurfaces, but achieved here with pump intensities that are two
orders of magnitude lower.


Abstract:
  We report results of large scale quantum Monte Carlo (QMC) simulations of
graphene. Using cutting-edge algorithmic improvements, we are able to consider
spatial volumes, corresponding to 20808 electrons, that allow us to access
energy scales of direct relevance to experiments. Using constrained random
phase approximation (cRPA) estimates of short ranged interactions combined with
a Coulomb tail akin to the vacuum, we are able to confront successfully
numerical and experimental estimates of the Fermi velocity renormalization.
These results and their comparison with perturbation theory not only show the
non-Fermi liquid character of graphene, but also prove the importance of
lattice-scale physics for the quantitative description of the experimental data
for the Fermi velocity renormalization in suspended graphene.


Abstract:
  To make informed health policy decisions regarding a treatment, we must
consider both its cost and its clinical effectiveness. In past work, we
introduced the net benefit separation (NBS) as a novel measure of
cost-effectiveness. The NBS is a probabilistic measure that characterizes the
extent to which a treated patient will be more likely to experience benefit as
compared to an untreated patient. Due to variation in treatment response across
patients, uncovering factors that influence cost-effectiveness can assist
policy makers in population-level decisions regarding resource allocation. In
this paper, we introduce a regression framework for NBS in order to estimate
covariate-specific NBS and find determinants of variation in NBS. Our approach
is able to accommodate informative cost censoring through inverse probability
weighting techniques, and addresses confounding through a semiparametric
standardization procedure. Through simulations, we show that NBS regression
performs well in a variety of common scenarios. We apply our proposed
regression procedure to a realistic simulated data set as an illustration of
how our approach could be used to investigate the association between cancer
stage, comorbidities and cost-effectiveness when comparing adjuvant radiation
therapy and chemotherapy in post-hysterectomy endometrial cancer patients.


Abstract:
  Reactor experiments are well suited to probe the possible loss of coherence
of neutrino oscillations due to wave-packets separation. We combine data from
the short-baseline experiments Daya Bay and the Reactor Experiment for Neutrino
Oscillation (RENO) and from the long baseline reactor experiment KamLAND to
obtain the best current limit on the reactor antineutrino wave-packet width,
$\sigma > 2.1 \times 10^{-4}$ nm at 90% CL. We also find that the determination
of standard oscillation parameters is robust, i.e., it is mostly insensitive to
the presence of hypothetical decoherence effects once one combines the results
of the different reactor neutrino experiments.


Abstract:
  In type theory, we can express many practical ideas by attributing some
additional data to expressions we operate on during compilation. For instance,
some substructural type theories augment variables' typing judgments with the
information of their usage. That is, they allow one to explicitly state how
many times - 0, 1, or many - a variable can be used. This solves the problem of
resource usage control and allows us to treat variables as resources.
  What's more, it often happens that this attributed information is interpreted
(used) during the same compilation and erased before we run a program. A case
in the point is that in the same substructural type theories, their type
checkers use these 0, 1, or many, to ensure that all variables are used as many
times as these attributions say them to be.
  Yet, there wasn't any programming language concept whose concern would be to
allow a programmer to express these attributions in the language itself. That
is, to let the programmer express which data the one wants to attribute to what
expressions and, most importantly, the meaning of the attributed data in their
program.
  As it turned out, the presence of such a concept allows us to express many
practical ideas in the language itself. For instance, with appropriate means
for assigning the meaning of these attributions, this concept would allow one
to express linear types as functionality in a separate program module, without
the need to refine the whole type system to add them.
  In this paper, we present such a concept - we propose type properties. It
allows a programmer to express these attributions while fulfilling the
requirement of being fully on the static level. That is, it allows one to
express how to interpret these attributions during compilation and erases them
before a program is passed to the runtime.


Abstract:
  With the growing interest in deep learning algorithms and computational
design in the architectural field, the need for large, accessible and diverse
architectural datasets increases. We decided to tackle this problem by
constructing a field-specific synthetic data generation pipeline that generates
an arbitrary amount of 3D data along with the associated 2D and 3D annotations.
The variety of annotations, the flexibility to customize the generated building
and dataset parameters make this framework suitable for multiple deep learning
tasks, including geometric deep learning that requires direct 3D supervision.
Creating our building data generation pipeline we leveraged architectural
knowledge from experts in order to construct a framework that would be modular,
extendable and would provide a sufficient amount of class-balanced data
samples. Moreover, we purposefully involve the researcher in the dataset
customization allowing the introduction of additional building components,
material textures, building classes, number and type of annotations as well as
the number of views per 3D model sample. In this way, the framework would
satisfy different research requirements and would be adaptable to a large
variety of tasks. All code and data are made publicly available.


Abstract:
  We report a scheme for generation of high-order vortex states using two-mode
photon-number squeezed states, generated via the non-linear process of
Spontaneous Parametric Down Conversion. By applying a parametric rotation in
quadrature space $(X,Y)$, using a $\phi$ converter, the Gaussian quadrature
profile of the photon-number squeezed input state can be mapped into a
superposition of Laguerre-Gauss modes with $N$ vortices or singularities, for
an input state containing $N$ photons, thus mapping photon-number fluctuations
to interference effects in quadrature space. Our scheme has the potential to
improve measurement sensitivity beyond the Standard Quantum Limit (SQL $\propto
\sqrt{N}$), by exploiting the advantages of optical vortices, such as ease of
creation and detection, high dimensionality or topological properties, for
applications requiring reduced uncertainty, such as quantum cryptography,
quantum metrology and sensing.


Abstract:
  The interplay between timeliness and rate efficiency is investigated in
packet erasure broadcast channels with feedback. A scheduling framework is
proposed in which coding actions, as opposed to users, are scheduled to attain
desired tradeoffs between rate and age of information (AoI). This tradeoff is
formalized by an upper bound on AoI as a function of the target rate
constraints and two lower bounds: one as a function of the communication rate
and one as a function of the arrival rate. Simulation results show that (i)
coding can be beneficial in reducing AoI in the regime of moderate arrival
rates even without rate constraints and the benefit increases with the number
of users, and (ii) AoI increases with both the target rate constraint and the
arrival rate when either is kept fixed, but decreases with them when they are
set to be equal.


Abstract:
  Recent acoustic and electrical-circuit experiments have reported the
third-order (or octupole) topological insulating phase, while its counterpart
in classical magnetic systems is yet to be realized. Here we explore the
collective dynamics of magnetic vortices in three-dimensional breathing
cuboids, and find that the vortex lattice can support zero-dimensional corner
states, one-dimensional hinge states, two-dimensional surface states, and
three-dimensional bulk states, when the ratio of alternating intralayer and
interlayer bond lengths goes beyond a critical value. We show that only the
corner states are stable against external frustrations because of the
topological protection. Full micromagnetic simulations verify our theoretical
predictions with good agreement.


Abstract:
  Until recently, human behavioral data from reading has mainly been of
interest to researchers to understand human cognition. However, these human
language processing signals can also be beneficial in machine learning-based
natural language processing tasks. Using EEG brain activity to this purpose is
largely unexplored as of yet. In this paper, we present the first large-scale
study of systematically analyzing the potential of EEG brain activity data for
improving natural language processing tasks, with a special focus on which
features of the signal are most beneficial. We present a multi-modal machine
learning architecture that learns jointly from textual input as well as from
EEG features. We find that filtering the EEG signals into frequency bands is
more beneficial than using the broadband signal. Moreover, for a range of word
embedding types, EEG data improves binary and ternary sentiment classification
and outperforms multiple baselines. For more complex tasks such as relation
detection, further research is needed. Finally, EEG data shows to be
particularly promising when limited training data is available.


Abstract:
  Asaoka & Irie recently proved a $C^{\infty}$ closing lemma of Hamiltonian
diffeomorphisms of closed surfaces. We reformulated their techniques into a
more general perturbation lemma for area-preserving diffeomorphism and proved a
$C^{\infty}$ closing lemma for area-preserving diffeomorphisms on a torus that
is isotopic to identity. i.e., we show that the set of periodic orbits is dense
for a generic diffeomorphism isotopic to identity area-preserving
diffeomorphism on torus. The main tool is the flux vector of area-preserving
diffeomorphisms which is, different from Hamiltonian cases, non-zero in
general.


Abstract:
  The main feature of Hom-algebras is that the identities defining the
structures are twisted by linear maps. The purpose of this paper is to
introduce and study a Hom-type generalization of pre-Malcev algebras and
M-dendriform algebras, called Hom-pre-Malcev algebras and Hom-M-dendriform
algebras. We also introduce the notion of $\mathcal{O}$-operators of Hom-Malcev
and Hom-pre-Malcev algebras and show the connections between Hom-Malcev,
Hom-pre-Malcev and Hom-M-dendriform algebras using $\mathcal{O}$-operators.
Hom-pre-Malcev algebras and Hom-M-dendriform algebras generalize Hom-pre-Lie
algebras and Hom-L-dendriform algebras respectively to the alternative setting
and fit into a bigger framework with a close relationship with
Hom-pre-alternative algebras and Hom-alternative quadri-algebras respectively.


Abstract:
  Treewidth is a well-known graph invariant with multiple interesting
applications in combinatorics. On the practical side, many NP-complete problems
are polynomial-time (sometimes even linear-time) solvable on graphs of bounded
treewidth. On the theoretical side, treewidth played an essential role in the
proof of the celebrated Robertson-Seymour graph minor theorem. While defining
treewidth-like invariants on graphs and treewidth analogues on other sorts of
combinatorial objects (incl. hypergraphs, digraphs) has been a fruitful avenue
of research, a direct, categorial description capturing multiple treewidth-like
invariants is yet to emerge. Here we report on our recent work on spined
categories (arXiv:2104.01841): categories equipped with extra structure that
permits the definition of a functorial analogue of treewidth, the triangulation
functor. The usual notion of treewidth is recovered as a special case, the
triangulation functor of a spined category with graphs as objects and graph
monomorphisms as arrows. The usual notion of treewidth for hypergraphs arises
as the triangulation functor of a similar category of hypergraphs.


Abstract:
  Despite their appealing flexibility, deep neural networks (DNNs) are
vulnerable against adversarial examples. Various adversarial defense strategies
have been proposed to resolve this problem, but they typically demonstrate
restricted practicability owing to unsurmountable compromise on universality,
effectiveness, or efficiency. In this work, we propose a more practical
approach, Lightweight Bayesian Refinement (LiBRe), in the spirit of leveraging
Bayesian neural networks (BNNs) for adversarial detection. Empowered by the
task and attack agnostic modeling under Bayes principle, LiBRe can endow a
variety of pre-trained task-dependent DNNs with the ability of defending
heterogeneous adversarial attacks at a low cost. We develop and integrate
advanced learning techniques to make LiBRe appropriate for adversarial
detection. Concretely, we build the few-layer deep ensemble variational and
adopt the pre-training & fine-tuning workflow to boost the effectiveness and
efficiency of LiBRe. We further provide a novel insight to realise adversarial
detection-oriented uncertainty quantification without inefficiently crafting
adversarial examples during training. Extensive empirical studies covering a
wide range of scenarios verify the practicability of LiBRe. We also conduct
thorough ablation studies to evidence the superiority of our modeling and
learning strategies.


Abstract:
  This article is not a proof of the Poincar\'{e} conjecture but a discussion
of the proof, its context, and some of the people who played a prominent role.
It is a personal, anecdotal account. There may be omission or transpositions as
these recollections are 40 years old and not supported by contemporaneous
notes, but memories feel surprisingly fresh. I have not looked up old papers to
check details of statements; this article is merely a download from my current
mental state.


Abstract:
  Time-evolving random graph models have appeared and have been studied in
various fields of research over the past decades. However, the rigorous
mathematical treatment of large graphs and their limits at the process-level is
still in its infancy. In this article, we adapt the approach of Athreya, den
Hollander and R\"ollin (2021+) to the setting of multigraphs and multigraphons,
introduced by Kolossv\'ary and R\'ath (2011). We then generalise the work of
R\'ath (2012) and R\'ath and Szak\'acs (2012), who analysed edge-flipping
dynamics on the configuration model -- in contrast to their work, we establish
weak convergence at the process-level, and by allowing removal and addition of
edges, these limits are non-deterministic.


Abstract:
  We tackle the challenge of Visual Question Answering in multi-image setting
for the ISVQA dataset. Traditional VQA tasks have focused on a single-image
setting where the target answer is generated from a single image. Image set
VQA, however, comprises of a set of images and requires finding connection
between images, relate the objects across images based on these connections and
generate a unified answer. In this report, we work with 4 approaches in a bid
to improve the performance on the task. We analyse and compare our results with
three baseline models - LXMERT, HME-VideoQA and VisualBERT - and show that our
approaches can provide a slight improvement over the baselines. In specific, we
try to improve on the spatial awareness of the model and help the model
identify color using enhanced pre-training, reduce language dependence using
adversarial regularization, and improve counting using regression loss and
graph based deduplication. We further delve into an in-depth analysis on the
language bias in the ISVQA dataset and show how models trained on ISVQA
implicitly learn to associate language more strongly with the final answer.


Abstract:
  Despite their practical success, modern seq2seq architectures are unable to
generalize systematically on several SCAN tasks. Hence, it is not clear if
SCAN-style compositional generalization is useful in realistic NLP tasks. In
this work, we study the benefit that such compositionality brings about to
several machine translation tasks. We present several focused modifications of
Transformer that greatly improve generalization capabilities on SCAN and select
one that remains on par with a vanilla Transformer on a standard machine
translation (MT) task. Next, we study its performance in low-resource settings
and on a newly introduced distribution-shifted English-French translation task.
Overall, we find that improvements of a SCAN-capable model do not directly
transfer to the resource-rich MT setup. In contrast, in the low-resource setup,
general modifications lead to an improvement of up to 13.1% BLEU score w.r.t. a
vanilla Transformer. Similarly, an improvement of 14% in an accuracy-based
metric is achieved in the introduced compositional English-French translation
task. This provides experimental evidence that the compositional generalization
assessed in SCAN is particularly useful in resource-starved and domain-shifted
scenarios.


Abstract:
  A textile fabric consists of countless parallel vertical yarns (warps) and
horizontal yarns (wefts). While common looms can weave repetitive patterns,
Jacquard looms can weave the patterns without repetition restrictions. A
pattern in which the warps and wefts cross on a grid is defined in a binary
matrix. The binary matrix can define which warp and weft is on top at each grid
point of the Jacquard fabric. The process can be regarded as encoding from
pattern to textile. In this work, we propose a decoding method that generates a
binary pattern from a textile fabric that has been already woven. We could not
use a deep neural network to learn the process based solely on the training set
of patterns and observed fabric images. The crossing points in the observed
image were not completely located on the grid points, so it was difficult to
take a direct correspondence between the fabric images and the pattern
represented by the matrix in the framework of deep learning. Therefore, we
propose a method that can apply the framework of deep learning via the
intermediate representation of patterns and images. We show how to convert a
pattern into an intermediate representation and how to reconvert the output
into a pattern and confirm its effectiveness. In this experiment, we confirmed
that 93% of correct pattern was obtained by decoding the pattern from the
actual fabric images and weaving them again.


Abstract:
  While it is often assumed that the orbital transport is short-ranged due to
strong crystal field potential and orbital quenching, we show that orbital
propagation can be remarkably long-ranged in ferromagnets. In contrast to spin
transport, which exhibits an oscillatory decaying behavior by spin dephasing,
the injected orbital angular momentum does not oscillate and decays slowly.
This unusual feature is attributed to nearly degenerate states in
$\mathbf{k}$-space, which form hot-spots for the intrinsic orbital response. We
demonstrate this in a bilayer consisting of a nonmagnet and a ferromagnet,
where the orbital Hall current is injected from a nonmagnet into a ferromagnet.
Interaction of the orbital Hall current with the magnetization in the
ferromagnet results in an intrinsic response of the orbital angular momentum
which propagates far beyond the spin dephasing length. This gives rise to a
distinct type of orbital torque on the magnetization, increasing with the
thickness of the ferromagnet. Such behavior may serve as critical long-sought
evidence of orbital transport to be directly tested in experiments. Our
findings open the possibility of using long-range orbital transport in
orbitronic device applications.


Abstract:
  In IPFS content identifiers are constructed based on the item's data
therefore the binding between an item's identifier and its data can be
deterministically verified. Nevertheless, once an item is modified, its
identifier also changes. Therefore when it comes to mutable content there is a
need for keeping track of the "latest" IPFS identifier. This is achieved using
naming protocols on top of IPFS, such as IPNS and DNSlink, that map a constant
name to an IPFS identifier, allowing at the same time content owners to update
these mappings. Nevertheless, IPNS relies on a cryptographic key pair that
cannot be rotated, and DNSlink does not provide content authenticity
protection. In this paper, we propose a naming protocol that combines DNSlink
and decentralized identifiers to enable self-verifiable content items. Our
protocol provides content authenticity without imposing any security
requirement to DNSlink. Furthermore, our protocol prevent fake content even if
attackers have access to the DNS server of the content owner or have access to
the content owner secret keys. Our proof of concept implementation shows that
our protocol is feasible and can be used with existing IPFS tools.


Abstract:
  Automatic program repair (APR) is crucial to improve software reliability.
Recently, neural machine translation (NMT) techniques have been used to fix
software bugs automatically. While promising, these approaches have two major
limitations. Their search space often does not contain the correct fix, and
their search strategy ignores software knowledge such as strict code syntax.
Due to these limitations, existing NMT-based techniques underperform the best
template-based approaches.
  We propose CURE, a new NMT-based APR technique with three major novelties.
First, CURE pre-trains a programming language (PL) model on a large software
codebase to learn developer-like source code before the APR task. Second, CURE
designs a new code-aware search strategy that finds more correct fixes by
focusing on compilable patches and patches that are close in length to the
buggy code. Finally, CURE uses a subword tokenization technique to generate a
smaller search space that contains more correct fixes.
  Our evaluation on two widely-used benchmarks shows that CURE correctly fixes
57 Defects4J bugs and 26 QuixBugs bugs, outperforming all existing APR
techniques on both benchmarks.


Abstract:
  A very popular model in machine learning is the feedforward neural network
(FFN). The FFN can approximate general functions and mitigate the curse of
dimensionality. Here we introduce FFNs which represent sections of holomorphic
line bundles on complex manifolds, and ask some questions about their
approximating power. We also explain formal similarities between the standard
approach to supervised learning and the problem of finding numerical Ricci flat
K\"ahler metrics, which allow carrying some ideas between the two problems.


Abstract:
  With the rapid growth of renewable energy, lots of small photovoltaic (PV)
prosumers emerge. Due to the uncertainty of solar power generation, there is a
need for aggregated prosumers to predict solar power generation and whether
solar power generation will be larger than load. This paper presents two
interpretable neural networks to solve the problem: one binary classification
neural network and one regression neural network. The neural networks are built
using TensorFlow. The global feature importance and local feature contributions
are examined by three gradient-based methods: Integrated Gradients, Expected
Gradients, and DeepLIFT. Moreover, we detect abnormal cases when predictions
might fail by estimating the prediction uncertainty using Bayesian neural
networks. Neural networks, which are interpreted by gradient-based methods and
complemented with uncertainty estimation, provide robust and explainable
forecasting for decision-makers.


Abstract:
  In this work, we study the behavior of blow-up solutions to the
multidimensional restricted Euler--Poisson equations which are the localized
version of the full Euler--Poisson system. We provide necessary conditions for
the existence of finite-time blow-up solutions in terms of the initial data,
and describe the asymptotic behavior of the solutions near blow up times. We
also identify a rich set of the initial data which yields global bounded
solutions.


Abstract:
  Typical fingerprint recognition systems are comprised of a spoof detection
module and a subsequent recognition module, running one after the other. In
this paper, we reformulate the workings of a typical fingerprint recognition
system. In particular, we posit that both spoof detection and fingerprint
recognition are correlated tasks. Therefore, rather than performing the two
tasks separately, we propose a joint model for spoof detection and matching to
simultaneously perform both tasks without compromising the accuracy of either
task. We demonstrate the capability of our joint model to obtain an
authentication accuracy (1:1 matching) of TAR = 100% @ FAR = 0.1% on the FVC
2006 DB2A dataset while achieving a spoof detection ACE of 1.44% on the LiveDet
2015 dataset, both maintaining the performance of stand-alone methods. In
practice, this reduces the time and memory requirements of the fingerprint
recognition system by 50% and 40%, respectively; a significant advantage for
recognition systems running on resource-constrained devices and communication
channels. The project page for our work is available at
https://www.bit.ly/ijcb2021-unified .


Abstract:
  The COVID19 pandemic created a worldwide emergency as it is estimated that
such a large number of infections are due to human-to-human transmission of the
COVID19. As a necessity, there is a need to track users who came in contact
with users having travel history, asymptomatic and not yet symptomatic, but
they can be in the future. To solve this problem, the present work proposes a
solution for contact tracing based on assisted GPS and cloud computing
technologies. An application is developed to collect each user's assisted GPS
coordinates once all the users install this application. This application
periodically sends assisted GPS data to the cloud. To determine which devices
are within the permissible limit of 5m, we perform clustering over assisted GPS
coordinates and track the clusters for about t mins to allow the measure of
spread. We assume that it takes around 3 or 5 mins to get the virus from an
infected object. For clustering, the proposed M way like tree data structure
stores the assisted GPS coordinates in degree, minute, and second format. Thus,
every user is mapped to a leaf node of the tree. We split the "seconds" part of
the assisted GPS location into m equal parts, which amount to d meter in
latitude(longitude). Hence, two users who are within d meter range will map to
the same leaf node. Thus, by mapping assisted GPS locations every t mins, we
can find out how many users came in contact with a particular user for at least
t mins. Our work's salient feature is that it runs in linear time O(n) for n
users in the static case, i.e., when users are not moving. We also propose a
variant of our solution to handle the dynamic case, that is, when users are
moving. Besides, the proposed solution offers potential hotspot detection and
safe-route recommendation as an additional feature, and proof of concept is
presented through experiments on simulated data of 10M users.


Abstract:
  We discuss three ways of obtaining the Born approximations for Coulomb
scattering: The standard way, making use of a convergence factor ("screening"),
Oppenheimer's way using cylindrical (instead of spherical) coordinates, and
finally Landau and Lifshitz' way. The last one although it does require some
background from the theory of generalized functions is nevertheless a very
instructive and important technique deserving more exposure to physicists.


Abstract:
  Online Travel Platforms are virtual two-sided marketplaces where guests
search for accommodations and accommodation providers list their properties
such as hotels and vacation rentals. The large majority of hotels are rated by
official institutions with a number of stars indicating the quality of service
they provide. It is a simple and effective mechanism that contributes to match
supply with demand by helping guests to find options meeting their criteria and
accommodation suppliers to market their product to the right segment directly
impacting the number of transactions on the platform. Unfortunately, no similar
rating system exists for the large majority of vacation rentals, making it
difficult for guests to search and compare options and hard for vacation
rentals suppliers to market their product effectively. In this work we describe
a machine learned quality rating system for vacation rentals. The problem is
challenging, mainly due to explainability requirements and the lack of ground
truth. We present techniques to address these challenges and empirical evidence
of their efficacy. Our system was successfully deployed and validated through
Online Controlled Experiments performed in Booking. com, a large Online Travel
Platform, and running for more than one year, impacting more than a million
accommodations and millions of guests.


Abstract:
  X-ray diffraction indicates that the structure of the recently discovered
room temperature carbonaceous sulfur hydride (C-S-H) superconductor is derived
from previously established van der Waals compounds found in the H$_2$S-H$_2$
and CH$_4$-H$_2$ systems. Crystals of the superconducting phase were produced
by a photochemical synthesis technique leading to the superconducting critical
temperature $T_c$ of 288 K at 267 GPa. Single-crystal x-ray diffraction
patterns measured from 124 to 178 GPa, within the pressure range of the
superconducting phase, give an orthorhombic structure derived from the
Al$_2$Cu-type determined for (H$_2$S)$_2$H$_2$ and (CH$_4$)$_2$H$_2$ that
differs from those predicted and observed for the S-H system to these
pressures. The formation and stability of the C-S-H compound can be understood
in terms of the close similarity in effective volumes of the H$_2$S and CH$_4$
components over a broad range of pressures. The relative amounts of carbon and
sulfur in the structure is not determined, and denser carbon-bearing S-H
structures may form at higher pressures. The results are consistent with
hole-doping enhancement of $T_c$ by carbon proposed for the room-temperature
superconductivity in this system.


Abstract:
  The availability of social media simplifies the companies-customers
relationship. An effort to engage customers in conversation networks using
social media is called Social Customer Relationship Management (SCRM). Social
Network Analysis helps to understand network characteristics and how active the
conversation network on social media. Calculating its network properties is
beneficial for measuring customer relationship performance. Financial
Technology, a new emerging industry that provides digital-based financial
services utilize social media to interact with its customers. Measuring SCRM
performance is needed in order to stay competitive among others. Therefore, we
aim to explore the SCRM performance of the Indonesia Fintech company. In terms
of discovering the market majority thought in conversation networks, we perform
sentiment analysis by classifying into positive and negative opinion. As case
studies, we investigate Twitter conversations about GoPay, OVO, Dana, and
LinkAja during the observation period from 1st October until 1st November 2019.
The result of this research is beneficial for business intelligence purposes
especially in managing relationships with customers.


Abstract:
  The amount of video content and the number of applications based on
multimedia information increase each day. The development of new video coding
standards is a challenge to increase the compression rate and other important
features with a reasonable increase in the computational load. Video Experts
Team (JVET) of ITU-T and the JCT group within ISO/IEC have worked together to
standardize the Versatile Video Coding, approved finally in July 2020 as ITU-T
H.266 | MPEG-I - Part 3 (ISO/IEC 23090-3) standard. This paper overviews some
interesting consumer electronic use cases, the compression tools described in
the standard, the current available real time implementations and the first
industrial trials done with this standard.


Abstract:
  Channel modeling is a critical issue when designing or evaluating the
performance of reconfigurable intelligent surface (RIS)-assisted
communications. Inspired by the promising potential of learning-based methods
for characterizing the radio environment, we present a general approach to
model the RIS end-to-end equivalent channel using the unsupervised
expectation-maximization (EM) learning algorithm. We show that an EM-based
approximation through a simple mixture of two Nakagami-$m$ distributions
suffices to accurately approximating the equivalent channel, while allowing for
the incorporation of crucial aspects into RIS's channel modeling as spatial
channel correlation, phase-shift errors, arbitrary fading conditions, and
coexistence of direct and RIS channels. Based on the proposed analytical
framework, we evaluate the outage probability under different settings of RIS's
channel features and confirm the superiority of this approach compared to
recent results in the literature.


Abstract:
  TANet is one of state-of-the-art 3D object detection method on KITTI and JRDB
benchmark, the network contains a Triple Attention module and Coarse-to-Fine
Regression module to improve the robustness and accuracy of 3D Detection.
However, since the original input data (point clouds) contains a lot of noise
during collecting the data, which will further affect the training of the
model. For example, the object is far from the robot, the sensor is difficult
to obtain enough pointcloud. If the objects only contains few point clouds, and
the samples are fed into model with the normal samples together during
training, the detector will be difficult to distinguish the individual with few
pointcloud belong to object or background. In this paper, we propose TANet++ to
improve the performance on 3D Detection, which adopt a novel training strategy
on training the TANet. In order to reduce the negative impact by the weak
samples, the training strategy previously filtered the training data, and then
the TANet++ is trained by the rest of data. The experimental results shows that
AP score of TANet++ is 8.98 higher than TANet on JRDB benchmark.


Abstract:
  We present a framework for the construction of portal effective theories
(PETs) that couple effective field theories of the Standard Model (SM) to light
hidden messenger fields. Using this framework we construct electroweak and
strong scale PETs that couple the SM to messengers carrying spin zero, one
half, or one. The electroweak scale PETs encompass all portal operators up to
dimension five, while the strong scale PETs additionally contain all portal
operators of dimension six and seven that contribute at leading order to
quark-flavour violating transitions. Using the strong scale PETs, we define a
set of portal currents that couple hidden sectors to QCD, and construct portal
chiral perturbation theories ($\chi$PTs) that relate these currents to the
light pseudoscalar mesons. We estimate the coefficients of the portal $\chi$PT
Lagrangian that are not fixed by SM observations using non-perturbative
matching techniques and give a complete list of the resulting one- and
two-meson portal interactions. From those, we compute transition amplitudes for
three golden channels that are used in hidden sector searches at fixed target
experiments: i) charged kaon decay into a charged pion and a spin zero
messenger, ii) charged kaon decay into a charged lepton and a spin one half
messenger, and iii) neutral pion decay into a photon and a spin one messenger.
Finally, we compare these amplitudes to specific expressions for models
featuring light scalar particles, axion-like particles, heavy neutral leptons,
and dark photons.


Abstract:
  A mesoporous bioactive glass (MBG) of molar composition 75SiO2-20CaO-5P2O5
(MBG-75S) has been synthetized as a potential bioceramic for bone regeneration
purposes. X-ray diffraction (XRD), Fourier transform infrared spectroscopy
(FT-IR), nitrogen adsorption studies and transmission electron microscopy (TEM)
demonstrated that MBG-75Spossess a highly ordered mesoporous structure with
high surface area and porosity, which would explain the high ionic exchange
rate (mainly calcium and silicon soluble species) with the surrounded media.
MBG-75S showed high biocompatibility in contact with Saos-2 osteoblast-like
cells. Concentrations up to 1 mg/ml did not lead to significant alterations on
either morphology or cell cycle. Regarding the effects on osteoclasts, MBG-75S
allowed the differentiation of RAW-264.7 macrophages into osteoclast-like cells
but exhibiting a decreased resorptive activity. These results point out that
MBG-75S does not inhibit osteoclastogenesis but reduces the osteoclast
boneresorbing capability. Finally, in vitro studies focused on the innate
immune response, evidenced that MBG-75S allows the proliferation of macrophages
without inducing their polarization towards the M1 pro-inflammatory phenotype.
This in vitro behavior is indicative that MBG-75S would just induce the
required innate immune response without further inflammatory complications
under in vivo conditions. The overall behavior respect to osteoblasts,
osteoclasts and macrophages, makes this MBG a very interesting candidate for
bone grafting applications in osteoporotic patients.


Abstract:
  Recent work has proposed stochastic Plackett-Luce (PL) ranking models as a
robust choice for optimizing relevance and fairness metrics. Unlike their
deterministic counterparts that require heuristic optimization algorithms, PL
models are fully differentiable. Theoretically, they can be used to optimize
ranking metrics via stochastic gradient descent. However, in practice, the
computation of the gradient is infeasible because it requires one to iterate
over all possible permutations of items. Consequently, actual applications rely
on approximating the gradient via sampling techniques. In this paper, we
introduce a novel algorithm: PL-Rank, that estimates the gradient of a PL
ranking model w.r.t. both relevance and fairness metrics. Unlike existing
approaches that are based on policy gradients, PL-Rank makes use of the
specific structure of PL models and ranking metrics. Our experimental analysis
shows that PL-Rank has a greater sample-efficiency and is computationally less
costly than existing policy gradients, resulting in faster convergence at
higher performance. PL-Rank further enables the industry to apply PL models for
more relevant and fairer real-world ranking systems.


Abstract:
  Cycling processes are important in many areas of physics ranging from lasers
to topological insulators, often offering surprising insights into dynamical
and structural aspects of the respective system. Here we report on a
quantum-nonlinear wave-mixing experiment where resonant lasers and an optical
cavity define a closed cycle between several ground and excited states of a
single atom. We show that, for strong atom-cavity coupling and steady-state
driving, the entanglement between the atomic states and intracavity photon
number suppresses the excited-state population via quantum interference,
effectively reducing the cycle to the atomic ground states. The system dynamics
then result from transitions within a harmonic ladder of entangled dark states,
one for each cavity photon number, and a quantum Zeno blockade that generates
antibunching in the photons emitted from the cavity. The reduced cycle
suppresses unwanted optical pumping into atomic states outside the cycle,
thereby enhancing the number of emitted photons.


Abstract:
  We describe the spectrum of the Laplacian matrix of the commuting graph
associated to a finite group, using group theoretic information. Various graph
invariants (such as diameter, isoperimetric number, clique number, mean
distance) of the commuting graph associated to a finite group are determined.
We produce a number of examples to illustrate applications of our results.


Abstract:
  Electromagnetic crimping is a high-velocity joining method to join highly
conductive workpieces where a pulsed magnetic field is applied without any
working medium or mechanical contact to deform the workpiece. This work
explores tube-to-tube joining of Copper outer tube and Stainless steel threaded
inner tube using electromagnetic crimping. A non-coupled simulation model is
developed for the finite element analysis. ANSYS Maxwell package is used to
obtain the magnetic field intensity, which is later converted to pressure using
an analytical equation, and this pressure is applied to the two-tube working
domain in ANSYS Explicit Dynamics. Numerical simulations are done for different
combinations of discharge energies and pitches of the thread to analyse
deformation, stress and strain. The converged finite element results are
validated using experimental data. The amount of deformation is found to be
proportional to discharge energy and the pitch of the thread used. An empirical
relation is developed for the deformation as a function of discharge energy and
pitch. The relation is able to predict the deformation for other discharge
energies, which is later verified with ANSYS simulations.


Abstract:
  Benefiting from the powerful expressive capability of graphs, graph-based
approaches have achieved impressive performance in various biomedical
applications. Most existing methods tend to define the adjacency matrix among
samples manually based on meta-features, and then obtain the node embeddings
for downstream tasks by Graph Representation Learning (GRL). However, it is not
easy for these approaches to generalize to unseen samples. Meanwhile, the
complex correlation between modalities is also ignored. As a result, these
factors inevitably yield the inadequacy of providing valid information about
the patient's condition for a reliable diagnosis. In this paper, we propose an
end-to-end Multimodal Graph Learning framework (MMGL) for disease prediction.
To effectively exploit the rich information across multi-modality associated
with diseases, amodal-attentional multi-modal fusion is proposed to integrate
the features of each modality by leveraging the correlation and complementarity
between the modalities. Furthermore, instead of defining the adjacency matrix
manually as existing methods, the latent graph structure can be captured
through a novel way of adaptive graph learning. It could be jointly optimized
with the prediction model, thus revealing the intrinsic connections among
samples. Unlike the previous transductive methods, our model is also applicable
to the scenario of inductive learning for those unseen data. An extensive group
of experiments on two disease prediction problems is then carefully designed
and presented, demonstrating that MMGL obtains more favorable performances. In
addition, we also visualize and analyze the learned graph structure to provide
more reliable decision support for doctors in real medical applications and
inspiration for disease research.


Abstract:
  We calculate high-precision constraints on Natural Inflation relative to
current observational constraints from Planck 2018 + BICEP/Keck(BK15)
Polarization + BAO on $r$ and $n_S$, including post-inflationary history of the
universe. We find that, for conventional post-inflationary dynamics, Natural
Inflation with a cosine potential is disfavored at greater than 95\% confidence
out by current data. If we assume protracted reheating characterized by
$\overline{w}>1/3,$ Natural Inflation can be brought into agreement with
current observational constraints. However, bringing unmodified Natural
Inflation into the 68\% confidence region requires values of $T_{\mathrm{re}}$
below the scale of electroweak symmetry breaking. The addition of a SHOES prior
on the Hubble Constant $H_0$ only worsens the fit.


Abstract:
  As cities become increasingly populated, urban planning plays a key role in
ensuring the equitable and inclusive development of metropolitan areas. MIT
City Science group created a data-driven tangible platform, CityScope, to help
different stakeholders, such as government representatives, urban planners,
developers, and citizens, collaboratively shape the urban scenario through the
real-time impact analysis of different urban interventions. This paper presents
an agent-based model that characterizes citizens' behavioural patterns with
respect to housing and mobility choice that will constitute the first step in
the development of a dynamic incentive system for an open interactive
governance process. The realistic identification and representation of the
criteria that affect this decision-making process will help understand and
evaluate the impacts of potential housing incentives that aim to promote urban
characteristics such as equality, diversity, walkability, and efficiency. The
calibration and validation of the model have been performed in a well-known
geographic area for the Group: Kendall Square in Cambridge, MA.


Abstract:
  Researchers have recently started to study how the emotional speech heard by
young infants can affect their developmental outcomes. As a part of this
research, hundreds of hours of daylong recordings from preterm infants' audio
environments were collected from two hospitals in Finland and Estonia in the
context of so-called APPLE study. In order to analyze the emotional content of
speech in such a massive dataset, an automatic speech emotion recognition (SER)
system is required. However, there are no emotion labels or existing indomain
SER systems to be used for this purpose. In this paper, we introduce this
initially unannotated large-scale real-world audio dataset and describe the
development of a functional SER system for the Finnish subset of the data. We
explore the effectiveness of alternative state-of-the-art techniques to deploy
a SER system to a new domain, comparing cross-corpus generalization, WGAN-based
domain adaptation, and active learning in the task. As a result, we show that
the best-performing models are able to achieve a classification performance of
73.4% unweighted average recall (UAR) and 73.2% UAR for a binary classification
for valence and arousal, respectively. The results also show that active
learning achieves the most consistent performance compared to the two
alternatives.


Abstract:
  We give an overview of our recent theoretical studies of the thermodynamics
of excitons, and other solid-state qubits, driven by time-dependent laser
fields. We consider a single such emitter and describe how the formation of
strong-field dressed states allows the emitter to absorb or emit acoustic
phonons in a controlled way. We present results for the heat absorption, and
show that the form of the driving field can be tailored to produce different
thermodynamic processes, including both reversible and irreversible heat
absorption. We discuss these effects from the perspective of quantum
thermodynamics and outline the possibility of using them for optical cooling of
solids to low temperatures.