Abstract:
  In this paper, we study the phenomenon of quantum interference in the
presence of external gravitational fields described by alternative theories of
gravity. We analyze both non-relativistic and relativistic effects induced by
the underlying curved background on a superposed quantum system. In the
non-relativistic regime, it is possible to come across a gravitational
counterpart of the Bohm-Aharonov effect, which results in a phase shift
proportional to the derivative of the modified Newtonian potential. On the
other hand, beyond the Newtonian approximation, the relativistic nature of
gravity plays a crucial r\^ole. Indeed, the existence of a gravitational time
dilation between the two arms of the interferometer causes a loss of coherence
that is in principle observable in quantum interference patterns. We work in
the context of generalized quadratic theories of gravity to compare their
physical predictions with the analogous outcomes in general relativity. In so
doing, we show that the decoherence rate strongly depends on the gravitational
model under investigation, which means that this approach turns out to be a
promising test bench to probe and discriminate among all the extensions of
Einstein's theory in future experiments.


Abstract:
  The analysis of the double-diffusion model and
$\mathbf{H}(\mathrm{div})$-conforming method introduced in [B\"urger, M\'endez,
Ruiz-Baier, SINUM (2019), 57:1318--1343] is extended to the time-dependent
case. In addition, the efficiency and reliability analysis of residual-based
{\it a posteriori} error estimators for the steady, semi-discrete, and fully
discrete problems is established. The resulting methods are applied to simulate
the sedimentation of small particles in salinity-driven flows. The method
consists of Brezzi-Douglas-Marini approximations for velocity and compatible
piecewise discontinuous pressures, whereas Lagrangian elements are used for
concentration and salinity distribution. Numerical tests confirm the properties
of the proposed family of schemes and of the adaptive strategy guided by the
{\it a posteriori} error indicators.


Abstract:
  As robots are becoming more and more ubiquitous in human environments, it
will be necessary for robotic systems to better understand and predict human
actions. However, this is not an easy task, at times not even for us humans,
but based on a relatively structured set of possible actions, appropriate cues,
and the right model, this problem can be computationally tackled. In this
paper, we propose to use an ensemble of long-short term memory (LSTM) networks
for human action prediction. To train and evaluate models, we used the MoGaze
dataset - currently the most comprehensive dataset capturing poses of human
joints and the human gaze. We have thoroughly analyzed the MoGaze dataset and
selected a reduced set of cues for this task. Our model can predict (i) which
of the labeled objects the human is going to grasp, and (ii) which of the macro
locations the human is going to visit (such as table or shelf). We have
exhaustively evaluated the proposed method and compared it to individual cue
baselines. The results suggest that our LSTM model slightly outperforms the
gaze baseline in single object picking accuracy, but achieves better accuracy
in macro object prediction. Furthermore, we have also analyzed the prediction
accuracy when the gaze is not used, and in this case, the LSTM model
considerably outperformed the best single cue baseline


Abstract:
  Pretraining on large labeled datasets is a prerequisite to achieve good
performance in many computer vision tasks like 2D object recognition, video
classification etc. However, pretraining is not widely used for 3D recognition
tasks where state-of-the-art methods train models from scratch. A primary
reason is the lack of large annotated datasets because 3D data is both
difficult to acquire and time consuming to label. We present a simple
self-supervised pertaining method that can work with any 3D data - single or
multiview, indoor or outdoor, acquired by varied sensors, without 3D
registration. We pretrain standard point cloud and voxel based model
architectures, and show that joint pretraining further improves performance. We
evaluate our models on 9 benchmarks for object detection, semantic
segmentation, and object classification, where they achieve state-of-the-art
results and can outperform supervised pretraining. We set a new
state-of-the-art for object detection on ScanNet (69.0% mAP) and SUNRGBD (63.5%
mAP). Our pretrained models are label efficient and improve performance for
classes with few examples.


Abstract:
  Open quantum systems can be systematically controlled by making changes to
their environment. A well-known example is the spontaneous radiative decay of
an electronically excited emitter, such as an atom or a molecule, which is
significantly influenced by the feedback from the emitter's environment, for
example, by the presence of reflecting surfaces. A prerequisite for a
deliberate control of an open quantum system is to reveal the physical
mechanisms that determine the state of the system. Here, we investigate the
Bose-Einstein condensation of a photonic Bose gas in an environment with
controlled dissipation and feedback realised by a potential landscape that
effectively acts as a Mach-Zehnder interferometer. Our measurements offer a
highly systematic picture of Bose-Einstein condensation under non-equilibrium
conditions. We show that the condensation process is an interplay between
minimising the energy of the condensate, minimising particle losses and
maximising constructive feedback from the environment. In this way our
experiments reveal physical mechanisms involved in the formation of a
Bose-Einstein condensate, which typically remain hidden when the system is
close to thermal equilibrium. Beyond a deeper understanding of Bose-Einstein
condensation, our results open new pathways in quantum simulation with optical
Bose-Einstein condensates.


Abstract:
  Non-abelian anyons are highly desired for topological quantum computation
purposes, with Majorana fermions providing a promising route, particularly zero
modes with non-trivial mutual statistics. Yet realizing Majorana zero modes in
matter is a challenge, with various proposals in chiral superconductors,
nanowires, and spin liquids, but no clear experimental examples. Heavy fermion
materials have long been known to host Majorana fermions at two-channel Kondo
impurity sites, however, these impurities cannot be moved adiabatically and
generically occur in metals, where the absence of a gap removes the topological
protection. Here, we consider an ordered lattice of these two-channel Kondo
impurities, which at quarter-filling form a Kondo insulator. We show that
topological defects in this state will host Majorana zero modes, or possibly
more complicated parafermions. These states are protected by the insulating gap
and may be adiabatically braided, providing the novel possibility of realizing
topological quantum computation in heavy fermion materials.


Abstract:
  The immersion and the interaction are the important features of the driving
simulator. To improve these characteristics, this paper proposes a low-cost and
mark-less driver head tracking framework based on the head pose estimation
model, which makes the view of the simulator can automatically align with the
driver's head pose. The proposed method only uses the RGB camera without the
other hardware or marker. To handle the error of the head pose estimation
model, this paper proposes an adaptive Kalman Filter. By analyzing the error
distribution of the estimation model and user experience, the proposed Kalman
Filter includes the adaptive observation noise coefficient and loop closure
module, which can adaptive moderate the smoothness of the curve and keep the
curve stable near the initial position. The experiments show that the proposed
method is feasible, and it can be used with different head pose estimation
models.


Abstract:
  In this article we solve this ancient problem of perfect tuning in all keys
and present a system were all harmonies are conserved at once. It will become
clear, when we expose our solution, why this solution could not be found in the
way in which earlier on musicians and scientist have been approaching the
problem. We follow indeed a different approach. We first construct a
mathematical representation of the complete harmony by means of a vector space,
where the different tones are represented in complete harmonic way for all keys
at once. One of the essential differences with earlier systems is that tones
will no longer be ordered within an octave, and we find the octave-like
ordering back as a projection of our system. But it is exactly by this
projection procedure that the possibility to create a harmonic system for all
keys at once is lost. So we see why the old way of ordering tones within an
octave could not lead to a solution of the problem. We indicate in which way a
real musical instrument could be built that realizes our harmonic scheme.
Because tones are no longer ordered within an octave such a musical instrument
will be rather unconventional. It is however a physically realizable musical
instrument, at least for the Pythagorean harmony. We also indicate how perfect
harmonies of every dimension could be realized by computers.


Abstract:
  We use idealized N-body simulations of equilibrium stellar disks embedded
within course-grained dark matter haloes to study the effects of spurious
collisional heating on disk structure and kinematics. Collisional heating
artificially increases the vertical and radial velocity dispersions of disk
stars, as well as the thickness and size of disks; the effects are felt at all
galacto-centric radii. The integrated effects of collisional heating are
determined by the mass of dark matter halo particles (or equivalently, by the
number of particles at fixed halo mass), their local density and characteristic
velocity dispersion, but are largely insensitive to the stellar particle mass.
The effects can therefore be reduced by increasing the mass resolution of dark
matter in cosmological simulations, with limited benefits from increasing the
baryonic (or stellar) mass resolution. We provide a simple empirical model that
accurately captures the effects of spurious collisional heating on the
structure and kinematics of simulated disks, and use it to assess the
importance of disk heating for simulations of galaxy formation. We find that
the majority of state-of-the-art zoom simulations, and a few of the
highest-resolution, smallest-volume cosmological runs, are in principle able to
resolve thin stellar disks in Milky Way-mass haloes, but most large-volume
cosmological simulations cannot. For example, dark matter haloes resolved with
fewer than $\approx 10^6$ particles will collisionally heat stars near the
stellar half-mass radius such that their vertical velocity dispersion increases
by $\gtrsim 10$ per cent of the halo's virial velocity in approximately one
Hubble time.


Abstract:
  Scattering polarization tends to dominate the linear polarization signals of
the Ca II 8542 A line in weakly magnetized areas ($B \lesssim 100$ G),
especially when the observing geometry is close to the limb. In this paper we
evaluate the degree of applicability of existing non-LTE spectral line
inversion codes (which assume that the spectral line polarization is due to the
Zeeman effect only) at inferring the magnetic field vector and, particularly,
its transverse component. To this end, we use the inversion code STiC to
extract the strength and orientation of the magnetic field from synthetic
spectropolarimetric data generated with the Hanle-RT code. The latter accounts
for the generation of polarization through scattering processes as well as the
joint actions of the Hanle and the Zeeman effects. We find that, when the
transverse component of the field is stronger than $\sim$80 G, the inversion
code is able to retrieve accurate estimates of the transverse field strength as
well as its azimuth in the plane of the sky. Below this threshold, the
scattering polarization signatures become the major contributors to the linear
polarization signals and often mislead the inversion code into severely over-
or under-estimating the field strength. Since the line-of-sight component of
the field is derived from the circular polarization signal, which is not
affected by atomic alignment, the corresponding inferences are always good.


Abstract:
  We present lattice results for the non-perturbative Collins-Soper (CS)
kernel, which describes the energy-dependence of transverse momentum-dependent
parton distributions (TMDs). The CS kernel is extracted from the ratios of
first Mellin moments of quasi-TMDs evaluated at different nucleon momenta.The
analysis is done with dynamical $N_f=2+1$ clover fermions for the CLS ensemble
H101 ($a=0.0854\,\mathrm{fm}$, $m_{\pi}=m_K=422\,\mathrm{MeV}$). The computed
CS kernel is in good agreement with experimental extractions and previous
lattice studies.


Abstract:
  One crucial objective of multi-task learning is to align distributions across
tasks so that the information between them can be transferred and shared.
However, existing approaches only focused on matching the marginal feature
distribution while ignoring the semantic information, which may hinder the
learning performance. To address this issue, we propose to leverage the label
information in multi-task learning by exploring the semantic conditional
relations among tasks. We first theoretically analyze the generalization bound
of multi-task learning based on the notion of Jensen-Shannon divergence, which
provides new insights into the value of label information in multi-task
learning. Our analysis also leads to a concrete algorithm that jointly matches
the semantic distribution and controls label distribution divergence. To
confirm the effectiveness of the proposed method, we first compare the
algorithm with several baselines on some benchmarks and then test the
algorithms under label space shift conditions. Empirical results demonstrate
that the proposed method could outperform most baselines and achieve
state-of-the-art performance, particularly showing the benefits under the label
shift conditions.


Abstract:
  We study the giant component problem slightly above the critical regime for
percolation on Poissonian random graphs in the scale-free regime, where the
vertex weights and degrees have a diverging second moment. Critical percolation
on scale-free random graphs have been observed to have incredibly subtle
features that are markedly different compared to those in random graphs with
converging second moment. In particular, the critical window for percolation
depends sensitively on whether we consider single- or multi-edge versions of
the Poissonian random graph.
  In this paper, and together with our companion paper with Bhamidi, we build a
bridge between these two cases. Our results characterize the part of the barely
supercritical regime where the size of the giant components are approximately
same for the single- and multi-edge settings. The methods for establishing
concentration of giant for the single- and multi-edge versions are quite
different. While the analysis in the multi-edge case is based on scaling limits
of exploration processes, the single-edge setting requires identification of a
core structure inside certain high-degree vertices that forms the giant
component.


Abstract:
  We present two generalized hybrid kinetic-Hall magnetohydrodynamics (MHD)
models describing the interaction of a two-fluid bulk plasma, which consists of
thermal ions and electrons, with energetic, suprathermal ion populations
described by Vlasov dynamics. The dynamics of the thermal components are
governed by standard fluid equations in the Hall MHD limit with the electron
momentum equation providing an Ohm's law with Hall and electron pressure terms
involving a gyrotropic electron pressure tensor. The coupling of the bulk,
low-energy plasma with the energetic particle dynamics is accomplished through
the current density (current coupling scheme; CCS) and the ion pressure tensor
appearing in the momentum equation (pressure coupling scheme; PCS) in the first
and the second model, respectively. The CCS is a generalization of two
well-known models, because in the limit of vanishing energetic and thermal ion
densities we recover the standard Hall MHD and the hybrid
kinetic-ions/fluid-electron model, respectively. This provides us with the
capability to study in a continuous manner the global impact of the energetic
particles in a regime extending from vanishing to dominant energetic particle
densities. The noncanonical Hamiltonian structures of the CCS and PCS, which
can be exploited to study equilibrium and stability properties through the
energy-Casimir variational principle, are identified. As a first application
here, we derive a generalized Hall MHD Grad--Shafranov--Bernoulli system for
translationally symmetric equilibria with anisotropic electron pressure and
kinetic effects owing to the presence of energetic particles using the PCS.


Abstract:
  Given a set $B\subset \mathbb{N}$, we investigate the existence of a set
$A\subset \mathbb{N}$ such that the sumset $A+B = \{a + b\,:\, a\in A, b\in
B\}$ has a prescribed asymptotic density. A set $B = \{b_1, b_2, \ldots\}$ is
said to be highly sparse if $B$ is either finite or infinite with
$\lim_{j\rightarrow\infty} b_{j+1}/b_j = \infty$. In this note, we prove that
if $B$ is highly sparse, such a set $A$ exists. This generalizes a recent
result by Faisant et al.


Abstract:
  We present the novel Efficient Line Segment Detector and Descriptor (ELSD) to
simultaneously detect line segments and extract their descriptors in an image.
Unlike the traditional pipelines that conduct detection and description
separately, ELSD utilizes a shared feature extractor for both detection and
description, to provide the essential line features to the higher-level tasks
like SLAM and image matching in real time. First, we design the one-stage
compact model, and propose to use the mid-point, angle and length as the
minimal representation of line segment, which also guarantees the
center-symmetry. The non-centerness suppression is proposed to filter out the
fragmented line segments caused by lines' intersections. The fine offset
prediction is designed to refine the mid-point localization. Second, the line
descriptor branch is integrated with the detector branch, and the two branches
are jointly trained in an end-to-end manner. In the experiments, the proposed
ELSD achieves the state-of-the-art performance on the Wireframe dataset and
YorkUrban dataset, in both accuracy and efficiency. The line description
ability of ELSD also outperforms the previous works on the line matching task.


Abstract:
  Quantum graphs are defined by having a Laplacian defined on the edges a
metric graph with boundary conditions on each vertex such that the resulting
operator, L, is self-adjoint. We use Neumann boundary conditions. The spectrum
of L does not determine the graph uniquely, that is, there exist non-isomorphic
graphs with the same spectra. There are few known examples of pairs of
non-isomorphic but isospectral quantum graphs. We have found all pairs of
isospectral but non-isomorphic equilateral connected quantum graphs with at
most seven vertices. We find three isospectral triplets including one involving
a loop. We also present a combinatorial method to generate arbitrarily large
sets of isospectral graphs and give an example of an isospectral set of four.
This has been done this using computer algebra. We discuss the possibilities
that our program is incorrect, present our tests and open source it for
inspection at github.com/meapistol/Spectra-of-graphs.


Abstract:
  Spin ensembles coupled to optical cavities provide a powerful platform for
engineering synthetic quantum matter. Recently, we demonstrated that cavity
mediated infinite range interactions can induce fast scrambling in a Heisenberg
$XXZ$ spin chain (Phys. Rev. Research {\bf 2}, 043399 (2020)). In this work, we
analyze the kaleidoscope of quantum phases that emerge in this system from the
interplay of these interactions. Employing both analytical spin-wave theory as
well as numerical DMRG calculations, we find that there is a large parameter
regime where the continuous $U(1)$ symmetry of this model is spontaneously
broken and the ground state of the system exhibits $XY$ order. This kind of
symmetry breaking and the consequent long range order is forbidden for short
range interacting systems by the Mermin-Wagner theorem. Intriguingly, we find
that the $XY$ order can be induced by even an infinitesimally weak infinite
range interaction. Furthermore, we demonstrate that in the $U(1)$ symmetry
broken phase, the half chain entanglement entropy violates the area law
logarithmically. Finally, we discuss a proposal to verify our predictions in
state-of-the-art quantum emulators.


Abstract:
  The detection and quantification of quantum coherence play significant roles
in quantum information processing. We present an efficient way of tomographic
witnessing for both theoretical and experimental detection of coherence. We
prove that a coherence witness is optimal if and only if all of its diagonal
elements are zero. Naturally, we obtain a bona fide homographic measure of
coherence given by the sum of the absolute values of the real and the imaginary
parts of the non-diagonal entries of a density matrix, together with its
interesting relations with other coherence measures like $l_1$ norm coherence
and robust of coherence.


Abstract:
  The growing size and complexity of software in embedded systems poses new
challenges to the safety assessment of embedded control systems. In industrial
practice, the control software is mostly treated as a black box during the
system's safety analysis. The appropriate representation of the failure
propagation of the software is a pressing need in order to increase the
accuracy of safety analyses. However, it also increase the effort for creating
and maintaining the safety analysis models (such as fault trees) significantly.
In this work, we present a method to automatically generate Component Fault
Trees from Continuous Function Charts. This method aims at generating the
failure propagation model of the detailed software specification. Hence,
control software can be included into safety analyses without additional manual
effort required to construct the safety analysis models of the software.
Moreover, safety analyses created during early system specification phases can
be verified by comparing it with the automatically generated one in the
detailed specification phased.


Abstract:
  We initiate the study of computational complexity of graph coverings, aka
locally bijective graph homomorphisms, for {\em graphs with semi-edges}. The
notion of graph covering is a discretization of coverings between surfaces or
topological spaces, a notion well known and deeply studied in classical
topology. Graph covers have found applications in discrete mathematics for
constructing highly symmetric graphs, and in computer science in the theory of
local computations. In 1991, Abello et al. asked for a classification of the
computational complexity of deciding if an input graph covers a fixed target
graph, in the ordinary setting (of graphs with only edges). Although many
general results are known, the full classification is still open. In spite of
that, we propose to study the more general case of covering graphs composed of
normal edges (including multiedges and loops) and so-called semi-edges.
Semi-edges are becoming increasingly popular in modern topological graph
theory, as well as in mathematical physics. They also naturally occur in the
local computation setting, since they are lifted to matchings in the covering
graph. We show that the presence of semi-edges makes the covering problem
considerably harder; e.g., it is no longer sufficient to specify the vertex
mapping induced by the covering, but one necessarily has to deal with the edge
mapping as well. We show some solvable cases, and completely characterize the
complexity of the already very nontrivial problem of covering one- and
two-vertex (multi)graphs with semi-edges. Our NP-hardness results are proven
for simple input graphs, and in the case of regular two-vertex target graphs,
even for bipartite ones. This provides a strengthening of previously known
results for covering graphs without semi-edges, and may contribute to better
understanding of this notion and its complexity.


Abstract:
  In this paper we introduce the stack of polarized twisted conics and we use
it to give a new point of view on $\overline{\mathcal{M}}_2$. In particular, we
present a new and independent approach to the computation of the integral Chow
ring of $\overline{\mathcal{M}}_2$, previously determined by Eric Larson.


Abstract:
  The proton electric and magnetic form factors, $G_E$ and $G_M$, are
intrinsically connected to the spatial distribution of charge and magnetization
in the proton. For decades, Rosenbluth separation measurements of the angular
dependence of elastic e$^-$-p scattering were used to extract $G_E$ and $G_M$.
More recently, polarized electron scattering measurements, aiming to improve
the precision of $G_E$ extractions, showed significant disagreement with
Rosenbluth measurements at large momentum transfers ($Q^2$). This discrepancy
is generally attributed to neglected two-photon exchange (TPE) corrections.
  At larger $Q^2$ values, a new `Super-Rosenbluth' technique was used to
improve the precision of the Rosenbluth extraction, allowing for a better
quantification of the discrepancy, while comparisons of e$^+$-p and e$^-$-p
scattering indicated the presence of TPE corrections, but at $Q^2$ values below
where a clear discrepancy is observed. In this work, we demonstrate the
significant benefits to combining the Super-Rosenbluth technique with positron
beam measurements. This approach provides a greater kinematic reach and is
insensitive to some of the key systematic uncertainties in previous positron
measurements.


Abstract:
  A matroid is uniform if and only if it has no minor isomorphic to
$U_{1,1}\oplus U_{0,1}$ and is paving if and only if it has no minor isomorphic
to $U_{2,2}\oplus U_{0,1}$. This paper considers, more generally, when a
matroid $M$ has no $U_{k,k}\oplus U_{0,\ell}$-minor for a fixed pair of
positive integers $(k,\ell)$. Calling such a matroid $(k,\ell)$-uniform, it is
shown that this is equivalent to the condition that every rank-$(r(M)-k)$ flat
of $M$ has nullity less than $\ell$. Generalising a result of Rajpal, we prove
that for any pair $(k,\ell)$ of positive integers and prime power $q$, only
finitely many simple cosimple $GF(q)$-representable matroids are \kl-uniform.
Consequently, if Rota's Conjecture holds, then for every prime power $q$, there
exists a pair $(k_q,\ell_q)$ of positive integers such that every excluded
minor of $GF(q)$-representability is $(k_q,\ell_q)$-uniform. We also determine
all binary $(2,2)$-uniform matroids and show the maximally $3$-connected
members to be $Z_5\backslash t, AG(4,2), AG(4,2)^*$ and a particular self-dual
matroid $P_{10}$. Combined with results of Acketa and Rajpal, this completes
the list of binary $(k,\ell)$-uniform matroids for which $k+\ell\leq 4$.


Abstract:
  Tracking non-rigidly deforming scenes using range sensors has numerous
applications including computer vision, AR/VR, and robotics. However, due to
occlusions and physical limitations of range sensors, existing methods only
handle the visible surface, thus causing discontinuities and incompleteness in
the motion field. To this end, we introduce 4DComplete, a novel data-driven
approach that estimates the non-rigid motion for the unobserved geometry.
4DComplete takes as input a partial shape and motion observation, extracts 4D
time-space embedding, and jointly infers the missing geometry and motion field
using a sparse fully-convolutional network. For network training, we
constructed a large-scale synthetic dataset called DeformingThings4D, which
consists of 1972 animation sequences spanning 31 different animals or humanoid
categories with dense 4D annotation. Experiments show that 4DComplete 1)
reconstructs high-resolution volumetric shape and motion field from a partial
observation, 2) learns an entangled 4D feature representation that benefits
both shape and motion estimation, 3) yields more accurate and natural
deformation than classic non-rigid priors such as As-Rigid-As-Possible (ARAP)
deformation, and 4) generalizes well to unseen objects in real-world sequences.


Abstract:
  The automatic analysis of fine art paintings presents a number of novel
technical challenges to artificial intelligence, computer vision, machine
learning, and knowledge representation quite distinct from those arising in the
analysis of traditional photographs. The most important difference is that many
realist paintings depict stories or episodes in order to convey a lesson,
moral, or meaning. One early step in automatic interpretation and extraction of
meaning in artworks is the identifications of figures (actors). In Christian
art, specifically, one must identify the actors in order to identify the
Biblical episode or story depicted, an important step in understanding the
artwork. We designed an automatic system based on deep convolutional neural
networks and simple knowledge database to identify saints throughout six
centuries of Christian art based in large part upon saints symbols or
attributes. Our work represents initial steps in the broad task of automatic
semantic interpretation of messages and meaning in fine art.


Abstract:
  The scaling of the turbulent spectra provides a key measurement that allows
to discriminate between different theoretical predictions of turbulence. In the
solar wind, this has driven a large number of studies dedicated to this issue
using in-situ data from various orbiting spacecraft. While a semblance of
consensus exists regarding the scaling in the MHD and dispersive ranges, the
precise scaling in the transition range and the actual physical mechanisms that
control it remain open questions. Using the high-resolution data in the inner
heliosphere from Parker Solar Probe (PSP) mission, we find that the sub-ion
scales (i.e., at the frequency f ~ [2, 9] Hz) follow a power-law spectrum f^a
with a spectral index a varying between -3 and -5.7. Our results also show that
there is a trend toward and anti-correlation between the spectral slopes and
the power amplitudes at the MHD scales, in agreement with previous studies: the
higher the power amplitude the steeper the spectrum at sub-ion scales. A
similar trend toward an anti-correlation between steep spectra and increasing
normalized cross helicity is found, in agreement with previous theoretical
predictions about the imbalanced solar wind. We discuss the ubiquitous nature
of the ion transition range in solar wind turbulence in the inner heliosphere.


Abstract:
  The theory of spectral filtering is a remarkable tool to understand the
statistical properties of learning with kernels. For least squares, it allows
to derive various regularization schemes that yield faster convergence rates of
the excess risk than with Tikhonov regularization. This is typically achieved
by leveraging classical assumptions called source and capacity conditions,
which characterize the difficulty of the learning task. In order to understand
estimators derived from other loss functions, Marteau-Ferey et al. have
extended the theory of Tikhonov regularization to generalized self concordant
loss functions (GSC), which contain, e.g., the logistic loss. In this paper, we
go a step further and show that fast and optimal rates can be achieved for GSC
by using the iterated Tikhonov regularization scheme, which is intrinsically
related to the proximal point method in optimization, and overcomes the
limitation of the classical Tikhonov regularization.


Abstract:
  It is well known that there are asymmetric dependence structures between
financial returns. In this paper we use a new nonparametric measure of local
dependence, the local Gaussian correlation, to improve portfolio allocation. We
extend the classical mean-variance framework, and show that the portfolio
optimization is straightforward using our new approach, only relying on a
tuning parameter (the bandwidth). The new method is shown to outperform the
equally weighted (1/N) portfolio and the classical Markowitz portfolio for
monthly asset returns data.


Abstract:
  We report on the fabrication of fractal dendrites by laser induced melting of
aluminum alloys. We target boron carbide (B4C) that is one of the most
effective radiation-absorbing materials which is characterised by a low
coefficient of thermal expansion. Due to the high fragility of B4C crystals we
were able to introduce its nanoparticles into a stabilization aluminum matrix
of AA385.0. The high intensity laser field action led to the formation of
composite dendrite structures under the effect of local surface melting. The
modelling of the dendrite cluster growth confirms its fractal nature and sheds
light on the pattern behavior of the resulting quasicrystal structure.


Abstract:
  Let $H$ be a simple undirected graph. The family of all matchings of $H$
forms a simplicial complex called the matching complex of $H$. Here , we give a
classification of all graphs with a Gorenstein matching complex. Also we study
when the matching complex of $H$ is Cohen-Macaulay and, in certain classes of
graphs, we fully characterize those graphs which have a Cohen-Macaulay matching
complex. In particular, we characterize when the matching complex of a graph
with girth at least 5 or a complete graph is Cohen-Macaulay.


Abstract:
  Ultra-relativistic heavy-ion collisions are expected to produce the strongest
electromagnetic fields in the known Universe. These highly-Lorentz contracted
fields can manifest themselves as linearly polarized quasi-real photons that
can interact via the Breit-Wheeler process to produce lepton anti-lepton pairs.
The energy and momentum distribution of the produced dileptons carry
information about the strength and spatial distribution of the colliding
fields. Recently it has been demonstrated that photons from these fields can
interact even in heavy-ion collisions with hadronic overlap, providing a purely
electromagnetic probe of the produced medium. In this review we discuss the
recent theoretical progress and experimental advances for mapping the
ultra-strong electromagnetic fields produced in heavy-ion collisions via
measurement of the Breit-Wheeler process.


Abstract:
  In this paper we are dealing with the issue of finding possibly short
synchronizing words in automata with weight assigned to each letter in the
alphabet $\Sigma$. First we discuss some complexity problems, and then we
present new approximation algorithm in four variations.


Abstract:
  In this paper, we consider the discrete Laguerre polynomials $P_{n, N}(z)$
orthogonal with respect to the weight function $w(x) = x^{\alpha} e^{-N cx}$
supported on the infinite nodes $L_N = \{ x_{k,N} = \frac{k^2}{N^2}, k \in
\mathbb{N} \}$. We focus on the "band-saturated region" situation when the
parameter $c > \frac{\pi^2}{4}$. As $n \to \infty$, uniform expansions for
$P_{n, n}(z)$ are achieved for $z$ in different regions in the complex plane.
Typically, the Airy-function expansions and Gamma-function expansions are
derived for $z$ near the endpoints of the band and the origin, respectively.
The asymptotics for the normalizing coefficient $h_{n, N}$, recurrence
coefficients $\mathscr{B}_{n, N}$ and $\mathscr{A}_{n, N}^2$, are also
obtained. Our method is based on the Deift-Zhou steepest descent method for
Riemann-Hilbert problems.


Abstract:
  Debugging is an essential process with a large share of the development
effort, being a relentless quest for offensive code through tracing, inspection
and iterative running sessions. Probably every developer has been in a
situation with a clear wish to rewind time just for a while, only to retry some
actions alternatively, instead of restarting the entire session. Well, the
genie to fulfill such a wish is known as a reverse debugger. Their inherent
technical complexity makes them very hard to implement, while the imposed
execution overhead turns them to less preferable for adoption. There are only a
few available, most being off-line tools, working on recorded, previously run,
sessions. We consider live reverse debuggers both challenging and promising,
since they can fit into existing forward debuggers, and we developed the first
live reverse debugger on top of LLDB, discussing in detail our implementation
approach.


Abstract:
  Graph structures are powerful tools for modeling the relationships between
textual elements. Graph-of-Words (GoW) has been adopted in many Natural
Language tasks to encode the association between terms. However, GoW provides
few document-level relationships in cases when the connections between
documents are also essential. For identifying sub-events on social media like
Twitter, features from both word- and document-level can be useful as they
supply different information of the event. We propose a hybrid Graph-of-Tweets
(GoT) model which combines the word- and document-level structures for modeling
Tweets. To compress large amount of raw data, we propose a graph merging method
which utilizes FastText word embeddings to reduce the GoW. Furthermore, we
present a novel method to construct GoT with the reduced GoW and a Mutual
Information (MI) measure. Finally, we identify maximal cliques to extract
popular sub-events. Our model showed promising results on condensing
lexical-level information and capturing keywords of sub-events.


Abstract:
  Environmental epidemiologists are increasingly interested in establishing
causality between exposures and health outcomes. A popular model for causal
inference is the Rubin Causal Model (RCM), which typically seeks to estimate
the average difference in study units' potential outcomes. An important
assumption under RCM is no interference; that is, the potential outcomes of one
unit are not affected by the exposure status of other units. The no
interference assumption is violated if we expect spillover or diffusion of
exposure effects based on units' proximity to other units and several other
causal estimands arise. Air pollution epidemiology typically violates this
assumption when we expect upwind events to affect downwind or nearby locations.
This paper adapts causal assumptions from social network research to address
interference and allow estimation of both direct and spillover causal effects.
We use propensity score-based methods to estimate these effects when
considering the effects of the Environmental Protection Agency's 2005
nonattainment designations for particulate matter with aerodynamic diameter
less than 2.5 micrograms per cubic meter (PM2.5) on lung cancer incidence using
county-level data obtained from the Surveillance, Epidemiology, and End Results
(SEER) Program. We compare these methods in a rigorous simulation study that
considers both spatially autocorrelated variables, interference, and missing
confounders. We find that pruning and matching based on the propensity score
produces the highest probability coverage of the true causal effects and lower
mean squared error. When applied to the research question, we found protective
direct and spillover causal effects.


Abstract:
  With the widespread use of Deep Neural Networks (DNNs), machine learning
algorithms have evolved in two diverse directions -- one with ever-increasing
connection density for better accuracy and the other with more compact sizing
for energy efficiency. The increase in connection density increases on-chip
data movement, which makes efficient on-chip communication a critical function
of the DNN accelerator. The contribution of this work is threefold. First, we
illustrate that the point-to-point (P2P)-based interconnect is incapable of
handling a high volume of on-chip data movement for DNNs. Second, we evaluate
P2P and network-on-chip (NoC) interconnect (with a regular topology such as a
mesh) for SRAM- and ReRAM-based in-memory computing (IMC) architectures for a
range of DNNs. This analysis shows the necessity for the optimal interconnect
choice for an IMC DNN accelerator. Finally, we perform an experimental
evaluation for different DNNs to empirically obtain the performance of the IMC
architecture with both NoC-tree and NoC-mesh. We conclude that, at the tile
level, NoC-tree is appropriate for compact DNNs employed at the edge, and
NoC-mesh is necessary to accelerate DNNs with high connection density.
Furthermore, we propose a technique to determine the optimal choice of
interconnect for any given DNN. In this technique, we use analytical models of
NoC to evaluate end-to-end communication latency of any given DNN. We
demonstrate that the interconnect optimization in the IMC architecture results
in up to 6$\times$ improvement in energy-delay-area product for VGG-19
inference compared to the state-of-the-art ReRAM-based IMC architectures.


Abstract:
  Currently, urban autonomous driving remains challenging because of the
complexity of the driving environment. Learning-based approaches, such as
reinforcement learning (RL) and imitation learning (IL), have indicated
superiority over rule-based approaches, showing great potential to make
decisions intelligently, but they still do not work well in urban driving
situations. To better tackle this problem, this paper proposes a novel
learning-based method that combines deep reinforcement learning with expert
demonstrations, focusing on longitudinal motion control in autonomous driving.
Our proposed method employs the soft actor-critic structure and modifies the
learning process of the policy network to incorporate both the goals of
maximizing reward and imitating the expert. Moreover, an adaptive prioritized
experience replay is designed to sample experience from both the agent's
self-exploration and expert demonstration, in order to improve the sample
efficiency. The proposed method is validated in a simulated urban roundabout
scenario and compared with various prevailing RL and IL baseline approaches.
The results manifest that the proposed method has a faster training speed, as
well as better performance in navigating safely and time-efficiently. The
ablation study reveals that the prioritized replay and expert demonstration
filter play important roles in our proposed method.


Abstract:
  Video quality assessment (VQA) is now a fast-growing subject, maturing in the
full reference (FR) case, yet challenging in the exploding no reference (NR)
case. We investigate variants of the popular VMAF video quality assessment
algorithm for the FR case, using both support vector regression and feedforward
neural networks. We extend it to the NR case, using some different features but
similar learning, to develop a partially unified framework for VQA. When fully
trained, FR algorithms such as VMAF perform very well on test datasets,
reaching 90%+ match in PCC and SRCC; but for predicting performance in the
wild, we train/test from scratch for each database. With an 80/20 train/test
split, we still achieve about 90% performance on average in both PCC and SRCC,
with up to 7-9% gains over VMAF, using an improved motion feature and better
regression. Moreover, we even get decent performance (about 75%) if we ignore
the reference, treating FR as NR, partly justifying our attempts at
unification. In the true NR case, we reduce complexity vs. leading recent
algorithms VIDEVAL, RAPIQUE, yet achieve performance within 3-5%. Moreover, we
develop a method to analyze the saliency of features, and conclude that for
both VIDEVAL and RAPIQUE, a small subset of their features are providing the
bulk of the performance. In short, we find encouraging improvements in
trainability in FR, while constraining training complexity against leading
methods in NR, elucidating the saliency of features for feature selection.


Abstract:
  Primordial black holes (PBHs), formed out of large overdensities in the early
Universe, are a viable dark matter (DM) candidate over a broad range of masses.
Ultra-light, asteroid-mass PBHs with masses around $10^{17}$ g are particularly
interesting as current observations allow them to constitute the entire DM
density. PBHs in this mass range emit $\sim$ MeV photons via Hawking radiation
which can directly be detected by the gamma ray telescopes, such as the
upcoming AMEGO. In this work we forecast how well an instrument with the
sensitivity of AMEGO will be able to detect, or rule out, PBHs as a DM
candidate, by searching for their evaporating signature when marginalizing over
the Galactic and extra-Galactic gamma-ray backgrounds. We find that an
instrument with the sensitivity of AMEGO could exclude non-rotating PBHs as the
only DM component for masses up to $7 \times 10^{17}$ g at 95% confidence level
(C.L.) for a monochromatic mass distribution, improving upon current bounds by
nearly an order of magnitude. The forecasted constraints are more stringent for
PBHs that have rotation, or which follow extended mass distributions.


Abstract:
  The diurnal cycle CO$_2$ emissions from fossil fuel combustion and cement
production reflect seasonality, weather conditions, working days, and more
recently the impact of the COVID-19 pandemic. Here, for the first time we
provide a daily CO$_2$ emission dataset for the whole year of 2020 calculated
from inventory and near-real-time activity data (called Carbon Monitor project:
https://carbonmonitor.org). It was previously suggested from preliminary
estimates that did not cover the entire year of 2020 that the pandemics may
have caused more than 8% annual decline of global CO$_2$ emissions. Here we
show from detailed estimates of the full year data that the global reduction
was only 5.4% (-1,901 MtCO$_2$, ). This decrease is 5 times larger than the
annual emission drop at the peak of the 2008 Global Financial Crisis. However,
global CO$_2$ emissions gradually recovered towards 2019 levels from late April
with global partial re-opening. More importantly, global CO$_2$ emissions even
increased slightly by +0.9% in December 2020 compared with 2019, indicating the
trends of rebound of global emissions. Later waves of COVID-19 infections in
late 2020 and corresponding lockdowns have caused further CO$_2$ emissions
reductions particularly in western countries, but to a much smaller extent than
the declines in the first wave. That even substantial world-wide lockdowns of
activity led to a one-time decline in global CO$_2$ emissions of only 5.4% in
one year highlights the significant challenges for climate change mitigation
that we face in the post-COVID era. These declines are significant, but will be
quickly overtaken with new emissions unless the COVID-19 crisis is utilized as
a break-point with our fossil-fuel trajectory, notably through policies that
make the COVID-19 recovery an opportunity to green national energy and
development plans.


Abstract:
  A laser multicharged ion source was used to perform interfacial treatment of
the 4H-SiC/ SiO2 interface using B and Ba ions. A Q-switched Nd:YAG laser
(wavelength {\lambda} = 1064 nm, pulse width {\tau} = 7 ns, and fluence F = 135
J/cm2) was used to ablate B and Ba targets to generate multicharged ions. The
ions were deflected by an electrostatic field to separate them from the
neutrals. The multicharged ions were used for nanometer layer growth and
shallow ion implantation in 4H-SiC. Several metal-oxide-semiconductor
capacitors (MOSCAP) were fabricated with a combination of B and Ba at the
SiC/SiO2 interface. High-low C-V measurements were used to characterize the
MOSCAPs. The B interfacial layer reduced the MOSCAP flatband voltage from 4.5
to 0.04 V, while the Ba layer had a negligible effect.


Abstract:
  Nanoconfinement has been shown to drastically affect the physical properties
of water. Its effects on reactivity and dissociation, however, remain
controversial, despite their importance to understand aqueous chemistry at
interfaces, pores, aerosolos or protein cavities, and the growing evidence
indicating the acceleration of chemical kinetics in confined environments. The
dissociation constant $K_w$ in nanospaces has been assessed from experiments
and simulations in a few specific cases, leading to dissimilar conclusions.
Here, using carefully designed ab-initio simulations and data-science tools, we
demonstrate, challenging current misconceptions, that the energetics of bulk
water dissociation remains unchanged to surprisingly small length-scales
including clusters of only a dozen water molecules. This is rooted in the fact
that most of the free-energy involved in water autoionization comes from
breaking the O-H covalent bond, which has a comparable barrier in the bulk
liquid or in droplets of nanometer size. The subsequent separation of the
hydroxide and hydronium species contributes a marginal fraction of the total
free-energy, for which reason it turns out that confinement exerts little
control on this process. The present work provides a definitive and fundamental
description of the mechanism and thermodynamics of water dissociation at
different scales with broader implications on water's self-ionization at the
air-liquid interface and on chemical reactivity under nanoconfinement.


Abstract:
  Recent experiments have demonstrated strong light-matter coupling between
electromagnetic nanoresonators and pristine sheets of two-dimensional
semiconductors, and it has been speculated whether these systems can enter the
quantum regime operating at the few-polariton level. To address this question,
we present a first-principles microscopic quantum theory for the interaction
between excitons in an infinite sheet of two-dimensional material and a
localised electromagnetic resonator. We find that the light-matter interaction
breaks the symmetry of the otherwise translation-invariant system and thereby
effectively generates a localised exciton mode, which is coupled to an
environment of residual exciton modes. This dissipative coupling increases with
tighter lateral confinement, and our analysis reveals this to be a potential
challenge in realising nonlinear exciton-exciton interaction. Nonetheless, we
predict that polariton blockade due to nonlinear exciton-exciton interactions
is well within reach for nanoresonators coupled to transition-metal
dichalcogenides, provided that the lateral resonator mode confinement can be
sufficiently small that the nonlinearity overcomes the polariton dephasing
caused by phonon interactions.


Abstract:
  The sample efficiency of Bayesian optimization(BO) is often boosted by
Gaussian Process(GP) surrogate models. However, on mixed variable spaces,
surrogate models other than GPs are prevalent, mainly due to the lack of
kernels which can model complex dependencies across different types of
variables. In this paper, we propose the frequency modulated (FM) kernel
flexibly modeling dependencies among different types of variables, so that BO
can enjoy the further improved sample efficiency. The FM kernel uses distances
on continuous variables to modulate the graph Fourier spectrum derived from
discrete variables. However, the frequency modulation does not always define a
kernel with the similarity measure behavior which returns higher values for
pairs of more similar points. Therefore, we specify and prove conditions for FM
kernels to be positive definite and to exhibit the similarity measure behavior.
In experiments, we demonstrate the improved sample efficiency of GP BO using FM
kernels (BO-FM).On synthetic problems and hyperparameter optimization problems,
BO-FM outperforms competitors consistently. Also, the importance of the
frequency modulation principle is empirically demonstrated on the same
problems. On joint optimization of neural architectures and SGD
hyperparameters, BO-FM outperforms competitors including Regularized
evolution(RE) and BOHB. Remarkably, BO-FM performs better even than RE and BOHB
using three times as many evaluations.


Abstract:
  The state-of-the-art on basic, single-antecedent anaphora has greatly
improved in recent years. Researchers have therefore started to pay more
attention to more complex cases of anaphora such as split-antecedent anaphora,
as in Time-Warner is considering a legal challenge to Telecommunications Inc's
plan to buy half of Showtime Networks Inc-a move that could lead to all-out war
between the two powerful companies. Split-antecedent anaphora is rarer and more
complex to resolve than single-antecedent anaphora; as a result, it is not
annotated in many datasets designed to test coreference, and previous work on
resolving this type of anaphora was carried out in unrealistic conditions that
assume gold mentions and/or gold split-antecedent anaphors are available. These
systems also focus on split-antecedent anaphors only. In this work, we
introduce a system that resolves both single and split-antecedent anaphors, and
evaluate it in a more realistic setting that uses predicted mentions. We also
start addressing the question of how to evaluate single and split-antecedent
anaphors together using standard coreference evaluation metrics.


Abstract:
  Starting with twisted bilayer graphene, graphene-based moir\'e materials have
recently been established as a new platform for studying strong electron
correlations. In this paper, we study twisted graphene monolayers on trilayer
graphene and demonstrate that this system can host flat bands when the twist
angle is close to the magic-angle of 1.16$^\circ$. When monolayer graphene is
twisted on ABA trilayer graphene, the flat bands are not isolated, but are
intersected by a Dirac cone with a large Fermi velocity. In contrast, graphene
twisted on ABC trilayer graphene (denoted AtABC) exhibits a gap between flat
and remote bands. Since ABC trilayer graphene and twisted bilayer graphene are
known to host broken-symmetry phases, we further investigate the ostensibly
similar magic angle AtABC system. We study the effect of electron-electron
interactions in AtABC using both Hartree theory and an atomic Hubbard theory to
calculate the magnetic phase diagram as a function of doping, twist angle, and
perpendicular electric field. Our analysis reveals a rich variety of magnetic
orderings, including ferromagnetism and ferrimagnetism, and demonstrates that a
perpendicular electric field makes AtABC more susceptible to magnetic ordering.


Abstract:
  Accurately describing and detecting 2D and 3D keypoints is crucial to
establishing correspondences across images and point clouds. Despite a plethora
of learning-based 2D or 3D local feature descriptors and detectors having been
proposed, the derivation of a shared descriptor and joint keypoint detector
that directly matches pixels and points remains under-explored by the
community. This work takes the initiative to establish fine-grained
correspondences between 2D images and 3D point clouds. In order to directly
match pixels and points, a dual fully convolutional framework is presented that
maps 2D and 3D inputs into a shared latent representation space to
simultaneously describe and detect keypoints. Furthermore, an ultra-wide
reception mechanism in combination with a novel loss function are designed to
mitigate the intrinsic information variations between pixel and point local
regions. Extensive experimental results demonstrate that our framework shows
competitive performance in fine-grained matching between images and point
clouds and achieves state-of-the-art results for the task of indoor visual
localization. Our source code will be available at [no-name-for-blind-review].


Abstract:
  Using the two-level approximation of the energy barrier, we perform extensive
kinetic Monte Carlo simulations to probe the relaxation characteristics in a
two-dimensional ($L^{}_x\times L^{}_y$) array of magnetic nanoparticle as a
function of dipolar interaction strength $h^{}_d$, aspect ratio
$A^{}_r=L^{}_y/L^{}_x$, and temperature $T$. In the case of weak dipolar
interaction ($h^{}_d\approx0$) and substantial temperature, the magnetic
relaxation follows the N\'eel Brown model as expected. Interestingly, the
dipolar interaction of enough strength is found to induce antiferromagnetic
coupling in the square arrangement of MNPs ($A^{}_r=1.0$), resulting in the
fastening of magnetic relaxation with $h^{}_d$. There is also a rapid increase
in relaxation even with $A^{}_r<100$ above a particular dipolar interaction
strength $h^{\star}_d$, which gets enhanced with $A^{}_r$. Remarkably, there is
a slowing down of magnetic relaxation with $h^{}_d$ for the highly anisotropic
system such as linear chain of MNPs. It is because the dipolar interaction
induces ferromagnetic interaction in such a case. The thermal fluctuations also
affect the relaxation properties drastically. In the case of weak dipolar
limit, magnetization relaxes rapidly with $T$ because of enhancement in thermal
fluctuations. The effect of dipolar interaction and aspect ratio on the
magnetic relaxation is also clearly indicated in the variation of N\'eel
relaxation time $\tau^{}_N$. In the presence of strong dipolar interaction
($h^{}_d>0.3$) and $A^{}_r=1.0$, $\tau^{}_N$ decreases with $h^{}_d$ for a
given temperature. On the other hand, there is an increase in $\tau^{}_N$ with
$h^{}_d$ for huge $A^{}_r$ $(>100)$. We believe that the concepts presented in
this work are beneficial for the efficient use of self-assembled MNPs array in
data storage and other related applications.


Abstract:
  A permutation $\pi$ contains a pattern $\sigma$ if and only if there is a
subsequence in $\pi$ with its letters are in the same relative order as those
in $\sigma$. Partially ordered patterns (POPs) provide a convenient way to
denote patterns in which the relative order of some of the letters does not
matter. This paper elucidates connections between the avoidance sets of a few
POPs with other combinatorial objects, directly answering five open questions
posed by Gao and Kitaev \cite{gao-kitaev-2019}. This was done by thoroughly
analysing the avoidance sets and developing recursive algorithms to derive
these sets and their corresponding combinatorial objects in parallel, which
yielded a natural bijection. We also analysed an avoidance set whose simple
permutations are enumerated by the Fibonacci numbers and derived an algorithm
to obtain them recursively.


Abstract:
  We study the implication of $J/\psi$ decay into invisible particles for light
sterile neutrino and sub-GeV dark matter (DM). The low-energy effective field
theories (EFTs) are used for the description of general neutrino interactions
and the Dirac fermion DM coupled to charm quark. For $J/\psi\to \gamma+{\rm
invisible}$, we perform the likelihood fits for the individual neutrino and DM
operators with distinct Lorentz structures and photon spectra. The limits on
the decay branching fractions are obtained for different neutrino or DM
scenarios and then converted to the lower bounds on the new energy scales. The
most stringent bounds on the energy scale in neutrino and DM EFTs are 12.8 GeV
and 11.6 GeV, respectively. The purely invisible decay $J/\psi\to {\rm
invisible}$ provides complementary constraints on the effective operators. The
relevant bound on the energy scale is above 100 GeV for the dipole operators.
We also evaluate the limit on the DM-nucleon scattering cross section converted
from $J/\psi$ data. The data of $J/\psi$ invisible decays are sensitive to the
light DM mass range where the DM direct detection experiments cannot probe yet.
The future Super Tau Charm Factory after one year run can push the limits down
by two orders of magnitude.


Abstract:
  Superpixels are higher-order perceptual groups of pixels in an image, often
carrying much more information than raw pixels. There is an inherent relational
structure to the relationship among different superpixels of an image. This
relational information can convey some form of domain information about the
image, e.g. relationship between superpixels representing two eyes in a cat
image. Our interest in this paper is to construct computer vision models,
specifically those based on Deep Neural Networks (DNNs) to incorporate these
superpixels information. We propose a methodology to construct a hybrid model
that leverages (a) Convolutional Neural Network (CNN) to deal with spatial
information in an image, and (b) Graph Neural Network (GNN) to deal with
relational superpixel information in the image. The proposed deep model is
learned using a generic hybrid loss function that we call a `hybrid' loss. We
evaluate the predictive performance of our proposed hybrid vision model on four
popular image classification datasets: MNIST, FMNIST, CIFAR-10 and CIFAR-100.
Moreover, we evaluate our method on three real-world classification tasks:
COVID-19 X-Ray Detection, LFW Face Recognition, and SOCOFing Fingerprint
Identification. The results demonstrate that the relational superpixel
information provided via a GNN could improve the performance of standard
CNN-based vision systems.


Abstract:
  We consider the Cauchy problem for the Hardy parabolic equation $\partial_t
u-\Delta u=|x|^{-\gamma}u^p$ with initial data $u_0$ singular at some point
$z$. Our main results show that, if $z\neq 0$, then the optimal strength of the
singularity of $u_0$ at $z$ for the solvability of the equation is the same as
that of the Fujita equation $\partial_t u-\Delta u=u^p$. Moreover, if $z=0$,
then the optimal singularity for the Hardy parabolic equation is weaker than
that of the Fujita equation. We also obtain analogous results for a fractional
case $\partial_t u+(-\Delta)^{\theta/2} u=|x|^{-\gamma}u^p$ with $0<\theta<2$.


Abstract:
  We extend the notion of quasi-transitive orientations of graphs to
2-edge-coloured graphs. By relating quasi-transitive $2$-edge-colourings to an
equivalence relation on the edge set of a graph, we classify those graphs that
admit a quasi-transitive $2$-edge-colouring. As a contrast to
Ghouil\'{a}-Houri's classification of quasi-transitively orientable graphs as
comparability graphs, we find quasi-transitively $2$-edge-colourable graphs do
not admit a forbiddden subgraph characterization. Restricting the problem to
comparability graphs, we show that the family of uniquely quasi-transitively
orientable comparability graphs is exactly the family of comparabilty graphs
that admit no quasi-transitive $2$-edge-colouring.


Abstract:
  The properties of exotic stars are investigated. In particular, we study
objects made entirely of dark matter and we take into account intrinsic
anisotropies which have been ignored so far. We obtain exact analytical
solutions to the structure equations and we we show that those solutions i) are
well behaved within General Relativity, and ii) are capable of describing
realistic astrophysical configurations.


Abstract:
  Spatial confinement of matter in functional nanostructures has propelled
these systems to the forefront of nanoscience, both as a playground for exotic
physics and quantum phenomena and in multiple applications including
plasmonics, optoelectronics, and sensing. In parallel, the emergence of
monochromated electron energy loss spectroscopy (EELS) has enabled exploration
of local nanoplasmonic functionalities within single nanoparticles and the
collective response of nanoparticle assemblies, providing deep insight into the
associated mechanisms. However, modern synthesis processes for plasmonic
nanostructures are often limited in the types of accessible geometry and
materials, and even then, limited to spatial precisions on the order of tens of
nm, precluding the direct exploration of critical aspects of the
structure-property relationships. Here, we use the atomic-sized probe of the
scanning transmission electron microscope (STEM) to perform precise sculpting
and design of nanoparticle configurations. Furthermore, using low-loss (EELS),
we provide dynamic analyses of evolution of the plasmonic response during the
sculpting process. We show that within self-assembled systems of nanoparticles,
individual nanoparticles can be selectively removed, reshaped, or arbitrarily
patterned with nanometer-level resolution, effectively modifying the plasmonic
response in both space and energy domains. This process significantly increases
the scope for design possibilities and presents opportunities for arbitrary
structure development, which are ultimately key for nanophotonic design.
Nanosculpting introduces yet another capability to the electron microscope.


Abstract:
  We show that a one-dimensional ordered fermionic lattice system with
power-law-decaying hopping, when connected to two baths at its two ends with
different chemical potentials at zero temperature, features two phases showing
sub-diffusive scaling of conductance with system size. These phases have no
analogues in the isolated system (i.e, in absence of the baths) where the
transport is perfectly ballistic. In the open system scenario, interestingly,
there occurs two chemical-potential-driven sub-diffusive to ballistic phase
transitions at zero temperature. We discuss how these phase transitions, to our
knowledge, are different from all the known non-equilibrium quantum phase
transitions. We provide a clear understanding of the microscopic origin of
these phases and argue that the sub-diffusive phases are robust against the
presence of arbitrary number-conserving many-body interactions in the system.
These phases showing sub-diffusive scaling of conductance with system size in a
two-terminal set-up are therefore universal properties of all ordered
one-dimensional number-conserving fermionic systems with power-law-decaying
hopping at zero temperature.


Abstract:
  Session types denote message protocols between concurrent processes, allowing
a type-safe expression of inter-process communication. Although previous work
demonstrate a well-defined notion of subtyping where processes have different
perceptions of the protocol, these formulations were limited to linear session
types where each channel of communication has a unique provider and client. In
this paper, we extend subtyping to shared session types where channels can now
have multiple clients instead of a single client. We demonstrate that this
generalization can statically capture protocol requirements that span multiple
phases of interactions of a client with a shared service provider, something
not possible in prior proposals. Moreover, the phases are manifest in the type
of the client.


Abstract:
  Zero Trust security model permits to secure cloud native applications while
encrypting all network communication, authenticating, and authorizing every
request. The service mesh can enable Zero Trust using a side-car proxy without
changes to the application code. To the best of our knowledge, no previous work
has provided a performance analysis of Zero Trust in a multi-cloud environment.
This paper proposes a multi-cloud framework and a testing workflow to analyze
performance of the data plane under load and the impact on the control plane,
when Zero Trust is enabled. The results of preliminary tests show that Istio
has reduced latency variability in responding to sequential HTTP requests.
Results also reveal that the overall CPU and memory usage can increase based on
service mesh configuration and the cloud environment.


Abstract:
  Modern Automatic Speech Recognition (ASR) systems can achieve high
performance in terms of recognition accuracy. However, a perfectly accurate
transcript still can be challenging to read due to disfluency, filter words,
and other errata common in spoken communication. Many downstream tasks and
human readers rely on the output of the ASR system; therefore, errors
introduced by the speaker and ASR system alike will be propagated to the next
task in the pipeline. In this work, we propose an ASR post-processing model
that aims to transform the incorrect and noisy ASR output into a readable text
for humans and downstream tasks. We leverage the Metadata Extraction (MDE)
corpus to construct a task-specific dataset for our study. Since the dataset is
small, we propose a novel data augmentation method and use a two-stage training
strategy to fine-tune the RoBERTa pre-trained model. On the constructed test
set, our model outperforms a production two-step pipeline-based post-processing
method by a large margin of 13.26 on readability-aware WER (RA-WER) and 17.53
on BLEU metrics. Human evaluation also demonstrates that our method can
generate more human-readable transcripts than the baseline method.


Abstract:
  Identifying an entanglement island requires exquisite control over the
entropy of quantum fields, which is available only in toy models. Here we
present a set of sufficient conditions that guarantee the existence of an
island and place an upper bound on the entropy computed by the island rule.
This is enough to derive the main features of the Page curve for an evaporating
black hole in any spacetime dimension. Our argument makes use of Wall's maximin
formulation and the Quantum Focusing Conjecture. As a corollary, we derive a
novel entropy bound.


Abstract:
  The present work reports on the numerical investigation of NOx in three
turbulent piloted diffusion flames of different levels of extinction. The study
involves two-dimensional axisymmetric modeling of combustion in these flames
with fairly detailed chemistry, i.e. GRI 3.0 mechanism. The main focus of the
study is to analyze the effects of the two different combustion model
approaches, such as infinitely fast chemistry based unsteady flamelet and
finite rate chemistry based EDC, in predicting the NOx formation in three
piloted methane jet flames (Sandia D, E, and F). The EDC approach is able to
predict the passive scalar quantities but shows over-prediction in the reactive
scalar quantities and NO prediction, while the unsteady flamelet modeling is
found to be essential in predicting the accurate formation of slow kinetic
species like NOx. The inability of flamelet and EDC approach in capturing
localized flame extinction is observed, which lead to an over-prediction of NOx
at larger downstream locations. Further, the dominance of NOx formation
pathways is investigated in all three flames.


Abstract:
  We prove that the fundamental group of a finite graph of convergence groups
with parabolic edge groups is a convergence group. Using this result, under
some mild assumptions, we prove combination theorems for a graph of convergence
groups with dynamically quasi-convex edge groups (Theorems 1.3, 1.5). In the
proofs of these results, we are generalizing Dahmani's technique. Finally, we
prove that the fundamental group of a graph of relatively hyperbolic groups
with edge groups either parabolic or infinite cyclic is relatively hyperbolic
and construct the Bowditch boundary.


Abstract:
  Energy efficiency and energy conservation are one of the most crucial
constraints for meeting the 20MW power envelope desired for exascale systems.
Towards this, most of the research in this area has been focused on the
utilization of user-controllable hardware switches such as per-core dynamic
voltage frequency scaling (DVFS) and software controlled clock modulation at
the application level. In this paper, we present a tuning plugin for the
Periscope Tuning Framework which integrates fine-grained autotuning at the
region level with DVFS and uncore frequency scaling (UFS). The tuning is based
on a feed-forward neural network which is formulated using Performance
Monitoring Counters (PMC) supported by x86 systems and trained using
standardized benchmarks. Experiments on five standardized hybrid benchmarks
show an energy improvement of 16.1% on average when the applications are tuned
according to our methodology as compared to 7.8% for static tuning.


Abstract:
  We investigate the sensitivity of the FASER$\nu$ detector to new physics in
the form of non-standard neutrino interactions. FASER$\nu$, which has recently
been installed 480 m downstream of the ATLAS interaction point, will for the
first time study interactions of multi-TeV neutrinos from a controlled source.
Our formalism -- which is applicable to any current and future neutrino
experiment -- is based on the Standard Model Effective Theory~(SMEFT) and its
counterpart, Weak Effective Field Theory~(WEFT), below the electroweak scale.
Starting from the WEFT Lagrangian, we compute the coefficients that modify
neutrino production in meson decays and detection via deep-inelastic
scattering, and we express the new physics effects in terms of modified flavor
transition probabilities. For some coupling structures, we find that FASER$\nu$
will be able to constrain interactions that are two to three orders of
magnitude weaker than Standard Model weak interactions, implying that the
experiment will be indirectly probing new physics at the multi-TeV scale. In
some cases, FASER$\nu$ constraints will become comparable to existing limits -
some of them derived for the first time in this paper - already with
$150~$fb${}^{-1}$ of data.


Abstract:
  The BKK theorem states that the mixed volume of the Newton polytopes of a
system of polynomial equations upper bounds the number of isolated torus
solutions of the system. Homotopy continuation solvers make use of this fact to
pick efficient start systems. For systems where the mixed volume bound is not
attained, such methods are still tracking more paths than necessary. We propose
a strategy of improvement by lifting a system to an equivalent system with a
strictly lower mixed volume at the expense of more variables. We illustrate
this idea providing lifting constructions for arbitrary bivariate systems and
certain dense-enough systems.


Abstract:
  In this paper, we are introducing a novel model of artificial intelligence,
the functional neural network for modeling of human decision-making processes.
This neural network is composed of multiple artificial neurons racing in the
network. Each of these neurons has a similar structure programmed independently
by the users and composed of an intention wheel, a motor core and a sensory
core representing the user itself and racing at a specific velocity. The
mathematics of the neuron's formulation and the racing mechanism of multiple
nodes in the network will be discussed, and the group decision process with
fuzzy logic and the transformation of these conceptual methods into practical
methods of simulation and in operations will be developed. Eventually, we will
describe some possible future research directions in the fields of finance,
education and medicine including the opportunity to design an intelligent
learning agent with application in business operations supervision. We believe
that this functional neural network has a promising potential to transform the
way we can compute decision-making and lead to a new generation of neuromorphic
chips for seamless human-machine interactions.


Abstract:
  An approach to reduce motion artifacts in Quantitative Susceptibility Mapping
using deep learning is proposed. We use an affine motion model with randomly
created motion profiles to simulate motion-corrupted QSM images. The simulated
QSM image is paired with its motion-free reference to train a neural network
using supervised learning. The trained network is tested on unseen simulated
motion-corrupted QSM images, in healthy volunteers and in Parkinson's disease
patients. The results show that motion artifacts, such as ringing and ghosting,
were successfully suppressed.


Abstract:
  In this paper, we determine the harvested power region of a two-user
multiple-input single-output (MISO) wireless power transfer (WPT) system for a
non-linear model of the rectennas at the energy harvester (EH) nodes. To this
end, we characterize the distributions of the transmit symbol vector that
achieve individual points on the boundary of this region. Each distribution is
obtained as solution of an optimization problem where we maximize a weighted
sum of the average harvested powers at the EH nodes under a constraint on the
power budget of the transmitter. We prove that the optimal transmit strategy
employs two beamforming vectors and scalar unit norm transmit symbols with
arbitrary phase. To determine the beamforming vectors, we propose an iterative
algorithm based on a two-dimensional grid search, semi-definite relaxation, and
successive convex approximation. Our numerical results reveal that the proposed
design outperforms two baseline schemes based on a linear EH model and a single
beamforming vector, respectively. Finally, we observe that the harvested power
region is convex and the power harvested at one EH node can be traded for a
higher harvested power at the other node.


Abstract:
  In graph theory and network analysis, node degree is defined as a simple but
powerful centrality to measure the local influence of node in a complex
network. Preferential attachment based on node degree has been widely adopted
for modeling network growth. However, many evidences exist which show deviation
of real network growth from what a pure degree-based model suggests. It seems
that node degree is not a reliable measure for predicting the preference of
newcomers in attaching to the network, or at least, it does not tell the whole
story. In this paper, we argue that there is another dimension to network
growth, one that we call node "coreness". The new dimension gives insights on
the global influence of nodes, in comparison to the local view the degree
metric provides. We found that the probability of existing nodes attracting new
nodes generally follows an exponential dependence on node coreness, while at
the same time, follows a power-law dependence on node degree. That is to say,
high-coreness nodes are more powerful than high-degree nodes in attracting
newcomers. The new dimension further discloses some hidden phenomena which
happen in the process of network growth. The power of node degree in attracting
newcomers increases over time while the influence of coreness decreases, and
finally, they reach a state of equilibrium in the growth. All these theories
have been tested on real-world networks.


Abstract:
  We have performed density functional calculations in conjunction with the
linearized Migdal-Eliashberg equations and the functional derivative approach,
which takes into account the energy-range dependence of the density of states
at the Fermi level ($N(\varepsilon)$ variable on the scale of phonon energy),
to determine the evolution of the critical temperature ($T_{c}$) and the
isotope effect coefficient ($\alpha$) of H$_{3}$S as a function of pressure on
its $lm\bar{3}m$ crystal-structure range (from 162 to 250 GPa). Such approach,
in comparison with $N(\varepsilon)$=$N(0)$=const., improves the agreement of
$T_{c}$ with available experiments on the whole range of studied pressure.
Considering for $\alpha$ two main contributions: one positive coming from the
electron-phonon (el-ph) and the other negative from the electron-electron
interaction (el-el), we obtained a monotonic decrement as a function of
pressure, independent of applied scheme ($N(\varepsilon)$ or $N(0)$). However,
when $N(\varepsilon)$ is taken into account, an important renormalization
occurs on both contributions, el-ph and el-el, improving the agreement with
experimental data, specially for the high-pressure regime. The observed
evolution of $T_{c}$ and $\alpha$ as a function of pressure indicates, thus,
the crucial role of the energy-dependence on $N(\varepsilon)$ for the proper
analysis and description of the superconducting state on high-$T_{c}$ metal
hydrides as H$_{3}$S, by considering the role of its van Hove singularities.


Abstract:
  We propose a novel deep Gaussian process (DGP) inference method for computer
model emulation using stochastic imputation. By stochastically imputing the
latent layers, the approach transforms the DGP into the linked GP, a
state-of-the-art surrogate model formed by linking a system of feed-forward
coupled GPs. This transformation renders a simple while efficient DGP training
procedure that only involves optimizations of conventional stationary GPs. In
addition, the analytically tractable mean and variance of the linked GP allows
one to implement predictions from DGP emulators in a fast and accurate manner.
We demonstrate the method in a series of synthetic examples and real-world
applications, and show that it is a competitive candidate for efficient DGP
surrogate modeling in comparison to the variational inference and the
fully-Bayesian approach. A $\texttt{Python}$ package $\texttt{dgpsi}$
implementing the method is also produced and available at
https://github.com/mingdeyu/DGP.


Abstract:
  We consider the two-dimensional ideal Fermi gas subject to a magnetic field
which is perpendicular to the Euclidean plane $\mathbb R^2$ and whose strength
$B(x)$ at $x\in\mathbb R^2$ converges to some $B_0>0$ as $\|x\|\to\infty$.
Furthermore, we allow for an electric potential $V_\varepsilon$ which vanishes
at infinity. They define the single-particle Landau Hamiltonian of our Fermi
gas (up to gauge fixing). Starting from the ground state of this Fermi gas with
chemical potential $\mu\ge B_0$ we study the asymptotic growth of its bipartite
entanglement entropy associated to $L\Lambda$ as $L\to\infty$ for some fixed
bounded region $\Lambda\subset\mathbb R^2$. We show that its leading order in
$L$ does not depend on the perturbations $B_\varepsilon := B_0 - B$ and
$V_\varepsilon$ if they satisfy some mild decay assumptions. Our result holds
for all $\alpha$-R\' enyi entropies $\alpha>1/3$; for $\alpha\le 1/3$, we have
to assume in addition some differentiability of the perturbations
$B_\varepsilon$ and $V_\varepsilon$. The case of a constant magnetic field
$B_\varepsilon = 0$ and with $V_\varepsilon= 0$ was treated recently for
general $\mu$ by Leschke, Sobolev and Spitzer. Our result thus proves the
stability of that area law under the same regularity assumptions on the
boundary $\partial \Lambda$.


Abstract:
  We theoretically address minimal search strategies of active, self-propelled
particles towards hidden targets in three-dimensional space. The particles can
sense if a target is close, e.g., by detecting signaling molecules released by
a target, but they cannot deduce any directional cues. We focus on composite
search strategies, where particles switch between extensive outer search and
intensive inner search; inner search is started when the proximity of a target
is detected and ends again when a certain inner search time has elapsed. In the
simplest strategy, active particles move ballistically during outer search, and
transiently reduce their directional persistence during inner search. In a
second, adaptive strategy, particles exploit a dynamic scattering effect by
reducing directional persistence only outside a well-defined target zone. These
two search strategies require only minimal information processing capabilities
and a single binary or tertiary internal state, respectively, yet increases the
rate of target encounter substantially. The optimal inner search time scales as
a power-law with exponent -2/3 with target density, reflecting a trade-off
between exploration and exploitation.


Abstract:
  Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be a
powerful method for data clustering. However, SNMF is mathematically formulated
as a non-convex optimization problem, making it sensitive to the initialization
of variables. Inspired by ensemble clustering that aims to seek a better
clustering result from a set of clustering results, we propose self-supervised
SNMF (S$^3$NMF), which is capable of boosting clustering performance
progressively by taking advantage of the sensitivity to initialization
characteristic of SNMF, without relying on any additional information.
Specifically, we first perform SNMF repeatedly with a random nonnegative matrix
for initialization each time, leading to multiple decomposed matrices. Then, we
rank the quality of the resulting matrices with adaptively learned weights,
from which a new similarity matrix that is expected to be more discriminative
is reconstructed for SNMF again. These two steps are iterated until the
stopping criterion/maximum number of iterations is achieved. We mathematically
formulate S$^3$NMF as a constraint optimization problem, and provide an
alternative optimization algorithm to solve it with the theoretical convergence
guaranteed. Extensive experimental results on $10$ commonly used benchmark
datasets demonstrate the significant advantage of our S$^3$NMF over $12$
state-of-the-art methods in terms of $5$ quantitative metrics. The source code
is publicly available at https://github.com/jyh-learning/SSSNMF.


Abstract:
  We present a background model for dark matter searches using an array of
NaI(Tl) crystals in the COSINE-100 experiment that is located in the Yangyang
underground laboratory. The model includes background contributions from both
internal and external sources, including cosmogenic radionuclides and surface
$^{210}$Pb contamination. To improve the model in the low energy region, with
the threshold lowered to 1 keV, we used a depth profile of $^{210}$Pb
contamination in the surface of the NaI(Tl) crystals determined in a comparison
between measured and simulated spectra. We also considered the effect of the
energy scale errors propagated from the statistical uncertainties and the
nonlinear detector response at low energies. The 1.7 years COSINE-100 data
taken between October 21, 2016 and July 18, 2018 were used for this analysis.
The Geant4 toolkit version 10.4.2 was utilized throughout the Monte Carlo
simulations for the possible internal and external origins. In particular, the
version provides a non-Gaussian peak around 50 keV originating from beta decays
of $^{210}$Pb in a good agreement with the measured background. This improved
model estimates that the activities of $^{210}$Pb and $^{3}$H are the dominant
sources of the backgrounds with an average level of 2.73$\pm$0.14
counts/day/keV/kg in the energy region of 1-6 keV, using COSINE-100 data with a
total exposure of 97.7 kg$\cdot$years.


Abstract:
  In this paper we prove the existence of a complete cap of ${\rm PG}(4n+1, q)$
of size $2(q^{2n+1}-1)/(q-1)$, for each prime power $q>2$. It is obtained by
projecting two disjoint Veronese varieties of ${\rm PG}(2n^2+3n, q)$ from a
suitable $(2n^2-n-2)$-dimensional projective space. This shows that the trivial
lower bound for the size of the smallest complete cap of ${\rm PG}(4n+1, q)$ is
essentially sharp.


Abstract:
  We study a classical model for the atom that considers the movement of $n$
charged particles of charge $-1$ (electrons) interacting with a fixed nucleus
of charge $\mu >0$. We show that two global branches of spatial relative
equilibria bifurcate from the $n$-polygonal relative equilibrium for each
critical values $\mu =s_{k}$ for $k\in \lbrack 2,...,n/2]$. In these solutions,
the $n$ charges form $n/h$-groups of regular $h$-polygons in space, where $h$
is the greatest common divisor of $k$ and $n$. Furthermore, each spatial
relative equilibrium has a global branch of relative periodic solutions for
each normal frequency satisfying some nonresonant condition. We obtain
computer-assisted proofs of the existence of several spatial relative
equilibria on global branches away from the $n$-polygonal relative equilibrium.
Moreover, the nonresonant condition of the normal frequencies for some spatial
relative equilibria is verified rigorously using computer-assisted proofs.


Abstract:
  For every given real value of the ratio $\mu:=A_X/G_X>1$ of the arithmetic
and geometric means of a positive random variable $X$ and every real $v>0$,
exact upper bounds on the right- and left-tail probabilities
$\mathsf{P}(X/G_X\ge v)$ and $\mathsf{P}(X/G_X\le v)$ are obtained, in terms of
$\mu$ and $v$. In particular, these bounds imply that $X/G_X\to1$ in
probability as $A_X/G_X\downarrow1$. Such a result may be viewed as a converse
to a reverse Jensen inequality for the strictly concave function $f=\ln$,
whereas the well-known Cantelli and Chebyshev inequalities may be viewed as
converses to a reverse Jensen inequality for the strictly concave quadratic
function $f(x) \equiv -x^2$. As applications of the mentioned new results,
improvements of the Markov, Bernstein--Chernoff, sub-Gaussian, and
Bennett--Hoeffding probability inequalities are given.


Abstract:
  Recently regular decision processes have been proposed as a well-behaved form
of non-Markov decision process. Regular decision processes are characterised by
a transition function and a reward function that depend on the whole history,
though regularly (as in regular languages). In practice both the transition and
the reward functions can be seen as finite transducers. We study reinforcement
learning in regular decision processes. Our main contribution is to show that a
near-optimal policy can be PAC-learned in polynomial time in a set of
parameters that describe the underlying decision process. We argue that the
identified set of parameters is minimal and it reasonably captures the
difficulty of a regular decision process.


Abstract:
  Recently, a new form of online shopping becomes more and more popular, which
combines live streaming with E-Commerce activity. The streamers introduce
products and interact with their audiences, and hence greatly improve the
performance of selling products. Despite of the successful applications in
industries, the live stream E-commerce has not been well studied in the data
science community. To fill this gap, we investigate this brand-new scenario and
collect a real-world Live Stream E-Commerce (LSEC) dataset. Different from
conventional E-commerce activities, the streamers play a pivotal role in the
LSEC events. Hence, the key is to make full use of rich interaction information
among streamers, users, and products. We first conduct data analysis on the
tripartite interaction data and quantify the streamer's influence on users'
purchase behavior. Based on the analysis results, we model the tripartite
information as a heterogeneous graph, which can be decomposed to multiple
bipartite graphs in order to better capture the influence. We propose a novel
Live Stream E-Commerce Graph Neural Network framework (LSEC-GNN) to learn the
node representations of each bipartite graph, and further design a multi-task
learning approach to improve product recommendation. Extensive experiments on
two real-world datasets with different scales show that our method can
significantly outperform various baseline approaches.


Abstract:
  Motivated by twisted graphene multilayers, we study interaction of a Chern
insulator with circularly polarized light. The interaction energy contains an
antisymmetric term that couples to the helicity of incident light. For a
two-band Chern insulator, this term is expressed as an integral involving the
Berry curvature of the system. Taking advantage of this interaction, we propose
an experimental protocol for switching topological memory based on orbital
magnetization by circularly polarized light and discuss its feasibility for the
Chern insulators found in twisted graphene multilayers.


Abstract:
  We describe a modular optically-pumped magnetometer (OPM) system which
enables fast prototyping and testing of new measurement schemes. Quick
reconfiguration of self-contained laser and sensor modules allow easy
construction of various array layouts. The modularity of this system enables
scaling of shared light-source arrays, and development of methods for high
density array management for magnetic imaging and sensing in both medical and
industrial fields. We demonstrate the OPM system in a first-order axial
gradiometer configuration with a magnetic field gradient sensitivity of 10.4
$fT/cm/\sqrt{Hz}$. To illustrate the capabilities of this system, we measured
alpha-rhythms from the brain of a human participant, and assessed the
magnetometer sensitivity both with single sensor channels and in a differential
gradiometer configuration.


Abstract:
  The debate on gravity theories to extend or modify General Relativity is very
active today because of the issues related to ultra-violet and infra-red
behavior of Einstein's theory. In the first case, we have to address the
Quantum Gravity problem. In the latter, dark matter and dark energy, governing
the large scale structure and the cosmological evolution, seem to escape from
any final fundamental theory and detection. The state of art is that, up to
now, no final theory, capable of explaining gravitational interaction at any
scale, has been formulated. In this perspective, many research efforts are
devoted to test theories of gravity by space-based experiments. Here we propose
straightforward tests by the GINGER experiment, which, being Earth based,
requires little modeling of external perturbation, allowing a thorough analysis
of the systematics, crucial for experiments where sensitivity breakthrough is
required. Specifically, we want to show that it is possible to constrain
parameters of gravity theories, like scalar-tensor or Horava-Lifshitz gravity,
by considering their post-Newtonian limits matched with experimental data. In
particular, we use the Lense-Thirring measurements provided by GINGER to find
out relations among the parameters of theories and finally compare the results
with those provided by LARES and Gravity Probe-B satellites.


Abstract:
  We generalize and unify the $f(R,T)$ and $f(R,L_m)$ type gravity models by
assuming that the gravitational Lagrangian is given by an arbitrary function of
the Ricci scalar $R$, of the trace of the energy-momentum tensor $T$, and of
the matter Lagrangian $L_m$, so that $L_{grav}=f(R,L_m,T)$. We obtain the
gravitational field equations in the metric formalism, the equations of motion
for test particles, and the energy and momentum balance equations, which follow
from the covariant divergence of the energy-momentum tensor. Generally, the
motion is non-geodesic, and takes place in the presence of an extra force
orthogonal to the four-velocity. The Newtonian limit of the equations of motion
is also investigated, and the expression of the extra acceleration is obtained
for small velocities and weak gravitational fields. The generalized Poisson
equation is also obtained in the Newtonian limit, and the Dolgov-Kawasaki
instability is also investigated. The cosmological implications of the theory
are investigated for a homogeneous, isotropic and flat Universe for two
particular choices of the Lagrangian density $f(R,L_m,T)$ of the gravitational
field, with a multiplicative and additive algebraic structure in the matter
couplings, respectively, and for two choices of the matter Lagrangian, by using
both analytical and numerical methods.


Abstract:
  We present a spectroscopic and imaging study of an abnormal active galactic
nucleus (AGN), 2MASX J00423991+3017515. This AGN is newly identified in the
hard X-rays by the Swift BAT All-Sky survey and found in an edge-on disk galaxy
interacting with a nearby companion. Here, we analyze the first optical spectra
obtained for this system (taken in 2011 and 2016), high-resolution imaging
taken with the Hubble Space Telescope and Chandra X-ray Observatory, and 1"
imaging with the Very Large Array. Two unique properties are revealed: the
peaks of the broad Balmer emission lines (associated with gas orbiting very
near the supermassive black hole) are blue shifted from the corresponding
narrow line emission and host galaxy absorption by 1540 km/s, and the AGN is
spatially displaced from the apparent center of its host galaxy by 3.8 kpc. We
explore several scenarios to explain these features, along with other
anomalies, and propose that 2MASX J00423991+3017515 may be an AGN with an
unusually strong wind residing in a uniquely configured major merger, or that
it is an AGN recoiling from either a gravitational "slingshot" in a three-body
interaction or from a kick due to the asymmetric emission of gravitational
waves following the coalescence of two progenitor supermassive black holes.


Abstract:
  Recent advances in meta-learning has led to remarkable performances on
several few-shot learning benchmarks. However, such success often ignores the
similarity between training and testing tasks, resulting in a potential bias
evaluation. We, therefore, propose a generative approach based on a variant of
Latent Dirichlet Allocation to analyse task similarity to optimise and better
understand the performance of meta-learning. We demonstrate that the proposed
method can provide an insightful evaluation for meta-learning algorithms on two
few-shot classification benchmarks that matches common intuition: the more
similar the higher performance. Based on this similarity measure, we propose a
task-selection strategy for meta-learning and show that it can produce more
accurate classification results than methods that randomly select training
tasks.


Abstract:
  We come back on the dynamical properties of k-essential cosmological models
and show how the interesting phenomenological features of those models are
related to the existence of boundaries in the phase surface. We focus our
attention to the branching points where the energy density has an extremum and
the effective speed of sound diverges. We discuss the behaviour of solutions of
a general class of cosmological models exhibiting such curves and give two
possible interpretations; the most interesting possibility regards the arrow of
time that is reversed in trespassing the branching curve. This study teaches to
us something new about general FLRW cosmologies where the fluids driving the
cosmic evolution have equations of state that are multivalued functions of the
energy density and other thermodynamical quantities.


Abstract:
  Results from higher order mean field calculations of light interacting with
atom arrays are presented for calculations of one- and two-time expectation
values. The atoms are approximated as two-levels and are fixed in space.
Calculations were performed for mean field approximations that include the
expectation value of one operator (mean field), two operators (mean field-2),
and three operators (mean field-3). For the one-time expectation values, we
examined three different situations to understand the convergence with
increasing order of mean field and some limitations of higher order mean field
approximations. As a representation of a two-time expectation value, we
calculated the $g^{(2)}(\tau )$ for a line of atoms illuminated by a
perpendicular plane wave at several emission angles and two different
intensities. For many cases, the mean field-2 will be sufficiently accurate to
quantitatively predict the response of the atoms as measured by one-time
expectation values. However, the mean field-3 approximation will often be
needed for two-time expectation values.