Abstract:
  Let $\mathbb{F}_q$ be a finite field of order $q$. In this paper, we study
the distribution of rectangles in a given set in $\mathbb{F}_q^2$. More
precisely, for any $0<\delta\le 1$, we prove that there exists an integer
$q_0=q_0(\delta)$ with the following property: if $q\ge q_0$ and $A$ is a
multiplicative subgroup of $\mathbb{F}^*_q$ with $|A|\ge q^{2/3}$, then any set
$S\subset \mathbb{F}_q^2$ with $|S|\ge \delta q^2$ contains at least $\gg
\frac{|S|^4|A|^2}{q^5}$ rectangles with side-lengths in $A$. We also consider
the case of rectangles with one fixed side-length and the other in a
multiplicative subgroup $A$.


Abstract:
  Usually, managers or technical leaders in software projects assign issues
manually. This task may become more complex as more detailed is the issue
description. This complexity can also make the process more prone to errors
(misassignments) and time-consuming. In the literature, many studies aim to
address this problem by using machine learning strategies. Although there is no
specific solution that works for all companies, experience reports are useful
to guide the choices in industrial auto-assignment projects. This paper
presents an industrial initiative conducted in a global electronics company
that aims to minimize the time spent and the errors that can arise in the issue
assignment process. As main contributions, we present a literature review, an
industrial report comparing different algorithms, and lessons learned during
the project.


Abstract:
  We study astrometric residuals from a simultaneous fit of Hyper Suprime-Cam
images. We aim to characterize these residuals and study the extent to which
they are dominated by atmospheric contributions for bright sources. We use
Gaussian process interpolation, with a correlation function (kernel), measured
from the data, to smooth and correct the observed astrometric residual field.
We find that Gaussian process interpolation with a von K\'arm\'an kernel allows
us to reduce the covariances of astrometric residuals for nearby sources by
about one order of magnitude, from 30 mas$^2$ to 3 mas$^2$ at angular scales of
~1 arcmin, and to halve the r.m.s. residuals. Those reductions using Gaussian
process interpolation are similar to recent result published with the Dark
Energy Survey dataset. We are then able to detect the small static astrometric
residuals due to the Hyper Suprime-Cam sensors effects. We discuss how the
Gaussian process interpolation of astrometric residuals impacts galaxy shape
measurements, in particular in the context of cosmic shear analyses at the
Rubin Observatory Legacy Survey of Space and Time.


Abstract:
  The system of two nonlinear coupled oscillators is studied. As partial case
this system of equation is reduced to the Duffing oscillator which has many
applications for describing physical processes. It is well known that the
inverse scattering transform is one of the most powerful methods for solving
the Cauchy problems of partial differential equations. To solve the Cauchy
problem for nonlinear differential equations we can use the Lax pair
corresponding to this equation. The Lax pair for ordinary differential or
systems or for system ordinary differential equations allows us to find the
first integrals, which also allow us to solve the question of integrability for
differential equations. In this report we present the Lax pair for the system
of coupled oscillators. Using the Lax pair we get two first integrals for the
system of equations. The considered system of equations can be also reduced to
the fourth-order ordinary differential equation and the Lax pair can be used
for the ordinary differential equation of fourth order. Some special cases of
the system of equations are considered.


Abstract:
  This paper continues the program initiated in the works by the authors [60],
[61] and [62] and by the authors with Li [51] and [52] to establish higher
order Poincar\'e-Sobolev, Hardy-Sobolev-Maz'ya, Adams and Hardy-Adams
inequalities on real hyperbolic spaces using the method of Helgason-Fourier
analysis on the hyperbolic spaces. The aim of this paper is to establish such
inequalities on the Siegel domains and complex hyperbolic spaces. Firstly, we
prove a factorization theorem for the operators on the complex hyperbolic space
which is closely related to Geller' operator, as well as the CR invariant
differential operators on the Heisenberg group and CR sphere. Secondly, by
using, among other things, the Kunze-Stein phenomenon on a closed linear group
$SU(1,n)$ and Helgason-Fourier analysis techniques on the complex hyperbolic
spaces, we establish the Poincar\'e-Sobolev, Hardy-Sobolev-Maz'ya inequality on
the Siegel domain $\mathcal{U}^{n}$ and the unit ball
$\mathbb{B}_{\mathbb{C}}^{n}$. Finally, we establish the sharp Hardy-Adams
inequalities and sharp Adams type inequalities on Sobolev spaces of any
positive fractional order on the complex hyperbolic spaces. The factorization
theorem we proved is of its independent interest in the Heisenberg group and CR
sphere and CR invariant differential operators therein.


Abstract:
  Low Earth orbit (LEO) satellite constellations rely on inter-satellite links
(ISLs) to provide global connectivity. However, one significant challenge is to
establish and maintain inter-plane ISLs, which support communication between
different orbital planes. This is due to the fast movement of the
infrastructure and to the limited computation and communication capabilities on
the satellites. In this paper, we make use of antenna arrays with either Butler
matrix beam switching networks or digital beam steering to establish the
inter-plane ISLs in a LEO satellite constellation. Furthermore, we present a
greedy matching algorithm to establish inter-plane ISLs with the objective of
maximizing the sum of rates. This is achieved by sequentially selecting the
pairs, switching or pointing the beams and, finally, setting the data rates.
Our results show that, by selecting an update period of 30 seconds for the
matching, reliable communication can be achieved throughout the constellation,
where the impact of interference in the rates is less than 0.7 % when compared
to orthogonal links, even for relatively small antenna arrays. Furthermore,
doubling the number of antenna elements increases the rates by around one order
of magnitude.


Abstract:
  Given a random real quadratic field from $\{ \mathbb{Q}(\sqrt{p}\,) ~|~ p
\text{ primes} \}$, the conjectural probability $\mathbb{P}(h=q)$ that it has
class number $q$ is given for all positive odd integers $q$. Some related
conjectures of the Cohen-Lenstra heuristic are given here as corollaries. These
results suggest that the set of real quadratic number fields may have some
natural hierarchical structures.


Abstract:
  Dispersionless bands -- \emph{flatbands} -- provide an excellent testbed for
novel physical phases due to the fine-tuned character of flatband tight-binding
Hamiltonians. The accompanying macroscopic degeneracy makes any perturbation
relevant, no matter how small. For short-range hoppings flatbands support
compact localized states, which allowed to develop systematic flatband
generators in $d=1$ dimension in Phys. Rev. B {\bf 95} 115135 (2017) and Phys.
Rev. B {\bf 99} 125129 (2019). Here we extend this generator approach to $d=2$
dimensions. The \emph{shape} of a compact localized state turns into an
important additional flatband classifier. This allows us to obtain analytical
solutions for classes of $d=2$ flatband networks and to re-classify and
re-obtain known ones, such as the checkerboard, kagome, Lieb and Tasaki
lattices. Our generator can be straightforwardly generalized to three lattice
dimensions as well.


Abstract:
  In this article we introduce the notion of a Ribaucour partial tube and use
it to derive several applications. These are based on a characterization of
Ribaucour partial tubes as the immersions of a product of two manifolds into a
space form such that the distributions given by the tangent spaces of the
factors are orthogonal to each other with respect to the induced metric, are
invariant under all shape operators, and one of them is spherical. Our first
application is a classification of all hypersurfaces with dimension at least
three of a space form that carry a spherical foliation of codimension one,
extending previous results by Dajczer, Rovenski and the second author for the
totally geodesic case. We proceed to prove a general decomposition theorem for
immersions of product manifolds, which extends several related results. Other
main applications concern the class of hypersurfaces of $\mathbb{R}^{n+1}$ that
are of Enneper type, that is, hypersurfaces that carry a family of lines of
curvature, correspondent to a simple principal curvature, whose orthogonal
$(n-1)$-dimensional distribution is integrable and whose leaves are contained
in hyperspheres or affine hyperplanes of $\mathbb{R}^{n+1}$. We show how
Ribaucour partial tubes in the sphere can be used to describe all
$n$-dimensional hypersurfaces of Enneper type for which the leaves of the
$(n-1)$-dimensional distribution are contained in affine hyperplanes of
$\mathbb{R}^{n+1}$, and then show how a general hypersurface of Enneper type
can be constructed in terms of a hypersurface in the latter class. We give an
explicit description of some special hypersurfaces of Enneper type, among which
are natural generalizations of the so called Joachimsthal surfaces.


Abstract:
  This paper proposes a method to relax the conditional independence assumption
of connectionist temporal classification (CTC)-based automatic speech
recognition (ASR) models. We train a CTC-based ASR model with auxiliary CTC
losses in intermediate layers in addition to the original CTC loss in the last
layer. During both training and inference, each generated prediction in the
intermediate layers is summed to the input of the next layer to condition the
prediction of the last layer on those intermediate predictions. Our method is
easy to implement and retains the merits of CTC-based ASR: a simple model
architecture and fast decoding speed. We conduct experiments on three different
ASR corpora. Our proposed method improves a standard CTC model significantly
(e.g., more than 20 % relative word error rate reduction on the WSJ corpus)
with a little computational overhead. Moreover, for the TEDLIUM2 corpus and the
AISHELL-1 corpus, it achieves a comparable performance to a strong
autoregressive model with beam search, but the decoding speed is at least 30
times faster.


Abstract:
  Physical-layer key generation (PKG) can generate symmetric keys between two
communication ends based on the reciprocal uplink and downlink channels. By
smartly reconfiguring the radio signal propagation, intelligent reflecting
surface (IRS) is able to improve the secret key rate of PKG. However, existing
works involving IRS-assisted PKG are concentrated in single-antenna wireless
networks. So this paper investigates the problem of PKG in the IRS-assisted
multiple-input single-output (MISO) system, which aims to maximize the secret
key rate by optimally designing the IRS passive beamforming. First, we analyze
the correlation between channel state information (CSI) of eavesdropper and
legitimate ends and derive the expression of the upper bound of secret key rate
under passive eavesdropping attack. Then, an optimal algorithm for designing
IRS reflecting coefficients based on Semi-Definite Relaxation (SDR) and Taylor
expansion is proposed to maximize the secret key rate. Numerical results show
that our optimal IRS-assisted PKG scheme can achieve much higher secret key
rate when compared with two benchmark schemes.


Abstract:
  We investigate $\lambda$-Hilbert transform, $\lambda$-Possion integral and
conjugate $\lambda$-Poisson integral on the atomic Hardy space in the Dunkl
setting and establish a new version of Paley type inequality which extends the
results in \cite{F} and \cite{ZhongKai Li 3}.


Abstract:
  Arc-locally semicomplete and arc-locally in-semicomplete digraphs were
introduced by Bang-Jensen as a common generalization of both semicomplete and
semicomplete bipartite digraphs in 1993. Later, Bang-Jensen (2004),
Galeana-Sanchez and Goldfeder (2009) and Wang and Wang (2009) provided a
characterization of strong arc-locally semicomplete digraphs. In 2009, Wang and
Wang characterized strong arc-locally in-semicomplete digraphs. In 2012,
Galeana-Sanchez and Goldfeder provided a characterization of all arc-locally
semicomplete digraphs which generalizes some results by Bang-Jensen. In this
paper, we characterize the structure of arbitrary connected arc-locally (out)
in-semicomplete digraphs and arbitrary connected arc-locally semicomplete
digraphs.


Abstract:
  We study Markov population processes on large graphs, with the local state
transition rates of a single vertex being linear function of its neighborhood.
A simple way to approximate such processes is by a system of ODEs called the
homogeneous mean-field approximation (HMFA). Our main result is showing that
HMFA is guaranteed to be the large graph limit of the stochastic dynamics on a
finite time horizon if and only if the graph-sequence is quasi-random. Explicit
error bound is given and being of order $\frac{1}{\sqrt{N}}$ plus the largest
discrepancy of the graph. For Erd\H{o}s R\'{e}nyi and random regular graphs we
show an error bound of order the inverse square root of the average degree. In
general, diverging average degrees is shown to be a necessary condition for the
HMFA to be accurate. Under special conditions, some of these results also apply
to more detailed type of approximations like the inhomogenous mean field
approximation (IHMFA). We pay special attention to epidemic applications such
as the SIS process.


Abstract:
  Downscaling aims to link the behaviour of the atmosphere at fine scales to
properties measurable at coarser scales, and has the potential to provide high
resolution information at a lower computational and storage cost than numerical
simulation alone. This is especially appealing for targeting convective scales,
which are at the edge of what is possible to simulate operationally. Since
convective scale weather has a high degree of independence from larger scales,
a generative approach is essential. We here propose a statistical method for
downscaling moist variables to convective scales using conditional Gaussian
random fields, with an application to wet bulb potential temperature (WBPT)
data over the UK. Our model uses an adaptive covariance estimation to capture
the variable spatial properties at convective scales. We further propose a
method for the validation, which has historically been a challenge for
generative models.


Abstract:
  Quantum spins of mesoscopic size are a well-studied playground for
engineering non-classical states. If the spin represents the collective state
of an ensemble of qubits, its non-classical behavior is linked to entanglement
between the qubits. In this work, we report on an experimental study of
entanglement in dysprosium's electronic spin. Its ground state, of angular
momentum $J=8$, can formally be viewed as a set of $2J$ qubits symmetric upon
exchange. To access entanglement properties, we partition the spin by optically
coupling it to an excited state $J'=J-1$, which removes a pair of qubits in a
state defined by the light polarization. Starting with the well-known W and
squeezed states, we extract the concurrence of qubit pairs, which quantifies
their non-classical character. We also directly demonstrate entanglement
between the 14- and 2-qubit subsystems via an increase in entropy upon
partition. In a complementary set of experiments, we probe decoherence of a
state prepared in the excited level $J'=J+1$ and interpret spontaneous emission
as a loss of a qubit pair in a random state. This allows us to contrast the
robustness of pairwise entanglement of the W state with the fragility of the
coherence involved in a Schr\"odinger cat state. Our findings open up the
possibility to engineer novel types of entangled atomic ensembles, in which
entanglement occurs within each atom's electronic spin as well as between
different atoms.


Abstract:
  High quality (HQ) video services occupy large portions of the total bandwidth
and are among the main causes of congestion at network bottlenecks. Since video
is resilient to data loss, throwing away less important video packets can ease
network congestion with minimal damage to video quality and free up bandwidth
for other data flows. Frame type is one of the features that can be used to
determine the importance of video packets, but this information is stored in
the packet payload. Due to limited processing power of devices in high
throughput/speed networks, data encryption and user credibility issues, it is
costly for the network to find the frame type of each packet. Therefore, a fast
and reliable standalone method to recognize video packet types at network level
is desired. This paper proposes a method to model the structure of live video
streams in a network node which results in determining the frame type of each
packet. It enables the network nodes to mark and if need be to discard less
important video packets ahead of congestion, and therefore preserve video
quality and free up bandwidth for more important packet types. The method does
not need to read the IP layer payload and uses only the packet header data for
decisions. Experimental results indicate while dropping packets under packet
type prediction degrades video quality with respect to its true type by 0.5-3
dB, it has 7-20 dB improvement over when packets are dropped randomly.


Abstract:
  Improving wind turbine efficiency is essential for reducing the costs of
energy production. The highly nonlinear dynamics of the wind turbines and their
uncertain operating conditions have posed many challenges for their control
methods. In this work, a robust control strategy based on sliding mode and
adaptive fuzzy disturbance observer is proposed for speed tracking in a
variable speed wind turbine. First, the nonlinear mathematical model that
describes the dynamics of the variable speed wind turbine is derived. This
nonlinear model is then used to derive the control methodology and to find
stability and robustness conditions. The control approach is designed to track
the optimal wind speed that causes maximum energy extraction. The stability
condition was verified using the Lyapunov stability theory. A simulation study
was conducted to verify the method, and a comparative analysis was used to
measure its effectiveness. The results showed a high tracking ability and
robustness of the developed methodology. Moreover, higher power extraction was
observed when compared to a classical control method.


Abstract:
  Many modern systems for speaker diarization, such as the recently-developed
VBx approach, rely on clustering of DNN speaker embeddings followed by
resegmentation. Two problems with this approach are that the DNN is not
directly optimized for this task, and the parameters need significant retuning
for different applications. We have recently presented progress in this
direction with a Leave-One-Out Gaussian PLDA (LGP) clustering algorithm and an
approach to training the DNN such that embeddings directly optimize performance
of this scoring method. This paper presents a new two-pass version of this
system, where the second pass uses finer time resolution to significantly
improve overall performance. For the Callhome corpus, we achieve the first
published error rate below 4% without any task-dependent parameter tuning. We
also show significant progress towards a robust single solution for multiple
diarization tasks.


Abstract:
  We use 3D fully kinetic particle-in-cell simulations to study the occurrence
of magnetic reconnection in a simulation of decaying turbulence created by
anisotropic counter-propagating low-frequency Alfv\'en waves consistent with
critical-balance theory. We observe the formation of small-scale
current-density structures such as current filaments and current sheets as well
as the formation of magnetic flux ropes as part of the turbulent cascade. The
large magnetic structures present in the simulation domain retain the initial
anisotropy while the small-scale structures produced by the turbulent cascade
are less anisotropic. To quantify the occurrence of reconnection in our
simulation domain, we develop a new set of indicators based on intensity
thresholds to identify reconnection events in which both ions and electrons are
heated and accelerated in 3D particle-in-cell simulations. According to the
application of these indicators, we identify the occurrence of reconnection
events in the simulation domain and analyse one of these events in detail. The
event is related to the reconnection of two flux ropes, and the associated ion
and electron exhausts exhibit a complex three-dimensional structure. We study
the profiles of plasma and magnetic-field fluctuations recorded along
artificial-spacecraft trajectories passing near and through the reconnection
region. Our results suggest the presence of particle heating and acceleration
related to small-scale reconnection events within magnetic flux ropes produced
by the anisotropic Alfv\'enic turbulent cascade in the solar wind. These events
are related to current structures of order a few ion inertial lengths in size.


Abstract:
  We propose the spatial-temporal aggregated predictor (STAP) modeling
framework to address measurement and estimation issues that arise when
assessing the relationship between built environment features (BEF) and health
outcomes. Many BEFs can be mapped as point locations and thus traditional
exposure metrics are based on the number of features within a pre-specified
spatial unit. The size of the spatial unit--or spatial scale--that is most
appropriate for a particular health outcome is unknown and its choice
inextricably impacts the estimated health effect. A related issue is the lack
of knowledge of the temporal scale--or the length of exposure time that is
necessary for the BEF to render its full effect on the health outcome. The
proposed STAP model enables investigators to estimate both the spatial and
temporal scales for a given BEF in a data-driven fashion, thereby providing a
flexible solution for measuring the relationship between outcomes and spatial
proximity to point-referenced exposures. Simulation studies verify the validity
of our method for estimating the scales as well as the association between
availability of BEFs' and health outcomes. We apply this method to estimate the
spatial-temporal association between supermarkets and BMI using data from the
Multi-Ethnic Atherosclerosis Study, demonstrating the method's applicability in
cohort studies.


Abstract:
  In a rectangular domain, a boundary-value problem is considered for a
mixed-type equation with a regularized Caputo-like counterpart of hyper-Bessel
differential operator and the bi-ordinal Hilfer's fractional derivative. Using
the method of separation of variables, Laplace transform, a unique solvability
of the considered problem has been established. Moreover, we have found the
explicit solution of initial problems for a differential equation with the
bi-ordinal Hilfer's derivative and regularized Caputo-like counterpart of the
hyper-Bessel differential operator with the non-zero starting point.


Abstract:
  Prediction of human actions in social interactions has important applications
in the design of social robots or artificial avatars. In this paper, we model
human interaction generation as a discrete multi-sequence generation problem
and present SocialInteractionGAN, a novel adversarial architecture for
conditional interaction generation. Our model builds on a recurrent
encoder-decoder generator network and a dual-stream discriminator. This
architecture allows the discriminator to jointly assess the realism of
interactions and that of individual action sequences. Within each stream a
recurrent network operating on short subsequences endows the output signal with
local assessments, better guiding the forthcoming generation. Crucially,
contextual information on interacting participants is shared among agents and
reinjected in both the generation and the discriminator evaluation processes.
We show that the proposed SocialInteractionGAN succeeds in producing high
realism action sequences of interacting people, comparing favorably to a
diversity of recurrent and convolutional discriminator baselines. Evaluations
are conducted using modified Inception Score and Fr{\'e}chet Inception Distance
metrics, that we specifically design for discrete sequential generated data.
The distribution of generated sequences is shown to approach closely that of
real data. In particular our model properly learns the dynamics of interaction
sequences, while exploiting the full range of actions.


Abstract:
  We consider, in general terms, the possible parameter space of thermal dark
matter candidates. We assume that the dark matter particle is fundamental and
was in thermal equilibrium in a hidden sector with a temperature $T'$, which
may differ from that of the Standard Model temperature, $T$. The candidates lie
in a region in the $T'/T$ vs. $m_{\rm dm}$ plane, which is bounded by both
model-independent theoretical considerations and observational constraints. The
former consists of limits from dark matter candidates that decoupled when
relativistic (the relativistic floor) and from those that decoupled when
non-relativistic with the largest annihilation cross section allowed by
unitarity (the unitarity wall), while the latter concerns big bang
nucleosynthesis ($N_{\rm eff}$ ceiling) and free streaming. We present three
simplified dark matter scenarios, demonstrating concretely how each fits into
the domain.


Abstract:
  We design a multi-purpose environment for autonomous UAVs offering different
communication services in a variety of application contexts (e.g., wireless
mobile connectivity services, edge computing, data gathering). We develop the
environment, based on OpenAI Gym framework, in order to simulate different
characteristics of real operational environments and we adopt the Reinforcement
Learning to generate policies that maximize some desired performance.The
quality of the resulting policies are compared with a simple baseline to
evaluate the system and derive guidelines to adopt this technique in different
use cases. The main contribution of this paper is a flexible and extensible
OpenAI Gym environment, which allows to generate, evaluate, and compare
policies for autonomous multi-drone systems in multi-service applications. This
environment allows for comparative evaluation and benchmarking of different
approaches in a variety of application contexts.


Abstract:
  The discovery of superconductivity in the infinite-layer nickelates has
opened new perspectives in the context of quantum materials. We analyze, via
first-principles calculations, the electronic properties of La$_2$NiO$_3$F --
the first single-layer T'-type nickelate -- and compare these properties with
those of related nickelates and isostructural cuprates. We find that
La$_2$NiO$_3$F is essentially a single-band system with a Fermi surface
dominated by the Ni-3$d_{x^2-y^2}$ states with an exceptional 2D character. In
addition, the hopping ratio is similar to that of the highest $T_c$ cuprates
and there is a remarkable $e_g$ splitting together with a charge transfer
energy of 3.6~eV. According to these descriptors, along with a comparison to
Nd$_2$CuO$_4$, we thus indicate single-layer T'-type nickelates of this class
as very promising analogs of cuprate-like physics while keeping distinct
Ni$^{1+}$ features.


Abstract:
  We conducted an investigation to find when a mistake was introduced in a
widely accessed Internet document, namely the RFC index. With great surprise,
we discovered that a it may go unnoticed for a very long period, namely more
that twenty-six years. This raises some questions to what does it mean to have
open access and the meaning of Linus' laws that "given enough eyeballs, all
bugs are shallow"


Abstract:
  In this paper, we reformulate the Bakry-\'Emery curvature on a weighted graph
in terms of the smallest eigenvalue of a rank one perturbation of the so-called
curvature matrix using Schur complement. This new viewpoint allows us to show
various curvature function properties in a very conceptual way. We show that
the curvature, as a function of the dimension parameter, is analytic, strictly
monotone increasing and strictly concave until a certain threshold after which
the function is constant. Furthermore, we derive the curvature of the Cartesian
product using the crucial observation that the curvature matrix of the product
is the direct sum of each component. Our approach of the curvature functions of
graphs can be employed to establish analogous results for the curvature
functions of weighted Riemannian manifolds. Moreover, as an application, we
confirm a conjecture (in a general weighted case) of the fact that the
curvature does not decrease under certain graph modifications.


Abstract:
  For the first time, basing both on experimental facts and our theoretical
consideration, we show that Fermi systems with flat bands should be tuned with
the superconducting state. Experimental measurements on magic-angle twisted
bilayer graphene of the Fermi velocity $V_F$ as a function of the temperature
$T_c$ of superconduction phase transition have revealed $V_F\propto T_c\propto
1/N_s(0)$, where $N_s(0)$ is the density of states at the Fermi level. We show
that the high-$T_c$ compounds $\rm Bi_2Sr_2CaCu_2O_{8+x}$ exhibit the same
behavior. Such observation is a challenge to theories of high-$T_c$
superconductivity, since $V_F$ is negatively correlated with $T_c$, for
$T_c\propto 1/V_F\propto N_s(0)$. We show that the theoretical idea of forming
flat bands in strongly correlated Fermi systems can explain this behavior and
other experimental data collected on both $\rm Bi_2Sr_2CaCu_2O_{8+x}$ and
twisted bilayer graphene. Our findings place stringent constraints on theories
describing the nature of high-$T_c$ superconductivity and the deformation of
flat band by the superconducting phase transition.


Abstract:
  This paper presents a novel, non-standard set of vector instruction types for
exploring custom SIMD instructions in a softcore. The new types allow
simultaneous access to a relatively high number of operands, reducing the
instruction count where applicable. Additionally, a high-performance
open-source RISC-V (RV32 IM) softcore is introduced, optimised for exploring
custom SIMD instructions and streaming performance. By providing instruction
templates for instruction development in HDL/Verilog, efficient FPGA-based
instructions can be developed with few low-level lines of code. In order to
improve custom SIMD instruction performance, the softcore's cache hierarchy is
optimised for bandwidth, such as with very wide blocks for the last-level
cache. The approach is demonstrated on example memory-intensive applications on
an FPGA. Although the exploration is based on the softcore, the goal is to
provide a means to experiment with advanced SIMD instructions which could be
loaded in future CPUs that feature reconfigurable regions as custom
instructions. Finally, we provide some insights on the challenges and
effectiveness of such future micro-architectures.


Abstract:
  The law of centripetal force governing the motion of celestial bodies in
eccentric conic sections, has been established and thoroughly investigated by
Sir Isaac Newton in his Principia Mathematica. Yet its profound implications on
the understanding of such motions is still evolving. In a paper to the royal
academy of science, Sir Willian Hamilton demonstrated that this law underlies
the circular character of hodographs for Kepler orbits. A fact which was the
object of ulterior research and exploration by Richard Feynman and many other
authors [1]. In effect, a minute examination of the geometry of elliptic
trajectories, reveals interesting geometric properties and relations,
altogether, combined with the law of conservation of angular momentum lead
eventually, and without any recourse to dealing with differential equations, to
the appearance of the equation of the trajectory and to the derivation of the
equation of its corresponding hodograph. On this respect, and for the sake of
founding the approach on solid basis, I devised two mathematical theorems; one
concerning the existence of geometric means, and the other is related to
establishing the parametric equation of an off-center circle, altogether
compounded with other simple arguments ultimately give rise to the inverse
square law of force that governs the motion of bodies in elliptic trajectories,
as well as to the equation of their inherent circular hodographs.


Abstract:
  3D point-clouds and 2D images are different visual representations of the
physical world. While human vision can understand both representations,
computer vision models designed for 2D image and 3D point-cloud understanding
are quite different. Our paper investigates the potential for transferability
between these two representations by empirically investigating whether this
approach works, what factors affect the transfer performance, and how to make
it work even better. We discovered that we can indeed use the same neural net
model architectures to understand both images and point-clouds. Moreover, we
can transfer pretrained weights from image models to point-cloud models with
minimal effort. Specifically, based on a 2D ConvNet pretrained on an image
dataset, we can transfer the image model to a point-cloud model by
\textit{inflating} 2D convolutional filters to 3D then finetuning its input,
output, and optionally normalization layers. The transferred model can achieve
competitive performance on 3D point-cloud classification, indoor and driving
scene segmentation, even beating a wide range of point-cloud models that adopt
task-specific architectures and use a variety of tricks.


Abstract:
  We present a study of the environment of 27 z=3-4.5 bright quasars from the
MUSE Analysis of Gas around Galaxies (MAGG) survey. With medium-depth MUSE
observations (4 hours on target per field), we characterise the effects of
quasars on their surroundings by studying simultaneously the properties of
extended gas nebulae and Lyalpha emitters (LAEs) in the quasar host haloes. We
detect extended (up to ~ 100 kpc) Lyalpha emission around all MAGG quasars,
finding a very weak redshift evolution between z=3 and z=6. By stacking the
MUSE datacubes, we confidently detect extended emission of CIV and only
marginally detect extended HeII up to ~40 kpc, implying that the gas is metal
enriched. Moreover, our observations show a significant overdensity of LAEs
within 300 km/s from the quasar systemic redshifts estimated from the nebular
emission. The luminosity functions and equivalent width distributions of these
LAEs show similar shapes with respect to LAEs away from quasars suggesting that
the Lyalpha emission of the majority of these sources is not significantly
boosted by the quasar radiation or other processes related to the quasar
environment. Within this framework, the observed LAE overdensities and our
kinematic measurements imply that bright quasars at z=3-4.5 are hosted by
haloes in the mass range ~ 10^{12.0}-10^{12.5} Msun.


Abstract:
  Although deep neural networks are successful for many tasks in the speech
domain, the high computational and memory costs of deep neural networks make it
difficult to directly deploy highperformance Neural Network systems on
low-resource embedded devices. There are several mechanisms to reduce the size
of the neural networks i.e. parameter pruning, parameter quantization, etc.
This paper focuses on how to apply binary neural networks to the task of
speaker verification. The proposed binarization of training parameters can
largely maintain the performance while significantly reducing storage space
requirements and computational costs. Experiment results show that, after
binarizing the Convolutional Neural Network, the ResNet34-based network
achieves an EER of around 5% on the Voxceleb1 testing dataset and even
outperforms the traditional real number network on the text-dependent dataset:
Xiaole while having a 32x memory saving.


Abstract:
  In this paper, we present a model-free learning-based control scheme for the
soft snake robot to improve its contact-aware locomotion performance in a
cluttered environment. The control scheme includes two cooperative controllers:
A bio-inspired controller (C1) that controls both the steering and velocity of
the soft snake robot, and an event-triggered regulator (R2) that controls the
steering of the snake in anticipation of obstacle contacts and during contact.
The inputs from the two controllers are composed as the input to a Matsuoka CPG
network to generate smooth and rhythmic actuation inputs to the soft snake. To
enable stable and efficient learning with two controllers, we develop a
game-theoretic process, fictitious play, to train C1 and R2 with a shared
potential-field-based reward function for goal tracking tasks. The proposed
approach is tested and evaluated in the simulator and shows significant
improvement of locomotion performance in the obstacle-based environment
comparing to two baseline controllers.


Abstract:
  The primary objective of this paper is the study of different instances of
the elliptic Stark conjectures of Darmon, Lauder and Rotger, in a situation
where the elliptic curve attached to the modular form $f$ has split
multiplicative reduction at $p$ and the arithmetic phenomena are specially
rich. For that purpose, we resort to the principle of improved $p$-adic
$L$-functions and study their $\mathcal L$-invariants. We further interpret
these results in terms of derived cohomology classes coming from the setting of
diagonal cycles, showing that the same $\mathcal L$-invariant which arises in
the theory of $p$-adic $L$-functions also governs the arithmetic of Euler
systems. Thus, we can reduce, in the split multiplicative situation, the
conjecture of Darmon, Lauder and Rotger to a more familiar statement about
higher order derivatives of a triple product $p$-adic $L$-function at a point
lying inside the region of classical interpolation, in the realm of the more
well-known exceptional zero conjectures.


Abstract:
  The allocation of venture capital is one of the primary factors determining
who takes products to market, which startups succeed or fail, and as such who
gets to participate in the shaping of our collective economy. While gender
diversity contributes to startup success, most funding is allocated to
male-only entrepreneurial teams. In the wake of COVID-19, 2020 is seeing a
notable decline in funding to female and mixed-gender teams, giving raise to an
urgent need to study and correct the longstanding gender bias in startup
funding allocation. We conduct an in-depth data analysis of over 48,000
companies on Crunchbase, comparing funding allocation based on the gender
composition of founding teams. Detailed findings across diverse industries and
geographies are presented. Further, we construct machine learning models to
predict whether startups will reach an equity round, revealing the surprising
finding that the CEO's gender is the primary determining factor for attaining
funding. Policy implications for this pressing issue are discussed.


Abstract:
  Macroscopic realism (MR) is the notion that a time-evolving system possesses
definite properties, irrespective of past or future measurements. Quantum
mechanical theories can, however, produce violations of MR. Most research to
date has focused on a single set of conditions for MR, the Leggett-Garg
inequalities (LGIs), and on a single data set, the "standard data set", which
consists of single-time averages and second-order correlators of a dichotomic
variable Q for three times. However, if such conditions are all satisfied, then
where is the quantum behaviour? In this paper, we provide an answer to this
question by considering expanded data sets obtained from finer-grained
measurements and MR conditions on those sets. We consider three different
situations in which there are violations of MR that go undetected by the
standard LGIs. First, we explore higher-order LGIs on a data set involving
third- and fourth-order correlators, using a spin-1/2 and spin-1 system.
Second, we explore the pentagon inequalities (PIs) and a data set consisting of
all possible averages and second-order correlators for measurements of Q at
five times. Third, we explore the LGIs for a trichotomic variable and
measurements made with a trichotomic operator to, again, identify violations
for a spin-1 system beyond those seen with a single dichotomic variable. We
also explore the regimes in which combinations of two and three-time LGIs can
be satisfied and violated in a spin-1 system, extending recent work. We discuss
the possible experimental implementation of all the above results.


Abstract:
  The carrier transport and the motion of a vortex system in a mixed state of
an electron-doped high-temperature superconductors Nd2-xCexCuO4 were
investigated. To study the anisotropy of galvanomagnetic effects of highly
layered NdCeCuO system we have synthesized Nd2-xCexCuO4/SrTiO3 epitaxial films
with non-standart orientations of the c-axis and conductive CuO2 layers
relative to the substrate. The variation ofe the angle of inclination of the
magnetic field B, relative to the current J, reveals that the behavior of both
the in-plane r_xx(B) and the out-plane r_xy(B) resistivities in the mixed state
is mainly determined by the perpendicular to J component of B, that indicates
the crucial role of the Lorentz force F_L~[JxB] and defines the motion of
Josephson vortices across the CuO2 layers.


Abstract:
  We consider the problem of finding an inductive construction, based on vertex
splitting, of triangulated spheres with a fixed number of additional edges
(braces). We show that for any positive integer $b$ there is such an inductive
construction of triangulations with $b$ braces, having finitely many base
graphs. In particular we establish a bound for the maximum size of a base graph
with $b$ braces that is linear in $b$. In the case that $b=1$ or $2$ we
determine the list of base graphs explicitly. Using these results we show that
doubly braced triangulations are (generically) minimally rigid in two distinct
geometric contexts arising from a hypercylinder in $\mathbb{R}^4$ and a class
of mixed norms on $\mathbb{R}^3$.


Abstract:
  The narrow escape problem is a first-passage problem concerned with randomly
moving particles in a physical domain, being trapped by absorbing surface traps
(windows), such that the measure of traps is small compared to the domain size.
The expected value of time required for a particle to escape is defined as mean
first passage time (MFPT), which satisfies the Poisson partial differential
equation subject to a mixed Dirichlet-Neumann boundary condition. The primary
objective of this work is a direct numerical simulation of multiple particles
undergoing Brownian motion in a three-dimensional sphere with boundary traps,
compute MFPT values by averaging Brownian escape times, and compare the results
with asymptotic results obtained by solving the Poisson PDE problem. A
comprehensive study of results obtained from the simulations shows that the
difference between Brownian and asymptotic results for the escape times mostly
not exceed $1\%$ accuracy. This comparison in some sense validates the narrow
escape PDE problem itself as an approximation (averaging) of the multiple
physical Brownian motion runs. This work also predicted that how many
single-particle simulations are required to match the predicted asymptotic
averaged MFPT values. The next objective of this work is to study dynamics of
Brownian particles near the boundary by estimating the average percentage of
time spent by Brownian particle near the domain boundary for both the
anisotropic and isotropic diffusion. It is shown that the Brownian particles
spend more in the boundary layer than predicted by the boundary layer relative
volume, with the effect being more pronounced in a narrow layer near the
spherical wall. It is also shown that taking into account anisotropic diffusion
yields larger times a particle spends near the boundary, and smaller escape
times than those predicted by the isotropic diffusion model.


Abstract:
  This paper considers the narrow escape problem of a Brownian particle within
a three-dimensional Riemannian manifold under the influence of the force field.
We compute an asymptotic expansion of mean sojourn time for Brownian particles.
As an auxiliary result, we obtain the singular structure for the restricted
Neumann Green's function which may be of independent interest.


Abstract:
  I propose the use of two magnetic Wollaston prisms to correct the linear
Larmor phase aberration of MIEZE, introduced by the transverse size of the
sample. With this approach, the resolution function of MIEZE can be optimized
for any scattering angle of interest. The optimum magnetic fields required for
the magnetic Wollaston prisms depend only on the scattering angle and the
frequency of the RF flippers and they are independent of the neutron wavelength
and beam divergence, which makes it suitable for both pulsed and constant
wavelength neutron sources.


Abstract:
  We consider $n$ independent $p$-dimensional Gaussian vectors with covariance
matrix having Toeplitz structure. We test that these vectors have independent
components against a stationary distribution with sparse Toeplitz covariance
matrix, and also select the support of non-zero entries. We assume that the
non-zero values can occur in the recent past (time-lag less than $p/2$). We
build test procedures that combine a sum and a scan-type procedures, but are
computationally fast, and show their non-asymptotic behaviour in both one-sided
(only positive correlations) and two-sided alternatives, respectively. We also
exhibit a selector of significant lags and bound the Hamming-loss risk of the
estimated support. These results can be extended to the case of nearly Toeplitz
covariance structure and to sub-Gaussian vectors. Numerical results illustrate
the excellent behaviour of both test procedures and support selectors - larger
the dimension $p$, faster are the rates.


Abstract:
  A graph $G$ is called interval colorable if it has a proper edge coloring
with colors $1,2,3,\dots$ such that the colors of the edges incident to every
vertex of $G$ form an interval of integers. Not all graphs are interval
colorable; in fact, quite few families have been proved to admit interval
colorings. In this paper we introduce and investigate a new notion, the
interval coloring thickness of a graph $G$, denoted
${\theta_{\mathrm{int}}}(G)$, which is the minimum number of interval colorable
edge-disjoint subgraphs of $G$ whose union is $G$.
  Our investigation is motivated by scheduling problems with compactness
requirements, in particular, problems whose solution may consist of several
schedules, but where each schedule must not contain any waiting periods or idle
times for all involved parties. We first prove that every connected properly
$3$-edge colorable graph with maximum degree $3$ is interval colorable, and
using this result, we deduce an upper bound on ${\theta_{\mathrm{int}}}(G)$ for
general graphs $G$. We demonstrate that this upper bound can be improved in the
case when $G$ is bipartite, planar or complete multipartite and consider some
applications in timetabling.


Abstract:
  CO$_2$ dissociation stimulated by vibrational excitation in non-equilibrium
discharges has drawn lots of attention. Ns-discharges are known for their
highly non-equilibrium conditions. It is therefore of interest to investigate
the CO$_2$ excitation in such discharges. In this paper, we demonstrate the
ability for monitoring the time evolution of CO$_2$ ro-vibrational excitation
with a well-selected wavelength window around 2289.0 cm$^{-1}$ and a single CW
quantum cascade laser (QCL) with both high accuracy and temporal resolution.
The rotational and vibrational temperatures for both the symmetric and the
asymmetric modes of CO$_2$ in the afterglow of CO$_2$ + He ns-discharge were
measured with a temporal resolution of 1.5 $\mu$s. The non-thermal feature and
the preferential excitation of the asymmetric stretch mode of CO$_2$ were
experimentally observed, with a peak temperature of $T_{v3, max}$ = 966 $\pm$
1.5 K, $T_{v12, max}$ = 438.4 $\pm$ 1.2 K and $T_{rot}$ = 334.6 $\pm$ 0.6 K
reached at 3 $\mu$s after the nanosecond pulse. In the following relaxation
process, an exponential decay with a time constant of 69 $\mu$s was observed
for the asymmetric stretch (001) state, consistent with the dominant
deexcitation mechanism due to VT transfer with He and deexcitation on the wall.
Furthermore, a synchronous oscillation of the gas temperature and the total
pressure was also observed and can be explained by a two-line thermometry and
adiabatic process. The period of the oscillation and its dependence on the gas
components is consistent with a standing acoustic wave excited by the
ns-discharge.


Abstract:
  Let $G=(V,E)$ be a graph and $P\subseteq V$ a set of points. Two points are
mutually visible if there is a shortest path between them without further
points. $P$ is a mutual-visibility set if its points are pairwise mutually
visible. The mutual-visibility number of $G$ is the size of any largest
mutual-visibility set. In this paper we start the study about this new
invariant and the mutual-visibility sets in undirected graphs. We introduce the
mutual-visibility problem which asks to find a mutual-visibility set with a
size larger than a given number. We show that this problem is NP-complete,
whereas, to check whether a given set of points is a mutual-visibility set is
solvable in polynomial time. Then we study mutual-visibility sets and
mutual-visibility numbers on special classes of graphs, such as block graphs,
trees, grids, tori, complete bipartite graphs, cographs. We also provide some
relations of the mutual-visibility number of a graph with other invariants.


Abstract:
  Binary metallic phosphide, Nb2P5, belongs to technologically important class
of materials. Quite surprisingly, a large number of physical properties of
Nb2P5, including elastic properties and their anisotropy, acoustic, electronic
(DOS, charge density distribution, electron density difference),
thermo-physical, bonding characteristics, and optical properties have not been
investigated at all. In the present work we have explored all these properties
in details for the first time employing density functional theory based
first-principles method. Nb2P5 is found to be a mechanically stable,
elastically anisotropic compound with weak brittle character. The bondings
among the atoms are dominated by covalent and ionic contributions with small
signature of metallic feature. The compound possesses high level of
machinability. Nb2P5 is a moderately hard compound. The band structure
calculations reveal metallic conduction with a large electronic density of
states at the Fermi level. Calculated values of different thermal properties
indicate that Nb2P5 has the potential to be used as a thermal barrier coating
material. The energy dependent optical parameters show close agreement with the
underlying electronic band structure. The optical absorption and reflectivity
spectra and the static index of refraction of Nb2P5 show that the compound
holds promise to be used in optoelectronic device sector. Unlike notable
anisotropy in elastic and mechanical properties, the optical parameters are
found to be almost isotropic.


Abstract:
  Bayesian nonparametric hierarchical priors are highly effective in providing
flexible models for latent data structures exhibiting sharing of information
between and across groups. Most prominent is the Hierarchical Dirichlet Process
(HDP), and its subsequent variants, which model latent clustering between and
across groups. The HDP, may be viewed as a more flexible extension of Latent
Dirichlet Allocation models (LDA), and has been applied to, for example, topic
modelling, natural language processing, and datasets arising in health-care. We
focus on analogous latent feature allocation models, where the data structures
correspond to multisets or unbounded sparse matrices. The fundamental
development in this regard is the Hierarchical Indian Buffet process (HIBP),
which utilizes a hierarchy of Beta processes over J groups, where each group
generates binary random matrices, reflecting within group sharing of features,
according to beta-Bernoulli IBP priors. To encompass HIBP versions of
non-Bernoulli extensions of the IBP, we introduce hierarchical versions of
general spike and slab IBP. We provide explicit novel descriptions of the
marginal, posterior and predictive distributions of the HIBP and its
generalizations which allow for exact sampling and simpler practical
implementation. We highlight common structural properties of these processes
and establish relationships to existing IBP type and related models arising in
the literature. Examples of potential applications may involve topic models,
Poisson factorization models, random count matrix priors and neural network
models


Abstract:
  We introduce a new model of the logarithmic type of wave like plate equation
with a nonlocal logarithmic damping mechanism. We consider the Cauchy problem
for this new model in the whole space, and study the asymptotic profile and
optimal decay rates of solutions as time goes to infinity in L^{2}-sense. The
operator L considered in this paper was first introduced to dissipate the
solutions of the wave equation in the paper studied by Charao-Ikehata in 2020.
We will discuss the asymptotic property of the solution as time goes to
infinity to our Cauchy problem, and in particular, we classify the property of
the solutions into three parts from the viewpoint of regularity of the initial
data, that is, diffusion-like, wave-like, and both of them.


Abstract:
  This paper details speckle observations of binary stars taken at the Lowell
Discovery Telescope, the WIYN Telescope, and the Gemini telescopes between 2016
January and 2019 September. The observations taken at Gemini and Lowell were
done with the Differential Speckle Survey Instrument (DSSI), and those done at
WIYN were taken with the successor instrument to DSSI at that site, the
NN-EXPLORE Exoplanet Star and Speckle Imager (NESSI). In total, we present 378
observations of 178 systems and we show that the uncertainty in the measurement
precision for the combined data set is ~2 mas in separation, ~1-2 degrees in
position angle depending on the separation, and $\sim$0.1 magnitudes in
magnitude difference. Together with data already in the literature, these new
results permit 25 visual orbits and one spectroscopic-visual orbit to be
calculated for the first time. In the case of the spectroscopic-visual
analysis, which is done on the trinary star HD 173093, we calculate masses with
precision of better than 1% for all three stars in that system. Twenty-one of
the visual orbits calculated have a K dwarf as the primary star; we add these
to the known orbits of K dwarf primary stars and discuss the basic orbital
properties of these stars at this stage. Although incomplete, the data that
exist so far indicate that binaries with K dwarf primaries tend not to have
low-eccentricity orbits at separations of one to a few tens of AU, that is, on
solar-system scales.


Abstract:
  For many real-world classification problems, e.g., sentiment classification,
most existing machine learning methods are biased towards the majority class
when the Imbalance Ratio (IR) is high. To address this problem, we propose a
set convolution (SetConv) operation and an episodic training strategy to
extract a single representative for each class, so that classifiers can later
be trained on a balanced class distribution. We prove that our proposed
algorithm is permutation-invariant despite the order of inputs, and experiments
on multiple large-scale benchmark text datasets show the superiority of our
proposed framework when compared to other SOTA methods.


Abstract:
  In this paper we prove the convergence of solutions to discrete models for
binary waveguide arrays toward those of their formal continuum limit, for which
we also show the existence of localized standing waves. This work rigorously
justifies formal arguments and numerical simulations present in the Physics
literature.


Abstract:
  What would be the effect of locally poking a static scene? We present an
approach that learns naturally-looking global articulations caused by a local
manipulation at a pixel level. Training requires only videos of moving objects
but no information of the underlying manipulation of the physical scene. Our
generative model learns to infer natural object dynamics as a response to user
interaction and learns about the interrelations between different object body
regions. Given a static image of an object and a local poking of a pixel, the
approach then predicts how the object would deform over time. In contrast to
existing work on video prediction, we do not synthesize arbitrary realistic
videos but enable local interactive control of the deformation. Our model is
not restricted to particular object categories and can transfer dynamics onto
novel unseen object instances. Extensive experiments on diverse objects
demonstrate the effectiveness of our approach compared to common video
prediction frameworks. Project page is available at https://bit.ly/3cxfA2L .


Abstract:
  We propose a straightforward implementation of the phenomenon of diffractive
focusing with uniform atomic Bose-Einstein condensates. Both, analytical as
well as numerical methods not only illustrate the influence of the atom-atom
interaction on the focusing factor and the focus time, but also allow us to
derive the optimal conditions for observing focusing of this type in the case
of interacting matter waves.


Abstract:
  Consider the first order differential system given by
  \begin{equation*}
  \begin{array}{l}
  \dot{x}= y, \qquad \dot{y}= -x+a(1-y^{2n})y, \end{array}
  \end{equation*} where $a$ is a real parameter and the dots denote derivatives
with respect to the time $t$. Such system is known as the generalized Rayleigh
system and it appears, for instance, in the modeling of diabetic chemical
processes through a constant area duct, where the effect of adding or rejecting
heat is considered. In this paper we characterize the global dynamics of this
generalized Rayleigh system. In particular we prove the existence of a unique
limit cycle when the parameter $a\ne 0$.


Abstract:
  The ALICE Collaboration reports the first fully-corrected measurements of the
$N$-subjettiness observable for track-based jets in heavy-ion collisions. This
study is performed using data recorded in pp and Pb$-$Pb collisions at
centre-of-mass energies of $\sqrt{s} = 7$ TeV and $\sqrt{s_{\rm NN}} = 2.76$
TeV, respectively. In particular the ratio of 2-subjettiness to 1-subjettiness,
$\tau_{2}/\tau_{1}$, which is sensitive to the rate of two-pronged jet
substructure, is presented. Energy loss of jets traversing the strongly
interacting medium in heavy-ion collisions is expected to change the rate of
two-pronged substructure relative to vacuum. The results are presented for jets
with a resolution parameter of $R = 0.4$ and charged jet transverse momentum of
$40 \leq p_{\rm T,\rm jet} \leq 60$ GeV/$c$, which constitute a larger jet
resolution and lower jet transverse momentum interval than previous
measurements in heavy-ion collisions. This has been achieved by utilising a
semi-inclusive hadron-jet coincidence technique to suppress the larger jet
combinatorial background in this kinematic region. No significant modification
of the $\tau_{2}/\tau_{1}$ observable for track-based jets in Pb$-$Pb
collisions is observed relative to vacuum PYTHIA6 and PYTHIA8 references at the
same collision energy. The measurements of $\tau_{2}/\tau_{1}$, together with
the splitting aperture angle $\Delta R$, are also performed in pp collisions at
$\sqrt{s}=7$ TeV for inclusive jets. These results are compared with PYTHIA
calculations at $\sqrt{s}=7$ TeV, in order to validate the model as a vacuum
reference for the Pb$-$Pb centre-of-mass energy. The PYTHIA references for
$\tau_{2}/\tau_{1}$ are shifted to larger values compared to the measurement in
pp collisions. This hints at a reduction in the rate of two-pronged jets in
Pb$-$Pb collisions compared to pp collisions.


Abstract:
  We present an ALMA 1.3 mm (Band 6) continuum survey of lensed submillimeter
galaxies (SMGs) at $z=1.0\sim3.2$ with an angular resolution of $\sim0.2$".
These galaxies were uncovered by the Herschel Lensing Survey (HLS), and feature
exceptionally bright far-infrared continuum emission ($S_\mathrm{peak} \gtrsim
90$ mJy) owing to their lensing magnification. We detect 29 sources in 20
fields of massive galaxy clusters with ALMA. Using both the Spitzer/IRAC
(3.6/4.5 $\mathrm{\mu m}$) and ALMA data, we have successfully modeled the
surface brightness profiles of 26 sources in the rest-frame near- and
far-infrared. Similar to previous studies, we find the median dust-to-stellar
continuum size ratio to be small ($R_\mathrm{e,dust}/R_\mathrm{e,star} =
0.38\pm0.14$) for the observed SMGs, indicating that star formation is
centrally concentrated. This is, however, not the case for two spatially
extended main-sequence SMGs with a low surface brightness at 1.3 mm ($\lesssim
0.1$ mJy arcsec$^{-2}$), in which the star formation is distributed over the
entire galaxy ($R_\mathrm{e,dust}/R_\mathrm{e,star}>1$). As a whole, our SMG
sample shows a tight anti-correlation between
($R_\mathrm{e,dust}/R_\mathrm{e,star}$) and far-infrared surface brightness
($\Sigma_\mathrm{IR}$) over a factor of $\simeq$ 1000 in $\Sigma_\mathrm{IR}$.
This indicates that SMGs with less vigorous star formation (i.e., lower
$\Sigma_\mathrm{IR}$) lack central starburst and are likely to retain a broader
spatial distribution of star formation over the whole galaxies (i.e., larger
$R_\mathrm{e,dust}/R_\mathrm{e,star}$). The same trend can be reproduced with
cosmological simulations as a result of central starburst and potentially
subsequent "inside-out" quenching, which likely accounts for the emergence of
compact quiescent galaxies at $z\sim2$.


Abstract:
  Single layer Pb on top of (111) surfaces of group IV semiconductors hosts
charge density wave and superconductivity depending on the coverage and on the
substrate. These systems are normally considered to be experimental
realizations of single band Hubbard models and their properties are mostly
investigated using lattice models with frozen structural degrees of freedom,
although the reliability of this approximation is unclear. Here, we consider
the case of Pb/Ge(111) at 1/3 coverage, for which surface X-ray diffraction and
ARPES data are available. By performing first principles calculations, we
demonstrate that the non-local exchange between Pb and the substrate drives the
system into a $3\times 3$ charge density wave. The electronic structure of this
charge ordered phase is mainly determined by two effects: the magnitude of the
Pb distortion and the large spin-orbit coupling. Finally, we show that the
effect applies also to the $3\times 3$ phase of Pb/Si(111) where the
Pb-substrate exchange interaction increases the bandwidth by more than a factor
1.5 with respect to DFT+U, in better agreement with STS data. The delicate
interplay between substrate, structural and electronic degrees of freedom
invalidates the widespread interpretation available in literature considering
these compounds as physical realizations of single band Hubbard models.


Abstract:
  Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) has
recently emerged as a promising technique to provide sustainable energy supply
for widely distributed low-power ground devices (GDs) in large-scale wireless
networks. Compared with the energy transmitters (ETs) in conventional WPT
systems which are deployed at fixed locations, UAV-mounted aerial ETs can fly
flexibly in the three-dimensional (3D) space to charge nearby GDs more
efficiently. This paper provides a tutorial overview on UAV-enabled WPT and its
appealing applications, in particular focusing on how to exploit UAVs'
controllable mobility via their 3D trajectory design to maximize the amounts of
energy transferred to all GDs in a wireless network with fairness. First, we
consider the single-UAV-enabled WPT scenario with one UAV wirelessly charging
multiple GDs at known locations. To solve the energy maximization problem in
this case, we present a general trajectory design framework consisting of three
innovative approaches to optimize the UAV trajectory, which are multi-location
hovering, successive-hover-and-fly, and time-quantization-based optimization,
respectively. Next, we consider the multi-UAV-enabled WPT scenario where
multiple UAVs cooperatively charge many GDs in a large area. Building upon the
single-UAV trajectory design, we propose two efficient schemes to jointly
optimize multiple UAVs' trajectories, based on the principles of UAV swarming
and GD clustering, respectively. Furthermore, we consider two important
extensions of UAV-enabled WPT, namely UAV-enabled wireless powered
communication networks (WPCN) and UAV-enabled wireless powered mobile edge
computing (MEC).


Abstract:
  We explore the intrinsic dynamics of spherical shells immersed in a fluid in
the vicinity of their buckled state, through experiments and 3D axisymmetric
simulations. The results are supported by a theoretical model that accurately
describes the buckled shell as a two-variable-only oscillator. We quantify the
effective "softening" of shells above the buckling threshold, as observed in
recent experiments on interactions between encapsulated microbubbles and
acoustic waves. The main dissipation mechanism in the neighboring fluid is also
evidenced.


Abstract:
  In this paper, we analyze the effect of transport infrastructure investments
in railways. As a testing ground, we use data from a new historical database
that includes annual panel data on approximately 2,400 Swedish rural
geographical areas during the period 1860-1917. We use a staggered event study
design that is robust to treatment effect heterogeneity. Importantly, we find
extremely large reduced-form effects of having access to railways. For real
nonagricultural income, the cumulative treatment effect is approximately 120%
after 30 years. Equally important, we also show that our reduced-form effect is
likely to reflect growth rather than a reorganization of existing economic
activity since we find no spillover effects between treated and untreated
regions. Specifically, our results are consistent with the big push hypothesis,
which argues that simultaneous/coordinated investment, such as large
infrastructure investment in railways, can generate economic growth if there
are strong aggregate demand externalities (e.g., Murphy et al. 1989). We used
plant-level data to further corroborate this mechanism. Indeed, we find that
investments in local railways dramatically, and independent of initial
conditions, increase local industrial production and employment on the order of
100-300% across almost all industrial sectors.


Abstract:
  The presence of relativistic electrons within the diffuse gas phase of galaxy
clusters is now well established, but their detailed origin remains unclear.
Cosmic ray protons are also expected to accumulate during the formation of
clusters and would lead to gamma-ray emission through hadronic interactions
within the thermal gas. Recently, the detection of gamma-ray emission has been
reported toward the Coma cluster with Fermi-LAT. Assuming that this gamma-ray
emission arises from hadronic interactions in the ICM, we aim at exploring the
implication of this signal on the cosmic ray content of the Coma cluster. We
use the MINOT software to build a physical model of the cluster and apply it to
the Fermi-LAT data. We also consider contamination from compact sources and the
impact of various systematic effects. We confirm that a significant gamma-ray
signal is observed within the characteristic radius $\theta_{500}$ of the Coma
cluster, with a test statistic TS~27 for our baseline model. The presence of a
possible point source may account for most of the observed signal. However,
this source could also correspond to the peak of the diffuse emission of the
cluster itself and extended models match the data better. We constrain the
cosmic ray to thermal energy ratio within $R_{500}$ to $X_{\rm
CRp}=1.79^{+1.11}_{-0.30}$\% and the slope of the energy spectrum of cosmic
rays to $\alpha=2.80^{+0.67}_{-0.13}$. Finally, we compute the synchrotron
emission associated with the secondary electrons produced in hadronic
interactions assuming steady state. This emission is about four times lower
than the overall observed radio signal, so that primary cosmic ray electrons or
reacceleration of secondary electrons is necessary to explain the total
emission. Assuming an hadronic origin of the signal, our results provide the
first quantitative measurement of the cosmic ray proton content in a
cluster.[Abridged]


Abstract:
  We study idempotent, model, and Toeplitz operators that attain the norm.
Notably, we prove that if $\mathcal{Q}$ is a backward shift invariant subspace
of the Hardy space $H^2(\mathbb{D})$, then the model operator $S_{\mathcal{Q}}$
attains its norm. Here $S_{\mathcal{Q}} = P_{\mathcal{Q}}M_z|_{\mathcal{Q}}$,
the compression of the shift $M_z$ on the Hardy space $H^2(\mathbb{D})$ to
$\mathcal{Q}$.


Abstract:
  Power system simulations that extend over a time period of minutes, hours, or
even longer are called extended-term simulations. As power systems evolve into
complex systems with increasing interdependencies and richer dynamic behaviors
across a wide range of timescales, extended-term simulation is needed for many
power system analysis tasks (e.g., resilience analysis, renewable energy
integration, cascading failures), and there is an urgent need for efficient and
robust extended-term simulation approaches. The conventional approaches are
insufficient for dealing with the extended-term simulation of multi-timescale
processes. This paper proposes an extended-term simulation approach based on
the holomorphic embedding (HE) methodology. Its accuracy and computational
efficiency are backed by HE's high accuracy in event-driven simulation, larger
and adaptive time steps, and flexible switching between full-dynamic and
quasi-steady-state (QSS) models. We used this proposed extended-term simulation
approach to evaluate bulk power system restoration plans, and it demonstrates
satisfactory accuracy and efficiency in this complex simulation task.


Abstract:
  The efficiency of the adiabatic demagnetization of nuclear spin system (NSS)
of a solid is limited, if quadrupole effects are present. Nevertheless, despite
a considerable quadrupole interaction, recent experiments validated the
thermodynamic description of the NSS in GaAs. This suggests that nuclear spin
temperature can be used as the universal indicator of the NSS state in presence
of external perturbations. We implement this idea by analyzing the modification
of the NSS temperature in response to an oscillating magnetic field at various
frequencies, an approach termed as the warm-up spectroscopy. It is tested in a
n-GaAs sample where both mechanical strain and built-in electric field may
contribute to the quadrupole splitting, yielding the parameters of electric
field gradient tensors for 75As and both Ga isotopes, 69Ga and 71Ga.


Abstract:
  Unmanned aerial vehicles (UAVs) play an increasingly important role in
military, public, and civilian applications, where providing connectivity to
UAVs is crucial for its real-time control, video streaming, and data
collection. Considering that cellular networks offer wide area, high speed, and
secure wireless connectivity, cellular-connected UAVs have been considered as
an appealing solution to provide UAV connectivity with enhanced reliability,
coverage, throughput, and security. Due to the nature of UAVs mobility, the
throughput, reliability and End-to-End (E2E) delay of UAVs communication under
various flight heights, video resolutions, and transmission frequencies remain
unknown. To evaluate these parameters, we develop a cellular-connected UAV
testbed based on the Long Term Evolution (LTE) network with its uplink video
transmission and downlink control\&command (CC) transmission. We also design
algorithms for sending control signal and controlling UAV. The indoor
experimental results provide fundamental insights for the cellular-connected
UAV system design from the perspective of transmission frequency, adaptability,
and link outage, respectively.


Abstract:
  TMs are a pattern recognition approach that uses finite state machines for
learning and propositional logic to represent patterns. In addition to being
natively interpretable, they have provided competitive accuracy for various
tasks. In this paper, we increase the computing power of TMs by proposing a
first-order logic-based framework with Herbrand semantics. The resulting TM is
relational and can take advantage of logical structures appearing in natural
language, to learn rules that represent how actions and consequences are
related in the real world. The outcome is a logic program of Horn clauses,
bringing in a structured view of unstructured data. In closed-domain
question-answering, the first-order representation produces 10x more compact
KBs, along with an increase in answering accuracy from 94.83% to 99.48%. The
approach is further robust towards erroneous, missing, and superfluous
information, distilling the aspects of a text that are important for real-world
understanding.


Abstract:
  Markerless motion capture and understanding of professional non-daily human
movements is an important yet unsolved task, which suffers from complex motion
patterns and severe self-occlusion, especially for the monocular setting. In
this paper, we propose SportsCap -- the first approach for simultaneously
capturing 3D human motions and understanding fine-grained actions from
monocular challenging sports video input. Our approach utilizes the semantic
and temporally structured sub-motion prior in the embedding space for motion
capture and understanding in a data-driven multi-task manner. To enable robust
capture under complex motion patterns, we propose an effective motion embedding
module to recover both the implicit motion embedding and explicit 3D motion
details via a corresponding mapping function as well as a sub-motion
classifier. Based on such hybrid motion information, we introduce a
multi-stream spatial-temporal Graph Convolutional Network(ST-GCN) to predict
the fine-grained semantic action attributes, and adopt a semantic attribute
mapping block to assemble various correlated action attributes into a
high-level action label for the overall detailed understanding of the whole
sequence, so as to enable various applications like action assessment or motion
scoring. Comprehensive experiments on both public and our proposed datasets
show that with a challenging monocular sports video input, our novel approach
not only significantly improves the accuracy of 3D human motion capture, but
also recovers accurate fine-grained semantic action attributes.


Abstract:
  Methods for stochastic trace estimation often require the repeated evaluation
of expressions of the form $z^T p_n(A)z$, where $A$ is a symmetric matrix and
$p_n$ is a degree $n$ polynomial written in the standard or Chebyshev basis. We
show how to evaluate these expressions using only $\lceil n/2\rceil$
matrix-vector products, thus substantially reducing the cost of existing trace
estimation algorithms that use Chebyshev interpolation or Taylor series.


Abstract:
  The inverse Higgs phenomenon, which plays an important r\^ole in physical
systems with Goldstone bosons (such as the phonons in a crystal) involves
nonholonomic mechanical constraints. By formulating field theories with
symmetries and constraints in a general way using the language of differential
geometry, we show that many examples of constraints in inverse Higgs phenomena
fall into a special class, which we call coholonomic constraints, that are dual
(in the sense of category theory) to holonomic constraints. Just as for
holonomic constraints, systems with coholonomic constraints are equivalent to
unconstrained systems (whose degrees of freedom are known as essential
Goldstone bosons), making it easier to study their consistency and dynamics.
The remaining examples of inverse Higgs phenomena in the literature require the
dual of a slight generalisation of a holonomic constraint, which we call
(co)meronomic. Our formalism simplifies and clarifies the many ad hoc
assumptions and constructions present in the literature. In particular, it
identifies which are necessary and which are merely convenient. It also opens
the way to studying much more general dynamical examples, including systems
which have no well-defined notion of a target space.


Abstract:
  This article discusses a dark energy cosmological model in the standard
theory of gravity - general relativity with a broad scalar field as a source.
Exact solutions of Einstein's field equations are derived by considering a
particular form of deceleration parameter $q$, which shows a smooth transition
from decelerated to accelerated phase in the evolution of the universe. The
external datasets such as Hubble ($H(z)$) datasets, Supernovae (SN) datasets,
and Baryonic Acoustic Oscillation (BAO) datasets are used for constraining the
model par parameters appearing in the functional form of $q$. The transition
redshift is obtained at $% z_{t}=0.67_{-0.36}^{+0.26}$ for the combined data
set ($H(z)+SN+BAO$), where the model shows signature-flipping and is consistent
with recent observations. Moreover, the present value of the deceleration
parameter comes out to be $q_{0}=-0.50_{-0.11}^{+0.12}$ and the jerk parameter
$% j_{0}=-0.98_{-0.02}^{+0.06}$ (close to 1) for the combined datasets, which
is compatible as per Planck2018 results. The analysis also constrains the omega
value i.e., $\Omega _{m_{0}}\leq 0.269$ for the smooth evolution of the scalar
field EoS parameter. It is seen that energy density is higher for the effective
energy density of the matter field than energy density in the presence of a
scalar field. The evolution of the physical and geometrical parameters is
discussed in some details with the model parameters' numerical constrained
values. Moreover, we have performed the state-finder analysis to investigate
the nature of dark energy.


Abstract:
  Electrical energy consumption data accessibility for low voltage end users is
one of the pillars of smart grids. In some countries, despite the presence of
smart meters, a fragmentary data availability and/or the lack of
standardization hinders the creation of post-metering value-added services and
confines such innovative solutions to the prototypal and experimental level. We
take inspiration from the technology adopted in Italy, where the national
regulatory authority actively supported the definition of a solution agreed
upon by all the involved stakeholders. In this context, smart meters are
enabled to convey data to low voltage end users through a power line
communication channel (CHAIN 2) in near real-time. The aim of this paper is
twofold. On the one hand, it describes the proof of concept that the channel
underwent and its subsequent validation (with performances nearing 99% success
rate). On the other hand, it defines a classification framework (I2MA) for
post-metering value-added services, in order to categorize each use case based
on both level of service and expected benefits, and understand its maturity
level. As an example, we apply the methodology to the 16 use cases defined in
Italy. The lessons learned from the regulatory, technological, and functional
approach of the Italian experience bring us to the provision of recommendations
for researchers and industry experts. In particular, we argue that a
well-functioning post-metering value-added services' market can flourish when:
i) distribution system operators certify the measurements coming from smart
meters; ii) national regulatory authorities support the technological
innovation needed for setting up this market; and iii) service providers create
customer-oriented solutions based on smart meters' data.


Abstract:
  Robust edge transport can occur when particles in crystalline lattices
interact with an external magnetic field. This system is well described by
Bloch's theorem, with the spectrum being composed of bands of bulk states and
in-gap edge states. When the confining lattice geometry is altered to be
quasicrystaline, then Bloch's theorem breaks down. However, we still expect to
observe the basic characteristics of bulk states and current carrying edge
states. Here, we show that for quasicrystals in magnetic fields, there is also
a third option; the bulk localised transport states. These states share the
in-gap nature of the well-known edge states and can support transport along
them, but they are fully contained within the bulk of the system, with no
support along the edge. We consider both finite and infinite systems, using
rigorous error controlled computational techniques that are not prone to
finite-size effects. The bulk localised transport states are preserved for
infinite systems, in stark contrast to the normal edge states. This allows for
transport to be observed in infinite systems, without any perturbations,
defects, or boundaries being introduced. We confirm the in-gap topological
nature of the bulk localised transport states for finite and infinite systems
by computing common topological measures; namely the Bott index and local Chern
marker. The bulk localised transport states form due to a magnetic aperiodicity
arising from the interplay of length scales between the magnetic field and
quasiperiodic lattice. Bulk localised transport could have interesting
applications similar to those of the edge states on the boundary, but that
could now take advantage of the larger bulk of the lattice. The infinite size
techniques introduced here, especially the calculation of topological measures,
could also be widely applied to other crystalline, quasicrystalline, and
disordered models.


Abstract:
  It is well established that glassy materials can undergo aging, i.e., their
properties gradually change over time. There is rapidly growing evidence that
dense active and living systems also exhibit many features of glassy behavior,
but it is still largely unknown how physical aging is manifested in such active
glassy materials. Our goal is to explore whether active and passive thermal
glasses age in fundamentally different ways. To address this, we numerically
study the aging dynamics following a quench from high to low temperature for
two-dimensional passive and active Brownian model glass-formers. We find that
aging in active thermal glasses is governed by a time-dependent competition
between thermal and active effects, with an effective temperature that
explicitly evolves with the age of the material. Moreover, unlike passive aging
phenomenology, we find that the degree of dynamic heterogeneity in active aging
systems is relatively small and remarkably constant with age. We conclude that
the often-invoked mapping between an active system and a passive one with a
higher effective temperature rigorously breaks down upon aging, and that the
aging dynamics of thermal active glasses differs in several distinct ways from
both the passive and athermal active case.


Abstract:
  The radio nebula W50 is a unique object interacting with the jets of the
microquasar SS433. The SS433/W50 system is a good target for investigating the
energy of cosmic-ray particles accelerated by galactic jets. We report
observations of radio nebula W50 conducted with the NSF's Karl G. Jansky Very
Large Array (VLA) in the L band (1.0 -- 2.0 GHz). We investigate the secular
change of W50 on the basis of the observations in 1984, 1996, and 2017, and
find that most of its structures were stable for 33 years. We revise the upper
limit velocity of the eastern terminal filament by half to 0.023$c$ assuming a
distance of 5.5 kpc. We also analyze the observational data of the Arecibo
Observatory 305-m telescope and identify the HI cavity around W50 in the
velocity range 33.77 km s$^{-1}$ -- 55.85 km s$^{-1}$. From this result, we
estimate the maximum energy of the cosmic-ray protons accelerated by the jet
terminal region to be above 10$^{15.5}$ eV. We also use the luminosity of the
gamma-rays in the range 0.5 -- 10 GeV to estimate the total energy of
accelerated protons below 5.2 $\times$ 10$^{48}$ erg.


Abstract:
  We present the design for a novel type of dual-band photodetector in the
thermal infrared spectral range, the Optically Controlled Dual-band quantum dot
Infrared Photodetector (OCDIP). This concept is based on a quantum dot ensemble
with a unimodal size distribution, whose absorption spectrum can be controlled
by optically-injected carriers. An external pumping laser varies the electron
density in the QDs, permitting to control the available electronic transitions
and thus the absorption spectrum. We grew a test sample which we studied by AFM
and photoluminescence. Based on the experimental data, we simulated the
infrared absorption spectrum of the sample, which showed two absorption bands
at 5.85 um and 8.98 um depending on the excitation power.


Abstract:
  We present a novel ultrastable superconducting radio-frequency (RF) ion trap
realized as a combination of an RF cavity and a linear Paul trap. Its RF
quadrupole mode at 34.52 MHz reaches a quality factor of $Q\approx2.3\times
10^5$ at a temperature of 4.1 K and is used to radially confine ions in an
ultralow-noise pseudopotential. This concept is expected to strongly suppress
motional heating rates and related frequency shifts which limit the ultimate
accuracy achieved in advanced ion traps for frequency metrology. Running with
its low-vibration cryogenic cooling system, electron beam ion trap and
deceleration beamline supplying highly charged ions (HCI), the superconducting
trap offers ideal conditions for optical frequency metrology with ionic
species. We report its proof-of-principle operation as a quadrupole mass filter
with HCI, and trapping of Doppler-cooled ${}^9\text{Be}^+$ Coulomb crystals.


Abstract:
  With Regulation UNECE R157 on Automated Lane-Keeping Systems, the first
framework for the introduction of passenger cars with Level 3 systems has
become available in 2020. In accordance with recent research projects including
academia and the automotive industry, the Regulation utilizes scenario based
testing for the safety assessment. The complexity of safety validation of
automated driving systems necessitates system-level simulations. The
Regulation, however, is missing the required parameterization necessary for
test case generation. To overcome this problem, we incorporate the exposure and
consider the heterogeneous behavior of the traffic participants by extracting
concrete scenarios according to Regulation's scenario definition from the
established naturalistic highway dataset highD. We present a methodology to
find the scenarios in real-world data, extract the parameters for modeling the
scenarios and transfer them to simulation. In this process, more than 340
scenarios were extracted. OpenSCENARIO files were generated to enable an
exemplary transfer of the scenarios to CARLA and esmini. We compare the
trajectories to examine the similarity of the scenarios in the simulation to
the recorded scenarios. In order to foster research, we publish the resulting
dataset called ConScenD together with instructions for usage with both
simulation tools. The dataset is available online at
https://www.levelXdata.com/scenarios.


Abstract:
  In this paper, we characterize the asymptotic and large scale behavior of the
eigenvalues of wavelet random matrices in high dimensions. We assume that
possibly non-Gaussian, finite-variance $p$-variate measurements are made of a
low-dimensional $r$-variate ($r \ll p$) fractional stochastic process with
non-canonical scaling coordinates and in the presence of additive
high-dimensional noise. The measurements are correlated both time-wise and
between rows. We show that the $r$ largest eigenvalues of the wavelet random
matrices, when appropriately rescaled, converge to scale invariant functions in
the high-dimensional limit. By contrast, the remaining $p-r$ eigenvalues remain
bounded. Under additional assumptions, we show that, up to a log
transformation, the $r$ largest eigenvalues of wavelet random matrices exhibit
asymptotically Gaussian distributions. The results have direct consequences for
statistical inference.


Abstract:
  Annually, a large number of injuries and deaths around the world are related
to motor vehicle accidents. This value has recently been reduced to some
extent, via the use of driver-assistance systems. Developing driver-assistance
systems (i.e., automated driving systems) can play a crucial role in reducing
this number. Estimating and predicting surrounding vehicles' movement is
essential for an automated vehicle and advanced safety systems. Moreover,
predicting the trajectory is influenced by numerous factors, such as drivers'
behavior during accidents, history of the vehicle's movement and the
surrounding vehicles, and their position on the traffic scene. The vehicle must
move over a safe path in traffic and react to other drivers' unpredictable
behaviors in the shortest time. Herein, to predict automated vehicles' path, a
model with low computational complexity is proposed, which is trained by images
taken from the road's aerial image. Our method is based on an encoder-decoder
model that utilizes a social tensor to model the effect of the surrounding
vehicles' movement on the target vehicle. The proposed model can predict the
vehicle's future path in any freeway only by viewing the images related to the
history of the target vehicle's movement and its neighbors. Deep learning was
used as a tool for extracting the features of these images. Using the HighD
database, an image dataset of the road's aerial image was created, and the
model's performance was evaluated on this new database. We achieved the RMSE of
1.91 for the next 5 seconds and found that the proposed method had less error
than the best path-prediction methods in previous studies.


Abstract:
  The paper provides a version of the rational Hodge conjecture for $\3\dg$
categories. The noncommutative Hodge conjecture is equivalent to the version
proposed in \cite{perry2020integral} for admissible subcategories. We obtain
examples of evidence of the Hodge conjecture by techniques of noncommutative
geometry. Finally, we show that the noncommutative Hodge conjecture for smooth
proper connective $\3\dg$ algebras is true.


Abstract:
  Let $(X, D)$ be a log smooth log canonical pair such that $K_X+D$ is ample.
Extending a theorem of Guenancia and building on his techniques, we show that
negatively curved K\"{a}hler-Einstein crossing edge metrics converge to
K\"{a}hler-Einstein mixed cusp and edge metrics smoothly away from the divisor
when some of the cone angles converge to $0$. We further show that near the
divisor such normalized K\"{a}hler-Einstein crossing edge metrics converge to a
mixed cylinder and edge metric in the pointed Gromov-Hausdorff sense when some
of the cone angles converge to $0$ at (possibly) different speeds.


Abstract:
  Novice programmers face numerous barriers while attempting to learn how to
code that may deter them from pursuing a computer science degree or career in
software development. In this work, we propose a tool concept to address the
particularly challenging barrier of novice programmers holding misconceptions
about how their code behaves. Specifically, the concept involves an inquisitive
code editor that: (1) identifies misconceptions by periodically prompting the
novice programmer with questions about their program's behavior, (2) corrects
the misconceptions by generating explanations based on the program's actual
behavior, and (3) prevents further misconceptions by inserting test code and
utilizing other educational resources. We have implemented portions of the
concept as plugins for the Atom code editor and conducted informal surveys with
students and instructors. Next steps include deploying the tool prototype to
students enrolled in introductory programming courses.


Abstract:
  We show that any Brauer tree algebra has precisely $\binom{2n}{n}$
$2$-tilting complexes, where $n$ is the number of edges of the associated
Brauer tree. More explicitly, for an external edge $e$ and an integer $j\neq0$,
we show that the number of $2$-tilting complexes $T$ with $g_e(T)=j$ is
$\binom{2n-|j|-1}{n-1}$, where $g_e(T)$ denotes the $e$-th of the $g$-vector of
$T$. To prove this, we use a geometric model of Brauer graph algebras on the
closed oriented marked surfaces and a classification of $2$-tilting complexes
due to Adachi-Aihara-Chan.


Abstract:
  We measure the evolution of the rest-frame UV luminosity function (LF) and
the stellar mass function (SMF) of Lyman-alpha (Lya) emitters (LAEs) from z~2
to z~6 by exploring ~4000 LAEs from the SC4K sample. We find a correlation
between Lya luminosity (LLya) and rest-frame UV (M_UV), with best-fit
M_UV=-1.6+-0.2 log10(LLya/erg/s)+47+-12 and a shallower relation between LLya
and stellar mass (Mstar), with best-fit log10( Mstar/Msun)=0.9+-0.1
log10(LLya/erg/s)-28+-4.0. An increasing LLya cut predominantly lowers the
number density of faint M_UV and low Mstar LAEs. We estimate a proxy for the
full UV LFs and SMFs of LAEs with simple assumptions of the faint end slope.
For the UV LF, we find a brightening of the characteristic UV luminosity
(M_UV*) with increasing redshift and a decrease of the characteristic number
density (Phi*). For the SMF, we measure a characteristic stellar mass
(Mstar*/Msun) increase with increasing redshift, and a Phi* decline. However,
if we apply a uniform luminosity cut of log10 (LLya/erg/s) >= 43.0, we find
much milder to no evolution in the UV and SMF of LAEs. The UV luminosity
density (rho_UV) of the full sample of LAEs shows moderate evolution and the
stellar mass density (rho_M) decreases, with both being always lower than the
total rho_UV and rho_M of more typical galaxies but slowly approaching them
with increasing redshift. Overall, our results indicate that both rho_UV and
rho_M of LAEs slowly approach the measurements of continuum-selected galaxies
at z>6, which suggests a key role of LAEs in the epoch of reionisation.


Abstract:
  Accurate numerical solutions for the Schr\"odinger equation are of utmost
importance in quantum chemistry. However, the computational cost of current
high-accuracy methods scales poorly with the number of interacting particles.
Combining Monte Carlo methods with unsupervised training of neural networks has
recently been proposed as a promising approach to overcome the curse of
dimensionality in this setting and to obtain accurate wavefunctions for
individual molecules at a moderately scaling computational cost. These methods
currently do not exploit the regularity exhibited by wavefunctions with respect
to their molecular geometries. Inspired by recent successful applications of
deep transfer learning in machine translation and computer vision tasks, we
attempt to leverage this regularity by introducing a weight-sharing constraint
when optimizing neural network-based models for different molecular geometries.
That is, we restrict the optimization process such that up to 95 percent of
weights in a neural network model are in fact equal across varying molecular
geometries. We find that this technique can accelerate optimization when
considering sets of nuclear geometries of the same molecule by an order of
magnitude and that it opens a promising route towards pre-trained neural
network wavefunctions that yield high accuracy even across different molecules.


Abstract:
  As a non-linear extension of the classic Linear Discriminant Analysis(LDA),
Deep Linear Discriminant Analysis(DLDA) replaces the original Categorical Cross
Entropy(CCE) loss function with eigenvalue-based loss function to make a deep
neural network(DNN) able to learn linearly separable hidden representations. In
this paper, we first point out DLDA focuses on training the cooperative
discriminative ability of all the dimensions in the latent subspace, while put
less emphasis on training the separable capacity of single dimension. To
improve DLDA, a regularization method on within-class scatter matrix is
proposed to strengthen the discriminative ability of each dimension, and also
keep them complement each other. Experiment results on STL-10, CIFAR-10 and
Pediatric Pneumonic Chest X-ray Dataset showed that our proposed regularization
method Regularized Deep Linear Discriminant Analysis(RDLDA) outperformed DLDA
and conventional neural network with CCE as objective. To further improve the
discriminative ability of RDLDA in the local space, an algorithm named Subclass
RDLDA is also proposed.


Abstract:
  A fog-radio access network (F-RAN) architecture is studied for an
Internet-of-Things (IoT) system in which wireless sensors monitor a number of
multi-valued events and transmit in the uplink using grant-free random access
to multiple edge nodes (ENs). Each EN is connected to a central processor (CP)
via a finite-capacity fronthaul link. In contrast to conventional
information-agnostic protocols based on separate source-channel (SSC) coding,
where each device uses a separate codebook, this paper considers an
information-centric approach based on joint source-channel (JSC) coding via a
non-orthogonal generalization of type-based multiple access (TBMA). By
leveraging the semantics of the observed signals, all sensors measuring the
same event share the same codebook (with non-orthogonal codewords), and all
such sensors making the same local estimate of the event transmit the same
codeword. The F-RAN architecture directly detects the events values without
first performing individual decoding for each device. Cloud and edge detection
schemes based on Bayesian message passing are designed and trade-offs between
cloud and edge processing are assessed.