Abstract:
  Data-efficiency and generalization are key challenges in deep learning and
deep reinforcement learning as many models are trained on large-scale,
domain-specific, and expensive-to-label datasets. Self-supervised models
trained on large-scale uncurated datasets have shown successful transfer to
diverse settings. We investigate using pretrained image representations and
spatio-temporal attention for state representation learning in Atari. We also
explore fine-tuning pretrained representations with self-supervised techniques,
i.e., contrastive predictive coding, spatio-temporal contrastive learning, and
augmentations. Our results show that pretrained representations are at par with
state-of-the-art self-supervised methods trained on domain-specific data.
Pretrained representations, thus, yield data and compute-efficient state
representations. https://github.com/PAL-ML/PEARL_v1


Abstract:
  Recently, the importance of the electronic many-body effect in the dark
matter (DM) detection has been recognized and a coherent formulation of the
DM-electron scattering in terms of the dielectric response of the target
material has been well established in literatures. In this paper, we put
relevant formulas into practical density functional theory (DFT) estimation of
the excitation event rates for the diamond and silicon semiconductor targets.
Moreover, we compare the event rates calculated from the energy loss functions
with and without the local field effects. For a consistency check of this
numerical method, we also compare the differential spectrum and detection reach
of the silicon with those computed with the $\mathtt{GPAW}$ code. It turns out
that this DFT approach is quite consistent and robust. As an interesting
extension, we also investigate the in-medium effect on the detection of the
solar-reflected DM flux in silicon-based detectors, where the screening effect
is found to be also remarkable in the optically thick regime, and to turn
insignificant in the optically thin regime, depending on the energies of the
reflected DM particles.


Abstract:
  As an application of the simultaneous and continuous measurement of
noncommutative observables formulated in our previous paper [C. Jiang and G.
Watanabe, Phys. Rev. A 102, 062216 (2020)], we propose a scheme to generate the
pure ideal quadrature squeezed state in an one-dimensional harmonic oscillator
system by the feedback control based on such type of measurement of
noncommutative quadrature observables. We find that, by appropriately setting
the strengths of the measurement and the feedback control, the pure ideal
quadrature squeezed state with arbitrary squeezedness can be produced. This is
in contrast to the scheme based on the single-observable measurement and the
feedback control, where only nonideal squeezed state with squeezing of the
measured quadrature are produced.


Abstract:
  Recent work has shown that Neural Ordinary Differential Equations (ODEs) can
serve as generative models of images using the perspective of Continuous
Normalizing Flows (CNFs). Such models offer exact likelihood calculation, and
invertible generation/density estimation. In this work we introduce a
Multi-Resolution variant of such models (MRCNF), by characterizing the
conditional distribution over the additional information required to generate a
fine image that is consistent with the coarse image. We introduce a
transformation between resolutions that allows for no change in the log
likelihood. We show that this approach yields comparable likelihood values for
various image datasets, with improved performance at higher resolutions, with
fewer parameters, using only 1 GPU. Further, we examine the out-of-distribution
properties of (Multi-Resolution) Continuous Normalizing Flows, and find that
they are similar to those of other likelihood-based generative models.


Abstract:
  Young exoplanets can offer insight into the evolution of planetary
atmospheres, compositions, and architectures. We present the discovery of the
young planetary system TOI 451 (TIC 257605131, Gaia DR2 4844691297067063424).
TOI 451 is a member of the 120-Myr-old Pisces--Eridanus stream (Psc--Eri). We
confirm membership in the stream with its kinematics, its lithium abundance,
and the rotation and UV excesses of both TOI 451 and its wide binary companion,
TOI 451 B (itself likely an M dwarf binary). We identified three candidate
planets transiting in the TESS data and followed up the signals with photometry
from Spitzer and ground-based telescopes. The system comprises three validated
planets at periods of 1.9, 9.2 and 16 days, with radii of 1.9, 3.1, and 4.1
Earth radii, respectively. The host star is near-solar mass with V=11.0 and
H=9.3 and displays an infrared excess indicative of a debris disk. The planets
offer excellent prospects for transmission spectroscopy with HST and JWST,
providing the opportunity to study planetary atmospheres that may still be in
the process of evolving.


Abstract:
  We explore changes in the adiabatic low-order g-mode pulsation periods of
0.526, 0.560, and 0.729 M$_\odot$ carbon-oxygen white dwarf models with
helium-dominated envelopes due to the presence, absence, and enhancement of
$^{22}$Ne in the interior. The observed g-mode pulsation periods of such white
dwarfs are typically given to 6$-$7 significant figures of precision. Usually
white dwarf models without $^{22}$Ne are fit to the observed periods and other
properties. The root-mean-square residuals to the $\simeq$ 150$-$400 s
low-order g-mode periods are typically in the range of $\sigma_{rms}$
$\lesssim$ 0.3 s, for a fit precision of $\sigma_{rms}/ P$ $\lesssim$ 0.3 %. We
find average relative period shifts of $\Delta P/P$ $\simeq$ $\pm$ 0.5 % for
the low-order dipole and quadrupole g-mode pulsations within the observed
effective temperature window, with the range of $\Delta P/P$ depending on the
specific g-mode, abundance of $^{22}$Ne, effective temperature, and mass of the
white dwarf model. This finding suggests a systematic offset may be present in
the fitting process of specific white dwarfs when $^{22}$Ne is absent. As part
of the fitting processes involves adjusting the composition profiles of a white
dwarf model, our study on the impact of $^{22}$Ne can provide new inferences on
the derived interior mass fraction profiles. We encourage routinely including
$^{22}$Ne mass fraction profiles, informed by stellar evolution models, to
future generations of white dwarf model fitting processes.


Abstract:
  A simple geometrical model with event-by-event fluctuations is suggested to
study elliptical and triangular eccentricities in the initial state of
relativistic heavy-ion collisions. This model describes rather well the ALICE
and ATLAS data for Pb+Pb collisions at center-of-mass energy $\sqrt{s_{NN}} =
5.02$~TeV per nucleon pair, assuming that the second, $v_2$, and third, $v_3$,
harmonics of the anisotropic flow are simply linearly proportional to the
eccentricities $\varepsilon_2$ and $\varepsilon_3$, respectively. We show that
the eccentricity $\varepsilon_3$ has a pure fluctuation origin and is
substantially dependent on the size of the overlap area only, while the
eccentricity $\varepsilon_2$ is mainly related to the average collision
geometry. Elliptic flow, therefore, is weakly dependent on the event-by-event
fluctuations everywhere except of the very central collisions 0--2%, whereas
triangular flow is mostly determined by the fluctuations. The scaling
dependence of the magnitude of the flow harmonics on atomic number, $v_n
\propto A^{-1/3}$, is predicted for this centrality interval.


Abstract:
  Let $n,k$ be positive integers such that $n\geq 3$, $k < \frac {n}{2} $. Let
$q$ be a power of a prime $p$ and $\mathbb{F}_q$ be a finite field of order
$q$. Let $V(q,n)$ be a vector space of dimension $n$ over $\mathbb{F}_q$. We
define the graph $S(q,n,k)$ as a graph with the vertex set $V=V_k \cup
V_{k+1}$, where $V_k$ and $V_{k+1}$ are the family of subspaces in $V(q,n)$ of
dimension $k$ and $k+1$ respectively, in which two vertices $v$ and $w$ are
adjacent whenever $v$ is a subspace of $w$ or $w$ is a subspace of $v$.
  It is clear that the graph $S(q,n,k)$ is a bipartite graph. In this paper, we
study some properties of this graph. In particular, we determine the spectrum
of the graph $S(q,n,k)$.


Abstract:
  3D point cloud semantic segmentation is a challenging topic in the computer
vision field. Most of the existing methods in literature require a large amount
of fully labeled training data, but it is extremely time-consuming to obtain
these training data by manually labeling massive point clouds. Addressing this
problem, we propose a superpoint-guided semi-supervised segmentation network
for 3D point clouds, which jointly utilizes a small portion of labeled scene
point clouds and a large number of unlabeled point clouds for network training.
The proposed network is iteratively updated with its predicted pseudo labels,
where a superpoint generation module is introduced for extracting superpoints
from 3D point clouds, and a pseudo-label optimization module is explored for
automatically assigning pseudo labels to the unlabeled points under the
constraint of the extracted superpoints. Additionally, there are some 3D points
without pseudo-label supervision. We propose an edge prediction module to
constrain features of edge points. A superpoint feature aggregation module and
a superpoint feature consistency loss function are introduced to smooth
superpoint features. Extensive experimental results on two 3D public datasets
demonstrate that our method can achieve better performance than several
state-of-the-art point cloud segmentation networks and several popular
semi-supervised segmentation methods with few labeled scenes.


Abstract:
  For a subset $A$ of $\{1,2,\ldots,N\}^2$ of size $\alpha N^2$ we show
existence of $(m,n)\neq(0,0)$ such that the set $A$ contains at least
$(\alpha^3 - o(1))N^2$ triples of points of the form $(a,b)$, $(a+m,b+n)$,
$(a-n,b+m)$. This answers a question by Ackelsberg, Bergelson, and Best from
arXiv:2101.02811. The same approach also establishes the corresponding result
for compact abelian groups. Furthermore, for a finite field $\mathbb{F}_q$ we
comment on exponential smallness of subsets of $(\mathbb{F}_q^n)^2$ that avoid
the aforementioned configuration. The proofs are minor modifications of the
existing proofs regarding three-term arithmetic progressions.


Abstract:
  We present analytic cooling and diffusion rates for a simplified model of
coherent electron cooling (CEC), based on a proton energy kick at each turn.
This model also allows to estimate analytically the rms value of electron beam
density fluctuations in the "kicker" section. Having such analytic expressions
should allow for better understanding of the CEC mechanism, and for a quicker
analysis and optimization of main system parameters. Our analysis is applicable
to any CEC amplification mechanism, as long as the wake (kick) function is
available.


Abstract:
  In this short paper, we argue that the chiral central charge $c_-$ of a
(2+1)d topological ordered state is sometimes strongly constrained by 't Hooft
anomaly of anti-unitary global symmetry. For example, if a (2+1)d fermionic
TQFT has a time reversal anomaly with $T^2=(-1)^F$ labeled as
$\nu\in\mathbb{Z}_{16}$, the TQFT must have $c_-=1/4$ mod $1/2$ for odd $\nu$,
while $c_-=0$ mod $1/2$ for even $\nu$. This generalizes the fact that the
bosonic TQFT with $T$ anomaly in a particular class must carry $c_-=4$ mod $8$
to fermionic cases. We also study such a constraint for fermionic TQFT with
$U(1)\times CT$ symmetry, which is regarded as a gapped surface of the
topological superconductor in class AIII.


Abstract:
  The application of the Cauchy distribution has often been discussed as a
potential model of the financial markets. In particular the way in which single
extreme, or "Black Swan", events can impact long term historical moments, is
often cited. In this article we show how one can construct Martingale
processes, which have marginal distributions that tend to the Cauchy
distribution in the large volatility limit. This provides financial
justification to approaches discussed by other authors, and highlights an
example of how quantum probability can be used to construct non-Gaussian
Martingales. We go on to illustrate links with hyperbolic diffusion, and
discuss the insight this provides.


Abstract:
  In the standard model of charge density wave (CDW) transitions, the
displacement along a single phonon mode lowers the total electronic energy by
creating a gap at the Fermi level, making the CDW a metal--insulator
transition. Here, using scanning tunneling microscopy and spectroscopy and ab
initio calculations, we show that VS$_2$ realizes a CDW which stands out of
this standard model. There is a full CDW gap residing in the unoccupied states
of monolayer VS$_2$. At the Fermi level, the CDW induces a topological
metal-metal (Lifshitz) transition. Non-linear coupling of transverse and
longitudinal phonons is essential for the formation of the CDW and the full gap
above the Fermi level. Additionally, x-ray magnetic circular dichroism reveals
the absence of net magnetization in this phase, pointing to coexisting charge
and spin density waves in the ground state.


Abstract:
  Recent gravitational-wave observations have begun to constrain the internal
physics of neutron stars. However, current detection searches for neutron star
systems assume that potential neutron stars are low-mass black holes, ignoring
any affect on the gravitational-wave signal due to the internal neutron-star
physics. We wish to create a template bank of binary neutron star waveforms
including the effect of tidal deformability. However, we find that the Fisher
matrix, which is commonly used to approximate match calculations when placing
template banks, is unsuitable to predict the match between two binary neutron
star waveforms. We find that the Fisher matrix can predict errors on the
mismatch that are larger than $100\%$ when attempting to identify waveforms
with a match of $0.97$. We explore the regime in which the Fisher matrix cannot
be trusted and examine why it breaks down. We demonstrate that including
higher-order terms in the Taylor series expansion of the match can reliably
compute matches for these examples, but that it is prohibitively
computationally expensive to do so. Finally, we demonstrate that stochastic
placement can still be used to construct a template bank of tidally deformed
neutron-star waveforms.


Abstract:
  This paper targets the problem of procedural multimodal machine comprehension
(M3C). This task requires an AI to comprehend given steps of multimodal
instructions and then answer questions. Compared to vanilla machine
comprehension tasks where an AI is required only to understand a textual input,
procedural M3C is more challenging as the AI needs to comprehend both the
temporal and causal factors along with multimodal inputs. Recently Yagcioglu et
al. [35] introduced RecipeQA dataset to evaluate M3C. Our first contribution is
the introduction of two new M3C datasets- WoodworkQA and DecorationQA with 16K
and 10K instructional procedures, respectively. We then evaluate M3C using a
textual cloze style question-answering task and highlight an inherent bias in
the question answer generation method from [35] that enables a naive baseline
to cheat by learning from only answer choices. This naive baseline performs
similar to a popular method used in question answering- Impatient Reader [6]
that uses attention over both the context and the query. We hypothesized that
this naturally occurring bias present in the dataset affects even the best
performing model. We verify our proposed hypothesis and propose an algorithm
capable of modifying the given dataset to remove the bias elements. Finally, we
report our performance on the debiased dataset with several strong baselines.
We observe that the performance of all methods falls by a margin of 8% - 16%
after correcting for the bias. We hope these datasets and the analysis will
provide valuable benchmarks and encourage further research in this area.


Abstract:
  Let $M$ be a cancellative monoid. It is known~\cite{Ta54} that if $M$ is left
amenable then the monoid ring $K[M]$ satisfies Ore condition, that is, there
exist nontrivial common right multiples for the elements of this ring.
In~\cite{Don10} Donnelly shows that a partial converse to this statement is
true. Namely, if the monoid $\mathbb Z^{+}[M]$ of all elements of $\mathbb
Z[M]$ with positive coefficients has nonzero common right multiples, then $M$
is left amenable. He asks whether the converse is true for this particular
statement.
  We show that the converse is false even for the case of groups. If $M$ is a
free metabelian group, then $M$ is amenable but the Ore condition fails for
$\mathbb Z^{+}[M]$. Besides, we study the case of the monoid $M$ of positive
elements of R.\,Thompson's group $F$. The amenability problem for it is a
famous open question. It is equivalent to left amenability of the monoid $M$.
We show that for this case the monoid $\mathbb Z^{+}[M]$ does not satisfy Ore
condition. That is, even if $F$ is amenable, this cannot be shown using the
above sufficient condition.


Abstract:
  In this paper we present an extensive analysis of the GW190521 gravitational
wave event with the current (fourth) generation of phenomenological waveform
models for binary black hole coalescences. GW190521 stands out from other
events since only a few wave cycles are observable. This leads to a number of
challenges, one being that such short signals are prone to not resolve
approximate waveform degeneracies, which may result in multi-modal posterior
distributions. The family of waveform models we use includes a new fast
time-domain model IMRPhenomTPHM, which allows us extensive tests of different
priors and robustness with respect to variations in the waveform model,
including the content of spherical harmonic modes. We clarify some issues
raised in a recent paper [Nitz&Capano], associated with possible support for a
high-mass ratio source, but confirm their finding of a multi-modal posterior
distribution, albeit with important differences in the statistical significance
of the peaks. In particular, we find that the support for both masses being
outside the PISN mass-gap, and the support for an intermediate mass ratio
binary are drastically reduced with respect to what Nitz&Capano found. We also
provide updated probabilities for associating GW190521 to the potential
electromagnetic counterpart from ZTF.


Abstract:
  An increasing number of models require the control of the spectral norm of
convolutional layers of a neural network. While there is an abundance of
methods for estimating and enforcing upper bounds on those during training,
they are typically costly in either memory or time. In this work, we introduce
a very simple method for spectral normalization of depthwise separable
convolutions, which introduces negligible computational and memory overhead. We
demonstrate the effectiveness of our method on image classification tasks using
standard architectures like MobileNetV2.


Abstract:
  We consider performing phase estimation under the following conditions: we
are given only one copy of the input state, the input state does not have to be
an eigenstate of the unitary, and the state must not be measured. Most quantum
estimation algorithms make assumptions that make them unsuitable for this
'coherent' setting, leaving only the textbook approach. We present novel
algorithms for phase, energy, and amplitude estimation that are both
conceptually and computationally simpler than the textbook method, featuring
both a smaller query complexity and ancilla footprint. They do not require a
quantum Fourier transform, and they do not require a quantum sorting network to
compute the median of several estimates. Instead, they use block-encoding
techniques to compute the estimate one bit at a time, performing all
amplification via singular value transformation. These improved subroutines
accelerate the performance of quantum Metropolis sampling and quantum Bayesian
inference.


Abstract:
  We propose NeuMIP, a neural method for representing and rendering a variety
of material appearances at different scales. Classical prefiltering
(mipmapping) methods work well on simple material properties such as diffuse
color, but fail to generalize to normals, self-shadowing, fibers or more
complex microstructures and reflectances. In this work, we generalize
traditional mipmap pyramids to pyramids of neural textures, combined with a
fully connected network. We also introduce neural offsets, a novel method which
allows rendering materials with intricate parallax effects without any
tessellation. This generalizes classical parallax mapping, but is trained
without supervision by any explicit heightfield. Neural materials within our
system support a 7-dimensional query, including position, incoming and outgoing
direction, and the desired filter kernel size. The materials have small storage
(on the order of standard mipmapping except with more texture channels), and
can be integrated within common Monte-Carlo path tracing systems. We
demonstrate our method on a variety of materials, resulting in complex
appearance across levels of detail, with accurate parallax, self-shadowing, and
other effects.


Abstract:
  Plant leaf diseases pose a significant danger to food security and they cause
depletion in quality and volume of production. Therefore accurate and timely
detection of leaf disease is very important to check the loss of the crops and
meet the growing food demand of the people. Conventional techniques depend on
lab investigation and human skills which are generally costly and inaccessible.
Recently, Deep Neural Networks have been exceptionally fruitful in image
classification. In this research paper, plant leaf disease detection employing
transfer learning is explored in the JPEG compressed domain. Here, the JPEG
compressed stream consisting of DCT coefficients is, directly fed into the
Neural Network to improve the efficiency of classification. The experimental
results on JPEG compressed leaf dataset demonstrate the efficacy of the
proposed model.


Abstract:
  We provide an extension to previous analysis of the localised beading
instability of soft slender tubes under surface tension and axial stretching.
The primary questions pondered here are: under what loading conditions, if any,
can bifurcation into circumferential buckling modes occur, and do such
solutions dominate localisation and periodic axial modes? Three distinct
boundary conditions are considered; in case 1 the tube's curved surfaces are
traction free and under surface tension, whilst in cases 2 and 3 the inner and
outer surfaces (respectively) are fixed to prevent radial displacement and
surface tension. A linear bifurcation analysis is conducted to determine
numerically the existence of circumferential mode solutions. In case 1 we focus
on the tensile stress regime given the preference of slender compressed tubes
towards Euler buckling over axial wrinkling. We show that tubes under several
loading paths are highly sensitive to circumferential modes; in contrast,
localised and periodic axial modes are absent, suggesting that the
circumferential buckling is dominant by default. In case 2, circumferential
mode solutions are associated with negative surface tension values and thus are
physically implausible. Circumferential buckling solutions are shown to exist
in case 3 for tensile and compressive axial loads, and we demonstrate for
multiple loading scenarios their dominance over localisation and periodic axial
modes within specific parameter regimes.


Abstract:
  We review the hypothetical interactions predicted beyond the Standard Model
which could be constrained by using the results of tabletop laboratory
experiments. These interactions are described by the power-type potentials with
different powers, Yukawa potential, other spin-independent potentials, and by
the spin-dependent potentials of different kinds. In all these cases the
current constraints on respective hypothetical interactions are considered
which follow from the Casimir effect and some other tabletop physics. The
exotic particles and constraints on them are discussed in the context of
problems of the quantum vacuum, dark energy, and the cosmological constant.


Abstract:
  Despite the success of reinforcement learning methods, they have yet to have
their breakthrough moment when applied to a broad range of robotic manipulation
tasks. This is partly due to the fact that reinforcement learning algorithms
are notoriously difficult and time consuming to train, which is exacerbated
when training from images rather than full-state inputs. As humans perform
manipulation tasks, our eyes closely monitor every step of the process with our
gaze focusing sequentially on the objects being manipulated. With this in mind,
we present our Attention-driven Robotic Manipulation (ARM) algorithm, which is
a general manipulation algorithm that can be applied to a range of
sparse-rewarded tasks, given only a small number of demonstrations. ARM splits
the complex task of manipulation into a 3 stage pipeline: (1) a Q-attention
agent extracts interesting pixel locations from RGB and point cloud inputs, (2)
a next-best pose agent that accepts crops from the Q-attention agent and
outputs poses, and (3) a control agent that takes the goal pose and outputs
joint actions. We show that current learning algorithms fail on a range of
RLBench tasks, whilst ARM is successful.


Abstract:
  Motivated by the recently observed failure of the kinetic theory for the bulk
viscosity, we in turn revisit the shear viscosity and the thermal conductivity
of two-component fermions with a zero-range interaction both in two and three
dimensions. In particular, we show that their Kubo formula evaluated exactly in
the high-temperature limit to the lowest order in fugacity is reduced to the
linearized Boltzmann equation. Previously, such a microscopic derivation of the
latter was achieved only incompletely corresponding to the relaxation-time
approximation. Here, we complete it by resuming all contributions that are
naively higher orders in fugacity but become comparable in the zero-frequency
limit due to the pinch singularity, leading to a self-consistent equation for a
vertex function identical to the linearized Boltzmann equation. We then compute
the shear viscosity and the thermal conductivity in the high-temperature limit
for an arbitrary scattering length and find that the Prandtl number exhibits a
nonmonotonic behavior slightly below the constant value in the relaxation-time
approximation.


Abstract:
  The generalized gamma convolutions class of distributions appeared in
Thorin's work while looking for the infinite divisibility of the log-Normal and
Pareto distributions. Although these distributions have been extensively
studied in the univariate case, the multivariate case and the dependence
structures that can arise from it have received little interest in the
literature. Furthermore, only one projection procedure for the univariate case
was recently constructed, and no estimation procedures are available. By
expanding the densities of multivariate generalized gamma convolutions into a
tensorized Laguerre basis, we bridge the gap and provide performant estimation
procedures for both the univariate and multivariate cases. We provide some
insights about performance of these procedures, and a convergent series for the
density of multivariate gamma convolutions, which is shown to be more stable
than Moschopoulos's and Mathai's univariate series. We furthermore discuss some
examples.


Abstract:
  Non-invasive cortical neural interfaces have only achieved modest performance
in cortical decoding of limb movements and their forces, compared to invasive
brain-computer interfaces (BCIs). While non-invasive methodologies are safer,
cheaper and vastly more accessible technologies, signals suffer from either
poor resolution in the space domain (EEG) or the temporal domain (BOLD signal
of functional Near Infrared Spectroscopy, fNIRS). The non-invasive BCI decoding
of bimanual force generation and the continuous force signal has not been
realised before and so we introduce an isometric grip force tracking task to
evaluate the decoding. We find that combining EEG and fNIRS using deep neural
networks works better than linear models to decode continuous grip force
modulations produced by the left and the right hand. Our multi-modal deep
learning decoder achieves 55.2 FVAF[%] in force reconstruction and improves the
decoding performance by at least 15% over each individual modality. Our results
show a way to achieve continuous hand force decoding using cortical signals
obtained with non-invasive mobile brain imaging has immediate impact for
rehabilitation, restoration and consumer applications.


Abstract:
  Quantum mass function has been applied in lots of fields because of its
efficiency and validity of managing uncertainties in the form of quantum which
can be regarded as an extension of classical Dempster-Shafer (D-S) evidence
theory. However, how to handle uncertainties in the form of quantum is still an
open issue. In this paper, a new method is proposed to dispose uncertain
quantum information, which is called two-dimensional quantum mass function
(TDQMF). A TDQMF is consist of two elements, TQ = (Qoriginal, Qindicative),
both of the Qs are quantum mass functions, in which the Qindicative is an
indicator of the reliability on Qoriginal. More flexibility and effectiveness
are offered in handling uncertainty in the field of quantum by the proposed
method compared with primary quantum mass function. Besides, some numerical
examples are provided and some practical applications are given to verify its
correctness and validity


Abstract:
  We investigate a simple quantitative genetics model subjet to a gradual
environmental change from the viewpoint of the phylogenies of the living
individuals. We aim to understand better how the past traits of their ancestors
are shaped by the adaptation to the varying environment. The individuals are
characterized by a one-dimensional trait. The dynamics -- births and deaths --
depend on a time-changing mortality rate that shifts the optimal trait to the
right at constant speed. The population size is regulated by a nonlinear
non-local logistic competition term. The macroscopic behaviour can be described
by a PDE that admits a unique positive stationary solution. In the stationary
regime, the population can persist, but with a lag in the trait distribution
due to the environmental change. For the microscopic (individual-based)
stochastic process, the evolution of the lineages can be traced back using the
historical process, that is, a measure-valued process on the set of continuous
real functions of time. Assuming stationarity of the trait distribution, we
describe the limiting distribution, in large populations, of the path of an
individual drawn at random at a given time $T$. Freezing the non-linearity due
to competition allows the use of a many-to-one identity together with
Feynman-Kac's formula. This path, in reversed time, remains close to a simple
Ornstein-Uhlenbeck process. It shows how the lagged bulk of the present
population stems from ancestors once optimal in trait but still in the tail of
the trait distribution in which they lived.


Abstract:
  This study focuses on comparing the individual polymer chain dynamics in an
entangled polymeric liquid under different shear and extension rates. Polymer
chains under various shear rates and extension rates were simulated using a
stochastic-tube model [J. Rheol. 56: 1057 (2012)]. We developed a Matlab code
to visualize and analyze the simulated configurations from the stochastic-tube
model. We introduced new variables to determine how the extent of linearity
changes with time for different shear rates, which is more useful than a
typical end-to-end distance analysis. We identified whether the polymer chains
undergo a tumbling rotation (slight elongation not accompanying contraction) or
flipping rotation (elongation accompanying contraction). The simulation results
indicate that the polymer chains exhibit a significant tendency to elongate at
higher shear rates and occasionally experience flipping, while lower shear
rates tend to exhibit very frequent tumbling. Furthermore, no rotations were
observed under extensional flows. These results help clarifying uncertainty of
previously proposed polymer deformation mechanisms of the convective constraint
release and the configuration-dependent friction coefficient.


Abstract:
  Electric vector potential $\Theta(\boldsymbol{r})$ is a legitimate but rarely
used tool to calculate the steady electric field in free-charge regions.
Commonly, it is preferred to employ the scalar electric potential
$\Phi(\boldsymbol{r})$ rather than $\Theta(\boldsymbol{r})$ in most of the
electrostatic problems. However, the electric vector potential formulation can
be a viable representation to study certain systems. One of them is the surface
electrode SE, a planar finite region $\mathcal{A}_{-}$ kept at a fixed electric
potential with the rest grounded including a gap of thickness $\nu$ between
electrodes. In this document we use the \textit{Helmholtz Decomposition
Theorem} and the electric vector potential formulation to provide integral
expressions for the surface charge density and the electric field of the SE of
arbitrary contour $\partial\mathcal{A}$. We also present an alternative
derivation of the result found in [M. Oliveira and J. A. Miranda 2001 Eur. J.
Phys. 22 31] for the gapless ($\nu=0$) surface electrode GSE without invoking
any analogy between the GSE and magnetostatics. It is shown that electric
vector potential and the electric field of the gapped circular SE at any point
can be obtained from an average of the gapless solution on the gap. Keywords:
Electric vector potential, surface-electrode, Helmholtz Decomposition, Green's
theorem.


Abstract:
  Thermodynamic properties of a system of interacting boson particles and
antiparticles at finite temperatures are studied within the framework of the
thermodynamically consistent Skyrme-like mean-field model. The mean field
contains both attractive and repulsive terms. Self-consistency relations
between the mean field and thermodynamic functions are derived. We assume
conservation of the isospin density for all temperatures. It is shown that,
independently of the strength of the attractive mean field, at the critical
temperature $T_{\rm c}$ the system undergoes the phase transition of
second-order to the Bose-Einstein condensate, which exists in the temperature
interval $0 \le T \le T_{\rm c}$. We obtained that the condensation represents
a discontinuity of the derivative of the heat capacity at $T = T_{\rm c}$, and
condensate occurs only for the component with a higher particle-number density
in the particle-antiparticle system.


Abstract:
  We provide strong evidence for single-electron spatial dynamics with a
macroscopic discontinuity. The dynamics is observed in an electron quantum
phase consisted of macroscopic quantum orbits similar to those responsible for
the integer quantum Hall effect. The dynamics is consistent with the standard
QM that predicts a "cloud-like" behavior of electron in quantum orbits
regardless of their lengthscale. Sinse the dynamics is beyond the relativistic
paradigm of movement, it may well be nonlocal. It thereby can be the basis for
a quantum concept of absolute simultaneity, which now rests not on the
meaningless notion of infinite speed but on empirically-tested quantum effect.
This concept revives the Bell-Popper idea to replace the current "kinematic"
version of relativity by the "dynamic" version by Lorentz and Poincare with a
revival of the classical view of space and time where the problem of preferred
reference frame now can be solved without to involve the vague notion "aether".
We argue that the concept of quantum absolute simultaneity opens the door to a
realistic interpretation of standard QM through a deeper insight of what is
called "reality" and ultimately to a harmony between quantum and relativity
theories.


Abstract:
  We present a model for differentiating between images that are authentic
copies of ones published by photographers, and images that have been
manipulated by cropping, splicing or downsampling after publication. The model
comprises an encoder that resides with the photographer and a matching decoder
that is available to observers. The encoder learns to embed imperceptible
positional signatures into image values prior to publication. The decoder
learns to use these steganographic positional ("stegapos") signatures to
determine, for each small image patch, the 2D positional coordinates that were
held by the patch in its originally-published image. Crop, splice and
downsample edits become detectable by the inconsistencies they cause in the
hidden positional signatures. We find that training the encoder and decoder
together produces a model that imperceptibly encodes position, and that enables
superior performance on established benchmarks for splice detection and high
accuracy on a new benchmark for crop detection.


Abstract:
  Extending DiNezza-Lu's approach to the setting of big cohomology classes, we
prove that solutions of degenerate complex Monge-Amp{\`e}re equations on
compact K{\"a}hler manifolds are continuous on a Zariski open set. This allows
us to show that singular K{\"a}hler-Einstein metrics on log canonical varieties
of general type have continuous potentials on the ample locus outside of the
non-klt part.


Abstract:
  The task of sequential sentence classification enables the semantic
structuring of research papers. This can enhance academic search engines to
support researchers in finding and exploring research literature more
effectively. However, previous work has not investigated the potential of
transfer learning with datasets from different scientific domains for this task
yet. We propose a uniform deep learning architecture and multi-task learning to
improve sequential sentence classification in scientific texts across domains
by exploiting training data from multiple domains. Our contributions can be
summarised as follows: (1) We tailor two common transfer learning methods,
sequential transfer learning and multi-task learning, and evaluate their
performance for sequential sentence classification; (2) The presented
multi-task model is able to recognise semantically related classes from
different datasets and thus supports manual comparison and assessment of
different annotation schemes; (3) The unified approach is capable of handling
datasets that contain either only abstracts or full papers without further
feature engineering. We demonstrate that models, which are trained on datasets
from different scientific domains, benefit from one another when using the
proposed multi-task learning architecture. Our approach outperforms the state
of the art on three benchmark datasets.


Abstract:
  For $n\leq d$, a family ${\cal F}=\{C_0,C_1,\ldots, C_n\}$ of compact convex
sets in $R^d$ is called an $n$-critical family provided any $n$ members of
${\cal F}$ have a non-empty intersection, but $\bigcap_{i=0}^n
C_i=\varnothing$. If $n=d$ then a lemma on the intersection of convex sets due
to Klee implies that the $d+1$ members of the $d$-critical family enclose a
`hollow' in $R^d$, a bounded connected component of
$R^d\setminus\bigcup_{i=0}^n C_i.$ Here we prove that the closure of the convex
hull of a hollow in $R^d$ is a $d$-simplex.


Abstract:
  In this article, the effect of geometrical parameters and flow conditions on
the performance of a swirl atomizer is studied. Dimensional analysis and
experimental investigations are utilized to define significant terms. The PDA
system used for the measurements was able to supply information about the size,
concentration, and particle velocity at each measurement location. The orifice
diameter, the spiral cone angle, and also the flow Reynolds number, which is
defined based on the injector orifice diameter, play an important role in spray
quality, and their significance is summarized in a correlation. In order to
achieve the appropriate combination of design variables that satisfy the design
constraints, a GA-based program was used in a reverse analysis process.
Finally, the advantages of human inspection were employed to provide true best
performers from a small group of final answers.


Abstract:
  There is no exact closed form formula for pricing of European options with
discrete cash dividends under the model where the underlying asset price
follows a piecewise lognormal process with jumps at dividend ex-dates. This
paper presents alternative expansions based on the technique of Etore and
Gobet, leading to more robust first, second and third-order expansions across
the range of strikes and the range of dividend dates.


Abstract:
  Existing malware detection approaches suffer from a simplistic trade-off
between false positive rate (FPR) and true positive rate (TPR) due to a single
tier classification approach, where the two measures adversely affect one
another. The practical implication for malware detection is that FPR must be
kept at an acceptably low level while TPR remains high. To this end, we propose
a two-tiered learning, called ``Echelon", from raw byte data with no need for
hand-crafted features. The first tier locks FPR at a specified target level,
whereas the second tier improves TPR while maintaining the locked FPR. The core
of Echelon lies at extracting activation information of the hidden layers of
first tier model for constructing a stronger second tier model. Echelon is a
framework in that it allows any existing CNN based model to be adapted in both
tiers. We present experimental results of evaluating Echelon by adapting the
state-of-the-art malware detection model ``Malconv" in the first and second
tiers.


Abstract:
  We develop several formal analogies between the logistic equation and the
spatially homogeneous and isotropic relativistic cosmology described by the
Einstein-Friedmann equations. These analogies produce an effective Lagrangian
and Hamiltonian and new symmetries for the logistic equation.


Abstract:
  We consider the manner in which the spacetime manifold emerges from a quantum
substratum through the transactional process, in which spacetime events and
their connections are established. In this account, there is no background
spacetime as is generally assumed in physical theorizing. Instead, the usual
notion of a background spacetime is replaced by the quantum substratum,
comprising quantum systems with nonvanishing rest mass. Rest mass corresponds
to internal periodicities that function as internal clocks defining proper
times, and in turn, inertial frames that are not themselves aspects of the
spacetime manifold, but are pre-spacetime reference structures. Specific
processes in the quantum substratum serve to distinguish absolute from relative
motion.


Abstract:
  A plethora of observational data obtained over the last couple of decades has
allowed cosmology to enter into a precision era and has led to the foundation
of the standard cosmological constant and cold dark matter paradigm, known as
the $\Lambda$CDM model. Given the many possible extensions of this concordance
model, we present here several novel consistency tests which could be used to
probe for deviations from $\Lambda$CDM. First, we derive a joint consistency
test for the spatial curvature $\Omega_{k,0}$ and the matter density
$\Omega_\textrm{m,0}$ parameters, constructed using only the Hubble rate
$H(z)$, which can be determined directly from observations. Second, we present
a new test of possible deviations from homogeneity using the combination of two
datasets, either the baryon acoustic oscillation (BAO) and $H(z)$ data or the
transversal and radial BAO data, while we also introduce two consistency tests
for $\Lambda$CDM which could be reconstructed via the transversal and radial
BAO data. We then reconstruct the aforementioned tests using the currently
available data in a model independent manner using a particular machine
learning approach, namely the Genetic Algorithms. Finally, we also report on a
$\sim 4\sigma$ tension on the transition redshift as determined by the $H(z)$
and radial BAO data.


Abstract:
  Within the $SP(2,R)$ symmetry, the Two-time model (2T model) has six
dimensions with two dimensions of time and the dilaton field that can be
identified as inflaton in a warm inflation scenario with potential of the form
$\sim\phi^4$. From that consideration, we derive the range of parameters for
the Higgs-Dilaton potential, the coupling constant between Higgs and Dialton
($\alpha$) is smaller than $3.85\times 10^{-7}$ when the mass of Dilaton is
lager than $200$ GeV. Therefore, the 2T-model indirectly suggests that
extra-dimension can also be a source of inflation.


Abstract:
  We study a general convergence theory for the analysis of numerical solutions
to the magnetohydrodynamic system describing the time evolution of
compressible, viscous, electrically conducting fluids in space dimension d (=
2; 3). First, we introduce the concept of dissipative weak solutions and prove
the weak-strong uniqueness property for dissipative weak solutions, meaning a
dissipative weak solution coincides with a classical solution emanating from
the same initial data on the lifespan of the latter. Next, we introduce the
concept of consistent approximations and prove the convergence of consistent
approximations towards the dissipative weak solution as well as the classical
solution. Interpreting the consistent approximation as the energy stability and
consistency of numerical solutions, we have built a nonlinear variant of the
celebrated Lax-equivalence theorem. Finally, as an application of this theory,
we show the convergence analysis for two numerical methods.


Abstract:
  Interacting spin networks are fundamental to quantum computing. Data-based
tomography of time-independent spin networks has been achieved, but an open
challenge is to ascertain the structures of time-dependent spin networks using
time series measurements taken locally from a small subset of the spins.
Physically, the dynamical evolution of a spin network under time-dependent
driving or perturbation is described by the Heisenberg equation of motion.
Motivated by this basic fact, we articulate a physics-enhanced machine learning
framework whose core is Heisenberg neural networks. In particular, we develop a
deep learning algorithm according to some physics motivated loss function based
on the Heisenberg equation, which "forces" the neural network to follow the
quantum evolution of the spin variables. We demonstrate that, from local
measurements, not only the local Hamiltonian can be recovered but the
Hamiltonian reflecting the interacting structure of the whole system can also
be faithfully reconstructed. We test our Heisenberg neural machine on spin
networks of a variety of structures. In the extreme case where measurements are
taken from only one spin, the achieved tomography fidelity values can reach
about 90%. The developed machine learning framework is applicable to any
time-dependent systems whose quantum dynamical evolution is governed by the
Heisenberg equation of motion.


Abstract:
  Radio interferometer arrays such as HERA consist of many close-packed dishes
arranged in a regular pattern, giving rise to a large number of `redundant'
baselines with the same length and orientation. Since identical baselines
should see an identical sky signal, this provides a way of finding a relative
gain/bandpass calibration without needing an explicit sky model. In reality,
there are many reasons why baselines will not be exactly identical, giving rise
to a host of effects that spoil the redundancy of the array and induce spurious
structure in the calibration solutions if not accounted for. In this paper, we
seek to build an understanding of how differences in the primary beam response
between antennas affect redundantly-calibrated interferometric visibilities and
their resulting frequency (delay-space) power spectra. We use simulations to
study several generic types of primary beam variation, including differences in
the width of the main lobe, the angular and frequency structure of the
sidelobes, and the beam ellipticity and orientation. For all of these types, we
find that additional temporal structure is induced in the gain solutions,
particularly when bright point sources pass through the beam. In comparison,
only a low level of additional spectral structure is induced. The temporal
structure modulates the cosmological 21cm power spectrum, but only at the level
of a few percent in our simulations. We also investigate the possibility of
signal loss due to decoherence effects when non-redundant visibilities are
averaged together, finding that the decoherence is worst when bright point
sources pass through the beam, and that its magnitude varies significantly
between baseline groups and types of primary beam variation. Redundant
calibration absorbs some of the decoherence effect however, reducing its impact
compared to if the visibilities were perfectly calibrated.


Abstract:
  Recent work has shown constrained Bayesian optimization to be a powerful
technique for the optimization of industrial processes. In complex
manufacturing processes, the possibility to run extensive sequences of
experiments with the goal of finding good process parameters is severely
limited by the time required for quality evaluation of the produced parts. To
accelerate the process parameter optimization, we introduce a parallel
acquisition procedure tailored on the process characteristics. We further
propose an algorithm that adapts to equipment status to improve run-to-run
reproducibility. We validate our optimization method numerically and
experimentally, and demonstrate that it can efficiently find input parameters
that produce the desired outcome and minimize the process cost.


Abstract:
  Learning from label proportions (LLP) aims at learning an instance-level
classifier with label proportions in grouped training data. Existing deep
learning based LLP methods utilize end-to-end pipelines to obtain the
proportional loss with Kullback-Leibler divergence between the bag-level prior
and posterior class distributions. However, the unconstrained optimization on
this objective can hardly reach a solution in accordance with the given
proportions. Besides, concerning the probabilistic classifier, this strategy
unavoidably results in high-entropy conditional class distributions at the
instance level. These issues further degrade the performance of the
instance-level classification. In this paper, we regard these problems as noisy
pseudo labeling, and instead impose the strict proportion consistency on the
classifier with a constrained optimization as a continuous training stage for
existing LLP classifiers. In addition, we introduce the mixup strategy and
symmetric crossentropy to further reduce the label noise. Our framework is
model-agnostic, and demonstrates compelling performance improvement in
extensive experiments, when incorporated into other deep LLP models as a
post-hoc phase.


Abstract:
  We propose a novel framework for off-chain execution and verification of
computationally-intensive smart contracts. Our framework is the first solution
that avoids duplication of computing effort across multiple contractors, does
not require trusted execution environments, supports computations that do not
have deterministic results, and supports general-purpose computations written
in a high-level language. Our experiments reveal that some intensive
applications may require as much as 141 million gas, approximately 71x more
than the current block gas limit for computation in Ethereum today, and can be
avoided by utilizing the proposed framework.


Abstract:
  In this paper, we propose reconfigurable intelligent surface (RIS)-assisted
unmanned aerial vehicles (UAVs) networks that can utilise both advantages of
UAV's agility and RIS's reflection for enhancing the network's performance. To
aim at maximising the energy efficiency (EE) of the considered networks, we
jointly optimise the power allocation of the UAVs and the phase-shift matrix of
the RIS. A deep reinforcement learning (DRL) approach is proposed for solving
the continuous optimisation problem with time-varying channels in a centralised
fashion. Moreover, a parallel learning approach is also proposed for reducing
the information transmission requirement of the centralised approach. Numerical
results show a significant improvement of our proposed schemes compared with
the conventional approaches in terms of EE, flexibility, and processing time.
Our proposed DRL methods for RIS-assisted UAV networks can be used for
real-time applications due to their capability of instant decision-making and
handling the time-varying channel with the dynamic environmental setting.


Abstract:
  A magnetic field parallel to an electrical current does not produce a Lorentz
force on the charge carriers. Therefore, orbital longitudinal magnetoresistance
is unexpected. Here we report on the observation of a large and non saturating
magnetoresistance in lightly doped SrTiO$_{3-x}$ independent of the relative
orientation of current and magnetic field. We show that this quasi-isotropic
magnetoresistance can be explained if the carrier mobility along all
orientations smoothly decreases with magnetic field. This anomalous regime is
restricted to low concentrations when the dipolar correlation length is longer
than the distance between carriers. We identify cyclotron motion of electrons
in a potential landscape tailored by polar domains as the cradle of
quasi-isotropic orbital magnetoresistance. The result emerges as a challenge to
theory and may be a generic feature of lightly-doped quantum paralectric
materials.


Abstract:
  We explore four approaches to the question of defectivity for a complex
projective toric variety $X_A$ associated with an integral configuration $A$.
The explicit tropicalization of the dual variety $X_A^\vee$ due to Dickenstein,
Feichtner, and Sturmfels allows for the computation of the defect in terms of
an affine combinatorial invariant $\rho(A)$. We express $\rho(A)$ in terms of
affine invariants $\iota(A)$ associated to Esterov's iterated circuits and
$\lambda(A)$, an invariant defined by Curran and Cattani in terms of a Gale
dual of $A$. Thus we obtain formulae for the dual defect in terms of iterated
circuits and Gale duals. An alternative expression for the dual defect of $X_A$
is given by Furukawa-Ito in terms of Cayley decompositions of $A$. We give a
Gale dual interpretation of these decompositions and apply it to the study of
defective configurations.


Abstract:
  We have measured the angular dependence of ferromagnetic resonance (FMR)
spectra for Fibonacci-distorted, Kagome artificial spin ice (ASI). The number
of strong modes in the FMR spectra depend on the orientation of the applied DC
magnetic field. In addition, discontinuities observed in the FMR
field-frequency dispersion curves also depend on DC field orientation, and
signal a multi-step DC magnetization reversal, which is caused by the reduced
energy degeneracy of Fibonacci-distorted vertices. The results suggest the
orientation of applied magnetic field and severity of Fibonacci distortion
constitute control variables for FMR modes and multi-step reversal in future
magnonic devices and magnetic switching systems.


Abstract:
  The existence of a gapped chiral spin liquid has been recently suggested in
the vicinity of the metal-insulator transition of the Hubbard model on the
triangular lattice, by intensive density-matrix renormalization group (DMRG)
simulations [A. Szasz, J. Motruk, M.P. Zaletel, and J.E. Moore, Phys. Rev. X
{\bf 10}, 021042 (2020)]. Here, we report the results obtained within the
variational Monte Carlo technique based upon Jastrow-Slater wave functions,
implemented with backflow correlations. As in DMRG calculations, we consider
$N$-leg cylinders. For $N=4$ and in the presence of a next-nearest neighbor
hopping, a chiral spin liquid emerges between the metal and the insulator with
magnetic quasi-long-range order. Within our approach, the chiral state is
gapped and breaks the reflection symmetry. By contrast, for both $N=5$ and
$N=6$, the chiral spin liquid is not the state with the lowest variational
energy: in the former case a nematic spin liquid is found in the entire
insulating regime, while for the less frustrated case with $N=6$ the results
are very similar to the one obtained on two-dimensional clusters [L.F. Tocchio,
A. Montorsi, and F. Becca, Phys. Rev. B {\bf 102}, 115150 (2020)], with an
antiferromagnetic phase close to the metal-insulator transition and a nematic
spin liquid in the strong-coupling regime.


Abstract:
  The earth system is exceedingly complex and often chaotic in nature, making
prediction incredibly challenging: we cannot expect to make perfect predictions
all of the time. Instead, we look for specific states of the system that lead
to more predictable behavior than others, often termed "forecasts of
opportunity." When these opportunities are not present, scientists need
prediction systems that are capable of saying "I don't know." We introduce a
novel loss function, termed the "NotWrong loss", that allows neural networks to
identify forecasts of opportunity for classification problems. The NotWrong
loss introduces an abstention class that allows the network to identify the
more confident samples and abstain (say "I don't know") on the less confident
samples. The abstention loss is designed to abstain on a user-defined fraction
of the samples via a PID controller. Unlike many machine learning methods used
to reject samples post-training, the NotWrong loss is applied during training
to preferentially learn from the more confident samples. We show that the
NotWrong loss outperforms other existing loss functions for multiple climate
use cases. The implementation of the proposed loss function is straightforward
in most network architectures designed for classification as it only requires
the addition of an abstention class to the output layer and modification of the
loss function.


Abstract:
  Direct reconstruction methods have been developed to estimate parametric
images directly from the measured PET sinograms by combining the PET imaging
model and tracer kinetics in an integrated framework. Due to limited counts
received, signal-to-noise-ratio (SNR) and resolution of parametric images
produced by direct reconstruction frameworks are still limited. Recently
supervised deep learning methods have been successfully applied to medical
imaging denoising/reconstruction when large number of high-quality training
labels are available. For static PET imaging, high-quality training labels can
be acquired by extending the scanning time. However, this is not feasible for
dynamic PET imaging, where the scanning time is already long enough. In this
work, we proposed an unsupervised deep learning framework for direct parametric
reconstruction from dynamic PET, which was tested on the Patlak model and the
relative equilibrium Logan model. The patient's anatomical prior image, which
is readily available from PET/CT or PET/MR scans, was supplied as the network
input to provide a manifold constraint, and also utilized to construct a kernel
layer to perform non-local feature denoising. The linear kinetic model was
embedded in the network structure as a 1x1 convolution layer. The training
objective function was based on the PET statistical model. Evaluations based on
dynamic datasets of 18F-FDG and 11C-PiB tracers show that the proposed
framework can outperform the traditional and the kernel method-based direct
reconstruction methods.


Abstract:
  Weak-scale secluded sector dark matter can reproduce the observed dark matter
relic density with thermal freeze-out within that sector. If nature is
supersymmetric, three portals to the visible sector - a gauge portal, a Higgs
portal, and a gaugino portal - are present. We present gamma ray spectra
relevant for indirect detection of dark matter annihilation in such setups.
Since symmetries in the secluded sector can stabilize dark matter, $R$-parity
is unnecessary, and we investigate the impact of $R$-parity violation on
annihilation spectra. We present limits from the Fermi Large Area Telescope
observations of dwarf galaxies and projections for Cherenkov Telescope Array
observations of the galactic center. Many of our results are also applicable to
generic, non-supersymmetric setups.


Abstract:
  The movement of large-scale (tens of Terabytes and larger) data sets between
high performance computing (HPC) facilities is an important and increasingly
critical capability. A growing number of scientific collaborations rely on HPC
facilities for tasks which either require large-scale data sets as input or
produce large-scale data sets as output. In order to enable the transfer of
these data sets as needed by the scientific community, HPC facilities must
design and deploy the appropriate data transfer capabilities to allow users to
do data placement at scale.
  This paper describes the Petascale DTN Project, an effort undertaken by four
HPC facilities, which succeeded in achieving routine data transfer rates of
over 1PB/week between the facilities. We describe the design and configuration
of the Data Transfer Node (DTN) clusters used for large-scale data transfers at
these facilities, the software tools used, and the performance tuning that
enabled this capability.


Abstract:
  Influencing spectral and directional features of exciton emission
characteristics from 2D transition metal dichalcogenides by coupling it to
plasmonic nano-cavities has emerged as an important prospect in nanophotonics
of 2D materials. In this paper we experimentally study the directional
photoluminescence emission from Tungsten disulfide (WS2) monolayer sandwiched
between a single-crystalline plasmonic silver nanowire (AgNW) waveguide and a
gold (Au) mirror, thus forming an AgNW-WS2-Au cavity. By employing
polarization-resolved Fourier plane optical microscopy, we quantify the
directional emission characteristics from the distal end of the AgNW-WS2-Au
cavity. Given that our geometry simultaneously facilitates local field
enhancement and waveguiding capability, we envisage its utility in 2D
material-based, on-chip nanophotonic signal processing, including nonlinear and
quantum optical regimes.


Abstract:
  Metamaterials are artificially created media, which allow introducing
additional degrees of freedom into electromagnetic design by controlling
constitutive material parameters. Reconfigurable time-dependent metamaterials
can further enlarge those capabilities by introducing a temporal variable as an
additional controllable parameter. Here we demonstrate a first-of-its-kind
reconfigurable volumetric metamaterial-based scatterer, wherein the
electromagnetic properties are controlled dynamically with light. In
particular, hybridized resonances in arrays of split ring resonators give rise
to a collective mode that presents properties of artificial high-frequency
magnetism for centimeter waves. Resonant behavior of each individual ring is
controlled with a photocurrent, which allows the fast tuning of macroscopic
effective permeability. Thus, the artificial gigahertz magnon resonant
excitation within a subwavelength spherical scatterer is governed by light
intensity. Four-dimensional control over electromagnetic scattering in both
space and time opens new venues for modern applications, including wireless
communications and automotive radars to name just a few.


Abstract:
  Progress towards the energy breakthroughs needed to combat climate change can
be significantly accelerated through the efficient simulation of atomic
systems. Simulation techniques based on first principles, such as Density
Functional Theory (DFT), are limited in their practical use due to their high
computational expense. Machine learning approaches have the potential to
approximate DFT in a computationally efficient manner, which could dramatically
increase the impact of computational simulations on real-world problems.
Approximating DFT poses several challenges. These include accurately modeling
the subtle changes in the relative positions and angles between atoms, and
enforcing constraints such as rotation invariance or energy conservation. We
introduce a novel approach to modeling angular information between sets of
neighboring atoms in a graph neural network. Rotation invariance is achieved
for the network's edge messages through the use of a per-edge local coordinate
frame and a novel spin convolution over the remaining degree of freedom. Two
model variants are proposed for the applications of structure relaxation and
molecular dynamics. State-of-the-art results are demonstrated on the
large-scale Open Catalyst 2020 dataset. Comparisons are also performed on the
MD17 and QM9 datasets.


Abstract:
  Connected and Automated Vehicles (CAVs) rely on the correctness of position
and other vehicle kinematics information to fulfill various driving tasks such
as vehicle following, lane change, and collision avoidance. However, a
malicious vehicle may send false sensor information to the other vehicles
intentionally or unintentionally, which may cause traffic inconvenience or loss
of human lives. Here, we take the advantage of cloud-computing and increase the
resilience of CAVs to malicious vehicles by assuming each vehicle shares its
local sensor information with other vehicles to create information redundancy
on the cloud side. We exploit this redundancy and propose a sensor fusion
algorithm for the cloud, capable of providing a robust state estimation of all
vehicles in the cloud under the condition that the number of malicious
information is sufficiently small. Using the proposed estimator, we provide an
algorithm for isolating malicious vehicles. We use numerical examples to
illustrate the effectiveness of our methods.


Abstract:
  The kinetic term of the $N$-body Hamiltonian system defined on the surface of
the sphere is non-separable. As a result, standard explicit symplectic
integrators are inapplicable. We exploit an underlying hierarchy in the
structure of the kinetic term to construct an explicit time-reversible
symplectic scheme of second order. We use iterative applications of the method
to construct a fourth order scheme and demonstrate its efficiency.


Abstract:
  In this paper, we study properties of quadratic variations of c\`{a}dl\`{a}g
paths within the framework of the It\^{o}--F\"{o}llmer calculus in Banach
spaces. We prove a generalized $C^1$-transformation formula for quadratic
variations. We also investigate relations between tensor and scalar quadratic
variations.


Abstract:
  We study interactions between strategic players and markets whose behavior is
guided by an algorithm. Algorithms use data from prior interactions and a
limited set of decision rules to prescribe actions. While as-if rational play
need not emerge if the algorithm is constrained, it is possible to guide
behavior across a rich set of possible environments using limited details.
Provided a condition known as weak learnability holds, Adaptive Boosting
algorithms can be specified to induce behavior that is (approximately) as-if
rational. Our analysis provides a statistical perspective on the study of
endogenous model misspecification.


Abstract:
  This tutorial paper surveys training alternatives to end-to-end
backpropagation (E2EBP) -- the de facto standard for training deep
architectures. Modular training refers to strictly local training without both
the forward and the backward pass, i.e., dividing a deep architecture into
several nonoverlapping modules and training them separately without any
end-to-end operation. Between the fully global E2EBP and the strictly local
modular training, there are "weakly modular" hybrids performing training
without the backward pass only. These alternatives can match or surpass the
performance of E2EBP on challenging datasets such as ImageNet, and are gaining
increased attention primarily because they offer practical advantages over
E2EBP, which will be enumerated herein. In particular, they allow for greater
modularity and transparency in deep learning workflows, aligning deep learning
with the mainstream computer science engineering that heavily exploits
modularization for scalability. Modular training has also revealed novel
insights about learning and has further implications on other important
research domains. Specifically, it induces natural and effective solutions to
some important practical problems such as data efficiency and transferability
estimation.


Abstract:
  Beyond generating long and topic-coherent paragraphs in traditional
captioning tasks, the medical image report composition task poses more
task-oriented challenges by requiring both the highly-accurate medical term
diagnosis and multiple heterogeneous forms of information including impression
and findings. Current methods often generate the most common sentences due to
dataset bias for individual case, regardless of whether the sentences properly
capture key entities and relationships. Such limitations severely hinder their
applicability and generalization capability in medical report composition where
the most critical sentences lie in the descriptions of abnormal diseases that
are relatively rare. Moreover, some medical terms appearing in one report are
often entangled with each other and co-occurred, e.g. symptoms associated with
a specific disease. To enforce the semantic consistency of medical terms to be
incorporated into the final reports and encourage the sentence generation for
rare abnormal descriptions, we propose a novel framework that unifies template
retrieval and sentence generation to handle both common and rare abnormality
while ensuring the semantic-coherency among the detected medical terms.
Specifically, our approach exploits hybrid-knowledge co-reasoning: i) explicit
relationships among all abnormal medical terms to induce the visual attention
learning and topic representation encoding for better topic-oriented symptoms
descriptions; ii) adaptive generation mode that changes between the template
retrieval and sentence generation according to a contextual topic encoder.
Experimental results on two medical report benchmarks demonstrate the
superiority of the proposed framework in terms of both human and metrics
evaluation.


Abstract:
  We investigate the repeated prisoner's dilemma game where both players
alternately use reinforcement learning to obtain their optimal memory-one
strategies. We theoretically solve the simultaneous Bellman optimality
equations of reinforcement learning. We find that the Win-stay Lose-shift
strategy, the Grim strategy, and the strategy which always defects can form
symmetric equilibrium of the mutual reinforcement learning process amongst all
deterministic memory-one strategies.


Abstract:
  We apply a modified vector meson dominance (VMD) model to analyze the
electromagnetic form factors of the $\Lambda_c$ hyperon in the time-like
reaction $e^+e^-\rightarrow \Lambda_c^+ \bar{\Lambda}_c^-$. In the model, we
include the contributions from the vector charmed mesons and their excitations
$\psi(1S)$, $\psi(2S)$, $\psi(3770)$, $\psi(4040)$, $\psi(4160)$ and
$\psi(4415)$. We perform a combined fit to the available data on the Born cross
section in reaction $e^+e^-\rightarrow \Lambda_c^+ \bar{\Lambda}_c^-$ and the
ratio of the electromagnetic form factors $|G_E/G_M|$ to obtain the values of
the model parameters. Our results show that the VMD model can simultaneously
describe the data of electromagnetic form factors from the Belle and BESIII
Collaborations, and the behavior of $|G_E/G_M|$ for the BESIII data can be
qualitatively reproduced by the VMD model prediction at the threshold region.
Moreover, we predict the single and double polarization observables in
$e^+e^-\rightarrow \Lambda_c^+ \bar{\Lambda}_c^-$ reactions, which are
experimentally accessible in the polarized process. We also obtain the form
factors of the $\Lambda_c$ hyperon in the space-like region via analytic
continuing the time-like form factors.


Abstract:
  We report on an unusually bright observation of PSR J2051$-$0827 recorded
during a regular monitoring campaign of black-widow pulsar systems with the
Effelsberg 100-m telescope. Through fortunate coincidence, a particularly
bright scintillation maximum is simultaneous with the eclipse by the companion,
enabling precise measurements of variations in the flux density, dispersion
measure (DM), and scattering strength throughout the eclipse. The flux density
is highly variable throughout the eclipse, with a peak 1.7 times the average
away from the eclipse, and yet does not significantly decrease on average. We
recover the flux density variations from the measured DM variations using
geometric optics, with a relative velocity as the only free parameter. We
measure an effective velocity of (470 $\pm$ 10) km/s, consistent with the
relative orbital motion of the companion, suggesting that the outflow velocity
of the lensing material is low, or is directly along the line of sight. The 2
per cent uncertainty on the effective velocity is a formal error; systematics
related to our current model are likely to dominate, and we detail several
extensions to the model to be considered in a full treatment of lensing. This
is a demonstration of the causal link between DM and lensing; the flux density
variations can be predicted directly through the derivatives of DM. Going
forward, this approach can be applied to investigate the dynamics of other
eclipsing systems, and to investigate the physical nature of scintillation and
lensing in the ionized interstellar medium.


Abstract:
  The proton elastic form factor ratio shows a discrepancy between measurements
using the Rosenbluth technique in unpolarized beam and target experiments and
measurements using polarization degrees of freedom. The proposed explanation of
this discrepancy is uncorrected hard two-photon exchange (TPE), a type of
radiative correction that is conventionally neglected. The effect size and
agreement with theoretical predictions has been tested recently by three
experiments. While the results support the existence of a small two-photon
exchange effect, they cannot establish that theoretical treatments are valid.
At larger momentum transfers, theory remains untested. This proposal aims to
measure two-photon exchange over an extended and so far largely untested $Q^2$
and $\varepsilon$ range with high precision using the {\tt CLAS12} experiment.
Such data are crucial to clearly confirm or rule out TPE as the driver for the
discrepancy as well as test several theoretical approaches, believed valid in
different parts of the tested $Q^2$ range.


Abstract:
  We study a smart grid with wind power and battery storage. Traditionally,
day-ahead planning aims to balance demand and wind power, yet actual wind
conditions often deviate from forecasts. Short-term flexibility in storage and
generation fills potential gaps, planned on a minutes time scale for 30-60
minute horizons. Finding the optimal flexibility deployment requires solving a
semi-infinite non-convex stochastic program, which is generally intractable to
do exactly. Previous approaches rely on sampling, yet such critical problems
call for rigorous approaches with stronger guarantees. Our method employs
probabilistic model checking techniques. First, we cast the problem as a
continuous-space Markov decision process with discretized control, for which an
optimal deployment strategy minimizes the expected grid frequency deviation. To
mitigate state space explosion, we exploit specific structural properties of
the model to implement an iterative exploration method that reuses pre-computed
values as wind data is updated. Our experiments show the method's feasibility
and versatility across grid configurations and time scales.


Abstract:
  The Leray-Serre and the Eilenberg-Moore spectral sequence are fundamental
tools for computing the cohomology of a group or, more generally, of a space.
We describe the relationship between these two spectral sequences when both of
them share the same abutment. There exists a joint tri-graded refinement of the
Leray-Serre and the Eilenberg-Moore spectral sequence. This refinement involves
two more spectral sequences, the preludes from the title, which abut to the
initial terms of the Leray-Serre and the Eilenberg-Moore spectral sequence,
respectively. We show that one of these always degenerates from its second page
on and that the other one satisfies a local-to-global property: It degenerates
for all possible base spaces if and only if it does so when the base space is
contractible. When the preludes degenerate early enough, they appear to echo
Deligne's d\'ecalage, but in general, this is an illusion. We discuss several
principal fibrations to illustrate the possible cases and give applications, in
particular, to Lie groups, torus bundles, and generalizations.


Abstract:
  Motivated by recent developments of hydrodynamical quantum mechanical analogs
[J. W. M. Bush, Annu. Rev. Fluid Mech. 47, 269-292 (2015)] we provide a
relativistic model for a classical particle coupled to a scalar wave-field
through a holonomic constraint. In presence of an external Coulomb field we
define a regime where the particle is guided by the wave in a way similar to
the old de Broglie phase-wave proposal. Moreover, this dualistic mechanical
analog of the quantum theory is reminiscent of the double-solution approach
suggested by de Broglie in 1927 and is able to reproduce the Bohr-Sommerfeld
semiclassical quantization formula for an electron moving in a atom.


Abstract:
  Tellurium trioxide, TeO3, is the only example of a trioxide adopting at
ambient conditions the VF3-type structure (a distorted variant of the cubic
ReO3 structure). Here we present a combined experimental (Raman scattering) and
theoretical (DFT modelling) study on the influence of high pressure (exceeding
100 GPa) on the phase stability of this compound. In experiment the
ambient-pressure VF3-type structure (R-3c symmetry) is preserved up to 110 GPa.
In contrast, calculations indicate that above 66 GPa the R-3c structure should
transform to a YF3-type polymorph (Pnma symmetry) with the coordination number
of Te6+ increasing from 6 to 8 upon the transition. The lack of this transition
in the room-temperature experiment is most probably connected with energetic
barriers, in analogy to what is found for compressed WO3. The YF3-type phase is
predicted to be stable up to 220 GPa when it should transform to a novel
structure of R-3 symmetry and Z = 18. We analyse the influence of pressure on
the band gap of TeO3, and discuss the present findings in the context of
structural transformations of trioxides and trifluorides adopting an extended
structure in the solid state


Abstract:
  We present a source-to-source transformation framework, GOCC, that consumes
lock-based pessimistic concurrency programs in the Go language and transforms
them into optimistic concurrency programs that use Hardware Transactional
Memory (HTM). The choice of the Go language is motivated by the fact that
concurrency is a first-class citizen in Go, and it is widely used in Go
programs. GOCC performs rich inter-procedural program analysis to detect and
filter lock-protected regions and performs AST-level code transformation of the
surrounding locks when profitable. Profitability is driven by both static
analyses of critical sections and dynamic analysis via execution profiles. A
custom HTM library, using perceptron, learns concurrency behavior and
dynamically decides whether to use HTM in the rewritten lock/unlock points.
Given the rich history of transactional memory research but its lack of
adoption in any industrial setting, we believe this workflow, which ultimately
produces source-code patches, is more apt for industry-scale adoption. Results
on widely adopted Go libraries and applications demonstrate significant (up to
10x) and scalable performance gains resulting from our automated transformation
while avoiding major performance regressions.


Abstract:
  Wearable biosensor technology enables real-time, convenient, and continuous
monitoring of users behavioral signals. Such include signals relative to body
motion, body temperature, biological or biochemical markers, and individual
grip forces, which are studied in this paper. A four step pick and drop image
guided and robot assisted precision task has been designed for exploiting a
wearable wireless sensor glove system. Individual spatio temporal grip forces
are analyzed on the basis of thousands of individual sensor data, collected
from different locations on the dominant and non-dominant hands of each of
three users in ten successive task sessions. Statistical comparisons reveal
specific differences between grip force profiles of the individual users as a
function of task skill level (expertise) and time.


Abstract:
  MDS codes are elegant constructions in coding theory and have mode important
applications in cryptography, network coding, distributed data storage,
communication systems et. In this study, a method is given which MDS codes are
lifted to a higher finite field. The presented method satisfies the protection
of the distance and creating the MDS code over the $F_q$ by using MDS code over
$F_p.$


Abstract:
  The mechanism of the generation of dark matter and dark radiation from the
evaporation of primordial black holes is very interesting. We consider the case
of Kerr black holes to generalize previous results obtained in the
Schwarzschild case. For dark matter, the results do not change dramatically and
the bounds on warm dark matter apply similarly: in particular, the Kerr case
cannot save the scenario of black hole domination for light dark matter. For
dark radiation, the expectations for $\Delta N_{eff}$ do not change
significantly with respect to the Schwarzschild case, but for an enhancement in
the case of spin 2 particles: in the massless case, however, the projected
experimental sensitivity would be reached only for extremal black holes.


Abstract:
  In this work, we introduce, justify and demonstrate the Corrective Source
Term Approach (CoSTA) -- a novel approach to Hybrid Analysis and Modeling
(HAM). The objective of HAM is to combine physics-based modeling (PBM) and
data-driven modeling (DDM) to create generalizable, trustworthy, accurate,
computationally efficient and self-evolving models. CoSTA achieves this
objective by augmenting the governing equation of a PBM model with a corrective
source term generated using a deep neural network. In a series of numerical
experiments on one-dimensional heat diffusion, CoSTA is found to outperform
comparable DDM and PBM models in terms of accuracy -- often reducing predictive
errors by several orders of magnitude -- while also generalizing better than
pure DDM. Due to its flexible but solid theoretical foundation, CoSTA provides
a modular framework for leveraging novel developments within both PBM and DDM.
Its theoretical foundation also ensures that CoSTA can be used to model any
system governed by (deterministic) partial differential equations. Moreover,
CoSTA facilitates interpretation of the DNN-generated source term within the
context of PBM, which results in improved explainability of the DNN. These
factors make CoSTA a potential door-opener for data-driven techniques to enter
high-stakes applications previously reserved for pure PBM.


Abstract:
  Background: The quantitative analysis of microscope videos often requires
instance segmentation and tracking of cellular and subcellular objects. The
traditional method consists of two stages: (1) performing instance object
segmentation of each frame, and (2) associating objects frame-by-frame.
Recently, pixel-embedding-based deep learning approaches these two steps
simultaneously as a single stage holistic solution. In computer vision,
annotated training data with consistent segmentation and tracking is resource
intensive, the severity of which is multiplied in microscopy imaging due to (1)
dense objects (e.g., overlapping or touching), and (2) high dynamics (e.g.,
irregular motion and mitosis). Adversarial simulations have provided successful
solutions to alleviate the lack of such annotations in dynamics scenes in
computer vision, such as using simulated environments (e.g., computer games) to
train real-world self-driving systems. Methods: In this paper, we propose an
annotation-free synthetic instance segmentation and tracking (ASIST) method
with adversarial simulation and single-stage pixel-embedding based learning.
Contribution: The contribution of this paper is three-fold: (1) the proposed
method aggregates adversarial simulations and single-stage pixel-embedding
based deep learning; (2) the method is assessed with both the cellular (i.e.,
HeLa cells) and subcellular (i.e., microvilli) objects; and (3) to the best of
our knowledge, this is the first study to explore annotation-free instance
segmentation and tracking study for microscope videos. Results: The ASIST
method achieved an important step forward, when compared with fully supervised
approaches: ASIST shows 7% to 11% higher segmentation, detection and tracking
performance on microvilli relative to fully supervised methods, and comparable
performance on Hela cell videos.


Abstract:
  An effective approach to improve the detection efficiency of nanoscale light
sources relies on a planar antenna configuration, which beams the emitted light
into a narrow cone. Planar antennas operate like optical Yagi-Uda antennas,
where reflector and director elements are made of metal films. Here we
introduce and investigate, both theoretically and experimentally, a scanning
implementation of a planar antenna. Using a small ensemble of molecules
contained in fluorescent nanobeads placed in the antenna, we independently
address the intensity, the radiation pattern and the decay rate as a function
of distance between a flat-tip scanning gold wire (reflector) and a thin gold
film coated on a glass coverslip (director). The scanning planar antenna
changes the radiation pattern of a single fluorescent bead and it beams light
into a narrow cone down to angles of 45{\deg} (full width at half maximum).
Moreover, the collected signal compared to the case of a glass coverslip is
larger than a factor of 3, which is mainly due to the excitation enhancement.
These results offer a better understanding of the modification of light-matter
interaction by planar antennas and they hold promise for applications, such as
sensing, imaging and diagnostics.


Abstract:
  Large MIMO transceivers are integral components of next-generation wireless
networks. However, for such systems to be practical, their channel estimation
process needs to be fast and reliable. Although several solutions for fast
estimation of sparse channels do exist, there is still a gap in understanding
the fundamental limits governing this problem. Specifically, we need to better
understand the lower bound on the number of measurements under which accurate
channel estimates can be obtained. This work bridges that knowledge gap by
deriving a tight asymptotic lower bound on the number of measurements. This not
only helps develop a better understanding for the sparse MIMO channel
estimation problem, but it also provides a benchmark for evaluating current and
future solutions.


Abstract:
  Here we study the transport properties of a nickelate compound, namely LaNiO2
using density functional theory (DFT) in conjunction with dynamical mean field
theory (DMFT). An interacting multi-orbital spin-resolved scenario for LaNiO2
yields a metallic ground state with ferromagnetic correlations. The latter
contrasts an antiferromagnetic order reported earlier. The metallic behaviour
persists even at large values of the interaction energies. Further support of
the metallic state is provided by the angular resolved photoemission
spectroscopy (ARPES) data.


Abstract:
  Large-scale distributed training of Deep Neural Networks (DNNs) on
state-of-the-art platforms is expected to be severely communication
constrained. To overcome this limitation, numerous gradient compression
techniques have been proposed and have demonstrated high compression ratios.
However, most existing methods do not scale well to large scale distributed
systems (due to gradient build-up) and/or fail to evaluate model fidelity (test
accuracy) on large datasets. To mitigate these issues, we propose a new
compression technique, Scalable Sparsified Gradient Compression (ScaleCom),
that leverages similarity in the gradient distribution amongst learners to
provide significantly improved scalability. Using theoretical analysis, we show
that ScaleCom provides favorable convergence guarantees and is compatible with
gradient all-reduce techniques. Furthermore, we experimentally demonstrate that
ScaleCom has small overheads, directly reduces gradient traffic and provides
high compression rates (65-400X) and excellent scalability (up to 64 learners
and 8-12X larger batch sizes over standard training) across a wide range of
applications (image, language, and speech) without significant accuracy loss.


Abstract:
  We present a method for comparing the classical and quantum calculations of
the electric quadrupolar Love number $k_2$ and show that our previous
derivation of the quantum Love number of a quantum blackhole matches exactly
the classical calculation of $k_2$ when quantum expectation values are replaced
by the corresponding classical quantities, as dictated by the Bohr
correspondence principle. The standard derivation of $k_2$ for classical
relativistic stars relies on fixing boundary conditions on the surface of the
star for the Einstein equations in the presence of an external perturbing
field. An alternative method for calculating $k_2$ uses properties of the
spectrum of the non-relativistic fluid modes of the star. We adopt this
alternative method and use it to derive an effective description of the
interior modes in terms of a collection of driven harmonic oscillators
characterized by different frequencies and amplitudes. We compare these two
classical methods and find that most of the interior information can be
integrated out, reducing the problem of calculating $k_2$ to fixing a single
boundary condition for the perturbed Einstein equations on the surface of the
deformed star. We then determine this single boundary condition in terms of the
spectrum of the object and proceed to identify the relationship between
classical quantities and quantum expectation values and to verify the agreement
between the results of the effective classical calculation and the quantum
calculation.


Abstract:
  In this paper we consider the moduli space of complete, conformally flat
metrics on a sphere with k punctures having constant positive Q-curvature and
positive scalar curvature. Previous work has shown that such metrics admit an
asymptotic expansion near each puncture, allowing one to define an asymptotic
necksize of each singular point. We prove that any set in the moduli space such
that the distances between distinct punctures and the asymptotic necksizes all
remain bounded away from zero is sequentially compact, mirroring a theorem of
D. Pollack about singular Yamabe metrics. Along the way we define a radial
Pohozaev invariant at each puncture and refine some a priori bounds of the
conformal factor, which may be of independent interest.


Abstract:
  Unmanned Aerial Vehicle (UAV) offers lots of applications in both commerce
and recreation. With this, monitoring the operation status of UAVs is crucially
important. In this work, we consider the task of tracking UAVs, providing rich
information such as location and trajectory. To facilitate research on this
topic, we propose a dataset, Anti-UAV, with more than 300 video pairs
containing over 580k manually annotated bounding boxes. The releasing of such a
large-scale dataset could be a useful initial step in research of tracking
UAVs. Furthermore, the advancement of addressing research challenges in
Anti-UAV can help the design of anti-UAV systems, leading to better
surveillance of UAVs. Besides, a novel approach named dual-flow semantic
consistency (DFSC) is proposed for UAV tracking. Modulated by the semantic flow
across video sequences, the tracker learns more robust class-level semantic
information and obtains more discriminative instance-level features.
Experimental results demonstrate that Anti-UAV is very challenging, and the
proposed method can effectively improve the tracker's performance. The Anti-UAV
benchmark and the code of the proposed approach will be publicly available at
https://github.com/ucas-vg/Anti-UAV.


Abstract:
  Dealing with nonlinear effects of the radio-frequency(RF) chain is a key
issue in the realization of very large-scale multi-antenna (MIMO) systems.
Achieving the remarkable gains possible with massive MIMO requires that the
signal processing algorithms systematically take into account these effects.
Here, we present a computationally efficient linear precoding method satisfying
the requirements for low peak-to-average power ratio (PAPR) and low-resolution
D/A-converters (DACs). The method is based on a sparse regularization of the
precoding matrix and offers advantages in terms of precoded signal PAPR as well
as processing complexity. Through simulation, we find that the method
substantially improves conventional linear precoders.


Abstract:
  The stochastic SIRS model is a continuous-time Markov chain modelling the
spread of infectious diseases with temporary immunity, in a
homogeneously-mixing population of fixed size $N$. We study the scaling
behaviour of the extinction time of stochastic SIRS models as $N$ tends to
infinity. When the initial size of infected population is small, we obtain the
closed-form expression of the asymptotic distribution of this extinction time,
and compare it with the data from Monte Carlo simulation.


Abstract:
  We prove a ``positive'' Jantzen sum formula for the Specht modules of the
cyclotomic Hecke algebras of type~$A$. That is, in the Grothendieck group, we
show that the sum of the pieces of the Jantzen filtration is equal to an
explicit non-negative linear combination of modules $E^\nu_{f,e}$, which are
modular reductions of simple modules for closely connected Hecke algebras in
characteristic zero. The coefficient of $E^\nu_{f,e}$ in the sum formula is
determined by the graded decomposition numbers in characteristic zero, which
are known, and the characteristic of the field. As a consequence we see that
the decomposition numbers of a cyclotomic Hecke algebra at an $e$th root of
unity in characteristic $p$ depend on the decomposition numbers of related
cyclotomic Hecke algebras at $ep^r$th roots of unity in characteristic zero,
for $r\ge0$.


Abstract:
  By using small computing devices deployed at user premises, Autonomous Demand
Response (ADR) adapts users electricity consumption to given time-dependent
electricity tariffs. This allows end-users to save on their electricity bill
and Distribution System Operators to optimise (through suitable time-dependent
tariffs) management of the electric grid by avoiding demand peaks.
Unfortunately, even with ADR, users power consumption may deviate from the
expected (minimum cost) one, e.g., because ADR devices fail to correctly
forecast energy needs at user premises. As a result, the aggregated power
demand may present undesirable peaks. In this paper we address such a problem
by presenting methods and a software tool (APD-Analyser) implementing them,
enabling Distribution System Operators to effectively verify that a given
time-dependent electricity tariff achieves the desired goals even when
end-users deviate from their expected behaviour. We show feasibility of the
proposed approach through a realistic scenario from a medium voltage Danish
distribution network.


Abstract:
  We present a brief review of power laws and correlation functions as measures
of criticality and the relation between them. By comparing phenomenology from
rain, brain and the forest fire model we discuss the relevant features of
self-organisation to the vicinity about a critical state. We conclude that
organisation to a region of extended correlations and approximate power laws
may be behaviour of interest shared between the three considered systems.


Abstract:
  We evaluated the DiscovErr system, in which we had previously implemented a
new methodology for assessment of compliance to continuous application of
evidence-based clinical guidelines, based on a bidirectional search from the
guideline objectives to the patient's longitudinal data, and vice versa. We
compared the system comments on 1584 transactions regarding the management,
over a mean of 5.23 years, of 10 randomly selected Type 2 diabetes patients, to
those of two diabetes experts and a senior family practitioner. After providing
their own comments, the experts assessed both the correctness (precision) and
the importance of each of the DiscovErr system comments. The completeness
(recall or coverage) of the system was computed by comparing its comments to
those made by the experts. The system made 279 comments. The experts made 181
unique comments. The completeness of the system was 91% compared to comments
made by at least two experts, and 98% when compared to comments made by all
three. 172 comments were evaluated by the experts for correctness and
importance: All 114 medication-related comments, and a random 35% of the 165
monitoring-related comments. The system's correctness was 81% compared to
comments judged as correct by both diabetes experts, and 91% compared to
comments judged as correct by a diabetes expert and at least as partially
correct by the other. 89% of the comments were judged as important by both
diabetes experts, 8% were judged as important by one expert, 3% were judged as
less important by both experts. The completeness scores of the three experts
(compared to the comments of all experts plus the validated system comments)
were 75%, 60%, and 55%; the experts' correctness scores (compared to their
majority) were respectively 99%, 91%, and 88%. Conclusion: Systems such as
DiscovErr can assess the quality of continuous guideline-based care.


Abstract:
  We study the mod $p$ motivic cohomology of homogeneous varieties such as
$GL_{n}/GL_{r}$ or $Sp_{2n}/Sp_{2n-2}$ along with the action of the Steenrod
operations, without restrictions on the characteristic of the base field. In
particular, we prove that certain quotient maps do not admit sections.