Abstract:
  We show that the infimum of the dual volume of the convex core of a convex
co-compact hyperbolic $3$-manifold with incompressible boundary coincides with
the infimum of the Riemannian volume of its convex core, as we vary the
geometry by quasi-isometric deformations. We deduce a linear lower bound of the
volume of the convex core of a quasi-Fuchsian manifold in terms of the length
of its bending measured lamination, with optimal multiplicative constant.


Abstract:
  Before the recent publication of the correspondence between Gauss and Encke,
nothing was known about the role that John Taylor, a cotton merchant from
Liverpool, had played in the life of Gotthold Eisenstein. In this article, we
will bring together what we have discovered about John Taylor's life.


Abstract:
  We introduce a theoretical framework for understanding and predicting the
complexity of sequence classification tasks, using a novel extension of the
theory of Boolean function sensitivity. The sensitivity of a function, given a
distribution over input sequences, quantifies the number of disjoint subsets of
the input sequence that can each be individually changed to change the output.
We argue that standard sequence classification methods are biased towards
learning low-sensitivity functions, so that tasks requiring high sensitivity
are more difficult. To that end, we show analytically that simple lexical
classifiers can only express functions of bounded sensitivity, and we show
empirically that low-sensitivity functions are easier to learn for LSTMs. We
then estimate sensitivity on 15 NLP tasks, finding that sensitivity is higher
on challenging tasks collected in GLUE than on simple text classification
tasks, and that sensitivity predicts the performance both of simple lexical
classifiers and of vanilla BiLSTMs without pretrained contextualized
embeddings. Within a task, sensitivity predicts which inputs are hard for such
simple models. Our results suggest that the success of massively pretrained
contextual representations stems in part because they provide representations
from which information can be extracted by low-sensitivity decoders.


Abstract:
  Heart Sound (also known as phonocardiogram (PCG)) analysis is a popular way
that detects cardiovascular diseases (CVDs). Most PCG analysis uses supervised
way, which demands both normal and abnormal samples. This paper proposes a
method of unsupervised PCG analysis that uses beta variational auto-encoder
($\beta-\text{VAE}$) to model the normal PCG signals. The best performed model
reaches an AUC (Area Under Curve) value of 0.91 in ROC (Receiver Operating
Characteristic) test for PCG signals collected from the same source. Unlike
majority of $\beta-\text{VAE}$s that are used as generative models, the
best-performed $\beta-\text{VAE}$ has a $\beta$ value smaller than 1. Further
experiments then find that the introduction of a light weighted KL divergence
between distribution of latent space and normal distribution improves the
performance of anomaly PCG detection based on anomaly scores resulted by
reconstruction loss. The fact suggests that anomaly score based on
reconstruction loss may be better than anomaly scores based on latent vectors
of samples


Abstract:
  We introduce a new family of particle evolution samplers suitable for
constrained domains and non-Euclidean geometries. Stein Variational Mirror
Descent and Mirrored Stein Variational Gradient Descent minimize the
Kullback-Leibler (KL) divergence to constrained target distributions by
evolving particles in a dual space defined by a mirror map. Stein Variational
Natural Gradient exploits non-Euclidean geometry to more efficiently minimize
the KL divergence to unconstrained targets. We derive these samplers from a new
class of mirrored Stein operators and adaptive kernels developed in this work.
We demonstrate that these new samplers yield accurate approximations to
distributions on the simplex, deliver valid confidence intervals in
post-selection inference, and converge more rapidly than prior methods in
large-scale unconstrained posterior inference. Finally, we establish the
convergence of our new procedures under verifiable conditions on the target
distribution.


Abstract:
  In this paper we consider anisotropic Lorentz-Karamata space $2\pi$ of
periodic functions of $m$ variables and Nikol'skii--Besov's class . In this
paper, we establish order-sharp estimates of the best approximation by
trigonometric polynomials with harmonic numbers from the step hyperbolic cross
of functions from the Nikol'skii - Besov class in the norm of the anisotropic
Lorentz-Karamata space.


Abstract:
  Multicore CPU architectures have been established as a structure for
general-purpose systems for high-performance processing of applications. Recent
multicore CPU has evolved as a system architecture based on non-uniform memory
architecture. For the technique of using the kernel space that shifts the tasks
to the ideal memory node, the characteristics of the applications of the
user-space cannot be considered. Therefore, kernel level approaches cannot
execute memory scheduling to recognize the importance of user applications.
Moreover, users need to run applications after sufficiently understanding the
multicore CPU based on non-uniform memory architecture to ensure the high
performance of the user's applications. This paper presents a user-space memory
scheduler that allocates the ideal memory node for tasks by monitoring the
characteristics of non-uniform memory architecture. From our experiment, the
proposed system improved the performance of the application by up to 25%
compared to the existing system.


Abstract:
  Using the Floquet Hamiltonian derived based on the time-dependent
perturbation theory, we investigated the quasienergy bands of a one-dimensional
time-Floquet photonic crystal. The time-Floquet photonic crystal contains two
alternating layers labeled as A and B, and the permittivity of A layer is
modulated periodically in time. We showed that the quasienergy bands are
reciprocal when the modulation function is a function of time only, while the
quasienergy bands could be nonreciprocal when the permittivity is modulated in
both time and space through an unique combination. In the former case, the
coupling between the positive (negative) and positive (negative) bands results
in quasienergy gaps, while the coupling between the positive and negative bands
leads to pairs of exception points, when the modulation is on the real part of
the permittivity. In the latter case, the coupling between the positive
(negative) and positive (negative) bands still results in quasienergy gaps.
However, the coupling between the positive and negative bands leads to
quasienergy gaps at a small modulation speed and pairs of exceptional points at
a high modulation speed.


Abstract:
  The many unusual properties of the enigmatic AT2018cow suggested that at
least some subset of the empirical class of fast blue optical transients
(FBOTs) represents a genuinely new astrophysical phenomenon. Unfortunately, the
intrinsic rarity and fleeting nature of these events have made it difficult to
identify additional examples early enough to acquire the observations necessary
to constrain theoretical models. We present here the Zwicky Transient Facility
discovery of AT2020xnd (ZTF20acigmel, the "Camel") at z=0.243, the first
unambiguous AT2018cow analog to be found and confirmed in real time. AT2018cow
and AT2020xnd share all key observational properties: a fast optical rise,
sustained high photospheric temperature, absence of a second peak attributable
to ejection of a radioactively-heated stellar envelope, extremely luminous
radio, millimetre, and X-ray emission, and a dwarf-galaxy host. This supports
the argument that AT2018cow-like events represent a distinct phenomenon from
slower-evolving radio-quiet supernovae, likely requiring a different progenitor
or a different central engine. The sample properties of the four known members
of this class to date disfavour tidal disruption models but are consistent with
the alternative model of an accretion powered jet following the direct collapse
of a massive star to a black hole. Contextual filtering of alert streams
combined with rapid photometric verification using multi-band imaging provides
an efficient way to identify future members of this class, even at high
redshift.


Abstract:
  This paper is concerned with the parabolic-elliptic Keller-Segel system with
nonlinear diffusion and signal-dependent sensitivity
\begin{align}\tag{KS}\label{system} \begin{cases}
u_t=\Delta(u+1)^m-\nabla\cdot(u\chi(v)\nabla v),\quad &x\in\Omega, t>0,\\
0=\Delta v-v+u, &x\in\Omega, t>0 \end{cases} \end{align} under homogeneous
Newmann boundary conditions and initial conditions, where
$\Omega=B_R(0)\subset\mathbb{R}^N$ ($N\geq3,\ R>0$) is a ball, $m\geq 1$,
$\chi$ is a function satisfying that $\chi(s)\geq\chi_0(a+s)^{-k}$ ($k>0$,
$\chi_0>0$, $a\geq 0$) for all $s>0$ and some conditions. If the case that
$m=1$ and $\chi(s)=\chi_0s^{-k}$, Nagai-Senba established finite-time blow-up
of solutions under the smallness conditions on a moment of initial data $u(x,
0)$ and some condition for $k\in(0,1)$. Moreover, if the case that
$\chi(s)\equiv(\mbox{const.})$, Sugiyama showed finite-time blow-up of
solutions under the condition $m\in[1,2-\frac{2}{N})$. According to two
previous works, it seems that the smallness conditions of $m$ and $k$ leads to
finite-time blow-up of solutions. The purpose of this paper is to give the
relationship which depends only on $m$, $k$ and $N$ such that there exists
initial data which corresponds finite-time blow-up solutions.


Abstract:
  The LIGO-Virgo gravitational-wave (GW) observation unveiled the new
population of black holes (BHs) that appears to have an extended mass spectrum
up to around $70M_\odot$, much heavier than the previously-believed mass range
($\sim 8M_\odot$). In this paper, we study the capability of a microlensing
observation of stars in the Milky Way (MW) bulge region to identify BHs of GW
mass scales, taking into account the microlensing parallax characterized by the
parameter $\pi_{\rm E}\propto M^{-1/2}$ ($M$ is the mass of a lens), which is a
dimension-less quantity defined by the ratio of the astronomical unit to the
projected Einstein radius. First, assuming that BHs follow the same spatial and
velocity distributions of stars as predicted by the standard MW model, we show
that microlensing events with long light curve timescales, $t_{\rm E}\gtrsim
100~{\rm days}$, and small parallax effects, $\pi_{\rm E}\sim 10^{-2}$, are
dominated by BH lenses compared to stellar-mass lenses. Second, using a Markov
chain Monte Carlo analysis of the simulated light curve, we show that BH lens
candidates are securely identified on individual basis, if the parallax effect
is detected or well constrained to the precision of a percent level in
$\pi_{\rm E}$. We also discuss that a microlensing event of an
intermediate-mass BH of $\sim 1000M_\odot$, if it occurs, can be identified in
a distinguishable way from stellar-mass BHs.


Abstract:
  In this work we propose a novel and fully automated method for extracting the
yarn geometrical features in woven composites so that a direct parametrization
of the textile reinforcement is achieved (e.g., FE mesh). Thus, our aim is not
only to perform yarn segmentation from tomographic images but rather to provide
a complete descriptive modeling of the fabric. As such, this direct approach
improves on previous methods that use voxel-wise masks as intermediate
representations followed by re-meshing operations (yarn envelope estimation).
The proposed approach employs two deep neural network architectures (U-Net and
Mask RCNN). First, we train the U-Net to generate synthetic CT images from the
corresponding FE simulations. This allows to generate large quantities of
annotated data without requiring costly manual annotations. This data is then
used to train the Mask R-CNN, which is focused on predicting contour points
around each of the yarns in the image. Experimental results show that our
method is accurate and robust for performing yarn instance segmentation on CT
images, this is further validated by quantitative and qualitative analyses.


Abstract:
  In this paper, we explore the use of pre-trained language models to learn
sentiment information of written texts for speech sentiment analysis. First, we
investigate how useful a pre-trained language model would be in a 2-step
pipeline approach employing Automatic Speech Recognition (ASR) and
transcripts-based sentiment analysis separately. Second, we propose a pseudo
label-based semi-supervised training strategy using a language model on an
end-to-end speech sentiment approach to take advantage of a large, but
unlabeled speech dataset for training. Although spoken and written texts have
different linguistic characteristics, they can complement each other in
understanding sentiment. Therefore, the proposed system can not only model
acoustic characteristics to bear sentiment-specific information in speech
signals, but learn latent information to carry sentiments in the text
representation. In these experiments, we demonstrate the proposed approaches
improve F1 scores consistently compared to systems without a language model.
Moreover, we also show that the proposed framework can reduce 65% of human
supervision by leveraging a large amount of data without human sentiment
annotation and boost performance in a low-resource condition where the human
sentiment annotation is not available enough.


Abstract:
  We analyze the properties of the scattering solutions obtained as the pole of
the S- and K-matrix with the help of the Jost function framework and the
Strum-Liouville theory within the Hartree-Fock-Bogoliubov(HFB) framework, and
clarify the scattering solutions which can be defined as the physical state. We
found that there are three types of the resonances; "{\it shape resonance}",
"{\it particle-type}" and "{\it hole-type quasiparticle resonances}", and
another two types of solutions are given as the independent S-matrix and
K-matrix poles. The shape resonance is formed by the Hartree-Fock(HF) mean
field potential, is not affected by the pairing correlation so much. The
particle-type and hole-type quasiparticle resonances originate from the
particle and hole states by the configuration mixing effect by pairing. All of
resonance are represented by the S-matrix pole which has the corresponding
K-matrix pole. Two other types of solutions are given by the independent
S-matrix and K-matrix poles. These poles are formed by the HF mean field
potential. The effect of pairing for the independent S-matrix pole is small,
but the one for the independent K-matrix pole has the remarkable effect. The
independent K-matrix pole destroys the quasiparticle resonance as it approaches
to the resonance by the pairing effect. The wave function of all resonances
have the characteristic structure of the metastable property. However, the
metastable structure of the wave function of the quasiparticle resonance can be
broken by the independent standing wave solution or the Fano effect.


Abstract:
  The multifractal formalism for measures in its original formulation is
checked for special classes of measures such as doubling, self-similar, and
Gibbs-like ones. Out of these classes, suitable conditions should be taken into
account to prove the validity of the multifractal formalism. In the present
work, a large class of measures satisfying a weak condition known as quasi
Ahlfors is considered in the framework of mixed multifractal analysis. A joint
multifractal analysis of finitely many quasi Ahlfors probability measures is
developed. Mixed variants of multifractal generalizations of Hausdorff and
packing measures, and corresponding dimensions are introduced. By applying
convexity arguments, some properties of these measures and dimensions are
established. Finally, an associated multifractal formalism is introduced and
proved to hold for the class of quasi Ahlfors measures.


Abstract:
  In recent work, G. E. Andrews and G. Simay prove a surprising relation
involving parity palindromic compositions, and ask whether a combinatorial
proof can be found. We extend their results to a more general class of
compositions that are palindromic modulo $m$, that includes the parity
palindromic case when $m=2$. We then provide combinatorial proofs for the cases
$m=2$ and $m=3$.


Abstract:
  With rapidly evolving internet technologies and emerging tools, sports
related videos generated online are increasing at an unprecedentedly fast pace.
To automate sports video editing/highlight generation process, a key task is to
precisely recognize and locate the events in the long untrimmed videos. In this
tech report, we present a two-stage paradigm to detect what and when events
happen in soccer broadcast videos. Specifically, we fine-tune multiple action
recognition models on soccer data to extract high-level semantic features, and
design a transformer based temporal detection module to locate the target
events. This approach achieved the state-of-the-art performance in both two
tasks, i.e., action spotting and replay grounding, in the SoccerNet-v2
Challenge, under CVPR 2021 ActivityNet workshop. Our soccer embedding features
are released at https://github.com/baidu-research/vidpress-sports. By sharing
these features with the broader community, we hope to accelerate the research
into soccer video understanding.


Abstract:
  The concept of $\check{H}^n-$bubles was defined and investigated. In this
paper we generalize this conception for some other functors $F$. Open questions
are formulated.


Abstract:
  Accelerators magnets must have minimal magnetic field imperfections for
reducing particle-beam instabilities. In the case of coils made of
high-temperature superconducting (HTS) tapes, the field imperfections from
persistent currents need to be carefully evaluated. In this paper we study the
use of superconducting screens based on HTS tapes for reducing the magnetic
field imperfections in accelerator magnets. The screens exploit the
magnetization by persistent currents to cancel out the magnetic field error.
The screens are aligned with the main field components, such that only the
undesired field components are compensated. The screens are passive,
self-regulating, and do not require any external source of energy. Measurements
in liquid nitrogen at 77 Kelvin show for dipole-field configurations a
significant reduction of the magnetic-field error up to a factor of four. The
residual error is explained via numerical simulations, accounting for the
geometrical imperfections in the HTS screens, thus achieving satisfactory
agreement with experimental results. Simulations show that if screens are
increased in width and thickness, and operated at 4.5 Kelvin, field errors may
be eliminated almost entirely for the typical excitation cycles of accelerator
magnets.


Abstract:
  Object Detection (OD) is an important computer vision problem for industry,
which can be used for quality control in the production lines, among other
applications. Recently, Deep Learning (DL) methods have enabled practitioners
to train OD models performing well on complex real world images. However, the
adoption of these models in industry is still limited by the difficulty and the
significant cost of collecting high quality training datasets. On the other
hand, when applying OD to the context of production lines, CAD models of the
objects to be detected are often available. In this paper, we introduce a fully
automated method that uses a CAD model of an object and returns a fully trained
OD model for detecting this object. To do this, we created a Blender script
that generates realistic labeled datasets of images containing the object,
which are then used for training the OD model. The method is validated
experimentally on two practical examples, showing that this approach can
generate OD models performing well on real images, while being trained only on
synthetic images. The proposed method has potential to facilitate the adoption
of object detection models in industry as it is easy to adapt for new objects
and highly flexible. Hence, it can result in significant costs reduction, gains
in productivity and improved products quality.


Abstract:
  The extension of the Standard Model with two gauge-singlet Majorana fermions
can simultaneously explain two beyond-the-Standard-model phenomena: neutrino
masses and oscillations, as well as the origin of the matter-antimatter
asymmetry in the Universe. The parameters of such a model are constrained by
the neutrino oscillation data, direct accelerator searches, big bang
nucleosynthesis, and requirement of successful baryogenesis. We show that the
combination of all these constraints still leaves an allowed region in the
parameter space below the kaon mass. This region can be probed by the further
searches of NA62, DUNE, or SHiP experiments.


Abstract:
  Dynamical models of Solar System evolution have suggested that P-/D-type
volatile-rich asteroids formed in the outer Solar System and may be genetically
related to the Jupiter Trojans, the comets and small KBOs. Indeed, their
spectral properties resemble that of anhydrous cometary dust.
High-angular-resolution images of P-type asteroid (87) Sylvia with VLT/SPHERE
were used to reconstruct its 3D shape, and to study the dynamics of its two
satellites. We also model Sylvia's thermal evolution. The shape of Sylvia
appears flattened and elongated. We derive a volume-equivalent diameter of 271
+/- 5 km, and a low density of 1378 +/- 45 kg.m-3. The two satellites orbit
Sylvia on circular, equatorial orbits. The oblateness of Sylvia should imply a
detectable nodal precession which contrasts with the fully-Keplerian dynamics
of the satellites. This reveals an inhomogeneous internal structure, suggesting
that Sylvia is differentiated. Sylvia's low density and differentiated interior
can be explained by partial melting and mass redistribution through water
percolation. The outer shell would be composed of material similar to
interplanetary dust particles (IDPs) and the core similar to aqueously altered
IDPs or carbonaceous chondrite meteorites such as the Tagish Lake meteorite.
Numerical simulations of the thermal evolution of Sylvia show that for a body
of such size, partial melting was unavoidable due to the decay of long-lived
radionuclides. In addition, we show that bodies as small as 130-150 km in
diameter should have followed a similar thermal evolution, while smaller
objects, such as comets and the KBO Arrokoth, must have remained pristine, in
agreement with in situ observations of these bodies. NASA Lucy mission target
(617) Patroclus (diameter~140 km) may, however, be differentiated.


Abstract:
  The ACM A.M. Turing Award is commonly acknowledged as the highest distinction
in the realm of computer science. Since 1960s, it has been awarded to computer
scientists who made outstanding contributions. The significance of this award
is far-reaching to the laureates as well as their research teams. However,
unlike the Nobel Prize that has been extensively investigated, little research
has been done to explore this most important award. To this end, we propose the
Turing Number (TN) index to measure how far a specific scholar is to this
award. Inspired by previous works on Erdos Number and Bacon Number, this index
is defined as the shortest path between a given scholar to any Turing Award
Laureate. Experimental results suggest that TN can reflect the closeness of
collaboration between scholars and Turing Award Laureates. With the correlation
analysis between TN and metrics from the bibliometric-level and network-level,
we demonstrate that TN has the potential of reflecting a scholar's academic
influence and reputation.


Abstract:
  We define the concept of energy-variational solutions for the Navier--Stokes
and Euler equations. The underlying relative energy inequality holds as an
equality for classical solutions and if the additional variable vanishes, these
solutions are equivalent to the weak formulation with the strong energy
inequality. By introducing an additional defect variable in time, all
restrictions and all concatenations of energy-variational solutions are again
energy-variational solutions. Via the criterion of maximal dissipation, a
unique solution is selected that is not only continuously depending on the data
but also turns out to be a unique weak solution.


Abstract:
  The first measurement of the production of pions, kaons, (anti-)protons and
$\phi$ mesons at midrapidity in Xe-Xe collisions at $\sqrt{s_{\rm NN}} = 5.44$
TeV is presented. Transverse momentum ($p_{\rm T}$) spectra and $p_{\rm
T}$-integrated yields are extracted in several centrality intervals bridging
from p-Pb to mid-central Pb-Pb collisions in terms of final-state multiplicity.
The study of Xe-Xe and Pb-Pb collisions allows systems at similar
charged-particle multiplicities but with different initial geometrical
eccentricities to be investigated. A detailed comparison of the spectral shapes
in the two systems reveals an opposite behaviour for radial and elliptic flow.
In particular, this study shows that the radial flow does not depend on the
colliding system when compared at similar charged-particle multiplicity. In
terms of hadron chemistry, the previously observed smooth evolution of particle
ratios with multiplicity from small to large collision systems is also found to
hold in Xe-Xe. In addition, our results confirm that two remarkable features of
particle production at LHC energies are also valid in the collision of
medium-sized nuclei: the lower proton-to-pion ratio with respect to the thermal
model expectations and the increase of the $\phi$-to-pion ratio with increasing
final-state multiplicity.


Abstract:
  Highlights are presented about the science to be done with SKA. as well as
state of the art science already done today with its precursors (MeerKAT,
ASKAP) and pathfinders (LOFAR, NenuFAR), with accent on the expected
breakthroughs.


Abstract:
  Federated Learning (FL) has received a significant amount of attention in the
industry and research community due to its capability of keeping data on local
devices. To aggregate the gradients of local models to train the global model,
existing works require that the global model and the local models are the same.
However, Internet of Things (IoT) devices are inherently diverse regarding
computation speed and onboard memory. In this paper, we propose an FL framework
targeting the heterogeneity of IoT devices. Specifically, local models are
compressed from the global model, and the gradients of the compressed local
models are used to update the global model. We conduct preliminary experiments
to illustrate that our framework can facilitate the design of IoT-aware FL.


Abstract:
  Microgrid (MG) energy management is an important part of MG operation.
Various entities are generally involved in the energy management of an MG,
e.g., energy storage system (ESS), renewable energy resources (RER) and the
load of users, and it is crucial to coordinate these entities. Considering the
significant potential of machine learning techniques, this paper proposes a
correlated deep Q-learning (CDQN) based technique for the MG energy management.
Each electrical entity is modeled as an agent which has a neural network to
predict its own Q-values, after which the correlated Q-equilibrium is used to
coordinate the operation among agents. In this paper, the Long Short Term
Memory networks (LSTM) based deep Q-learning algorithm is introduced and the
correlated equilibrium is proposed to coordinate agents. The simulation result
shows 40.9% and 9.62% higher profit for ESS agent and photovoltaic (PV) agent,
respectively.


Abstract:
  The phenomenon of quantum entanglement marks one of the furthest departures
from classical physics and is indispensable for quantum information processing.
Despite its fundamental importance, the distribution of entanglement over long
distances trough photons is unfortunately hindered by unavoidable decoherence
effects. Entanglement distillation is a means of restoring the quality of such
diluted entanglement by concentrating it into a pair of qubits. Conventionally,
this would be done by distributing multiple photon pairs and distilling the
entanglement into a single pair. Here, we turn around this paradigm by
utilising pairs of single photons entangled in multiple degrees of freedom.
Specifically, we make use of the polarisation and the energy-time domain of
photons, both of which are extensively field-tested. We experimentally chart
the domain of distillable states and achieve relative fidelity gains up to 13.8
%. Compared to the two-copy scheme, the distillation rate of our single-copy
scheme is several orders of magnitude higher, paving the way towards
high-capacity and noise-resilient quantum networks.


Abstract:
  We theoretically study the superconducting properties of multi-band
two-dimensional transition metal oxide superconductors by analyzing not only
the role played by conventional singlet pairings, but also by the triplet order
parameters, favored by the spin-orbit couplings present in these ma- terials.
In particular, we focus on the two-dimensional electron gas at the (001)
interface between LaAlO3 and SrTiO3 band insulators where the low electron
densities and the sizeable spin-orbit couplings affect the superconducting
features. Our theoretical study is based on an extended su- perconducting
mean-field analysis of the typical multi-band tight-binding Hamiltonian, as
well as on a parallel analysis of the effective electronic bands in the
low-momentum limit, including static on-site and inter-site intra-band
attractive potentials under applied magnetic fields. The presence of triplet
pairings is able to strongly reduce the singlet order parameters which, as a
result, are no longer a monotonic function of the charge density. The interplay
between the singlet and the triplet pairings affects the dispersion of
quasi-particle excitations in the Brillouin zone and also induces anisotropy in
the superconducting behavior under the action of an in-plane and of an out-
of-plane magnetic fields. Finally, non-trivial topological superconducting
states become stable as a function of the charge density, as well as of the
magnitude and of the orientation of the magnetic field. In addition to the
chiral, time-reversal breaking, topological superconducting phase, favored by
the linear Rashba couplings and by the on-site attractive potentials in the
presence of an out- of-plane magnetic field, we find that a time-reversal
invariant topological helical superconducting phase is promoted by not-linear
spin-orbit couplings and by the inter-site attractive interactions in the
absence of magnetic field.


Abstract:
  In classical Iwasawa theory, we mainly study codimension one behavior of
arithmetic modules. Relatively recently, F. M. Bleher, T. Chinburg, R.
Greenberg, M. Kakde, G. Pappas, R. Sharifi, and M. J. Taylor started studying
higher codimension behavior of unramified Iwasawa modules which are conjectured
to be pseudo-null. In this paper, by developing a general algebraic theory on
perfect complexes, we obtain a new perspective of their works. That allows us
to extend the results to equivariant settings and, even in non-equivariant
settings, to obtain more refined results concerning the higher codimension
behavior.


Abstract:
  We compare and contrast the stellar structures of isolated Local Group dwarf
galaxies, as traced by their oldest stellar populations, with the satellite
dwarf galaxies of the Milky Way and M31. All Local Group dwarfs with Mv < -6
and surface brightness < 26.5 mags. per square arcsec. are considered, taking
advantage of measurements from surveys that use similar observations and
analysis techniques. For the isolated dwarfs, we use the results from Solitary
Local (Solo) Dwarf Galaxy Survey. We begin by confirming that the structural
and dynamical properties of the two satellite populations are not obviously
statistically different from each other, but we note that there many more
satellites around M31 than around the Milky Way down to equivalent magnitude
and surface brightness limits. We find that dwarfs in close proximity to a
massive galaxy generally show more scatter in their Kormendy relations than
those in isolation. Specifically, isolated Local Group dwarf galaxies show a
tighter trend of half-light radius versus magnitude than the satellite
populations, and similar effects are also seen for related parameters. There
appears to be a transition in the structural and dynamical properties of the
dwarf galaxy population around ~400 kpc from the Milky Way and M31, such that
the smallest, faintest, most circular dwarf galaxies are found closer than this
separation. We discuss the impact of selection effects on our analysis, and we
argue that our results point to the significance of tidal interactions on the
population of systems within approximately 400 kpc from the MW and M31.


Abstract:
  Quantifying the amount of polarization is crucial for understanding and
studying political polarization in political and social systems. Several
methods are used commonly to measure polarization in social networks by purely
inspecting their structure. We analyse eight of such methods and show that all
of them yield high polarization scores even for random networks with similar
density and degree distributions to typical real-world networks. Further, some
of the methods are sensitive to degree distributions and relative sizes of the
polarized groups. We propose normalization to the existing scores and a minimal
set of tests that a score should pass in order for it to be suitable for
separating polarized networks from random noise. The performance of the scores
increased by 38%-220% after normalization in a classification task of 203
networks. Further, we find that the choice of method is not as important as
normalization, after which most of the methods have better performance than the
best-performing method before normalization. This work opens up the possibility
to critically assess and compare the features and performance of different
methods for measuring structural polarization.


Abstract:
  Equilibrium statistical mechanics rests on the assumption of ergodic dynamics
of a system modulo the conservation laws of local observables: extremization of
entropy immediately gives Gibbs' ensemble (GE) for energy conserving systems
and a generalized version of it (GGE) when the number of local conserved
quantities (LCQ) is more than one. Through the last decade, statistical
mechanics has been extended to describe the late-time behaviour of periodically
driven (Floquet) quantum matter starting from a generic state. The structure
built on the fundamental assumptions of ergodicity and identification of the
relevant "conservation laws" in this inherently non-equilibrium setting. More
recently, it has been shown that the statistical mechanics has a much richer
structure due to the existence of {\it emergent} conservation laws: these are
approximate but stable conservation laws arising {\it due to the drive}, and
are not present in the undriven system. Extensive numerical and analytical
results support perpetual stability of these emergent (though approximate)
conservation laws, probably even in the thermodynamic limit. This banks on the
recent finding of a sharp ergodicity threshold for Floquet thermalization in
clean, interacting non-integrable Floquet systems. This opens up a new
possibility of stable Floquet engineering in such systems. This review intends
to give a theoretical overview of these developments. We conclude by briefly
surveying the experimental scenario.


Abstract:
  In this paper we will solve an open problem raised by Man\'asevich and Mawhin
twenty years ago on the structure of the periodic eigenvalues of the vectorial
$p$-Laplacian. This is an Euler-Lagrangian equation on the plane or in higher
dimensional Euclidean spaces. The main result obtained is that for any exponent
$p$ other than $2$, the vectorial $p$-Laplacian on the plane will admit
infinitely many different sequences of periodic eigenvalues with a given
period. These sequences of eigenvalues are constructed using the notion of
scaling momenta we will introduce. The whole proof is based on the complete
integrability of the equivalent Hamiltonian system, the tricky reduction to
$2$-dimensional dynamical systems, and a number-theoretical distinguishing
between different sequences of eigenvalues. Some numerical simulations to the
new sequences of eigenvalues and eigenfunctions will be given. Several further
conjectures towards to the panorama of the spectral sets will be imposed.


Abstract:
  We give asymptotic upper and lower bounds for the real and imaginary parts of
cycle integrals of the classical modular j-function along geodesics that
correspond to Markov irrationalities.


Abstract:
  We report sub-arcsecond ALMA observations between 272 - 375 GHz towards Sgr
A*'s Circumnuclear disk (CND). Our data comprises 8 individual pointings, with
significant SiO (8(7) - 7(6)) and SO (7 - 6) emission detected towards 98
positions within these pointings. Additionally, we identify H2CS (9(1,9) -
8(1,8)), OCS (25 - 24) and CH3OH (2(1,1) - 2(0,2)) towards a smaller subset of
positions. By using the observed peak line flux density together with a
Bayesian Inference technique informed by radiative transfer models, we
systematically recover the physical gas conditions towards each of these
positions. We estimate that the bulk of the surveyed gas has temperature T <
500 K and density n $\lessapprox 10^{6}$ cm$^{-3}$, consistent with previous
studies of similar positions as traced by HCN clumps. However, we identify an
uncharacteristically hot (T $\approx 600$ K) and dense (n $\approx 10^{6}$
cm$^{-3}$) source in the Northeastern Arm. This position is found to be
approximately consistent with a gravitationally bound region dominated by
turbulence. We also identify a nearby cold (T $\approx 60$ K) and extremely
dense (n $\approx 10^{7}$ cm$^{-3}$) position that is again potentially bound
and dominated by turbulence. We also determine that the total gas mass
contained within the CND is M $\approx 4 \times 10^{4}$ $M_{\odot}$.
Furthermore, we qualitatively note that the observed chemical enrichment across
large scales within the CND is consistent with bulk grain processing, though
multiple desorption mechanisms are plausibly responsible. Further chemical
modelling is required to identify the physical origin of the grain-processing,
as well as the localised H2CS and OCS emission.


Abstract:
  The rampant phenomenon of overpopulation and the remarkable increase of human
movements over the last decade have caused an aggressive re-emergence of dengue
fever, which made it the subject of several research fields. In this regard,
mathematical modeling, and notably through compartmental systems, is considered
as an eminent tool to obtain a clear overview of this disease's prevalence
behavior. In reality, and like all epidemics, the dengue spread phenomenon is
often subject to some randomness due to the different natural environment
fluctuations. For this reason, a mathematical formulation that considers
suitably as much as possible the external stochasticity is indeed required. By
this token, we strive in this work to present and analyze a generalized
stochastic dengue model that incorporates both slight and huge environmental
perturbations. More precisely, our proposed model is represented under the form
of an It\^o-L\'evy stochastic differential equations system that we demonstrate
its mathematical well-posedness and biological significance. Based on some
novel analytical techniques, we prove, and under appropriate hypothetical
frameworks, of course, two important asymptotic properties, namely: extinction
and persistence in the mean. The theoretical findings show that the dynamics of
our disturbed dengue model are mainly determined by the parameters that are
narrowly related to the small perturbations' intensities and the jumps
magnitudes. In the end, we give certain numerical illustrative examples to
support our theoretical findings and to highlight the effect of the adopted
mathematical techniques on the results.


Abstract:
  The advent of Stage IV weak lensing surveys will open up a new era in
precision cosmology. These experiments will offer more than an
order-of-magnitude leap in precision over existing surveys, and we must ensure
that the accuracy of our theory matches this. Accordingly, it is necessary to
explicitly evaluate the impact of the theoretical assumptions made in current
analyses on upcoming surveys. One effect typically neglected in present
analyses is the Doppler-shift of the measured source comoving distances. Using
Fisher matrices, we calculate the biases on the cosmological parameter values
inferred from a Euclid-like survey, if the correction for this Doppler-shift is
omitted. We find that this Doppler-shift can be safely neglected for Stage IV
surveys. The code used in this investigation is made publicly available.


Abstract:
  Recent simulations have shown that asymmetries in the ejecta distribution of
supernova remnants (SNR) can still reflect asymmetries from the initial
supernova explosion. Thus, their study provides a great means to test and
constrain model predictions in relation to the distributions of heavy elements
or the neutron star kicks, both of which are key to better understanding the
explosion mechanisms in core-collapse supernovae. The use of a novel blind
source separation method applied to the megasecond X-ray observations of the
well-known Cassiopeia A SNR has revealed maps of the distribution of the ejecta
endowed with an unprecedented level of detail and clearly separated from
continuum emission. Our method also provides a three-dimensional view of the
ejecta by disentangling the red- and blue-shifted spectral components and
associated images of the Si, S, Ar, Ca and Fe, providing insights into the
morphology of the ejecta distribution in Cassiopeia A. These mappings allow us
to thoroughly investigate the asymmetries in the heavy elements distribution
and probe simulation predictions about the neutron star kicks and the relative
asymmetries between the different elements. We find in our study that most of
the ejecta X-ray flux stems from the red-shifted component, suggesting an
asymmetry in the explosion. In addition, the red-shifted ejecta can physically
be described as a broad, relatively symmetric plume, whereas the blue-shifted
ejecta is more similar to a dense knot. The neutron star also moves directly
opposite to the red-shifted parts of the ejecta similar to what is seen with
44Ti. Regarding the morphological asymmetries, it appears that heavier elements
have more asymmetrical distributions, which confirms predictions made by
simulations. This study is a showcase of the capacities of new analysis methods
to revisit archival observations to fully exploit their scientific content.


Abstract:
  Pixel-wise regression is probably the most common problem in fine-grained
computer vision tasks, such as estimating keypoint heatmaps and segmentation
masks. These regression problems are very challenging particularly because they
require, at low computation overheads, modeling long-range dependencies on
high-resolution inputs/outputs to estimate the highly nonlinear pixel-wise
semantics. While attention mechanisms in Deep Convolutional Neural
Networks(DCNNs) has become popular for boosting long-range dependencies,
element-specific attention, such as Nonlocal blocks, is highly complex and
noise-sensitive to learn, and most of simplified attention hybrids try to reach
the best compromise among multiple types of tasks. In this paper, we present
the Polarized Self-Attention(PSA) block that incorporates two critical designs
towards high-quality pixel-wise regression: (1) Polarized filtering: keeping
high internal resolution in both channel and spatial attention computation
while completely collapsing input tensors along their counterpart dimensions.
(2) Enhancement: composing non-linearity that directly fits the output
distribution of typical fine-grained regression, such as the 2D Gaussian
distribution (keypoint heatmaps), or the 2D Binormial distribution (binary
segmentation masks). PSA appears to have exhausted the representation capacity
within its channel-only and spatial-only branches, such that there is only
marginal metric differences between its sequential and parallel layouts.
Experimental results show that PSA boosts standard baselines by $2-4$ points,
and boosts state-of-the-arts by $1-2$ points on 2D pose estimation and semantic
segmentation benchmarks.


Abstract:
  The cosmological term, $\Lambda$, was introduced $104$ years ago by Einstein
in his gravitational field equations. Whether $\Lambda$ is a rigid quantity or
a dynamical variable in cosmology has been a matter of debate for many years,
especially after the introduction of the general notion of dark energy (DE).
$\Lambda$ is associated to the vacuum energy density, $\rho_{\rm vac}$, and one
may expect that it evolves slowly with the cosmological expansion. Herein we
present a devoted study testing this possibility using the promising class of
running vacuum models (RVM's). We use a large string $SNIa+BAO+H(z)+LSS+CMB$ of
modern cosmological data, in which for the first time the CMB part involves the
full Planck 2018 likelihood for these models. We test the dependence of the
results on the threshold redshift $z_*$ at which the vacuum dynamics is
activated in the recent past and find positive signals up to $\sim4.0\sigma$
for $z_*\simeq 1$. The RVM's prove very competitive against the standard
$\Lambda$CDM model and give a handle for solving the $\sigma_8$ tension and
alleviating the $H_0$ one.


Abstract:
  Task-parallel programs often enjoy deadlock freedom under certain
restrictions, such as the use of structured join operations, as in Cilk and
X10, or the use of asynchronous task futures together with deadlock-avoiding
policies such as Known Joins or Transitive Joins. However, the promise, a
popular synchronization primitive for parallel tasks, does not enjoy
deadlock-freedom guarantees. Promises can exhibit deadlock-like bugs; however,
the concept of a deadlock is not currently well-defined for promises.
  To address these challenges, we propose an ownership semantics in which each
promise is associated to the task which currently intends to fulfill it.
Ownership immediately enables the identification of bugs in which a task fails
to fulfill a promise for which it is responsible. Ownership further enables the
discussion of deadlock cycles among tasks and promises and allows us to
introduce a robust definition of deadlock-like bugs for promises.
  Cycle detection in this context is non-trivial because it is concurrent with
changes in promise ownership. We provide a lock-free algorithm for precise
runtime deadlock detection. We show how to obtain the memory consistency
criteria required for the correctness of our algorithm under TSO and the Java
and C++ memory models. An evaluation compares the execution time and memory
usage overheads of our detection algorithm on benchmark programs relative to an
unverified baseline. Our detector exhibits a 12% (1.12$\times$) geometric mean
time overhead and a 6% (1.06$\times$) geometric mean memory overhead, which are
smaller overheads than in past approaches to deadlock cycle detection.


Abstract:
  Program verification is to develop the program's proof system, and to prove
the proof system soundness with respect to a trusted operational semantics of
the program. However, many practical program verifiers are not based on
operational semantics and can't seriously validate the program. Matching logic
is proposed to make program verification based on operational semantics. In
this paper, following Grigore Ro{\c{s}}u 's work, we consider matching logic
for parallel imperative language(PIMP). According to our investigation, this
paper is the first study on matching logic for PIMP. In our matching logic, we
redefine "interference-free" to character parallel rule and prove the soundness
of matching logic to the operational semantics of PIMP. We also link PIMP's
operational semantics and PIMP's verification formally by constructing a
matching logic verifier for PIMP which executes rewriting logic semantics
symbolically on configuration patterns and is sound and complete to matching
logic for PIMP. That is our matching logic verifier for PIMP is sound to the
operational semantics of PIMP. Finally, we also verify the matching logic
verifier through an example which is a standard problem in parallel
programming.


Abstract:
  Ordinary differential equations (ODEs) are widely used to model complex
dynamics that arises in biology, chemistry, engineering, finance, physics, etc.
Calibration of a complicated ODE system using noisy data is generally very
difficult. In this work, we propose a two-stage nonparametric approach to
address this problem. We first extract the de-noised data and their higher
order derivatives using boundary kernel method, and then feed them into a
sparsely connected deep neural network with ReLU activation function. Our
method is able to recover the ODE system without being subject to the curse of
dimensionality and complicated ODE structure. When the ODE possesses a general
modular structure, with each modular component involving only a few input
variables, and the network architecture is properly chosen, our method is
proven to be consistent. Theoretical properties are corroborated by an
extensive simulation study that demonstrates the validity and effectiveness of
the proposed method. Finally, we use our method to simultaneously characterize
the growth rate of Covid-19 infection cases from 50 states of the USA.


Abstract:
  We calculate gluon self-energy using quark energy projectors in a general
quark-gluon plasma. By separating the quark field into a positive- and a
negative-energy mode, the quark loop constructed with the same mode is always
convergent, and the divergence appears only in the mixed loop with different
modes and is medium independent. After removing the divergence in vacuum, we
obtain the one-loop gluon self-energy at finite temperature, chemical potential
and quarks mass without approximation. With the method of quark loop
resummation, we calculate non-perturbatively the gluon Debye mass and
thermodynamic potential. In the limit of small gluon momentum in comparison
with temperature, chemical potential and quark mass, our calculation comes back
to the known HTL/HDL results in literature.


Abstract:
  Braiding operations are challenging to create topological quantum computers.
It is unclear whether braiding operations can be executed with any materials.
Although various calculations based on Majorana fermions show braiding
possibilities, a braiding operation with a Majorana fermion has not yet been
experimentally proven. Herein, braiding operations are demonstrated using a
molecular topological superconductor (MTSC) that utilizes the topological
properties intrinsic in molecules. The braiding operations were implemented by
controlling two MTSC modules made by pelletizing crystals of
4,5,9,10-tetrakis(dodecyloxy)pyrene, which is proposed as the first MTSC
material through n-MOSFETs. It shows the elements of topological quantum
computers that can be demonstrated without an external magnetic field at room
temperature.


Abstract:
  Most water in the universe may be superionic, and its thermodynamic and
transport properties are crucial for planetary science but difficult to probe
experimentally or theoretically. We use machine learning and free energy
methods to overcome the limitations of quantum mechanical simulations, and
characterize hydrogen diffusion, superionic transitions, and phase behaviors of
water at extreme conditions. We predict that a close-packed superionic phase
with mixed stacking is stable over a wide temperature and pressure range, while
a body-centered cubic phase is only thermodynamically stable in a small window
but is kinetically favored. Our phase boundaries, which are consistent with the
existing-albeit scarce-experimental observations, help resolve the fractions of
insulating ice, different superionic phases, and liquid water inside of ice
giants.


Abstract:
  Real-time rendering and animation of humans is a core function in games,
movies, and telepresence applications. Existing methods have a number of
drawbacks we aim to address with our work. Triangle meshes have difficulty
modeling thin structures like hair, volumetric representations like Neural
Volumes are too low-resolution given a reasonable memory budget, and
high-resolution implicit representations like Neural Radiance Fields are too
slow for use in real-time applications. We present Mixture of Volumetric
Primitives (MVP), a representation for rendering dynamic 3D content that
combines the completeness of volumetric representations with the efficiency of
primitive-based rendering, e.g., point-based or mesh-based methods. Our
approach achieves this by leveraging spatially shared computation with a
deconvolutional architecture and by minimizing computation in empty regions of
space with volumetric primitives that can move to cover only occupied regions.
Our parameterization supports the integration of correspondence and tracking
constraints, while being robust to areas where classical tracking fails, such
as around thin or translucent structures and areas with large topological
variability. MVP is a hybrid that generalizes both volumetric and
primitive-based representations. Through a series of extensive experiments we
demonstrate that it inherits the strengths of each, while avoiding many of
their limitations. We also compare our approach to several state-of-the-art
methods and demonstrate that MVP produces superior results in terms of quality
and runtime performance.


Abstract:
  We make quantitative improvements to recently obtained results on the
structure of the image of a large difference set under certain quadratic forms
and other homogeneous polynomials. Previous proofs used deep results of
Benoist-Quint on random walks in certain subgroups of
$\operatorname{SL}_r(\mathbb{Z})$ (the symmetry groups of these quadratic
forms) that were not of a quantitative nature. Our new observation relies on
noticing that rather than studying random walks, one can obtain more
quantitative results by considering polynomial orbits of these group actions
that are not contained in cosets of submodules of $\mathbb{Z}^r$ of small
index. Our main new technical tool is a uniform Furstenberg-S\'{a}rk\"{o}zy
theorem that holds for a large class of polynomials not necessarily vanishing
at zero, which may be of independent interest and is derived from a density
increment argument and Hua's bound on polynomial exponential sums.


Abstract:
  Person image synthesis, e.g., pose transfer, is a challenging problem due to
large variation and occlusion. Existing methods have difficulties predicting
reasonable invisible regions and fail to decouple the shape and style of
clothing, which limits their applications on person image editing. In this
paper, we propose PISE, a novel two-stage generative model for Person Image
Synthesis and Editing, which is able to generate realistic person images with
desired poses, textures, or semantic layouts. For human pose transfer, we first
synthesize a human parsing map aligned with the target pose to represent the
shape of clothing by a parsing generator, and then generate the final image by
an image generator. To decouple the shape and style of clothing, we propose
joint global and local per-region encoding and normalization to predict the
reasonable style of clothing for invisible regions. We also propose
spatial-aware normalization to retain the spatial context relationship in the
source image. The results of qualitative and quantitative experiments
demonstrate the superiority of our model on human pose transfer. Besides, the
results of texture transfer and region editing show that our model can be
applied to person image editing.


Abstract:
  Given a closed manifold of positive Yamabe invariant and for instance
positive Morse functions upon it, the conformally prescribed scalar curvature
problem raises the question, whether or not such functions can by conformally
changing the metric be realised as the scalar curvature of this manifold. As we
shall quantify depending on the shape and structure of such functions, every
lack of a solution for some candidate function leads to existence of
energetically uniformly bounded solutions for entire classes of related
candidate functions.


Abstract:
  In the animation industry, cartoon videos are usually produced at low frame
rate since hand drawing of such frames is costly and time-consuming. Therefore,
it is desirable to develop computational models that can automatically
interpolate the in-between animation frames. However, existing video
interpolation methods fail to produce satisfying results on animation data.
Compared to natural videos, animation videos possess two unique characteristics
that make frame interpolation difficult: 1) cartoons comprise lines and smooth
color pieces. The smooth areas lack textures and make it difficult to estimate
accurate motions on animation videos. 2) cartoons express stories via
exaggeration. Some of the motions are non-linear and extremely large. In this
work, we formally define and study the animation video interpolation problem
for the first time. To address the aforementioned challenges, we propose an
effective framework, AnimeInterp, with two dedicated modules in a
coarse-to-fine manner. Specifically, 1) Segment-Guided Matching resolves the
"lack of textures" challenge by exploiting global matching among color pieces
that are piece-wise coherent. 2) Recurrent Flow Refinement resolves the
"non-linear and extremely large motion" challenge by recurrent predictions
using a transformer-like architecture. To facilitate comprehensive training and
evaluations, we build a large-scale animation triplet dataset, ATD-12K, which
comprises 12,000 triplets with rich annotations. Extensive experiments
demonstrate that our approach outperforms existing state-of-the-art
interpolation methods for animation videos. Notably, AnimeInterp shows
favorable perceptual quality and robustness for animation scenarios in the
wild. The proposed dataset and code are available at
https://github.com/lisiyao21/AnimeInterp/.


Abstract:
  Tensor network states have been a very prominent tool for the study of
quantum many-body physics, thanks to their physically relevant entanglement
properties and their ability to encode symmetries. In the last few years, the
formalism has been extended and applied to theories with local symmetries to -
lattice gauge theories. In the contraction of tensor network states as well as
correlation functions of physical observables with respect to them, one uses
the so-called transfer operator, whose local properties dictate the long-range
behaviour of the state. In this work we study transfer operators of tensor
network states (in particular, PEPS - projected entangled pair states) in the
context of lattice gauge theories, and consider the implications of the local
symmetry on their structure and properties. We focus on the Wilson loop - a
nonlocal, gauge-invariant observable which is central to pure gauge theories,
whose long range decay behaviour probes the confinement or deconfinement of
static charges. Using the symmetry, we show how to handle its contraction, and
formulate conditions relating local properties to its decay fashion.


Abstract:
  We optimised the magnetic field homogeneity of two canonical designs for
mobile microfluidic NMR applications: two parallel magnets with an air gap and
a modified Halbach array. Along with the influence of the sample length,
general design guidelines will be presented. For a fair comparison the
sensitive length of the sample has been chosen to be the same as the gap size
between the magnets to ensure enough space for the transmitting and receiving
unit, as well as basic electric shimming components. Keeping the compactness of
the final device in mind, a box with an edge length 5 times the gap size has
been defined, in which the complete magnet configuration should fit. With the
chosen boundary conditions, the simple parallel cuboid configuration reaches
the best homogeneity without active shimming (0.5$\mathrm{B_{s}}$, 41 ppm),
while the Pseudo-Halbach configuration has the highest field strength
(0.9$\mathrm{B_{s}}$, 994 ppm), assuming perfect magnets. However, permanent
magnet configurations suffer from imperfections, such as magnetisation,
fabrication and positioning errors, which results in worse magnetic field
homogeneities than expected from simulations using a fixed optimised parameter
set. We present a sensitivity analysis for a magnetic cube and the results of
studies of the variations in the magnetisation and angle of magnetisation of
magnets purchased from different suppliers, composed of different materials and
coatings, and of different sizes. We performed a detailed Monte Carlo
simulation on the effect of the measured distribution of magnetic properties on
the mentioned configurations. The cuboid design shows a mean homogeneity of 430
ppm (std dev. 350 ppm), the Pseudo-Halbach has a mean homogeneity of 1086 ppm
(std dev. 8 ppm).


Abstract:
  Over the last decade, Programmable Logic Controllers (PLCs) have been
increasingly targeted by attackers to obtain control over industrial processes
that support critical services. Such targeted attacks typically require
detailed knowledge of system-specific attributes, including hardware
configurations, adopted protocols, and PLC control-logic, i.e. process
comprehension. The consensus from both academics and practitioners suggests
stealthy process comprehension obtained from a PLC alone, to conduct targeted
attacks, is impractical. In contrast, we assert that current PLC programming
practices open the door to a new vulnerability class based on control-logic
constructs. To support this, we propose the concept of Process Comprehension at
a Distance (PCaaD), as a novel methodological and automatable approach for
system-agnostic exploitation of PLC library functions, leading to the targeted
exfiltration of operational data, manipulation of control-logic behavior, and
establishment of covert command and control channels through unused memory. We
validate PCaaD on widely used PLCs, by identification of practical attacks.


Abstract:
  Emil Post's tag system problem is the question of whether or not a tag system
$\{N=3, P(0)=00, P(1)=1101\}$ has a configuration, simulation of which will
never halt or end up in a loop. For the past decades, there were several
attempts to find an answer to this question, including a recent study by
Wolfram (2021), during which the first $2^{84}$ initial configurations were
checked. This paper presents a family of configurations of this type in a form
of strings $a^{n} b c^{m}$, that evolve to $a^{n+1} b c^{m+1}$ after a finite
amount of steps. The proof of this behavior for all non-negative $n$ and $m$ is
described further in a paper as a finite verification procedure, which is
computationally bounded by 20000 iterations of tag. All corresponding code can
be found at
https://github.com/nikitakurilenko/post-tag-infinitely-growing-configurations.


Abstract:
  3D reconstruction has lately attracted increasing attention due to its wide
application in many areas, such as autonomous driving, robotics and virtual
reality. As a dominant technique in artificial intelligence, deep learning has
been successfully adopted to solve various computer vision problems. However,
deep learning for 3D reconstruction is still at its infancy due to its unique
challenges and varying pipelines. To stimulate future research, this paper
presents a review of recent progress in deep learning methods for Multi-view
Stereo (MVS), which is considered as a crucial task of image-based 3D
reconstruction. It also presents comparative results on several publicly
available datasets, with insightful observations and inspiring future research
directions.


Abstract:
  Traditional approaches to activity recognition involve the use of wearable
sensors or cameras in order to recognise human activities. In this work, we
extract fine-grained physical layer information from WiFi devices for the
purpose of passive activity recognition in indoor environments. While such data
is ubiquitous, few approaches are designed to utilise large amounts of
unlabelled WiFi data. We propose the use of self-supervised contrastive
learning to improve activity recognition performance when using multiple views
of the transmitted WiFi signal captured by different synchronised receivers. We
conduct experiments where the transmitters and receivers are arranged in
different physical layouts so as to cover both Line-of-Sight (LoS) and non LoS
(NLoS) conditions. We compare the proposed contrastive learning system with
non-contrastive systems and observe a 17.7% increase in macro averaged F1 score
on the task of WiFi based activity recognition, as well as significant
improvements in one- and few-shot learning scenarios.


Abstract:
  3D printing has revolutionized the manufacturing of volumetric components and
structures for use in various fields. Owing to the advent of photo-curable
resins, several fully volumetric light-based techniques have been recently
developed to push further the resolution and speed limitations of 3D printing.
However, these new approaches only work with homogeneous and relatively
transparent resins so that the incident light patterns used for
photo-polymerization are not impacted along their propagation through the
material. Herein, we describe a strategy to print in scattering materials. It
consists of characterizing how light is distorted by the curable resin and then
applying a digital correction to the light patterns to counteract the effect of
scattering. Using a tomographic volumetric printer, we experimentally
demonstrate the importance of taking light scattering into account when
computing the projected patterns and show that our applied correction
significantly improves printability, even when the object size exceeds the
scattering mean free path of light.


Abstract:
  Brain function relies on a precisely coordinated and dynamic balance between
the functional integration and segregation of distinct neural systems.
Characterizing the way in which neural systems reconfigure their interactions
to give rise to distinct but hidden brain states remains an open challenge. In
this paper, we propose a Bayesian model-based characterization of latent brain
states and showcase a novel method based on posterior predictive discrepancy
using the latent block model to detect transitions between latent brain states
in blood oxygen level-dependent (BOLD) time series. The set of estimated
parameters in the model includes a latent label vector that assigns network
nodes to communities, and also block model parameters that reflect the weighted
connectivity within and between communities. Besides extensive in-silico model
evaluation, we also provide empirical validation (and replication) using the
Human Connectome Project (HCP) dataset of 100 healthy adults. Our results
obtained through an analysis of task-fMRI data during working memory
performance show appropriate lags between external task demands and
change-points between brain states, with distinctive community patterns
distinguishing fixation, low-demand and high-demand task conditions.


Abstract:
  For globally connected devices like smart phones, personal computers and
Internet-of-things devices, the ability to generate random numbers is essential
for execution of cryptographic protocols responsible for information security.
Generally, a random number generator should be small, robust, utilize as few
hardware and energy resources as possible, yet provide excellent randomness at
a high enough speed (bitrate) for a given purpose. In this work we present a
quantum random number generator (QRNG) which makes use of a photoelectric
effect in single-photon avalanche diodes (SPADs) as a source of randomness and
is scalable to any desired bitrate. We use the random flip-flop method in which
random bits are obtained by periodic sampling of a randomly toggling flip-flop.
For the first time we investigate this method in detail and find that, out of
two main imperfections, bias is due only to hardware imperfections while
autocorrelation predominantly resides with the method itself. SPADs are
integrated on a silicon chip together with passive quenching and digital
pulse-shaping circuitry, using a standard 180 nm CMOS process. A separate FPGA
chip derives random numbers from the detection signals. The basic QRNG cell,
made of only two SPADs and a few logic circuits, can generate up to 20 Mbit/s
that pass NIST statistical tests without any further postprocessing. This
technology allows integration of a QRNG on a single silicon chip using readily
available industrial processes.


Abstract:
  The rise of personal assistants has made conversational question answering
(ConvQA) a very popular mechanism for user-system interaction. State-of-the-art
methods for ConvQA over knowledge graphs (KGs) can only learn from crisp
question-answer pairs found in popular benchmarks. In reality, however, such
training data is hard to come by: users would rarely mark answers explicitly as
correct or wrong. In this work, we take a step towards a more natural learning
paradigm - from noisy and implicit feedback via question reformulations. A
reformulation is likely to be triggered by an incorrect system response,
whereas a new follow-up question could be a positive signal on the previous
turn's answer. We present a reinforcement learning model, termed CONQUER, that
can learn from a conversational stream of questions and reformulations. CONQUER
models the answering process as multiple agents walking in parallel on the KG,
where the walks are determined by actions sampled using a policy network. This
policy network takes the question along with the conversational context as
inputs and is trained via noisy rewards obtained from the reformulation
likelihood. To evaluate CONQUER, we create and release ConvRef, a benchmark
with about 11k natural conversations containing around 205k reformulations.
Experiments show that CONQUER successfully learns to answer conversational
questions from noisy reward signals, significantly improving over a
state-of-the-art baseline.


Abstract:
  Let $K$ be a field and $X$, $Y$ denote matrices such that, the entries of $X$
are either indeterminates over $K$ or $0$ and the entries of $Y$ are
indeterminates over $K$ which are different from those appearing in $X$. We
consider ideals of the form $I_{1}(XY)$, which is the ideal generated by the
$1\times 1$ minors of the matrix $XY$. We prove that the quotient ring $K[X,
Y]/I_{1}(XY)$ admits an ASL structure for certain $X$ and $Y$.


Abstract:
  We apply the diabatic approach, specially suited for a QCD based study of
conventional (quark-antiquark) and unconventional (quark-antiquark +
meson-meson) meson states, to the description of hidden-bottom mesons. A
spectral analysis of the $I=0$, $J^{++}$ and $1^{--}$ resonances with masses up
to about $10.8$ GeV is carried out. Masses and widths of all the experimentally
known resonances, including conventional and unconventional states, can be well
reproduced. In particular, we predict a significant $B\bar{B}^{\ast}$ component
in $\Upsilon(10580)$. We also predict the existence of a not yet discovered
unconventional $1^{++}$ narrow state, with a significant
$B_{s}\bar{B}_{s}^{\ast}$ content making it to decay into $\Upsilon(1S)\phi$,
whose experimental discovery would provide definite support to our theoretical
analysis.


Abstract:
  In a previous paper, we computed the energy density and the non-linear energy
cascade rate for transverse kink waves using Elsasser variables. In this paper,
we focus on the standing kink waves, which are impulsively excited in coronal
loops by external perturbations. We present an analytical calculation to
compute the damping time due to the non-linear development of the
Kelvin-Helmholtz instability. The main result is that the damping time is
inversely proportional to the oscillation amplitude. We compare the damping
times from our formula with the results of numerical simulations and
observations. In both cases we find a reasonably good match. The comparison
with the simulations show that the non-linear damping dominates in the high
amplitude regime, while the low amplitude regime shows damping by resonant
absorption. In the comparison with the observations, we find a power law
inversely proportional to the amplitude $\eta^{-1}$ as an outer envelope for
our Monte Carlo data points.


Abstract:
  Multilingual Neural Machine Translation (MNMT) has aroused widespread
interest due to its efficiency. An exciting advantage of MNMT models is that
they could also translate between unsupervised (zero-shot) language directions.
Language tag (LT) strategies are often adopted to indicate the translation
directions in MNMT. In this paper, we demonstrate that the LTs are not only
indicators for translation directions but also crucial to zero-shot translation
qualities. Unfortunately, previous work tends to ignore the importance of LT
strategies. We demonstrate that a proper LT strategy could enhance the
consistency of semantic representations and alleviate the off-target issue in
zero-shot directions. Experimental results show that by ignoring the source
language tag (SLT) and adding the target language tag (TLT) to the encoder, the
zero-shot translations could achieve a +8 BLEU score difference over other LT
strategies in IWSLT17, Europarl, TED talks translation tasks.


Abstract:
  Cohort analysis is a pervasive activity in web analytics. One divides users
into groups according to specific criteria and tracks their behavior over time.
Despite its extensive use, academic circles do not discuss cohort analysis to
evaluate user behavior online. This work introduces an unsupervised
non-parametric approach to group Internet users based on their activities. In
comparison, canonical methods in marketing and engineering-based techniques
underperform. COHORTNEY is the first machine learning-based cohort analysis
algorithm with a robust theoretical explanation.


Abstract:
  We study ancient Ricci flows which admit asymptotic solitons in the sense of
Perelman. We prove that the asymptotic solitons must coincide with Bamler's
tangent flows at infinity. Furthermore, we show that Perelman's
$\nu$-functional is uniformly bounded on such ancient solutions; this fact
leads to logarithmic Sobolev inequalities and Sobolev inequalities. Lastly, as
an important tool for the proofs of the above results, we also show that, for a
complete Ricci flow with bounded curvature, the bound of the Nash entropy
depends only on the local geometry around an $H_n$-center of its base point.


Abstract:
  A deep equilibrium model uses implicit layers, which are implicitly defined
through an equilibrium point of an infinite sequence of computation. It avoids
any explicit computation of the infinite sequence by finding an equilibrium
point directly via root-finding and by computing gradients via implicit
differentiation. In this paper, we analyze the gradient dynamics of deep
equilibrium models with nonlinearity only on weight matrices and non-convex
objective functions of weights for regression and classification. Despite
non-convexity, convergence to global optimum at a linear rate is guaranteed
without any assumption on the width of the models, allowing the width to be
smaller than the output dimension and the number of data points. Moreover, we
prove a relation between the gradient dynamics of the deep implicit layer and
the dynamics of trust region Newton method of a shallow explicit layer. This
mathematically proven relation along with our numerical observation suggests
the importance of understanding implicit bias of implicit layers and an open
problem on the topic. Our proofs deal with implicit layers, weight tying and
nonlinearity on weights, and differ from those in the related literature.


Abstract:
  Owing to its low vapor pressure, low toxicity, high thermal and electrical
conductivities, eutectic Ga-In (EGaIn) has shown a great potential for smart
material applications in flexible devices, cooling in micro-devices,
self-healing reconfigurable materials, and actuators. For such applications,
EGaIn is maintained above its melting point, below which it undergoes
solidification and complex phase separation. A scientific understanding of the
structural and compositional evolution during thermal cycling could help
further assess the application range of Ga and other low-melting-point fusible
alloys. Here, we use an integrated suite of cryogenically-enabled advanced
microscopy & microanalysis to better understand phase separation and (re)mixing
processes in EGaIn. We reveal an overlooked thermal-stimulus-response behavior
for frozen mesoscale EGaIn at cryogenic temperatures, with a sudden volume
expansion observed during in-situ heat-cycling, associated with the
immiscibility between Ga and In during cooling and the formation of metastable
Ga phases. These results emphasize the importance of the kinetics of
rejuvenation, and open new paths for EGaIn in sensor applications.


Abstract:
  Deep learning has achieved impressive performance on many tasks in recent
years. However, it has been found that it is still not enough for deep neural
networks to provide only point estimates. For high-risk tasks, we need to
assess the reliability of the model predictions. This requires us to quantify
the uncertainty of model prediction and construct prediction intervals. In this
paper, We explore the uncertainty in deep learning to construct the prediction
intervals. In general, We comprehensively consider two categories of
uncertainties: aleatory uncertainty and epistemic uncertainty. We design a
special loss function, which enables us to learn uncertainty without
uncertainty label. We only need to supervise the learning of regression task.
We learn the aleatory uncertainty implicitly from the loss function. And that
epistemic uncertainty is accounted for in ensembled form. Our method correlates
the construction of prediction intervals with the uncertainty estimation.
Impressive results on some publicly available datasets show that the
performance of our method is competitive with other state-of-the-art methods.


Abstract:
  We improve on a construction of Mestre--Shioda to produce some families of
curves $X/\mathbb{Q}$ of record rank relative to the genus $g$ of $X$. Our
first main result is that for any integer $g \geqslant 8$ with $g \equiv 2
\pmod 3$, there exist infinitely many genus $g$ hyperelliptic curves over
$\mathbb{Q}$ with at least $8g+32$ $\mathbb{Q}$-points and Mordell--Weil rank
$\geqslant 4g + 15$ over $\mathbb{Q}$. Our second main theorem is that if $g+1$
is an odd prime and $K$ contains the $g+1$-th roots of unity, then there exist
infinitely many genus $g$ hyperelliptic curves over $K$ with Mordell--Weil rank
at least $6g$ over $K$.


Abstract:
  Simultaneous localization and mapping (SLAM) is a fundamental capability
required by most autonomous systems. In this paper, we address the problem of
loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our
approach utilizes a deep neural network exploiting different cues generated
from LiDAR data for finding loop closures. It estimates an image overlap
generalized to range images and provides a relative yaw angle estimate between
pairs of scans. Based on such predictions, we tackle loop closure detection and
integrate our approach into an existing SLAM system to improve its mapping
results. We evaluate our approach on sequences of the KITTI odometry benchmark
and the Ford campus dataset. We show that our method can effectively detect
loop closures surpassing the detection performance of state-of-the-art methods.
To highlight the generalization capabilities of our approach, we evaluate our
model on the Ford campus dataset while using only KITTI for training. The
experiments show that the learned representation is able to provide reliable
loop closure candidates, also in unseen environments.


Abstract:
  Due to its high lethality amongst the elderly, the safety of nursing homes
has been of central importance during the COVID-19 pandemic. With test
procedures becoming available at scale, such as antigen or RT-LAMP tests, and
increasing availability of vaccinations, nursing homes might be able to safely
relax prohibitory measures while controlling the spread of infections (meaning
an average of one or less secondary infections per index case). Here, we
develop a detailed agent-based epidemiological model for the spread of
SARS-CoV-2 in nursing homes to identify optimal prevention strategies. The
model is microscopically calibrated to high-resolution data from nursing homes
in Austria, including detailed social contact networks and information on past
outbreaks. We find that the effectiveness of mitigation testing depends
critically on the timespan between test and test result, the detection
threshold of the viral load for the test to give a positive result, and the
screening frequencies of residents and employees. Under realistic conditions
and in absence of an effective vaccine, we find that preventive screening of
employees only might be sufficient to control outbreaks in nursing homes,
provided that turnover times and detection thresholds of the tests are low
enough. If vaccines that are moderately effective against infection and
transmission are available, control is achieved if 80% or more of the
inhabitants are vaccinated, even if no preventive testing is in place and
residents are allowed to have visitors. Since these results strongly depend on
vaccine efficacy against infection, retention of testing infrastructures,
regular voluntary screening and sequencing of virus genomes is advised to
enable early identification of new variants of concern.


Abstract:
  Membership inference (MI) attacks affect user privacy by inferring whether
given data samples have been used to train a target learning model, e.g., a
deep neural network. There are two types of MI attacks in the literature, i.e.,
these with and without shadow models. The success of the former heavily depends
on the quality of the shadow model, i.e., the transferability between the
shadow and the target; the latter, given only blackbox probing access to the
target model, cannot make an effective inference of unknowns, compared with MI
attacks using shadow models, due to the insufficient number of qualified
samples labeled with ground truth membership information.
  In this paper, we propose an MI attack, called BlindMI, which probes the
target model and extracts membership semantics via a novel approach, called
differential comparison. The high-level idea is that BlindMI first generates a
dataset with nonmembers via transforming existing samples into new samples, and
then differentially moves samples from a target dataset to the generated,
non-member set in an iterative manner. If the differential move of a sample
increases the set distance, BlindMI considers the sample as non-member and vice
versa.
  BlindMI was evaluated by comparing it with state-of-the-art MI attack
algorithms. Our evaluation shows that BlindMI improves F1-score by nearly 20%
when compared to state-of-the-art on some datasets, such as Purchase-50 and
Birds-200, in the blind setting where the adversary does not know the target
model's architecture and the target dataset's ground truth labels. We also show
that BlindMI can defeat state-of-the-art defenses.


Abstract:
  We propose a novel high dynamic range (HDR) video reconstruction method with
new tri-exposure quad-bayer sensors. Thanks to the larger number of exposure
sets and their spatially uniform deployment over a frame, they are more robust
to noise and spatial artifacts than previous spatially varying exposure (SVE)
HDR video methods. Nonetheless, the motion blur from longer exposures, the
noise from short exposures, and inherent spatial artifacts of the SVE methods
remain huge obstacles. Additionally, temporal coherence must be taken into
account for the stability of video reconstruction. To tackle these challenges,
we introduce a novel network architecture that divides-and-conquers these
problems. In order to better adapt the network to the large dynamic range, we
also propose LDR-reconstruction loss that takes equal contributions from both
the highlighted and the shaded pixels of HDR frames. Through a series of
comparisons and ablation studies, we show that the tri-exposure quad-bayer with
our solution is more optimal to capture than previous reconstruction methods,
particularly for the scenes with larger dynamic range and objects with motion.


Abstract:
  We derive a limiting absorption principle on any compact interval in
$\mathbb{R} \backslash \{0\}$ for the free massless Dirac operator, $H_0 =
\alpha \cdot (-i \nabla)$ in $[L^2(\mathbb{R}^n)]^N$, $n \geq 2$,
$N=2^{\lfloor(n+1)/2\rfloor}$, and then prove the absence of singular
continuous spectrum of interacting massless Dirac operators $H = H_0 +V$, where
$V$ decays like $O(|x|^{-1 - \varepsilon})$.
  Expressing the spectral shift function $\xi(\,\cdot\,; H,H_0)$ as normal
boundary values of regularized Fredholm determinants, we prove that for
sufficiently decaying $V$, $\xi(\,\cdot\,;H,H_0) \in C((-\infty,0) \cup
(0,\infty))$, and that the left and right limits at zero, $\xi(0_{\pm};
H,H_0)$, exist.
  Introducing the non-Fredholm operator $\boldsymbol{D}_{\boldsymbol{A}} =
\frac{d}{dt} + \boldsymbol{A}$ in
$L^2\big(\mathbb{R};[L^2(\mathbb{R}^n)]^N\big)$, where $\boldsymbol{A} =
\boldsymbol{A_-} + \boldsymbol{B}$, $\boldsymbol{A_-}$, and $\boldsymbol{B}$
are generated in terms of $H, H_0$ and $V$, via $A(t) = A_- + B(t)$, $A_- =
H_0$, $B(t)=b(t) V$, $t \in \mathbb{R}$, assuming $b$ is smooth, $b(-\infty) =
0$, $b(+\infty) = 1$, and introducing $\boldsymbol{H_1} =
\boldsymbol{D}_{\boldsymbol{A}}^{*} \boldsymbol{D}_{\boldsymbol{A}}$,
$\boldsymbol{H_2} = \boldsymbol{D}_{\boldsymbol{A}}
\boldsymbol{D}_{\boldsymbol{A}}^{*}$, one of the principal results in this
manuscript expresses the $k$th resolvent regularized Witten index
$W_{k,r}(\boldsymbol{D}_{\boldsymbol{A}})$ ($k \in \mathbb{N}$, $k \geq \lceil
n/2 \rceil$) in terms of spectral shift functions as \[
W_{k,r}(\boldsymbol{D}_{\boldsymbol{A}}) = \xi(0_+; \boldsymbol{H_2},
\boldsymbol{H_1}) = [\xi(0_+;H,H_0) + \xi(0_-;H,H_0)]/2. \] Here
$L^2(\mathbb{R};\mathcal{H}) = \int_{\mathbb{R}}^{\oplus} dt \, \mathcal{H}$
and $\boldsymbol{T} = \int_{\mathbb{R}}^{\oplus} dt \, T(t)$ abbreviate direct
integrals.


Abstract:
  The one-loop contributions to the trilinear neutral gauge boson couplings
$ZZV^\ast$ ($V=\gamma,Z,Z'$), parametrized in terms of one $CP$-conserving
$f_5^{V}$ and one $CP$-violating $f_4^{V}$ form factors, are calculated in
models with $CP$-violating flavor changing neutral current couplings mediated
by the $Z$ gauge boson and an extra neutral gauge boson $Z'$. Analytical
results are presented in terms of both Passarino-Veltman scalar functions and
closed form functions. Constraints on the vector and axial couplings of the $Z$
gauge boson $\left|g_{{VZ}}^{tu}\right|< 0.0096$ and
$\left|g_{{VZ}}^{tc}\right|<0.011$ are obtained from the current experimental
data on the $t\rightarrow Z q$ decays. It is found that in the case of the
$ZZ\gamma^\ast$ vertex the only non-vanishing form factor is $f_5^{\gamma}$,
which can be of the order of $10^{-3}$, whereas for the $ZZZ^\ast$ vertex both
form factors $f_5^{Z}$ and $f_4^{Z}$ are non-vanishing and can be of the order
of $10^{-6}$ and $10^{-5}$, respectively. Our estimates for $f_5^{\gamma}$ and
$f_5^{Z}$ are smaller than those predicted by the standard model, where
$f_4^{Z}$ is absent up to the one loop level. We also estimate the $ZZ{Z'}^{*}$
form factors arising from both diagonal and non-diagonal $Z'$ couplings within
a few extension models. It is found that in the diagonal case $f_{5}^{Z'}$ is
the only non-vanishing form factor and its real and imaginary parts can be of
the order of $10^{-1}-10^{-2}$ and $ 10^{-2}-10^{-3}$, respectively, with the
dominant contributions arising from the light quarks and leptons. In the
non-diagonal case $f_{5}^{Z^\prime}$ can be of the order of $10^{-4}$, whereas
$f_4^{Z'}$ can reach values as large as $10^{-7}-10^{-8}$, with the largest
contributions arising from the $Z'tq$ couplings.


Abstract:
  Large-scale sky surveys have played a transformative role in our
understanding of astrophysical transients, only made possible by increasingly
powerful machine learning-based filtering to accurately sift through the vast
quantities of incoming data generated. In this paper, we present a new
real-bogus classifier based on a Bayesian convolutional neural network that
provides nuanced, uncertainty-aware classification of transient candidates in
difference imaging, and demonstrate its application to the datastream from the
GOTO wide-field optical survey. Not only are candidates assigned a
well-calibrated probability of being real, but also an associated confidence
that can be used to prioritise human vetting efforts and inform future model
optimisation via active learning. To fully realise the potential of this
architecture, we present a fully-automated training set generation method which
requires no human labelling, incorporating a novel data-driven augmentation
method to significantly improve the recovery of faint and nuclear transient
sources. We achieve competitive classification accuracy (FPR and FNR both below
1%) compared against classifiers trained with fully human-labelled datasets,
whilst being significantly quicker and less labour-intensive to build. This
data-driven approach is uniquely scalable to the upcoming challenges and data
needs of next-generation transient surveys. We make our data generation and
model training codes available to the community.


Abstract:
  The weighted $k$-server problem is a natural generalization of the $k$-server
problem in which the cost incurred in moving a server is the distance traveled
times the weight of the server. Even after almost three decades since the
seminal work of Fiat and Ricklin (1994), the competitive ratio of this problem
remains poorly understood, even on the simplest class of metric spaces -- the
uniform metric spaces. In particular, in the case of randomized algorithms
against the oblivious adversary, neither a better upper bound that the doubly
exponential deterministic upper bound, nor a better lower bound than the
logarithmic lower bound of unweighted $k$-server, is known. In this article, we
make significant progress towards understanding the randomized competitive
ratio of weighted $k$-server on uniform metrics. We cut down the triply
exponential gap between the upper and lower bound to a singly exponential gap
by proving that the competitive ratio is at least exponential in $k$,
substantially improving on the previously known lower bound of about $\ln k$.


Abstract:
  Recently, many authors have embraced the study of certain properties of
modules such as projectivity, injectivity and flatness from an alternative
point of view. Rather than saying a module has a certain property or not, each
module is assigned a relative domain which, somehow, measures to which extent
it has this particular property. In this work, we introduce a new and fresh
perspective on flatness of modules. However, we will first investigate a more
general context by introducing domains relative to a precovering class $\x$. We
call these domains $\x$-precover completing domains. In particular, when $\x$
is the class of flat modules, we call them flat-precover completing domains.
This approach allows us to provide a common frame for a number of classical
notions. Moreover, some known results are generalized and some classical rings
are characterized in terms of these domains.


Abstract:
  Cometary outbursts offer a valuable window into the composition of comet
nuclei with their forceful ejection of dust and volatiles in explosive events,
revealing the interior components of the comet. Understanding how different
types of outbursts influence the dust properties and volatile abundances to
better interpret what signatures can be attributed to primordial composition
and what features are the result of processing is an important task best
undertaken with a multi-instrument approach. The European Space Agency
\textit{Rosetta} mission to 67P/Churyumov-Gerasimenko carried a suite of
instruments capable of carrying out this task in the near-nucleus coma with
unprecedented spatial and spectral resolution. In this work we discuss two
outbursts that occurred November 7 2015 and were observed by three instruments
on board: the Alice ultraviolet spectrograph, the Visual Infrared and Thermal
Imaging Spectrometer (VIRTIS), and the Optical, Spectroscopic, and Infrared
Remote Imaging System (OSIRIS). Together the observations show that mixed gas
and dust outbursts can have different spectral signatures representative of
their initiating mechanisms, with the first outburst showing indicators of a
cliff collapse origin and the second more representative of fresh volatiles
being exposed via a deepening fracture. This analysis opens up the possibility
of remote spectral classification of cometary outbursts with future work.


Abstract:
  The search for suitable materials for solid-state stationary storage of green
hydrogen is pushing the implementation of efficient renewable energy systems.
This involves rational design and modification of cheap alloys for effective
storage in mild conditions of temperature and pressure. Among many
intermetallic compounds described in the literature, TiFe-based systems have
recently regained vivid interest as materials for practical applications since
they are low-cost and they can be tuned to match required pressure and
operation conditions. This work aims to provide a comprehensive review of
publications involving chemical substitution in TiFe-based compounds for
guiding compound design and materials selection in current and future hydrogen
storage applications. Mono- and multi-substituted compounds modify TiFe
thermodynamics and are beneficial for many hydrogenation properties. They will
be reviewed and deeply discussed, with a focus on manganese substitution.


Abstract:
  We report a series of measurements of the effect of an electric field on the
frequency of the ultranarrow linewidth $^7F_0 \rightarrow$ $^5D_0$ optical
transition of $\rm Eu^{3+}$ ions in an $\rm Y_2SiO_5$ matrix at cryogenic
temperatures. We provide linear Stark coefficients along two dielectric axes
and for the two different substitution sites of the $\rm Eu^{3+}$ ions, with an
unprecedented accuracy, and an upper limit for the quadratic Stark shift. The
measurements, which indicate that the electric field sensitivity is a factor of
seven larger for site 1 relative to site 2 for a particular direction of the
electric field are of direct interest both in the context of quantum
information processing and laser frequency stabilization with rare-earth doped
crystals, in which electric fields can be used to engineer experimental
protocols by tuning transition frequencies.


Abstract:
  We investigate the lattice regularization of $\mathcal{N} = 4$ supersymmetric
Yang-Mills theory, by stochastically computing the eigenvalue mode number of
the fermion operator. This provides important insight into the non-perturbative
renormalization group flow of the lattice theory, through the definition of a
scale-dependent effective mass anomalous dimension. While this anomalous
dimension is expected to vanish in the conformal continuum theory, the finite
lattice volume and lattice spacing generically lead to non-zero values, which
we use to study the approach to the continuum limit. Our numerical results,
comparing multiple lattice volumes, 't Hooft couplings, and numbers of colors,
confirm convergence towards the expected continuum result, while quantifying
the increasing significance of lattice artifacts at larger couplings.


Abstract:
  We use the data from Gaia Early Data Release 3 (EDR3) to study the kinematic
properties of Milky Way globular clusters. We measure the mean parallaxes and
proper motions (PM) for 170 clusters, determine the PM dispersion profiles for
more than 100 clusters, uncover rotation signatures in more than 20 objects,
and find evidence for radial or tangential PM anisotropy in a dozen richest
clusters. At the same time, we use the selection of cluster members to explore
the reliability and limitations of the Gaia catalogue itself. We find that the
formal uncertainties on parallax and PM are underestimated by 10-20% in dense
central regions even for stars that pass numerous quality filters. We explore
the the spatial covariance function of systematic errors, and determine a lower
limit on the uncertainty of average parallaxes and PM at the level 0.01 mas and
0.025 mas/yr, respectively. Finally, a comparison of mean parallaxes of
clusters with distances from various literature sources suggests that the
parallaxes (after applying the zero-point correction suggested by Lindegren et
al. 2021) are overestimated by 0.01+-0.003 mas. Despite these caveats, the
quality of Gaia astrometry has been significantly improved in EDR3 and provides
valuable insights into the properties of star clusters.


Abstract:
  We consider the task of minimizing the sum of smooth and strongly convex
functions stored in a decentralized manner across the nodes of a communication
network whose links are allowed to change in time. We solve two fundamental
problems for this task. First, we establish the first lower bounds on the
number of decentralized communication rounds and the number of local
computations required to find an $\epsilon$-accurate solution. Second, we
design two optimal algorithms that attain these lower bounds: (i) a variant of
the recently proposed algorithm ADOM (Kovalev et al., 2021) enhanced via a
multi-consensus subroutine, which is optimal in the case when access to the
dual gradients is assumed, and (ii) a novel algorithm, called ADOM+, which is
optimal in the case when access to the primal gradients is assumed. We
corroborate the theoretical efficiency of these algorithms by performing an
experimental comparison with existing state-of-the-art methods.